Skip to main content

Advertisement

Log in

Facilitators and Barriers of Artificial Intelligence Adoption in Business – Insights from Opinions Using Big Data Analytics

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Barabási, A. L. (2013). Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371, 20120375

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Dittrich, K., Guérard, S., & Seidl, D. (2016). Talking about routines: The role of reflective talk in routine change. Organization Science, 27(3), 678–697

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Floridi, L. (2008). Information ethics: A reappraisal. Ethics and Information Technology, 10, 189–204

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Hansen, M. T., Nohria, N., & Tierney, T. (1999). What’s your strategy for managing knowledge? Harvard Business Review, 77(2), 106–116

    Google Scholar 

  • Helfat, C. E., & Peteraf, M. A. (2015). Managerial cognitive capabilities and the microfoundations of dynamic capabilities. Strategic Management Journal, 36(6), 831–850

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Kolb, D. A. (2015). Experiential learning: experience as the source of learning and development. Pearson Education

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Morikawa, M. (2017). Firms’ expectations about the impact of ai and robotics: Evidence from a survey. Economic Enquiry, 55(2), 1054–1063

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Sheridan, C. (2004). A taste of the future. Nature Biotechnology, 22(10), 1203–1205. https://doi.org/10.1038/nbt1004-1203

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Simon, H. A. (1991). Bounded rationality and organizational learning. Organization Science, 2(1), 125–134. https://doi.org/10.1287/orsc.2.1.125

    Article  Google Scholar 

  • Sitkin, S. B. (1992). Learning through failure: the strategy of small losses. Research in Organizational Behavior, 14, 231–266

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Trudel, R. (2019). Sustainable consumer behavior. Consumer Psychology Review, 2(1), 85–96. https://doi.org/10.1002/arcp.1045

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • West, M. A., & Farr, J. L. (1989). Innovation at work: Psychological perspectives. Social Behaviour, 4(1), 15–30

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Arpan Kumar Kar.

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-021-10219-4

Keywords

Navigation