{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T11:14:19Z","timestamp":1787915659415,"version":"build-2784847793"},"reference-count":106,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T00:00:00Z","timestamp":1643068800000},"content-version":"vor","delay-in-days":116,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Intelligent Sys in Account"],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>\n                    Artificial intelligence (AI) in business processes and academic research in AI has significantly increased. However, the adoption of AI in organizational strategy is yet to be explored in extant literature. This study proposes two conceptual frameworks showing hierarchical relationships among the various drivers and barriers to AI adoption in organizational strategy. In a two\u2010step approach, the literature study is first done to identify eight drivers of and nine barriers to AI adoption and validated by academic and industry experts. In the second step, MICMAC (matrice d'impacts croises\u2010multiplication appliq\u00fae a un classment\n                    <jats:italic>or<\/jats:italic>\n                    cross\u2010impact matrix multiplication applied to classification) analysis categorizes the drivers and barriers to AI adoption in organizational strategy. Total interpretive structural modeling (TISM) is developed to understand the complex and hierarchical associations among the drivers and barriers. This is the first attempt to model the drivers and barriers using a methodology like TISM, which provides a comprehensive conceptual framework with hierarchical relationships and relative importance of the drivers and barriers to AI adoption. AI solutions' decision\u2010making ability and accuracy are the most influential drivers that influence other driving factors. Lack of an AI adoption strategy, lack of AI talent, and lack of leadership commitment are the most significant barriers that affect other barriers. Recommendations for senior leadership are discussed to focus on the leading drivers and barriers. Also, the limitations and future research scope are addressed.\n                  <\/jats:p>","DOI":"10.1002\/isaf.1503","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T23:54:57Z","timestamp":1643154897000},"page":"217-238","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":91,"title":["Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective"],"prefix":"10.1002","volume":"28","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-6320-0232","authenticated-orcid":false,"given":"Sudatta","family":"Kar","sequence":"first","affiliation":[{"name":"Capgemini Engineering  Gurugram India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-4186-4887","authenticated-orcid":false,"given":"Arpan Kumar","family":"Kar","sequence":"additional","affiliation":[{"name":"Department of Management Studies, Indian Institute of Technology  New Delhi India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manmohan Prasad","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Management Studies, Indian Institute of Technology  New Delhi India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,1,25]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jjimei.2021.100022"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.7208\/chicago\/9780226613475.001.0001"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12063-019-00143-x"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1177\/0972262920988398"},{"key":"e_1_2_8_6_1","first-page":"26","article-title":"Artificial intelligence adoption: AI\u2010readiness at firm\u2010level","volume":"6","author":"Alsheibani S.","year":"2018","journal-title":"Artificial Intelligence"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2020.106548"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40547-017-0085-8"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2021.102929"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-010-0362-z"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106948"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.35940\/ijitee.K1030.09811S19"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.5120\/ijca2019919695"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.07.059"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207543.2018.1530476"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1108\/JEIM-06-2020-0233"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1017\/jmo.2016.55"},{"key":"e_1_2_8_18_1","doi-asserted-by":"crossref","unstructured":"Brynjolfsson E. 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