Purpose: Technological evaluation and e-HRM gained momentum during the COVID-19 pandemic. In order to fully understand their influence on users’ intention to continuous using e-HRM systems, this study integrated IS success model and technology continuity theory (TCT), examining how attitudes and satisfaction moderate the impact on the continuous usage of e-HRM post-COVID-19. Method: The study is based on the integrated IS success model and the TCT model, a survey data’s set of 260 samples. The study deployed structural equation model (SEM) and an importance-performance map analysis (IPMA) to find the determinants of continuous usage intention of e-HRM. Results: The study found that information quality (IQ) was positively associated with perceived ease of use (PEU), satisfaction and continuous use of e-HRM, whereas system quality (SQ) was insignificantly associated with PEU but did not support attitude or continued use of e-HRM. According to IPMA, management needs to pay extra attention to perceived ease of use (PEU), satisfaction (SA), and information quality (IQ) to keep employee using e-HRM. This research has proposed and validated an integrated model by incorporating IS success model and TCT theory. Conclusions: E-HRM is the use of information technology to support human resource management (HRM) activities, resulting in improved efficiency, effectiveness and employee satisfaction. Therefore, this research has added new value for determining professional E-HRM continuous intention to use in developing countries like Bangladesh.
KeywordsInformation QualitySystem QualitySatisfactionAttitude and Continuous Intention to Use
Al Amin, M., Arefin, M. S., Sultana, N., Islam, M. R., Jahan, I., & Akhtar, A. (2020). Evaluating the Customers’ Dining Attitudes, E-Satisfaction and Continuance Intention toward Mobile Food Ordering Apps (MFOAs): Evidence from Bangladesh. European Journal of Management and Business Economics, 30, 211-229. https://doi.org/10.1108/ejmbe-04-2020-0066
Ali, B. M., & Younes, B. (2013). The Impact of Information Systems on User Performance: An Exploratory Study. Journal of Knowledge Management, Economics and Information Technology , 3 , 128-154.
Al-Rahmi, W. M., Uddin, M., Alkhalaf, S., Al-Dhlan, K. A., Cifuentes-Faura, J., Al-Rahmi, A. M. et al. (2022). Validation of an Integrated IS Success Model in the Study of E-government. Mobile Information Systems, 2022, 1-16. https://doi.org/10.1155/2022/8909724
Alshibly, H. H. (2014a). A Free Simulation Experiment to Examine the Effects of Social Commerce Website Quality and Customer Psychological Empowerment on Customers’ Satisfaction. Journal of Business Studies Quarterly, 5, 21-40.
Alshibly, H. H. (2014b). Evaluating E-HRM Success: A Validation of the Information Systems Success Model. International Journal of Human Resource Studies , 4, Article 107.
Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25, 351-370. https://doi.org/10.2307/3250921
Cohen, J., & Cohen, P. (1983). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates, Inc.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13, 319-340. https://doi.org/10.2307/249008
Djuitaningsih, T., & Arifiyantoro, D. (2020). Individual and Organizational Impacts: Information and System Quality Influence on Attitude towards Use and User Satisfaction of Agency-Level Financial Application System. Acta Informatica Malaysia, 4, 10-18. https://doi.org/10.26480/aim.01.2020.10.18
Duy Phuong, N. N., & Dai Trang, T. T. (2018). Repurchase Intention: The Effect of Service Quality, System Quality, Information Quality, and Customer Satisfaction as Mediating Role: a PLS Approach of M-Commerce Ride Hailing Service in Vietnam. Marketing and Branding Research, 5, 78-91.
Fahmi, F., Fajeriadi, H., & Irhasyuarna, Y. (2021). Feasibility of the Prototype of Teaching Materials on the Topic of Classification of Living Things Based on the Advantage of Local Wetland. BIO-INOVED: Jurnal Biologi-Inovasi Pendidikan, 3, 113-118. https://doi.org/10.20527/bino.v3i2.10322
Fathima, A. Y., & Muthumani, S. (2019). Client Cluster Identification of Internet Bank Services. Journal of Computational and Theoretical Nanoscience , 16, 3554-3559.
Garver, M. S., & Mentzer, J. T. (1999). Logistics Research Methods: Employing Structural Equation Modeling to Test for Construct Validity. Journal of Business Logistics , 20, 33-57.
Giri, A., Paul, P., Chatterjee, S., Bag, M., & Aich, A. (2019). Intention to Adopt e-HRM (Electronic-Human Resource Management) in Indian Manufacturing Industry: An Empirical Study Using Technology Acceptance Model (TAM). International Journal of Management , 10, 205-215.
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
Hair, Jr., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate Data Analysis (6th ed.). Pearson-Prentice Hall.
Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W. et al. (2014). Common Beliefs and Reality about PLS. Organizational Research Methods, 17, 182-209. https://doi.org/10.1177/1094428114526928
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS Path Modeling in New Technology Research: Updated Guidelines. Industrial Management & Data Systems, 116, 2-20. https://doi.org/10.1108/imds-09-2015-0382
Henseler, J., Ringle, C. M., & Sarstedt, M. (2012). Using Partial Least Squares Path Modeling in Advertising Research: Basic Concepts and Recent Issues. In Handbook of Research on International Advertising . Edward Elgar Publishing. https://doi.org/10.4337/9781781001042.00023
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The Use of Partial Least Squares Path Modeling in International Marketing. In R. R. Sinkovics, & P. N. Ghauri (Eds.), New Challenges to International Marketing (pp. 277-319). Emerald Group Publishing Limited.
Hoelter, J. W. (1983). The Analysis of Covariance Structures. Sociological Methods & Research, 11, 325-344. https://doi.org/10.1177/0049124183011003003
Huang, T., & Liao, S. (2015). A Model of Acceptance of Augmented-Reality Interactive Technology: The Moderating Role of Cognitive Innovativeness. Electronic Commerce Research, 15, 269-295. https://doi.org/10.1007/s10660-014-9163-2
Iranmanesh, M., Zailani, S., & Nikbin, D. (2017). RFID Continuance Usage Intention in Health Care Industry. Quality Management in Health Care, 26, 116-123. https://doi.org/10.1097/qmh.0000000000000134
Islam, M. A., Hassan, M. R., & Hossen, M. M. (2020). Macroeconomic Determinants of FDI: A Case Study of Bangladesh. Journal of SUB, 10, 1-23.
Islami, M. M., Asdar, M., & Baumassepe, A. N. (2021). Analysis of Perceived Usefulness and Perceived Ease of Use to the Actual System Usage through Attitude Using Online Guidance Application. Hasanuddin Journal of Business Strategy, 3, 52-64. https://doi.org/10.26487/hjbs.v3i1.410
Jaafreh, A. B. (2017). Evaluation Information System Success: Applied DeLone and McLean Information System Success Model in Context Banking System in KSA. International Review of Management and Business Research , 6, 829-845.
Khan, S. A., Liang, Y., & Shahzad, S. (2015). An Empirical Study of Perceived Factors Affecting Customer Satisfaction to Re-Purchase Intention in Online Stores in China. Journal of Service Science and Management, 8, 291-305. https://doi.org/10.4236/jssm.2015.83032
Khayer, A., & Bao, Y. (2019). The Continuance Usage Intention of Alipay. The Bottom Line, 32, 211-229. https://doi.org/10.1108/bl-07-2019-0097
Khayer, A., Talukder, M. S., Bao, Y., & Hossain, M. N. (2020). Cloud Computing Adoption and Its Impact on SMEs’ Performance for Cloud Supported Operations: A Dual-Stage Analytical Approach. Technology in Society, 60, Article 101225. https://doi.org/10.1016/j.techsoc.2019.101225
Kumar, A., & Lata, S. (2021). The System Quality and Customer Satisfaction with Website Quality as Mediator in Online Purchasing: A Developing Country Perspectives. Journal of Operations and Strategic Planning, 4, 7-26. https://doi.org/10.1177/2516600x21991945
Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2014). Antecedents of the Adoption of the New Mobile Payment Systems: The Moderating Effect of Age. Computers in Human Behavior, 35, 464-478. https://doi.org/10.1016/j.chb.2014.03.022
Liu, Y., Song, Y., Sun, J., Sun, C., Liu, C., & Chen, X. (2020). Understanding the Relationship between Food Experiential Quality and Customer Dining Satisfaction: A Perspective on Negative Bias. International Journal of Hospitality Management, 87, Article 102381. https://doi.org/10.1016/j.ijhm.2019.102381
Lutfi, A. (2023). Factors Affecting the Success of Accounting Information System from the Lens of Delone and Mclean IS Model. International Journal of Information Management Data Insights, 3, Article 100202. https://doi.org/10.1016/j.jjimei.2023.100202
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
McQuitty, S. (2004). Statistical Power and Structural Equation Models in Business Research. Journal of Business Research, 57, 175-183. https://doi.org/10.1016/s0148-2963(01)00301-0
Mirkovski, K., Rouibah, K., Lowry, P., Paliszkiewicz, J., & Ganc, M. (2023). Cross-Country Determinants of Citizens’ E-Government Reuse Intention: Empirical Evidence from Kuwait and Poland. Information Technology & People, 37 , 1864-1896. https://doi.org/10.1108/ITP-08-2022-0651
Mohamad, L., Osman, Z., Mohamad, R. K., Ismail, Z., & Mohd Din, M. I. (2023). The Perceived Attitude of Bank Customers towards the Intention to Use Digital Banking in Malaysia. International Journal of Academic Research in Business and Social Sciences, 13, 1308-1323. https://doi.org/10.6007/ijarbss/v13-i1/15570
Mollel, H. L., & Rutenge, M. M. (2024). Adoption and Use of Electronic Human Resources Management Systems for Service Delivery in Tanzania: A Case to Tanzania Airports Authority. African Journal of Empirical Research, 5, 617-626. https://doi.org/10.51867/ajernet.5.4.50
Nascimento, B., Oliveira, T., & Tam, C. (2018). Wearable Technology: What Explains Continuance Intention in Smartwatches? Journal of Retailing and Consumer Services, 43, 157-169. https://doi.org/10.1016/j.jretconser.2018.03.017
Noerman, T., Erlando, A., & Riyanto, F. D. (2021). Factors Determining Intention to Continue Using E-HRM. The Journal of Asian Finance, Economics and Bu siness, 8, 1079-1089.
Obeidat, S. M. (2016). The Link between E-HRM Use and HRM Effectiveness: An Empirical Study. Personnel Review, 45, 1281-1301. https://doi.org/10.1108/pr-04-2015-0111
Pavlou, P. A. (2003). Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model. International Journal of Electronic Commerce, 7, 101-134.
Podsakoff, P. M., MacKenzie, S. B., Lee, J., & Podsakoff, N. P. (2003). Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. Journal of Applied Psychology, 88, 879-903. https://doi.org/10.1037/0021-9010.88.5.879
Rahi, S., Ghani, M. A., & Ngah, A. H. (2020). Factors Propelling the Adoption of Internet Banking: The Role of E-Customer Service, Website Design, Brand Image and Customer Satisfaction. International Journal of Business Information Systems, 33, 549-569. https://doi.org/10.1504/ijbis.2020.105870
Rahi, S., Khan, M. M., & Alghizzawi, M. (2021a). Extension of Technology Continuance Theory (TCT) with Task Technology Fit (TTF) in the Context of Internet Banking User Continuance Intention. International Journal of Quality & Reliability Management, 38, 986-1004. https://doi.org/10.1108/ijqrm-03-2020-0074
Rahi, S., Khan, M. M., & Alghizzawi, M. (2021b). Factors Influencing the Adoption of Telemedicine Health Services during COVID-19 Pandemic Crisis: An Integrative Research Model. Enterprise Information Systems, 15, 769-793. https://doi.org/10.1080/17517575.2020.1850872
Rahman, M., Mordi, C., & Nwagbara, U. (2018). Factors Influencing E-HRM Implementation in Government Organisations. Journal of Enterprise Information Management, 31, 247-275. https://doi.org/10.1108/jeim-05-2017-0066
Rawashdeh, A. M., Bakheet Elayan, M., Alhyasat, W., & Dawood Shamout, M. (2021). Electronic Human Resources Management Perceived Usefulness, Perceived Ease of Use and Continuance Usage Intention: The Mediating Role of User Satisfaction in Jordanian Hotels Sector. International Journal for Quality Research, 15, 679-696. https://doi.org/10.24874/ijqr15.02-20
Roul, J., Mohapatra, L. M., Pradhan, A. K., & Kamesh, A. V. S. (2024). Analysing the Role of Modern Information Technologies in HRM: Management Perspective and Future Agenda. Kybernetes . https://doi.org/10.1108/k-11-2023-2512
Sareen, P. (2015). Study of Employee Satisfaction towards E-HRM System. European Journal of Applied Business and Management , 1, 1-18.
Schaupp, L. C., Weiguo Fan,, & Belanger, F. (2006). Determining Success for Different Website Goals. In Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS ’ 06) (pp. 107b). IEEE. https://doi.org/10.1109/hicss.2006.122
Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review. The Journal of Educational Research, 99, 323-338. https://doi.org/10.3200/joer.99.6.323-338
Singh, P., & Koneru, K. (2022). A Mediated Moderation Analysis of E-HRM Practices and Long-Term Competitive Advantage from the Perspective of HR Practitioners. Journal of Positive School Psychology, 6, 4436-4458.
Sivo, S. A., Fan, X., Witta, E. L., & Willse, J. T. (2006). The Search for “Optimal” Cutoff Properties: Fit Index Criteria in Structural Equation Modeling. The Journal of Experimental Education, 74, 267-288. https://doi.org/10.3200/jexe.74.3.267-288
Slade, E. L., Dwivedi, Y. K., Piercy, N. C., & Williams, M. D. (2015). Modeling Consumers’ Adoption Intentions of Remote Mobile Payments in the United Kingdom: Extending UTAUT with Innovativeness, Risk, and Trust. Psychology & Marketing, 32, 860-873. https://doi.org/10.1002/mar.20823
Talwar, S., Dhir, A., Khalil, A., Mohan, G., & Islam, A. K. M. N. (2020). Point of Adoption and beyond. Initial Trust and Mobile-Payment Continuation Intention. Journal of Retailing and Consumer Services, 55, Article 102086. https://doi.org/10.1016/j.jretconser.2020.102086
Taylor, S., & Todd, P. A. (1995). Understanding Information Technology Usage: A Test of Competing Models. Information Systems Research, 6, 144-176. https://doi.org/10.1287/isre.6.2.144
Upadhyay, A. K., & Khandelwal, K. (2018). Applying Artificial Intelligence: Implications for Recruitment. Strategic HR Review, 17, 255-258. https://doi.org/10.1108/shr-07-2018-0051
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46, 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Voermans, M., & Van Veldhoven, M. J. P. M. (2007). Attitude towards E‐HRM: An Empirical Study at Philips. Personnel Review , 36, 887-902.
Wang, W. C., Weng, S. W., Wang, S. H., & Chen, C. Y. (2014). Integrating Building Information Models with Construction Process Simulations for Project Scheduling Support. Automation in Construction, 37, 68-80.
Widianto, M. H. (2020). Analysis of Application of Online Work Exchange Using Technology Acceptance Model and Innovation Diffusion Theory. Journal of Theoretical and Applied Information Technology, 98, 1697-1711.
Wu, R., & Tian, X. (2021). Investigating the Impact of Critical Factors on Continuous Usage Intention Towards Enterprise Social Networks: An Integrated Model of IS Success and TTF. Sustainability, 13, Article 7619. https://doi.org/10.3390/su13147619
Xu, J., Benbasat, I., & Cenfetelli, R. T. (2013). Integrating Service Quality with System and Information Quality: An Empirical Test in the E-Service Context. MIS Quarterly, 777-794.
Yang, Y., Asaad, Y., & Dwivedi, Y. (2017). Examining the Impact of Gamification on Intention of Engagement and Brand Attitude in the Marketing Context. Computers in Human Behavior, 73, 459-469. https://doi.org/10.1016/j.chb.2017.03.066
Yusliza, M. Y., & Ramayah, T. (2011). Explaining the Intention to Use Electronic HRM among HR Professionals: Results from a Pilot Study. Australian Journal of Basic and Applied Sciences , 5, 489-497.
Zhang, Y., Liu, C., Luo, S., Xie, Y., Liu, F., Li, X. et al. (2019). Factors Influencing Patients’ Intentions to Use Diabetes Management Apps Based on an Extended Unified Theory of Acceptance and Use of Technology Model: Web-Based Survey. Journal of Medical Internet Research, 21, e15023. https://doi.org/10.2196/15023