Abstract
Data-driven predictions have become an inseparable part of business decisions. Artificial Intelligence (AI) has started helping the product and support teams perform more accurate experiments in various business settings. This study proposes a framework for businesses based on inductive learnings related to success and barriers shared on social media platforms. Our goal is to analyse the signals emerging from these conversational opinions from the early adoption of AI, with a focus towards facilitators and barriers faced by teams. Factors like efficiency, innovation, business research, product novelty, manual intervention, adaptability, emotion, support, personal growth, experiential learning, fear of failure and fear of upgradation have been identified based on an exploratory study and then a confirmatory study. We present the learnings through a roadmap for practitioners. This study contributes to the IS literature by delineating AI as a determinant of success and introduces a lot of organizational factors into the model.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Aguinis, H., Gottfredson, R. K., & Culpepper, S. A. (2013). Recommendations for estimating cross-level interaction effects using multilevel modeling. Academy of Management Proceedings, 2013(1), 10839. https://doi.org/10.5465/ambpp.2013.10839abstract
Ahuja, M. K., & Thatcher, J. B. (2005). Moving beyond Intentions and toward the Theory of trying: Effects of work environment and gender on post-adoption information technology use. MIS Quarterly, 29(3), 427–459. https://doi.org/10.2307/25148691
Al-Gahtani, S. S., & King, M. (1999). Attitudes, satisfaction and usage: Factors contributing to each in the acceptance of information technology. Behaviour & Information Technology, 18(4), 277–297. https://doi.org/10.1080/014492999119020
Andersson, L. M., & Pearson, C. M. (1999). Tit for Tat? The spiraling effect of incivility in the workplace. Academy of Management Review, 24(3), 452–471. https://doi.org/10.5465/amr.1999.2202131
Archak, N., Ghose, A., & Ipeirotis, P. G. (2011). Deriving the pricing power of product features by mining consumer reviews. Management Science, 57(8), 1485–1509. https://doi.org/10.1287/mnsc.1110.1370
Argote, L., & Miron-Spektor, E. (2011). Organizational learning: from experience to knowledge. Organization Science, 22(5), 1123–1137. https://doi.org/10.1287/orsc.1100.0621
Arjun, R., Kuanr, A., & Kr, S. (2021). Developing banking intelligence in emerging markets: Systematic review and agenda. International Journal of Information Management Data Insights, 1(2), 100026. https://doi.org/10.1016/j.jjimei.2021.100026
Asuncion, A. G., & Lam, W. F. (1995). Affect and impression formation: influence of mood on person memory. Journal of Experimental Social Psychology, 31(5), 437–464. https://doi.org/10.1006/jesp.1995.1019
Bader, V., & Kaiser, S. (2019). Algorithmic decision-making? The user interface and its role for human involvement in decisions supported by artificial intelligence—Verena Bader, Stephan Kaiser, 2019. Organization Science, 26(5), 655–672
Baird, A., & Maruping, L. M. (2021). The next generation of research on is use: a theoretical framework of delegation to and from agentic is artifacts. MIS Quarterly, 45(1), 315–341. https://doi.org/10.25300/MISQ/2021/15882
Balakrishnan, J., Dwivedi, Y. K., Hughes, L., & Boy, F. (2021). Enablers and inhibitors of AI-powered voice assistants: a dual-factor approach by integrating the status quo bias and technology acceptance model. Information Systems Frontiers. https://doi.org/10.1007/s10796-021-10203-y
Barabási, A. L. (2013). Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371, 20120375
Barrodale, I., & Roberts, F. D. (1978). Solution of the constrained, ℓ1 linear approximation problem. ACM Transactions on Mathematical Software, 6(9), 231–235
Bartunek, J. M., & Ragins, B. R. (2015). Extending a provocative tradition: book reviews and beyond at AMR. Academy of Management Review, 40(3), 474–479. https://doi.org/10.5465/amr.2015.0029
Becker, L., & Jaakkola, E. (2020). Customer experience: Fundamental premises and implications for research. Journal of the Academy of Marketing Science, 48(4), 630–648. https://doi.org/10.1007/s11747-019-00718-x
Benlian, A., Kettinger, W. J., Sunyaev, A., Winkler, T. J., & EDITORS, G. (2018). Special section: the transformative value of cloud computing: a decoupling, platformization, and recombination theoretical framework. Journal of Management Information Systems, 35(3), 719–739. https://doi.org/10.1080/07421222.2018.1481634
Berger, J., Sorensen, A. T., & Rasmussen, S. J. (2010). Positive effects of negative publicity: when negative reviews increase sales. Marketing Science, 29(5), 815–827. https://doi.org/10.1287/mksc.1090.0557
Bergstein, B. (2019). Can AI pass the smell test? MIT Technology Review, 122(2): 82–86
Börner, K., Sanyal, S., & Vespignani, A. (2007). Network science. Annual Review of Information Science and Technology, 41(1), 537–607
Braga, A., & Logan, R. K. (2017). The emperor of strong AI has no clothes: limits to artificial intelligence. Information, 8(4), 156. https://doi.org/10.3390/info8040156
Brock, J. K. U., & Von Wangenheim, F. (2019). Demystifying AI: What digital transformation leaders can teach you about realistic artificial intelligence. California Management Review, 61(4), 110–134
Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, July Issue
Büschken, J., Otter, T., & Allenby, G. M. (2013). The dimensionality of customer satisfaction survey responses and implications for driver analysis. Marketing Science, 32(4), 533–553. https://doi.org/10.1287/mksc.2013.0779
Cambre, M. A., & Cook, D. L. (1985). Computer anxiety: definition, measurement, and correlates. Journal of Educational Computing Research, 1(1), 37–54. https://doi.org/10.2190/FK5L-092H-T6YB-PYBA
Cariani, P. (2010). On the importance of being emergent. Constructivist Foundations, 5, 86–91
Cave, S., & ÓhÉigeartaigh, S. S. (2019). Bridging near- and long-term concerns about AI | Nature Machine Intelligence. Nature Machine Intelligence, 1, 5–6
Dai, T., & Singh, S. (2020). Conspicuous by its absence: diagnostic expert testing under uncertainty. Marketing Science, 39(3), 540–563. https://doi.org/10.1287/mksc.2019.1201
Daugherty, P., & Wilson, H. J. (2018). Human + machine: Reimagining work in the age of AI. Harvard Business Review
Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340
Day, D. V., & Lord, R. G. (1992). Expertise and problem categorization: The role of expert processing in organizational sense-making. Journal of Management Studies, 29(1), 35–47
de Jong, M. G., Lehmann, D. R., & Netzer, O. (2012). State-dependence effects in surveys. Marketing Science, 31(5), 838–854. https://doi.org/10.1287/mksc.1120.0722
Deichmann, D., & van den Ende, J. (2013). Rising from failure and learning from success: the role of past experience in radical initiative taking. Organization Science, 25(3), 670–690. https://doi.org/10.1287/orsc.2013.0870
Dittrich, K., Guérard, S., & Seidl, D. (2016). Talking about routines: The role of reflective talk in routine change. Organization Science, 27(3), 678–697
Drexler, J. A. (1977). Organizational climate: Its homogeneity within organizations. Journal of Applied Psychology, 62(1), 38–42. https://doi.org/10.1037/0021-9010.62.1.38
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Edmondson, A. C. (2004). Learning from mistakes is easier said than done: group and organizational influences on the detection and correction of human error. The Journal of Applied Behavioral Science, 40(1), 66–90. https://doi.org/10.1177/0021886304263849
Ellis, S., Carette, B., Anseel, F., & Lievens, F. (2014). Systematic reflection: implications for learning from failures and successes. Current Directions in Psychological Science, 23(1), 67–72. https://doi.org/10.1177/0963721413504106
Floridi, L. (2008). Information ethics: A reappraisal. Ethics and Information Technology, 10, 189–204
Furlan, A., Galeazzo, A., & Paggiaro, A. (2019). Organizational and perceived learning in the workplace: a multilevel perspective on employees’ problem solving. Organization Science, 30(2), 280–297. https://doi.org/10.1287/orsc.2018.1274
Gal, D., & Rucker, D. D. (2011). Answering the unasked question: response substitution in consumer surveys—David Gal, Derek D. Rucker 48(1), 185–195
Gargiulo, F., Cafiero, F., Guille-Escuret, P., Seror, V., & Ward, J. K. (2020). Asymmetric participation of defenders and critics of vaccines to debates on French-speaking Twitter. Scientific Reports, 10(1), 6599. https://doi.org/10.1038/s41598-020-62880-5
Ghosh, I., & Sanyal, M. K. (2021). Introspecting predictability of market fear in Indian context during COVID-19 pandemic: An integrated approach of applied predictive modelling and explainable AI. International Journal of Information Management Data Insights, 1(2), 100039. https://doi.org/10.1016/j.jjimei.2021.100039
Grover, P., Kar, A. K., Dwivedi, Y. K., & Janssen, M. (2019). Polarization and acculturation in US Election 2016 outcomes – Can twitter analytics predict changes in voting preferences. Technological Forecasting and Social Change, 145, 438–460. https://doi.org/10.1016/j.techfore.2018.09.009
Grover, P., Kar, A. K., & Ilavarasan, P. V. (2017). Understanding nature of social media usage by mobile wallets service providers –An exploration through SPIN framework. Procedia Computer Science, 122, 292–299. https://doi.org/10.1016/j.procs.2017.11.372
Grover, P., Kar, A. K., Janssen, M., & Ilavarasan, P. V. (2019). Perceived usefulness, ease of use and user acceptance of blockchain technology for digital transactions – insights from user-generated content on Twitter. Enterprise Information Systems, 13(6), 771–800. https://doi.org/10.1080/17517575.2019.1599446
Gunasekaran, A., & Ngai, E. W. T. (2012). The future of operations management: An outlook and analysis. International Journal of Production Economics, 135(2), 687–701. https://doi.org/10.1016/j.ijpe.2011.11.002
Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14
Hansen, M. T., Nohria, N., & Tierney, T. (1999). What’s your strategy for managing knowledge? Harvard Business Review, 77(2), 106–116
Helfat, C. E., & Peteraf, M. A. (2015). Managerial cognitive capabilities and the microfoundations of dynamic capabilities. Strategic Management Journal, 36(6), 831–850
Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: biased estimation for nonorthogonal problems. Technometrics, 12(1), 55–67. https://doi.org/10.1080/00401706.1970.10488634
Igbaria, M., Parasuraman, S., & Baroudi, J. J. (1996). A motivational model of microcomputer usage. Journal of Management Information Systems, 13(1), 127–143. https://doi.org/10.1080/07421222.1996.11518115
Janssen, O., van de Vliert, E., & West, M. (2004). The bright and dark sides of individual and group innovation: A Special Issue introduction. Journal of Organizational Behavior, 25(2), 129–145. https://doi.org/10.1002/job.242
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586
Johns, G. (2001). In Praise of Context. Journal of Organizational Behavior
Johns, G. (2006). The essential impact of context on organizational behavior. Academy of Management Review, 31(2), 386–408. https://doi.org/10.5465/amr.2006.20208687
Johns, G. (2017). Reflections on the 2016 decade award: Incorporating context in organizational research. Academy of Management Review, 42(4), 577–595. https://doi.org/10.5465/amr.2017.0044
Kar, A. K., & Dwivedi, Y. K. (2020). Theory building with big data-driven research – Moving away from the “What” towards the “Why. International Journal of Information Management, 54, 102205. https://doi.org/10.1016/j.ijinfomgt.2020.102205
KC, D., Staats, B. R., & Gino, F. (2013). Learning from my success and from others’ failure: evidence from minimally invasive cardiac surgery. Management Science. https://doi.org/10.1287/mnsc.2013.1720
Kellogg, K. C., Valentine, M. A., & Christin, A. (2019). Algorithms at work: the new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174
Kim, H., & Kankanhalli, A. (2009). Investigating user resistance to information systems implementation: a status quo bias perspective. MIS Quarterly, 33(3), 567–582. https://doi.org/10.2307/20650309
Kolb, D. A. (2015). Experiential learning: experience as the source of learning and development. Pearson Education
Kumar, S., Kar, A. K., & Ilavarasan, P. V. (2021). Applications of text mining in services management: A systematic literature review. International Journal of Information Management Data Insights, 1(1), 100008. https://doi.org/10.1016/j.jjimei.2021.100008
Kushwaha, A. K., & Kar, A. K. (2020a). Language model-driven chatbot for business to address marketing and selection of products. In S. K. Sharma, Y. K. Dwivedi, B. Metri, & N. P. Rana (Eds.), Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation (pp. 16–28). Springer International Publishing. https://doi.org/10.1007/978-3-030-64849-7_3
Kushwaha, A. K., & Kar, A. K. (2020b). Micro-foundations of artificial intelligence adoption in business: making the shift. In S. K. Sharma, Y. K. Dwivedi, B. Metri, & N. P. Rana (Eds.), Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation (pp. 249–260). Springer International Publishing. https://doi.org/10.1007/978-3-030-64849-7_22
Kushwaha, A. K., & Kar, A. K. (2021a). Information Labelling of Medical Forum Posts by Non-Clinical Text Information Retrieval. 12
Kushwaha, A. K., & Kar, A. K. (2021b). MarkBot – A language model-driven chatbot for interactive marketing in post-modern world | SpringerLink. Information Systems Frontiers, 1–18. https://doi.org/10.1007/s10796-021-10184-y
Kushwaha, A. K., Kar, A. K., & Vigneswara Ilavarasan, P. (2020a). Predicting information diffusion on Twitter a deep learning neural network model using custom weighted word features. Responsible Design, Implementation and Use of Information and Communication Technology, 456–468. https://doi.org/10.1007/978-3-030-44999-5_38
Kushwaha, A. K., Kar, A. K., & Vigneswara Ilavarasan, P. (2020b). Predicting information diffusion on Twitter a deep learning neural network model using custom weighted word features. Responsible Design, Implementation and Use of Information and Communication Technology, 456–468. https://doi.org/10.1007/978-3-030-44999-5_38
Kushwaha, A. K., Mandal, S., Pharswan, R., Kar, A. K., & Ilavarasan, P. V. (2020c). Studying online political behaviours as rituals: a study of social media behaviour regarding the CAA. In Sharma, S. K., Dwivedi, Y. K., Metri, B., & Rana, N. P. (Eds.), Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation (pp. 315–326). Springer International Publishing. https://doi.org/10.1007/978-3-030-64861-9_28
Kushwaha, A. K., Kar, A. K., & Dwivedi, Y. K. (2021a). Applications of big data in emerging management disciplines: A literature review using text mining. International Journal of Information Management Data Insights, 1(2), 100017. https://doi.org/10.1016/j.jjimei.2021.100017
Kushwaha, A. K., Kar, A. K., & Ilavarasan, P. V. (2021b). Predicting retweet class using deep learning. Trends in Deep Learning Methodologies, 89–112. https://doi.org/10.1016/B978-0-12-822226-3.00004-0
Kushwaha, A. K., Kumar, P., & Kar, A. K. (2021c). What impacts customer experience for B2B enterprises on using AI-enabled chatbots? Insights from Big data analytics. Industrial Marketing Management, 98, 207–221. https://doi.org/10.1016/j.indmarman.2021.08.011
Kushwaha, A. K., Pharswan, R., & Kar, A. K. (2021d). Always Trust the Advice of AI in Difficulties? Perceptions Around AI in Decision Making. In Dennehy, D., Griva, A., Pouloudi, N., Dwivedi, Y. K., Pappas, I., & Mäntymäki, M. (Eds.), Responsible AI and Analytics for an Ethical and Inclusive Digitized Society (pp. 132–143). Springer International Publishing. https://doi.org/10.1007/978-3-030-85447-8_12
Lakhiwal, A., & Kar, A. K. (2016). Insights from Twitter Analytics: Modeling Social Media Personality Dimensions and Impact of Breakthrough Events. In Dwivedi, Y. K., Mäntymäki, M., Ravishankar, M. N., Janssen, M., Clement, M., Slade, E. L., Rana, N. P., Al-Sharhan, S., & Simintiras, A. C. (Eds.), Social Media: The Good, the Bad, and the Ugly (pp. 533–544). Springer International Publishing. https://doi.org/10.1007/978-3-319-45234-0_47
Lindebaum, D., Vesa, M., & den Hond, F. (2019). Insights from “The Machine Stops” to better understand rational assumptions in algorithmic decision making and its implications for organizations. Academy of Management Review, 45(1), 247–263. https://doi.org/10.5465/amr.2018.0181
Llewellyn, C., Grover, C., Alex, B., Oberlander, J., & Tobin, R. (2015). Extracting a topic specific dataset from a Twitter archive. In S. Kapidakis, C. Mazurek, & M. Werla (Eds.), Research and Advanced Technology for Digital Libraries (pp. 364–367). Springer International Publishing. https://doi.org/10.1007/978-3-319-24592-8_36
Ludwig, S., de Ruyter, K., Friedman, M., Brüggen, E. C., Wetzels, M., & Pfann, G. (2013). More than words: the influence of affective content and linguistic style matches in online reviews on conversion rates. Journal of Marketing, 77(1), 87–103. https://doi.org/10.1509/jm.11.0560
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: machines vs. humans: the impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 38(6), 937–947. https://doi.org/10.1287/mksc.2019.1192
McGrath, R. G. (1999). Falling forward: real options reasoning and entrepreneurial failure. Academy of Management Review, 24(1), 13–30. https://doi.org/10.5465/amr.1999.1580438
Mcilroy, D., Sadler, C., & Boojawon, N. (2007). Computer phobia and computer self-efficacy: Their association with undergraduates’ use of university computer facilities. Computers in Human Behavior, 23(3), 1285–1299. https://doi.org/10.1016/j.chb.2004.12.004
Meinhart, W. A. (1966). Artificial intelligence, computer simulation of human cognitive and social processes, and management thought. Academy of Management Journal, 9(4), 294–307. https://doi.org/10.5465/254948
Meske, C., Bunde, E., Schneider, J., & Gersch, M. (2020). Explainable artificial intelligence: objectives, stakeholders, and future research opportunities. Information Systems Management, 0(0), 1–11. https://doi.org/10.1080/10580530.2020.1849465
Metcalf, L., Askay, D. A., Rosenberg, L. B., Askay, D. A., & Rosenberg, L. B. (2019). Keeping humans in the loop: pooling knowledge through artificial swarm intelligence to improve business decision making—Metcalf, L., Askay, D. A., Rosenberg, L. B.. California Management Review, 61(4), 84–109
Mohamed Ridhwan, K., & Hargreaves, C. A. (2021). Leveraging Twitter data to understand public sentiment for the COVID-19 outbreak in Singapore. International Journal of Information Management Data Insights, 1(2), 100021. https://doi.org/10.1016/j.jjimei.2021.100021
Morikawa, M. (2017). Firms’ expectations about the impact of ai and robotics: Evidence from a survey. Economic Enquiry, 55(2), 1054–1063
Nair, R. S., Agrawal, R., Domnic, S., & Kumar, A. (2021). Image mining applications for underwater environment management—A review and research agenda. International Journal of Information Management Data Insights, 1(2), 100023. https://doi.org/10.1016/j.jjimei.2021.100023
Neogi, A. S., Garg, K. A., Mishra, R. K., & Dwivedi, Y. K. (2021). Sentiment analysis and classification of Indian farmers’ protest using twitter data. International Journal of Information Management Data Insights, 1(2), 100019. https://doi.org/10.1016/j.jjimei.2021.100019
Newell, A., Shaw, J. C., & Simon, H. A. (1959). Report on a general problem solving program. International Conference on Information Processing, 256–264
Newell, A., & Simon, H. (1956). The logic theory machine—A complex information processing system. IRE Transactions on Information Theory, 2, 61–79
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., Jennings, N. R., Kamar, E., Kloumann, I. M., Larochelle, H., Lazer, D., McElreath, R., Mislove, A., Parkes, D. C., Pentland, A. ‘Sandy,’ … Wellman, M. (2019). Machine behaviour. Nature, 568(7753), 477–486. https://doi.org/10.1038/s41586-019-1138-y
Raisch, S., & Krakowski, S. (2020). Artificial intelligence and management: the automation-augmentation paradox. Academy of Management Review. https://doi.org/10.5465/2018.0072
Rajendran, D. P. D., & Sundarraj, R. P. (2021). Using topic models with browsing history in hybrid collaborative filtering recommender system: Experiments with user ratings. International Journal of Information Management Data Insights, 1(2), 100027. https://doi.org/10.1016/j.jjimei.2021.100027
Rathore, A. K., Kar, A. K., & Ilavarasan, P. V. (2017). Social media analytics: literature review and directions for future research. Decision Analysis, 14(4), 229–249. https://doi.org/10.1287/deca.2017.0355
Reynolds, M., & Vince, R. (2004). Critical management education and action-based learning: synergies and contradictions. Academy of Management Learning & Education, 3(4), 442–456. https://doi.org/10.5465/amle.2004.15112552
Riley, T. (2018). Get ready, this year your next job interview may be with an A.I. robot. CNBC. https://www.cnbc.com/2018/03/13/ai-job-recruiting-tools-offered-by-hirevue-mya-other-start-ups.html
Schmitt, B. (1999). Experiential marketing. Journal of Marketing Management, 15(1–3), 53–67. https://doi.org/10.1362/026725799784870496
Schuetz, S., & Venkatesh, V. (2020). The rise of human machines: how cognitive computing systems challenge assumptions of user-system interaction. Journal of the Association for Information Systems, 21(2), 460–482
Seufert, S., Guggemos, J., & Sailer, M. (2020). Technology-related knowledge, skills, and attitudes of pre- and in-service teachers: The current situation and emerging trends. Computers in Human Behavior, 106552. https://doi.org/10.1016/j.chb.2020.106552
Sharma, S. K., Sharma, H., & Dwivedi, Y. K. (2019). A hybrid SEM-neural network model for predicting determinants of mobile payment services. Information Systems Management, 36(3), 243–261. https://doi.org/10.1080/10580530.2019.1620504
Sharma, S. K., & Sharma, M. (2019). Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation. International Journal of Information Management, 44, 65–75. https://doi.org/10.1016/j.ijinfomgt.2018.09.013
Sharma, S., Rana, V., & Kumar, V. (2021). Deep learning based semantic personalized recommendation system. International Journal of Information Management Data Insights, 1(2), 100028. https://doi.org/10.1016/j.jjimei.2021.100028
Sheridan, C. (2004). A taste of the future. Nature Biotechnology, 22(10), 1203–1205. https://doi.org/10.1038/nbt1004-1203
Simon, H. A. (1987). Two heads are better than one: the collaboration between AI and OR. INFORMS Journal on Applied Analytics, 17(4), 8–15. https://doi.org/10.1287/inte.17.4.8
Simon, H. A. (1991). Bounded rationality and organizational learning. Organization Science, 2(1), 125–134. https://doi.org/10.1287/orsc.2.1.125
Sitkin, S. B. (1992). Learning through failure: the strategy of small losses. Research in Organizational Behavior, 14, 231–266
Stephan, M., Brown, D., & Erickson, R. (2017). Talent acquisition through predictive hiring | Deloitte Insights. https://www2.deloitte.com/us/en/insights/focus/human-capital-trends/2017/predictive-hiring-talent-acquisition.html
Taylor, S. E. (1991). Asymmetrical effects of positive and negative events: The mobilization-minimization hypothesis. Psychological Bulletin, 110(1), 67-85
Thumin, F. J., & Thumin, L. J. (2011). The measurement and interpretation of organizational climate. The Journal of Psychology, 145(2), 93–109. https://doi.org/10.1080/00223980.2010.538754
Trudel, R. (2019). Sustainable consumer behavior. Consumer Psychology Review, 2(1), 85–96. https://doi.org/10.1002/arcp.1045
Van de Ven, A. H. (1986). Central problems in the management of innovation. Management Science, 32(5), 590–607 (JSTOR)
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., & Speier, C. (1999). Computer technology training in the workplace: a longitudinal investigation of the effect of mood. Organizational Behavior and Human Decision Processes, 79(1), 1–28. https://doi.org/10.1006/obhd.1999.2837
Vimalkumar, M., Sharma, S. K., Singh, J. B., & Dwivedi, Y. K. (2021). ‘Okay google, what about my privacy?’: User’s privacy perceptions and acceptance of voice based digital assistants. Computers in Human Behavior, 120, 106763. https://doi.org/10.1016/j.chb.2021.106763
von Krogh, G. (2018). Artificial intelligence in organizations: new opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4(4), 404–409. https://doi.org/10.5465/amd.2018.0084
Wang, Y., Meister, D. B., & Gray, P. H. (2013). Social influence and knowledge management systems use: evidence from panel data. MIS Quarterly, 37(1), 299–313
West, M. A., & Farr, J. L. (1989). Innovation at work: Psychological perspectives. Social Behaviour, 4(1), 15–30
Woodman, R. W., Sawyer, J. E., & Griffin, R. W. (1993). Toward a theory of organizational creativity. The Academy of Management Review, 18(2), 293–321. https://doi.org/10.2307/258761 JSTOR
Yuan, F., & Woodman, R. W. (2010). Innovative behavior in the workplace: the role of performance and image outcome expectations. The Academy of Management Journal, 53(2), 323–342 (JSTOR)
Zhao, Y., Yang, S., Narayan, V., & Zhao, Y. (2013). Modeling consumer learning from online product reviews. Marketing Science, 32(1), 153–169. https://doi.org/10.1287/mksc.1120.0755
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of Interest
Authors have no conflict of interests to declare.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kar, A.K., Kushwaha, A.K. Facilitators and Barriers of Artificial Intelligence Adoption in Business – Insights from Opinions Using Big Data Analytics. Inf Syst Front 25, 1351–1374 (2023). https://doi.org/10.1007/s10796-021-10219-4
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10796-021-10219-4