Published date: Feb 01 2023

Journal Title: KnE Social Sciences

Issue title: Economies of the Balkan and Eastern European Countries (EBEEC)

Pages: 435–448

DOI: 10.18502/kss.v8i1.12663

Bilal Hmoudpostgraduate PhD, H-4032 Debrecen, Böszörményi út 138

Laszlo Varallyaivarallyai.laszlo@econ.unideb.huassociate professor, H-4032 Debrecen, Böszörményi út 138

The primary goal of this article is to contribute to the field of technology adoption research by providing researchers, organizations, HR leaders, service providers, and decision-makers with advanced understanding and valid inputs on the development of AI-based HR solutions and the determinants of adoption. The overall objective of this research is to determine the general attitude of HR managers toward the adoption of AI in HRM and to assess the factors that determine the adoption of AI from the perspective of HR managers. The proposed adoption factors were grouped into four constructs, innovation characteristics, trust, technology-organizational-environment (TOE) factors, and emphasized HR roles within the organization. The research was conducted among HR managers in Middle Eastern countries, specifically Jordan, Kuwait, Saudi Arabia, and Qatar. An online questionnaire was used to collect data from a total of 389 respondents. The results showed that respondents were largely positive toward AI applications in HRM. This positive attitude can be inferred from the mean values of two variables, relative advantage and attitude toward the application of AI in HRM. The research results showed that HR managers have a positive attitude and confidence that emerging AI applications can contribute to supporting the efficiency, effectiveness, and quality of HRM. In addition, the results showed a constructive perception of the relative benefits of AI. Researchers, policymakers, and service providers are also recommended to investigate the phenomenon from two perspectives, first, the impact of attitudes on actual adoption decisions and second, the factors that influence this impact.

Keywords: artificial intelligence, HRM, technology adoption, HR leaders, technologicalorganizational- environmental

[1] Al-Dmour RH, Love S, Al-Debei MM. Factors influencing the organisational adoption of human resource information systems: A conceptual model. International Journal of Business Innovation and Research. 2016;11(2):161–207.

[2] Ball KS. The use of human resource information systems: A survey. Personnel Review. 2001;30(6):677–693.

[3] Chan PYP, Mills AM. Motivators and inhibitors of e-commerce technology adoption: Online stock trading by small brokerage firms in New Zealand. Journal of Information Technology Case and Application Research. 2002;4(3):38–56.

[4] Florkowski GW, Olivas-Luján MR. The diffusion of human-resource informationtechnology innovations in US and non-US firms. Personnel Review. 2006;35(6):684– 710.

[5] Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate data analysis. 7th ed. Pearson Education Limited; 2014.

[6] Hausdorf PA, Duncan D. Firm size and internet recruiting in Canada: A preliminary investigation. Journal of Small Business Management. 2004;42(3):325–334.

[7] Kim G, Shin B, Lee HG. Understanding dynamics between initial trust and usage intentions of mobile banking. Information Systems Journal. 2009;19(3):283–311.

[8] Kimberly JR. Organizational innovation: The influence of individual, organizational, and contextual factors on hospital adoption of technological and administrative innovations. Academy of Management Journal. 1981;24(4):689–713.

[9] Kovach KA, Cathcart CE. Human resource information systems (HRIS): Providing business with rapid data access, information exchange and strategic advantage. Public Personnel Management. 1999;28(2):275–281.

[10] Kovach KA, Hughes AA, Fagan P, Maggitti PG. Administrative and strategic advantages of HRIS. Employment Relations Today. 2002;29(2):43–48.

[11] Lippert SK, Davis M. A conceptual model integrating trust into planned change activities to enhance technology adoption behaviour. Journal of Information Science. 2006;32(5):434–448.

[12] Low C, Chen Y, Wu M. Understanding the determinants of cloud computing adoption. Industrial Management and Data Systems. 2011;111(7):1006–1023.

[13] Oliveira T, Martins MF. Understanding e-business adoption across industries in European countries. Industrial Management and Data Systems. 2010;110(9):1337– 1354.

[14] Oliveira T, Thomas M, Espadanal M. Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information and Management. 2014;51(5):497–510.

[15] Martins R, Oliveira T, Thomas MA. An empirical analysis to assess the determinants of SaaS diffusion in firms. Computers in Human Behavior. 2016;62:19–33.

[16] Ngai EWT, Wat FKT. Human resource information systems: A review and empirical analysis. Personnel Review. 2004;35(3):297–314.

[17] Palos-Sanchez PR, Arenas-Marquez FJ, Aguayo-Camacho M. Cloud computing (SaaS) adoption as a strategic technology: Results of an empirical study. Mobile Information Systems. 2017:2536040.

[18] Parasuraman R, Sheridan TB, Wickens CD. Situation awareness, mental workload, and trust in automation: viable, empirically supported cognitive engineering constructs. Journal of Cognitive Engineering and Decision Making. 2008;2(2):140– 160.

[19] Perrault R, Shoham Y, Brynjolfsson E, Clark J, Etchemendy J, Grosz B, et al. Artificial Intelligence Index 2019 annual report. Stanford, CA: AI Index Steering Committee, Human-Centered AI Institute, Stanford University; 2019.

[20] Premkumar G, Ramamurthy K. The role of interorganizational and organizational factors on the decision model for adoption of interorganizational systems. Decision Sciences. 1995;26(3):303–336.

[21] Robinson MF. AI in hiring: Understanding attitudes and perspectives of HR practitioners. ProQuest Dissertations Publishing. 2019.

[22] Rogers EM. Diffusion of innovations. 5th ed. The Free Press; 2003. Available from: https://hai.stanford.edu/sites/g/files/sbiybj10986/f/ai_index_2019_report.pdf

[23] Sharma R, Yetton P. The contingent effects of management support and task interdependence on successful information systems implementation. MIS Quarter. 2003;27(4):533–555.

[24] Strohmeier S. Research in e-HRM: Review and implications. Human Resource Management Review. 2007;17(1):19–37.

[25] Strohmeier S, Kabst R. Organizational adoption of e-HRM in Europe: An empirical exploration of major adoption factors. Journal of Managerial Psychology. 2009;24(6):482–501.

[26] Thong JYL, Yap CS, Raman KS. Top management support, external expertise and information systems implementation in small businesses. Information Systems Research. 1996;7(2):248–267.

[27] To ML, Ngai EWT. Predicting the organisational adoption of B2C e-commerce: An empirical study. Industrial Management and Data Systems. 2006;106(8):1133–1147.

[28] Teo T, Lim GS, Fedric SA. The adoption and diffusion of human resources information systems in Singapore. Asia Pacific Journal of Human Resources. 2007;45(1):44–62.

[29] Teo TSH, Lin S, Lai KH. Adopters and non-adopters of e-procurement in Singapore: An empirical study. Omega. 2009;37(5):972–987.

[30] Tornatzky L, Fleischer M, Chakrabarti K. The process of technology innovation. Lexington Books; 1990.

[31] Ulrich D. HR of the future. Human Resource Management. 1997;36(1):175–179.

[32] Voermans M, Van Veldhoven M. Attitude towards E-HRM: An empirical study at Philips. Personnel Review. 2007;36(6):887–902.

[33] Wang YS, Li HT, Li CR, Zhang DZ. Factors affecting hotels’ adoption of mobile reservation systems: A technology-organization-environment framework. Tourism Management. 2016;53:163–172.

[34] Zhu K, Kraemer KL, Xu S. The process of innovation assimilation by firms in different countries: A technology diffusion perspective on e-business. Management Science. 2006;52(10):1557–1576.