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2020 | OriginalPaper | Chapter

Micro-foundations of Artificial Intelligence Adoption in Business: Making the Shift

Authors : Amit Kumar Kushwaha, Arpan Kumar Kar

Published in: Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation

Publisher: Springer International Publishing

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Abstract

Artificial Intelligence has gradually materialized as an independent research field within information systems and business domains. The new forms of work evolving in the business require substantial experimentation, lead generations, and real-time recommendations. This has driven the extraordinary increase in the adoption of Artificial Intelligence technologies. Even with front runner organizations across the domain envisioning the advantages of early adoption of Artificial Intelligence technologies, some organizations scuffle the adoption owing to various barriers. This paper analyzes the characteristics that lead to and factors inhibiting the adoption of Artificial Intelligence at the organization-level. Through this paper, we report the results of Twitter conversations involving small and medium scale organizations about their level of adoption of Artificial Intelligence and barriers that they are facing. Through this analysis, we provide insights and agenda to help the executives of small and medium scale organizations to prepare for the adoption of Artificial Intelligence.

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Agrawal, A., Gans, J., Goldfarb, A.: The simple economics of machine intelligence. Harv. Bus. Rev., 17 November 2016

Lerner, S.: The evolution of artificial intelligence. Enterp. Digitalization, 11 February 2019

Gartner’s Top 10 Strategic Technology Trends for 2017. www.gartner.com/smarterwithgartner/gartners-top-10-technology-trends-2017

06-technology-background.pdf. https://www.calpers.ca.gov/docs/board-agendas/201801/full/day1/06-technology-background.pdf

PricewaterhouseCoopers: PwC’s Global Artificial Intelligence Study: Sizing the prize. PwC. https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html

Applying Artificial Intelligence to Drive Business Transformation: A Gartner Trend Insight Report

Alsheibani, S., Cheung, Y., Messom, C.: Artificial intelligence adoption: AI-readiness at firm-level, p. 9 (2018)

Perc, M., Ozer, M., Hojnik, J.: Social and juristic challenges of artificial intelligence. Palgrave Commun. 5(1), 61 (2019). https://doi.org/10.1057/s41599-0190278-xCrossRef

Becker, L., Jaakkola, E.: Customer experience: fundamental premises and implications for research. J. Acad. Mark. Sci. 48(4), 630–648 (2020). https://doi.org/10.1007/s11747-019-00718-xCrossRef

10.

Kar, A.K.: Bio inspired computing – a review of algorithms and scope of applications. Exp. Syst. Appl. 59, 20–32 (2016). https://doi.org/10.1016/j.eswa.2016.04.018CrossRef

11.

McCarthy, J., Hayes, P.J.: Some philosophical problems from the standpoint of artificial intelligence. In: Webber, B.L., Nilsson, N.J. (eds.) Readings in Artificial Intelligence, pp. 431–450. Morgan Kaufmann (1981)

12.

Lighthill, M.J.: Large-amplitude elongated-body theory of fish locomotion. Proc. R. Soc. Lond. B Biol. Sci. 179(1055), 125–138 (1971)CrossRef

13.

Miner, A.S., Laranjo, L., Kocaballi, A.B.: Chatbots in the fight against the COVID-19 pandemic. Digit. Med. 3(1), 65 (2020). https://doi.org/10.1038/s41746-0200280-0CrossRef

14.

Imerman, M.B., Fabozzi, F.J.: Cashing in on innovation: a taxonomy of FinTech. J. Asset. Manag. 21(3), 167–177 (2020). https://doi.org/10.1057/s41260-020-00163-4CrossRef

15.

Marín-Morales, J., et al.: Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors. Sci. Rep. 8(1), 13657 (2018). https://doi.org/10.1038/s41598-018-32063-4CrossRef

16.

Kushwaha, A.K., Kar, A.K., Vigneswara Ilavarasan, P.: Predicting information diffusion on Twitter a deep learning neural network model using custom weighted word features. In: Hattingh, M., et al. (eds.) I3E 2020. LNCS, vol. 12066, pp. 456–468. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44999-5_38CrossRef

17.

Topol, E.J.: High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25(1), 44–56 (2019). https://doi.org/10.1038/s41591-018-0300-7CrossRef

18.

Rogers, E.M.: Diffusion of innovations: modifications of a model for telecommunications. In: Stoetzer, M.W., Mahler, A. (eds.) Die Diffusion von Innovationen in der Telekommunikation. Schriftenreihe des Wissenschaftlichen Instituts für Kommunikationsdienste, vol. 17, pp. 25–38. Springer, Heidelberg (1995). https://doi.org/10.1007/978-3-642-79868-9_2CrossRef

19.

Pierce, J.L., Delbecq, A.L.: Organization structure, individual attitudes and innovation. AMR 2(1), 27–37 (1977). https://doi.org/10.5465/amr.1977.4409154CrossRef

20.

Cebon, P., Love, E.G.: Why do late adopters perform poorly: symbolic adoption, or cultural incongruity? Acad. Manage. Proc. 2002(1), I1–I6 (2002). https://doi.org/10.5465/apbpp.2002.7518359CrossRef

21.

Doherty, E., Carcary, M., Conway, G.: Migrating to the cloud: examining the drivers and barriers to adoption of cloud computing by SMEs in Ireland: an exploratory study. J. Small Bus. Enterp. Dev. 22(3), 512–527 (2015). https://doi.org/10.1108/jsbed-05-2013-0069CrossRef

22.

Cennamo, C., Marchesi, C., Meyer, T.: Two sides of the same coin? Decentralized versus proprietary blockchains and the performance of digital currencies. AMD (2020). https://doi.org/10.5465/amd.2019.0044

23.

Sadhya, V., Sadhya, H.: Barriers to adoption of Blockchain technology. In: AMCIS 2018 Proceedings, August 2018

24.

Pankomera, R., van Greunen, D.: Opportunities, barriers, and adoption factors of mobile commerce for the informal sector in developing countries in Africa: a systematic review. Electron. J. Inf. Syst. Dev. Ctries. 85(5), e12096 (2019). https://doi.org/10.1002/isd2.12096CrossRef

25.

Arif, I., Aslam, W., Hwang, Y.: Barriers in adoption of internet banking: A structural equation modeling - neural network approach. Technol. Soc. 61, 101231 (2020). https://doi.org/10.1016/j.techsoc.2020.101231CrossRef

26.

Cox, B., Ghoneim, S.: Drivers and barriers to adopting EDI: a sector analysis of UK industry. Eur. J. Inf. Syst. 5(1), 24–33 (1996). https://doi.org/10.1057/ejis.1996.9CrossRef

27.

Mergel, I.: Open innovation in the public sector: drivers and barriers for the adoption of Challenge.gov. Pub. Manage. Rev. 20(5), 726–745 (2018). https://doi.org/10.1080/14719037.2017.1320044CrossRef

28.

Ransbotham, S., Kiron, D., Gerbert, P., Reeves, M.: Reshaping Business With Artificial Intelligence: Closing the Gap Between Ambition and Action. MIT Sloan Management Review; Cambridge, vol. 59, no. 1 (2017)

29.

Kruse, C.S., Kristof, C., Jones, B., Mitchell, E., Martinez, A.: Barriers to electronic health record adoption: a systematic literature review. J. Med. Syst. 40(12), 1–7 (2016). https://doi.org/10.1007/s10916-016-0628-9CrossRef

30.

Aswani, R., Ghrera, S.P., Kar, A.K., Chandra, S.: Identifying buzz in social media: a hybrid approach using artificial bee colony and k-nearest neighbors for outlier detection. Soc. Netw. Anal. Min. 7(1), 1–10 (2017). https://doi.org/10.1007/s13278-017-0461-2CrossRef

31.

Grover, P., Kar, A.K., Vigneswara Ilavarasan, P.: Blockchain for businesses: a systematic literature review. In: Al-Sharhan, Salah A., et al. (eds.) I3E 2018. LNCS, vol. 11195, pp. 325–336. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02131-3_29CrossRef

32.

Kumar, V., Umashankar, N., Kim, K.H., Bhagwat, Y.: Assessing the influence of economic and customer experience factors on service purchase behaviors. Mark. Sci. 33(5), 673–692 (2014)CrossRef

33.

Pine II, B.J., Gilmore, J.H.: Welcome to the experience economy. Harv. Bus. Rev. (1998). (From the July–August 1998 Issue)

34.

Eisenhardt, K.M.: Making fast strategic decisions in high-velocity environments. AMJ 32(3), 543–576 (1989). https://doi.org/10.5465/256434CrossRef

35.

Grover, P., Kar, A.K., Dwivedi, Y.K., Janssen, M.: Polarization and acculturation in US Election 2016 outcomes – can twitter analytics predict changes in voting preferences. Technol. Forecast. Soc. Chang. 145, 438–460 (2019)CrossRef

36.

Kassarjian, H.H.: Content analysis in consumer research. J. Consum. Res. 4(1), 8–18 (1977). https://doi.org/10.1086/208674CrossRef

37.

Nooralahzadeh, F., Arunachalam, V., Chiru, C.-G.: 2012 presidential elections on Twitter – an analysis of how the US and French election were reflected in tweets. https://doi.org/10.1109/cscs.2013.72

38.

Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATH

39.

Kar, A.K.: 5 Present-Day Applications of Artificial Intelligence – Tech Talk. https://techtalk.org/2018/02/16/5-present-day-applications-of-artificial-intelligence

Title: Micro-foundations of Artificial Intelligence Adoption in Business: Making the Shift
Authors: Amit Kumar Kushwaha
Arpan Kumar Kar
Publisher: Springer International Publishing
Book: Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation
Print ISBN: 978-3-030-64848-0

Electronic ISBN: 978-3-030-64849-7

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-64849-7_22

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