Adoption of Artificial Intelligence and Robotics in Organisations: A Systematic Literature Review

Al-Momani Ala’a

Al-Momani Ala’a

Abstract

Organisations have increasingly used artificial intelligence (AI) to innovate and compete successfully. Therefore, knowing the factors that affect the adoption of AI is crucial. This study aims to investigate the theories and models utilised in studying the adoption of AI among organisations and identify the factors influencing AI adoption among organisations across countries. Hence, a systematic literature review (SLR) and the PRISMA framework were used. This SLR contains eleven earlier studies that uncovered a growth in research focusing on AI adoption among organisations. Furthermore, this SLR discovered that the TAM, TOE, and UTAUT models are the most common ones. Three factors influencing organisations’ adoption of AI across countries are perceived usefulness, perceived ease of use, and intention. In developed countries, 30 factors have been discovered as having a significant influence on AI adoption while in developing countries, 27 factors have been identified. There is a lack of prior literature using SLR to analyse the technological adoption frameworks and models for AI adoption among organisations. Studies on identifying the crucial factors influencing AI adoption among organisations are limited. Therefore, the findings of this study contribute to the current body of knowledge on AI adoption among organisations. The results of this SLR can also help managers make the right decisions and build effective strategies for adopting AI among organisations.

References

Ajzen, I. (1991). The Theory of Planned Behavior Organizational Behavior and Human Decision Processes. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Ala’a, A. M., & Ramayah, T. (2023a). Predicting the Behavioural Intention of Jordanian Healthcare Professionals to Use Blockchain-Based EHR Systems : An Empirical Study. Journal of System and Management Sciences, 13(4), 107–139. https://doi.org/10.33168/JSMS.2023.0407.
Ala’a, A. M., & Ramayah, T. (2023b). A Review of the Technology Acceptance Model in Electronic Health Records. International Journal of Business and Technology Management, 5(2), 8–19. https://doi.org/10.55057/ijbtm.2023.5.2.2.
Al-Sharafi, M. A., Al-Emran, M., Iranmanesh, M., Al-Qaysi, N., Iahad, N. A., & Arpaci, I. (2022). Understanding the impact of knowledge management factors on the sustainable use of AI-based chatbots for educational purposes using a hybrid SEM-ANN approach. Interactive Learning Environments, 0(0), 1–20. https://doi.org/10.1080/10494820.2022.2075014.
Alshehhi, K., Cheaitou, A., & Rashid, H. (2022). Adoption Frameworks for Artificial Intelligence in the Public Sector: A Systematic Review of Literature. Proceedings of the 3rd South American International Industrial Engineering and Operations Management, 919–929.
Bai, X. J., & Li, J. J. (2020). The best configuration of collaborative knowledge innovation management from the perspective of artificial intelligence. Knowledge Management Research and Practice, 00(00), 1–13. https://doi.org/10.1080/14778238.2020.1834886.
Basit, A., Zafar, M., Liu, X., Javed, A. R., Jalil, Z., & Kifayat, K. (2021). A comprehensive survey of AI-enabled phishing attacks detection techniques. Telecommunication Systems, 76(1), 139–154. https://doi.org/10.1007/s11235-020-00733-2.
Blanco-González-Tejero, C., Ribeiro-Navarrete, B., Cano-Marin, E., & McDowell, W. C. (2023). A systematic literature review on the role of artificial intelligence in entrepreneurial activity. International Journal on Semantic Web and Information Systems (IJSWIS), 19(1), 1–16.
Borges, A. F. S., Laurindo, F. J. B., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57, 102225. https://doi.org/10.1016/j.ijinfomgt.2020.102225.
Boustani, N. M. (2022). Artificial intelligence impact on banks clients and employees in an Asian developing country. Journal of Asia Business Studies, 16(2), 267–278. https://doi.org/10.1108/JABS-09-2020-0376.
Briner, R. B., & Denyer, D. (2012). Systematic review and evidence synthesis as a practice and scholarship tool.
Chatterjee, S., Chaudhuri, R., Vrontis, D., Thrassou, A., & Ghosh, S. K. (2021). Adoption of artificial intelligence-integrated CRM systems in agile organizations in India. Technological Forecasting and Social Change, 168(March), 120783. https://doi.org/10.1016/j.techfore.2021.120783.
Chatterjee, S., Nguyen, B., Ghosh, S. K., Bhattacharjee, K. K., & Chaudhuri, S. (2020). Adoption of artificial intelligence integrated CRM system: an empirical study of Indian organizations. The Bottom Line, 33(4), 359–375. https://doi.org/10.1108/BL-08-2020-0057.
Cheng, B., Lin, H., & Kong, Y. (2023). Challenge or hindrance? How and when organizational artificial intelligence adoption influences employee job crafting. Journal of Business Research, 164(April 2022), 113987. https://doi.org/10.1016/j.jbusres.2023.113987.
Chintalapati, S., & Pandey, S. K. (2022). Artificial intelligence in marketing: A systematic literature review. International Journal of Market Research, 64(1), 38–68. https://doi.org/10.1177/14707853211018428.
Darlington, N., & Patience, A. M. (2023). Artificial Intelligence Marketing Practices : The Way Forward to Better Customer Experience Management in Africa ( Systematic Literature Review). International Academy Journal of Management, Marketing and Entrepreneurial Studies, 9(2), 44–62.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982.
Denyer, D., & Tranfield, D. (2009). Producing a systematic review.
Dinh, T. N., & Thai, M. T. (2018). AI and Blockchain: A Disruptive Integration. Computer, 51(9), 48–53. https://doi.org/10.1109/MC.2018.3620971.
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48(January), 63–71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021.
Elareshi, M., Habes, M., Youssef, E., Salloum, S. A., Alfaisal, R., & Ziani, A. (2022). SEM-ANN-based approach to understanding students’ academic-performance adoption of YouTube for learning during Covid. Heliyon, 8(4), e09236. https://doi.org/10.1016/j.heliyon.2022.e09236.
Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2008). Comparison of PubMed, Scopus, Web of Science, and Google Scholar: strengths and weaknesses. The FASEB Journal, 22(2), 338–342. https://doi.org/10.1096/fj.07-9492LSF.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Contemporary Sociology, 6(2), 244–245.
Ghani, E. K., Ariffin, N., & Sukmadilaga, C. (2022). Factors Influencing Artificial Intelligence Adoption in Publicly Listed Manufacturing Companies: A Technology, Organisation, and Environment Approach. International Journal of Applied Economics, Finance and Accounting, 14(2), 108–117. https://doi.org/10.33094/ijaefa.v14i2.667.
Giuggioli, G., & Pellegrini, M. M. (2023). Artificial intelligence as an enabler for entrepreneurs: a systematic literature review and an agenda for future research. International Journal of Entrepreneurial Behaviour and Research, 29(4), 816–837. https://doi.org/10.1108/IJEBR-05-2021-0426.
Harzing, A. W., & Alakangas, S. (2016). Google Scholar, Scopus and the Web of Science: a longitudinal and cross-disciplinary comparison. Scientometrics, 106(2), 787–804. https://doi.org/10.1007/s11192-015-1798-9.
Hmoud, B. (2021). The adoption of artificial intelligence in human resource management. Forum Scientiae Oeconomia, 9(1), 5–18. https://doi.org/10.23762/fso_Vol9_no1_7.
Islam, M., Mamun, A. Al, Afrin, S., Ali Quaosar, G. M. A., & Uddin, M. A. (2022). Technology Adoption and Human Resource Management Practices: The Use of Artificial Intelligence for Recruitment in Bangladesh. South Asian Journal of Human Resources Management, 9(2), 324–349. https://doi.org/10.1177/23220937221122329.
Jatobá, M. N., Ferreira, J. J., Fernandes, P. O., & Teixeira, J. P. (2023). Intelligent human resources for the adoption of artificial intelligence: a systematic literature review. Journal of Organizational Change Management. https://doi.org/10.1108/JOCM-03-2022-0075.
Kaushal, N., Kaurav, R. P. S., Sivathanu, B., & Kaushik, N. (2023). Artificial intelligence and HRM: identifying future research Agenda using systematic literature review and bibliometric analysis. Management Review Quarterly, 73(2), 455–493.
Lee, M. C. M., Scheepers, H., Lui, A. K. H., & Ngai, E. W. T. (2023). The implementation of artificial intelligence in organizations: A systematic literature review. Information and Management, 60(5), 103816. https://doi.org/10.1016/j.im.2023.103816.
Li, J. J., Bonn, M. A., & Ye, B. H. (2019). Hotel employee’s artificial intelligence and robotics awareness and its impact on turnover intention: The moderating roles of perceived organizational support and competitive psychological climate. Tourism Management, 73(April 2018), 172–181. https://doi.org/10.1016/j.tourman.2019.02.006.
Malik, A., Budhwar, P., Patel, C., & Srikanth, N. R. (2022). May the bots be with you! Delivering HR cost-effectiveness and individualised employee experiences in an MNE. International Journal of Human Resource Management, 33(6), 1148–1178. https://doi.org/10.1080/09585192.2020.1859582.
Mariani, M. M., Machado, I., Magrelli, V., & Dwivedi, Y. K. (2023). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 122(May 2022), 102623. https://doi.org/10.1016/j.technovation.2022.102623.
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097.
Ooi, K. B., Hew, J. J., & Lin, B. (2018). Unfolding the privacy paradox among mobile social commerce users: a multi-mediation approach. Behaviour and Information Technology, 37(6), 575–595. https://doi.org/10.1080/0144929X.2018.1465997.
Pan, Y., Froese, F., Liu, N., Hu, Y., & Ye, M. (2022). The adoption of artificial intelligence in employee recruitment: The influence of contextual factors. The International Journal of Human Resource Management, 33(6), 1125–1147. https://doi.org/10.1080/09585192.2021.1879206.
Parhi, S., Joshi, K., Wuest, T., & Akarte, M. (2022). Factors affecting Industry 4.0 adoption – A hybrid SEM-ANN approach. Computers and Industrial Engineering, 168(March 2021). https://doi.org/10.1016/j.cie.2022.108062.
Pereira, V., Hadjielias, E., Christofi, M., & Vrontis, D. (2023). A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective. Human Resource Management Review, 33(1), 100857. https://doi.org/10.1016/j.hrmr.2021.100857.
Phuoc, N. Van. (2022). The Critical Factors Impacting Artificial Intelligence Applications Adoption in Vietnam: A Structural Equation Modeling Analysis. Economies, 10(6), 129. https://doi.org/10.3390/economies10060129.
Pietronudo, M. C., Croidieu, G., & Schiavone, F. (2022). A solution looking for problems? A systematic literature review of the rationalizing influence of artificial intelligence on decision-making in innovation management. Technological Forecasting and Social Change, 182(April 2021). https://doi.org/10.1016/j.techfore.2022.121828.
Pittaway, L., Robertson, M., Munir, K., Denyer, D., & Neely, A. (2004). Networking and innovation: a systematic review of the evidence. International Journal of Management Reviews, 5–6(3–4), 137–168. https://doi.org/10.1111/j.1460-8545.2004.00101.x.
Razak, N. A., & Ismail, K. (2022). Factors Influencing The Adoption Of Robotic Process Automation Among Accounting Personnel In Malaysia. Management and Accounting Review, 21(3), 181–207. https://doi.org/10.24191/MAR.V21i03-08.
Rehman, I. H., Ahmad, A., Akhter, F., & Aljarallah, A. (2022). A Dual-Stage SEM-ANN Analysis to Explore Consumer Adoption of Smart Wearable Healthcare Devices. Journal of Global Information Management, 29(6), 1–30. https://doi.org/10.4018/JGIM.294123.
Reis, J., Espírito Santo, P., & Melão, N. (2021). Influence of artificial intelligence on public employment and its impact on politics: A systematic literature review1. Brazilian Journal of Operations and Production Management, 18(3), 1–22. https://doi.org/10.14488/BJOPM.2021.010.
Scott, J. E., & Walczak, S. (2009). Cognitive engagement with a multimedia ERP training tool: Assessing computer self-efficacy and technology acceptance. Information and Management, 46(4), 221–232. https://doi.org/10.1016/j.im.2008.10.003.
Sethibe, T., & Naidoo, E. (2022). The adoption of robotics in the auditing profession. South African Journal of Information Management, 24(1), 1–7. https://doi.org/10.4102/sajim.v24i1.1441.
Straub, D., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information and Management, 33(1), 1–11. https://doi.org/10.1016/S0378-7206(97)00026-8.
Tian, M., Deng, P., Zhang, Y., & Salmador, M. P. (2018). How does culture influence innovation? A systematic literature review. Management Decision, 56(5), 1088–1107. https://doi.org/10.1108/MD-05-2017-0462.
Tuffaha, M., & Perello-Marin, M. R. (2021). Artificial intelligence definition, applications and adoption in Human Resource Management: a systematic literature review. International Journal of Business Innovation and Research, 1(1), 1. https://doi.org/10.1504/IJBIR.2021.10040005.
Upadhyay, N., Upadhyay, S., & Dwivedi, Y. K. (2022). Theorizing artificial intelligence acceptance and digital entrepreneurship model. International Journal of Entrepreneurial Behaviour and Research, 28(5), 1138–1166. https://doi.org/10.1108/IJEBR-01-2021-0052.
Vărzaru, A. A. (2022). Assessing Artificial Intelligence Technology Acceptance in Managerial Accounting. Electronics (Switzerland), 11(14). https://doi.org/10.3390/electronics11142256.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3), 425–478. https://doi.org/10.2307/30036540.
Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002. https://doi.org/10.1016/j.jjimei.2020.100002.
Votto, A. M., Valecha, R., Najafirad, P., & Rao, H. R. (2021). Artificial Intelligence in Tactical Human Resource Management: A Systematic Literature Review. International Journal of Information Management Data Insights, 1(2), 100047. https://doi.org/10.1016/j.jjimei.2021.100047.
Wan, S. M., Cham, L. N., Tan, G. W.-H., Lo, P.-S., Ooi, K.-B., & Chatterjee, R.-S. (2022). What’s Stopping You from Migrating to Mobile Tourism Shopping? Journal of Computer Information Systems, 62(6), 1223–1238. https://doi.org/10.1080/08874417.2021.2004564.
Wanner, J., Herm, L.-V., Heinrich, K., & Janiesch, C. (2022). The effect of transparency and trust on intelligent system acceptance: Evidence from a user-based study. Electronic Markets, 32(4), 2079–2102. https://doi.org/10.1007/s12525-022-00593-5.