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AI Adoption Determinants and Its Impacts on HRM Effectiveness within MES in Tanzania

DOI: 10.4236/ojbm.2024.124131, PP. 2532-2552

Keywords: AI Adoption, Human Resource Management, TOE Model, HRM Effectiveness, Medium Enterprises, Tanzania

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Abstract:

This study intends to explore the determinants of AI adoption and its impact on HRM effectiveness in Tanzanian medium enterprises (MEs). With a focus on providing insights for HR professionals and decision-makers, data from 185 respondents comprising HR professionals, IT professionals, and CEOs who have already adopted AI was analyzed using PLS-SEM, where factors of Relative advantage, Complexity, Compatibility, Security/Privacy, Top management, Organisation readiness, Competitive pressure, External support and Government support were tested to the adoption of AI. Results highlight relative advantage, compatibility, and competitive pressure as key drivers of AI adoption in Tanzania’s context, subsequently enhancing HR systems’ effectiveness. The study bridges the existing gaps and offers recommendations for AI integration into HRM practices. Implications for managers and solution providers were discussed to facilitate a better understanding of the determinants influencing the adoption process within Tanzanian MEs. The study underlies the theoretical understanding of AI adoption by utilizing the TOE model and incorporating technological, organizational, and environmental factors. This study recommends future exploration of additional factors and including a larger sample to enhance the universality of the results.

References

[1]  Agarwal, A. (2022). AI Adoption by Human Resource Management: A Study of Its Antecedents and Impact on HR System Effectiveness. Foresight, 25, 67-81.
https://doi.org/10.1108/fs-10-2021-0199
[2]  Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Prentice-Hall.
https://search.worldcat.org/title/Understanding-attitudes-and-predicting-social-behavior/oclc/5726878
[3]  Alam, M. G. R., Masum, A. K. M., Beh, L., & Hong, C. S. (2016). Critical Factors Influencing Decision to Adopt Human Resource Information System (HRIS) in Hospitals. PLOS ONE, 11, e0160366.
https://doi.org/10.1371/journal.pone.0160366
[4]  Almaiah, M. A., Alfaisal, R., Salloum, S. A., Hajjej, F., Shishakly, R., Lutfi, A. et al. (2022). Measuring Institutions’ Adoption of Artificial Intelligence Applications in Online Learning Environments: Integrating the Innovation Diffusion Theory with Technology Adoption Rate. Electronics, 11, Article 3291.
https://doi.org/10.3390/electronics11203291
[5]  Anderson, J., & Johnson, S. (2017). The Impact of Artificial Intelligence on HRM Effectiveness. Journal of Applied Management, 10, 45-62.
[6]  Basnet, S. (2024). Artificial Intelligence and Machine Learning in Human Resource Management: Prospect and Future Trends. International Journal of Research Publication and Reviews, 5, 281-287.
https://doi.org/10.55248/gengpi.5.0124.0107
[7]  Bhatiasevi, V., & Naglis, M. (2018). Elucidating the Determinants of Business Intelligence Adoption and Organizational Performance. Information Development, 36, 78-96.
https://doi.org/10.1177/0266666918811394
[8]  Boonsiritomachai, W., McGrath, G. M., & Burgess, S. (2016). Exploring Business Intelligence and Its Depth of Maturity in Thai Smes. Cogent Business & Management, 3, Article ID: 1220663.
https://doi.org/10.1080/23311975.2016.1220663
[9]  Campbell, S., Greenwood, M., Prior, S., Shearer, T., Walkem, K., Young, S. et al. (2020). Purposive Sampling: Complex or Simple? Research Case Examples. Journal of Research in Nursing, 25, 652-661.
https://doi.org/10.1177/1744987120927206
[10]  Chong, J. L., & Olesen, K. (2017). A Technology-Organization-Environment Perspective on Eco-Effectiveness: A Meta-Analysis. Australasian Journal of Information Systems, 21.
https://doi.org/10.3127/ajis.v21i0.1441
[11]  Chong, L. Y. Q., & Lim, T. S. (2022). Pull and Push Factors of Data Analytics Adoption and Its Mediating Role on Operational Performance. Sustainability, 14, Article 7316.
https://doi.org/10.3390/su14127316
[12]  Cooper, D. R., & Schindler, P. S. (2014). Business Research Methods. McGraw-Hill.
https://thuvienso.hoasen.edu.vn/v/web/viewer.html?file=/bitstream/handle/123456789/10310/Contents.pdf?sequence=5&isAllowed=y
[13]  Croucher, R., Stumbitz, B., Quinlan, M., & Vickers, I. (2013). Can Better Working Conditions Improve the Performance of SMEs? An International Literature Review. International Labour Office.
[14]  Dhamija, P., & Bag, S. (2020). Role of Artificial Intelligence in Operations Environment: A Review and Bibliometric Analysis. The TQM Journal, 32, 869-896.
https://doi.org/10.1108/tqm-10-2019-0243
[15]  Dincbas, T., Ergeneli, A., & Yigitbasioglu, H. (2021). Clean Technology Adoption in the Context of Climate Change: Application in the Mineral Products Industry. Technology in Society, 64, Article ID: 101478.
https://doi.org/10.1016/j.techsoc.2020.101478
[16]  Fenwick, A., Molnar, G., & Frangos, P. (2024). Revisiting the Role of HR in the Age of AI: Bringing Humans and Machines Closer Together in the Workplace. Frontiers in Artificial Intelligence, 6, Article 1272823.
https://doi.org/10.3389/frai.2023.1272823
[17]  Fowler Jr., F. J. (2009). Survey Research Methods. John Wiley & sons.
[18]  Gangwar, H. (2018). Understanding the Determinants of Big Data Adoption in India a: An Analysis of the Manufacturing and Services Sectors. Information Resources Management Journal, 31, 1-22.
https://doi.org/10.4018/irmj.2018100101
[19]  Goswami, M., Jain, S., Alam, T., Deifalla, A. F., Ragab, A. E., & Khargotra, R. (2023). Exploring the Antecedents of AI Adoption for Effective HRM Practices in the Indian Pharmaceutical Sector. Frontiers in Pharmacology, 14, Article 1215706.
https://doi.org/10.3389/fphar.2023.1215706
[20]  Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial Least Squares Structural Equation Modeling (PLS-SEM): An Emerging Tool in Business Research. European Business Review, 26, 106-121.
https://doi.org/10.1108/ebr-10-2013-0128
[21]  Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19, 139-152.
https://doi.org/10.2753/mtp1069-6679190202
[22]  Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance. Long Range Planning, 46, 1-12.
https://doi.org/10.1016/j.lrp.2013.01.001
[23]  Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to Use and How to Report the Results of PLS-SEM. European Business Review, 31, 2-24.
https://doi.org/10.1108/ebr-11-2018-0203
[24]  Heinze, C. (2023). 5 Advantages and Disadvantages of Using AI in HR.
https://www.techtarget.com/searchhrsoftware/feature/Advantages-and-disadvantages-of-using-AI-in-HR
[25]  Holl, F., Kircher, J., Hertelendy, A. J., Sukums, F., & Swoboda, W. (2024). Tanzania’s and Germany’s Digital Health Strategies and Their Consistency with the World Health Organization’s Global Strategy on Digital Health 2020-2025: Comparative Policy Analysis. Journal of Medical Internet Research, 26, e52150.
https://doi.org/10.2196/52150
[26]  Horani, O. M., Khatibi, A., AL-Soud, A. R., Tham, J., & Al-Adwan, A. S. (2023). Determining the Factors Influencing Business Analytics Adoption at Organizational Level: A Systematic Literature Review. Big Data and Cognitive Computing, 7, Article 125.
https://doi.org/10.3390/bdcc7030125
[27]  Hossin, S., Arije Ulfy, M., Ali, I., Karim, W., & Karim, M. W. (2021). Challenges in Adopting Artificial Intelligence (AI) in HRM Practices: A study on Bangladesh Perspective. International Fellowship Journal of Interdisciplinary Research, 1, 66-73.
https://doi.org/10.5281/zenodo.4480245
[28]  International Finance Corporation (2022). Banking on SMEs: Driving Growth, Creating Jobs Global SME Finance Facility Progress Report.
[29]  Jeje, K. (2022). Innovation and Performance of Small and Medium-Sized Bakeries in Tanzania. Acta Universitatis Danubius, 18, 126-158.
[30]  Jöhnk, J., Weißert, M., & Wyrtki, K. (2020). Ready or Not, AI Comes—An Interview Study of Organizational AI Readiness Factors. Business & Information Systems Engineering, 63, 5-20.
https://doi.org/10.1007/s12599-020-00676-7
[31]  Kaur, M., Rekha, A. G., & Vikas, S. (2021). Adoption of Artificial Intelligence in Human Re-source Management: A Conceptual Model. Indian Journal of Industrial Relations, 57, 333-338.
[32]  Kshetri, N. (2020). Artificial Intelligence in Developing Countries. IT Professional, 22, 63-68.
https://doi.org/10.1109/mitp.2019.2951851
[33]  Kumar, A., Singh, R. K., & Swain, S. (2022). Adoption of Technology Applications in Organized Retail Outlets in India: A TOE Model. Global Business Review.
https://doi.org/10.1177/09721509211072382
[34]  Kurup, S., & Gupta, V. (2022). Factors Influencing the AI Adoption in Organizations. Metamorphosis: A Journal of Management Research, 21, 129-139.
https://doi.org/10.1177/09726225221124035
[35]  Lutfi, A., Alsyouf, A., Almaiah, M. A., Alrawad, M., Abdo, A. A. K., Al-Khasawneh, A. L. et al. (2022). Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMES. Sustainability, 14, Article 1802.
https://doi.org/10.3390/su14031802
[36]  Malik, S., Chadhar, M., Vatanasakdakul, S., & Chetty, M. (2021). Factors Affecting the Organizational Adoption of Blockchain Technology: Extending the Technology-Organization-Environment (TOE) Framework in the Australian Context. Sustainability, 13, Article 9404.
https://doi.org/10.3390/su13169404
[37]  Maroufkhani, P., Iranmanesh, M., & Ghobakhloo, M. (2022). Determinants of Big Data Analytics Adoption in Small and Medium-Sized Enterprises (SMEs). Industrial Management & Data Systems, 123, 278-301.
https://doi.org/10.1108/imds-11-2021-0695
[38]  Meister, J. (2023). 5 Benefits of Artificial Intelligence in Human Resources.
https://www.retorio.com/blog/5-benefits-artificial-intelligence-in-human-resources
[39]  Ministry of Industry and Trade (2003). Small and Medium Enterprise Development Policy. Ministry of Industry and Trade Dar es Salaam.
[40]  Mujahed, H. M. H., Musa Ahmed, E., & Samikon, S. A. (2021). Factors Influencing Palestinian Small and Medium Enterprises Intention to Adopt Mobile Banking. Journal of Science and Technology Policy Management, 13, 561-584.
https://doi.org/10.1108/jstpm-05-2020-0090
[41]  Na, S., Heo, S., Han, S., Shin, Y., & Roh, Y. (2022). Acceptance Model of Artificial Intelligence (AI)-Based Technologies in Construction Firms: Applying the Technology Acceptance Model (TAM) in Combination with the Technology-Organisation-Environment (TOE) Framework. Buildings, 12, Article 90.
https://doi.org/10.3390/buildings12020090
[42]  Neumann, O., Guirguis, K., & Steiner, R. (2022). Exploring Artificial Intelligence Adoption in Public Organizations: A Comparative Case Study. Public Management Review, 26, 114-141.
https://doi.org/10.1080/14719037.2022.2048685
[43]  Nguyen, T. H., Le, X. C., & Vu, T. H. L. (2022). An Extended Technology-Organization-Environment (TOE) Framework for Online Retailing Utilization in Digital Transformation: Empirical Evidence from Vietnam. Journal of Open Innovation: Technology, Market, and Complexity, 8, 200.
https://doi.org/10.3390/joitmc8040200
[44]  Park, J., & Kim, Y. B. (2019). Factors Activating Big Data Adoption by Korean Firms. Journal of Computer Information Systems, 61, 285-293.
https://doi.org/10.1080/08874417.2019.1631133
[45]  Pillai, R., & Sivathanu, B. (2020). Adoption of Artificial Intelligence (AI) for Talent Acquisition in IT/ITeS Organizations. Benchmarking: An International Journal, 27, 2599-2629.
https://doi.org/10.1108/bij-04-2020-0186
[46]  Qahtani, E., & Alsmairat, M. (2023). Assisting Artificial Intelligence Adoption Drivers in Human Resources Management: A Mediation Model. Acta logistica, 10, 141-150.
https://doi.org/10.22306/al.v10i1.371
[47]  Rodgers, W., Murray, J. M., Stefanidis, A., Degbey, W. Y., & Tarba, S. Y. (2023). An Artificial Intelligence Algorithmic Approach to Ethical Decision-Making in Human Resource Management Processes. Human Resource Management Review, 33, Article ID: 100925.
https://doi.org/10.1016/j.hrmr.2022.100925
[48]  Rogers (1985). Rogers-DOI-CH5. Diffusion of Innovation.
[49]  Satell, G., & Sutton, J. (2019). We Need AI That Is Explainable, Auditable, and Transparent. Harvard Business Review.
[50]  Sekaran, U., & Bougie, R. (2016). Research Methods for Business: A Skill Building Approach (7th Edition).
https://www.wiley.com/en-us/Research+Methods+For+Business%3A+A+Skill+Building+Approach%2C+7th+Edition-p-9781119266846
[51]  Sharma, S., Singh, G., Islam, N., & Dhir, A. (2024). Why Do SMEs Adopt Artificial Intelligence-Based Chatbots? IEEE Transactions on Engineering Management, 71, 1773-1786.
https://doi.org/10.1109/tem.2022.3203469
[52]  Shet, S. V., Poddar, T., Wamba Samuel, F., & Dwivedi, Y. K. (2021). Examining the Determinants of Successful Adoption of Data Analytics in Human Resource Management—A Framework for Implications. Journal of Business Research, 131, 311-326.
https://doi.org/10.1016/j.jbusres.2021.03.054
[53]  Singh, A., & Pandey, J. (2024). Artificial Intelligence Adoption in Extended HR Ecosystems: Enablers and Barriers. An Abductive Case Research. Frontiers in Psychology, 14, Article 1339782.
https://doi.org/10.3389/fpsyg.2023.1339782
[54]  Strusani, D., & Houngbonon, G. V. (2019). The Role of Artificial Intelligence in Supporting Development in Emerging Markets.
https://www.ifc.org/thoughtleadership
[55]  Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2013). Using Multivariate Statistics (7th ed). Pearson.
[56]  Tornatzky, L. G., & Fleischer, M. (1990). The Processes of Technological Innovation. Lexington Books.
[57]  Tuffaha, M., & Rosario Perello-Marin, M. (2022). Adoption Factors of Artificial Intelligence in Human Resources Management. Future of Business Administration, 1, 1-12.
https://doi.org/10.33422/fba.v1i1.140
[58]  Visier (2013). How to Use AI in HR.
https://www.visier.com/ai/ai-in-hr/
[59]  wael AL-khatib, A. (2023). Drivers of Generative Artificial Intelligence to Fostering Exploitative and Exploratory Innovation: A TOE Framework. Technology in Society, 75, Article ID: 102403.
https://doi.org/10.1016/j.techsoc.2023.102403
[60]  Wamba-Taguimdje, S., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of Artificial Intelligence (AI) on Firm Performance: The Business Value of AI-Based Transformation Projects. Business Process Management Journal, 26, 1893-1924.
https://doi.org/10.1108/bpmj-10-2019-0411
[61]  World Economic Forum (2020). The Future of Jobs Report 2020.
https://www.weforum.org/publications/the-future-of-jobs-report-2020/
[62]  Xu, S., Kee, K. F., Li, W., Yamamoto, M., & Riggs, R. E. (2023). Examining the Diffusion of Innovations from a Dynamic, Differential-Effects Perspective: A Longitudinal Study on AI Adoption among Employees. Communication Research.
https://doi.org/10.1177/00936502231191832
[63]  Yabanci, O. (2019). From Human Resource Management to Intelligent Human Resource Management: A Conceptual Perspective. Human-Intelligent Systems Integration, 1, 101-109.
https://doi.org/10.1007/s42454-020-00007-x
[64]  Yoon Kin Tong, D., & Sivanand, C. N. (2005). E-Recruitment Service Providers Review. Employee Relations, 27, 103-117.
https://doi.org/10.1108/01425450510569337

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