Dr. Basanta Adhikari

OCEM Journal of
Management,Technology&SocialSciences 1
OCEM Journal of
Management, Technology
& Social Sciences
Multi Disciplinary Peer Reviewed Journals
Patron
Professor, Er. Hari Bhandari
Principal
Oxford College of Engineering and Management, Gaindakot-2, Nawalpur
Journal Advisory Board
Dr. Cha-Hsuan Liu (Utrecht University, Netherlands).
Tilak Panthi (Vice Principal, OCEM)
Bhim Bhandari (HoD. Engineering Department)
Suresh Baral (HoD, BCA)
Prem Sharma (Associate Professor of BBA)
Surya Narayan Poudel (Associate Professor of BBA)
Sanjeev Mishra (Ph.D. scholar)
Ganga Sapkota (Associate Professor of BBA).
Nawaraj Gautam (Assistant Professor of BBA)
Binod Babu Poudel (Assistant Lecturer, BBA)
Members of the Peer Review Board
Dr. Deepak Bahadhur Bhandari (Pokhara University, Nepal)
Dr. Bijaya Lal Pradhan (Asso. Prof., TU, Nepal)
Dr. Gyanu Subedi (Pokhara University, Nepal)
Mr. Kapil Deb Subedi (Asso. Prof. Saptagandaki Multiple Campus, Nepal)
Publisher:
RESEARCH DEPARTMENT
Gaindakot-2, Nawalpur
http://www.oxfordcollege.edu.np
Volume 1 Issue 1 December 2019

OCEMJournalof
Management,Technology&SocialSciences2

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Congratulations!!! OCEM
No doubt, education can be strengthened through the research activities conducted either by the
teachers or students. Oxford College of Engineering and Management has proved its academic
excellence in the results of Pokhara University examinations. However, the gap of research
journal publication had not been fulfilled yet. Now, this gap is also going to be filled. It is true
that the thin bright edge of the dark cloud is enough for the successful journey of the light. As an
example OCEM journal which is in your hands now. It was a dream but now is an achievement
of the College, faculties and students. There are many grounds or sources of knowledge, among
all these forms can be considered as worth for achieving outstanding academic performance.
Whatever success we achieve in Business, science and technology, we can’t ignore research
work as well. Even if we fly in the sky, the final point of rest would be the land to be landed. So,
whatsoever practices have been done in investigation of knowledge, it is not enough yet. It can
bring breakthrough in our life.
Finally I would like to thank to the Prof. Er. Hari Prasad Bhandari, Dr. Basant Adhikari and to
the entire team for their incessant efforts to deliver this piece of work.
Tilak Ram Panthi
Overall Coordinator
Oxford College Of Engineering and Management
Gaindakot-2, Nawalparasi
panthi.tilak@oxfordcollege.edu.np

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Acknowledgement of
Engineering Department
Concept, capability and confidence help professionals in practicing with high morale and
professional integrity. Oxford College of Engineering and Management is one of the leading
technical institution, shares challenging opportunity through appropriate blending of nation’s
requirement with young generations’ wildlings. Innovation and creativity are nurtured by
reinventing and revitalizing engineering services for nation building when the ethical values
and social services are the mile stone. It is only achievable if learning teaching environment
is research evidence-based and outcome of study is directly applicable for infrastructure
development through high-end technology, safety and glocalization. For this system, OCEM
research department has taken a great initiation of publishing the research journal. We extend our
appreciation and sincere to our research head, who is bringing peer reviewed journal in our hand.
We believe that this journal will soon be one of the greatest journals in the world.
Challenges of today's engineering education are emergent, necessitating calls for its reformation
to empower future engineers function optimally as innovative leaders, in both national and
international contexts. These challenges: keeping pace with technological dynamism; high
attrition; and most importantly, quality teaching/learning require multifaceted approaches. And
this platform will open all the doors to faculties to leverage on quality evidence-based teaching.
Nevertheless, linkages to equivalent global perspectives are presented from Nepal.
Assoc. Prof. Bhim Bhandari
BE Co-ordinator
Oxford College Of Engineering and Management
Gaindakot-2, Nawalparasi

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Table of Contents
The Consequences of Mother International Migration To The Left
Behind Girls Under 16 For Their Education, Health & Psycho-Social
Development in Chitwan District
Dr. Basanta Prasad Adhikari 7
Literature review of the most cited articles in selected 5 educational
technology journals during 2013 to 2017 – Identifying the champions Dr. Basanta Prasad Adhikari 23
A Review of Literature on MBA-Expectations and Reality
Mr. Narayan Sapkota
Factors Influencing Students’ Satisfaction in Oxford College of
Engineering and Management, Gaindakot-2, Nawalpur of Nepal. Dr. Basanta Prasad Adhikari 48
Factor Influencing Customer Satisfaction at BBSM, Bharatpur, Chitwan Dr. Basanta Prasad Adhikari
58
StudentSatisfactionatSecondaryLevelinOxfordCollegeofEngineering
& Management
Dr. Basanta Prasad Adhikari
73
The impact of information technology to make rational strategic decision
making in educational institutions in Nepal Professor, Er. Hari Bhandari 84
Factors Influencing Customer Satisfaction in Buddha Air, Bharatpur
Chitwan
An Elaborative Study in the Market Potential of Home Automation and
Security Products: A Case Study of Chitwan District in Urban Nepal Mr. Samir Raj Bhandari 109
The study of internet addiction among adolescent of Oxford College of
Engineering and Management (OCEM)
Mr. Ganga Prasad Sapkota 123
Correlation and Regression Analysis Using SPSS
Mr. Sarad Chandra Kafle
134

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OCEM Journal of
TheConsequencesofMotherInternationalMigration
ToThe Left Behind Girls Under16 ForTheirEducation,
Health&Psycho-SocialDevelopmentinChitwanDistrict
(Research Head and International Relationship Officer)
Email: adhikari_bp@ymail.com
Abstract
The primary objective of this study was to examine the consequences of Mother International Migration
(MIM) to the Left Behind Girls under the age of 16 on their Education, Health and Psychosocial
Development in Nepal. A mixed methods approach was used where the survey study and qualitative
interview were used as data collection methods. A five-point Likert scale survey questionnaire and the
Semi-structured Interview were used as research instruments to collect data. The consent form was sent
to immediate parents and the left behind girls for their acceptance to take part in this study. In the first
stage, twenty different schools were selected randomly and later purposive sampling method was used
to select the interviewees. Two hundred and fifty questionnaires were dispatched but two hundred and
thirty-seven survey questionnaires were returned by the returnees which was more than 94.8 % response
rate. Approximately, 45% of the sampled girls were under the age of 12 and 55% of them were between
the age of 12 to 16 in this study. The results show that there was positive relationship between the MIM
and feeling of loneliness, poor health suggestion, poor health condition, negative neighbour’s attitudes,
problem of relationship, and unsupportive house environment (p < 0.05). Again, there is significant
negative association between MIM and social attachment, use of social media and outdoor activities,
better health condition, positive psychosocial feeling, family support and availability of desired food
(p < 0.05). About 80% interviewees realized that their overall development of education, health and
psychosociology have been affected after their MIM. Approximately, 60% interviewees argued that the
development of the education health and psychosocial development were negatively affected by their
MIM. The qualitative results supported the quantitative results to foreground the phenomenon and to get
additional information on something that wasn’t expected on the impact of MIM to the LBGS
Recent increases in MIM to European, Arabic and other countries have invited an upwelling of interest
in how the absence of mothers affect the left-behind girls in Nepal. This study has supported the previous
findings on (MIM) that the LBGs had been negatively affected by the MIM for their education, health &
psychological development. The implication of this study is to aware the policy makers and governmental
administrators about the positive and negative consequences MIM on LBGs for their education, health
and psychosocial development. The main contribution of this study is to add new knowledge on the
consequences of MIM to the LBGs in the archive of foreign employment in the Nepalese context.
Keywords: Mother international migration, left behind girls, education, health and psychosocial
development, respondents, significant relationship.

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1. Introduction
Among various consequences of MIM, family constellation, a number of siblings, birth order of the
siblings and the educational, health and psychosocial development of the left behind girls (LBGs) under
the age of 16 have gained increasing attention from the migration and sociology scholars as well as the
social science researchers in theAsian context (Adhikari, 2018). The primary objective of this study was to
examine the consequences of MIM to the LBGs for their education, health and psychosocial development
in the Asian countries based on the family constellation, birth order, gender, age differences and a number
of siblings in mother migrant households (Cortes, 2015; Lahaie, Hayes, Peng & Wong, 2015; Piper, &
Heymann, 2009; Thimothy & Sasikumar, 2012; Yeoh & Lam, 2016; Meyerhoefer & Chen, 2010; Bhadra,
2007). It is not yet known that both theoretically and practically, as to whether the LBGs are particularly
vulnerable or not. It is also not known that how, when and under what circumstances, the LBGs are
suffering after their MIM (Adhikari, 2018; Dhar, 2012; Torgler & Valev, 2016). The previous studies
on MIM have largely been focused on macro determinants and economic and demographic changes,
however; the special issues of educational, health and psychosocial development of the LBGs have been
marginalized and less prioritized (Resurreccion, 2005; Adhikari, 2018; Battistella & Conaco, 1998; Rossi,
2009). Many LBGs have already turned on antisocial activities (for example, addiction of alcohol and
drugs, unprotected sexual attempts, prostitution, criminal activities) which have been increased due to
the lack of mother’s physical attachment with them (Abramsky et al., 2018; Adhikari, 2018). As a result,
the negative consequences have been increased on educational, health and psychosocial development of
the LBGs in the Asian countries, like Nepal (Adhikari, 2018; Bouchoucha, 2013; Mazhuvanchery, 2015;
Pescaru, 2015).
ThenextissuesofconsequencesofMIMtotheLBGsareembeddedinthenumberofsiblingsinfamily,their
age, gender, relationships with siblings, relationship with parents and their birth order which can inherently
impact on the educational, health and psychosocial development of the LBGs. Based on the socialization
and interaction perspectives, the experiences of the childhood with siblings are possible indicators to
affect the individual’s gender identity, intellectual development, and personality characteristics which
can affect the outcomes of educational and career development (Recchia & Wainryb, 2014). Four major
characteristics of sibling relations in early childhood are embedded in the sibling interactions; intimacy;
large individual differences and the age difference between siblings. Resources and opportunities are
embedded in different extent in the sibling structure in each child in the family which accompanies
socialization practices among siblings, but the higher birth order is also closely related to large sibship
size which is also negatively related to educational outcomes (Hauser & Sewell 1985; Black, Devereux
& Salvanes, 2016). The number of siblings, age and the birth order of siblings are also directly embedded
in girl’s educational development. With additional siblings, each child’s average share of parents’ time,
energy, and money which will be lowered or leading to lower educational attainment (Group of colleagues,
2012). Socially, boys are provided with more educational opportunities than girls because parents believe
that they will be able to support financially to their elderly age. Conversely, girls are taught to cook and
clean so that they will be able to take care of their own families after marriage. Again, the household
responsibilities, together with play activities, are the only socialization areas in which both parents treat
girls differently from boys (Lytton and Romney 1991; Quadlin, 2018; Alekseeva, Rzhanova, Fominykh
& Zyryanova, 2016). The previous literature reveals that girls’ time spent on domestic work increases in

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the presence of brothers, but not in the presence of sisters; boys’ housework time increases more in the
presence of brothers and less in the presence of sisters in 16 developing countries. The role of first-born
girl of mother migrant households is compulsorily responsible to take care for her younger siblings in
the Nepalese societies because they are regarded as the second mothers to take care for their younger
siblings and to support for their health educational and psychosocial development (Mechoulan & Wolff,
2015). On the other hand, there are some serious issues of sibling’s conflicts for the purpose of holding
the leadership role in the mother migrant households and individual disagreement among siblings. The
consequences of age difference between siblings often makes the issues of power and control, sources
of contention for children, rivalry and jealousy which can affect psychosocial development of the LBGs.
Additionally, the conflict of siblings frequent, poorly resolved and sometimes highly aggressive, violent
or even abusive which breaks the peaceful house environment and eventually girls are negatively affected
for their psychosocial development because of their patient nature and high tolerance capacity (Kolak
& Volling, 2011). The number of siblings, age and the birth order of siblings are also directly embedded
to girl’s health and psychosocial development in the Asian context because children are socialized into
appropriate gender roles according to their age where parents expect older siblings to undertake more
responsibility and become role models for their younger siblings in multi-child families which only
applies for the girls not for boys (Adhikari, 2018; Edmonds, 2006). It is consistently found an inverse
relationship between sibship size and educational outcomes (Booth & Kee 2008; Lu & Treiman 2008).
The primary objective of the study was to examine the consequences of MIM to the LBGs under the
age of 16 on education, health and psychosocial development between the mother migrant and non-
migrant households. The secondary objective was to compare the education, health and psychosocial
development of the LBGs between migrant and non-migrant households.
2. Literature Review of the Study
The word migration signifies both male and female migrants, but it does not specify directly for male or
female migrants. When the motivations, outcomes, and obstacles to international migration are studied,
there is a growing awareness in social science research that consideration of gender is critical phenomenon
(Rossi, 2009; Nguyen Yeoh, & Toyota, 2006). A little effort has been done to model explicitly for the
differences between male and female migrants with respect to determinants of international migration
and their changes overtime. This misunderstanding is a serious shortcoming in the international female
migration history because there is not any clear definition of male and female migrants (Bank, 2007). It
is argued that theoretical model and empirical findings focusing on male migration cannot adequately
describe female migration. More importantly, the studies that do not differentiate between males and
female migrants can state wrongly the effects of independent variables on migration for both genders
(Morrison, Schiff & Sjöblom, 2007; Moore, 2016; Rossi, 2009). MIM is defined as a form of family
transition which breaks the inherent relationship between both mothers and children (Mberu & Pongou,
2012; Wimalaratana, 2017).
2.1. Theoretical Framework of the Study
MIM and overall development of the LBGs are interconnected to education, health and psychosocial
development of the LBGs because mothers had undoubtedly played the primary role for overall

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Left Behind Girls
Consequences
development of the LBGs all over the world. The LBGs who lived in incomplete family environment
were easily neglected, received inadequate care and suffered from the worse school performance; physical
health/physical well-being; higher risk of injury, higher proportion of poor behaviour and lower nutrition
(Adhikari, 2018; Cohen, 1996). The LBGs who were cared by younger caretakers had been suffered from
behaviour of excessive alcohol drinking; smoking; internet addiction and the problem of mental health
in the mother-migrant households (Pescaru, 2015). They were also found of lower socioeconomic status
and had more psychological problems in mother-migrant households than father-migrant households
(Lam & Yeoh, 2016). More psychosocial problem was found in adolescence LBGs older than the age of
14 (Adhikari, 2018).
Figure 1. Theoretical Framework of the Proposed Study (c.f. Cortes, 2015)
2.2 Empirical literature on mother roles for LBGs
Cortes (2015) found that there was larger negative consequence to LBGs in mother-migrant households
than the father-migration households. The same study further summarized that school enrolment of
the younger girls had been less likely affected by household economic resources, but they had been
negativelyaffectedbytheir MIM inPhilippines.Again, Moran-Taylor (2008) notedthatthelargernegative
consequence of MIM was found in girls than in boys for their educational, health and psychosocial
development in Guatemala. The same study further disclosed that immediate parents of LBGs were
unable to maintain a watchful eye and strong parental control over them which resulted lower level of
child development. Many LBGs were found promiscuous in mother-migration households which had led
to an increase in single motherhood between the age of 12 and 13 (c.f., Gajos & Beaver, 2015). Similarly,
Grimes (1998) noted that the increase in the number of single mother was one of the most negative
consequences of MIM in Putla for the overall development of the LBGs. Cortes (2015) disclosed that the
LBGs were found more likely to involve in unprotected sex and excessive alcoholic habit and sometimes
involved in prostitution for the pocket money and foods in mother-migrant households which had
resulted unwanted social practice and directly affected for their educational outcomes (Pfeiffer Tailor,
Mother International Migration
Roles of
Mothers to LBGS
Psychosocial
Development
of the LBGS
Development of
Health and Well
Beings of the LBGS
Development of
Education of
the LBGS

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2007). Adhikari (2018) and Tong, Luo & Piotrowski (2015) concluded that MIM had greater negative
consequences to the LBGs than the father international migration. The same study further argued that
mothers were found naturally, culturally and maternally able to motivate their daughters better than their
fathers and also found usually skilful in nurturing and caring for the children in the comparison of father
migration households.
Similarly, Hugo and Ukwatta (2010) and Peng and Wong (2015) disclosed that the separation between
mother and child had created feelings of loneliness, helplessness, regretfulness and guiltiness which
had created the feeling of vulnerability and insecurity for girls from their male counterparts. The same
study further found that 60 out of the 400 mother-migrant households reported that LBGs had suffered
by mental and physical health problems due to the lack of mother’s primary care. It was concluded that
teenage daughters of mother-migrant households were forced to do extra household duties, for example,
cooking, washing, cleaning, cattle rearing that had diminished the level of educational performance
(Bank, 2007; Cortes, 2015; Jampaklay, 2006; Jaupart, 2018). The same studies also reported that mothers
had a bigger spiritual role in family formation than fathers to support the LBGs and also concluded
that the extended family members who helped the fathers did not involve in the spiritual development
of the children. It was importantly noted that a long-term absence of fathers did not have any serious
consequences for the child’s educational achievements compared with mother long-term absenteeism.
It was also concluded that boys culturally were found involved in less household works and spent more
time for outdoor games compared with girls in SAARC countries (Adhikari, 2018). Conversely, the
LBGs were negatively affected for their educational, health and psychosocial development in mother-
migrant households because they have to do extra household duties (Hugo & Ukwatta, 2010). Research
study of Cortes (2015) and Adhikari (2018) found that the gender roles are still very rigid in many Asian
countries for example, Nepal, India, Pakistan, Shri Lanka where the mother’s main role is to support
for the development of children and the father main role is to be the breadwinner (Sijapati, 2015). The
same study disclosed that MIM had been perceived as a much larger disruption in a child life than father
international migration. The repeated news of Kathmandu Post reported by Mahata (2018) found that
many LBGs were even raped by their fathers after the MIM and sometimes found sexual relationship
between father and daughter. Meyerhoefer and Chen (2010) found that MIM was associated with a
significant holdup in the educational degradation of the left behind girls in China. The same study further
argued that the level of education was negatively affected due to shifting the time allocation of LBGs
toward household duties. Cortes (2015) and Battistella and Conaco (1998) concluded that MIM was
found more detrimental than father international migration in the Philippines. The role of mother seemed
more attentive; skilful and more professional on how to care their children than the roles of fathers
(Gunduz, Karbeyaz & Ayranci, 2011). Thus, children without their mothers seemed more problematic
in mother-migrant households compared with father-migrant households (Fletcher et al., 2007; Gajos &
Beaver, 2015). Yeoh and Lam (2016) found that fathers were scared to care their matured and teenaged
daughters in mother-migrant households. The same research further disclosed that the left behind teenaged
and matured girls had expressed their strong preference for mothers’ support for their proper care and
development of health, education and psychosocial issues during the age of 13-16 in the Asian countries
(c.f., Cortes, 2015).

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3. Research Task, Data Collection & Analysis
Thisresearchstudyhad used amixedmethoddesignthatis bothhypothesistestingand hypothesis generating.
Girls aged, 10-16 years as the secondary schoolers were identified by visiting local government offices in
Chitwan District of Nepal. The key informants and immediate parents of the LBGs were contacted for the
collection of data of both quantitative and qualitative approaches Moreover, the sample population was
selected from both private and public institutions (for example, Schools, Hospitals, Police Departments,
Local Child Clubs, Nongovernment Organizations (Cohen et al., 2007). Two hundred and thirty seven left
behind girls (LBGs) were selectated randomly. All the sample population were contacted by the field visits,
email, and personal contact, telephone conversation, via local government and regional authorities and other
means of communication. Data analysis tools of this study were content analysis and descriptive statistics
analysis (Cohen et al, 2007; Lichtman, 2006; Thomas, 2009). The Factors Reduction Method was applied
to reduce the number of variables. After that, the Logistic Regression Model via Principal Component
Analysis Method was used to find the relationship between dependent and independent variables. The
descriptive statistics analysis was also computed to calculate subscales, grand mean values, and standard
deviation. Again, the values of Cronbach’s Alpha were computed to examine the reliability and internal
consistency of the subscales of this study (Creswell & Plano Clark, 2018; Cohen et al, 2011). This research
project had followed the UN Convention on the Rights of the Child (UNCRC) which became useful to
recap the main principles here. The UNCRC to children research was fully followed to minimize the ethical
dilemmas for the LBGs. Again, all the principles of child ethic were fully followed during the period of
the data collection and analysis. A consent form was sent in advance, follow-up was continued until the
consent forms returned. Personal data of each participant and interviewee were guaranteed not to publish
(Gibb, 2007). A short interview with six interviewees was conducted with six left behind girls to deepen the
consequences of MIM to the LBGs for their health, education & psychosocial development.
4. Results
The results of the 237 survey respondents were involved in this study where approximately, 45% of the
sampled girls were under the age of 12 and 55% of them were between the ages of 12 to16. Thirteen
respondents did not return the survey questionnaires. The response rate was approximately95% which was
excellent response rate. The analysis was based on Factor Reduction Model via Principal Component to
find the new Principal Components. The new PCs were named based on the grouped variable decided by
the Factor Reduction Model. In the second phase, subscales were identified based on descriptive statistics
where the values of grand mean and Standard Deviation (SD) were calculated. The analysis further
applied the Binary Logistic Regression (BLR) Analysis which examined the relationship between the
independent and dependent variables. The BLR model examined the positive and negative consequences
of MIM to the LBGS for their education, health and psychosocial development. The results have also
presented the summary of the vales of mean, SD, Cronbach’s Alpha and p values. The Wholesome
Model for the significant indicators was computed to examine the consequences of MIM to the LBGs.
(Jampaklay, Richter, Tangchonlatip & Nanthamongkolchai, 2018). The mean values of the subscales
less than 3.00 signify that the LBGS were not adequately supported by their immediate parents for their
education, health and psychosocial development after their MIM.

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4.1 Summary of mean, standard deviation and Cronbach’s Alpha of the Subscales (n = 237).
Descriptive statistics was computed to find the mean and SD. Similarly, the scale reliability was computed
to calculate Cronbach’s Alpha and an independent t-test was computed to calculate p values.
Table 1. Values of the mean, SD and Cronbach’s Alpha of the subscales
Subscales Mean SD Cronbach's Alpha
Number od
variables
Unmet needs of parental affection 3.05 1.13 .81 8
Health stress 3.05 1.26 .70 7
Unsupportive roles of immediate parents 3.10 1.25 .75 9
Poor neighbouring behaviour 3.10 1.28 .73 7
Lack of family support 3.10 1.21 .71 10
Communication activities 3.19 1.22 .71 9
Poor health condition 3.25 1.12 .82 8
Social isolation 3.29 1.22 .71 10
House environment 3.32 1.02 .70 10
Adverse psychosocial thinking 3.33 1.31 .87 9
Depressive symptoms 2.20 1.26 .72 8
Neighbour's attitude to neighbours 2.45 .924 .71 7
Feeling of loneliness 2.57 0.99 .70 8
Use of social media and outdoor activities 2.92 1.05 .75 10
Social injustice to LBGs 2.97 0.987 .74 9
The survey respondents were approximately undecided on the statements that unmet needs of parental
affection, health stress issues, unsupportive roles of immediate parents of the LBGs, poor neighbour
behaviour, and the lack of family support signifying that the mean values of these subscales were noticed
around 3.00-3.10. But, respondents were approximately agreed with the statements that communication
activities with their parents, poor health condition, social isolation, house environment, and adverse
psychosocial thinking signifying that the LBGs had been affected on education, health, and psychosocial
social development by their MIM. Most of the subscales were found having a bit lower and average
mean values signifying that the LBGs were not adequately supported by their immediate parents for their
education, health and psychosocial development. One of the interviewees note that:
“I am lacking my mother’s support so that I could not improve my educational performance which made
me so frustrated and depressive” (Interviewee-3).
“I am so much frustrated that my family members never understand my problems, specially, health and
educational issues. My mother was so concerned about my demands, support to my education and social
involvement but I missed now in the absence of my mother” (Interviewee-6).
“I am now feeling how my mother could understand what I really preferred eating as my best food, what
I really wearing as my best cloths and what I really visiting as my best place and relatives but now it is
my dream to get my best food, best dress and best places to visit (Interviewee-4).
“I now realized that my mother understood my choices, demands when she was with me. I really prefer
eating as my best food with mother, what I really wearing as my best cloths and visiting as my best place

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and relatives but now it is my dream to get my best food, best dress and best places to visit (Interviewee-1).
“My mother always cared me about my food and health. Similarly, my immediate parents also did high
care for my health and my best food. I do not need to wait for my mother’s return to get my best food
because my immediate parents always ask me what food I prefer” (Interviewee-5).
“I am really missing my mother’s supporting roles because my immediate parents never tried to know
what I really want”(Interviewer-2).
The qualitative results show that there were both negative and positive consequences of MIM to the LBGs
for their education, health and psychosocial development because most of the statements quoted by the
interviewees were found negative signifying that the LBGs were not supported as their requirement (see in
the Table 1) after their mother international migration. Five interviewees out of six disclosed that they were
not adequately supported by their immediate parents in the mother migrant households. But one interviewee
positively perceived the roles of immediate parents for her education, health and psychosocial development.
4.2 Summary of the significant indicators of the Wholesome Logistic Regression Model (WLRM)
There were four research problems in the analysis section. Each research question was answered by the
survey research instrument. Factor Reduction Method had had extracted twelve significant indicators
for the consequences of MIM to LBGs on their education, health and psychosocial development. The
results identified twelve significant indicators in the quantitative analysis (see in the Appendix 1 at Table
3). All the twelve significant indicators were entered the Wholesome Binary Logistic Regression Model
to examine the consequence of MIM to LBGs. But the results of WLRM show that only threeindicators
were found significant to the LBGs on their education, health and psychosocial development (the use of
social media and outdoor activities, sound psychosocial feeling, and the family support).
Table 3. Wholesome Model of the Binary Logistic Regression Model (N = 237)
Independent variables B S. E Wald df Sig Exp(B)
95% C.I.for EXP (B)
Lower Upper
Social attachment -.103 .202 .260 1 .610 .902 .608 1.340
Use of social media andoutdoor activities .576 .260 4.897 1 .027 1.780 1.068 2.965
Better health condition -.580 .527 1.208 1 .272 .560 .199 1.575
Feeling of loneliness -525 .294 3.187 1 .074 .591 .332 1.053
Poor health condition .175 .256 .465 1 .495 1.191 .721 1.967
Lack of health suggestion -.349 .271 1.658 1 .198 .705 .414 1,200
Negative neighbour's attitude to LBGs -.540 .411 1.726 1 .189 .583 .261 1.304
Sound psychosocial feeling .900 .298 9.132 1 .003 2.459 1.372 4.408
Problems of relationship and connection .864 .502 2.965 1 .085 2.374 .887 6.349
Unsupportive house environment .457 .240 3.619 1 .057 1.580 .986 2.532
Family support .679 .264 6.619 1 .010 1.972 1.176 3.309
Availability of desirable food -.498 .256 3.771 1 .052 .608 .368 1.005
Constant -.010 .165 .004 1 .949 .990 - -
The Omnibus Tests (Chi-Square = 63.043, df = 12, p = .001) and associated significance level less than
0.05, the present model shows a decrease in deviance in prediction from the base model. The model

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summary Table shows the values of -2Log Likehood (232.205), Cox and Snell R2
and Nagelkerke R2
[25.60 % (Cox and Snell) and 35.20 % (Nagelkerke)] variance of the model was explained by the
independent variables. Hosmer and Lemeshow Test shows that p = 0.280 > 0.05 is insignificant which
was good to support for the regression model fit. The classification Table shows that out of 104 LBGs who
chose the first option they were affected by their MIM, this model predicts 29 LBGs were not affected
for their education, health and psychosocial development after their MIM. Again, out of 109 LBGs who
chose the second option that they were not affected by their mother out migration, 30 of them were found
affected by their MIM. Thus, this model predicts the impact of MIM to the LBGs on education, health
and psychosocial development with 71.4 percent accuracy for those who said they were affected and also
predicts 73.1 percent of accuracy of prediction for the LBGs who chose the second option that they were
not affected by their MIM.
The results further confirmed that the overall percentage of correctness of observed data was 72.3 %.
The results also show that there was significant association between the use of social media and outdoor
activities, sound psychosocial feeling and family support to LBGs and MIM (p < 0.05 with odds ratio
1.780, 2.459, 1.972) (see in the Table 3). Again, when the independent variable the use of social media
and outdoor activities increases one unit, the impact of MIM can be predicated to increase around 1.780
times if other variables are controlled signifying that the use of social media and outdoor activities has
positive impact on education, health and psychosocial development of the LBGs after their MIM (Odd
ratio = 1.780 > 1, B = 0.576 > 0). The current study has supported the previous finding of Dhar (2012)
because the previous and the current studies have found that there was positive correlation between using
social media and the education, health and psychosocial development of the LBGs.
Similarly, when the independent variable sound psychosocial feeling increases one unit, the impact of
MIM can be predicated to increase around 1.972 times if other variables are controlled signifying that
psychosocial feeling has positive impact on education, health and psychosocial development of LBGs
after their MIM (Odd ratio = 2.459 > 1, B=.0.900 > 0). Again, when the independent variable family
support increases one unit, the impact of MIM can be predicated to increase around 2.459 times if other
variables are controlled signifying that family support has positive impact on education, health and
psychosocial development after MIM (Odd ratio = 1.972, B = 0.679 > 0). This study has also supported
the study of Jensen, Giorguli Saucedo & Hernández Padilla (2018) because the previous and the current
studies have found that family support to the LBGs has positively correlated for the education, health and
psychosocial development of the LBGs.
4.3 Results on categorical variables of the Linear Regression Model
The categorical variables on the ages of the LBGs and their mothers’ feeling were entered the Linear
Regression Model of the SPSS to find the correlation between them.
Table 4. The correlation between categorical variables and
the remembrance of mothers by the LBGs
a. Predictors: (Constant), Fifteen to sixteen years, fourteen to fifteen years
b. Dependent Variable: QNo15 Do you remember your mum now?
Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 .173a
.030 .021 .435 2.036

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The outputs of the first Table show the model summary and overall fit statistics. The results indicate
that the R value is .173. Therefore, remembrance of mothers is positively correlated with the ages of the
LBGs, signifying a weak relationship between the remembrance of mothers by the LBGs and the ages of
the LBGs. Again, the R² value is .0.030 signifying that the independent variables (ages of the left behind
girls) have explained total variances of 3 % on dependent variable (remembrance of mothers by the
LBGs) which is a very small variation between the remembrance of mothers by the LBGs and different
ages of them. Again, the adjusted R² of the model is 0.021 with the R² = .030 that means the linear
regression explains 2.10 % of the variance in the data which is very small difference so that the regression
equation does not appear to be useful for making predictions for the different ages of the LBGS since the
value of R² is very lower than 1. The Durbin-Watson d = 2.036, which is between the two critical values
of 1.5 < d < 2.5 and therefore we can assume that there is no first order linear auto-correlation in the data.
Table 5. Results of ANNOVA
Model Sum of squares df F Sig
Regression 1.157 2 .629 3.329 038b
Residual 40.975 217 .189
Total 42.232 219
a. Predictors: (Constant), Fifteen to sixteen years, fourteen to fifteen years b. Dependent Variable: QNo15
Do you remember your mum now?
The results of the Table 5 show that the regression model was the statistical significance that was run.
Here, p < 0.038, which is less than 0.05, indicating that, overall, the regression model statistically
significantly predicts the level of mothers’ remembrance by the LBGs which a good fit for the data is.
Table 6. Results of coefficients
Coefficientsa
Model 1
Unstandardized
Coefficients
Standardized
Coefficients
Sig
95.0% Confidence
interval for B
B Error Std. Beta t Upper Lower
Constant 1.205 .046 26.004 .000 1.113 1.296
Fourteen to fifteen years -.008 .076 -.008 -.111 .912 -.159 .142
Age of the LBGs (15 and 16 Years .153 .067 .169 2.294 .023 .022 .285
We are 95% confident that the slope of the true regression line is somewhere between –0.159 and 0.142.
In other words, we are 95% confident that for the LBGs whose ages lie between 14 to 15, the level of
mothers’ remembrance by the BGs decreases somewhere between –0.159 to 0.142. It is concluded that
on average, for the LBGs whose ages lie between 14 to 15 years, the level of mothers’ remembrance
will decrease -.008 times. Again, we are 95% confident that for the LBGs whose ages lie between 15
to 16, the level of mothers’ remembrance by the BGs increases somewhere between .022 to 0.285. It is
concluded that on average, for the LBGs whose ages lie between 15 to 16 years, the level of mothers’
remembrance will increase by 0.153 times.

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6. Discussion & Conclusion
This study was conducted at Chitwan District to examine both positive and negative consequences of
MIM to the LBGs for their education, health and psychosocial development among the mother migrant
households. The MIM and its consequences on the LBGs is a very debatable issue for the women and
gender study in the Asian context. The empirical research reveals that there was both negative and
positive consequences of MIM to the LBGs for their education, health and psychosocial development
in MIM households. A mixed method research approach was applied to collect data. Two hundred and
thirty-seven LBGs were involved in the survey study and six LGBs as interviewees were involved in the
qualitative study. There were twenty-two subscales with the values of mean, SD and Cronbach’s Alpha
(see in the Table 1) and also twenty-two independent variables in this study. The results indicate that there
were twelve significant indicators for the consequences of MIM (p < 0. 05) [see in the table 23]. The
results show that there was significant association between the consequences of MIM and the use of social
media and outdoor activities, positive psychosocial feeling and family support (p< 0.05 with odds ratio
1.780, 2.459, 1.972) in the Wholesome Model of Binary Logistic Regression Analysis. The implication
of the study is to support local government to formulate the child friendly policy and make aware the
local government to protect child rights in Chitwan District. The findings of the current study can be
generalized in the same context of larger population because of the larger quantitative sample population
involvement in this study. The results further conclude that the linear regression model was the statistical
significance where, p < 0.038, which is less than 0.05, indicating that, overall, the regression model
statistically significantly predicts the outcome variables which is a good fit for the data. The development
of the left behind girls under the age of 16 on education, health and psychosocial development is a globally
debatable issue so that researchers, academicians, police officers, policy makers and the government have
to focus on their future research for the children rights, security, safety and their overall development.
The universe is based on variation on mankind, geographical structure, population, resources, political
system, form and nature of governments so that there are the variations in the condition of the LBGS
among each country. Nepal is an underdeveloped country where the condition of the LBGs is adverse and
unfavourable for their overall development. The issue of the MIM and its negative consequences have to
be addressed in the future research of the international researchers and academic institutions. This study
is the ongoing research phenomenon to collect he larger scale of and analyzing holistically in future. It
is estimated that two thousand respondents for the survey study, fifty-one interviews for the qualitative
study and five Focus Group Discussion have been targeted to complete the study in future. The doctors,
police officers, immediate parents, the LBGs between the aged of 10 to 16 years, compounders, doctors
and social workers will be focused to collect qualitative data and quantitative data to enlarge this study.
References
Adhikari, B. (2018). The negative impacts of MIM to the left behind girls to the left behind girls under 16
years old on education, health and psychosocial development in Chitwan District. Retrieved from
https://www.researchgate.net/search.
Adhikari, J. (2006). Nepali women and foreign labour migration (1st Ed.). Kathmandu: UNIFEM.
Agbola, F., & Acupan, A. (2010). An empirical analysis of international labour migration in the
Philippines. Economic Systems, 34(4), 386-396.

OCEMJournalof
Antman, F. (2012). Gender, educational attainment, and the impact of parental migration on girls left
behind. Journal of Population Economics, 25(4), 1187-1214.
Bank, W. (2007). The International Migration of Women. Washington: World Bank.
Basnet, A. (2013). Impact of Increased Migration Opportunity on Human Capital Accumulation of Young
International Migrants: A Case of Nepal. Retrieved fromhttps://www.eur.nl/
Battistella, G., & Conaco, M. (1998). The Impact of Labour Migration on the Children Left Behind: A
Study of Elementary School Children in the Philippines. Journal of Social Issues in Southeast
Asia, 13(2), 220-241.
Bhadra, G. (2007). Twins in the shadow of a dead baby. Infant Observation, 10(2), 195-202.
Botezat, A., & Pfeiffer, F. (2014). The Impact of Parents Migration on the Well-Being of Girls Left
Behind Initial Evidence from Romania. SSRN Electronic Journal. http://dx.doi.org/10.2139.
Bouchoucha, I. (2013). The impact of parental migration on children left behind: The case of Tunisia.
Qscience Proceedings, 2013(1), 14. doi: 10.5339/qproc.2013.fmd.19.
Cebotari, V., Siegel, M., & Mazzucato, V. (2016). Migration and the education of girls who stay behind
in Moldova and Georgia. International Journal of Educational Development, 51, 96-107.
Chandra, B. (2007). International Labour Migration of Nepalese Women: Impact of their Remittances on
Poverty Reduction. Retrieved from https://www.econstor.eu/bitstream/10419/17.
Cohen, L., Manion, L., & Morrison, K. (2011). Research methods in education. London, Routledge
Publication.
Cohen, R. (1996). Theories of migration. Cheltenham, UK: E. Elgar.
Cortes, P. (2015). The Feminization of International Migration and its Effects on the Left Behind children:
Evidence from the Philippines. World Development, 65, 62-78.
Dhar, R. (2012). Women and International Migration: A Cross-cultural Analysis. Social Change, 42(1),
93102.
Gao, Y., Li, L., Chan, E., Lau, J., & Griffiths, S. (2013). Parental Migration, Self-Efficacy and Cigarette
Smoking among Rural Adolescents in South China. Plos ONE, 8(3), e57569. doi:
10.1371/journal.pone.0057569.
Giannelli, G., & Mangiavacchi, L. (2010). Girls Schooling and Parental Migration: Empirical Evidence
on the ‘Left-behind’ Generation in Albania. LABOUR, 24, 76-92.
Gibson, J., McKenzie, D., & Stillman, S. (2011). The Impacts of International Migration on Remaining
Household Members: Omnibus Results from a Migration Lottery Program. Review of Economics and
Statistics, 93(4), 1297-1318. doi: 10.1162/rest_a_00129
Gunduz, T., Karbeyaz, K., & Ayranci, U. (2011). Evaluation of the Adjudicated Incest Cases in Turkey:
Difficulties in Notification of Incestuous Relationships. Journal of Forensic Sciences, 56(2), 438-443.
Guo, Q., Sun, W., & Wang, Y. (2016). Effect of Parental Migration on Women Health in Rural China.
Review of Development Economics. http://dx.doi.org/10.1111/rode.12289
Hoang, L., Lam, T., Yeoh, B., & Graham, E. (2014). Transnational migration, changing care arrangements
and left-behind girls’ responses in South-east Asia. Girls Geographies, 13(3), 263277.
Hujo, K., & Piper, N. (2007). South South Migration: Challenges for development and social policy.
Development, 50(4), 19-25.

OCEM Journal of
Hysing, M., Petrie, K., Bøe, T., & Sivertsen, B. (2017). Parental work absenteeism is associated with
increased symptom complaints and school absence in adolescent children. BMC Public Health,
17(1). doi: 10.1186/s12889-017-4368-7
Jampaklay, A., Richter, K., Tangchonlatip, K., & Nanthamongkolchai, S. (2018). The impact of parental
absence on early childhood development in the context of Thailand. Asian and Pacific Migration
Journal, 27(2), 209-230.
Jensen, B., Giorguli Saucedo, S., & Hernández Padilla, E. (2018). International Migration and the
Academic Performance of Mexican Adolescents. International Migration Review, 52(2), 559-596
Lahaie, C., Hayes, J., Piper, T., & Heymann, J. (2009). Work and family divided across borders: the
impact of parental migration on Mexican girls in transnational families. Community. Work &
Family, 12(3), 299312.
Lu, Y. (2012). Education of Girls Left Behind in Rural China. Journal of Marriage and Family, 74(2),
328341.
Lu, Y. (2015). Internal migration, international migration, and physical growth of left-behind children: A
study of two settings. Health & Place, 36, 118-126
Man, Y., Mengmeng, L., Lezhi, L., Ting, M., & Jingping, Z. (2017). The psychological problems and
related influential factors of left-behind adolescents (LBA) in Hunan, China: a cross sectional
study. International Journal for Equity in Health, 16(1). doi: 10.1186/s12939-017-0639-2
Mazhuvanchery, S. (2015). 6. International Labour Organization (ILO). Yearbook of International
Environmental Law, 26, 583-587.
Meyerhoefer, C., & Chen, C. (2010). The effect of parental labour migration on children’s educational
progress in rural china. Review of Economics of the Household, 9(3), 379-396.
Peng, Y., & Wong, O. (2015). Who Takes Care of My Left-Behind Girls? Migrant Mothers and Caregivers
in Transnational Child Care. Journal of Family Issues, 37(14), 2021-2044.
Pescaru, M. (2015). Consequences of Parents’ Migration on Children Rearing and Education. Procedia -
Social and Behavioural Sciences, 180, 674-681.
Resurreccion, B. (2005). International Workshop on the Impacts of Migration on ‘Left-Behind’ in Asia.
Gender, Technology and Development, 9(3), 437-440.
Rossi, A. (2009). The Impact of Migration on Children Left Behind in Developing Countries: Outcomes
Analysis and Data Requirements. SSRN Electronic Journal. doi: 10.2139/ssrn.2490380
Sijapati, B, (2015). Women Labour Migration from Asia to the Pacific; Opportunity and Challenges;
International Organization for Migration, 12, 1-16.
Tong, Y., Luo, W., & Piotrowski, M. (2015). The Association between Parental Migration and Childhood
Illness in Rural China. European Journal of Population, 31(5), 561-586.
Torgler, B., & Valev, N. (2016). Women and Illegal Activities: Gender Differences and Women’s
Willingness to Comply Over Time. SSRN Electronic Journal. http://dx.doi.org/10.2139/ssrn.
Urias, D.(2012).Theimmigration &education nexus (1sted.).Rotterdam: SensePublishers.Vandevivere,
E., Braet, C., Bosmans, G., Mueller, S., & De Raedt, R. (2014). Attachment and Children’s Biased
Attentional Processing: Evidence for the Exclusion ofAttachment-Related Information. Plos ONE,
9(7), e103476. doi: 10.1371/journal.pone.0103476

OCEMJournalof
Viet Nguyen, C. (2016). Does parental migration really benefit left-behind girls? Comparative evidence
from Ethiopia, India, Peru and Vietnam. Social Science & Medicine, 153, 230-239. Wise, P., &
Blair, M. (2007). The UNICEF Report on Child Well-Being.Ambulatory Pediatrics, 7(4), 265-266.
Wickramage, K., Siriwardhana, C., Vidanapathirana, P.,Weerawarna, S., Jayasekara, B., & Pannala, G. et
al. (2015). Risk of mental health and nutritional problems for left-behind children of international
labour migrants. BMC Psychiatry, 15(1). doi: 10.1186/s12888-015-0412-2
Yeoh, B., & Lam, T. (2016). Immigration and Its (Dis) Contents. American Behavioural Scientist, 60(5-
6), 637-658.
Zhao, C., Wang, F., Li, L., Zhou, X., & Hesketh, T. (2017). Long-term impacts of parental migration on
Chinese children’s psychosocial well-being: mitigating and exacerbating factors. Social Psychiatry and
Psychiatric Epidemiology, 52(6), 669-675.

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Literature review of the most cited articles in selected 5
educational technology journals during 2013 to
2017 – Identifying the champions
Abstract
The aim of the current review study was to examine the characteristics of the most cited articles, derived
from five selected journals in the field of educational technology between 2013 to 2017. The research
method of this study was the review of the most cited published articles. Total forty one (n=41) articles
were reviewed first and later only seven most cited articles were selected for the father analysis. The
results indicate that the most cited published article from the five selected per journal and the years
between 2013 to 2017 was entitled “The gamifying learning experiences” (Citation count = 801 times).
The results further highlight that three articles were derived from the journal of Computer and Education.
The results also show that the five most cited articles were published in 2013 and other two articles were
published in 2014 and 2015. A mixed methods approach, review of the empirical articles and quantitative
approach were used as research methods in the most cited five selected journals. The results confirm
that the journal of Computer and Education was found the most dominating in the field of educational
technology research in all years. The results also show that the year 2013 was the most dominating years
for the published articles reviewed in this study. The primary implication of findings will be beneficial for
the novice researchers, Master Degree students and academicians to know the current issues of the educational
technology and its future improvement. The limitation of this studyis the issue of generalization because of the
limited number of reviewed most cited published articles in the current study.
Keyword: Characteristics, educational technology, most cited articles, computer and education.
1. Introduction
Reviewing published most cited articles is one of the primary tasks for the novice researchers. The
reviewer of this research study will be named as the current researcher in the following texts. The research
findings of the reviewed articles can be not only recognized in the academic community but also be
beneficial for applying tenure, promotion, grants and scholar awards by the publications to advance
their professional careers. Similarly, education researchers often view the publications of research
findings in academic journals as a significant work for their professional development (Tsai & Lydia
Wen, 2005). More importantly, reviewing most cited published educational technology journals help the
novice researchers to understand the required field in greater depth. Educators can be supported by the
systematic analysis of the most cited published articles in academic journals to discover the current status
and future trends of educational technology research (Lee, Wu & Tsai, 2009). Various methods have
been used to review different empirical published most cited articles. Reviewing journal articles is also
regarded a key effort to find the most debatable and emergent issue of educational technology research in

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the current educational context. Review of journal articles is also connected for selecting a new research
topic for further investigation.
In the current study, the aim of the analysis was to identify the most cited articles in educational
technology between 2013 to 2017. The five selected journals were entitled “British Journal of Educational
Technology (BJET), the Journal of Computer and Education (JCE), the Journal of Computer Assisted
Learning (JCAL), the Journal of IEEE Transactions on Learning Technologies (IEEE TLT) and the
Journal of Educational Technology Research and Development (JETRD). The method of reviewing
the most cited published articles was content analysis method where selected most cited published
articles were compared and summarized on the basis of the citation counts, published years, titles of
the published articles and existing theories. The current era of education is connected with worldwide
educational systems which demands for the holistic research in the educational technology to support
the learners and teachers (Pathek & Chaudhay, 2012). Many changes have been globally taken place in
the political, economic and demographical sectors which also demand the systematic research on the
emergence of educational technologies in teaching and learning activities. Furthermore, the research on
educational technology has covered, for example, the issues of social media, serious games, and adaptive
software to improve the outcomes of education. Similarly, the emerging practices on openness and user
modelling have to be focused in future research because global education has demanded the innovations
and new practices in digital learning contexts which have been facing complexities and unavailable
technological resources in teaching and learning activities (Pathek & Chaudhay, 2012). The roles of
educational technology have been increasing day by day in the educational sector for the improvement
of the students’ achievements and educational quality. So, the review of most cited published articles is
emerging to focus on the current demands of educational technology and its integration in educational
institutions (Aksnes, 2003; Tondeur, van Braak, Siddiq & Scherer, 2016). The outcomes of education
will be fruitful for all nations if computer and Technological tools are integrated in their educational
system. More importantly, this is the era of Information and Communication Technology (ICT) where all
official and none-official works, private and public activities have been made so convenient. So, current
educational leaders and practitioners have to at least understand the importance of ICT for effective and
efficient teaching and learning activities (Onifade, 2011; Picatoste, Pérez-Ortiz & Ruesga-Benito, 2018).
1.1 Importance of technology in classroom teaching and learning activities
The current era of education is more likely emerging to connect with educational technology research
because teacher educators are still struggling with how to create positive, interactive, open learning
environment in educational institutions. Creating a powerful learning experiences is one step ahead to
transform teachers’ efforts into classroom practice (Putnam & Borko, 2000). The roles of technology in
education has been emerged since two decades ago to now because the use of education technology can
identify the demands of students, enriches teachers how to apply technology in instructions and tracking
the their performance (Onifade, 2011). Additionally, educational technology can enhance students’
performance, keep students engage effectively in learning activities, improve students’ performance and
make student response to adapt the new learning environment (Spector, 2017). Van Thiel (2018) States
that “Technology integration in schools involves implementation of computers for effective and efficient
use in meaningful curriculum-driven ways that enhance student learning by allowing for flexibility,
creativity and collaboration, while making real-world connections” (p.2). Educational technology is

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important for teaching and learning activities because it integrates computer and teaching activities.
It also enhances teachers’ teaching skills and makes them easy to manage their classroom (Onifade,
2011). The use of technology in classroom teaching can support teachers for effective and efficient use
of curriculum contents which can increase student achievements. The use of technology also enhances
teachers’ beliefs for external commands and opportunities and permits them to access for resources
(Christensen et al., 2018). “Technology in education is an integral part of effective teaching and learning.
It is crucial to prepare learning leaders who can guide and support innovative and effective technology
enhanced learning in the classroom” (Christensen et al., 2018, p.458). Educational technology also
supports students and teachers to be more innovatives to improve their performance, & how to get good
results effectively and efficiently (Alexander, 2018).
Gupta (2015) states that; “The field of education has been affected by the penetrating influence of
information and communication technology. Undoubtedly, ICT has impacted on the quality and quantity
of teaching, learning, and research in traditional and distance education institutions” (p.316). It is noted
that current educational systems and teaching and learning practices have been positively influenced
for delivering actual chances for individualized instruction in classroom teaching by the educational
technology through its dynamic, interactive, and engaging contents (Cuny, 2011). It also enhances the
capability of accelerating, inspiring, and deepening skills; motivating and engaging students in teaching
and learning activities. Technology is also useful tool to teachers for helping to relate school experiences
to work practices; creating economic viability for tomorrow’s workforces; underwriting to fundamental
changes in school; strengthening teaching and providing opportunities for connection between the school
and the society (Onifade, 2011).
1.2 Research Problems and Questions
The current chapter has focused on the main research questions of the current study where one main
research question and 3 sub-questions were designed to facilitate the analysis section. The primary
research questions are rooted in the differences of citation counts; published years of journal and the
differences of the contents. The firstly, forty-one highly cited articles were selected & secondly, only
seven articles were selected. The next issue of the research question is deeply rooted in the variations of
per five selection journals and the published papers based on their characteristics. The primary research
question is related to identifying and analyzing the most cited of the five selected journals in the field of
educational technology during the year 2013 to 2017. The primary research question has been divided
into three sub-questions.
1. What are the characteristics/differences between the most cited published articles per five selected
journals?
2. What are the characteristics differences between the most cited published articles per year among
the five selected journals between 2013 to 2017?
3. What are the differences between the most cited published articles per five selected journals and per
year among the seven selected journals between 2013 to 2017?
At first, the most cited five journal articles were derived from Publish and Perish Tool. The first journal
BJET was the main source of academic journal articles for researchers and academicians in the arena of
digital educational and training technology throughout the universe. The publications of BJET are deeply

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rooted in theoretical outlooks, methodological developments and high quality observed studies that
signify whether and how applications of educational systems, tools, and resources guide to developments
in both formal and informal education at all sectors (Dalby & Swan, 2018). The second journal was
JCE that is helpful to increase knowledge and understanding of different ways by using computer
technologies in teaching and learning activities. More importantly, the journal of JCE was also the main
source of educational technology research. Additionally, it primarily focuses on digital technology in
order to enhance educational practices through the publication of high quality research materials which
eventually increases the level of the theory and practice of education. It is significantly noted that JCE has
highly demanded articles because it has revolutionary increased the importance of research on Computer
and Education all over the world (Robins, 2015).
The third journal was JCAL which is connected for using of computers to support the education of
people, to describe the application of computers and also includes the instructions for computer-based
learning activities. Moreover, the meaning of JCAL is defined as an interactive instructional technique
where a computer can remarkably present the instructional materials for teaching and learning activities
(Arteaga Sánchez, Cortijo & Javed, 2014: De Witte, Haelermans & Rogge, 2014).
The fourth journal was IEEE TLT which is connected for using technology in teaching and learning activities
to improve the outcomes of education. In more details, learning technologies have been deeply rooted in
computer-based learning method which is supported by the application of technology for the improvement
of teaching methods (Buckley & Doyle, 2017). Furthermore, computer-based learning is directly linked
in using the multimedia materials and also using of different networks and communication systems to assist
learning activities (Innovation in Technologies for Educational Computing, 2016). The words equality, future,
mobile, motivation, social, updates, assessments, global, and convenience have been used for the importance
of learning technologies in educational sectors. The fifth journal was the JETRD. The meaning of educational
technology research and development is understood by a single scholarly journal focusing entirely on research
and development in educational technology. The next origin of education technology has been anticipated
among working professionals, for example, technology coordinators, instructional designers, school library
media specialists, training directors, and technology teachers (Januszewski,2001).
2. Research Method
The purpose of the current study is to compare and contrast the seven most cited published articles
among the forty-one highly cited articles per five selected journals and per year among the five selected
journals (See in Appendix 1). The current research method has mainly focused on the topics of per five
selected journals and per year among the five selected journals of the forty-one published papers between
2013 to 2017 in the current study. The main research method is embedded in the content analysis of the
seven most cited published papers. The seven most cited articles among 41 articles are presented in pie-
chart mentioning their citation counts, published year of the articles and the percentage of each article in
the given pie-chart. Furthermore, forty-one articles are also mentioned in the Table 1 to make analysis
section clear. Chapter two introduces the research design of the current study where the content analysis
focuses on analyzing the data. It also explores the methods of data analysis and key contents for the
further analysis.
The research design also focuses on different issues of data analysis. Chapter three introduces the results
of the current study and further identifies and analyzes the key characteristics of the highly citedarticles

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7
6 1
259, [PERCENTAGE]
54, [PERCENTAGE] 2
1
5
801, [PERCENTAGE]
352, [PERCENTAGE] 3
4
4
410, [PERCENTAGE]
2
3 606, [PERCENTAGE] 5
506, [PERCENTAGE]
6
Gamifying learning experiences:
Practical implications and outcomes
Current status, opportunities and
challenges of augmented reality in
education
Flipping the classroom and
instructional technology integration in
a college-level information
Technological pedagogical content
knowledge –areviewoftheliterature
Assessing the effects of gamification
in the classroom
Puttingtwittertothetest:Assessing
outcomesforstudentcollaboration,
engagement andsuccess
based on per five selected journals and per year among the five selected journals. The results section
further explores the details analysis of 7 highly cited articles based on the publication years, citation
counts and the percentage covered by each article in each Pie-chart. The fourth part of the current
dissertation introduces the summary and conclusion of the whole part of this study which also compares,
contrasts and synthesizes the key findings of the results section. The purpose of the current research
design was to analyze the most cited seven articles per five selected journals and per year among the
five selected journals between 2013 to 2017. The contents for the analysis are years of publication and
citation counts of the most cited seven published articles among forty one published papers. First of all,
five journals entitled the CE, CAL, BJET, IEEE TLT and JETRD were selected. The number of citation
counts might be more in the forthcoming day, but the current researcher does not consider the citation
counts after 20th
May 2018. In the current study, the research topics of each published article have been
embedded in different subjects and different areas of the educational and technology research (see in the
Appendix 1 and 2). Twenty-five most cited published papers were derived from per five selected journals.
Similarly, another twenty-five most cited published papers per year among five selected journals between
2013 to 2017 were selected. The selected articles mentioned in the Table 1 and 2 are embedded in the total
citation counts of each published article, published years and the name of five selected journals. There
are five rows and five columns in the Table 1 and 2 where forty-one published articles are mentioned as
well. Only the fortyone published articles are mentioned in the Table 1 and 2.
3. Results
3.1 Analysis of Seven the Most Cited Articles among Horty one highly Cited articles
The seven the most cited articles among the forty-one the most cited published articles according to per
five selected journals and per year five selected journals were selected for the further analysis but other
most cited articles were excluded in the analysis The analysis has mainly focused on the citation counts,
published years and the five selected journals among fourty one most cited published articles.
Detailed analysis of the seven most cited published articles per journal and per year
Figure 1. Seven most cited published articles among five selected journals

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The Pie Chart in the Figure 1 has presented the number of citation counts, percentage covered by each
article and title of each most cited article. The first most cited article was derived from JCE which was
“Gamifying learning experience: Practical implications and outcomes” published in 2013 cited 801 times
(27%). The second most cited published article was derived from JCE which was entitled “Current status,
opportunities and challenges of augmented reality in education” published in 2013 cited by 606 times
(20%). The third most cited article was derived from JETRD which was “Flipping the classroom and
instructional technology integration in a college-level information system spreadsheet course” published
in 2013 cited by 506 times and has covered 17%. (Davies, Dean & Ball, 2013). The fourth most cited
article was derived from JCAL which was “Technological pedagogical content knowledge (TPACK)” and
published in 2013, published in 2015 cited by 410 times (14%). The fifth most cited article was derived
from JCE which was “Assessing the effects of gamification in the classroom: A longitudinal study on
intrinsic motivation, social comparison, satisfaction, effort, and academic performance” published in
2014 which was cited 352 times (12%).
The sixth most cited article was derived from BJET which was “Putting twitter to the test: Assessing
outcomes for student collaboration, engagement and success” published in 2013 which was cited 259
times (8%) (Junco, Elavsky & Heiberger, 2013). The seventh most cited article was entitled “Delving
into Participants’ Profiles and Use of Social Tools in MOOCs” which was derived from IEEE TLT,
published in 2013 cited 54 times (2%). The research theme of the seven most cited articles was the
massive open online courses and educational technology. The article had cover the participants’ profiles
on MOOCs, social tools on MOOCs and digital education of the future. Here, the observations of the
current researcher also conclude that JCE has been seen as demonizing journal according to per selected
journals and per year among the five selected journals. It was also noted that different types of research
methods were used in the most cited seven articles, for example, a mixed methods design, review
method, longitudinal survey method, the cross sectional survey method, qualitative interview method,
and quantitative method. The results further indicate that a reviewed method was used in many of the
reviewed articles and research objects mentioned in the most cited articles had given the same message
that ICT has to be interconnected in teaching and learning activities in our classroom for the quality
education. Meanwhile reviewed method was the first and the mixed method was seemed the next second
dominating research approach among the seven most cited published articles. In all seven most cited
articles, different research approaches, for example, quantitative method, a mixed methods research,
review method and the qualitative research method. Similarly, different research instruments were used
in the seven most cited articles, for example, the survey questionnaire, the qualitative interview question
and focus group discussion. There were many similarities and contrasts among the seven most cited
articles, for example, the research method and research instrument and key words used in the articles
(Creswell, 2017).
The review of seven most cited articles according to per five selected journals and per year among the
five selected journals between 2013 to 2017 has highlighted the key results in the field of educational
technology research. The current study has supported the empirical studies of Abramovich, Schunn
and Higashi (2013) because the study of Abramovich et al. (2013) had also concluded that the articles
published in the former years had greater number of citation counts than articles published in the later

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years as the current study concluded. The current study has also identified that the current trends in
education technology is highly connected with the computer and education in teaching activities because
most of the published most cited published articles were derived from the journal of Computer and
Education (n=21). The result importantly conclude that the key words used in different articles were
varied in seven published articles, but the mostly repeated keywords from seven published articles were
identified as learning, technology, collaboration, game, mobile and education. Furthermore, the current
study signifies that the trends of current educational technology research has focused on computer
and education technology research. Finally, the current study also confirmed that most of the repeated
published articles were also derived from the JCE (Sun & Shen, 2014).
The current researcher had faced many difficulties during this study, for example, finding the most cited
articles because there was variation in the citation counts among different online sources. Some online
sites showed greater number of citation counts and some online resources showed lesser number of
citation counts. The next limitation of the current study is the analysis of the limited number of most cited
articles because the current study had reviewed only seven most cited articles. So, the findings cannot
be generalized for the larger sample size in the similar context. The next limitation of the this study was
the limited analysis of characteristics of the only seven most cited articles because the current study has
analyzed articles based on per year among the five selected journals and per five selected journals. The
current researcher has also realized that the findings would be more valid and reliable if the greater number
of the most cited articles had been selected and added in the analysis section. Again, it was further noticed
that reviewing most cited articles can give more depth knowledge to select future research topics and also
helpful to know the current trends of educational technology research. The most crucial findings for the
current researcher was embedded in knowing the emerging issues of educational technology to integrate
in teaching and learning activities for improving the quality of education and students’ performance
(Margaryan, Bianco & Littlejohn, 2015). It is obvious that the educational technology research of JCE is
emerging in educational institutions so the researchers have to focus on reviewing the most cited articles
on the journal of JCE.
Recommendations
The future research has also to focus on reviewing the most cited articles of longitudinal studies which
would give more citation counts and reflect more advanced knowledge of educational technology for
the novice researchers. This study recommends that the future researchers need to focus on reviewing
the greater number of the most cited journals of CE separately to foreground the specific knowledge
of educational technology to enhance educational quality by which an innovative and contemporary
knowledge of educational technology and computer education can be generated for future generation. If
the future research focuses on reviewing the most cited articles of per selected five journals, it would be
more beneficial for practitioners, school leaders and the different levels teachers to gain more knowledge
how to intergrade computer technology into classroom teaching. More importantly, the future research
needs to focus on reviewing the articles of the former years which would give more citation counts
and deep knowledge for conducting the future primary research. This study also recommends that the
future research also has to select the most cited published articles of per five selected journals and needs

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to review them separately so that it can help the future researchers to know the special issues of each
journal and to conduct primary research on different issues, for example, BJET, CE, JCAL, IEEE TLT,
JETRD. It is also recommended that the future research has to focus on different characteristics (for
example, strengths and weakness, contents, abstracts, citation counts, published years). Finally, in order
to generalize the results obtained in this study, similar analysis of the most cited articles per five journals
and per year among the five selected journals should be made on reviewing most cited published articles
between 2013 to 2017.
References
Abramovich, S., Schunn, C. and Higashi, R. (2013). Are badges useful in education? It depends upon
the type of badge and expertise of learner. Educational Technology Research and Development,
61(2), 217-232.
Aksnes, D. (2003). Characteristics of most cited articles. Research Evaluation, 12(3), 159-170.
Arteaga Sánchez, R., Cortijo, V. and Javed, U. (2014). Students’ perceptions of Facebook for academic
purposes. Computers & Education, 70, pp.138-149.
Buckley, P.and Doyle, E. (2017). Individualising gamification: An investigation of the impact of learning
styles and personality traits on the efficacy of gamification using a prediction market. Computers
& Education, 106(1), 43-55.
Creswell, J. (2017). Designing & Conducting Mixed Methods Research + The Mixed Methods Reader.
[S.L.]: Sage Publications.
Dalby, D., & Swan, M. (2018). Using digital technology to enhance formative assessment in mathematics
classrooms. British Journal of Educational Technology. doi: 10.1111/bjet.12606Daldrup-
Davies, R., Dean, D. and Ball, N. (2013). Flipping the classroom and instructional technology integration
in a college-level information systems spreadsheet course. Educational Technology Research and
Development, 61(4), pp.563-580.
De Witte, K., Haelermans, C., & Rogge, N. (2014). The effectiveness of a computer-assisted math learning
program. Journal of Computer Assisted Learning, 31(4), 314-329.
Innovation in technologies for educational computing. (2016). IEEE Transactions on Learning
Technologies, 9(1), 96-96.
Januszewski, A. (2001). Educational technology. Englewood, Colo.: Libraries Unlimited.
Lee, M., Wu, Y., & Tsai, C. (2009). Research Trends in Science Education from 2003 to 2007: A content
analysis of publications in selected journals. International Journal of Science Education, 31(15),
1999-2020.
Margaryan, A., Bianco, M. and Littlejohn, A. (2015). Instructional quality of Massive Open Online
Courses (MOOCs). Computers & Education, 80, pp.77-83.
Onifade, A. (2011). The third millennium secretary and information & communication technology:
Nigerian experience. International Journal of Management & Information Systems (IJMIS), 13(2),
39.
Pathek, R., & Chaudhay, J. (2012). Educational technology. New Delhi: Dorling Kindersley.

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Picatoste, J., Pérez-Ortiz, L., & Ruesga-Benito, S. (2018). A new educational pattern in response to new
technologies and sustainable development. Enlightening ICT skills for youth employability in the
European Union. Telematics and Informatics, 35(4), 1031-1038.
Robins, A. (2015). The ongoing challenges of computer science education research. Computer Science
Education, 25(2), 115-119.
Sun, G. and Shen, J. (2014). Facilitating social collaboration in mobile cloud-based learning: A teamwork
as a service (TaaS) approach. IEEE Transactions on Learning Technologies, 7(3), pp.207-220.
Sung, H. and Hwang, G. (2013). A collaborative game-based learning approach to improving students’
learning performance in science courses. Computers & Education, 63, pp.43-51.
Tsai, C., & Lydia Wen, M. (2005). Research and trends in science education from 1998 to 2002: a content
analysis of publication in selected journals. International Journal of Science Education, 27(1), 3-
14.
Tondeur, J., van Braak, J., Siddiq, F., & Scherer, R. (2016). Time for a new approach to prepare
future teachers for educational technology use: Its meaning and measurement. Computers &
Education, 94, 134-150.
Topîrceanu, A. (2017). Gamified learning: A role-playing approach to increase student in-class
motivation. Procedia Computer Science, 112, pp.41-50.
Appendix 1
Table 1. Five most cited articles per journal based on Journal (Using Publish and Perish Tool)
British Journal
of Educational
Technology
Computer and
Education
Journal of
Computer Assisted
Learning
IEEE transactions
on learning
technologies
Journal of educational
technology research and
development
1.Putting twitter to
the test: Assessing
outcomes for student
collaboration,
engagement and
success-259 times
(2013)
1. Gamifying
learning
experiences:
Practical
implications and
outcomes-801 times
(2013).
1.Technological
pedagogical
content knowledge
- A review of the
literature
410 times (2013).
1. Developing into
participants' profiles
and use of social
tools in MOOCs
54 times (2014)
1. Flipping the classroom
and instructional
technology
integration in a college-
level information system
spreadsheet course-506
times (2013).
2.Mapping learning
and game mechanics
for serious games
analysis-211 times
(2015)
2.Current status,
opportunities and
challenges of
augmented reality
in education
606 times (2013).
2. Is it a tool suitable
for learning? A
critical review of
the literature on
Facebook as a
technology-enhanced
learning environment
263 times (2013).
2. Metafora: A
web-based platform
for learning to learn
together in science
and mathematics
52 times (2013)
2. Are badges useful in
education? It depends
upon the type of badge and
expertise of learner-239
times (2013).
3. Critical success
factors for transforming
pedagogy with mobile
Web 2.0
154 times (2015)
3. Assessing
the effects of
gamification in the
classroom: -352
times (2015)
3. Challenges
to learning and
schooling in the
digital networked
world of the 21st
century
191 times (2013)
3. Providing
collaborative
support to virtual
and remote
laboratories
47 times (2013)
3. Instructor experiences
with a social networking
site
in a higher education
setting: expectations,
frustrations,
appropriation, and
compartmentalization
97 times (2013).

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4.Ethical and privacy
principles for learning
analytics-121 times
(2014)
4. Here and now
mobile learning: An
experimental study
on the use of mobile
technology-329
times (2013).
4. A mixed methods
assessment of
students’ flow
experiences during
a mobile augmented
reality
science game-126
times (2013).
4. GreedEx: A
visualization tool for
experimentation and
discovery learning
of greedy algorithms
39 times (2013)
4. Enhancing socially
shared regulation in
collaborative
learning groups: designing
for CSCL regulation
tools-89 times (2015).
5.The research and 5.Instructional 5. Blending 5. Facilitating 5. Improving learning
evaluation of serious quality of Massive student technology social collaboration achievements, motivations
games: Toward Open Online experiences in formal in mobile cloud- and problem-solving skills
a comprehensive Courses (MOOCs) and informal learning based learning: through a peer
methodology-119 times 300 times 108 times(2013) A teamwork as assessment-based game
(2014) (2015) a service (TaaS) development approach
approach 78 times (2014).
25 times (2014)
Table 2. Five highly cited articles per journal based on published
year 2013-2017 (Using Publish and Perish Tool).
2013 2014 2015 2016 2017
1. Gamifying learning 1. Effectiveness of 1. Assessing
the effects of
gamification in
the classroom: A
longitudinal study on
intrinsic motivation,
social comparison,
satisfaction, effort,
and academic
performance
352 times
Computers &
Education
1. The effects 1. Self-regulated learning
experiences: Practical virtual reality-based of integrating strategies predict learner
implications and instruction on students' mobile devices behavior and goal
outcomes, 801 times learning outcomes with teaching attainment in Massive
Computers & in K-12 and higher and learning on Open Online Courses
Education education: A meta- students' learning 45 times
analysis performance: A Computers & Education
273 times meta-analysis and
Computers & Education research synthesis
167 times
Computers &
Education
2. Current status, 2. It's not about seat 2.Instructional 2. An update to
the systematic
literature review of
empirical evidence
of the impacts
and outcomes of
computer games
and serious games
148 times
Computers &
Education
2. Some guidance on
opportunities and time: Blending, quality of Massive conducting and reporting
challenges of flipping, and efficiency Open Online qualitative studies
augmented reality in in active learning Courses (MOOCs) 28 times
education, classrooms 300 times Computers & Education
606 times 252 times Computers &
Computers & Computers & Education Education
Education
3. Flipping the 3. Students' perceptions 3. Mapping learning 3. Mobile apps for 3. Perceiving learning at
classroom and of Facebook for and game mechanics science learning: a glance: A systematic
instructional academic purposes for serious games Review of research literature review of
technology integration 231 times analysis-211 times 80 times learning dashboard
in a college-level Computers and British Journal Computers & research
information systems Education of Educational Education 25 times
spreadsheet course, Technology IEEE Transactions on
506 times Learning Technologies
Educational
Technology Research
and Development

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4.Technological
pedagogical content
knowledge - A review
of the literature
410 times
Journal of Computer
Assisted Learning
4. Is FLIP enough?
Or should we use
the FLIPPED model
instead?
203 times
Computers and
Education
4. Understanding
the MOOCs
continuance: The
role of openness and
reputation
156 times
Computers &
Education
4. Virtual
laboratories
for education
in science,
technology, and
engineering: A
review
77 times
Computers &
Education
4. Individualising
gamification: An
investigation of the
impact of learning
styles and personality
traits on the efficacy of
gamification using a
prediction market
15 times
Computers & Education
5. Here and now 5. Experimenting with 5. Critical 5. Facebook 5. Studies of student
mobile learning: An electromagnetism success factors and the others. engagement in gamified
experimental study using augmented for transforming Potentials and online discussions
on the use of mobile reality: Impact on flow pedagogy with obstacles of Social 10 times
technology 329 times student experience mobile Web 2.0 Media for teaching Computers & Education
Computers & and educational 154 times in higher education
Education effectiveness British Journal 74 times
159 times of Educational Computers &
Computers and Technology Education
Education

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A Review of Literature on
MBA-Expectations and Reality
Mr. Narayan Sapkota1 Dr. Basanta Prasad Adhikari2
Research Head and International Relationship Officer
Abstract
The objective of this this review was to understand the existing knowledge on the current program of Master
of Business Administration (MBA) in the global context. The next objective was to find out the knowledge gap
between the existing knowledge and skills delived by the MBA program and the required skills demanding by
the global industries and companies. The research method of this study was based on reviewing method. The
reviewed journal articles were entitled “the Journal of Higher Education Policy and Management, Academy
of Management Learning & Education, Journal of Applied Psychology, Journal of Leadership Education,
Academy of Management Review, Journal of Business Ethics, Journal of Management Development, Consulting
Psychology Journal: Practice and Research, Innovative Marketing, Women in Management Review, Journal of
Public Policy & Marketing, Nursing Management (Springhouse) and Human Resource Development Review”
The results highlighted that more than ten (n=20) articles were reviewed to understand the knowledge gap
between thedeliveredskillsbythecurrent MBAand requiremanagerial skills demandingbythe global industries
and companies. The reviewed results highlighted that MBA programs need to set of pedagogical practices to
teach leadership in a global context that value awareness, reflection and development of the leadership skills.The
results also indicate that many graduate students from reputed business schools were unable to shows integrative
thinking as compare to undergraduates from other domains. The results also confirmed that most of the business
courses and schools were being criticized to make money for the University and their professors and there was
a little relevance of the output on career development and managerial practices. The results also highlighted
that students were not aware of what they needed to do after complication of the MBA Degree and they lack
of technical and human skills which made them confused toward their conceptual skills to use at appropriate
time during their professional work. In addition, the results also show that the scholars were not happy with the
pedagogy of delivering the MBA degree skills. The implication of this study will be useful to academicians and
MBA course designers to reform the existing courses to meet the current global demand of leader’s skills to
employ at global companies and industries in future. The limitation of this study was the reviewed of the imitated
number of journal articles which does not guarantee for the generalization of the findings in the similar context
in future. It is recommended that the future research needs to focus to review the most cited published journal
articles to deepen the knowledge gap between the existing managerial skills delivered by the MBAprogram and
the required skills demanding by the global companies andindustries.
Keywords: Master of business Administration, review, knowledge gap, MBA course, global required
leadership skills.
1. Introduction
Master of Business Administration (MBA) is one of the most popular subjects in the field of business and
management. Moreover, students of other disciplines, e.g., Engineering, Medical Science, Technology are
also showing their interest to get the fusion degree. Additionally, many universities are introducing the dual
degree combing MBA and other disciplines. In addition, (Dubas, 2017) found that MBA program plays a
vital role to minimize the gap between the companies’ expectations and managerial skills delivered to the

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graduate students. The primary propose of this reviewed journal article was to identify the gap between
the expectation of companies and the teaching learning processes implemented by the business schools.
This review is embedded in examining the following questions a) What expectation do companies are
looking for through MBA graduates? b) What are the thoughts of scholars about the MBA program
organizers c) What are the best approaches for business school to meet the current global expectations of
companies and students. This review was based on the theoretical arguments of the previous studies. This
review articles were organized on major four parts i.e. introduction, review of literature, methodology and
theoretical answer of the research questions, discussion and conclusions. The reasons for undertaking this
study toward an MBA are widely documented in the following section. A recent survey showed that self-
improvement, career development, enhancing business skills, having a positive impact on society are the
most important to MBAs immediately after they receive their degrees. Other reasons such as networking
opportunities, experiencing a foreign culture (for overseas students) and increased professional and personal
effectiveness are also proposed (Blackburn, 2011). Students in the MBA program are usually entered in
their late twenties with experience across small, medium and large organizations, and come from diverse
professional backgrounds, e.g. Engineering, Automotive, Law, Marketing, Banking, Defense and Tourism
Management, Consulting, Entrepreneurship (The Aspen Institute, 2008). Many national and international
universities have invested a large amount of public funds but the rate of the students moving to other
international markets rather than the home countries has been increased steadily and created a great problem.
It is universally identified that the curriculum contents and practical skills required to MBA program have
to be modified and improved. It is expected that future managers and company leaders have to able to scan
both internal and external company’s environment to achieve their pre-determined objectives (Lawrence,
Dunn & Weisfeld-Spolter, 2018).
2. Literature Review
2.1 MBA Expectations and reality
In today’s globalized world, most of the business schools are desperate to get the business leader, who
can be able to achieve the competitive advantage in this competetive global markets. And, the primary
source of it seems to be the business schools. However, companies have a greater dissatisfaction toward
the graduate students of business and management, programs like MBAs and EMBA. Current executive
programs are also fail fulfill the demand of companies. Soft skills are the most important for the business
leaders, but MBA Program also need to focus on functional and technical skills. In addition, the common
requirements of MBA programs are embedded in thoughtful, awareness, sensitive, flexible and adaptive
capability of readiness to be a global executive. But the bigger questions have been raised for business
school’s capability to develop every dimensions of leadership skills. Because some abilities like
communication ability, leadership interpersonal skills, and wisdom skill alongwith “the ability to weave
together and make use of different kinds of knowledge” (Mintzberge & Gosling, 2002:28). But these
skills are at once less easily transferred to others and these skills are highly valued in the competition
for leadership positions that occur in organizations. In result of these coherence gaps between the
skills needed in business and taught program and companies look for alternative source. Here are few
examples to support it, “Boston Consulting Group hired 20% of its consultants without MBAs in 2000”;
“Hamilton planned to hire one third of its people without graduate business degrees” and “more than half
of the consultants at McKinsey and Company do not have a Master of Business Administration degree”

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(Leonhardt, 2000:1) “Not only that, many graduate students from reputed business schools are unable
to shows integrative thinking as compared to undergraduates from other domains” (Petriglieri, Wood, &
Petriglieri, 2011, P.17). Many companies introduced the 3-weeks basic business training programs for
new hire. The research study of Shepherd, Douglas & Fitzsimmons (2008) believe that (70 – 90) percent
of work place learning occurs through on-the-job experience, informal training, coaching and mentoring.
Now, the biggest questions arise for business school is “Can they fulfill the expectations of the current
global companies”? Business schools have to prove the answer not only for company but also need to
assure the students to gain the career success and professional achievement, such as handsome salary and
higher position. However, many business schools had been facing the numbers of obstacles like cost,
faculty and staff, status-based system and status quo. Leavitt &m Leavitt (2012) argue that “business
schools have been designed without practical fields”. Moreover, the curriculum of MBA and E-MBAhave
not supported for succeeding in business outcomes because it is focused on the functions of business not
in practical skills of managing business institutions (Mintzberg & Gosling, 2002). Due to the impractical
culture, there is little evidence to provide learning required skills. Even, the assumptions of learning are
also incorrect and focused on external incentive such as grading impeded rather than enhance learning
outcomes and managerial skills (Steiner & Watson, 2006). Another issue faced by the business schools is
the method of instructions for example case method, combination of the practical knowledge to professional
skills but few examples are established business schools are there much clinical training or learning by doing-
experiential learning where “concrete experience is the basis for observation and reflection” (Твердола &
Tverdola, 2018. p.22). Likewise, the selection criteria, GMATis also negatively perceived by the students and
it is believed that managerial success depends on the mind-set of the students to be successful entrepreneurial
rather than a qualified manager (Mintzberg and Gosling, 2002).
Most of the courses of business schools are being criticized to make money for the University and their
professors and there is little relevance of the output on career development and managerial practices.
Most of the Universities perceive MBA program as “Cash cow”. The most common perspective and
approach to business school education is supposed to address the issue of relevance most of the common
practices of MBA program are shared for experienced students, multidisciplinary program, how people
think about business issues, application of learning in groups and individual’s current job and company.
Business schools need to think differently to get the success in the competative business world in future.
It is important to convert the valuable practices into culture that helps to institutionalize it our practices.
These practices are embedded in the quality enhancement, attraction of high performer faculty and
staff, research practices, systematic assessments of the products and evaluation of competitive global
environment (Waddock & Lozano, 2013).
2.2 Challenges to Develop the Business Leader
Developing business leaders is not a simple task. It is a human development process which is incomparable
with the product development or other tasks. On the other hand, the current market is more dynamic and
competitive. In this situation most of the business schools are struggling to cope with the challenges to
develop global leaders. The initial challenge of developing business leader starts with the assumption
about learning practices and it raise the few questions like ‘how our receptions are perceiving the learning
process?’ ‘Does it fulfill the actual meaning of teaching and learning outcomes?’ ‘Does it really meet
requirement of the external incentive likes grading and motivation?’ (Blackburn, 2011). It is not easy to

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answer the questions mentioned above because these questions are embedded in our perception, belief
and social thought. The second most important issue is about the pedagogy. The biggest question that
come up with the pedagogy is what type of pedagogy is perfect to solve the contemporary problem.
Likewise, instruction also plays a vital role for leadership development. But the questions aroused?
Does the methods like, case study, presentation, group discussion, reading article, doing assignments and
lecturers are sufficient for the leadership development ? If not, what could be the best way of instruction
for developing business leaders and what about the practical skills for them?
The previous study of Brett and Atwater (2001) argue that the selection of instrument and tools should
create the ownership by students. It could be done by supportive organizational structure and engagement
of faculty, importance of protégé beliefs and performance as a leader, mentoring, self-reflection, absorb
negativefeedback,trulycapableofleadership,emotionalandfrequentlyinvolvementsinpractices.Further,
Klimoski and Amos (2012) highlighted that it should focus on clear program goals, responsibility for
direction an articulated pedagogical framework, MBA programs, student ownership, and greater reliance
on experience and the use of assessments in order to provide evidence of impact. The other challenge that
needs to face by the development program is the number of available faculty members, their nature and
duration and sequencing of learning activities with functional subjects and specializations. (Lawrence
Dunn & Weisfeld-Spolter, 2018). Similarly, most business school’s faculties were not properly trained in
pedagogy and curriculum design, and they may not be able to face the challenge of teaching leadership
with the most appropriate research findings in mind (Klimoski & Amos, 2012). Some of the business
schools are facing the financial crisis and they are adopting the cost minimization strategy like increasing
the size of sections, increasing the average class size and reduce the number of smaller classes or at a
minimum to hold class sizes constant. But the question arises here. Does this strategy help us to achieve
our aim? Or Are the business schools really doing a business? Another challenge faced by business
schools is status-based system, it is scarcely in the interests of those schools winning the competitive war
for status to change the rules of the game that have put them on top. “As with any status-based system, it
is scarcely in the interests of those schools winning the competitive war for status to change the rules of
the game that have put them on top”. And finally, the status quo is maintained by the taken-for-granted
aspect of so much of business education, the fact that what we do and how we do it has become truly
institutionalized (Blackburn, 2011).
Developing female business leader is another challenge for most of the business schools. The number of
female students is not only low in the classroom, they are also low in the business and employment sector
specially managers and executive directors (Marlow & Carter, 2004; Reed, 1992). In some societies there
is clear separation of profession by gender for example in Nepal ‘Male students are not allowed to enroll
in Nursing and air hostage course, whereas Scandinavian countries give women greater opportunities to
fill top executive positions. However, in the arena of world business, the number of female graduates is
around thirty percent (30%), which seems as a hitting the ‘glass ceiling’ (DeRue & Ashford, 2010; Datar,
Garvin & Cullen, 2014) in today’s scenario, many business schools are trying to increase the number of
female students to fulfill the demand of companies for the female talent, build their pipeline of female
leaders, and compensate the gender imbalance that exists in top levels of management (Dragoni, Tesluk,
Russell & Oh, 2009). Some of the top business schools have introduced the fellowships and scholarship
to attract and encourage female leaders and to create awareness of career potential in business. Moreover,
partnership between business schools and external organizations also provide a platform like focused

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events and activities, including conferences and recruitment opportunities (Anderson, 2006).
2.3 Contemporary Approach to Fulfill the Expectations & Cope the Challenges
Thebusinessenvironmentisbeingmorecomplexdaybyday,whichisdemandingmoretalented,innovative
and dynamic leaders. Leadership development is a stage of enhancement in the life cycle which helps,
encourages and supports the expansion of knowledge and expertise required to optimize one’s leadership
competencies & performance (Dator, Gravin, & Cullen, 2014). It is complex and multidimensional field
that continues to evolve time and again (Montgomery, 2005). On the other hand, business schools are
criticized to not teach the right contents, whether that is ethical management, decision making or a greater
emphasis on input of globalization (Bazerman & Moore, 2009; Collinson, 2014). Furthermore, MBA
program has not been given enough effort to training for leadership development skills (Mintzberg &
Gosling, 2002; Pfeffer & Fong, 2002). MBA programs have to set of pedagogical practices to enhance
leadership skills in a context that value for the awareness, reflection and development (Roseser & Peck,
2009; Waddock, &Lozano, 2013).
The contemporary approach of leadership (new pedagogy) development focuses on the opportunities
to learn about the experience, motives, values aspiration and their interaction with the people around
them that influence how they are and how they lead the business organizations (Pfeffer & Sutton, 2006).;
Lawrence et al., 2018). Furthermore, it should be based on values awareness, reflection and development
designed to foster personal and professional growth (Lawrence, Dunn & Weisfeld-Spolter, 2018; Roeser
& Peck, 2009). In addition, the published articles were failed to link between theory and practices because
book learning and skills building are also essentials to develop the dynamic leaders which is not found in the
reviewed articles (Benjamin & O Reilly, 2011; Peffer & Sutton, 1999). The learning material (pedagogy)
should focus on pedagogy which could build ability to interact with other leaders, followers and organizational
actors, who exist from dynamic environment (Podsakoff, MacKenzie, Lee & Podsakoff, 2003; Collinson,
2014). It is a transformational experience where they should gain self-insight and self-knowledge, desire and
motivation to be a great leader. They must feel confident and being a great leader, self-efficiency in acting
like a leader, think like a leader, mastering critical task and to cope with stress and emotions (Klimoski &
Amos, 2012). The recent evidence of business leadership development programs is located on self-awareness,
iterative learningand reflection, and leadership coaching for development utilizing an assessment of leadership
potential with established reliable and valid measure (Lawrence et al., 2018) but current MBA programs were
failed todeliver the practical skills for professional leaders.
It is important to select the good instruments and tools to develop the leader who can help themselves and
others. Good instruments present the seven scale tools i.e. drives, experiences, awareness, learning ability,
leadership traits, capability and derailment risks (Gapper, 2005) which will help to be an accountable,
handle the complexity and be able to create the scope (Hooijberg & Lane, 2009). With the support of
these instruments, it was also thought that other tools are also valuable to develop the competent leader
like, multisource 360 feedback system (Breft & Atwater, 2001; Hooijberg V lane, 2009), Service learning
(Steiner & Watson, 2006), Personality assessment (Brungardt, 1997 & Carvan, 2015); Clinical counseling
(Chermack & Passmore, 2005) which are the common tools preferred by the universities. On the other hand,
the question has raised to know the capability to lead in this complex environment. The review articles have
presented the norms to compare the competency with successful global leader at each level from individual
to CEO to identify the strength and weakness or knowing oneself which means, examine the ability to

OCEM Journal of
convert classroom practice into professional life (Lester, Hannah, Harms, Vogelgesang & Avolio, 2011).
Similarly, introducing one to one partnership under coaching of trained and certified mentors are valuable
for leadership development (Hooijberg & Lane, 2009).
3. Methodology
The research methodology of this paper was review of previous articles based on theoretical review of
the selected ten (n = 10) published articles which helps to identify new knowledge about an emerging
topic of MBA programs (Torraco, 2005). The review method study has followed the study of Chermack
and Passmore (2005) which argue that the review approach is a key research method for summarizing
the current body of literature pertinent to MBA programs and leadership skills. This approach helps this
researcher to provide the framework of the research method. Throughout the examination of the different
articles based on MBA programs and leadership skills in different journal (Academy Of Management
Learning & Education, the Journal of Leadership Education, the Journal of Higher Education Policy
And Management, the Journal of Marketing Education Review, Harvard Business Review Press, Journal
of Business Ethics), finally 20 articles have been chosen from the five different management journals
from 2002 to 2018. The reviewed journal articles have highlighted the key knowledge on MBA program
and its delivered skills. The model summary tables include the name of the sample article, the name of
journal, key finding, published years and key words (Ibeh, Carter, Poff, & Hamill, 2008).
4. Findings & Discussion
4.1 Summary and the Conclusions
The review results show that MBA programs have to set pedagogical practices to teach leadership
in a context that value awareness, reflection and development. The results also indicate that many
graduate students from reputed business schools are unable to show integrative thinking as compared
to undergraduates from other domains. It also confirms that most of the business courses and schools
were being criticized only to make money for the University and their professors and there was little
relevance of the output on career development and managerial practices. The review results also note
that the selection of the good instruments and tools are essential for the development of a leader who can
help themselves and others. It is further summarized that drives, experiences, awareness, learning ability,
leadership traits, capability and derailment risks were the seven scale instruments for the leadership
development. This reviews also that shows that Business Schools have to work to fulfill the expectation
of the global companies and MBA students. The results also highlighted that students were not aware of
what they needed to do after completion of the MBA Degree. Further, results show that MBA students
were found of lacking technical and human skills which make them confused toward their conceptual
skills to use at appropriate time. In addition, the results also indicate that the scholars were not happy with
the pedagogy of delivering the MBA Degree skills. The results further noted that students of Business
Schools perceived MBA programs for making money as a cash cow. It was also noted that the MBA
Degree was developed for the development of leadership skills for business purposes which was possible
through behavioral aspects, e.g. self-awareness, assessment, reflection and coaching. Similarly, the results
indicate that the pedagogical development was essential for development of dynamic leaders to compete
with this tough and competitive business environment. The results importantly disclosed that pedagogy,
material, ability to coach, self-awareness, reflection ability and level of assessment were found to be the
key indicators of developing a qualified leader. It was also highlighted that the institutional and individual

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success of developing leadership skills primarily depends on determination of all stakeholders, clear
vision of the program director and the devotion to prepare a dynamic MBA graduate leader. Additionally,
the review results confirmed that most of the business schools were struggling to cope with the challenges
to develop the professional business leader. The most common challenges of MBA programs were found
as the assumption, pedagogy, instruction, instrument, manpower, cost, status-based system and status
quo. Finally, the results importantly indicate that the business schools were criticized not to teach the
right contents and global leadership skills.
4.2 Future Recommendations and Limitations
This study recommends that the future research has to focus on several limitations of MBA program
on practical skills to develop a qualified leader. This study also recommends that future research has
to focus on how to enhance the skills for development of dynamic leaders to compete with this tough
and competitive business environment. It is also recommended that future research has to emphasise on
how the graduate MBA students can ahieve necessary leadership skills and to able to show integrative
thinking as compared to undergraduates from other domains. Future research has also to address on the
necessary leadership skills via business courses to make money for the universities and to focus on the
relevance of the outputs on career development and managerial practices. Future research is also sought
for the balancing of practical and theoretical skills of MBA programs. This review is embedded in the
selection of twenty articles which may create conflict conclusion because of missing some important
contemporary data. There is no specific approach or guideline used for selection of the published articles.
This review has provided the limited research gap between the MBA programs and current demand of
global companies to fulfill the expectation of students and companies, universities or school of business
need to do the further research in pedagogical development. Again future research is required for the
new policy reform. Future research need to address the periodical examination on reforming the MBA
program to find the specific expectation of the companyies (Hooijberg & Lane, 2009). This review has
covered limited articles so that results cannot guarantee the reliability and validity of the findings. On
the other hand, the review is based on the secondary data so that the current researcher cannot take the
guarantee of the review data and findings.
References
Anderson, L. (2006). Building Confidence in Creativity: MBA Students. Marketing Education
Review, 16(1), 91-96.
Bazerman, M.H., & Moore, D. A. (2009). Improving decision making. In Judgment in managerial
decision making (7th
ed.): 179-199. USA, Hoboken, NJ: John Wiley & Sons.
Benjamin, B. and O’Reilly, C. (2011). Becoming a Leader: Early Career Challenges Faced by MBA
Graduates. Academy of Management Learning & Education, 10(3), 452-472.
Blackburn, G. (2011). Which Master of Business Administration (MBA)? Factors influencing prospective
students’ choice of MBA programme – an empirical study. Journal of Higher Education Policy and
Management, 33(5), 473-483.
Brett, J.andAtwater,L.(2001).360°feedback:Accuracy,reactions,and perceptionsofusefulness. Journal
of Applied Psychology, 86(5), 930-942.

OCEM Journal of
Brungardt, C. (1997). The Making of Leaders:AReview of the Research in Leadership Development and
Education. Journal of Leadership Studies, 3(3), pp.81-95.
Carvan, M. (2015). Leadership Education for the Volatile, Uncertain, Complex, and Ambiguous Now: A
Challenge to the Field. The Journal of Leadership Education, 14(4), 3-10.
Chermack, T. J., & Passmore, D. L. (2005). Using journals and databases in research. In R. A. Swanson &
E. F. Holton (Eds.), Research in organizations: Foundations and methods of inquiry (pp. 401-418).
San Francisco, CA: Berrett-Koehler.
Collinson, D. (2014). Dichotomies, dialectics and dilemmas: New directions for critical leadership
studies? Leadership, 10(1), 36-55.
Datar, S., Garvin, D. and Cullen, P.(2014). Rethinking the MBA. Boston: Harvard Business Review Press.
DeRue, D. and Ashford, S. (2010). Who will Lead and Who will Follow? a Social Process of Leadership
Identity Construction in Organizations. Academy of Management Review, 35(4), 627-647.
Dragoni, L., Tesluk, P.,Russell, J. and Oh, I. (2009). Understanding Managerial Development: Integrating
Developmental Assignments, Learning Orientation, and Access to Developmental Opportunities
in Predicting Managerial Competencies. Academy of Management Journal, 52(4), 731-743.
Gapper, J. (2005). Comment on Sumantra Ghoshal’s “Bad Management Theories Are Destroying Good
Management Practices”. Academy of Management Learning & Education, 4(1), 101-103.
Hooijberg, R. and Lane, N. (2009). Using Multisource Feedback Coaching Effectively in Executive
Education. Academy of Management Learning & Education, 8(4), 483-493.
Ibeh, K., Carter, S., Poff, D., & Hamill, J. (2008). How Focused are the World’s Top-Rated Business
Schools on Educating Women for Global Management? Journal of Business Ethics,83(1), 65-83.
Klimoski, R., & Amos, B. (2012). Practicing Evidence-Based Education in Leadership
Development. Academy of Management Learning & Education, 11(4), 685-702.
Lawrence, E., Dunn, M. and Weisfeld-Spolter, S. (2018). Developing leadership potential in graduate
students with assessment, self-awareness, reflection and coaching. Journal of Management
Development, 37(8), .634-651.
Lawrence, E., Dunn, M., & Weisfeld-Spolter, S. (2018). Developing leadership potential in graduate
students with assessment, self-awareness, reflection and coaching. Journal of Management
Development, 37(8), 634-651.
Leavitt, J., & Leavitt, L. (2012). Political Economy of Public Health. New West Indian Guide / Nieuwe
West-Indische Gids, 86(1-2), 90-95.
Lester, P., Hannah, S., Harms, P., Vogelgesang, G. and Avolio, B. (2011). Mentoring Impact on Leader
EfficacyDevelopment:AFieldExperiment.AcademyofManagementLearning &Education,10(3),
pp.409-429. McCall, McCall, M. (1994). Identifying leadership potential in future international
executives: Developing a concept. Consulting Psychology Journal: Practice and Research, 46(1),
49-63.
M. Dubas, K. (2017). Effective design and assessment of an MBA degree program through
benchmarking. Innovative Marketing, 13(4), 25-34.

OCEMJournalof
Marlow, S., & Carter, S. (2004). Accounting for change: professional status, gender disadvantage and
self‐employment. Women in Management Review, 19(1), 5-17.
Mintzberg, H. and Gosling, J. (2002). Educating Managers Beyond Borders. Academy of Management
Learning & Education, 1(1), 64-76.
Montgomery, D. (2005). Asian Management Education: Some Twenty-First-Century Issues. Journal of
Public Policy & Marketing, 24(1), 150-154.
Petriglieri, G., Wood, J. and Petriglieri, J. (2011). Up Close and Personal: Building Foundations for
Leaders’ Development Through the Personalization of Management Learning. Academy of
Management Learning & Education, 10(3), 430-450.
Pfeffer, J. and Fong, C. (2002). The End of Business Schools? Less Success Than Meets the Eye. Academy
of Management Learning & Education, 1(1), 78-95.
Pfeffer, J., & Fong, C. (2002). The End of Business Schools? Less Success Than Meets the Eye. Academy
Pfeffer, J., & Sutton, R. I. (2006). Hard facts, dangerous half-truths and total nonsense: Predicting from
evidence-based management. Boston, MA: Harvard Business School Press.
Podsakoff, P., MacKenzie, S., Lee, J. and Podsakoff, N. (2003). Common method biases in behavioral
research: A critical review of the literature and recommended remedies. Journal of Applied
Psychology, 88(5), 879-903.
REED, J. (1992). Situational Leadership. Nursing Management (Springhouse), 23(1), 63-64
Roeser, R. and Peck, S. (2009). An Education in Awareness: Self, Motivation, and Self-Regulated
Learning in Contemplative Perspective. Educational Psychologist, 44(2), 119-136.
Shepherd, D., Douglas, E., & Fitzsimmons, J. (2008). MBA Admission Criteria and an Entrepreneurial
Mind-Set: Evidence From “Western” Style MBAs in India and Thailand. Academy of Management
Steiner, S. and Watson, M. (2006). The Service-Learning Component in Business Education: The Values
Linkage Void. Academy of Management Learning & Education, 5(4), 422-434.
Torraco, R. (2005). Writing Integrative Literature Reviews: Guidelines and Examples. Human Resource
Development Review, 4(3), 356-367.
Твердола, Н. and Tverdola, N. (2018). Leadership and Leadership Development Tools in a Vuca
World. Management of the Personnel and Intellectual Resources in Russia, 7(6), 14-18.
Waddock, S. and Lozano, J. (2013). Developing More Holistic Management Education: Lessons Learned
from Two Programs. Academy of Management Learning & Education, 12(2), 265-284.
Montgomery, D. (2005). Asian Management Education: Some Twenty-First-Century Issues. Journal of
Public Policy & Marketing, 24(1), 150-154.
Ibeh, K., Carter, S., Poff, D., & Hamill, J. (2008). How Focused are the World’s Top-Rated Business
Schools on Educating Women for Global Management? Journal of Business Ethics, 83(1), 65-83

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Shepherd, D., Douglas, E., & Fitzsimmons, J. (2008). MBA Admission Criteria and an Entrepreneurial
Mind-Set: Evidence From “Western” Style MBAs in India and Thailand. Academy of Management
Pfeffer, J., & Fong, C. (2002). The End of Business Schools? Less Success Than Meets the Eye. Academy
Appendix 1
S.
N.
Topic Article
Name of
Journal
Key finding
Published
year
Key word
1 Developing
leadership
potential in
graduate students
with assessment,
self-awareness,
reflection and
coaching
Journal of
Management
Development
 New approach to developing leadership
potential i.e. integrative model stimulates
a process of awareness, reflection and
intentional development, and supports the
identification a pursuit of goal-directed
learning opportunities throughout
students MBA program.
2018, Vol.
37 issue 8,
pp. 634-
651
Leadership
development,
Educational
innovation,
Assessments,
Coaching, MBA,
Self-development
Type: Research paper
2 Asian
Management
Education: Some
Twenty-First-
Century Issues
Journal
of Public
Policy &
Marketing
 Increasing opportunity in the field of
management
 Asian Based research are required
 Policy maker need to focus on it more.
2005, Vol.
24. No. 1,
pp. 150-
154
N/A
3. How focused
are the world’s
top-rated
business schools
on Education
women
for global
management?
Journal of
Business
Ethics
 Average 30% in the sample business
schools
 Only 10% of these business schools have
a specialist center for developing women
business leaders and only a third offered
women focused programs or executive
education courses, including flextime
options.
2008, Vol.
83, No. 1,
pp. 65-83
Women, female,
top management,
business schools,
globalization,
business education,
women networks
4. MBA Admission
Criteria and an
entrepreneurial
mind-set:
Evidence form
“Western” style
MBAs in India
and Thailand
Academy of
Management
 GMAT may discriminate against
applicants with a greater propensity of
behave entrepreneurially.
 The fast-moving global economy requires
managers to have an entrepreneurial
mind-set
2008
Vol. 7
No.2
PP. 158-
172
N/A
5 The End of
Business
Schools? Less
success than
Meets the Eye
Academy of
Management
Learning &
Education
 Business schools are not very effective
 Neither possessing an MBA degree
 Nor grades earned in courses correlate
with career success
 Little evidence that business school
research is influential on management
practices
2002
Vol. 1,
No.1
pp. 78-79

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Original Article
Factors Influencing Students’ Satisfaction in Oxford
College of Engineering and Management,
Gaindakot-2, Nawalpur of Nepal.
Abstract
The objective of this study was to examine the students’ recommendation to their kith and kin to enrol at
Oxford College of Engineering and Management (OCEM) for the higher education study. The previous
studies reveal that students’ satisfaction was embedded in collage physical facilities, administrative
facilities, program quality, quality of academic staff, location of college and reputation of colleges.
Quantitative research approach was used as research methodology and the survey study was use as
research method applied to collect data from the respondents. The sampling methods was first purpsive
and the second was random sampling method. Two hundred and thirty seven respondents (n=237) were
participated in this study. The response rate of the survey questionnaire was 94.8 %. The reliability
analysis was used to find the value of Cronbach’s Alpha in order to find out the reliability and consistency
of the data. Twelve subscales were extracted from the variables of each Principal Component. Similarly,
Student t-Test was used to find the differences in boys and girls for their recommendation to enrol their
kith and kin at OCEM, Nawalpur of Nepal. Fifty seven male (24 %) and one hundred and eighty female
(76 %) students were participated in this study. The results highlighted that female students were more
satisfied than the male students at OCEM.
The results aslo show that strict student development schedule was positively and statistically significantly
associated with the preference of students’ recommendation to enrol their kith and kin at OCEM (p <
0.05, B = .486). Similarly, the results further show that physical facilities of OCEM was positively and
statistically significantly associated to students’ preference to recommend their kith and kin to enrol at
OCEM (p < 0.05. B = 1.038). The results of Multiple Regression Analysis also highlighted that there
is significance association between students’ preference and locations of the college. The implications
of the findings will be beneficial for the private and public colleges to understand the reason behind the
declining trends of students’ enrolment at Chitwan and Nawalparasi Districts. It will be also fruitful
for the policy makers of higher educational institutions to formulate new student friendly strategies and
student motivation policies.
Keywords: Student satisfaction, physical facilities, academic qualities, administrative facilities, location
and reputation of the college, extracurricular activities, Principal Component Analysis
Introduction
All the college level organizations have been facing the challenges of student’s retention globally. This
has increased in recent years as the participation in higher education has increased significantly and

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diversified (Mihanović, Batinić & Pavičić, 2016).Acertain percentage of students will be always expected
to drop out of colleges but an effort has to be made to minimize it (Meling, Kupczynski, Mundy & Green,
2012). In today’s global world, economic growth depends on the capacity to produce knowledge, and
higher education institutions are key role players in developing a knowledge-based economy. Students
need to learn more in less time, and quality has become increasingly important issue in higher educational
institutions (Sweeney, 2016). It is obvious that good performance could make students more satisfied with
their study experience, thus improving their acquired knowledge and career development (Bassi, 2019).
Consequently, more effective degree courses at colleges may attract more motivated students and receive
increased funding from the government and other institutional lenders, with the result of improving
their competitive position (Langstrand, Cronemyr & Poksinska, 2014). To satisfy this requirement, it is
important to modify and make more effective organisation and contents of teaching activities, as well as
to offer adequate services to students (Bassi, 2019). An important concern for private colleges and public
colleges is retaining students and understanding the reasons why students of different programs choose
to leave a programme (Gibson, 2010). Additionally, college education is considered an essential means
for the social, economic and political development of a country (Hussein & Bahmani 2012). The right
to access higher education is mentioned in a number of international human rights agreements; it should
be the responsibility of governments and educational service providers to ensure broad access and high
standards of quality of the educational training processes in each and every college (Langstrand et al.,
2014).
More specifically, colleges should achieve high standards of quality in teaching, research, administrative
services and available facilities to pursue their mission better in future. In most cases, good quality
is synonymous with good performance even though the definition of quality in colleges’ context is
quite complex and challenging (Pounder 1999). Student satisfaction is deeply rooted in academic,
managerial, infrastructure and technological factors in educational institutions. Student satisfaction is
also embedded in the current status of college surrounding, lecturers’ educational qualification, teaching
pedagogy, placement practices, students’ support systems, faculty support, roles of faculty head; roles
of principal and library and lab facilities (Uprety & Chhetri, 2014). College education is considered
as the essential means for the social, economic and political development of a country. The right to
access higher education is mentioned in a number of international human rights agreements; it should
be the responsibility of governments and educational service providers to ensure broad access and high
standards of quality of the training processes in college level education (Moller, 2006). More specifically,
colleges should achieve high standards of quality in teaching, research, administrative services and
available facilities to pursue their mission better. Good performance could make students more satisfied
with their study experience, thus improving their acquired knowledge and college career. The primary
objective of this study was to examine the students’ preference to recommend their kith and kin to study
at private colleges and the preference of students to continue their higher education at private colleges in
Nawalpur District of Nepal. The secondary objectives of this study was to examine students’ satisfaction
on managerial factor; support service factor; administrative factor; infrastructure factor on students’
preference to recommend for their kith and kin (Chen, 2014). Student satisfaction is a highly debatable
global phenomenon in educational sector. The rate of high student turn-over is a serious problem at

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private and public colleges in Nepal. A large number of students exist from Nepal to foreign countries.
There is always fluctuation in student enrollment in colleges due to student’s dissatisfaction on academic;
managerial; organizational; infrastructure factors, location and reputation of colleges. Students have
been treated as customers since a long time ago but their satisfaction level is very poor and debatable.
Due to the lack of student satisfaction in different colleges, student turnover has been regarded as a big
threat for educational practitioners in Nepal. It is also true that student dissatisfaction directly impacts
for both quality of education and college financial situation by which students’ enrollment trends have
gone down in most of the colleges (Douglas, Douglas & Barnes, 2006). The declining trends of students
along with the biggest number of higher education institutions changed the intensity of competition
among colleges in Nepal and attracted much more attention to marketing efforts, which was so far highly
neglected particularly by Nepalese public institutions (Sojkin, Bartkowiak & Skuza, 2011). Students are
seeking for the student centered learning pedagogy, lifelong skills and international standard education
in our colleges but the current outcomes are just embedded in securing high marks without focusing on
delivering lifelong skills to our students (Uprety & Chhetri, 2014). .
1. Satisfaction:
The financial anxiety, low quality of lecturers and weak teaching practices, traditional organizational
managerial practices, a lack of student involvement in college decision making practices, limited learning
resources, poor service facilities, and high priority in theoretical education and less priority in lifelong
skills have undermined the student preference to recommend their kith and kin and to continue their
higher level education in the same colleges in Nepal (Uprety & Chhetri, 2014). Student satisfaction
level has become a major focus of academic practitioners and researchers in the competitive learning
environment owing to its strong impact on the success of educational institutes and prospective student
registration since the past few decades (Langstrand, Cronemyr & Poksinska, 2014; Weerasinghe &
Fernando, 2018). More specifically, colleges should accomplish high standards of quality in teaching,
research, administrative services and available facilities to pursue their mission to meet the contemporary
demands of students (Bini & Masserini, 2015).
1.1 Customer Satisfaction:
The word “satisfaction” is defined by Uprety and Chhetri (2014) as a state of feeling of a person who has
experienced performance or an outcome that fulfils his/her expectation. In terms of students, expectation
may go as far as before the students even enter the higher education, suggesting that it is important to
the educational practitioners to determine first what the students expect before entering the colleges. It
is believed that satisfaction actually covers the issues of students‟ perception and experiences during the
college years. It is considered that student satisfaction is a match between what students expect while
entering colleges, and perception and experiences they develop during the college years (Carey,
Cambiano, & De Vore, 2002). While most studies on satisfaction focus on the perspective of customers
and researchers who are facing a problem of creating a standard definition for student satisfaction. Thus
providing a need of customer satisfaction theory to be selected and modified so that it can explain the
exact meaning of student satisfaction (Hom, 2002). Similarly, William (2002) mentioned that even

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though it is arguable to view students as customers, but given the current atmosphere of higher education
marketplace, there is a new moral privilege that students have become “customers” and therefore can, as
fee payers, reasonably demand that their views should be heard and acted upon so as this study considers
students as “customers” (Weerasinghe & Fernando, 2018).
1.2 Student Satisfaction
Retention is a big challenge for all the higher education institutions, especially among the first with more
than half of students that drop out doing so in their first year. Many students who endeavour to earn a
college degree fail to continue until graduation. Therefore, an effort should be made to keep this dropping
trends to a minimum extent (Mukhtar, Ahmed, Anwar & Baloch, 2015). The level of student satisfaction
in educational contexts can be defined as a short-term attitude based on students’ educational experiences.
“Satisfaction in education is a positive originator of student loyalty to institutions and also is an outcome
of a successful educational system. Thus, student satisfaction levels can be defined as a function of the
relative perceived levels of the quality of experiences and higher educational institutions’ performance in
providing educational services (Sojkin, Bartkowiak & Skuza, 2011). Elliott and Healy (2001) mentioned
that “A short-term attitude resulting from an evaluation of a students’ educational experience is generally
accepted as student satisfaction. Student satisfaction results when actual performance meets or exceeds
the students’ expectations” (p.8). Student satisfaction is defined as multi-dimensional and depended on
the clarity of student goals as reported by (Mihanović, Batinić & Pavičić, 2016). They further found that
satisfaction was significantly influenced by trust. Educational practitioners of higher education can build
trust by treating students in a consistent and equitable manner, meeting and handling their expectations
and complaints in a caring manner. Bassi (2019) concluded that perceived quality of an educational
experience is a consequence of student satisfaction. By analyzing the earlier mentioned definitions of
student’s satisfaction reveal that understanding the contemporary expectations and demands of students
almostly signifies the definition of student satisfaction.
2. The current study
The current study explores the complex phenomenon of student preference to recommend their kith and
kin for the enrollment at OCEM. As main starting point, the study puts forward the idea that the moment
at which students prefer not to enroll their kith and kin may have an important impact on their motives
for quitting from OCEM. In addition, gender and types of enrollment stream, educational level, family
income, religions and collage location are incorporated as control variables. The following research
questions are guided my investigation:
(1) Does the student satisfaction (preference) vary according to personal variables, such having actual
experience with academic factors or not, gender, family income, and collage location?
(2) What motives do existing students at OCEM have for their preference to recommend their kith and kin?
(3) Do the satisfaction and preference differ according to whether or not existing students have in
academic, managerial, physical and infrastructure factors and does this distinction remain after
controlling for other personal variables (gender, location, family monthly income and college
location).

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3. Methods
To answer the research questions mentioned in the section 2, a large-scale survey study was conducted
in OCEM Gaindakot-2, Nawalpur. OCEM instead of the whole colleges of Nawalpur was chosen as
the collage of investigation as the authority for students’ preference to recommend their kith and kin
with the regional college not with the national colleges. Given the fact, reginal facilities on academic,
managerial, psychical and infrastructure condition differ and that these differences might influence
students’ preferences to recommend their kith and kin, I opted to include only Signal College(OCEM).
3.1 Sample
Given the differences in enrolment, duration of the study and orientation of the aforementioned students
satisfaction for academic, managerial, psychical and infrastructure facilities, I opted to investigate
students experiences, satisfaction and preference for the recommendation to their kith and kin in a signal
program (BBA). As the majority of the students enrolled in four years (BBA program affiliated with
Pokhara University), I conducted my study in this program.
For the purpose of the current study, it was necessary to reach both students who have just commenced
their BBA and those students who already completed their BBA at OCEM. All the students from different
semesters were invited to participate in the study by providing contact information on students who had
successfully completed their BBA from OCEM. In total students of eight different semesters agreed to
participate in the study. Enrollment in these semesters was 35 to 40 students in each semester. Participants
per semester (first, second, third, fourth, fifth, sixth, seventh and eighth) ranged 30 to 45 students. Out of
two hundred and thirteen respondents, fifty seven (n=57) respondent was male and one hundred and eighty
(n=180) respondents was female. The response rate of the survey instrument was 94.8 % [237/250x100].
The Cronbach’s Alpha was computed to check the reliability of the data (see in the Table2).
3.2 Instruments
Information on the personal variables gender, location of the college, family monthly incomes of the
students and religions was obtained through the student administration of the participating collage
(OCEM). To gain insight into students’ satisfaction and preference for existing students and graduated
students, the seven questionnaires were developed. Existing literature was reviewed for students;’
satisfaction and preference to recommend their kith and kin. To design the instrument as broadly as
possible, no single model or theoretical framework (students satisfaction, expectations, perceive quality,
student loyalty) was used as reference. Instead all possible motives were inventoried. The resulting
instrument was piloted with tem graduated BBA students who did not study anymore to check our face-
validity and possible missing motives of students. For each motive, respondents had to indicate on a five-
point scale whether the reason had ranged from completely disagreed to completely agree.
3.3. Analysis
Previous study has sometimes relied heavily on single-item indicators of students’ satisfaction and
preference or raw frequency counts of motives. This approach maximizes the possibility of measurement
error (e.g. Watt &Richardson, 2007). To construct this caveat, I choose to work with more encompassing

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constructs, measured by multiple items. To identify these underlying themes in my questionnaire, a
Principal Component Analysis (PCA) was run. Subsequently, an Exploratory Factor Analysis (EFA) with
Varimax rotation was carried out to refine and interpret these components. Eigenvalues, the scree plot and
theoretical interpretability were used to make a decision on the number of factors. A factor loading of at
least [0.40] was taken as cut-off point to incorporate a specific item as an indicator for an understanding
motive. To explore the relation between students’ preference and personal variables (RQ1), descriptive
statistics and cross tabulations were computed. Descriptive statistics were also computed to analyze
students’ motives (preferences) for the recommendation to enroll at OCEM (RQ2). To explore the effect
of having actual college’s facilities experience after graduation on preference for the recommendation
after controlling for gender and different college locations, family income levels and different religions
of the students (RQ3), a stepwise strategy was followed. First a Binary Logistic Regression Model was
computed to assess the impact of the predictor and control variables on all motives. Both significant
levels and effective sizes were considered using Cohen’s d cut-off points (Cohen, 1998). The next, the
Chi-square Test and Student t-Test was computed to examine the association between two variables
measured on categorical scales (Pandya, Bulsari & Sinha, 2018).
4. Results
4.1. Preliminary analyses: subscales with mean, SD, reliabilities and p values
Mean calculation was carried out for an analysis tool because all the variables are in the normal
distributions and also variables are in order. Again, the distribution of variables has been well studied and
is well understood (e.g. normally distributed). The data analysis was carried out to compare the values of
mean, SD, Cronbach’s Alpha and p values of the twelve subscales. The subscales were categorized into
three groups which is 2.00 to 2.50 as the first group, 2.50 to 3.00 as the second group and 3.00 to 3.50 as
the third group respectively (see in the Table 2).
Table 2. Descriptive statistical analysis on academic factors on student’s satisfaction (N=237).
Scales Mean SD Cronbach's Alpha p values
Classroom facilities 2.04 0.64 0.71 .594
Faculty support for maintaining quality 2.13 0.82 0.75 .031
Technological facilities 2.29 0.75 0.70 .049
Physical facilities 2.32 0.74 0.70 .163
Emphasis on punctuality 2.35 .81 0.71 .396
Health and safety issues 2.43 0.91 0.70 .656
Using technologyin teaching and learning activities 2.47 0.77 0.72 .603
Emphasis on quality of extracurricular activities 2.58 0.69 0.73 .881
Strict nature of principal 2.81 1.11 0.80 .001
Strict students' career development schedule 2.92 0.90 0.71 .927
Availability of teaching resources 3.12 1.33 0.81 .794
Canteen services 3.33 1.20 0.80 .026
The mean value of the first subscale “classroom facilities” had been calculated as 2.04 signifying that
students were disagreed with the statements that they had sufficient furniture, the class room were well

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ventilated, they had sufficient light and their classrooms had sufficient place at OCEM. Similarly, the
mean value of the second subscale “faculty support for maintaining quality” had been calculated as 2.13
signifying that students had showed their disagreement with the statements that the overall coordinator
were always concerned about their issues, to solve my problem on time, to listen about their problems
and their .their principal had motivated them to secure high marks in the final exam. The third subscale
“technological issues” had been calculated as 2.29 signifying that students somehow disagreed and
somehow undecided with the statements that their classroom were seasonally equipped to bear outsider
heat and cold, the classrooms were well technologically equipped and the administrative buildings were
well equipped in their college. Again, the mean value of the fourth subscale “physical facilities” had
been calculated as 2.32 signifying that students were disagreed with the statements that the canteen of
OCEM was hygienic, all books had been available which they needed during their study period, the
transport system was comfortable, the parking space was sufficient and the lab facilitators were helpful
to support them. Furthermore, the mean value of the fifth subscale “emphasis on punctuality” had been
calculated as 2.35 signifying that students showed their disagreement with the statements that the faculty
members were capable to manage time, .the faculty heads were available all the time when they required
to complete their courses and .the faculty members were able to create positive learning environment in
their college. Again, the sixth subscale “health and safety issue” had been calculated as 2.43 signifying
that students were somehow disagreed and somehow undecided with the statements that number of rest
rooms were sufficient, they had safe drinking water and water facility was sufficient in their college.
Again the seventh subscale “using technology in teaching and learning activities” had been calculated as
2.47 signifying that students were somehow disagreed and somehow undecided with the statements that
lecturers were cooperative, modern technology had been used in teaching.
Students were also somehow found undecided and somehow dissatisfied with the current learning
activities and the technology used in the classrooms of OCEM. Moreover, the mean value of the eighth
subscale” emphasis on the quality of extracurricular activities” had been calculated 2.58 signifying that
students were approximately close to neither disagreed nor agreed with the statements that of the co-
curricular activities were compulsory, board members of the BBA were strict, extracurricular activities
were sufficient and they had learnt practical skills in their college. Again, the mean value of the ninth
subscale “strict roles of principal” had been calculated as 2.81 signifying that students had been seen
undecided for the statements that their principal was rational to make managerial decision, helpful and
focus on academic quality. Similarly, the mean value of the tenth subscale “strict career development
schedule” had been calculated as 2.92 signifying that students were exactly neither agreed nor disagreed
with the statements that internal exams had been run matching with predetermined schedule of the
examination, undecided on students’ future grooming career path at OCEM and they were also undecided
for the availability of interactive learning environment in their college. The mean value of the eleventh
subscale “teaching resources” had been calculated as 3.12 signifying that students were mostly undecided
and somehow agreed with the statements that they had sufficient computers in lab and library facilities
were available on time in OCEM. Finally, the mean value of the eleventh subscale “canteen services” had
been calculated as 3.33 signifying that students were agreed with the statements that the cost of food was
reasonable and canteen’s service was satisfactory at OCEM.

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4.2. Relationship between students’ preference personal variable gender
The first H1
assumes equal variances and the second H2
does not. The Levene’s test decides which
version of the t-test to report. If the Levene’s test shows no significance violations of the assumption, we
should report the “equal variances assumed” version of the t-test. Conversely, if the Levene’s test shows
significance violations of the assumption, we should report the “not equal variances assumed” version
of the t-test (Pandya et al., 2018). I have set the null and alternative hypotheses for Levene’s Test for
equality of variances are as follows.
H1
: Variances of two groups are equal.
H2
: Variances of two groups are not equal.
The mean score of the male students of the first subscale classroom facilities (M = 2.04, SD = 0.75)
is not statistically significantly differ [t (235) = 0.446, p = 0.594] than that of the female students on
the same variable (M = 2.00, SD = 0.61). Similarly, the mean score of the male students of the second
subscale faculty support for maintaining quality (M = 2.33, SD = 0.90) is statistically significantly higher
[t (91.54) = 2.165, p = 0.031, Cohen’s d = 0.31] than that of the female students on the same variable (M
= 2.07, SD = 0.77), signifying that male students had higher preference to recommend their kith and kin
to enroll at OCEM which is minimums effect.. Again, the mean score of the male students of the third
subscale technological facilities (M = 3.28, SD = 1.22) is statistically significantly higher [t (91.54) =
3.425, p = 0.001 than that of the female students on the same variable (M = 2.66, SD = 1.03, Cohen’s d
= 0.31) signifying that male students had seen more happy for the recommendation their kith and kin to
join at OCEM which has medium effect on it. Similarly, the mean score of the male students of the fourth
subscale physical facilities (M = 2.43, SD = 0.88) is not statistically significantly differ [t (235) = -1.398,
p = 0.163)] than that of the female students on the same variable (M = 2.28, SD = 0.67). Again, the mean
score of the male students of the fifth subscale emphasis on punctuality (M = 2.43, SD = 0.84) is not
statistically significantly differ [t (235) = 0.851, p = 0.396] than that of the female students on the same
variable (M = 2.32, SD = .81). Again, the mean score of the male students of the sixth subscale health
facilities (M = 2.64, SD = 0.97) is statistically significantly lower [t (86.67) = 1.171, p = 0.04, Cohen’s
d = 0.29] than that of the female students on the same variable (M = 2.37, SD = 0.87), signifying that
female students had higher preference to recommend their kith and kin to enroll at OCEM. Similarly,
the mean score of the male students of the seventh subscale using technology in teaching and learning
activities (M = 2.91, SD = 0.96) is not statistically significantly differ [t (235) =, p = 0.603) than that of
the female students on the same variable (M = 2.92, SD = 0.89).
Again, the mean score of the male students of the eighth subscale emphasis on quality of extracurricular
activities (M = 2.59, SD = 0.68) is not statistically significantly differ [t (235) = 0.150, p = 0.881] than
that of the female students on the same variable (M = 2.58, SD = .70). Again, the mean score of the male
students of the ninth subscale strict nature of principal (M = 3.28, SD = 1.22) is statistically significantly
higher [t (82.94) = 3.428, p = 0.001, Cohen’s d = 0.54] than that of the female students on the same
variable (M = 2.66, SD = 1.03), signifying that male students had higher preference to recommend
their kith and kin to enroll at OCEM which is minimum effect. Furthermore, the mean score of the male
students of the tenth subscale strict students’ career development (M = 2.91, SD = 0.96) is not statistically
significantly differ [t (235) = -.092, p = 0.927] than that of the female students on the same variable (M

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= 2.92, SD = 0.89). Similarly, the mean score of the male students of the eleventh subscale availability
of teaching resources (M = 3.07, SD = 1.22) is not statistically significantly differ [t (234) = 0.262, p =
0.794] than that of the female students on the same variable (M = 3.07, SD = 1.37). Finally, the mean
score of the male students of the twelvelth subscale canteen facilities (M = 3.00, SD = 1.13) is statistically
significantly lower [t (235) = -.092, p = 0.927, Cohen’s d = -.0. 37] than that of the female students on
the same variable (M = 3.44, SD = 1.21), signifying that female students’ preference to recommend their
kith and kin to enroll l at OCEM which is minimum effect.
4.3. Results of Chi-square Test
Chi-square Test was carried out to examine the association or statistical independence between two or more
variables measured on categorical scales. The null and alternative hypotheses for Chi-square test bare:
H0
: There is no association between the row (Gender) and column (Students’ preference to enroll l at
OCEM).
H1
: There is association between the row (Gender) and column (Students’ preference to enroll l at OCEM).
Table 4. Chi-Square Test between gender and students’ preference to
recommend for the admission at OCEM.
Count: Do you recommend your kith and kin to join at OCEM to study?
Gender
Options 1=Yes 2= No
Yeah No Total
Male 36 21 57
Female 153 27 180
Total 189 48 237
Crosstabulation of gender and options of the students’ preference of recommendation to their kith and kin
to join at OCEM shows that out of 57 male students, 36 intended to recommend their kith and kin and
21 did not intend to recommend their kith and kin to enroll at OCEM. Again, out of 180 female students,
153 intended to recommend their kith and kin to study at OCEM and 27 female students did not intend to
recommend their kith and kin to study at OCEM. This shows that there is association between gender and
students’preference for recommendation for the enrollment at the college where they are studying now.
Table 5. Chi-Square table of gander and students’ recommendation preference
Particulars Value df
Asymptotic Signifi-
cance (2-sided)
Exact Sig.
(2-sided)
Exact Sig.
(1-sided)
Pearson Chi-Square 12.787a
1 .000
Continuity Correction 11.471 1 .001
Likelihood Ratio 11.645 1 .001
Fisher's Exact Test .001 .001
Linear-by-Linear Association 12.733 1 .000
N of Valid Cases 237
The table 4 provides that the value of Chi-Square is11.471 and associated significance value is 0.001<0.05.
Therefore, the hull hypothesis is rejected, and signifying that there is association between the gender and
students’ preference to recommend their kith and kin to study at OCEM.

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Table 6 Chi-Square Test between gender and students’ preference to
continue their higher education at OCEM
Crosstabulation of Gender and options of the students’ preference to continue their higher education at
OCEM shows that out of 57 male students, 28 intended to continue their higher education at OCEM and
29 did not intend to continue their higher education at OCEM. Again, out of 180 female students, 127
intended to continue their higher education at OCEM and 53 female students did not intend to continue
their higher education at OCEM. This shows that there is association between gender and students’
preference to continue their higher education at OCEM.
Table 7. Chi-Square table of gander and students’ preference to
continue their higher education at OCEM
a. 0 cells (0.0%) have expected count less than 5. b. Computed only for a 2x2 table
The Table 7 shows that the value of Chi-Square is 8.788 and associated significance value is 0.004<0.05.
Therefore, the hull hypothesis is rejected, and signifying that there is association between the gender and
students’ preference to continue their higher education at private colleges.
4.4 Analysis of the significant indicators of Binary Logistic Regression Wholesome Model
The wholesome model of the Binary Logistic Regression was applied to find the indicators of student’s
recommendation to join their kith and kin at OCEM. It is a basic and commonly applied method of
predictive analysis for examining whether a set of predictor variable does a good work in predicting an
outcome (dependent variable) and which variables are significant predictors of the outcome variables or
in what way they are indicated by the sign of the Beta estimates- impact on the outcome variable and
its magnitude (Cohen et al, 2007). There were twelve basic measurement scales in quantitative result
section, but only nine indicators were found significant for the students’ satisfaction to recommendation
their kith and kin to join at OCEM (see in the Table 3). Binary Logistic Regression Model also used to
find the association between all significant independent variables and dependent variable, signifying the
key indicators in the Wholesome Model.
Count: Do you continue your higher study at Oxford College of Engineering and Management?
Gender
Options 1 = Yeah 2 = No
Yeah No Total
Male 28 29 57
Female 127 53 180
Total 155 82 237
Asymptotic Signifi-
cance (2-sided)
Exact Sig.
(2-sided)
Exact Sig.
(1-sided)
Pearson Chi-Square 8.788a
1 .004
Continuity Correction 7.867 1 .003
Likelihood Ratio 8.506 1 .002
Fisher's Exact Test .004 .003

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Table 8 Significant indicators of Binary Logistic Regression Wholesome Model
Variables in the equation (n = 237)
Independent variables B S.E Wald df Sig Exp(B)
95% C.I.for EXP(B)
Lower Upper
Emphasis on quality of extracurricular -.220 .324 .462 1 ..497 .802 .425 1.515
Strict student development schedule .486 .241 4.052 1 .044 1.625 1.013 2.607
Better teaching environment .510 .292 3.046 1 .081 1.664 .939 2.950
Strict nature of principal .239 .194 1.525 1 .217 1.271 ..869 1.858
Emphasis on punctuality -.305 .286 1.138 1 .286 .737 .421 1.290
Requirement of high quality .177 .255 .480 1 .488 1.193 .724 1.968
Physical facilities 1.038 .377 9.482 1 .002 2.822 1.458 5.463
Teaching resources .074 .166 .202 1 .653 1.077 .779 1.490
Health issue .260 .249 1.088 1 .297 1.297 .796 2.115
Consent 1.785 .228 61.361 1 .000 .001
The Omnibus Tests [Chi-Square = 50.404, df = 9, p =.001] and associated significance level is less
than 0.05, the present model shows a decrease in deviance from the base model because Chi-Square
is positive, showing this model is better fit compared the base model. The model summary table
shows the values of -2Log Likehood (187.987), Cox and Snell R2
and Nagelkerke R2
[19.20 % (Cox
and Snell) and 30.20 % (Nagelkerke)] variance of the model was explained by the independent
variables. Hosmer and Lemeshow Test shows that p = 0.054 > 0.05 is insignificant which is good
to support for the regression model fit. The classification Table shows that out of 212 students who
showed their preference to recommend their kith and kin to join at OCEM, this model predicts 181
students intended to recommend their kith and kin to join at OCEM but 31 students intended not
to recommend their kith and kin to join at OCEM. The classification Table further shows that out
of 24 students who did not intent to preference to recommend their kith and kin to join at OCEM,
17 of them intended to recommend their kith and kin to join at OCEM. Thus, it predicts students
who intended to recommend their kith and kin to join at OCEM with 96.3 percent accuracy and also
predicts that students who did not intend to recommend their kith and kin to join at OCEM with 35.4
percent accuracy.
The results further show that the overall percentage of correctness of observed data was 83.9 %. The
results also show that there was association between students’ preference to recommend to their kith
and kin to enroll at OCEM and strict schedule of student development (p< 0.05 with odds ratio 1.625,
B = .486 >1) in the Wholesome Analysis of Binary Logistic Regression Model indicating the positive
impact on the schedule of the internal examination, grooming the student’s career path and availability
of interactive learning environment at OCEM. Similarly, the results further indicate that there was
significant association between the student recommendation to their kith and kin to enroll l at OCEM
and physical facilities of OCEM (p< 0.05 with odds ratio 2.822, B = 1.038) in the Wholesome Analysis
of Binary Logistic Regression Model indicating the positive impact on the availability of books at the
library and the comfortable transport system, management of the hygienic canteen and the management
of the better lab facilities (see in the Table 8).

OCEM Journal of
4.5. Results on multiple regression on categorical variables location and students’ preference
Table 9. Model Summary of Linear Regression of categorical variables
a. Dependent Variable: Student preference to recommend
b. Predictors (Constant): Western Chitwan, Eastern Chitwan, Central Chitwan
The coefficient of multiple determination is 0.082; therefore, about 8.20 % of the variation in the location
of OCEM is explained by Eastern, Western and Central Chitwan. The regression equation appears to be
very useful for making predictions since the value of R2
is close to 1 but the value of R-square is not close
to 1 so the regression equation appears to be not useful for making predictions.
Table 10. Results of ANNOVA on multiple regression analysis
Model Sum of Square df Mean Square f Sig
1 Regression 3.405 3 1.135 7.132 0.000c
Residual 38.035 239 .159
Total 41.440 242
a. Dependent Variable: Student preference to recommend
b. Predictors (Constant): Western Chitwan, Eastern Chitwan, Central Chitwan students’ preference and
college location
The results from ANNOVA Table (10) show that when α = 0.001 level of significance, there exists
enough evidence to conclude that at least one of the predictors (Eastern, Western and Central Chitwan)
is useful for predicting students’ preference to recommend for the enrollment at OCEM; therefore the
model finds useful.
Table 11. Coefficients of multiple regression
Model
Unstandardized
B
Coefficient Std
Errors
Standardized
Coefficient Beta
t Sig
(Constant) .776 .031 25.378 .000
1. Eastern Chitwan -.776 .284 -.170 -2.737 .007
Central Chitwan -.776 .232 -.208 -3.342 .001
Western Chitwan .076 .057 .083 1.336 .183
Theresultsagain showthat when=α=0.007level ofsignificance, thereexists enough evidencetoconclude
that the slope of the location of Eastern Chitwan is not zero and, hence, the location Eastern Chitwan is
useful (with number of locations) as a predictor of students’ preference for the recommendation to enroll
at OCEM. Again, the results further show that when α = 0.001 level of significance, there exists enough
evidence to conclude that the slope of the location of Central Chitwan is not zero and, hence, that Central
Chitwan is useful (with number of locations) as a predictor of students’ preference on recommendation to
enroll their kith and kin at OCEM. Finally, the results show that when α = 0.183 level of insignificance,
there does not exist enough evidence to conclude that the slope of the location of Western Chitwan is not
zero and, hence, that Central Chitwan is not useful (with number of locations) as a predictor of students’
preference (Western, Eastern, Central Chitwan).
Model R R Squareb Adjusted R Square Std. Error of the Estimate
1 .287a .082 .071 .399

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5. Discussion & Conclusion
The purpose of the current study was to examine the students’ preference to recommend their kith and
kin to enrol and to continue their higher degree at OCEM for the further study. The quantitative research
approach along with the survey method was used to examine the opinions, experiences and ideas of
students on their preference to recommend and to continue their further education at OCEM. The study
was conducted inside the OCEM premises which had followed full criteria of research ethics. This study
had clearly defined purpose and common concepts. The research procedure was described in sufficient
detail to permit another research to repeat the research for further advancement, keeping the continuity
of what has already been attained, reported with complete frankness, clear flaws in procedural design and
has estimated the effects of all issues mentioned earlier paragraph upon the findings. The data analysis
was adequate to reveal its significance and the methods of analysis was appropriate, the validity and
reliability of the data were checked with the minimum value of Cronbach’s Alpha (0.60) and the research
design was carefully planned to yield results that were as objectives as possible. The Factor Reduction
Model of Principal Component Analysis was used to find the relationship among different variables of
each instrument.
The data analysis was based on descriptive statistics model where mean, Standard Deviation, Independent
Sample t-Test of two different groups and Chi-Square Test were computed to find the association
between gender and students’ preference to recommend and to continue student’s preference for the
further education at OCEM. The Binary Logistic Regression of PCA was applied to find the association
between the dependent and independent variables. The results show that there is significant relationship
between emphasis on quality of extracurricular activities, strict student development schedule, better
teaching environment, nature of principal, emphasis on punctuality, requirement of high quality, physical
facilities, teaching resources and health and safety issues (p<0.05, B = -.500. -.449, -.429, -.490, -.404,
-.428, -.904, -.410, -.295 and -.931) respectively. This study reveals that there was association between
students’ preference to recommend to their kith and kin to join at OCEM and strict student career
development schedule (p< 0.05 with odds ratio 1.625, B = .486) in the Wholesome Analysis of Binary
Logistic Regression Model indicating the positive impact on the schedule of the internal examination,
grooming the student’s career path and availability of interactive learning environment at OCEM.
Similarly, the results further confirm that there was significant association between the male and female
for the recommendation to their kith and kin to join at OCEM and physical facilities of OCEM (p< 0.05
with odds ratio 2.822, B = 1.038) in the Wholesome Analysis of Binary Logistic Regression Model
indicating the positive impact on the availability of books for the study and the comfortable transport
system, management of the hygienic canteen and management of the better lab facilities. The implication
of this study would be useful for the college administration to formulate new student admission strategies
and to reform different internal student centered policies.
Acknowledgement
The author thanks all the Department Heads, lecturers and students of Oxford College of Engineering
and Management [OCEM] Gaindakot-2 Nawalpur of Nepal who made substantial contributions to this
work. This work was fully funded by the OCEM. The supporting roles and contributions of Professor

OCEM Journal of
Er. Hari Bhandari to complete this great work was very much admirable and appreciative. Ms. Rabina
Lamichhane Magar, Shashikala Sapkota & Sushmita Chaudhary are highly appreciated for their great
contribution of data collection during this study.
References
Adhikari, B. (2016). The Gender Discrimination in Childhood Education in Nepal. [Online] Academia.
Available at: https://www.academia.edu/26193972 [Accessed 16 Jun. 2019].
Alves, H. and Raposo, M. (2010). The influence of university image on student behaviour. International
Journal of Educational Management, 24(1), 73-85.
Bassi, F. (2019). Students’ satisfaction in higher education: the role of practices, needs and beliefs of
teachers. Quality Assurance in Education, 27(1), 56-69.
Bini, M. and Masserini, L. (2015). Students’ Satisfaction and Teaching Efficiency of University
Offer. Social Indicators Research, 129 (2), 847-862.
Cassel, C. and Eklöf, J. (2001). Modelling customer satisfaction and loyalty on aggregate levels:
Experience from the ECSI pilot study. Total Quality Management, 12(7-8), 834-841.
Chen, Y.C. (2014) “An empirical examination of factors affecting college students’ proactive stickiness
with a web-based English learning environment,” Computers in Human Behavior; 31: 1,159-171
Cohen, L., Manion, L. and Morrison, K. (2011). Research methods in education. London: Routledge.
Coskun, L. (2014). Investigating the Essential Factors on Student Satisfaction:ACase ofAlbanian Private
University. Journal of Educational and Social Research.
Douglas, J., Douglas, A., & Barnes, B. (2006). Measuring student satisfaction at a UK university. Quality
Assurance in Education, 14(3), 251–267.
Elliott, K. and Healy, M. (2001). Key Factors Influencing Student Satisfaction Related to Recruitment
and Retention. Journal of Marketing for Higher Education, 10(4), 1-11.
Farahmandian, S. (2013). Perceived service quality and student satisfaction in higher education. IOSR
Journal of Business and Management, 12(4), 65-74.
García-Aracil, A. (2008). European graduates’ level of satisfaction with higher education. Higher
Education, 57(1), pp.1-21.
Gibson, A. (2010). Measuring business student satisfaction: A review and summary of major predictors.
Journal of Higher Education Policy and Management, 32(3), 251-59
Hanssen, T. and Solvoll, G. (2015). The importance of university facilities for student satisfaction at a
Norwegian University. Facilities, 33(13/14), 744-759.
Hernadewita et al., H. (2019). PLS-SEM Based Analysis of Service of Learning, Service Quality and
Satisfaction of College Student in Polytechnic. International Journal of Mechanical and Production
Engineering Research and Development, 9(3), 861-870.
Insch, A. and Sun, B. (2013). University students’ needs and satisfaction with their host city. Journal of
Place Management and Development, 6(3), 178-191.
Kärnä, S. and Julin, P. (2015). A framework for measuring student and staff satisfaction with university
campus facilities. Quality Assurance in Education, 23(1), 47-66.

OCEMJournalof
Kreber, C. (2009).Academics’teacher identities, authenticity and pedagogy. Studies in Higher Education,
35(2), 171-194.
Langstrand, J., Cronemyr, P. and Poksinska, B. (2014). Practise what you preach: quality of education
in education on quality. Total Quality Management & Business Excellence, 26(11-12), 1202-1212.
Mihanović, Z., Batinić, A. and Pavičić, J. (2016). The link between students’ satisfaction with faculty,
overall students’ satisfaction with student life and student performances. Review of Innovation and
Competitiveness, 2(1), 37-60.
Mukhtar, U., Ahmed, U., Anwar, S. and Baloch, M.A. (2015). “Factors affecting the service quality of
public and private sector universities comparatively: an empirical investigation; Journal of Arts,
Science & Commerce; 3(1), 132-142.
Nogueira, M. (2018). Measuring Academic Life Satisfaction in Portuguese Students. Nursing &
Healthcare International Journal, 2(1).
Pandya, K., Bulsari, S. and Sinha, S. (2018). SPSS in simple steps. New Delhi: Dreamteach, 1-179.
Quality Improvement Based on a Process Management Approach, with a Focus on University Student
Satisfaction. (2016). Acta Polytechnic a Hungarica, 13(6). http://dx.doi.org/10.12700.
Sweeney, L (2016). A Predictive Model of Student Satisfaction,” Irish Journal of Academic Practice:
5(1), 1-31 (https://arrow.dit.ie/ijap/vol5/iss1/8)
Tucker, B. (2013). Student evaluation to improve the student learning experience: anAustralian university
case study. Educational Research and Evaluation, 19(7), 615-627.
Uprety, R. and Chhetri, S. (2014). College Culture and Student Satisfaction. Journal of Education and
Research, 4(1), 77-92.
Vogt, W. (2011). SAGE quantitative research methods. Los Angeles [Calif.]: SAGE.
Weerasinghe, I. and Fernando, R. (2018). Critical factors affecting students’ satisfaction with higher
education in Sri Lanka. Quality Assurance in Education, 26(1), 115-130.
Yusoff, M., McLeay, F. and Woodruffe-Burton, H. (2015). Dimensions driving business student satisfaction
in higher education. Quality Assurance in Education, 23(1), 86-104.

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Factor Influencing Customer Satisfaction at
BBSM, Bharatpur, Chitwan
Author
(Research Head, OCEM)
Abstract
This study aims to examine the customer satisfaction against the price factors, service quality, time
management to deliver goods to customer and the customer management practice in BhatBhateni Super
Market (BBSM). The survey method was applied to collect data using structured questionnaire and the
respondents were customers visiting for shopping at BBSM, Bharatpur, Chitwan. The sampling method
was the random sampling technique. One hundred and ninety respondents were selected for this study.
Out of 190 respondents, 39.12 % (n =76) were male customers and 60. 88 % (n = 114) were female
customers. The response rate of the survey questionnaire was 87.5 %. Univariate analysis were carried
out by using different simple descriptive statistical tools The Chi-Square test, Factor Reduction Model
and Logistic Regression Analysis Model were multivariate the statistical techniques employed to get the
results. Previous studies on customer satisfaction show that it merely depends upon the price factors,
service quality, time management to deliver goods to customer and customer management factor. The
results showed that there was statistically significant association between the better customer relationship
management and customer satisfaction at BBSM (p < 0.05, B= .438). But the results also showed that
there is no significant association between customers centered service facilities and equipped technology
used by BBSM (p > 0.05). The implication of this study will be beneficial to the board members of the
company executives to formulate new customer-center strategy and also useful to the branch managers
of BBSM all over the country.
Keyword: Strategy, Customer Management, Association, Factor Analysis, Logistic Regression.
1. Introduction
The degree of fulfilment of customer’s expectation, needs and demands with the level of service is
consumer satisfaction. Simon & Gómez (2013) define customer satisfaction as “a person’s feeling of
pleasure or disappointment from comparing a product’s perceived performance in relation to his or her
expectations” (p.15). The definition of the customer’s satisfaction is embedded in reasonable price of
the product, quality of the product, service after sales, and the behaviour of the staff of the company.
Additionally, customer satisfaction is simply stated as a customer’s evaluation of their purchase and
consumption experience with a product, service, brand, or company (Kotler & Armstrong, 2012). More
significantly, customer’s satisfaction is deeplyrooted inaffecting customers’repeating purchase decisions

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CUSTOMER
SATISFACTION
Time
Customer
Management
Quality of
Goods
Price of Product
and subsequent companyprofits. Customer’s satisfaction is now aprominent business performancemetric.
Again, the customer’s satisfaction is a subjective measurement, which is rarely used in the performance
measurement of stakeholders.
Figure 1. Factors affecting Supermarket customer satisfaction
Price of the products
Previous studies suggest that price, as a determinant element of satisfaction, is varied by super market
store format. Price image has implications for store support, and strategic decisions related to selecting
a target customer base and creating in-store environments (Hassan, 2018). Grocery pricing strategy, for
example high-low (HILO) pricing, has a direct consequence on customer purchase habit in conventional
grocery stores: large basket customers prefer a store which offers an everyday low-price format, while
small basket shoppers desire a store that offers a HILO format. People who shop for economical brands
also tend to select “economical” store formats. It was found that low prices were second most important
store characteristic for supermarket shoppers; store location was the first (Baltas & Papastathopoulou,
2003).
1.1 Quality of product
Product quality and product features were considered the most important product choice criteria in a
study of Greek grocery customers (Baltas and Papastathopoulou, 2003). Quality is seen as “a satisfaction-
maintaining factor in the supermarket sector” in that improvements in quality have a small positive
impact on satisfaction while reductions in quality of the same magnitude have a significantly greater
chance of reducing satisfaction (Gómez, McLaughlin & Wittink, 2004, p. 273). For specialty store
customers, merchandise quality is an important differentiating factor. Previous study found the result that,
specialty store customers scored product quality higher in comparison to other store formats, the result
demonstrates the importance of product quality for these customers. A similar study by King and Ring,
1980, also found product quality to rank considerably higher for specialty customers when compared to
mass merchandiser and department store customers.

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1.2 Management of customers
While the literature on customer perceptions of service and its impact on food store shopping experiences
is sparse, empirical work drawing comparisons between specialty and department store customers
provides guidance on the strength and direction of these characteristics to store support. Specialty store
shoppers view service to be one of the most important determinants of store support. Sales associates play
a pivotal role in a customer service situation, with the most important attributes being store clerk attitude
and treatment of customers (Kotler & Armstrong, 2012). In a study of customer service in specialty and
conventional grocery stores, customer perceptions of service were found to vary greatly. It was also
found that customers who shop small grocery chains placed greater importance on service quality than
patrons of large grocery store chains (Kirkup et al., 2004).
1.3 Time Management
Time management to check out the products is another influencing factor of customer satisfaction. Study
in recent years have pointed to the checkout stand as a massive headache for retail customers. As shopping
has migrated online, where a few clicks are all it takes to complete a transaction, consumers have grown
less and less patient with a process that has remained much the same for years. Limitations in technology
and the supermarket format have long prevented grocers from speeding up their checkouts. Customers are
very busy today and do not want to spend more time in shopping goods and services (Cheriyah et al., 2013).
The research study of Cheriyah, Sulistyowati, Cornelia & Viverita (2013) found that customer satisfaction
is significantly positively associated with waiting time in the checkout process Super MarketStores.
BBSM is the leading brand for retail superstore in Nepal. It has all together 16 branches all over the Nepal
and it is on process of expansion to other big cities too. There are many customers who go shopping
in BhatBhateni Super Market (BBSM). Customer can get varieties of products from FMCG goods to
luxurious goods below a single roof. Almost 120000 varieties of goods are available there. The BBSM
branch of Chitwan was opened on Baisakh 11, 2073. The flow of customer to BBSM, Chitwan are high
but the sales of the store is not as expected as the flow of customers.
1.4 Purpose of the study
The primary purpose of the study was to examine the customers' satisfaction with BBSM located at
Narayanagarh Chitwan. The specific purposes of this study were to examine the opinions and thoughts
of regular customers for the cost of products, quality of goods; customer management approach and
time management to customers. The secondary objective of this research is to measure the consumer
satisfaction level towards BBSM at Chitwan District.
1.5 Statement of the problem
The customers are attracted to visit the store, but sales figure is not high as compared to the volume of
customer flow (Kotler & Amstrong, 2012). Many people visit there for sightseeing and for fun. It is a big
question for BBSM to have loyal customers. If the BBSM want loyal customers, the customers must be
satisfied, and it should understand customers’ need. Keeping in view of the above, the main problem of the
study is: Are customers satisfied by the services provided by the BBSM in the selected districts of Nepal.

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2. Research Methods
The survey method was used to collect data for this study where 190 random customers of BBSW were
selected in different opening days and time. Thus collected primary data was tested for the reliability
using Cronbach’s Alpha, and various statistical tests were also applied. Chi-Square Test was computed
to find the differences of their preference between the male and female respondents. The Binary Logistic
Regression Analysis was used to find the association between the dependent variable (customer's
satisfaction) and independent variables (price of the product, employees' behaviour with customers,
discount rate, utilization of technology in buying and selling activities). The target population was
one thousand (n = 1000) customers and sample population was one hundred and ninety (n = 190). The
proportion of the sample population was [(n/Nx100)] 19 %. Two hundred and ten (n=210) respondents
were requested to fill the structured questionnaire, but only one hundred and ninety (n=190) respondents
filled the dispatched questionnaire. The response rate was [190/210x100] 90.47 %. Cronbach's Alpha
was computed for reliability of collected data of this study. The proportion of the male respondent
was [76/190x100] 39.12% (n =76) and the female respondent was [114/190x100] 60. 88% (n=114) had
participated in this study. After computing reliability test of the collected data, the data analysis was
carried out using different simple statistical tools (Cohen, Manion, Morrison, & Bell, 2011).
3. Results
Each survey instrument was examined by computing the factor analysis for the classification of variables
or detecting structure in relationship between variables. There were methods based on the assumption
that some variability in data was not explained by all the components. However, this study has limited
the discussion to use of factor analysis for the data reduction which has focused only on Principal
Component Model. The analysis has finalized the price of goods at BBSM, lower discount rate, discount
rate at BBSM, facilities and quality of products, facility of furniture and waiting room, better BBSM
employees behaviour, use of technology and clean environment, customer-centered services, varieties of
new goods and sound customer management, facilities and equipped technology, varieties of goods and
quality services and customer’s facilities and management (see in the Table 2). After computing, factors
loading of the survey instrument as the sub-scales of PCs (see in the Table 1). The analysis is based
on the empirical literature of customer relationship management (CRM) system for improved business
profitability, better customer-centered decision making, enhanced customer relations, and good quality
of services and product offerings. The underpinning of the customer-oriented managing concept is that
identification and satisfaction of customer needs lead to improved customer retention, which is based on
corporate profitability (Mithas, Krishnan & Fornell, 2005).
3.1 Factors loading of variables
The survey instrument has been divided in to four parts namely Group A, B, C and D. Each group has
questions measured in Likert scale. Factor Reduction Model was applied to find the close relationship
among variables within a group and to segregate variables in respective group of each survey group.
The groups are later given the name sub-scales. Following Table 1 shows variables of different groups
of questionnaires with their factors loading, these factors loading were used to group the variable in to
different subgroups.

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Table 1. Factor loadings of each variable (N=190).
Groups Variables Factor loadings
A
(Price Factor)
The price of the products in BBSM fluctuates time and again. 0.804
I find goods in BBSM are cheap. 0.798
The cost of product is equal with another store in BBSM 0.792
Goods are cheaper in BBSM than other super markets 0.779
The cost of products in BBSM is higher than other stores 0.714
The discount rate of BBSM is leaser than other super markets. 0.714
The discount rate of BBSM is equal to other stores. 0.63
There is not price fluctuation in BBSM. 0.508
The rate of discount on products is greater than other stores 0.484
B
(Service
Factor)
BBSM have enough inventory store for goods. 0.786
BBSM has drinkable water for customers 0.786
There is customer's waiting room at BBSM 0.783
BBSM has money exchange facility. 0.703
I feel comfort while buying at BBSM 0.667
BBSM has no sound pollution. 0.633
BBSM has sound pollution. 0.619
BBSM has neat and clean environment. 0.58
BBSM has verities in shopping goods. 0.556
There is comfortable furniture for the customers while sitting. 0.531
BBSM have quality food products. 0.481
BBSM has the facility of using Visa Card. 0.324
C
(Quality
Factor)
BBSM has voice pollution. 0.938
BBSM has neat and clean environment 0.824
BBSM has verities of goods. 0.777
BBSM has quality food service. 0.772
There is no sound pollution in BBSM. 0.745
BBSM has managed enough space for the customers. 0.735
I feel comfort when I go to BBSM to buy goods. 0.726
BBSM has well management for drinkable water to customers. 0.72
BBSM has enough inventory store in BBSM. 715
BBSM has comfortable waiting room. 0.585
There are the facilities of money exchange. 0.439
BBSM accepts Visa Card for the payment. 0.398
D
(Customer
Management)
New goods are available in BBSM. 0.842
The BBSM takes a shorter time in money exchange. 0.799
The employees of BBSM answer the customers' inquiry 0.972
The employees' behaviour of BBSM is not good 0.753
The BBSM understands customers' demands/needs. 0.732
The customers of BBSM are satisfied service facilities 0.714
The BBSM solves the problems of customers. 0.706
New goods are available in BBSM. 0.62
The BBSM service is punctual and quick. 0.582
I will take the service of BBSM again. 0.576
Sometimes. I take service from other super markets. 0.5
Customers are available in BSSM Store. 0.492

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Factors loading for different variables under four groups (sub scales) is shown in Table 1, variables with
highest factor loading within the group are highlighted. Based on the Factor Loading, question in each
group has been classified in to different subgroups as suggested by SPSS outputs. Table 2 shows the sub
groups of different groups and their variation and different statistical values.
Table 2. Subscales of variables of each Principal Component (n =190)
Group Subgroup Variations KMO Mean SD Alpha
Group A
(Price Factor)
Price of goods at BBSM 19.25 %
0.63
2.65 0.87 0.63
Lower discount rate 16.99 % 2.69 0.85 0.62
Discount rate at BBSM 12.72 % 2.73 0.79 0.61
Group B
(Service Factor)
Facilities and quality of products 38.77 %
0.77
2.80 0.081 0.081
Facility of furniture and waiting room 11.42 % 3.10 0.85 0.77
Use of technology and clean environment 9.11 % 2.71 0.91 0.6
Group C
(Quality Factor)
Better BBSM employees behaviour 32.26 %
0.71
2.65 0.87 0.63
Customer centered services 13.23 % 2.73 0.85 0.61
Verities of new goods and sound
management
9.60 % 2.69 0.79 0.6
Group D
(Better
Customer
Management
Factor)
Facilities and equipped technology 22.02 %
0.62
2.80 0.84 0.78
Verities of goods and quality service 13.55 % 2.80 0.102 0.65
Customer's facilities and management 13.37 % 2.76 0.76 0.61
Reliability of the data was confirmed by the computing reliability scales of the Cronbach’s Alpha as all
the subgroups created using Factor loading have Cronbach’s Alfa greater than 0.6. Also, the adequacy
of the sample was confirmed by the calculated value of KMO > 0.60. The first largest variation among
the subgroup is embedded in the second group. Similarly, the second and third largest variation of the
subgroup is embedded in the third and fourth group respectively. But, the least variation among the
subscales (subgroups) is embedded in the variables in first part of the questionnaire as shown in the Table
2. The results show that the facility of furniture and waiting room has the highest mean value (3.10)
signifying that customers were approximately satisfied with available furniture in the waiting room.
But customers were neither satisfied nor dissatisfied with the price level of the products, quality of the
products and customer management at BBSM Bharatpur because the mean values were found less than
3.00. It is concluded that customers were not really satisfied with the overall current price level of the
products, quality of products, service of the employees to customers and customer management at BBSM
in Bharatpur Chitwan of Nepal.
3.2 Results of Chi-Square on gender and costumers' intention to continue buying at BBSM in future.
To examine the association between gender and customer's intention to continue future recommendation
to BBSM for their kith and kin and their self also, Chi Square test was conducted. Its cross tabulation is
shown in Table 3 and test statistics value is presented in Table 4.

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Table 3 Chi-Square Test between gender and students' intention to continue their buying at BBSM
(n=190).
Gender
Continue buying products in future at BBSM
Total
Yeah No
Male 44 36 80
Female 77 33 110
Total 155 82 190
Above Table 3 shows that out of 80 male customers, 44(55%) intended to continue their buying habits in
future and 36 (45 %) customers did not intend to continue their buying habit. Again, out of 110 female
customers, 77 (70 %) intended to continue their buying habits and 33 (30 %) female customers did not
intend to continue their buying habits at BBSM. This result shows that there is association between
gender and customers' intention to continue their buying habits at BBSM.
Table 4. Chi-Square table of association between gender and students'
intention to continue their buying at BBSM (n = 190).
Asymptotic Signifi-
cance (2-sided)
Exact Sig.
(2-sided)
Exact Sig.
(1-sided)
Pearson Chi-Square 4.506a 1 0.034
Continuity Correction 3.881 1 0.049
Likelihood Ratio 4.489 1 0.034
Fisher's Exact Test 0.047 0.025
Linear-by-Linear Association 4.482 1 0.034
Here Chi Square Test is applicable because no cell has expected frequency less than five. The Table 4
provides that the value of Chi-Square is 4.506 at 1 degree of freedom with P-value value 0.034 < 0.05.
The Null hypothesis of “there is no association between gender and students' intention to continue their
buying at BBSM” is rejected and signifying that there is no association.
3.3 Wholesome Binary Logistic Regression Model for relationship of customers' opinion on their
satisfaction at BBSM.
Binary Logistic Regression Model was used to find the relationship between the level of customer
satisfaction and quality of products, service quality, and employees' behaviour at BBSM Bharatpur
Chitwan. There were twelve independent variables but only seven variables were found significant in the
Wholesome Model of Binary Logistic Regression (BLR). So, the seven significant variables entered the
Binary Logistic Wholesome Model.

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Table 4. Summary of the significant predictors of the Wholesome Model of BLR (n = 190).
Independent variables B S. E. Wald df Sig.
Exp
(B)
95%C.IforExp(B)
Upper Lower
Use of technology and clean environment 0.134 0.19 0.497 1 0.481 1.143 1.657 0.788
Better Behaviour of BBSM employees 0.155 0.219 0.501 1 0.479 1.168 1.795 0.76
Customer Centered Service -0.096 0.18 0.286 1 0.593 0.908 1.293 0.638
Facilities and equipped store technology -0.329 0.18 3.336 1 0.068 0.72 1.024 0.506
Verities of products and quality service 0.193 0.215 0.804 1 0.37 1.213 1.849 0.795
Better customer management 0.438 0.171 6.552 1 0.01 1.549 2.166 1.108
Customers' facilities at BBSM -0.17 0.166 1.037 1 0.309 0.844 1.17 0.609
Constant -0.642 0.168 14.59 1 0.175
Before carrying out Binary Logistic Regression, some pre-required tests were conducted, the Omnibus
Tests [Chi-Square = 15.421, df = 7, p = .031] and associated P-value found less than 0.05, the present
model shows a decrease in deviance in prediction from the base model, showing that this model is better
fit compared to the base model. Hosmer and Lemeshow Test [5.641] shows that p = 0.687 > 0.05 is
insignificant which is good to support for the regression model fit. Again, the model summary table
shows the values of 2Log Likehood (213.274), Cox and Snell R2
and Nagelkerke R2
[8.30 % (Cox and
Snell) and 11.60 % (Nagelkerke)] variance of the model was explained by the independent variables.
Also the result shows that overall model gives 65.7 % percent correct prediction. The classification table
shows that the base model though, predicts correctly the number of satisfied customers but it does not
correctly predict the number of dissatisfied customers. Thus, it predicts satisfied customers with 90.2
percent accuracy and predicts 22.2 percent accuracy of dissatisfied customers at BBSM.
Results show that, out of 150 satisfied customers, this model predicts that 101 customers are satisfied
and 49 are dissatisfied. Again, out of 25 dissatisfied customers, this model predicts that 11 customers
are satisfied and 14 are dissatisfied (see in the Appendix 1). The results show that there is positively
statistically significant correlation between the better customer relationship management and customer
satisfaction at BBSM (p <0.05, B= .438). Again, when the independent variable the better customer
management increases one unit, customer satisfaction can be predicated to increase around 1.459 times
if other variables are controlled. This study has supported the findings of Mithas, Krishnan & Fornell
(2005). The study along with the current study summarized that the use of CRM applications is positively
associated with improved customer knowledge and improved customer satisfaction. This study also shows
that gains in customer knowledge are enhanced when firms share their customer-related information with
their supply chain partners.
But the results show that there is no significant relationship between customers centered service, facilities
and equipped technology used by BBSM, use of technology and maintain clean environment, varieties
of new and quality products and facilities and quality products of BBSM (p > 0.05). This study has
supported the empirical findings of Mithas, Krishnan & Fornell (2005) because both studies found that
customer relationship management was likely to have a positive effect on customer satisfaction, for
example, CRM applications enable firms to customize their offerings to each customer and also help
firms to gain customer knowledge which support firms improve their customer satisfaction infuture.

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12% 9%
9%
20%
50%
Behaviour of the employees
Cost of the products
Quality of products
Customer Management
Other issues
20% 18%
24%
23%
12%
3%
Agriculture
Business
Self-employed
Foreign employemnt
National employement
Others
Reasons of customers' choice to BBSM
Figure 1. The reasons of choosing BBSM by the customers
The results show that quality of products is the first reason (50%) of choosing BBSM, Consumer
Management (20 %) is the second reason of choosing the BBSM, Other reason (12 %) is the third main
reason of choosing BBSM, Behaviour of employees (9 %) is the fourth reason of choosing BBSM and
the last reason of choosing BBSM is cost of product (9 %). Seventy-one (n=70) males and one hundred
and one (n=101) females go to buy their goods.
Figure 2. Profession of BBSM customers
The highest percentage of profession who did shopping at BBSM Bharatpur was from the households
from National Service (24 %), the second highest profession of the customers was business (23 %), the
third profession of the customers was self-employed (20 %), the fourth highest profession was customers
was agriculture (18 %), the fifth highest profession of the customers was foreign employment (12 %) and
the least percentage of profession was others (3 %).

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Customers' monthly income of BBSM customers
Above 30000
21000-30000
16000 - 20000
11000- 15000
5000 - 10000
Less than Rs 5000
0 20 40 60 80 100 120
Number of persons
Figure-3: Customers' monthly income
The results show that the highest number of BBSM customers' monthly income was ranked from NRS
21000 to NRS 30000. Again, the least percentage of BBSM customers' income level was more than NRS
30000 per month (see in the Chart 1).
4. Discussion and Conclusion
The primary objective of this study was to examine the customers' satisfaction at BBSM Bharatpur
Chitwan of Nepal. The quantitative approach was used as research methodology and the survey study was
used to as research method. The survey questionnaire was used to know the opinions and experiences of
the sampled customers on their satisfaction based on service quality, price level, employees’ service and
customer relationship management. The target population was one thousand and the sampled customers
were one hundred and ninety which is 19 % as the sampled population. The proportion of the male and
female population was [76/190*100] 39.12 % (n =76) and the female respondent was and [114/190*100]
60. 88 % respectively.
The total sample customers participated in this study was one hundred and ninety-one where the
response rate was 84.88%. The results show that there is positively statistically significant relationship
between use the better customer management and customer satisfaction at BBSM Bharatpur Chitwan
(p <0.05, B= 0.438). Again, when the independent the better customer management increases one unit,
customer satisfaction can be predicated to increase around 0.438 times if other variables are controlled.
The current study has supported the findings of ROH, AHN & HAN (2005). Both studies summarized
that the CRM system success model that consists of CRM initiatives: process fit, customer information
quality, and system support; intrinsic success: efficiency and customer satisfaction; and extrinsic
success: profitability. The results show that the main reason of choosing BBSM by the customer was
quality of products. The results further show that there is no significant relationship between customers
centered service, facilities and equipped technology used by BBSM, use of technology and maintain
clean environment, verities of new and quality products and facilities and quality products of BBSM (p >
0.05). The monthly incomes of the majority of customers was fallen on NRS 21000 to 30000. The results
Income(NRS)

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further show that there is association between the gender and customers' intention to continue their
buying habits at BBSM Bharatpur Chitwan. The profession of the respondent was summarized as the
national service (24 %), business (23 %), self-employed (20 %), agriculture (18 %), foreign employment
(12 %) and other profession was (3 %). This study is based on customer relationship management which
is a combination of people, processes and technology that seeks to understand a company’s customers.
CRM has evolved from advances in information technology and organizational changes in customer-
centric processes. Companies that successfully implement CRM had gained the rewards in customer
loyalty and long run profitability.
References
Baltas, G. and Papastathopoulou, P. (2003), “Shopper characteristics, product and store choice criteria: a
survey in the Greek grocery sector”, International Journal of Retail & Distribution Management,
31:.10, 498-507.
Cheriyah, Y., Sulistyowati, W., Cornelia, A., & Viverita, V. (2013). Factors Affecting Customers’
Satisfaction and Perception: Case Study of Islamic Banks’ Service Quality. ASEAN Marketing
Journal, 2(1), 25-30
Cheriyah, Y., Sulistyowati, W., Cornelia, A., & Viverita, V. (2013). Factors Affecting Customers’
Satisfaction and Perception: Case Study of Islamic Banks’ Service Quality. ASEAN Marketing
Journal, 2(1), 25-30
Cohen, L., Manion, L., Morrison, K., & Bell, R. (2011). Research methods in education (1st ed.). London:
Routledge.
Gómez, M., McLaughlin, E. and Wittink, D. (2004). Customer satisfaction and retail sales performance:
an empirical investigation. Journal of Retailing, 80(4), 265-278.
Hassan, N. (2018). Factor Affecting Customer Satisfaction Towards Service Quality of Front Office Staff
at the Hotel Putra Regency. SSRN Electronic Journal. 16(3), 34-45.
Kirkup, M., De Kervenoael, R., Hallsworth, A., Clarke, I., Jackson, P. and Perez del Aguila,
R. (2004). Inequalities in retail choice: exploring consumer experiences in suburban
neighborhoods. International Journal of Retail & Distribution Management, 32(11), 511-522.
Kotler, P., & Armstrong, G. (2012). Principles of marketing. Boston: Pearson Prentice Hall.
Mithas,S., Krishnan,M.,&Fornell,C. (2005).WhyDoCustomerRelationshipManagementApplications
Affect Customer Satisfaction? Journal of Marketing, 69(4), 201-209.
Mithas,S., Krishnan,M.,&Fornell,C.(2005).WhyDoCustomerRelationshipManagementApplications
Affect Customer Satisfaction? Journal of Marketing, 69(4), 201-209.
ROH, T., AHN, C., & HAN, I. (2005). The priority factor model for customer relationship management
system success. Expert Systems with Applications, 28(4), 641-654.
APPENDIX 1
Observed
Predicted QN16
Percentage Correct
Yeah No
QN16 Intention to recommend 150 49 90.2
Does not intend to recommend 25 11 22.2
Overall Percentage 65.67

OCEMJournalof
Original Article
Student Satisfaction at Secondary Level in Oxford
College of Engineering & Management
Abstract
The objective of this study was to examine the student satisfaction level at grade 11 and grade 12 in
Oxford College of Engineering and Management (OCEM). Quantitative methodology approach
along with the survey study was applied in this study. The survey questionnaire was used as research
instrument to collect data in this study. The target population was four hundred and fourty and the
sampled population was two hundred and four. There were two hundred and four (N= 204) respondents
where the boy’s population was 55.88 % and girl’s population was 41.11 %. The response rate was
94.22%. The Cronbach’s Alpha was calculated to find the reliability of the data. Independent sample t-
test was used to find the differences between the male and female students’ intention to recommand for
the enrollment of their kith and kin at OCEM. The previous studies reveal that students’ satisfaction at
the secondary level schools were embedded in the factor of quality of education, school administrative
factor, managerial factor, psysical factor and school location. The results show that lifelong academic
skills, standard and qualified lecturers, student centered activities, strong faculty management, proactice
faculty support, better college environment and facilities, punctuality of the transfort facilities, strong
security environment, better lab facilities and advanced library facilities, advanced physical facilities and
college infrastructure facilities were extracted as the key subscales of the analysis section. The results
show that there was significant relationship between existing students’ recommendation to enrol and
student centered activities, advanced lab and library facilities and college facilities at Oxford College of
Engineering and Management (OCEM) at Nawalpur of Nepal (p < 0.05, B = -.342, B = -.309. B = -.398).
The implications of findings will be beneficial for college principals, school leaders, academicians,
Head of Department, college promoters to formulate student centered strategies. It will be also useful to
college policy makers to formulate new student-centered strategy to motivate students for the enrolment.
In generalizing the results of the present study, there is some cause for concern due to a sampling method
and representativeness of the boys and girls
Keyword: student satisfaction, quality of education, school administrative factor, managerial factor,
physical factor and school location.
1. Introduction
Student satisfaction is a debatable issue in the global context because the higher education market is
strongly affected by internal and external environment of the colleges. This has produced a competitive
market for educational services and increased competition to attract students (Nogueira, 2018). As

OCEM Journal of
competition among higher education institutions (HEIs) has increased, these institutions have been forced
to adopt market-oriented strategies to differentiate themselves from their competitors and thereby attract
as many students as possible (Butt & Rehman, 2010). HEIs have also realized that their sector represents
a business-like service industry and have begun to focus more on meeting or exceeding the needs of
their students (Gruber et al., 2010; Mihanović, Batinić & Pavičić, 2016). The primary objective of this
study is to examine the experiences and opinions of students of grade 11 & 12 on the current available
academic, managerial, physical and infrastructure facilities for their intention to recommend their kith
and kin. Students satisfaction level is embedded in the internal and external and external environment
of the educational institutions which covers image of college, ideal location of the college, quality of
college facilities, quality of college academic program experiences and the quality of administrative staff.
The secondary objective of this study is to identify the student’s intention to continue their higher level
education at OCEM. Student satisfaction is a short-term attitude resulting from an evaluation of a
student’s educational experience (Hossain & Islam, 2012), and as such, it is important to understand
for a number of reasons for example, to motivate students, to generate more profit and to penetrate
in the new market. Satisfied customers tend to have a higher probability of generating positive word-
of-mouth (Kwun, Ellyn & Choi, 2013; Nogueira, 2018). Thus, it is more likely that satisfied students
engage in positive word-of-mouth communication than do less satisfied students. Feedback from students
can be used to improve those factors where satisfaction is lower than the normal standard and because
student satisfaction has been found to be associated with the perceived quality of the institution. Kwun
et al., (2013) concluded that improving the level of student satisfaction will eventually improve public
perception with respect to the quality of the institution. The level of student dissatisfaction has been
increased in the Nepalese institutions (Sahayogee, 2019) and student retention has seen a big challenge
to the educational practitioners in higher education. If educational organizations are failed to satisfy their
students, the future of higher educational institutions will be in risk. Student centered marketing strategy
has emerged to fulfil the contemporary demands of students in higher education sector (Upreti & Chhetri,
2013). The current study will be beneficial for higher educational academic leaders and practitioners
to focus their marketing strategy to satisfy the students. Similarly, this study will also helpful for local
government to know the current demand of students and to regularize the local education system and to
associate with student mankind.
2. Theoretical Model of The Study
Student recommendation is deeply rooted in their satisfaction level where they are currently studying as
a student of higher education. Generally, they evaluate the current facilities available by their respective
college where the quality of administrative staff; college program; image of the college; ideal location of
the college and external environment of the college are the key indicators to satisfy them (Weerasinghe
& Fernando, 2018). Factors affecting student’s satisfaction are also concluded as a student’s culture,
subculture, social class; reference groups, aspirational groups, member groups, family roles and status,
age and life-cycle stage, occupation, economic circumstances, lifestyle, personality and self-concept,
perception, learning, beliefs, and attitudes (Attreya, 2018). Again, student’s satisfaction is deeply rooted
in 7P’s of the service marketing which are mentioned as product, price, placement, promotion, people,

OCEMJournalof
s
n
f
o
l
l
i
Recommendation
of
Students’
Stafisfaction
f
process, physical evidence as mentioned by Gajic (2011). Here product means college program, price
means, fee of each course, placement means, internship and job guarantee, promotion means advertisement
of college, people means lecturers and administrative staff, process means different stages of program
completion and physical evidence means physical facilities of college (Prentice, Brady & McLaughlin,
2018). Again, improving the college program, reducing the tuition fee; improving the connection
with economic environment; the image of college; the academic staff; the management activities, and
improving the college facilities are key influencing factors of student’s satisfaction in higher educational
institutions (Hanssen & Solvoll, 2015).
Figure 1. Student Satisfaction Model for Higher Education in Nepal
3. Research Task and Problems
The quantitative research approach was applied in this study because this approach is useful to cover
larger sample population generates and statistically robust results that can be derived from quantitative
research are good for estimating the probability of success. The research method is the survey study and
the research instrument is the service questionnaire. The target population was four hundred and fifty
(n=440) where the sample population was two hundred and four (n=204) There were two hundred and
four (n= 204) respondents where the boy’s population was one hundred and fourteen (N=114) [55.88 %]
and girl’s population was ninety (n = 90) [41.11 % ].
The target population was four hundred and fifty (n = 440). The main research problem will examine the
student’s intention to recommend to recommend their kith and kin to enrol at higher secondary schools.
The first sub problem will examine the student’s experiences and opinions at grade 11 and 12 grade

OCEM Journal of
Quantitative
Approach
[The Survey
Research
Method]
Research
Instrument
[The Survey
Questionnaire]
Data Analysis
Method
[The
Descriptive
Stastistics]
Stastistical Tool
[Principal
Component
Analysis and
Chi-square]
Reliability
[Relibility Scale
Factor]
students on academic factor for their intention to recommend their friends/family members/relatives to
enrol at OCEM. The second sub problem will examine the student’s experiences and opinions of grade
11 and 12 grade students on managerial factors for their intention to recommend their kith and kin at
secondary level schools. The third sub problem will examine student’s experiences and opinions on
college’s physical factor for their intention to recommend their kith and kin to enrol at Secondary Level
Schools. The fourth sub problem will examine the experiences and opinions on college infrastructure
facilities for their recommendation to enrol their kith and kin to enrol at schools where they are currently
studying (Cohen, Manion & Morrison, 2011; Tucker, 2013. Saying so, the first main problem and its sub
problems have been presented as follows.
1. What are the key influencing factors affecting student’s intention to recommend their kith and kin to
of grade 11 and 12 in the college?
1.1. What is the impact of academic factor on student’s intention to recommend their friends/relatives/
family members to enrol at the college where they are currently studying?
1.2. What is the impact of managerial factor on student’s intention to recommend their friends/
relatives/family members to enrol at the college where they are currently studying?
1.3. What is the impact of physical factor on student’s intention to recommend their friends/relatives/
family members to enrol at the college where they are currently studying?
1.4. What is the impact of infrastructure factor on student’s intention to recommend their friends/
relatives/family members to enrol at the college where they are currently studying?
3. Methods
The survey research design was applied to collect data on student’s recommendation to their kith and kin
to enrol at the same college because this method is useful to cover a large sample population. The survey
research design which is used in this study has been presented below.
Source: Kothari, 2004
Figure 2 Research design of quantitative method

OCEMJournalof
The research of this study mentioned in Figure 1 signifies that quantitative method is embedded the
survey research method, research instrument, data analysis method, statistical tools and reliability scale
factor. The five point Likert Scales survey questionnaire was used as research instrument to know the
experiences and opinions of grade 11 & 12 students. Two hundred and fifteen (N=215) questionnaires
were distributed but the respondents returned two hundred and four (N=204) questionnaire at the Research
Department of OCEM. The response rate was 94.22 % where the reliability of the data was examined
by computing Cronbach’s Alpha value (0.70). The descriptive statistics and Binary Logistic Regression
Model was applied to find the association between the independent and dependent variables. The structure
of Binary Logistic Regression Equation is mentioned as prob(event) is equal to b0+b1
x1
+b2
x2
+………..
bnxn (Cohen, Manion & Morrison, 2011 ; Vogt, 2011).
4. Data Analysis
The first, second, third and the fourth sub-problems have examined the students’experiences and opinions
on academic facilities, managerial facilities, service facilities and the infrastructure facilities for their
intention to recommend their friends/relatives/family members at Oxford College of Engineering and
Management in Gaindakot-2, Nawalpur of Nepal. The first, second, third and the fourth instruments
were entitled the “the academic factor; the managerial factor, physical and infrastructure factors. The
instrument was based on the five point Likert scales, for example, 1 = I strongly disagree, 2 = I disagree,
3 = I do not know, 4 = I agree and 5 = I strongly agree. Factor Reduction Model of Principal Component
Analysis has been applied to reduce the number of variables and to extract the new principal components.
The descriptive statistics analysis was applied to compute mean and Standard Deviation of each
subscales. Later on, the Binary Logistic Regression Model (BLRM) was applied to find the association
between dependent and independent variables. Chi-Square Test and Student T-test were applied to find
the association between the gender and student’s recommendation for the enrolment at OCEM.
4.1 Results
Therewerefoursub problems undertheonemain problem inthisstudy.Thefirst sub problem has examined
the student’s opinions and experiences of students for the current quality of academic program. Similarly,
the second sub problem has examined the available managerial support on the student’s intention to
recommend their kith and kin to enrol. Again, the third sub problem has examined the available physical
facilities on student’s intention to recommend for the enrolment.
4.1 Academic factor
Academic factors are embedded in delivering the practical skills, student centered activities, innovative
teaching pedagogy, interactive teaching environment, better internal evaluation system, cooperative
teaching environment and using modern educational technology in classroom teaching (Hanssen and
Solvoll, 2015; Kreber, 2009).
4.2 Managerial factor
The managerial factors are embedded in the role of faculty members to solve students’’ problems, the
role of principal to motivate students, the concentration of overall coordinator to address student issues
and helpful role of principal. Management of time schedule, teaching resources, availability of faculty

OCEM Journal of
head and high attention of faculty head to solve students’ problem (Upreti & Chhetri, 2014). Managerial
factors are also signify that student support centre, students’ involvement in decision making and also the
role of student union in decision making (Hernadewita et al., H. 2019).
4.3. Physical factors
The physical factors are embedded in available sport facilities, neat and clean college environment,
library and lab facilities, hygienic canteen, parking facilities, prompt and easy transport facilities, secured
college environment and available educational technology resources and other teaching materials (Kärnä
& Julin, 2015).
4.4. Infrastructure factors
The infrastructure factors are embedded in the availability of furniture, availability of clean drinking
water, availability of educational technology, advanced and technologically equipped classrooms, and a
large playground (Sweeney, 2016).
5. Subscales of Principal Components on academic, managerial, physical and
infrastructure factors.
5.1All the subscales were initially examined their reliability by using scale reliability analysis where
the accepted value of Cronbach’s Alpha was 0.070.
Table 1. The values of mean, SD and Cronbach’s Alpha on different subscales
Subscales Mean SD
Cronbach's
Alpha
P
values
Number
of
variables
ACADEMIC
SCALES
Standard lecturers
Lifelong academic skills
Strict student centred activities
2.22
2.24
2.45
0.78
0.59
0.95
0.70
0.71
0.75
0.157
0.014
0.016
10
9
8
10
10
9
10
10
11
10
MANAGERIAL
SCALES
Strong faculty management
Proactive faculty support
2.06
2.52
0.72
1.04
0.76
0.72
0.373
0.214
PSYCHICAL
SCALES
Better lab and library facilities 2.28 0.83 0.70 0.287
Strong security mechanism 2.93 0.83 0.70 0.341
Better college facilities 2.34 0.70 0.71 0.041
College furniture facilities
Best transportation facilities
2.44 0.90 0.74 0.162
2.76 0.99 0.73 0.377
INFRASTRUCTURE
SCALES
Weak college infrastructure
facilities
2.92 1.150 0.70 0.924 11
The results show that the subscales are categorized into four groups where standard lecturers, strict
student centred activities and lifelong academic skills are categorized as academic scales (Mean values
= 2.22 & 2.24). Similarly, strong faculty management and proactive faculty support are categorized
as managerial scales (Mean values = 2.06, 2,52 & 2.45). Again, better lab and library facilities, strong
security mechanism, better college facilities, college furniture facilities and better transportation facilities

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(Mean = 2.28, 2.93, 2.34, 2.44 & 2.76). Finally, the college buildings are categorized as the infrastructure
scale (Mean = 2.92). The results show that the mean value of the subscale “strong faculty management”
had been calculated as 2.06 signifying that students showed their disagreement with the statements
that faculty members were capable to manage time schedule to complete the course, manage teaching
and learning resources and the faculty head was available all the time when students needed some
support to solve the problems. Similarly, the mean value of the subscale “standard lecturers” had been
calculated as 2.22 signifying that students were somehow disagreed and somehow undecided with the
statements that teachers had used modern educational technology during classroom teaching, teachers
had followed the international evaluation system and creation of cooperative teaching environment by
teachers. Again, the mean value of the subscale “student cantered activities” had been calculated as 2.23
signifying that students were somehow disagreed and somehow undecided with the statements that they
had been motivated by their principal, the overall coordinator was always concerned about their issues
in their college, the capacity of principal to make rational decision and the supportive roles of principal
(Langstrand, Cronemyr & Poksinska, 2014). Furthermore, the results show that the mean value of the
subscale” lifelong academic skills” had been calculated 2.24 signifying that students were approximately
disagreed with the statements that of delivering the excellent teaching and learning activities, using
modern teaching pedagogy, availability of interactive teaching environment, grooming student’s career
path, and using modern technology during classroom teaching at grade 11 &12 class at OCEM. Similarly,
the mean value of the subscale “better lab and library facilities” had been calculated as 2.28 signifying that
students were somehow disagreed and somehow undecided with the statements that library facilities were
helpful and available on time. Additionally, the mean value of the subscale “better college environment
and facilities” had been calculated as 2.34 signifying that students had showed their disagreement with
the statements that of the books were available when they needed, maintaining the neat and clean college
environment; hygienic and satisfactory service of canteen and availability of sufficient parking lane
(Insch & Sun, 2013). The next mean value of the subscale “college physical facilities” had been
calculated as 2.44 signifying that students were somehow disagreed and somehow undecided with the
statements that college had sufficient furniture, sufficient clean drinking water and in college, availability
of the technologically equipped classrooms at OCEM (Yusoff, McLeay and Woodruffe-Burton, 2015).
Again, the mean value of the subscale “proactive faculty support” had been calculated as 2.52 signifying
that students were somehow disagreed and somhow undecided with the statements that faculty members
listenedtheirproblemsandsolvedontime. Again,themeanvalueoftheninthsubscale“besttransportation
system”hadbeencalculatedas2.76signifyingthatstudentswereapproximatelyagreedwiththestatements
that the punctuality of transport, reasonable cost and comfortable transport system.Again, the mean value
of the subscale “strong security environment” had been calculated as 2.93 signifying that students were
agreed with the statements that they were satisfied with the college security concern. Finally, the mean
value of the subscale “weak college infrastructure facilities” had been calculated as 2.92 signifying that
students were approximately agreed with that statements that collage building was safe, had sufficient
space, and technologically equipped administrative buildings in OCEM (Quality Improvement Based on
a Process Management Approach, with a Focus on University Student Satisfaction, 2016). The overall
mean values notified that the mean values ranged from 2.06 to 2.92 signifying that students were higher

OCEM Journal of
than the disagreed to natural to recommend their kith and kin to enrol at OCEM. The results show that
the mean score of the male student of the subscale strong faculty management (M = 2.10, SD = .80) do
not statistically significantly differ [t (202) = .893, p = 0.373 from that of the female students on the same
variable (M = 2.01, SD = .60). Similarly, the mean score of the male students of the subscale standard
lecturers (M = 2.29, SD = 0.85) did not differ statistically significantly [t (202) = 1.419, p = 0.157 from
that of the female students on the same variable (M = 2.13 SD = 0.67). But, the mean score for the male
students on the subscale student cantered activities (M = 2.36, SD = 0.87) is statistically significantly
higher [t (200.80) = 2.605, p = .012] from that of the female student on the same variable (M = 2.06, SD
= 0.71, Cohen’s d = 0.37) signifying that boys had have more intended to recommend their kith and kin to
enrol at OCEM than girls. The results further show that the mean score for the male students (n=116) on
the subscale lifelong academic skills (M = 2.33, SD = 0.60) is statistically significantly higher [t (194.45)
= 2.505, p = .013] than that of the female students (n=88) on the same variable (M = 2.12, SD = 0.56,
Cohen’s d 0.36) signifying that male students had have high intention to recommend their kith and kin
for the enrolment at OCEM than the female students. Moreover, the mean score of the male students of
the subscale better lab and library facilities (M = 2.33, SD = .82) do not statistically significantly lower
[t (202) = 1.068, p = 0.287 than that of the female students on the same variable (M = 2.21, SD = .84).
Again, the mean score of the male students of the subscale better college environment and facilities (M
= 2.43, SD = .74) is statistically significantly higher [t (199.72) = 2.102, p = 0.03] from that of the female
students on the same variable (M = 2.23, SD = .62), signifying that boys were more intended to recommend
their kith and kin for the enrolment where they are currently studying. Additionally, the mean score of the
male students of the subscale college physical facilities (M = 2.52, SD = .93) do not statistically significantly
differ [t (202) = 1.405, p = 0.162] than that of the female students on the same variable (M = 2.34, SD =
.85). Again, the mean score for the male students on the subscale proactive faculty support (M = 2.60, SD
= 1.05) did not differ statistically significantly [t (202) = 1.245, p = .214] from that of the female student on
the same variable (M = 2.42, SD = 1.01). Again, the mean score of the male students of the subscale better
transportation system (M = 2.81, SD = 1.03) do not statistically significantly differ [t (202) = .885, p = 0.377
from that of the female students on the same variable (M = 2.69, SD = .95). Again, the mean score for the
male students on the subscale college infrastructure facilities (M = 2.91, SD = .80) did not differ statistically
significantly [t (202) = -.906, p = 0.656] than that of the female student on the same variable (M = 2.92, SD
= .89). Finally, the mean score for the male students on the subscale strong security environment (M = 2.86,
SD = 1.14) did not differ statistically significantly [t (202) = -.954, p = .341] from that of the female student
on the same variable (M = 3.02, SD =1.18).
5.1ResultsofChi-SquareTestbetweenthelocationoftheexistingstudentsandtheirrecommendation
to enrol at OCEM.
The results of crosstabulation of different locations of existing students and the students’ intention of
recommendation to their kith and kin to enrol at OCEM shows that out of 204 sample students, 50
students from campus periphery, 42 students from Eastern Chitwan, 9 students from Western Chitwan
and 29 from other location were found positive to recommend their kith and kin. But out of 204 students,
22 students from campus periphery, 22 from Eastern Chitwan, and 15 from Western Chitwan and 14

OCEMJournalof
from other location showed their intention not to recommend their kith and kin to enrol at OCEM. This
shows that there is association between different locations of existing students and students’ intention for
recommendation for the enrolment at OCEM.
Table 3. Chi-Square table of location of existing students at OCEM
and their recommendation preference (N=204).
The Table 3 provides that the value of Chi-Square is 10.273a
and associated significance value is
0.036<0.05. Therefore, the hull hypothesis is rejected, and signifying that there is association between the
location of existing students and students’ intention to recommend their friends/relatives/family members
to study at OCEM.
5.2 Logistic regression Wholesome Model of the significant indicators
Three independent variables were found significance from the whole independent variables of this
study. All three significant indicators of student’s intention to recommend their friends/family members/
relatives for the enrolment in the same college where they are currently enrolled students were entered
into the Binary Regression Model. Only two indicators were found significant for the student’s intention
to recommend for the enrolment at OCEM. The equation of independent and dependent variable under
the Binary Logistic Regression Model is embedded in logit(P) = b0+b1x1+b2x2+………..bnxn where p
is used to represent the odds ratio and the formula of odds ratio[ odds = p/1-p i.e. numerators p denotes
probability of presence and denominator p is equal to probability of absence(Cohen et al,. 2007).
Table 2. Binary Logistic Regression Wholesome Model of the impact of different factors on the
intention of student’s recommendation for the enrolment at their own college (N = 204).
Independent variables B S. E Wald df Sig.
Exp
(B)
95% C.I forExp (B)
Upper Lower
Strict student centred activities -.342 .158 4.676 1 .032 .710 .521 .968
Better ab and library facilities .309 .168 3.389 1 .066 1.362 .980 1.891
Weakcollegeinfrastructurefacilities -.398 .157 6.934 1 .011 .672 .494 1.891
Constants -.640 .156 16.913 1 .000 .527
The Omnibus Tests [Chi-Square = 16.712, df = 3, p =.110 and associated significance level is greater than
0.05, the present model shows a decrease in deviance from the base model because Chi-Square is positive,
showing this model is better fit compared the base model. The model summary table shows the values
of -2 Log Likehood (243.483), Cox and Snell R2
and Nagelkerke R2
[8 % (Cox and Snell) and 11% %
(Nagelkerke)] variance of the model was explained by the independent variables. Hosmer and Lemeshow
Test shows that p = 0.110 > 0.05 is insignificant which is good to support for the regression model fit. The
Chi-Square Tests
Values df Asymptotic Significance (2-sided)
Pearson Chi-Square 8.957
4 0.036
Likelihood Ratio 8.574 4 0.037

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results show that out of 210, 174 students who initially showed their intention to recommend their kith
and kin to enrol at OCEM, this model predicts only 118 students intended to recommend their kith and
kin to enrol at OCEM but 46 students intended not to recommend their kith and kin to enrol at OCEM.
The results further show that out of 36 students who did not intent to recommend their kith and kin to
enrol at OCEM, 11 students intended to recommend their kith and kin to enrol at OCEM (see in the
Appendix 1). Thus, it predicts students who intended to recommend their kith and kin to enrol at OCEM
with 91.5 percent accuracy and also predicts that students who did not intend to recommend their kith
and kin to enrol at OCEM with 35.2 percent accuracy. The results also indicate that the overall percentage
of correctness of observed data was 71.5 %. The results show that there is association between students’
intention to recommend to their kith and kin to enrol at OCEM and strict student centred activities
(p< 0.05 with odds ratio .710, B = .352) in the wholesome analysis of Binary Logistic Regression
Model indicating the negative experiences on their principal motivational roles, the concerned of the
overall coordinator to hear their issues in their college, rational role of their principal to make managerial
decision and his helpful roles to them. The current study has supported the previous findings of Calder
(2013) because the study of Calder had found that students were found dissatisfied with the strict student
centred activities in their college and did not want to recommend their kith and kin. Similarly, the results
further reveals that there was significant association between the recommendation of students to their kith
and kin to enrol at OCEM and college infrastructure facilities of OCEM (p< 0.05 with odds ratio .672, B
= -.398) in the wholesome analysis of Binary Logistic Regression Model indicating the negative impact
on safety college building in all aspect, sufficient space of their classroom and equipped administrative
builds at college. The current study has supported the previous study of Weerasinghe and Fernando
(2018) because the study of Weerasinghe and Fernando had also found that students were dissatisfied
with the weak infrastructure facilities by which students did not want to recommend their kith and kin to
enrol at their existing colleges.
Discussion and Conclusion
The objective of this study was to examine the students’ intention to recommend their kith and kin to
enrol at OCEM Gaindakot-2 Nawalpur of Nepal. Quantitative research method was used along with
the survey study to collect data on students’ intention for the current facilities of academic, managerial,
physical and quality of college programs. The response rate of the survey questionnaire was 94.22%. The
results has concluded that lifelong academic skills, standard and qualified lecturers, student centered
activities, strong faculty management, proactice faculty support, better college environment and facilities,
punctual transfort facilities, strong security environment, better lab and library facilities, college psychical
facilities and college infrastructure facilities as the subscales of this study. The results of the Chi-square
show that there is significant association between students’ recommendation to their kith and kin and
different locals of the college. The results further show that there was association between the intention
of existing students’ to recommend their kith and kin to enrol at OCEM and student centered activities,
better lab and library facilities and college buildings facilities (p < 0.05, B = -.342 , .309, -.398). The
current study has also supported the previous study of Mullamaa (2017) because the previous study
of Mullamaa had found that student’s centred activities and better lab and library facilities motivated

OCEMJournalof
college students to recommend their kith and kin in their existing college to study. The findings of the
current study has supported the previous findings of Gajic (2011) because Gajic has found that students
were found satisfied with the rich infrastructure facility. The Chi-square Test was applied to measure
the association between existing students’ locations and their intention to recommend their kith and kin
to enrol at OCEM. The results show that there is significant relationship between the different locations
of students and their intention to recommend for the enrolment at OCEM. The findings of this study is
significant for the Department Head, administrative staff and the principal of OCEM to formulate new
policies and strategies. It will be also important to other colleges of the same characteristics to know the
students’ perception to the private colleges in Nawalpur and Chitwan District.
Recommendation
This study recommends that academicians of OCEM need to deliver lifelong academic skills, student-
cantered activities and updated lecturer during their classroom teaching. Similarly, the faculty heads
need to improve their management activities, quick faculty support to students and supportive college
environment and facilities and should apply new educational technology in their classroom teaching.
Again, top level management needs to revise the current students’ security system for the strong security
environment, improve lab and library facilities, to improve college infrastructure facilities and other
physical facilities. The future research has to cover the large sample population both private and the
public colleges in order to generalize findings for the larger population which makes the future research
more valid and transferable in other aspects of factors influencing to student satisfaction in Chitwan
District.
Acknowledgement
Theauthorthanksallthe Department Heads,lecturersandstudents ofgrade11and12at OCEMGaidakot-2
Nawalpur of Nepal who made substantial contributions to this work. This work was fully funded by the
OCEM Gaindakot-2 Nawalpur Nepal. The supporting roles and contributions encouragement from our
Principal, Professor Er. Hari Bhandari and draft correction from Vice Principal Mr. Tilak Ram Panthi are
very much admirable and appreciative to complete this great work.
References
Attreya, B. (2018). Consumer Buying Behaviour. Journal of Advances and Scholarly Researches in
Allied Education, 15(9), 1-4.
Butt, B. & Rehman, K. (2010). A study examining the students’ satisfaction in higher education. Procedia
- Social and Behavioral Sciences, 2(2), 5446-5450.
Calder, N. (2013). Mathematics in student-centred inquiry learning: Student engagement. Teachers And
Curriculum, 13. doi: 10.15663/tandc.v13i0.15
Cohen, L., Manion, L. and Morrison, K. (2011). Research methods in education. London: Routledge.
Gajic, J. (2011). Measurement of student satisfaction in higher education. Marketing, 42(1), 71-80.
Gruber, T., Fuß, S., Voss, R. and Gläser‐Zikuda, M. (2010). Examining student satisfaction with higher
education services. International Journal of Public Sector Management, 23(2), 105-123.

OCEM Journal of
Hanssen, T. and Solvoll, G. (2015). The importance of university facilities for student satisfaction at a
Norwegian University. Facilities, 33(13/14), 744-759.
Hernadewita et al., H. (2019). PLS-SEM Based Analysis of Service of Learning, Service Quality and
Satisfaction of College Student in Polytechnic. International Journal of Mechanical and Production
Engineering Research and Development, 9(3), 861-870.
Hossain, M. J. & Islam, M. A. (2012) Understanding perceived service quality and satisfaction: A study
of Dhaka University Library, Bangladesh. Performance Measurement and Metrics, 13, 169-182.
Insch, A. and Sun, B. (2013). University students’ needs and satisfaction with their host city. Journal of
Place Management and Development, 6(3), 178-191.
Kreber, C. (2009).Academics’teacher identities, authenticity and pedagogy. Studies in Higher Education,
35(2), 171-194.
Kwun, D. J. W.,Ellyn, E. & Choi, Y.(2013) Campus Foodservice Attributes and their Effects on Customer
Satisfaction, Image, and Word-of-mouth. Journal of Foodservice Business Res
Langstrand, J., Cronemyr, P. and Poksinska, B. (2014). Practise what you preach: quality of education in
education on quality. Total Quality Management & Business Excellence, 26(11-12),1202-1212.
Mihanović, Z., Batinić, A. and Pavičić, J. (2016). The link between students’ satisfaction with faculty,
overall students’ satisfaction with student life and student performances. Review of Innovation
and Competitiveness, 2(1), 37-60.
Mukhtar, U., Ahmed, U., Anwar, S. and Baloch, M.A. (2015). “Factors affecting the service quality of
public and private sector universities comparatively: an empirical investigation; Journal of Arts,
Science & Commerce; 3(1), 132-142.
Mullamaa, K. (2017). Student centred teaching and motivation. Advances In Social Sciences Research
Journal, 4(16). doi: 10.14738/assrj.416.3593
Nogueira, M. (2018). Measuring Academic Life Satisfaction in Portuguese Students. Nursing &
Healthcare International Journal, 2(1), 21-25
Prentice, G., Brady, J. and McLaughlin, C. (2018). Education Service Quality, Value and Satisfaction on
Student Customer Intentions and Behaviour. DBS Business Review, 2.
Quality Improvement Based on a Process Management Approach, with a Focus on University Student
Satisfaction. (2016). Acta Polytechnic a Hungarica, 13(6). http://dx.doi.org/10.12700.
Sahayogee, J. (2019). Best Wishes For First Day of College - Good Luck Messages & Quotes. Retrieved
4 November 2019, from https://bestwishes.imnepal.com/best-wishes/lifestyle/best-wishes-first-
day-college-good-luck-messages-status-quotes/
Sweeney, L (2016). A Predictive Model of Student Satisfaction,” Irish Journal of Academic Practice:
5(1), p.1-31 (https://arrow.dit.ie/ijap/vol5/iss1/8)
Tucker, B. (2013). Student evaluation to improve the student learning experience: an Australian university
case study. Educational Research and Evaluation, 19(7), 615-627.
Uprety, R. and Chhetri, S. (2014). College Culture and Student Satisfaction. Journal of Education and
Research, 4(1), 77-92.

OCEMJournalof
Vogt, W. (2011). SAGE quantitative research methods. Los Angeles [Calif.]: SAGE.
Weerasinghe, I. and Fernando, R. (2018). Critical factors affecting students’ satisfaction with higher
education in Sri Lanka. Quality Assurance in Education, 26(1), 115-130.
Yusoff,M.,McLeay,F.andWoodruffe-Burton,H.(2015).Dimensionsdrivingbusinessstudentsatisfaction
in higher education. Quality Assurance in Education, 23(1), 86-104.
APPENDIX 1
Observed
Predicted QN17
Percentage
Correct
Intention to
recommend
Does not intend to
recommend
QN17
Intention to recommend 168 36 91.5
Does not intend to recommend 0 11 35.2
Overall Percentage 71.5

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The Impact of Information Technology to Make
Rational Strategic Decision Making in
Educational Institutions in Nepal
Professor, Er. Hari Bhandari
Principal, Oxford College of Engineering and Management, Nawalpur, Nepal
hari_ocem@hotmail.com
Abstract
The primary objective of this study was to examine the impact of information factors for the rational
strategic decision making (RSDM) when information is accessed using technology. In educational
institutions. The previous studies reveal that time content, form of information and technology were
found influential factors for the appropriate rational strategic decision making. The quantitative method
was applied along with the survey study was used as a research method to collect data where the
administered survey structured questionnaire was used as a research instrument to collect data. In
the first stage, fourteen private and 10 public colleges were selected purposively and then twenty four
respondents were selected randomly from the twenty-four colleges. The results show that the proportion
of male and female respondent was 79.20 % and 20.8 % respectively and the proportion of private
and public college was 58.33 % and 41.66 % respectively. The results indicate that the values of the
subscales were found lower than the average mean value signifying the less importance of information
factors to make the RSDM. Additionally, the results also highlighted that there was an insignificant
association between the value of information, the purity of information, the efficiency of information, the
details of information, the quality of information, the advanced technology adopted human resources, the
performance of information, the formats of information, the perfectness of the information and the role
of information in RSDM (p > 0.05). The results further show that the mean score of the private college
of the subscale ‘purity of the information’ was statistically significantly higher from that of the public
college. Similarly, the mean score of the private college for the subscale quality of information was
statistically significantly lower from that of the public college signifying that private college did not give
more importance to quality of information for the impact of RSDM. The implication of this study will be
beneficial for the college executives and principals to understand the role of information to make RSDM
in educational institutions. The limitation of this research is very limited number of survey respondents
which has affected the results of the Binary Logistic Regression Analysis.
Keywords: Information, rational strategic decision making, subscales, principal components, technology.
Introduction
An organization behaves as an open system that takes in information, material and energy from the
external environment, transforms these resources into knowledge, processes and structures that
produce services which are then consumed somewhere in the world. An educational organization uses
information strategically to make sense of changes in its setting to create new knowledge for innovation

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and to make decision about its course of action (Citroen, 2011). The primary objective of this study is to
examine the impact of information factors to make rational decision making in educational institutions.
This study will also address the need for more data about the effects of information technology on the
strategic decision-making process in educational institutions. The research study of Aharoni, Tihanyi &
Connelly (2011) found that strategic rational decision-making processes were positively correlated with
the factual and relevant information delivered by the college IT Department. Similarly, the research
study of Nutt and Wilson (2010) found that strategic decision making was negatively correlated with the
poor technological performance. Moreover, some recent approaches to strategic decision making have
concentrated upon the more micro aspects of how college executives think, act, and interpret strategic
decisions. The micro approach has been termed the strategy as practice perspective (Szymaniec-Mlicka,
2017). Many studies in strategic management take the position that executives reach strategic decisions
based on a structured process of careful consideration of circumstances, alternatives and consequences
of the available information which approach is known as a ‘rational process. Information on matters such
as competition, markets, technologies and trends in the societal environment affecting the organization
is used as a basis for the judgement on the implications of feasible alternatives for the decision to be
made in such a rational process. It is universally obvious that the use of information contributes to the
reduction of uncertainty. However, aspects of the role of information in the decision-making process have
got less priority in management research to make a rational strategic decision. For that reason, this study
investigates whether this research can add a new viewpoint to this field, specifically to that of the role
and value of modern information resources and access as a prerequisite for the structuring of the strategic
decision-making process. This study will also observe in detail the use of information during the process
of a number of actual recent strategic decisions taken by executives in the educational institutions. The
emphasis is on the factors of information for the rational decision-making process, not on the substance
or quality of the resulting decisions (Nutt & Wilson, 2010).
2.1 Meaning of the information and decision making
Information is an intrinsic component of nearly every activity in the organization so much that its function
has become transparent (Choo, 1996, p.329). Without a firm grasp of how it creates, transforms and uses
information, an organization would lack the coherent vision to manage and integrate its information
processes, information resources and information technologies (Petersen & Laustsen, 2019). Current
thinking in management and organization theory recognizes three distinct areas in which the creation
and use of information play a strategic role in determining an organization’s capacity to make rational
strategic decision.
Nutt & Wilson (2010, p.3) state the following statements for the meaning of strategic decision making.
“The term strategic decision making is often used to indicate important or key decisions made in
organizations of all types. The term organization includes any collective social, economic or political
activity involving a plurality of human effort. Strategic decisions emphasize the social practice of decision
making as it is carried out among and between individuals in the organization. When studying decision
making, both the organizing of decision activity as a collective phenomenon and the cognitiveprocesses

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of individual decision makers take centre stage. Strategic decision making is more than computation
carried out to make judgements and choices. Various branches of mathematics can inform us about risk,
options, game theory and choice”.
The meaning of strategic decision making is embedded to judge and choose the tricks to make key
rational decisions to sustain the educational organizations. The strategic decision making is a plan, play,
pattern, position and perspective to sustain the organizations in this competitive business era. In the past,
sometime, it was defined as a plan, sometimes play, position, and perspective focusing on organizational
sustainability for the future sustainability (Nutt & Wilson, 2010).
2.2 A rational approach to decision making
An important theme in research into strategic decision-making concerns the approach that is followed in
making a rational decision and the structure of decision making process. In a rational decision-making
process, executives have to reach strategic decisions without a prejudiced opinion about the eventual
decision and only after a structured process of careful consideration of circumstances, alternative lines
of thought and consequences of the decision made. Information on matters are embedded in time,
contents, form and technological factors affecting the organization are needed to judge the implications
of the feasible alternatives for the decision to be made (Szymaniec-Mlicka, 2017). “First, organizations
search for and evaluate information in order to make important decisions. In theory, this choice is to be
made rationally, based upon complete information about the organization’s goals, feasible alternatives,
probable outcomes of these alternatives, and the values of these outcomes to the organization. In practice,
rational choice-making is muddled by the pushing of interests among organizational stakeholders,
bargaining and negotiation between powerful groups and individuals, the limitations and idiosyncracies
of personal choice making, the lack of information, and so on. Despite the complications addressed in
earlier paragraph , an organization must keep up at least an impression of rational, reasoned behaviour,
both to sustain internal trust, and to preserve external legitimacy” (Lunenburg, 2010, p.8). “The second
area of strategic information use is when the organization makes sense of changes and developments
in its external environment. Organizations thrive in a dynamic, uncertain world. A dependable supply
of materials, resources, and energy must be secured to make rational strategic decision making. Market
forces and dynamics modulate the organization’s success or failure. The third area of strategic information
use is embedded in organizations’ creating, organizing and processing information in order to generate
new knowledge through organizational learning. New knowledge is then applied to design new products
and services, enhance existing offerings, and improve organizational processes” (Citroen, 2010, p.493).
2.3. Information as a factor in strategic decision-making
In management research publications, the role of information in the process of decision-making is seldom
recognized, discussed or analyzed as such, probably because management information is considered a
production factor that is readily available, and its accessibility is “taken for granted” in many studies on
company performance. Although input of information is often mentioned in order to be able to consider
parameters such as the business environment, internal and external issues and changing conditions during
the decision-making process, information is seldom seen as a determining factor of rational decision

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making in educational organizations (Citroen, 2010). As consequences, the characterises of information
in strategic management such as the quality, the sources and actual use of available information during the
process of strategic decision making are not recognized as important issues (Mishra, Allen & Pearman,
2014).
2.4 Information and communication technology (ICT)
Today computers have surprisingly supported to find applications for practically every business process
in the educational institutions, this development has had a great influence on the way college executives
need to operate nowadays. If we restrict ourselves to the more strategic issues, the decision-making
process has completely changed over the last decade by the way information has become available and
travels over communication services that are common now (Citroen, 2010). The potential influence
of ICT on strategic decision-making can be summarised as better forecasting accuracy and decision-
making time horizon, more unanimous decision-making processes through better internal and external
communication and thus being able to conclude an accurate decision-making process . The decision can
be postponed if organizations have not sufficient information to make rational strategic making (Marques,
Moniz & de Sousa, 2018). There is little research into the use of the Internet as an information source
for strategic decision-making. On the use of the Internet as ‘decision support information technology
for college leaders and executives in both the private and public sector’, concludes that “The Internet is
used in all levels of management involving a number of functional areas which is perceived by college
executives as a decision-support information technology that contributes positively in improving their
rational decision making practices in (Elbanna, 2006).
2.5 The role of information in the decision process
The information is so important in this competitive world to make a rational strategic decision making
because organizational operations have to cope with high costs, small margins and fixed markets, so
management has to be very alert and perform proper analyses on, e.g. educational market developments
before decisions can be taken. The educational institutions is more opportunity driven now and can react
faster with sufficient information (Citroen, 2010 ). For each strategic issue decision, the best decision
structure can only be obtained when it is clear that all information is available in the proper format and
is reliable and can be understood by all stakeholders. College executives comment that after collecting
additionalinformationaneffortisrequiredforstudyingandanalyzingthisadditionalinformation.Firsthand
information mostly come from consultations with internal staff from the departments involved. Lacking
this expertise or in cases where an external opinion is indicated, studies are also often commissioned to
external organizations or consultancies. Therefore, it is concluded that both internal and external first
hand information is a backbone of the rational strategic decision making (Aharoni, Tihanyi & Connelly,
2011).
2.6 Quality of information for strategic decision-making
The college executives are always in the stress of the characteristics of the quality of the information
required by the board. Correct strategic decisions can only be taken on correct and complete information.

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One phrase given by one executive “Quality of information means integrity, robustness, able to stand
up for scrutiny, but very important is also a guarantee of completeness, wholeness”. Or another phrased
exploredbythenextexecutive:“Werelyonwellchecked,reliable,robustandrelevanceratedinformation”.
Generally, information that arrived ‘bottom up’ was trusted more than information provided by external
sources. If information become available from uncertain in sources or is not reliable at first sight, it
is thoroughly scrutinized for its credibility and robustness before being accepted by the departments
responsible for supplying information to the board. But even so, executives sometimes double-check
information themselves, one reason being that these departments are not always aware of the strategic
plans of the board (Citroen, 2010 ).
3. Research Methodology
Researcher asked a selected group of executives in colleges whether they would be willing to complete
the survey questionnaire with recently administered entitled the content, form, technological and time
factors of the information to make rational strategic decisions in their colleges. Twenty-four executive
level respondents were asked to complete the survey questionnaires to observe in which way they use
information during the decision making process. Thirty executive level college administrators were
sending the survey questionnaires but twenty-four of them returned which is 80% response rate. Data
analysis was based on descriptive statistics along with the Principal Component Analysis. Student’s t-Test
is used to find out the average differences in decision process in public & private colleges. The Logistic
Regression Enter Model was used to find the association between the impact of the information factors
and rational decision making in both private and public colleges.
3.1. Fieldwork
The sixteen executives that current researcher sent questionnaires were selected from members of the
college board or directors (n=16) who also belonged to the Management, Engineering, Education and
Information Technology Departments of the selected colleges, three from Nawalparasi District and
thirteen from Chitwan District. The type of college executives that agreed to take part in the research and
the functions of the survey questionnaires were either chairman or member of the board/management
team or were directly involved in strategic school management.
3.2 Sample Population
The target population was one hundred and ten college executives (N=110) and sample population was
twenty-four (n=24) so that the proportion of sample population is (24/110*100), i.e. 21.81%. The gender
proportions of the sample were (19/24) 79.20% male executives and five (5/19) female school executives
(20.8 %). The proportion of private college was (n/N) 58.33% and public college was 41.66 %.
4. Results
The analysis has focused on the roles of different factors of information to make rational decision in
an academic institution. The analysis highlights that the ages of respondent were categorized as (35-
35) years (25 %), (35-40) years (12.5 %), (40-45) years (37.5 %) and more than 45 years (25 %). The

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results show that respondents from province 3 have 81.25% and rest was from province Gandaki. All the
nine Principal Compnents (PCs) were computed via Factor Reduction Model. The analysis has secondly
focused on Binary Logistic Regression Model to find the association between the independent and
dependent variables.
4.1 The management of information
During the decision-making process, there are two phases in which information is mostly collected and
analyzed by the board, the preparation phase and the analysis and review phase. The titles of departments
that supply this information to the board can be Corporate Development, Strategy Development,
Business Development, & Innovation or the Market Intelligence Group. Furthermore, most business
units collect information about their own branches and send summaries of analysed information up to the
executive management. “The technical possibilities to define queries have become much easier so that no
information specialists and fewer external experts are needed any more to formulate database searches”
and also that “The interpretation of data and ensuring the relevancy of information for the executives is
now the bottleneck, not the process of searching”.
4.1.1 Factor Dimension Method
Principal Component (PC) Method has extracted three different principal components from the first
survey instrument. According to the result obtained 76.26 % total variance explained on RSDM, the first
PC accounts for 37.32 % total variance explained, the second PC accounts for 23.72 % total variance
explained, the third PC accounts for 15.16 % total variance explained. The PCs were named as values of
information, purity of information and efficiency of information. Again, the same method extracted two
different principal components from the second survey instrument. According to 66.34 % total variance
explained, the fourth PC accounts for 42.62 % total variance explained, the fifth PC accounts 23.72 %
total variance explained. The PCs were named as importance of details of information and quality of
information. Similarly, PCM has extracted two different principal components from the forth survey
instrument. According to 77.73 % total variance explained, the sixth PC accounts for 50.68 % total
variance explained, the seventh PC accounts for 27.05 % total variance explained. The PCs were named as
formats of information and perfectness of information. Again, PCM has extracted two different principal
components from the fifth survey instrument. According to 71.61 % total variance explained, the eighth
PC accounts for 51.12 % total variance explained, the ninth PC accounts for 20.49 % total variance
explained. The PCs were named as advanced technology adapted human resource and availability of
advanced technology.
Table 1. Varimax rotated principal components matrix on time, content, form and technological
factors of the information for the rational strategic decision making (n = 24).
Independent variables
Loadings
1 2 3
VALUE OF INFORMATION
Currency of the information is crucial for RSDM. .953
Relevant of the information is crucial for RSDM. .855

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Timeliness of the information is crucial RSDM. .754
PURITY OF THE INFORMATION
Sufficient of the information is crucial for RSDM. .896
Quality of the information is crucial for RSDM. .843
EFFICIENCY OF INFORMATION
Frequency of the information is crucial for RSDM. .963
Time period of the information is crucial for RSDM. .945
DETAILS OF INFORMATION
Completeness of the information is crucial for RSDM. .892
Relevance of the information is crucial for RSDM. .884
QUALITY OF INFORMATION
Performance of the information is crucial for RSDM. .965
Scope of the information is crucial for RSDM. .961
FORMATS OF INFORMATION
Presentation of the information is crucial for RSDM. .928
Detail of the information is crucial for RSDM .924
Media of the information is crucial for RSDM .923
Order of the information is crucial for RSDM .824
PERFECTNESS OF INFORMATION
Comparable of the information is crucial for RSDM .929
Unambiguous of the information is crucial for RSDM .887
Clarity of the information is crucial for RSDM .835
ADVANCED TECHNOLOGY ADAPTED HR
Skill of human resource is crucial for RSDM. .928
Use of the technology is crucial for rational strategic decision making RSDM. .789
Capacity of the technology is crucial for RSDM .750
Knowledge about technology is crucial for RSDM .689
Latest version of the technology is crucial for RSDM. .686
PERFORMANCE OF TECHNOLOGY
Speed of the technology is crucial for RSDM .948
Durability of the technology is crucial for RSDM .941
Availability of the technology is crucial for RSDM .792
The results show that the highest loadings were computed as 0.965 and the lowest loadings was 0.728.
The total loadings were 28 and total Principle Components were nine.

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4.1.2 Subscales of the variables
All the variables were used to obtain a rating that contributes to measurement on a larger scale. Table
2 has presented the mean values, standard deviation, values of Cronbach’s Alpha and number of
variables in each subscale. Nine subscales were computed from the four main factors, i.e. time factor,
content factor, form factor and technology factor of information.
Table 2. Mean, standard deviation and Cronbach’s Alpha for the scales of time factors
for the rational strategic decision making (n = 24).
The mean values of the three subscales of the time factor are lower than the average mean values
signifying that respondents strongly disagreed with the statements of currency of the information is
crucial for RSDM, relevants of the information is crucial for RSDM and timeliness of the information
is crucial for the RSDM. Similarly, the respondents showed their disagreement with the statements of
enough and quality of the information is crucial for the RSDM. Again, the respondents also showed their
opinions with the statements of frequency of the information is crucial for RSDM and time period of the
information is crucial for RSDM. Comparatively, respondents prioritized purity of information in the
first importance and the value of information in the least importance to make rational strategic decision
making. The mean values of the two subscales of the content factor are lower than the average mean
values (3). The results show that respondents did not give much importance to time factors of information
for the rational strategic decision making in educational institutions. The mean values of the details
of the information is close to the average mean value signifying that respondents neither agreed nor
disagreed with the statements of completeness of the information is crucial for RSDM and relevance of
the information is crucial for RSDM. But, the mean value of the quality of information is lower than the
average mean value signifying that respondents were dissatisfied with the statements of the performance
of the information is crucial for RSDM and scope of the information is crucial for RSDM. The results
show that respondents did not give much priority to content factors to make strategic rational decision
making in educational institutions. Comparatively, the mean values show that respondents have prioritized
details of information in the first rank and the quality of information in the second rank. The mean values
of the formats of information is lower than the average value signifying that the respondents disagreed
with the statements of detail of the information is crucial for RSDM, order of the information (arrange
Subscales Mean SD
Cronbach’s
Alpha
Number of
variables
Time
Factor
1. Value of the information 1.34 0.577 0.82 3
2. Purity of information 1.75 0.807 0.62 2
3. Efficiency of information 1.70 0.440 0.70 2
Content
Factor
4. Details of information 2.70 0.494 0.78 2
5. Quality of information 1.77 1.20 0.97 2
Form
Factor
6. Formats of information 2.08 1.06 .91 4
7. Perfectness of information 1.58 .549 .72 3
Technology
Factor
8. Advanced technology adapted HR 1.85 .641 .92 5
9. Performance of technology 2.06 .613 .70 3
Total variables 26

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in predetermined sequence) is crucial for RSDM, Presentation of the information (narrative, numeric,
graphic, sound, animated form etc.) is crucial for RSDM and media of the information (in the form of
printed paper documents, video display and other media) is crucial for RSDM. Similarly, the mean value
of the perfectness of the information is lower than the formats of information signifying that respondents
perceived their opinions between the strongly disagree and disagree with the statements of comparable of
the information is crucial for RSDM, unambiguous of the information is crucial for RSDM and clarity of
the information is crucial for RSDM. Finally, the mean value of the advanced technology adapted human
resource is also lower than the average value signifying that respondents showed their disagreement
with the statements of skill of human resource is crucial for RSDM., use of the technology is crucial for
rational strategic decision making RSDM, capacity of the technology is crucial for RSDM, knowledge
about technology is crucial for RSDM and the latest version of the technology is crucial for RSDM. But
the mean value of the performance of the technology is higher than the advanced technology adapted
HR and lower than the average mean value signifying that respondents showed their disagreement with
the statements of speed of the technology is crucial for RSDM, durability of the technology is crucial for
RSDM and availability of the technology is crucial for RSDM.
4.1.3 Results of the independent sample t-Test
Two basic experimental designs were employed to examine differences in two groups (Private College
& pubic college).
H0: There is no significant difference in average percentage of impact of information to make RSDM in
educational institutions.
H1
: There is significant difference in average percentage of impact of information to make RSDM in
educational institutions.
The results show that the mean score for the private college (n = 14) on the first subscale value of
information (M = 1.46, SD = 0 .67) did not differ statistically significantly [t (22) = 1.331, p = 0.197] from
that of public college (n = 10) for the same variable (M =1.48, SD = 0.29), hence the null hypothesis is
accepted. Similarly, the mean score of the private college of the second subscale purity of the information
(M = 1.43, SD = 0.53) is statistically significantly higher [t (11.11) = -.2.472, p = 0.01] than that of the
public college (M = 2.27, SD = 0.93), hence H1
is rejected. Again, the mean score of third subscale
for the private college on efficiency of the information (M = 1.63, SD = 0.48) was not statistically
significantly different [t (22) = -1.081, p = 0.291] from that of the public college (M = 1.83, SD = 0.35).
Similarly, Again, the mean score of the fourth subscale details of information for the private college on
the fourth subscale details of the information and growth (M = 1.53, SD = 0.71) did not differ statistically
significantly [t (22) = 1.405, p = 0.174] from that of public college for the same variable (M =1.20, SD
= .258). Similarly, the mean score of the private college of the fifth subscale quality of information (M
= 2.00, SD = 1.01) was statistically significantly lower [t (119.70) = -3.673 p = 0.001] than that of the
public college (M =3.70, SD = 1.18) signifying that private college does not give importance to quality
of information for the RSDM than the public college. The results show that the mean score for the private
college on the sixth subscale formats of information (M = 2.25, SD = 1.13) did not differ statistically
significantly [t (22) = .901, p = 0.377] from that of public college for the same variable (M =1.85, SD

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= 0.241), hence the null hypothesis is accepted. Similarly, the mean score for the private college on the
seventh subscale perfectness of information (M = 1.59, SD = .681) did not differ statistically significantly
[t (22) = .123, p = 0.903] from that of public college for the same variable (M =1.56, SD = 0.316), hence
the null hypothesis is accepted. Additionally, the results show that the mean score for the private college
on the eighth subscale advanced technology adapted human resource (M = 1.81, SD = .778) did not
differ statistically significantly differ [t (22) = 0.390, p = 0.700] from that of public college for the same
variable (M =1.85, SD = 0.241), hence the null hypothesis is accepted. Similarly, the mean score for the
private college on the ninth subscale performance of information (M = 2.16, SD = .448) did not differ
statistically significantly [t (22) = .915, p = 0.370] from that of public college for the same variable (M
=1.93, SD = 0.798).
4.1.4. Results of Logistic Regression Model
Binary Logistic Regression Model (BLRM) was used to find the effects of the independent variable (the
value of information, the purity of information, the efficiency of information, the details of information,
the quality of information, the advanced technology adapted human resource) on the dependent variables
(the rational strategic decision making).
Table 3. Summary of the independent’s predictors of the Wholesome
Model of Quantitative findings (n = 24).
There were nine basic measurement scales in quantitative result section, but all nine indicators were found
insignificant for the rational strategic decision making (see in the table 3). With the Omnibus Tests [Chi-
Square = 18.08, df = 9, p =.034 and associated significance level less than 0.05, the present model shows
a decrease in deviance from the base model because Chi-Square is positive, showing this model is better
fit compared to the base model. The model summary shows the values of -2Log Likelihood (0.000a
), Cox
and Snell R2
and Nagelkerke R2
[52.90 % % (Cox and Snell) and 100 % (Nagelkerke)] variance of the
model was explained by the independent variables. Hosmer and Lemeshow Test shows that p = 1.00 >
0.05 is insignificant which is good to support for the regression model fit. The classification table shows
that out of 24 school leaders 21 showed their opinion on the role of information is important to make
rational strategic decision making in educational institutions, this model predicts 3 school leaders showed
Independent variables B S. E. Wald df Sig. Exp (B)
The value of information 5.194 3.27 2.510 1 .113 180.142
The purity of information 2.334 1.672 1.949 1 .163 10.318
The efficiency of information 5.330 2.907 3.363 1 .067 206.435
The details of information 6.535 5.014 1.699 1 .192 689.025
The quality of information 1.855 1.701 1.190 1 .275 6.394
The advanced technology adapted HR -12.619 7.593 2.762 1 .097 .000
The performance of technology -3.457 1.927 3.218 1 .073 .032
The format of information 3.251 2.412 1.817 1 .178 25.820
The perfectness of information -1.369 1.071 1.634 1 .201 .254
Constant -6.575 3.473 3.584 1 .058 .001

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their opinions on the role of information is not important to make rational strategic decision making. Thus,
it predicts school leaders who showed their opinion for the importance of information to make rational
strategic decision in the educational institutions with 100% percent accuracy and predicts 100 percent
accuracy of school leaders who said the role of information to make rational strategic decision is not
important in educational institutions. The results further show that the overall percentage of correctness
of observed data was 100 %. The results show that there was a insignificant association between the value
of information, the purity of information, the efficiency of information, the details of information, the
quality of information, the advanced technology adapted human resource, the performance of information,
the formats of information and the perfectness of the information (p > 0.05) and the rational strategic
decision making in the wholesome analysis. Due to the insignificant association between the independent
variables and independent variable, further analysis of the independent variables was ignored.
5. Discussion and conclusions
The primary objective of this study was to examine the association between the impact of information
factors for rational strategic decision making in educational institutions. To fulfil this objective results
show that mean score of the private colleges of the second subscale purity of the information is statistically
significantly higher than that of the public colleges. Similarly, the mean score of the private colleges
of the fifth subscale quality of information was statistically significantly lower than that of the public
college signifying that private college does not give importance to quality of information for the impact
of RSDM. The results of the nine subscales highlight that details of information have covered the greatest
value of mean and the efficiency of information has the lowest mean value signifying that educational
executives do not give more attention for the positive role of information factors to make rational strategic
decision in educational institutions. Additionally, the results also show that the low mean value of each
subscale is lower than the average mean value (3) signifying that there is no impact of information to
make rational strategic decision is in educational institutions. The results confirmed that there was an
insignificant association between the factors value of information, the purity of information, the efficiency
of information, the details of information, the quality of information, the advanced technology adapted
human resource, the performance of information, the formats of information and the perfectness of the
information (p > 0.05) and the rational strategic decision making in the wholesome analysis. This study
did not support the studies of Frishammar (2003); Citroen (2011) and Szymaniec-Mlicka (2017) because
all three previous studies had concluded that there was significant association between the time, content,
form and technology factors of information and the rational strategic decision making. The results are
somehow surprising because not a single independent variable had significant association with the impact
of information in rational decision making. The results of the study provide new information on the
specific knowledge of information on how to improve decision-making efficiency and effectiveness at
each stage of the strategic decision process in educational institutions.
The limitations of this study are very small sample size and limited number of the survey instruments used
in this study. The findings of this study cannot be generalized in the similar situations because the number
of sample size was very small which would be the possible reason for insignificant association between
the independent variables and dependent variable. The implication of this study will be beneficial for the

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college executives and college principals to understand the importance of information to make rational
strategic decision making. It was learnt that a big sample population and a mixed methods approach would
be better for the future research studies. More importantly, there are very limited empirical research on
the impact of the information factors to make rational strategic decision making. It is recommended that
future research needs to focus on the impact of the information factors to make rational strategic decision
in educational institutions in Nepal. The study of the impact of information technology to make rational
strategic decision making in educational institutions in Nepal is imperative on large population in Nepal
to foreground the limitation of this research work.
References
Aharoni, Y.,Tihanyi, L., & Connelly, B. (2011). Managerial decision-making in international business: A
forty-five-year retrospective. Journal of World Business, 46(2), 135-142.
Chapple, J. (2015). Mission accomplished? School mission statements in NZ and Japan: what they reveal
and conceal. Asia Pacific Education Review, 16(1), 137-147.
Choo, C. (1996). The knowing organization: How organizations use information to construct meaning,
create knowledge and make decisions. International Journal of Information Management, 16(5),
329-340.
Citroen, C. (2011). The role of information in strategic decision-making. International Journal of
Information Management, 31(6), 493-501. doi: 10.1016/j.ijinfomgt.2011.02.005
Elbanna,S.(2006).Strategicdecision-making:Processperspectives.InternationalJournalofManagement
Reviews, 8(1), 1-20.
Frishammar, J. (2003). Information use in strategic decision making. Management Decision, 41(4), 318-
326.
Lunenburg, F. (2010). Decision Making Process. National Forum of Educational Administration and
Supervision Journal, 27(4), 1-12. Retrieved from http://file:///C:/Users/USER/Downloads/
Documents/Lunenburg,%20Fred%20C.%20The%20Decision%20Making%20Process%20
NFEASJ%20V27%20N4%202010.pdf.
Marques, C., Moniz, S., & de Sousa, J. (2018). Strategic decision-making in the pharmaceutical industry:
A unified decision-making framework. Computers & Chemical Engineering, 119,171-189.
Matarazzo, J. (1998). The knowing organization: How organizations use information to construct
meaning, create knowledge, and make decisions. The Journal of Academic Librarianship, 24(6),
492-493.
Nutt, P., & Wilson, D. (2010). Handbook of decision making. Chichester: John Wiley.
Petersen,M.,&Laustsen,L.(2019).Dominantleadersandthepoliticalpsychologyoffollowership.Current
Opinion in Psychology, 33, 136-141. doi: 10.1016/j.copsyc.2019.07.005
Szymaniec-Mlicka, K. (2017). The decision-making process in public healthcare entities – identification
of the decision-making process type. Management, 21(1), 191-204.

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Original Article
Factors Influencing Customer Satisfaction in
Buddha Air, Bharatpur Chitwan
Abstract
The primary purpose of this study was to examine the customer satisfaction on quality and price of the
products, customer management and employees’ behaviour of Buddha Air at Bharatpur, Chitwan. The
surveystudywas used as research method and the surveyquestionnaire was used as the research instrument
to collect data in this study. One hundred and eighty-five respondents had been selected randomly where
one hundred and eight was male population (58.37 %) and seventy-six was female population (41.63
%). The response rate was 92.5%. The Factor Reduction Method via Principal Component Analysis
was applied to find the relationship between the dependent variable and the independent variables.
The results show that there was significant association between customer satisfaction and strict flight
schedule and long security checking process, fluctuation in ticket price, employee motivation skills and
politeness, customer centered strategy and positive behaviour of employees and adequate facilities and
proper customer management skills (p < 0.05). The results further show that customers were found
dissatisfied with the current ticket prices, service quality, employee’s behaviour and customer relationship
management practices in Buddha Air, Bharatpur, Chitwan, Nepal. The previous studies reveal that
customer satisfaction is embedded in effective and efficient customer management, high quality product,
better customer relationship management and politeness of employees’ behaviour. The implication of
this study will be beneficial for the board members of the company executive level of Buddha Air to
formulate new customer-centered strategies and also be useful for the branch managers of Buddha Air all
over the country to improve their managerial skills and to penerate in new market.
Keyword: Customer satisfaction, the survey respondents, Principal Component Analysis, customer
management.
1. Introduction
In Nepal, the airlines history has begun since 1958 as the first airline named Royal Nepal Airlines based
on Tribhuvan International Airport, Kathmandu. It’s been long time since the airlines facilities has been
competing with prices and service quality to win the heart of customers. It is obvious that, customer
satisfaction is the key measure of products and services quality to meet the customers’ expectation.
Buddha Air Pvt. Ltd is a private air travel company founded on 23 April 1996. It is the best domestic
airline company of the nation. It has over 13 domestic and more than two international destinations. It has
facility to operate the famous for the Everest Experience Flight. It is in the process of further expansion
in international sectors. After 20 years of dedicated non-stop service, more than 100,000 flight hours
logged in with over 10 million passengers flown to thirteen destinations with permanent runways in the
country, Buddha Air today is the largest domestic air travel operator in Nepal employing more than 900
experienced professionals (“Buddha Air”,2018).

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The main office of this airline is based on Jawalakhel, Lalitpur in Nepal. This study intends to studyservice
quality, price, customer management and employees’ behaviour related to customer satisfaction (Fripp,
2018). The primary objective of this study was to examine the customer satisfaction at Buddha Airline.
The secondary objectives were to examine the level of customers’ satisfaction level in relation to price
of ticket, the service quality, customer management and in relation to employee behaviour at Buddha
Air. The previous studies reveal that customer relationship management (CRM) had become the most
important influence on customer satisfaction. CRM is a strategic approach that is concerned with creating
improved shareholder value through the development of appropriate relationships with key customers
and customer segments (Boettger, 2019). This study is for providing a greater understanding in customers’
needs through the service quality, price of the products, employees’ behaviour and customer relationship
management factors. Customers are the king of every business. Satisfied customers are the important
property of the business enterprises. Conversely, dissatisfied customers are the main reason of business
risk (Khashab, Gulliver and Ayoubi,2018). There is a tough competition among airline industries. Airlines
should satisfy customers to survive in the competitive airline market. Customer service shouldn’t just be
a department, it should be the entire company services including the quality, brand image and customer
loyalty (Study on Citilink Airline Passengers, 2019). Hence, the results obtained from this research might
be helpful for management in making plans for the improvement in services quality. The previous study
shows that a majority of the customers were not satisfied with service provided by different Airlines. So,
they are diversified to other means of transportation. Transport and the financial status of the airlines has
seemed in degrading trends (Aboulafia & Michaels, 2018). Therefore, Airline industries have to focus on
customer center strategies.
2. Research Design
This study used quantitative methods design. During the quantitative phase, the survey method was used
to collect data from the respondents because this method can cover the larger number of respondents
which ensures the generalization of the findings (Kothari, 2004).
Ethical consideration
Ethical approval was obtained from the administration of Buddha Airs and other ethical considerations
were also fulfilled during this study. Research Department of OCEM has provided permission to go to
Buddha Air for the data collection along with the acceptance letter of Buddha Air to collect data with the
customers.
Quantitative phase
A questionnaire was developed using the survey instruments from previous research studies in the area of
customer satisfaction. The questionnaire was piloted with five pediatric customers. The questionnaire was
designed to examine the experiences and opinions of respondents and their demographic information.
Sampling Design
The target population of this study was five hundred (n= 500) where the sample population was one
hundred and eighty-five (n = 185). Two hundred and twenty questionnaires were dispatched but only the
one hundred and eighty five questionnaires were returned by the returnees. The response rate was 84.09
%. Among one hundred and eighty-five respondents, one hundred and five (n = 105) respondent was
female population and eighty (n = 80) was male population.

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Method of Data Collection
The questionnaire was circulated to all 185 customers registered with the Buddha Air, Bharatpur Chitwan.
The customers were all registered in Buddha Air’s Webpage before two years ago. A link to the web-
based questionnaire was sent via email to all paediatric customers in Buddha Air. A reminder email was
circulated 2 weeks later. The responses were anonymous and could not be linked to the email address.
Processing and Analyzing of Data
The survey data were analysed using simple descriptive statistics and correlations. Principal compnent
analysis via Factor Reduction Model was applied to find the new principal components (PCs). Again,
Linear Regression Model was used to find the correlation between the selction of Buddha Air and gender
of the population.
3. Results
The data analysis was based on descriptive statistics analysis. The analysis is embedded in the subscales,
Chi-square test, categorical variables of the Linear Regression Model and the principal components.
3.1 Data Analysis
Factor analysis was used to reduce the large number of variables to a small number of components. The
demand for the air services has increased manifold in the past some years. Buddha Air as an air service
providerwas examinedforfactorsinfluencingcustomersatisfactionagainstitscurrentticketprices,service
quality, employees’ behaviour and customer management. This study undertakes a survey of 185 service
users of Buddha Air who fly from Bharatpur to Kathmandu and vice versa. Respondents were contacted
via telephone and were asked to rate forty-eight statements on their perceptions and experiences about
the Airline’s service quality, employee’s behaviour, customer management strategy and ticket’s prices on
a 5-point Likert scale [Completely dissatisfied =1, Dissatisfied =2, I do not know =3, Satisfied =4 and
completely satisfied =5]. The concept of data reduction is based on the fact that few components explain
most of the variance in dependent variable (Factors influencing customer’s satisfaction) (Pandya et al.,
2018). KMO and Bartlett’s Test was used to ensure the sample sufficiency for the further analysis of the
PCs where the minimum value of KMO was fixed < 0.60. Previous study had sometimes relied heavily
on a single-item indicator of customer’s’ satisfaction and preference which maximizes the possibility of
measurement error (e.g. Watt &Richardson, 2007). To construct this requirement, this study has chosen
to work with more encompassing constructs, measured by multiple items. To identify these underlying
themes inthequestionnaire, aPrincipal Component Analysis (PCA)was run. Subsequently,an Exploratory
Factor Analysis (EFA) with Varimax rotation was carried out to refine and interpret these components.
The reliability of the data was checked by computing scale analysis where the minimum value of the
Cronbach’s Alpha was considered over 0.60 (Cohen et al. 2011).
Eigenvalues, the screen plot and theoretical interpretability were also used to make a decision on the
number of factors. A factor loading of at least [0.40] was taken as cut-off point to incorporate a specific
item as an indicator for an understanding motive. To explore the relation between customers’ satisfaction
and personal variables, descriptive statistics and cross tabulations were computed (Pandya, Bulsari &
Sinha, 2018). Descriptive statistics was further employed to analyze customers’ ‘ motives (satisfaction) for
current service facilities, prices of the tickets, customer management strategy and employee’s behaviour

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towards customer’s satisfaction at Buddha Air. Again, the Chi-square Test was computed to examine the
association between customer satisfaction and categorical variables (gender, average family income level,
profession of the customers, main reasons of choosing Buddha Air, different religions of the customers).
A stepwise strategy was followed (Easterby-Smith, Thorpe & Jackson, 2012). Secondly, a Binary Logistic
Regression Model was used to assess the impact of the predictor and control variables on all motives of
customer’s satisfaction. Both significant levels and effective sizes were considered using Cohen’s d cut-off
points (Cohen, Manion, & Morrison, 2011). Finally, the Wholesome Binary Logistic Regression Model
was applied to find the association between all the significant indicators and customer satisfaction.
3.2. Quality Factor
The first research instrument was examined by the first survey instrument where respondents were
asked to share their experiences and perceptions on environmental cleanness, noise pollution, customers
waiting place, easy and comfortable seats, quality of drinking water, facility of using Visa/Master/Debit/
Credit Card to purchase tickets, feeling of customers’ facilities, money exchange facility, punctuality of
flights, adequate overhead facilities and safety of airline flights.
Table1. Varimax rotated principal components matrix on the quality of services for the customers
satisfaction before and after service of Buddha Airs (N = 185).
The Principal Component Model extracted three PCs where the first PC has five variables, the second
PC has three variables and the third PC has four variables. The variances of the first, second, and third
account were 26.05 %, 13.43 %, and 10.80 % respectively [KMO = 0.0678]. The first, second, and third
PCs were named as the proper shopping environment, quality of services and strict flight schedule and
security respectively in Buddha Air.
Variables
Loadings
1 2 3
PROPER SHOPPING ENVIRONMENTAND CUSTOMER MANAGEMENT
There is no sound pollution in the location of Buddha Airs. .700
The seats are comfortable and easy. .689
The is sufficient waiting place for customers in Buddha Air’s Office .655
The environment is neat and clean in Buddha Airs. .614
There is no sound pollution while taking off Buddha Air. .424
QUALITY SERVICES
Buddha Air Service accepts Visa and other online payment cards. .807
The food and beverage are quality and satisfactory. .748
I feel comfortable service of Buddha Airs. .645
STRICT FLIGHT SCHEDULE AND SECURITY
Buddha Air is punctual in its schedule. .826
The Airlines is safety than other Airlines. .762
The is the facility of money exchange around the counter. .623
The Airlines has overhead luggage facility. .591

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Table2.Mean, standard deviation and Cronbach’sAlpha forthescalesforquality of
services of Buddha Airs for customers’ satisfaction (N=185).
The mean values of three subscales were 3.41, 3.16, and 3.37 respectively. The overall mean values of the
first, second and third subscales had been seen more than the average value signifying that customers were
approximately agreed with the statements that proper shopping environment and customer management,
service qualityand strict flightscheduleandsecurityweresatisfactoryin BuddhaAir(seein detailin Table2).
Table3. Binary logistic regression model of the quality of services for customers’
satisfaction (N = 185).
With the Omnibus Tests [Chi-Square = 36.273, df = 3, p = .001] and associated significance level less
than 0.05, the present model shows a decrease in deviance in prediction from the base model because
the value of Chi-Square is positive. So that this model is better fit compared the base model. The model
summary table shows the values of -2Log Likehood, Cox and Snell R2
and Nagelkerke R2
[17.80 % (Cox
and Snell) and 38.80 % (Nagelkerke)] variance of the model was explained by the independent variables.
Hosmer and Lemeshow Test shows that p = 0.129 > 0.05 is insignificant which is good to support for
the regression model fit. Out of 176 customers who chose the first option [satisfied with the service of
Buddha Air], this model predicts 163 customers showed their satisfaction for Buddha Air services and
13 customers showed their dissatisfaction for the Airline services. Again, out of 9 customers who showed
their dissatisfaction for Buddha Air services, the results show that 5 customers were found dissatisfied
and 4 customers were found satisfied for the services of Buddha Airs. Thus, it predicts that customers
who showed their satisfaction for the services with 97.00 percent accuracy and the customers who
showed their dissatisfaction for the airline services was 23.5 percentage accuracy. The classification table
shows that the overall percentage of correct prediction was 90.3 percent. The results show that there was
significant association between strict flight schedule and security in and customers’ satisfaction (p < 0.05
with odds ratio = .198 < 1, B = -1.621 <0) indicating a negative impact on customers’ satisfaction. When
the independent variable high-level facilities and security increases one unit, customer satisfaction can
be predicated to decrease around 0.198 times if other variables are controlled. This study has supported
the previous findings of de Lange, Samoilovich & van der Rhee (2013) because both the current and the
previous studies de Lange et al (2013) have found that airlines’ customers were dissatisfied with strict
flight schedule and lengthy securityprocesses.
Subscales Mean SD Cronbach’s Alpha
Proper shopping environment and customer management 3.41 0.69 0.65
Quality of services 3.16 0.81 0.70
High level facilities and security 3.37 0.72 0,60
Independent Variables B S. E. Wald df Sig.
Exp
(B)
95% C.I for
Exp (B)
Upper Lower
Proper shopping Env. and customer
management
-.457 .242 3.565 1 0.059 .633 1.018 .394
Quality of services -.391 .323 1.459 1 0.227 .677 1.275 .359
Strict flight schedule and security -1.621 .346 21.833 1 .000 .198 .390 .100
Constant -3.384 .491 47.424 1 .000 .034

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4.2. Price factor
The second research instrument intends to examine the perceptions and experiences of customers on the
price level of Buddha Air’s ticket and their satisfaction level. The survey instrument was embedded in the
price fluctuation, the comparison of ticket’s price, facility and discount issues of online ticket buying and
selling, and reasonable price of air tickets (Chow, 2014).
Table4. Varimax rotated principal components matrix on the price of Buddha Air
ticket for the customers satisfaction (N = 185).
The Principal Component Model extracted two PCs where the first PC has three variables, and the second
PC has four variables. The variances of the first and second, Principal Components account for 30.37%
and 14.85% respectively [KMO = 0.0658]. The first and second PCs were named as the price of ticket and
nature of ticket pricerespectively.
Table5. Mean, standard deviation and Cronbach’sAlpha forthe scales forthe price of
Buddha Air ticket for the customers satisfaction (N=185).
The mean values of two subscales were 2.49 and 2.93 respectively. The overall mean values of the first and
second subscales had been lower than the average value signifying that customers were not satisfied with
the statements that the price of the ticket in Buddha Air was cheaper and the fluctuations in ticket price
occur time and again (see in detail in Table 5).
Table6. Binary logistic regression model of the price of Buddha Air ticket
for the customers’ satisfaction (N = 185).
The Omnibus Tests [Chi-Square = 9.295, df = 2, p = .010] and associated significance level less than 0.05,
Variables
Loadings
1 2
Price of Tickets
Online ticket purchase price of Buddha Air is similar with other airlines. .859
The cost price of ticket in Buddha Air is equal to other Air lines. .803
The price of the ticket in earlier booking is cheaper in Buddha Airs. .487
Fluaction in Ticket Price
There is price fluctuation in Buddha Airs. .865
The ticket price is consistence in Buddha Airs. .682
The ticket price of the Buddha Air is cheaper. .525
The ticket price in Buddha Air is constant. .520
Price of tickets 2.49 .086 0.65
Fluctuation in ticket price 2.93 0.60 0.60
Independent variables B S. E. Wald df Sig. Exp (B)
95% C.I for Exp (B)
Upper Lower
Prices of tickets -.154 .259 .355 1 .552 .857 1.423 .517
Fluctuation in ticket price -.747 .252 8.776 1 .003 .474 .777 .289
Constant .304 -2.526 68.880 1 .000 .080

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the present model shows a decrease in deviance in prediction from the base model because the value of
Chi-Square is positive. So this model is better fit compared to the base model. The model summary table
shows the values of -2Log Likehood, Cox and Snell R2
and Nagelkerke R2
[4.90 % (Cox and Snell) and
10.70 % (Nagelkerke)] variance of the model was explained by the independent variables. Hosmer and
Lemeshow Test shows that p = 0.268 > 0.05 is insignificant which is good to support for the regression
model fit. Out of 185 customers who chose the first option [satisfied with the price of Buddha Air],
this model predicts 168 customers showed their satisfaction for the ticket price of Buddha Airs and 17
customers showed their dissatisfaction for the price of Airline services. Thus, it predicts that customers
who showed their satisfaction for the price of tickets with 100.00 percent accuracy. The results show that
the overall percentage of correct prediction is 90.8 percent. The results show that there was significant
association between fluctuations in tickets’ price and customers’ satisfaction (p < 0.05 with odds ratio =
.474 <1, B = -747 < 0) indicating a negative impact of ticket price on customers’ satisfaction in Buddha
Air Service. When the independent variable fluctuation in tickets’ price increases one unit, customer
satisfaction can be predicated to decrease around 0.474 times if other variables are controlled. This study
has supported the previous study of Aligholi (2014) because this study has also highlighted that fluctuation
in tickets’ price made customers dissatisfied which is also highlighted by this study.
3.3. Service quality of the employees of Buddha Airs
The third research instrument intended to examine the association between employees behaviour and
customers satisfaction in Buddha Air. The third survey instrument was embedded in the polite behaviour
ofAirhostess,employees’politenesstocustomers,motivationofemployeestodeliverservicetocustomers,
servicesforentertainment,useofnewtechnologicaltools,cooperativebehaviourofemployees,satisfaction
of the services delivered by Buddha Airs, realization of mistakes by employees, service of ATM around
Airline counters, fulfillment of employees’ responsibilities on time, customer centered employees and
polite behaviour of pilots.
Table7.Varimaxrotated principal components matrix on the employees’
behaviour on the customers satisfaction (N = 185).
Variables
Loadings
1 2 3 4
SERVICE QUALITY AND EMPLOYEE’S BEHAVIOUR
Employees are polite in the area of Buddha Air’ counter .841
Employees are highly interested to provide services to customers. .667
The service quality of Buddha Airs is satisfactory. .620
EMPLOYEE MISTAKES AND ENTERTAINMENT
There are entertainment services in Buddha Airs. .721
Employees of Buddha Airs realize their mistakes while dealing. .630
Employees are customer centred in Buddha Airs. .594
PILOT BEHAVIOUR AND EMPLOYEE COOPERATION
Buddha Air has used new technological tools in its services. .831
The employees of Buddha Airs are cooperative and helpful. .603
The pilots are polite while dealing with customers. .501

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COMPETENT EMPLOYEES AND ATM SERVICE FACILITY
18.9. There is ATM service around the ticket counter. .851
18.10. The employees fulfil their assigned duties on time. .739
18.1. The behaviour of Air Hostess is polite and helpful .523
The Principal Component Model extracted four PCs where the first, second, third and the fourth PC have
three variables each. The variances of the first, second, third and the fourth Principal Components account
for 34.88 %, 12.85 %, 10 % and 9 % respectively [KMO = 0.721]. The first and second, third and the fourth
PCs were named as employee motivation and politeness, customer centered strategy and positive attitude
of employees, pilot behaviour and employees’ cooperation and competent employees and service facilities
respectively.
Table8.Mean, standarddeviationand Cronbach’sAlphaforthescalesforemployees’
behaviour for the customers satisfaction(N=185).
The mean values of four subscales were 2.41, 2.74, 2.66 and 2.55 respectively. The overall mean values
of the first, second, third and fourth subscales had been seen lower than the average value signifying
that customers were approximately dissatisfied with the statements that service quality and employees’
behaviour, employee mistakes and entertainment facilities, pilot behaviour and employees’ cooperation
and competent employees and service facilities from Buddha Air Service (see in details in table 8).
Table9. Binary logistic regression model of employees’ behaviour
for customers’ satisfaction (N =185).
The Omnibus Tests [Chi-Square = 14.844, df = 4, p = .005] and associated significance level less than 0.05,
the present model shows a decrease in deviance in prediction from the base model because the value of Chi-
Square is positive. So that this model is better fit compared the base model. The model summarytable shows
the values of -2Log Likehood, Cox and Snell R2
and Nagelkerke R2
[7.700 % (Cox and Snell) and 16.80 %
(Nagelkerke)] variance of the model was explained by the independent variables. Hosmer and Lemeshow
Test shows that p = 0.119 > 0.05 is insignificant which is good to support for the regression model fit. Out
of 181 customers who chose the first option [satisfied with the employee behaviour of Buddha Airs], this
Service quality and employee's behaviour 2.41 0.78 0.67
Employee mistakes and entertainment facilities 2.74 .080 0.60
Pilot behaviour and employees' cooperation 2.66 0.91 0.65
Competent employees and service facilities 2.55 0.86 0.63
Exp
(B)
95 % C.I for
Exp (B)
Upper Lower
Service quality and employee’s behaviour -.566 .244 5.396 1 .020 .568 .915 .362
Employee mistakes and entertainment facilities -.649 .302 4.627 1 .031 .523 .944 .289
Pilot behaviour and employees’ cooperation -.307 .267 1.318 1 .251 .736 .1.242 .436
Competent employees and service facilities .041 .252 0.26 1 .872 1.042 1.708 .635
Constant -2.631 .323 66.303 1 .000 .072

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model depicts that 176 customers show their satisfaction for Buddha Airs’ employees behaviour and 17
customers showed their dissatisfaction for the Airline’s employees behaviour. Again, out of 4 customers who
showed their dissatisfaction for Buddha Air’s employee behaviour, the results show that 4 customers were
found dissatisfied for the employee behaviour of Buddha Air. Thus, it predicts that customers who showed
their satisfaction for the employee behaviour with 97.60 percent accuracy and the customers who showed
their dissatisfaction for the airline services was 0 percentage accuracy. The results show that the overall
percentage of correct prediction is 88.60 percent. The results also show that there was significant association
between service quality and employees’ behaviour and customers’ satisfaction (p < 0.05 with odds ratio
= .568 < 1, B = -.566 < 0) indicating a negative impact on customers’ satisfaction. When the independent
variable service quality and employee’s behaviour increases one unit, customer satisfaction can be predicated
to decrease around 0.568 times if other variables are controlled. Similarly, there is significant association
between employee mistakes and entertainment facilities and customer’s satisfaction (p < 0.05 with odds
ratio = .523 <1, B = -.649 < 0) indicating a negative impact on customers’ satisfaction. Again, when the
independent variable customers centered strategy and positive attitude of the employee increases one unit,
customer satisfaction can be predicated to decrease around 0.649 times if other variables are controlled.
This study supported the research findings of Kattara, Weheba & El-Said (2008) because both studies found
that there was positive correlation between service quality, employee’s behaviour and customers satisfaction.
The previous study had also found that customers were satisfied when they received quality airline services
and employees’ polite behaviour. Importantly, the previous research had also concluded that employees’
behaviours have great effect on overall customer satisfaction regardless of customers’ gender, nationality, and
purpose of visit, number of visits and length of stay.
3.4. Customer Relationship ManagementCRM)
The fourth research instrument intended to examine perceptions and experiences of respondents on the
customers’ management and their satisfaction level at Buddha Air. The fourth survey instrument was
embedded in availability of air tickets in each ticket counter, ease of ticket availability, time consuming in
check-in and check-out, distance between ticket counter and airline take off destination, facility of ticket
cancellation and holding, comparison of Buddha Air with other air services, management of waiting place,
andthemanagementofloyaltycard.TheempiricalstudieshadprioritizedtheimportanceofCRM incompany
business strategy. CRM is an integration of technologies and business processes used to satisfy the needs
of a customer during any given interactions. More specifically, CRM involves acquisition. CRM life-cycle
follows eight stages which are planning, research, system analysis, design, construction, implementation,
maintenance and documentation and adaption (Amoah Mensah, Quaye & Mensah,2018).
Table10. Varimax rotated principal components matrix on the customer
management for the customers satisfaction (N = 185).
Variables
Loadings
1 2 3 4
FACILITIES AND CUSTOMER MANAGEMENT
Buddha Air provides all services on time. .766
The facilities of Buddha Airs are satisfactory. .759
Employees answer the customers inquiry .699

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There is proper waiting room management for customers in Buddha. .490
FACILITIES TO BUY TICKETS
The ticket is easily available to customers. .840
Tickets are available in each service counter. .819
FACILITIES OF TICKET POSTPONE AND CANCELLATION
There is the facility of ticket postpone. .831
There is ticket cancellation facility. .769
Ticket counter is close to plane take off area. .523
USE OF ADVANCED TECHNOLOGY FOR CUSTOMER MANAGEMENT
Less time is consumed in check-in and check-out. .646
Buddha Air is better than other airlines. .645
There is the facility of Loyalty card in Buddha Air Service. .500
The Principal Component Model extracted four PCs where the first PC has four variables, the second PC
has two variables, the third PC has three variables and the fourth PC has three variables respectively. The
variances of the first, second, third and fourth Principal Components account for 40.45%, 20.37%, 15.35%
and 14.85% respectively [KMO = 0.0628]. The first, second, third and the fourth PCs were named as ‘facility
and customer management facilities to buy tickets, facilities to postpone & cancel tickets and use of advanced
technology’ for customersatisfaction.
Table11.Mean, standard deviation and Cronbach’sAlphaforthescalesfor
employees’ behaviour for the customers satisfaction (n=185).
The mean values of four subscales were 2.49, 2.85, 2.59 and 3.27 respectively. The overall mean values
of the first, second, and third subscales had been seen a bit lower than the average value signifying that
customerswereapproximatelydissatisfied with the statements thatthefacilities tobuytickets, and facilities
of ticket postpone and cancellation in Buddha Air. But the mean value of the fourth subscales had seemed
higher than the average value signifying that customer were approximately satisfied with the technology
used to manage customers in Buddha Air (see in detail in table 8).
Table 12. Binary Logistic Regression Model on Customer Satisfaction at Buddha Air (N = 185).
Exp
(B)
95%C.IforExp(B)
Upper Lower
Facilities and customer management .700 .347 4.082 1 .043 2.014 3.973 1.021
Facilities to buy tickets .006 .256 0.001 1 .980 1.006 1.661 .610
Facilities of ticket postpone and cancellation .682 .371 3.381 1 .066 1.978 4.092 .956
Use of advanced technology for customer management -.427 .402 1.125 1 .289 .653 1.436 .297
Constant 4.728 1.742 7.369 1 .007 .009
Facilities and customer management 2.49 0.73 0.66
Facilities to buy tickets 2.85 1.14 0.76
Facilities of ticket postpone and cancellation 2.59 0.70 0.61
Use of advanced technology for customer management 3.27 0.71 0.60

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The Omnibus Tests [Chi-Square = 10.602, df = 4, p = .031] and associated significance level less than
0.05, the present model shows a decrease in deviance in prediction from the base model because the
value of Chi-Square is positive. So that this model is better fit compared with the base model. The result
of model summary show the values of -2Log Likehood, Cox and Snell R2
and Nagelkerke R2
[5.60 % (Cox
Hosmer and Lemeshow Test shows that p = 0.087 > 0.05 was insignificant which is good to support
for the regression model fit. Out of 185 customers who chose the first option [satisfied with the customer
management at Buddha Air], this model depicts that 168 customers showed their satisfaction for customer
management at Buddha Airs and 17 customers showed their dissatisfaction for the customer management at
Buddha Airline. Thus, it shows that customers who showed their satisfaction for the customer management
at Buddha Air with 100.00 percent accuracy. The results show that the overall percentageof correct prediction
is 90.80 percent. The results also show that there is significant association between facilities and customer
management and customers’ satisfaction (p < 0.05 with odds ratio = B = .700 > 0) indicatinga positive impact
on customers’ satisfaction. When the independent variable facilities and customer management increases
one unit, customer satisfaction can be predicated to increase around 2.014 times if other variables are
controlled. This study has supported the study of Hui, Zhang & Zheng (2013) because Hui et al. (2013) had
also found that facilities and customer management of communal facilities was the most crucial dimension
with regard to the overall customer satisfaction and communication efficiency and efficacious promotion
events are alsoimportant for maintaining customer satisfaction.
Binary Logistic Wholesome Model on Customer Satisfaction at Buddha Air
All the significant indicators selecting from each Binary Logistic Regression Tables (see in the table 3, 6,
9.12) were entered the Binary Logistic Regression Model. The main purpose of this analysis was to find
the Wholesome Model on customer satisfaction at BuddhaAir.
Table 13. Binary Logistic Wholesome Model on Customer Satisfaction at Buddha Air (N = 185).
Exp
(B)
95 % C.I for Exp (B)
Upper Lower
Fluctuation in ticket price -.582 .289 4.046 1 .044 .599 .985 .317
Employee motivation and politeness -.451 .245 3.396 1 .065 .637 .1.029 .394
Customer centered strategy and
positive employees
-.278 .337 .684 1 .408 .757 1.464 .392
Facilities and customer management .258 .319 .655 1 .418 1.295 2.421 .693
Strict flight schedule and security -1.512 .397 14.469 1 .000 .221 .481 .101
Constant -3.609 .568 40.411 1 .000 .027
The Omnibus Tests [Chi-Square = 39.888, df = 5, p = .001] and associated significance level less than
0.05, the present model shows a decrease in deviance in prediction from the base model because the
value of Chi-Square is positive. So that this model is better fit compared with the base model. The model
summary table shows the values of -2Log Likehood, Cox and Snell R2
and Nagelkerke R2
[19.40 % (Cox
Hosmer and Lemeshow Test shows that p = 0.654 > 0.05 is insignificant which is good to support for
the regression model fit. Out of 176 customers who chose the first option [satisfied with the customer

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management at Buddha Air], this model predicts 165 customers showed their satisfaction for customer
management at Buddha Air and 11 customers showed their dissatisfaction for the customer management
at Buddha Airline. Again, out of 9 customers who chose the second option dissatisfaction, this model
predicts that 3 were still dissatisfied and 6 were found satisfied with the price of the tickets, quality of
service, employee behaviour and customer management. Thus, it predicts that customers showed their
satisfaction for the customer management at Buddha Air with 98.20 percent accuracy and also predicts
that customers showed their dissatisfaction for the cost price of ticket, quality of services, employee
behaviour and customer management at Buddha Air with 98.20 percent accuracy which predicts 35.30
percent accuracy. The results show that the overall percentage of correct prediction is 92.40 percent. The
results also show that there was significant association between fluctuation in ticket price and customers’
satisfaction (p < 0.05 with odds ratio = .599 <1, B = -.582<0) indicating a negative impact on customers’
satisfaction. When the independent variable fluctuation in ticket price increases one unit, customer
satisfaction can be predicated to decrease around 0.559 times if other variables are controlled. This study
has supported the previous study of “The Effect of Price and Service Quality on Customer Satisfaction in
Mutiara Hotel Bandung” (2016) because both previous and current studies found that there is negative
association between the price fluctuation in ticket price and customers’ satisfaction. The previous study
also disclosed that customers were found dissatisfied when the price of the ticket price goes up and down.
Similarly, there was significant association between strict flight schedule and security (p < 0.05 with
odds ratio = .221 <1, B = -.1.512 < 0) indicating a negative impact on customers’ satisfaction. When the
independent variable strict flight schedule and security increases one unit, customer satisfaction can be
predicated to decrease around 0.559 times if other variables are controlled. This study has supported the
study of Fornell, Mithas, Morgeson & Krishnan (2006) because the previous and the current studies had
found that there was negative association between strict flight schedule, lengthy security processes and
customers’ satisfaction.
3.5. Results on categorical variables of the Linear Regression Model
ThecategoricalvariablesonreasonsofchoosingBuddhaAirandgenderwereenteredtheLinearRegression
Model of the SPSS to find the correlation between them.
Table 14. The correlation between gender and the reasons for choosing Buddha Air
Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson
1 .274a
.075 .055 .282 1.892
The outputs of the first Table 14 show the model summary and overall fit statistics. The results show
that the R value is .274. Therefore, the customer satisfaction is positively correlated with the reasons of
choosing Buddha Air and signifying a weak relationship between the customer satisfaction and reasons
for choosing Buddha Air. Again, the R² value is 0.075 signifying that the independent variables (price
of the tickets, customer management, service quality and employees’ behaviour) have explained total
variances of 7.50 % on dependent variable customer satisfaction which shows a very weak relationship
between the customer satisfaction and reasons of choosing Buddha Air. Again, the adjusted R² of the
model was 0.055 with the R² = .075 that means that the linear regression explains 5.50 % of the variance
in the data which is not a large variation so that the regression equation does not appear to be useful for
making predictions for the reasons of choosing Buddha Air since the value of R² is lower than 1. Again,
the Durbin-Watson d = 1.982, which is between the two critical values of 1.5 < d < 2.5 and therefore we
can assume that there was no first order linear auto-correlation in the data.

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Table 15. Results of ANNOVA
Model Sum of squares df Mean square F Sig
Regression 1.162 4 .271 3.664 0.007
Residual 14.276 180 .079
Total 15.438 184
The results show that the regression model was the statistical significance that was run. Here, p < 0.007,
which is less than 0.05, indicating that, overall, the regression model statistically significantly predicts the
outcome variables of customer satisfactions with Buddha Air which is a good fit for the data.
Table 16. Results of coefficients
Model
Unstandardized
Coefficients
Standardized
Coefficients Sig
95.0% Confidence
interval for B
B Std. Error Beta t Upper Lower
1. Constant 1.037 .038 27.060 .000 1.113 .961
Employee’s behaviour .034 .066 .043 .524 .601 .164 -.095
Price of the tickets -.037 .066 -.046 -.565 .573 .092 -.166
Service quality .098 .055 .152 1.783 .076 .206 -.010
Customer Management .224 .070 .256 3.192 .002 .382 .085
We are 95% confident that the slope of the true regression line is somewhere between .164 and -.095.
In other words, we are 95% confident that customer satisfaction with Buddha Air, the level of customer
satisfaction increases somewhere between .164 to -.095. It is concluded that on average, for the reasons of
choosing Buddha Air “employee behaviour”, “the level of customer satisfaction” will increase .034 times.
Again, we are 95 % confident that for the reason of choosing Buddha Air “Price of the Tickets” decreases
-.037 times. Again, we are 95% confident that the reason of choosing Buddha Air “Service Quality”
increases .098 times. Finally, we are 95 % confident that the reason of choosing Buddha Air “Customer
Management” increases .224 times.
4. Discussion and Conclusion
The objective of this study was to examine the customers’ satisfaction level against current ticket’s price,
service quality,employees’behaviour, andcustomer management at BuddhaAir.The empirical studies reveal
that customer satisfaction is embedded in price level of the ticket, service quality, employees’ behaviour and
customer management. Four research instruments were used to examine the perceptions and experiences of
customers on current rate of ticket prices, service quality, and customer behaviour and customer management.
The research method used in this study was the survey method where the survey questionairs was used as
research instrument. The survey questionnaire was returned by one hundred and eighty-five respondents.
One hundred and eight (58.37%) was male population and seventy-six (41.63 %) was female population.
The response rate was 92.5%. The results show that there is significant association between fluctuation in
ticket price, employee motivation and politeness, customer centered strategy, positive employees’ behaviour,
facilities and customer management and strict flight schedule and security and customer satisfaction.
Promotors, company’s policy makers, branch managers, researchers and students will be benefited by the
implication of this study to understand the perceptions of customers towards the price factor, quality factor,
service quality of employees and customer relationship management. More importantly, the findings of this

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study would be importantly helpful for company’s leaders on how to satisfy their customers at Bharatpur
Chitwan. The results further show that the customer management had not a buffering effect on initial levels
of customers’ satisfaction but affected change over time. In generalizing the results of the present study,
there was some cause for concern due to a sampling method and representativeness of the male and female
population. The facilities in different airlines, price of tickets, service quality, employees’ behaviour and
customermanagement vary in each airline.The conclusions of this research will be beneficial to otherairlines
to identify the needs and preference of customers so that they can formulate new customer-centred strategies
in future. It was summarized by the previous study that customer satisfaction has always been considered
a vital business goal because of its crucial role in the formation of customers’ desire for future purchase or
tendency to buy more. The growing of airlines industry provided opportunities as well as challenges to the
business entities in the Airline industry. The opportunities were due to the increasing demand for the airline
services, while the challenges were high level of competition between airlines but also due to the growing
customer demands for betterservices.
References
Aboulafia, R., & Michaels, K. (2018). Opinion: Global Aerospace Industry May Be at Record High.
Retrieved from http://aviationweek.com/farnborough-airshow-2018/opinion-global-aerospace-
industry-may-be-record-high.
Aligholi, M. (2014). Investigation of Link between Customer Satisfaction and Customers Price
Sensitivity. Mediterranean Journal of Social Sciences. doi: 10.5901/mjss.2014.v5n20p3098
Amoah Mensah, A., Quaye, D. and Mensah, I. (2018). Customer relationship management practices
affecting customer loyalty supporting small airline carriers in Ghana. International Journal of
Electronic Customer Relationship Management, 11(4), 411.
Boettger, T. (2019). What Drives Customer Inspiration? A Goal-Systemic Perspective. SSRN Electronic
Journal.
Chow, C. (2014). Customer satisfaction and service quality in the Chinese airline industry. Journal of Air
Transport Management, 35, 102-107.
Routledge.
Creswell,J.,&PlanoClark,V.(2011).Designingandconductingmixedmethodsresearch(1sted.).Thousand
deLange,R.,Samoilovich,I.,&vanderRhee,B.(2013).Virtualqueuingatairportsecuritylanes.European
Journal of Operational Research, 225(1), 153-165.
Easterby-Smith, M., Thorpe, R. and Jackson, P. (2012). Management Research. London: Sage.
Fornell,C., Mithas, S., Morgeson, F.,&Krishnan, M.(2006).Customer Satisfaction and Stock Prices:High
Returns, Low Risk. Journal of Marketing, 70(1), 3-14.
Kattara, H., Weheba, D., & El-Said, O. (2008). The impact of employee behaviour on customers’ service
quality perceptions and overall satisfaction. Tourism And Hospitality Research, 8(4), 309-323.
Oaks: Sage. Creswell, J. (2014). Educational research (1st ed.). Harlow, Essex: Pearson.
Service Quality, Brand Image and Customer Satisfaction Influence Loyalty (Study on Citilink Airline
Passengers). (2019). European Journal of Business and Management.
Sinha, A., Sinha, A. and Sinha, A. (2018). Using SPSS, New Delhi Panjab Publication.

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An Elaborative Study in the Market Potential of Home
Automation and Security Products: A Case Study of
Chitwan District in Urban Nepal
Mr. Samir Raj Bhandari
Oxford College of Engineering and Management
E-mail: Vertical16horizon@gmail.com
Abstract
The objective of this study was to make people aware of automation products and its importance in the
field of human convenience and security and also to focus on security, energy management and comfort.
Quantitative research approach was used in this study. The research was conducted in two phases, i.e.
collective interview with the guardians of the students by distributing the questionnaire to the students
and providing them necessary guidance to fill the questionnaire and field visit to different institutes,
banks, homes, hotels, industries in the year of 2018. The sampling technique was Random, Quota and
convenience sampling. The results show that around 78.2 % families had Wi-Fi connection in their
homes where 61.3 % was male and 37.1 % was female. Out of 124 members participating in research,
48.4 % of respondent was graduate student. The results show that approximately 96.8 % respondents
show their interest in technology product. Among them 60.2 % respondents were between the age group
of 30-50. The results also show that 90.3 % of family had more than three family members where
27.3 % respondents had monthly income above Nepalese Currency 90,000. About 51.7 % respondents
perceived that security was the key feature of automation products whereas only 17.7 % responded that
energy management and comfort were major issues for automation. The results importantly highlighted
that approximately, 82.3% were familiar with home automation and 89.5 % respondents trusted in home
automation products. The results also show that 84.7 % people showed interest in keeping home automation
products. The empirical studies reveal that home automation is the most customized and reliable automation
services. This study has tried to relate the advancement in the field of automation and the market potential of
those products in Chitwan, Nepal. The implication of this research will be beneficial to city people who have
the lack of deep knowledge of automation products and uses. The limitation of this study is the concern of
proportion of the sample population of male and female participants.
Keywords: Home Automation, Security, Comfort, Smart Home
Introduction
Background of Study
Home automation is derived from two different words “Home” and the “Automation” where home is the
place where we live inside the four walls and “Automation” means the act of implementing the controls of
equipment with advance tech usually involving electronic hardware (Asadullah & Raza, 2016). Therefore
“Home automation” gives the sense of smart house. All home automation system controls the lighting,
temperature, comfort, entertainment and other appliance inside house and with essential features about

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security such as fire alarm and cctv cameras which are getting popularity now days (Asadullah & Raza,
2016). The objective of home automation is all about comfort, efficient operation, reduction in energy
consumption and increasing the life standard. Certainly, with elderly and disabled people can get quality
of life because of the home automation (Asadullah & Raza, 2016). It is doubtless to say that a home
automation is connected with the “server” which is also known as hub. User can control home activities
within one click, even it he is far away from his house. The user needs to connect with any internet
source from anywhere so he/she can get notification instantly gadgets like cell phone laptops etc. Where
every gadgets and house equipment are connected with IOT so that every object can complete task and
communicate with user each other. The table below shows more clearly how automation is connected
with user. The control & automation is limted to the user alone (Asadullah & Raza, 2016).
Block diagram Home Automation
Theautomationindustryis ina re-evaluationstagewithsignificanttechnological advancements.Developments
in automation industry, introduction of upgraded devices and technology, also known as Smart Home and
Smart Building, has changed the way products and services are being delivered. With focus on enhancing
consumer experience, these technologies are witnessing continues research and development to equip the
products as per compatibility with Smart & Sustainable Home and Building projects (YANG, 2005). The
market for home automation is forecast to grow steadily to become US$ 116.26 Billion by 2026 from US$
64.67 Billion in 2017, at a CAGR of 6.8 % (Transparency Market, 2017)
Moreover, the market for home automation products and solutions in developing economies across the
globe such as China, India, and Brazil, are witnessing increasing adoption due to significant rise in
disposable income of the mid-income group and rising preference for luxurious lifestyle (Transparency
Market, 2017). Furthermore, other Asian countries, for instance Indonesia, Taiwan, and South Korea, are
projected to fuel the growth of the home automation market during the forecast period in this geography
(Transparency Market, 2017). This research is initially trying to understand the necessary outlet showing,
55.6 % choose online store, showing people interest in using technology with 46 % people thought that
this kind of product is very preferable to Home. Respondents view on the Products like automatic water
pumps which are available in the market with the price ranging from NRs. 1500-2500 Nepalese Rupees
(NRs) and remote-controlled lights and fans whose price in the market is NRs 15000-20000 were about
10.5 % people strongly agreed in the requirement of automatic water pump in present scenario. Products
like. Market research showed that about 72.7 % people had income level less than NRs. 90,000. Even
though people of Chitwan are aware of automation, Home automation is a completely new market.
SECURITY
ENERGY
MANAGEMENT
MAIN SYSTEM
COMFORT
USER

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Problem Statement
Problem definition
We are living in the 21st
century but still follow traditional methods for comfort, security, and energy
management. Presently we have system that can be easily installed, cost efficient, and able to provide
genuine home automation to consumers. We are wasting the energy (more specifically electrical energy)
in different fields such as Agriculture, Hospitals, Education, and Apartments etc (Transparency Market,
2017). which can be due to unwanted operation of different loads or equipment.
The market of Home solution is wide and includes variety of consumers of different age group starting
from kids to senior citizens. The demand and type of solutions vary as per the consumer. The main problem
we encountered from the on-field survey was problems with an integrated system capable of controlling
their comfort, security, and energy management issues. This research surveyed for the likeliness of a
single integrated system incorporating all the components of a home network which solves the issues of
comfort, security, and energy management to fit the present scenario.
Market Potential:
The market potential of is very high as it consists of 579,984 population (Statistics, 2017).
Target Market: Map of Nepal showing Chitwan
It has an area of 2,238.39 km2
and in 2011 had a population of 579,984 (279,087 male and 300,897
female) (Statistics, 2017). Chitwan has a huge opportunity for home automation product. Bharatpur
is major commercial and service Centre of Chitwan as well as Nepal and major destination for higher
education, health care and transportation in the region (UNFCO, 2009). At present Bharatpur is the
largest business area of Chitwan. Chitwan district is also known as the medical city of Nepal. There are
many top-rated medical institutions in the district are located in Bharatpur. High rank schools, hotels,
apartments, hospitals and industries are also present in abundant amount (UNFCO, 2009). Hence market
potential of home automation products in Chitwan is very high.

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Research Methodology
Research Approach
Initially exploratory design procedure was used for convenience and to get tentative idea about the
market. Later conclusive research was conducted to get precise idea of the market. Under conclusive
research design procedure, we conducted causal research procedure by formulating questionnaire that
was asked to 120 respondents.
Population and Sampling
The population of Chitwan is 579,984 population (Statistics, 2017) which is relatively larger as compared
to other cities of Nepal. But for our convenience we selected 120 samples for our research. The sample
included respondent from Bharatpur and its nearby areas. The sampling was carried out through
convenience, quota and random sampling procedure.
Questionnaire and Administration
Home automation (HA) is one of the new concepts for comfort, security and energy management. After
the formulation of questionnaires, the research was divided into two parts. In first part different students
at Oxford College of Engineering and Management were included by providing them questionnaire. Each
and every student of the class was provided with proper instruction and was asked to fill the questionnaire
through their guardian. The answered questionnaire was collected in the next day.
In second part the research was conducted on targeted area like hotel and restaurant, educational institute,
home etc. Both the approach gave positive and sound feedback regarding the need of automation products.
This research was based on the study of demand of technology so the prepared questionnaires were for
urban area pertaining to high class and middle-class family.
The research was conducted relating to technology and taking reviews through well-structured and
chronological questionnaires. The questionnaire was divided into three parts (consent, screening and
respondent field questions). The time of interview was around 10-15 minutes. On screening part, personal
and demographic information of the respondent as well as their willingness in the technology was taken.
It clarifies which income group, gender and what age group of people was interested in technology
products.
Data Analysis
A separate column is provided for unanswered questions (unanswered questions have no label in the figure)
Figure 1 : Interest in keeping the technology Products
120.0
100.0
96.8
80.0
60.0
40.0
20.0
2.4
0.0
YES NO
Percentage

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The bar diagram indicates that out of 120 respondents taken into our survey 96.8 % showed interest in
technology products. This result supports the cause of our research as people are preferring technological
products over traditional products like conventional switches.
60.0
40.0
20.0
0.0
41.3
20-30 30-60 60-90 90 above
Salary of Respondent in Thousand Nepalese Rupees
Figure 2 : Salary Level of the Respondent
If salary of the respondent were below 60-90 limit there would have been no reason for us to carry out
the survey as our products targeted to preferably middle-class and high-class people. As in the graph
it is clearly shows that our 7.4 % of respondents had salary above 60-90 thousand and 27.3 % had
salary above 90 thousand which clearly indicates the interest and inclination of people with high income
towards the technology products.
2.4
18.5
48
21.0
9.7
SLC GRA DUA TE POS T
GAR AD UAT E
Education level
OT HE R S
Figure 3 : Respondent Education Level
The survey that we carried also tried to corelate the tendency of respondents to use technology products
with education. As the bar depicts, only 18.5 % were just SLC passed also showed their interest in
technology products. This clearly indicates the possibility in that areas.
Figure 4 : WIFI Connection in Respondent House
24.0 27.3
7.4
200.0
150.0
100.0
50.0 98.4
0.0
YES
1.6
NO
-50.0
-100.0
Percentage
PercentagePercentage
.4

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As our product was based on Wi-fi so we had to understand the popularity of internet among our respondents,
almost every respondent had a Wi-Fi connection at their home or using any forms of internet services.
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
YES NO
Figure 5 : Ownership of the house
In this section respondent had to verify whether they owned any house, if the respondents had no house
then probably there was no need to keep any automation products. Surprisingly 76.6 %v respondents had
their own house.
100.0
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
YES NO
Figure 6 : Respondents Home Automization Product
8.9 % respondents did not trust the reliability of automation products rest of the respondents showed their
trust in these kinds of products.
60.0
50.0
51.6
40.0
30.0
20.0
10.0
0.0
SECURITY ENERGY
MANAGEMENT
COMFORT
Figure 7 : Respontent’s Rating for Automization System
17.7 17.7
12.9
Percentage
76.6
21.8
1.6
89.5
8.9

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As the main agenda of our research was to identify whether the respondents find security, energy
management or comfort as main priority of automation products. 51.6 % of the respondents explained and
answered in the favor of security and 17.7 % respondents answered in the favor of energy management
and comfort. Rest 12.9 % did not find any of them important.
49.2
28.2
STRONGLY AGREE
AGREE
NITHER AGREE NOR DISAGREE
DISAGREE
STRONGLY DISAGREE
Figure 8 : Respontent’s Mentality in the Workability in terms of Security by Home Products
49.2 % respondents agreed that home automation kept their home secure only 4 % showed dis-agreement
in the workability of automation products. But 28.2 % respondent strongly agreed to the workability in
terms of security.
OFFICIAL RETAILERS
ONLINE STORE
0.0 10.0 20.0 30.0 40.0 50.0 60.0
Figure 9 : Respontent’s Choice of Outlets
On the todays competitive market there are two types of outlets namely: official retailers and online store.
As anticipated by the people’s interest in technology products 55.6 % respondents chose online store over
official retailers. But around 10.5 % of respondents did not like to answer the question.
7.3
4.0.8 0 10.5
33.9
55.6
10.5

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YOURSELF
EXPERT ADVICE
PROFESSIONAL
11.3
COSTUM
Figure 10 : Method of Installation of Home Automization Product
As Nepal being a developing country, we hoped that no respondents would like to install these automation
technologies by themselves but interestingly about 6.5 % of respondents find installing these automation
products by themselves. But 64.5 % would seek professional’s help to install these automation products
in their home. Some 11.3 % would seek expert advice and rest 6.5 % respondent would like to take the
custom service provided by the company.
60.0
50.0
53.2
40.0
30.0
20.0
10.0
0.0
TOO
EXPENSIVE
TOO MUCH
ATTENTION
NO
COMPLETE
RELIABILITY
NO
FLEXIBILITY
HARD TO
MANAGE
Figure 11: Expectation of Respondents to adopt Home Automization
As 41.3 % of our respondents had their income level between 20-30 thousand Nepalese rupees, around
53.2 % respondent marked “Too Expensive” as the main reason of not buying the automation products.
Around 13.7 % thought it requires too much attention where as 13.7 % thought it would be hard to mange
despite showing interest in technology. Others choices like no complete reliability and no flexibility we
not the major concerns of the respondents.
6.5 0 11.3
6.5
64.5
13.7 13.7
5.6
8.9
4.8

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100.0
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
95.2
YES NO
Figure 12: Recommendation of Automization
As this was the main question in our entire survey, 95.2 % respondents would like to recommend the
automation products to their friends and relatives.
120.0
100.0
80.0
60.0
40.0
20.0
0.0
98.4
1.6
YES NO
Figure 13: Demand of Automization Product in Future
98.4 % of the respondents answered YES to the question regarding the demand of automation in future,
what we conclude out of this huge sublimation that they don’t think that automation is the present need
of general public of Chitwan, rather it to be a future need.
Summary of Findings
As seen from the data which was gathered from the survey it is clear that there is huge potential of
automation products in Chitwan district of Nepal. Many respondents showing interest in technology
products proves the future of these technologies in Nepal as well. This research also shows that with the
increase in the salary of respondents the tendency to use automation for comfort and energy management
increases. But as if for moderate respondents with salary of 60-90 thousand Security remains to be most
important. Ownership of a house, Wi-Fi connections and educational qualifications of respondents had
direct connections with the whether to select or not select automation for their household. But as most
respondents answered NO for the possibility of using automation products in the present scenario, we
found the research to be far cited than for this present scenario.
1.6 3.2
Percentage

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Discussion
From our survey we concluded that Home Automation could be a well-suited technology in near future.
Reasons monitored that people are not likely to purchase Digital security system and Home Automation
System includes-they are expensive, hard to maintain, high false alarm rate etc. Major sector where
future of product can be seen includes home, industries, schools etc. Only 6 % of people disagreed to
use products made in Nepal which put light on the interest of Nepalese people to use Nepalese products.
During our survey respondent’s we found that price of the product played a vital role for 47.6 % of our
respondent. 53.2 % people thought that the expense of automation product will be the main aspect of not
using automation product, but unexpectedly only 13.7 % thought that this kind of product will be hard
to manage at the first place. We also tried to find that what would change the mind of respondent for
adopting automation products, impressively 41.9 % agreed in transparency of the investment and cost
rather than affordable price which was just 29.8 %.
Our plan is to generate awareness and find out the market potential for the need of home automation
system. The home automation revenue is expected to rise as people become aware of its capabilities.
Some recommendations that this research provides that will ensure good customer relations are as regular
maintenance, relevant percentage of discount, a year warranty and scheme for custom installments, easy
graphical user interface and application control system, security and EMI scheme and remote info sharing.
References
Asadullah, M., & Raza, A. (2016). An overview of home automation systems. 2nd International
Conference on Robotics and Artificial Intelligence (ICRAI), . Rawalpindi, Pakistan.
Statistics, C. B. (2017). National Population and Housing Census 2011. Kathmandu: Government of
Nepal.
Transparency Market, R. (2017). Home Automation Market. Retrieved July 18, 2018, from https://
www.transparencymarketresearch.com/home-automation-market.html
UNFCO, U. F. (2009). An Overview of the Central Development Region (CR). UN House, Pulchowk,
Kathmandu, Nepal: United Nations Resident and Humanitarian Coordinator’s Office.
YANG, J. (2005). DISCOVER INTEGRATED APPROACHES TO SMART AND SUSTAINABLE
HOUSING DEVELOPMENT. 2005 World Sustainable Building Conference. Tokyo.
Appendix 1
One question had relations with other, so that there will be uniformity of response.
On the session 1:
Answers to the questions below were our key concerns.
1. Are you interested in technology products?
Reason: It shows will of the respondent in buying home automation products.
2. Family Monthly Income (in thousands, Nepalese rupees)
Reason: Family income determines the capacity of the respondent to buy our product.
3. Gender
Reason: Which gender is more likely to buy our products?

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4. Whether they own that house or not?
5. Education status of the respondent.
6. Do they have Wi-Fi connection in the house?
Reason: Our product as a full package is internet based hence Wi-Fi is the essential part of the
survey
On the session 2:
Following questionnaire had important role:
1. Do you trust in home automation products?
2. How would you like to link with us?
3. If you were to install home automation products, how would you prefer to do?
4. Which of these places you prefer to have automation systems?
5. Do you recommend automation products to your friends/relatives?
6. Do you see demand of home automation products in FUTURE?
7. If this product is available in the market from today, how likely would you be to buy the product?
8. What would change your mind about adopting automation?
9. Are Automatic water pumps the most essential product in today’s scenario?
10. Will Home automation would make your home secure?
Code Book for questionnaires
We used code book for coding and to serve as documentation of the layout and code definition of the
data file. It will ease us on data analysis and decoding of the survey research. Code book is given below.
Concerns
We discussed on the behavior categories for the data analysis because it is not easy to determine the
respondent response in demographic classification. Demographic classification is also included so that
more accurate analysis of data is obtained.
To obtain the effective output of the data of HA we revised the questionnaires many times consulting
with senior and our team members. The main purpose of analyzing data is to obtained useable and useful
information. Questionnaires was made to analyze the each of the response of the individual so data were
analyzed by using frequency distribution and visual technique based upon the behavior classification and
draw inference. We used statistical analysis (regression) to relate dependent and independent variable.

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The study of internet addiction among adolescent of Oxford
CollegeofEngineeringandManagement(OCEM)
Ganga Prasad Sapkota
Lecturer, HOD statistics (BMC)
E-mail: gangasapkota3067@gmail.com
Abstract
The primary objective of this study was to determine the prevalence of internet addiction (IA) among
contributory factors and to determine the association between socio-demographic variables and
influencing factors of using internet among Oxford College of Engineering and Management (OCEM)
students. The quantitative approach along with the survey study was used as a research method and the
survey questionnaire was used as the research instrument. Participants were selected through simple
random sampling. The cross sectional analytical study was conducted among 169 adolescents of OCEM
students. The results show that prevalence rate of non-addictive internet users were 20.1% while
79.9% were addictive users. Among addictive users, 38.5% were found mild addiction, 40.8% were
found moderate addiction, only 0.6% were found in severe addiction. The results also shows that the
prevalence of internet addiction was significantly high among young generation. Internet addiction was
also statistically significant with various demographic variables and internet use factors. The previous
studies reveal that internet has become an integral part of contemporary life, bringing huge benefits in
terms of expanding access to knowledge, information, social interaction, and entertainment and further
noted that around 40% of the today’s world population has an internet access. The implications of this
study will be beneficial to parents, educational leaders, school and college principals to understand the
internet addiction problems and to formulate new academic policy to minimize the over use of internet
during teaching and learning activities. It is also recommended that internet addiction can affect the
physical and mental health of the students so that the problem of internet addiction should be prevented
through it’s awareness program on the negative effects of over use of internet.
Keywords: Prevalence; Internet addiction; Inter addiction; youth of OCEM
1. Introduction
Internet was established in the early 1960 by the U.S. Department of Defense primarily for military
purposes. Since then, the continual improvement of the internet technology has provided an extraordinary
level of public accessibility to wide range forms of communication Intra-organizational and inter-
organizational email; data storage, management transfer, social websites like Facebook, twitter, and so
forth. Due to the development and spread of cheaper and more user-friendly computer technology and
software (e.g., portable computers, Microsoft Word), the use of the internet has increased dramatically
(Wanajak, 2011). Today around 50 % of the world population has an internet connection. In 1995, it was

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less than 5 %. The number of internet users has increased tenfold from 1999 to 2013 and reached first
billion in 2005, second billion in 2010 third billion in 2014 and penetration population of internet in the
world is 46.1% till July 1, 2016. As of, June 30, 2016 internet users in Asia is 49.5% with the highest
users and lowest user in Africa with 9.8 %. Similarly, In China Penetration population of internet user
is 52.2 %, in India 43 % and in Nepal 63 % (Internet Live Stats, 2019). A cross sectional descriptive
study was conducted to determine the prevalence of IA and contributor factors to determine internet
related behavior patterns among students of Science, using stratified random sampling method. Of 236
participants, 74.6% were females. The study revealed that 50.8 % had mild addiction, 40.7 % moderate
and 1.3 % had severe addiction. The finding of the study concluded that prevalence of IA is significantly
high (Adhikari B, 2015).
A cross-sectional school-based study was conducted in four cities in China among 13,723 students
(aged 12-20 years) to evaluate the associations between problematic Internet use and physical and
psychological symptoms. The Multidimensional Sub-health Questionnaire of, Pittsburgh Sleep
Quality Index and demographic variables were used to measure adolescents sleep quality, physical and
psychological symptoms respectively. Problematic internet use was assessed by the 20-item Young IA
Test. Prevalence rate of internet Addiction, physical symptoms, psychological symptoms, and poor sleep
quality were 11.7 %, 24.9 %, 19.8 %, and 26.7 %, respectively. Excessive internet use may not only
have direct adverse health consequences but also have indirect negative effects through sleep deprivation
(Van Ameringen, Simpson, Patterson, Turna & Khalesi, 2016). A cross-sectional survey was conducted
among 17,599 students in eight cities of China to test the relationship between Problematic internet use
and psychosomatic symptoms and life satisfaction among Adolescents. PIU was assessed by the 20-item
Young IA Test (YIAT). About 8.1 % of subjects showed PIU. Adolescents with PIU were associated
with males, high school students, urban, eastern and western areas, upper self-report family economy,
service type mostly used for entertainment and relieving loneliness and more frequency of internet use.
Compared with normal internet users, adolescents with PIU were more likely to suffer from psychosomatic
symptoms (P<0.001), including lack of physical energy (P<0.001), physiological disfunction (P<0.001),
weakened immunity (P<0.001), emotional symptoms (P<0.001), behavioral symptoms (P<0.001) and
social adaptation problems (P<0.001). Adolescents with PIU had lower scores on total and all dimensions
of life satisfaction (all P < 0.001) (Bozkurt, Özer, Şahin & Sönmezgöz, 2017).
Multistage sampling was conducted in the sampling procedure where student participants from Baguio
City were selected. The IA Test was used. Total of 1059 valid questionnaires were analyzed. Findings
suggest that adolescents are frequent online users and that there are significant differences in terms of
gender, school type, and online behaviors; social desirability had a strong positive relationship with
adolescent IA(Waldo, 2014). A survey was administered among 1097 adolescents aged between 11 and
18 years to explore the addictive symptomatology among British adolescents. A convenience sampling
technique was used. Only 71.8 % correctly completed all the Problematic Internet Entertainment Use
Scale for Adolescents [PIEUSA; PIEUSA items] (i.e. was., 1097 out of 1528 participants). The results
indicated that prevalence of online problem users was 5.290 and most of them were younger males that

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engaged in online gaming for more than two hours most days. The majority of online problem users
displayed negative addictive symptoms, especially ‘loss of control’ and ‘conflict’ (Lopez-Fernandez,
Honrubia-Serrano, Gibson & Griffiths, 2014). In distinct to my research a cross-sectional survey with a
sample size of 3560 students was conducted among high schools in Connecticut, USA. Demographic data,
characteristics of internet use, health measures and risk behaviors were assessed. the overall prevalence
was about 4% with no significant difference between genders. (Desaaiet,al.. 201l) In distinct to my
research a cross sectional study was conducted among adolescents of ages 13 to 18 years, registered on
the secondary school registry in Guangzhou city using a stratified random sampling technique. IA was
assessed using the Internet Addiction Test (IAT). The majority of respondents were classified as normal
users of the internet (n = 1,392, 89.2%), with 158 (10.2%) moderately and 10 (0.6%) severely addicted
to the Internet. (Lam et, al, 2009).
3. Research design
The Strategic plan structure of data were taken from OCEM student of class 11 and 12. The study was
examined to IA with youth. It was descriptive, analytical and cross tab in nature. The sample survey data
were collected from youth. The study was based on those students coming from rural and urban areas
from the different places. A Cross-sectiona1 analytical study design among 169 students was used to
assess IA among adolescents of 11th
and 12th
grade, whose age ranges between 15 to 19 years of OCEM
College.
3.1 Inclusion criteria and Exclusion criteria
The study included adolescents of 11th
and 12th
grade with age ranging from 15 to 19 years, were available
and willing to participate in the study. Those students whose age ranged below 15 years and above 19
years and absent were excluded from the sample population.
3.2 Data Collection Procedure
Permission was obtained from the concerned authorities. Pre-testing was done among 10% of samples.
The objectives of the study were informed to the respondents and written consent was taken. Parental
consent form was distributed to those whose age is < 18 years and signature of parents were taken as
the permission to involve their child in the research. All the respondents who met the inclusion criteria
were given a structured self-administered questionnaire. Respondents were assured for confidentiality
of information as it was only used for study purpose. Similarly, a cross sectional survey was conducted
between May and June 2010, using a self-administered questionnaire distributed to randomly selected
770 secondary schools students, using 20-item Young’s IA test. and the Center for epidemiological studies
depression scale with questions related to demographic, social, academic and internet use factors. 716
Students answered the questionnaire 391 are males and 325 are females. Prevalence was 5.3%, with male
predominance. IA was associated with a lower degree of school performance, more hours using internet
everyday (Cohen, Manion, Morrison & Bell, 2011).

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4. RESULTS
4.1 Internet Addiction and Socio-Demographic Variables
In this digitalized world, the internet has become a fundamental tool for information, entertainment and
social communication. It has been widely adopted especially by adolescents, as a low-cost, easy-to-
access platform for social interaction and leisure activities. Currently, 93% of adolescents and young
adults go online in the USA and almost 70% adolescents in Europe spend 2–4h daily on computer-
games surfing and chatting via the internet (Tsitsika et al., 2016). Given this high usage and amount of
time spent on internet use, internet addiction, often referred to as ‘problematic internet use’ (PIU), is a
growing concern. The reported prevalence of PIU varies widely, from 1% to 9% in Europe, 1 % to 12 %
in the Middle East and 2 % to 18 % in Asia. PIU in adolescents and young adults appears to be associated
with negative health consequences, such as Depression, low educational performance, Attention Deficit
Hyperactivity Disorder, daytime sleepiness, alcohol abuse and injuries (Mangoulia, 2014). It was found
that socioeconomic variables seem to increase the risk of childhood and adolescent obesity. Indeed,
previous research suggests an inverse correlation between childhood obesity and parental occupation,
education and income level (Moreno, 2011).
All the collected data were reviewed, checked and organized daily for the completeness and accuracy.
Coding and organizing was done before data entry. The data were entered and analyzed in the SPSS
version 20. Mann-Whitney U & Kruskal-Wallis H test was used to find out the association between
Internet Addiction, socio-demographic variables and Internet use factors. Data has been presented in
different table form.
Table 1. Internet Addiction and Socio-Demographic Variables
Factors Categories N Z score P-value
Age
15 to 17 136
1.192 0.233
18 to 19 33
Sex
Male 87
3.475 0.001*
Female 82
Marital Status
Married 2
7.56 0.45
Unmarried 167
Educational Faculty
Science 86
3.932 0.000*
Management 83
Education Level
11 84
2.255 0.024*
12 85
*Significance level at 5%, *p<0.005
The results show that the association between IA scores and socio-demographic variables. It is found that
IA is statistically significant with sex (z=3.47, p=0.01, education faculty z=3.932, p=0.000, education
level z=2.255, p=0.024 (see in the Table 1). Likewise, the results show that the academic performance
of the respondent are also associated with Internet Addiction. However it is not statistically significant
to other variables. The current study has supported the previous findings of Stavropoulos, Alexandra &
Motti-Stefanidi (2013) because both the current and the previous studies have highlighted that there is
significant association between the internet user students and academic performance. This study also

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support the previous study of Heo, Oh, Subramanian, Kim & Kawachi (2014) because both studies the
similar findings that there was significant associations between addictive Internet use and ages of students,
school grade and marital status. It was further found that female students in girls’ schools were more
likely to use Internet addictively than those in coeducational schools. Our results also revealed significant
gender differences of addictive Internet use in its associated individual- and school-level factors.
4.1 The Internet Use Factors
TheuseoftheInternethasallowedustheconvenienceofaccessinganythingatourfingertips.Inadolescents
especially,theInternethasbecomeareadilyaccessiblemeansforentertainment,communication,education
and information retrieval. Nonetheless, the negative impact of addiction has pervasively affected day to
day function; school performance and relationships with their parents; Worst of all, extensive Internet
use may generate adverse effects on the psychosocial development of adolescents, which may result in
many of them experiencing mental health problems including depression, loneliness, low self-esteem,
and anxiety. An increasing number of studies have revealed that addictive online behaviors are very
similar to alcoholism, substance addiction and pathological gambling. With the increased popularity of
the Internet, Internet addiction has emerged as a social and mental health issue among youths. Although
official diagnostic criteria do not currently exist, Young defined Internet addiction as the excessive,
obsessive–compulsive, uncontrollable, tolerance-causing use of the Internet, which also causes significant
distress and impairments in daily functioning. Internet addiction has the following types: cyber-sexual
addiction, cyber-relational addiction, game addiction, information overload, and net compulsions. In
recent years, Internet addiction has been reported in both Western and Eastern societies among adult and
adolescent groups. Several studies have also examined the prevalence of Internet addiction during the past
few years. Although data from those studies reported inconsistent occurrence rate of Internet addiction,
there is no doubt that Internet addiction has emerged as a rapidly growing problem in young people that
has attracted world-wide attention. Adolescence is a critical period for addiction vulnerability, when
compared to adults, adolescents are more likely to adopt patterns of excessive Internet use. Generally
speaking, Internet addiction is common among adolescents, and related factors are found at both home and
school. Close attention should be paid by both parents and teachers to these factors. Effective measures
are needed to prevent the spread of this problem.
Table 2. Association between IA and Internet use factors
Factors N Z score p-value
Internet access at home
Yes 168
0 1
No 1
Own gadget
Yes 163
2.188 0.29
No 6
Type of gadget owned Computer
Yes 39
1.461 0.144
No 124
Smart Phone
Yes 123
0.654 0.513
No 39
Ipad/Tablet
Yes 38
1.029 0.304
No 125

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Alternative to use if don’t own gadget
Family members 5
3 0.77
Internet Café 1
Time of using internet more
Evening 72
3.791 0.000*
Night 97
Purpose of internet use study
Yes 120
2.26 0.024*
No 49
Online Games
Yes 71
3.619 0.000*
No 98
Chatting
Yes 152
535.5 0.001
No 15
Gambling
Yes 12
1.656 0.098
No 157
Pornography
Yes 27
2.668 0.008*
No 141
Social network sites
Yes 126
2.417 0.016*
No 43
Blogs
Yes 6
0.166 0.868
No 163
Downloading movies
Yes 6
1.128 0.855
No 163
News
Yes 6
0.142 0.254
No 163
Online shopping
Yes 18
1.142 1.142
No 151
Communicated with strangers
Yes 119
3.554 0.000*
No 50
Exchange phone number
Yes 50
3.206 0.001
No 119
Exchanges photos with strangers
Yes 52
4.315 0.000*
No 117
Met online friends
Yes 40
4.279 0.000*
No 129
Cyber bullying
Yes 8
0.685 0.493
No 161
Family relationship effects
Yes 23
4.031 0.000*
No 146
Health effects
Yes 53
2.154
0.031*
No 116
Kruskal Wallis Test
Factors N H df P-value
Experience of using internet
<6 months 4
3 0.037*
6 months to 2 years 43
>2 years to 5 years 66
>5 years 56
Average hour of internet use per day
<2 hours 84
3 0.000*
2-3 hours 40
>3-5 hours 27
>5 hours 18

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Sleeping hour
<7 hours 47
2 0.641<7 to 8 hours 104
>8 hours 18
Monthly expense
100-500 rupees 69
0.003*>500-1000 rupees 53
>1000 rupees 47
The results indicate that Internet addiction is associated with various socio-demographic and internet
use factors. This study revealed that prevalence rate of addictive internet users were 79.90 % and non-
addictive internet users were 20.1%. Internet addiction has been classified as none users which was
20.l%. The results further show that mild, addiction was 38.5%, moderate addiction was 40.8% and
severe addiction was 0.6%. Likewise, among all of the respondents’ age group, adolescents of 17 years
(34.91 %) were found to more addicted whereas, 15 years (1.18 %) group adolescents were less addicted
than other groups. Regarding sex, male (45 %) were highly addicted than female (34.9%). Likewise,
89.9% use Internet for chatting, 70.4 % for study purpose, 74.6 % for social networking sites. 62.7 %
for downloading movies/music, 42.0% for online games, 23.1% for news, 16.0 % for pornography. 10.7
% for online shopping. 7.1 % for gambling and 3.6 % for websites/blogging. The study revealed that
50.8% had mild addiction. 40.7 % modern and 1.3 % had severe addiction. (see in the Table 2). This
study has supported the previous study of Wu et al. (2016) because both studies have found that a variety
of related factors have significant effects on Internet addiction, for example, parental control, per capita
annual household income, academic performance, the access to Internet and online activities. The results
also show that Internet addiction was negatively correlated with social support and positively associated
with depression.
Discussion & Conclusions
The results show that, addictive internet users (79.9%) were higher than non-addictive internet users
(20.1%) among the respondents. Moderate Addiction was highest among others (40.8%) followed by
mild addiction (38.5%). Likewise severe addiction has only 1 (0.6 %). The results also show that the
academic performance of the respondent were also associated with Internet Addiction. However it was
not statistically significant to other variables.
Internet addiction is becoming a significant problem among adolescents. IA is growing problem, which
has psychological, physical, and social impact on adolescents’ life, and requires preventive strategies as
well as therapeutic interventions. IA is statistically significant with sex, educational faculty, educational
level and academic performance of the respondents. IA is strongly significant of using internet more at
night time. IA score is affected by the purpose of using 1ntemet. lA score is significant to online games,
chatting, viewing’ pornography‚ using social networking sites, respectively. IA score is highly significant
with Communicating with strangers, exchanging phone numbers, exchanging photos with strangers
meeting online, Family relationships and health. IA scores were significantly affected by experience in
using the internet, those who has been using internet for > 5 years are highly affected than others. Daily
average internet using hours is also significant to those who use internet> 5 hours a day and monthly
expenditure is also significant to IA.

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References
Adhikari B, M. (2015). Internet Addiction and Associated Factors among Health Sciences Students
in Nepal. Journal Of Community Medicine & Health Education, 05(04). doi: 10.4172/2161-
0711.1000362
Bozkurt, H., Özer, S., Şahin, S., & Sönmezgöz, E. (2017). Internet use patterns and IAin children and
adolescents with obesity. Pediatric Obesity, 13(5), 301-306.
Christakis, D.A., Moreno, M.M., Jelenchick, L., Myaing, M.T. & Zhou, C., (2011). Problematic internet
usage in US college students: a pilot study. BMC medicine, 9 (1), l.
Routledge.
Hawi, N. (2012). Internet Addiction among adolescents in Lebanon. Computers in Human Behavior, 28
(3), 1044-1053.
Heo, J., Oh, J., Subramanian, S., Kim, Y., & Kawachi, I. (2014). Addictive Internet Use among Korean
Adolescents: A National Survey. Plos ONE, 9(2), e87819. doi: 10.1371/journal.pone.0087819
Internet Live Stats. (2019). Internet Live Stats - Internet Usage & Social Media Statistics. Retrieved 21
November 2019, from https://www.internetlivestats.com/
Lam, L.T., Peng, Z.W., Mai, J.C. & Jing, J.(2009). Factors associated with Internet Addiction among
adolescents. Cyber Psychology& Behavior, 12(5), pp.551-555
Lopez-Fernandez, O., Honrubia-Serrano, M., Gibson, W., & Griffiths, M. (2014). Problematic Internet
use in British adolescents: An exploration of the addictive symptomatology. Computers In Human
Behavior, 35, 224-233.
Mangoulia, P. (2014). Internet Addiction and Psychopathological Symptoms in Greek University
Students. Journal Of Addictive Behaviors Therapy & Rehabilitation, 03(03). doi: 10.4172/2324-
9005.1000125.
Moreno, M. (2011). Problematic Internet Use Among US Youth. Archives Of Pediatrics & Adolescent
Medicine, 165(9), 797-801.
Stavropoulos, V., Alexandraki, K., & Motti-Stefanidi, F. (2013). Recognizing internet addiction:
Prevalence and relationship to academic achievement in adolescents enrolled in urban and rural
Greek high schools. Journal Of Adolescence, 36(3), 565-576.
Tsitsika, A., Andrie, E., Psaltopoulou, T., Tzavara, C., Sergentanis, T., & Ntanasis-Stathopoulos, I. et
al. (2016). Association between problematic internet use, socio-demographic variables and obesity
among European adolescents. The European Journal Of Public Health, 26(4), 617-622.
Van Ameringen, M., Simpson, W., Patterson, B., Turna, J., & Khalesi, Z. (2016). Internet Addiction
or psychopathology in disguise? Results from a survey of college-aged internet users. European
Neuropsychopharmacology, 26, S700-S701.doi: 10.1016/s0924-977x(16)31834-x.
Waldo, A. (2014). Correlates of Internet Addiction among Adolescents. Psychology, 05(18), 1999-2008.
Wu, X., Zhang, Z., Zhao, F., Wang, W., Li, Y., & Bi, L. et al. (2016). Prevalence of Internet addiction and
its association with social support and other related factors among adolescents in China. Journal Of
Adolescence, 52, 103-111. doi: 10.1016/j.adolescence.2016.07.012
Wanajak, K. (2011). Internet use and its impact on secondary school students in Chiang Mai,
Thailand (Doctoral). Cowan University.

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Correlation and Regression Analysis Using SPSS
Sarad Chandra Kafle
Asst. Professor
Birendra Multiple Campus, Bharatpur
1. Introduction
In quantitative study, researcher willing to use very famous statistical tool regression & correlation, however
due to lack ofsufficientknowledgeon regression & correlation analysis theirdesired havenot fulfilled or even
they use the tool, the tool haven’t been properly used. To provide clear cut idea on correlation regression, its
use way of interpretation of output of analysis, this research article has been prepared. Relation between two
or more variables can be studied by using Correlation and Regression. Two variables are said to be related if
change in the value of one variable changes the value of other variable. Here the term change implies either
increase or decrease in the value of variable. Relationship between variables can be studied by the method
of correlation or regression. Such an analysis of relationship can be carried for quantitative or qualitative
variable however this paper includes only the analysis of relationship between quantitative variables. Those
variables which are measurable and thus have unit are quantitative variables. Study of relationship between
two quantitative variables at a time is simple regression or simple correlation and relationship between more
than two quantitative variables may be partial correlation or multiple correlation or multiple regression
according to the objective/nature of study and variables included in the study (Sthapit, Yadav, Khanal, &
Dangol, 2017).
Strength of relationship between two or more variables is studied by using Correlation. Correlation is
statistical tool that measures how strong relationship exists between variables. Value of correlation lies in
between -1 and +1. Nearer the value of correlation to zero weak is the relationship between the variables,
similarly if the value of correlation close to one implies higher (close) relation between variables. Hence
correlation is a value which tries to explain degree of association between variables whereas regression tries
to explain the relationship between variables using a mathematical function. (Gupta & Kapoor,2014).
Abstract
The objective of this study is to share knowledge on how to use Correlation and Regression Analysis
through Statistical Package for Social Science (SPSS). This study has used secondary data to demonstrate
the way of using very popular statistical tool on using correlation and regression analysis for novice
researchers. Among various statistical tools, correlation and regression analysis are mostly used tools in
many research works., e.g. the field of management, medicine, social science and education. However,
not all the researchers may know whether the tools are fit to use, how to carry the analysis and how
to interpret the obtained results. The results shows that novice researchers need to know the proper
knowledge and skill to analyse the quantitative data. The implications of this study is willing to share
the knowledge on correlation and regression analysis and the way of analyzing through very popular
software package SPSS.
Keyword: Statistical tools, Test of Significance, p-value, Hypothesis, Dependent and Independent
variables

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6
5
4
Y
3
2
1
0
0 5
X
10
1.1 Correlation Analysis:
The correlation analysis refers the degree of relationship between variables. But it does not explain about
which of the variable is cause and which one is the effect. Study of correlation between two variables is
called simple and between more than two variables may be partial or multiple.
Correlation can be studied by two methods, diagrammatic method and mathematic method.
Diagrammatically it is studied with the help of scatter diagram which cannot provide exact value of
correlation in all case. Mathematically many methods and formulae are there however Karl Pearson’s
Method is widely used (Magnello, 2009).
1.2 Diagrammatic method:
Diagrammatically correlation can be studied by scatter diagram. This is presented in figure-1. To plot
a scatter diagram, a dot is provided for each pair of data for X and Y, plotting the value in X axis and
That of Y on respective Axis. More closer and the arranged point shows higher correlation between two
variables. Analysis of the strength of relationship is based on the trend which is seen in scatter diagram.
If increase in the value of one variable makes increase in the value of other variable, (direct relationship),
then the correlation is said to be positive whereas if the scatter shows opposite trend to that then the
relation is negative. (Shrestha, Khanal, & Kafle, 2014).
Following scatter diagram helps to clearly the different types of correlation between two variables X and Y.
Fig-1: Scatter diagram
Perfect Positive correlation(r = +1)
perfect negative correlation(r = -1)
Highly negative correlation
No correlation (r =0)
(Fig Source: (Shrestha, Khanal, & Kafle, 2014) )
10
8
6
Y
4
2
0
0 5
X
10
15
10
Y
5
0
0 2 4 6 8
X
14
12
10
y
8
6
4
2
0
0 5
x
10

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1.3 Karl Pearson’s correlation coefficient:
This is mathematical method to study the degree of association between two variables. It is used to study
the correlation between two quantitative variables and denoted by r. Formula to calculate Karl Pearson’s
correlation coefficient is as follow (Sthapit, Yadav, Khanal, & Dangol, 2017) -
cov (X, Y)
xy
or, r =
nXY - XY
nX2
- (X)2
nY2
-(Y)2
1.4 Spearman’s rank correlation:
Tostudy the degree of association between two variables whose values are written in rank, rank correlation
is used. For quantitative variables ranks can be provided according to their increasing or decreasing order
of magnitude. Rank correlation is denoted by rs.
and its formula for calculation is as
6 d2
rs = 1 - n3
- n
; When the ranks are not repeated.
6

d2
+
1
(m 3
- m ) +
1
(m 3
- m ) +..................


 12 1
1 12 2 2

= 1 -
repeated (Magnello, 2009)
n3
- n
; when ranksare
1.5 Kendal tau:
It also rank correlation and can be used in the case where spearman’s rank correlation can be calculated.
It is denoted by ῑ(tau). Formula to calculate Kendal tau is as
τ = ( )
; when ranks are not repeated
√ ( ) √ ( ) = ; when ranks are repeated
(Gupta & Kapoor, 2014)
1.6 Interpretation of Correlation Coefficient:
Correlation calculated using any formula and method stated above can be interpreted as below
If r = 1, the correlation is said to be perfect positive.
If r = -1, the correlation is said to be perfect negative.
If r = 0, the variables X and Y are said to be uncorrelated.
If 0< r ≥ 0.4, low correlation.
If 0.4 ≤ r < 0.7, moderate correlation.
If 0.7 ≤ r < 1, high correlation.
The value of correlation coefficients nearer to +1 or -1 be interpreted as very high positive or negative
correlation and nearing zero is considered as very low (Gupta & Kapoor, 2014).
r =

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1.7 Partial correlation:
Correlation between two variables keeping the effect of remaining variable constant is partial correlation.
If we are interested to study the relationship between two variables X1
and X2
while there exists another
variable X3
then the correlation between X1
and X2
keeping the value of X3
constant is partial correlation
between X1 and X2 keeping X3 constant, denoted by r12.3. Value of partial correlation lies in between -1
and +1.
r12.3 =
√( ) ( )
1. 8 Multiple Correlation:
Correlation between predicted and the actual values of the dependent variable in a linear regression model
that includes an intercept. In other words it is the relationship between dependent variable and joint effect
of independent variable on dependent variable In statistics, the coefficient of multiple correlation is a
measure of how well a given variable can be predicted using a linear function of a set of othervariables.
If X1
be dependent variable which is described by X2
and X3
then the correlation between actual value of
X1
predicted value of X1
is denoted by R1.23,
in other way,
it is the correlation between dependent variable
X1
and joint effect of X2
and X3
on X1
The value of multiple correlation lies in between 0 and 1.
R1.23 = √
1.9 Regression analysis:
Regression analysis tries to study the relationship between two or variables with the help of equation, the
equation is called regression line. The line is also called line of best fit since it is obtained by the method
of least square. Least Square Method is estimation of parameters of regression equation by minimizing
the error sum of square of dependent.
Regression analysis established the nature of relationship between two or more variables and then
estimates the unknown variable (dependent variable) with the help of known variable (independent
variables). In other words there are two types of variables in a regression analysis. The variables, which
is used to predict the variable of interest is called the independent or explanatory variable or predictor,
and the variable whose value is to be predicted is called the dependent variable or explained variable or
regressed. (Montgomery, 1982)
1.10 Simple regression:
If relationship between two (one dependent and other independent) variables is studied at a time then the
regression is called simple, whereas the study of more than two variables at a time is multiple.
If Y is a dependent variable and X is an independent variable then regression equation of Y is-
Y = a + b X
Where,
a = y intercept = constant = value of Y when X = 0
b = regression coefficient = slope coefficient = change in the value of Y per unit change in the value
of X.

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1.11 Multiple Regressions:
Let ‘y’ is the dependent variable and x1
, x2,
x3.....................................
xk
be the ‘k’ independent variables. Then the
multiple regression model is defined as
y   0  1 x1   2 x2  .............   k xk  e
Where,
y = dependent variable and x1
, x2,
x3
…………… xk
are independent variables.
0
= y-intercept.
1
= Slope of y with variable x1
holding the remaining variables x2,
x3
…,xk
constant or Regression
coefficient of y on x1
holding the remaining variables x2,
x3
…………… xk
constant. And so on.
(Dendukuri & Reinhold, 2005)
Some pre-requisities to carry linear regression model are
- There is linear relationship between quantitative dependent and independent variables
- There is no presence of autocorrelation of residuals.
- The mean of residuals is zero.
- There is equal variance of residuall or presence of homoscedasticity.
- The independent variables are uncorrelated with errors.
- There is absence of multicollinearity. (Zaid, 2015)
1.12 SPSS
SPSS refers to Statistical Package for Social Science. It is statistical software which eases to compile and
analyze data. We can compile or entry collected primary data or secondary as same as Microsoft Excel.
Its menu bar is helpful to analyze the data thus entered easily. Many statistical analysis can be carried
using SPSS (Arkkelin, 2014).
Many researchers have applied the correlation and regression analysis in their thesis, articles and their
documents, however; they are not yet confident for the appropriate use of correlation and regression
analysis and how to fit these statistical tools in their research works. In some cases, their interpretation
may mislead their research studies. Many novice researchers are willing to use correlation and regression
analysis but they don’t know how to use these tools during their data analysis. The primary objective of
this study is to share knowledge on regression and correlation analysis and required conditions to use in
their research paper.
2. Method & Materials
This study is based on sampled secondary data of 423 maternity women respondents admitted in
Chitwan Medical Sciences(CMS), Bharatpur, Chitwan, Nepal during the period 2017 July to August
2017 for maternity. The data used in this study were accessed via library of Chitwan Medical Sciences.
The sample data of infant’s ages and weight were entered into computer software (SPSS) and analyzed
using regression and correlation. Different published articles were googled through online resources, for
example, google, bookboon.com, uef.fi, and http://www.oxfordcollege.edu.np. All the research materials
were embedded in correlation and regression analysis. The collected materials were initially observed
their abstracts, methods and findings to find the deep knowledge on the research phenomenon.
3. Results & Discussion
To study the association between quantitative variables, correlation analysis can be carried in SPSS. To
start this analysis, at first select Analyze then define the variables between which variable researchers
wants to determine correlation and then choose Pearson’s correlation, Kendal tau or spearman’s according
to the nature of data. For test of significance tail of the test can be defined. After completing these actions



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and clicking on ok button an output window will show result of correlation analysis as in
Table 1. Correlation output table using SPSS
Age of respondent in month Height of respondent in cm
Age of
respondent in
month
Pearson Correlation 1 .853**
Sig. (2-tailed) .000
N 423 423
Height of
respondent in
cm
Pearson Correlation .853**
1
Sig. (2-tailed) .000
N 423 423
**. Correlation is significant at the 0.01 level (2-tailed).
Table 1 is correlation analysis output table for correlation between age and height of respondents. The
correlation coefficient is 0.853 which is high degree of positive correlation between height and weight
of the respondents. Also the correlation coefficient is significant as its p-value is 0.00 and is less than
significance level(α = 5 % ).
To find out how these two variables are related regression analysis is carried. To carry this analysis
researcher has selected ‘Analyze’ then ‘Regression’ and then ‘Linear’ successively. Then researcher
define dependent and independent and independent variable and then clicking on ‘Ok’, followingoutput
table is obtained as shown in Table-2, Table-3 and Table-4.
Table 2. Model Summary
Model 1 R R Square Adjusted R Square Std. Error of the Estimate
1 .853a
.728 .727 7.67832
a. Predictors: (Constant), Age of respondent in month
Table 2 shows coefficient of determination ( R square) 0.728, which means 72.8% variation in dependent
variable ( Height) is explained by independent variable (Age).
Table 3. ANOVA
Model Sum of Squares df Mean Square F Sig.
1
Regression 66311.832 1 66311.832 1124.755 .000b
Residual 24820.758 421 58.957
Total 91132.590 422
a. Dependent Variable: Height of respondent in cm
b. Predictors: (Constant), Age of respondent in month
Table 3 tries to test overall goodness of fit of fitted regression model. From above table it can be concluded
that the fitted model is significant as P-value of F statistics is 0.00 and it is less than level of significance
level(α = 5% ).
Table 4. Coefficient table
Model
Unstandardized
Coefficients
Standardized
Coefficients t Sig.
B Std. Error Beta
(Constant) 59.350 0.679 87.373 0.000
Age of respondent in month 0.811 0.024 0.853 33.537 0.000
a. Dependent Variable: Height of respondent in cm
Coefficient table helps to determine the regression equation, the column Unstandardized Coefficients and
its sub column ‘B’ provides the regression coefficients. First one is constant or y intercept and second one
is regression coefficient of height (Y) on age(X). Hence the regression equation using coefficient table is
Y = 59.35 + 0.811 X
The regression coefficient of height on age is found to be 0.811 which implies that any child which is
one month elder than other child is 0.811 centimeter taller than earlier. Also, the regression coefficient is

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significant as p-value (0.00) is less than level of significance level (α = 5 % ).
4. Discussion
The results show that using correlation and regression via SPSS is useful for the novice researchers. The
results also highlighted that the using correlation and regression is embedded only in quantitative data. In
practical life researcher can find many quantitative variables which are related to each other, their degree of
relationship can be measured by correlation and how two or more variables are related can be described by
an equation, e.g. an equation is regression equation. Manually, the calculation of regression equation and
correlation is very complex for big data ,so it requires software via SPSS which is very easy and faster.
The results also highlighted that correlation and regression are two key data analysis tools in quantitative
approach because Logistic Regression Model helps in predicting probability of occurrences of y dependent
variable to x independent variables, when the dependent variable is dichotomous. Researchers can use
dichotomous variables, e.g. health status(sick or not), employment status( employed or unemployed),
labour force participation (part or not part of the labour force) and family planning method (which type).
The results also summarized that Logistic Regression Analysis is more flexible method because it makes no
assumptions about the nature of relationship between independent and dependent variables. The limitations
of this study are the secondary data analysis, limited research materials, limited knowledge on statistical
tools, limited literature review, limited areas of research knowledge, limited knowledge on correlation and
regression analysis. Due to these limitations of this research, the current research cannot give the guarantee
for the radiality and validity of data and findings. It is recommended that future research has to focus on
rich literature review and primary research on how correlation and regression can be effectively use in data
analysis processes of quantitative methods. It is also recommended that a details steps of correlation and
regression analysis has to focus in future research study to make helpful for the novice researchers.
Reference
Arkkelin, D. (2014). Using Spss to Understand Research and Data Analysis. Valparaiso: Valparaiso
University.
Dendukuri, N., & Reinhold, C. (2005). Correlation and Regression. American journal of Roentgenology,
3-18.
Draper, N. R., & Smith, H. (2011). Applied Regression Analysis. Noida: Wiley India Pvt. Ltd.
Gujarati, D. N., C, P. D., & Gunasekar, S. (2015). Basic Econometrics. New Delhi: McGraw Hill
Education (india) Pvt. Ltd.
Gupta, S. C., & Kapoor, V. K. (2014). Fundamentals of Mathematical Statistics. Mumbai: Sultan Chand
and Sons.
Magnello, M. (2009). Karl Pearson and the Establishment of Mathematical Statistics. MInternational
Statistical Review / Revue Internationale De Statistique, 3-29.
Mehta, B. C., & Kapoor, K. (2005). Fundamentals of Econometrics. Mumbai: Himalaya Publishing
House.
Montgomery, D. (1982). Introduction to linear Regression Analysis. New Delhi: Willy.
Shrestha, M. P., Khanal, P. R., & Kafle, S. C. (2014). Business Statistics. Kathmandu: Sabdartha
Publication.
Sthapit,A. B.,Yadav,R. P.,Khanal, S. P.,& Dangol, P.M. (2017). Fundamentals of Statistics. Kathmandu:
Asmita Publication.
Zaid Mohamed Ahmed, (2015). Correlation and Regression Analysis; Statistical Economic and Research
and Training Centre for islamic countries.

Dr. Basanta Adhikari

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