Factors Influencing the Intention of Leaders to Use HRIS Software in Small and Medium-Sized Enterprises
DOI:
https://doi.org/10.47654/v28y2024i3p127-153Keywords:
Human Resource Information System (HRIS), Intention to Use, Leaders, Small and Medium-sized Enterprises (SMEs), Technology Acceptance ModelAbstract
Purpose: This study aims to identify the factors that influence the intention of small and medium-sized enterprise (SME) leaders in Hanoi to use human resource information system (HRIS) software.
Design/methodology/approach: The quantitative research method with a sample size of 416 SME leaders, combined with the Theory of Planned Behavior (TPB), Theory of Reasoned Action (TRA), Task-Technology Fit Acceptance Model (TTF-TAM), and Task-Technology Fit Unified Theory of Acceptance and Use of Technology (TTF-UTAUT) models.
Findings: The research results show five leading factors that affect SME leaders' intention to use HRIS: perceived usefulness, perceived ease of use, Social influence, facilitating conditions, and Task-technology fit. HRIS software companies can use the research findings to develop marketing and sales strategies focusing on the factors that drive SME leaders' intention to use HRIS.
Originality/value: SME leaders can use the research findings to better understand the factors that influence their intention to use HRIS software and make informed decisions about adopting HRIS. Effective HRIS adoption can help SMEs improve their HR management efficiency and increase productivity and profitability. This study narrows the gap in understanding the factors influencing SME leaders' adoption of HRIS, providing valuable insights for software companies to address management skepticism and promote HRIS usage in SMEs. The integrated approach of combining different theoretical models and focusing on the specific context of SMEs in Hanoi is a novel and unique feature of this study.
References
Aggarwal, N., & Kapoor, M. (2012). Human resource information systems (HRIS)-Its role and importance in business competitiveness. Gian Jyoti E-Journal, 1(2), 1-13.
Agudo-Peregrina, Á. F., Hernández-García, Á., & Acquila-Natale, E. (2016). The effect of income level on e-commerce adoption: A multigroup analysis. In Encyclopedia of E-commerce development, implementation, and management (pp. 2239-2255). IGI Global Scientific Publishing.
Ajzen, I. (1985). From Intentions to Actions: A Theory of Planned Behavior. Springer Berlin Heidelberg.
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
Aljarboa, S., & Miah, S. J. (2022). An integration of UTAUT and task-technology fit frameworks for assessing the acceptance of clinical decision support systems in the context of a developing country. In Proceedings of Sixth International Congress on Information and Communication Technology: ICICT 2021, London, Volume 2 (pp. 127-137). Springer Singapore.
Al-Shibly, H. (2011). Human resources information systems success assessment: An integrative model. Australian Journal of Basic and Applied Sciences, 5(5), 157-169.
Amoako, R., Jiang, Y., Frempong, M. F., Tetteh, S., & Adu-Yeboah, S. S. (2022). Examining the effect of organizational leadership, organizational structure, and employee technological capability on the success of electronic human resource management. SAGE Open, 12(2), 21582440221088852.
Anupa, M. (2021). Role of human resources information system (hris) in accelerating organizational effectiveness–it companies perspective. International Journal of Management and Humanities (IJMH), 5(6), 22-25.
Beadles II, N. A., Lowery, C. M., & Johns, K. (2005). The impact of human resource information systems: An exploratory study in the public sector. Communications of the IIMA, 5(4), 6.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Dery, K., Grant, D., Wiblen, S., & Studies, O. (2009, August). Human resource information systems (HRIS): Replacing or enhancing HRM. In Proceedings of the 15th World Congress of the International Industrial Relations Association IIRA (pp. 24-27).
DeSanctis, G. (1986). Human resource information systems: A current assessment. MIS quarterly, 15-27.
Dohan, M. S., & Tan, J. (2013). Perceived Usefulness and Behavioral Intention to Use Consumer-Oriented Web-Based Health Tools: A Meta-Analysis. In AMCIS.
Elkaseh, A. M., Wong, K. W., & Fung, C. C. (2016). Perceived ease of use and perceived usefulness of social media for e-learning in Libyan higher education: A structural equation modeling analysis. International Journal of Information and Education Technology, 6(3), 192.
Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research.
Fitrianie, S., Horsch, C., Beun, R. J., Griffioen-Both, F., & Brinkman, W.-P. (2021). Factors affecting user’s behavioral intention and use of a mobile-phone-delivered cognitive behavioral therapy for insomnia: A small-scale UTAUT analysis. Journal of Medical Systems, 45, 1-18.
Fjerrnestad, J., & Hiltz, S. R. (1997, January). Experimental studies of group decision support systems: an assessment of variables studied and methodology. In Proceedings of the Thirtieth Hawaii International Conference on System Sciences (Vol. 2, pp. 45-65). IEEE.
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS quarterly, 213-236.
Haines, V. Y., & Petit, A. (1997). Conditions for successful human resource information systems. Human Resource Management: Published in Cooperation with the School of Business Administration, The University of Michigan and in alliance with the Society of Human Resources Management, 36(2), 261-275.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A Primer on SEM (2nd ed.), Sage
Harsono, L. D., & Suryana, L. A. (2014). Factors affecting the use behavior of social media using UTAUT 2 model. In proceedings of the first Asia-Pacific Conference on global business, economics, finance and social sciences, vol 4.
Hendrickson, A. R. (2003). Human resource information systems: Backbone technology of contemporary human resources. Journal of Labor Research, 24(3), 381.
Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International journal of medical informatics, 101, 75-84.
Jasin, M. (2022). The effect of perceived ease of use on behavior intention through perceived enjoyment as an intervening variable on digital payment in the digital era. Journal of Industrial Engineering & Management Research, 3(5), 127-133.
Kang, H.-J., Han, J., & Kwon, G. H. (2022). The acceptance behavior of smart home health care services in South Korea: an integrated model of UTAUT and TTF. International Journal of Environmental Research and Public Health, 19(20), 13279.
Kavanagh, M. J., & Johnson, R. D. (2017). Human resource information systems: Basics, applications, and future directions. Sage Publications.
Khalilzadeh, J., Ozturk, A. B., & Bilgihan, A. (2017). Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers in human behavior, 70, 460-474.
Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling (4th ed
Klopping, I. M., & McKinney, E. (2004). Extending the technology acceptance model and the task-technology fit model to consumer e-commerce. Information Technology, Learning & Performance Journal, 22(1).
Lengnick-Hall, M. L., & Moritz, S. (2003). The impact of e-HR on the human resource management function. Journal of Labor Research, 24(3), 365.
Maruping, L. M., & Agarwal, R. (2004). Managing team interpersonal processes through technology: A task-technology fit perspective. Journal of applied psychology, 89(6), 975.
Mei-Ying, W., Pei-Yuan, Y., & Weng, Y.-C. (2012). A study on user behavior for i pass by UTAUT: Using taiwan's MRT as an example. Asia Pacific Management Review, 17(1).
Ngai, E., & Wat, F. K. T. (2006). Human resource information systems: a review and empirical analysis. Personnel review, 35(3), 297-314.
Nguyen, N., Nguyen, T., Chu, T., & Nguyen, D. (2019). Factors affecting the application of management accounting in small and medium enterprises in Hanoi, Vietnam. Management Science Letters, 9(12), 2039-2050.
Nguyen, V., Hoang, T., Mai, T., Lam, T., & Pham, H. (2023). The factors affecting digital transformation in small and medium enterprises in Hanoi city. Uncertain Supply Chain Management, 11(4), 1705-1718.
Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in health sciences education, 15(5), 625-632. https://doi.org/10.1007/s10459-010-9222-y
Ololade, A. J., Paul, S. O., Morenike, A. T., & Esitse, D. A. (2023). Bolstering the role of human resource information system on employees’ behavioural outcomes of selected manufacturing firms in Nigeria. Heliyon, 9(1).
Putriani, S., Putriana, S., Fuad, K., Widawati, M. W., & Aji, N. P. (2023). The Role of Digital Technology Self-Efficacy and Digital Technostress on Intention to Use FinTech: A Study on MSMEs in Surakarta City. Riset Akuntansi dan Keuangan Indonesia, 8(3), 287-300.
Rai, R. S., & Selnes, F. (2019). Conceptualizing task-technology fit and the effect on adoption–A case study of a digital textbook service. Information & Management, 56(8), 103161.
Rhemtulla, M., Brosseau-Liard, P. É., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological methods, 17(3), 354. https://doi.org/10.1037/a0029315
Satispi, E., Rajiani, I., Murod, M., & Andriansyah, A. (2023). Human Resources Information System (HRIS) to Enhance Civil Servants’ Innovation Outcomes: Compulsory or Complimentary? Administrative Sciences, 13(2), 32.
Strong, D. M., Dishaw, M. T., & Bandy, D. B. (2006). Extending task technology fit with computer self-efficacy. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 37(2-3), 96-107.
Tamrakar, B., & Shrestha, A. (2022). Factors influencing Use of Human Resource Information System in Nepali Organizations. Journal of Business and Management Research, 4(01), 1-16.
Tannenbaum, S. I. (1990). Human resource information systems: User group implications. Journal of Systems management, 41(1), 27.
Tansley, C., & Watson, T. (2000). Strategic exchange in the development of human resource information systems (HRIS). New Technology, Work and Employment, 15(2), 108-122.
Thomas, T., Singh, L., & Gaffar, K. (2013). The utility of the UTAUT model in explaining mobile learning adoption in higher education in Guyana. International Journal of Education and Development using ICT, 9(3).
Tursunbayeva, A., Bunduchi, R., & Pagliari, C. (2020). “Planned Benefits” Can Be Misleading in Digital Transformation Projects: Insights From a Case Study of Human Resource Information Systems Implementation in Healthcare. SAGE Open, 10(2), 2158244020933881.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
Zigurs, I., & Buckland, B. K. (1998). A theory of task/technology fit and group support systems effectiveness. MIS quarterly, 313-334.
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