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.
References
[1]
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. EuropeanJournalofManagementandBusinessEconomics,30, 211-229. https://doi.org/10.1108/ejmbe-04-2020-0066
[2]
Ali, B. M., & Younes, B. (2013). The Impact of Information Systems on User Performance: An Exploratory Study. JournalofKnowledgeManagement,EconomicsandInformationTechnology,3, 128-154.
[3]
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. MobileInformationSystems,2022, 1-16. https://doi.org/10.1155/2022/8909724
[4]
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.
[5]
Alshibly, H. H. (2014b). Evaluating E-HRM Success: A Validation of the Information Systems Success Model. InternationalJournalofHumanResourceStudies, 4, Article 107.
[6]
Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MISQuarterly,25, 351-370. https://doi.org/10.2307/3250921
[7]
Cohen, J., & Cohen, P. (1983). AppliedMultipleRegression/CorrelationAnalysisfortheBehavioralSciences (2nd ed.). Lawrence Erlbaum Associates, Inc.
[8]
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MISQuarterly,13, 319-340. https://doi.org/10.2307/249008
[9]
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. ActaInformaticaMalaysia,4, 10-18. https://doi.org/10.26480/aim.01.2020.10.18
[10]
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.
[11]
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:JurnalBiologi-InovasiPendidikan,3, 113-118. https://doi.org/10.20527/bino.v3i2.10322
[12]
Fathima, A. Y., & Muthumani, S. (2019). Client Cluster Identification of Internet Bank Services. JournalofComputationalandTheoreticalNanoscience, 16, 3554-3559.
[13]
Garver, M. S., & Mentzer, J. T. (1999). Logistics Research Methods: Employing Structural Equation Modeling to Test for Construct Validity. JournalofBusinessLogistics, 20, 33-57.
[14]
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). InternationalJournalofManagement, 10, 205-215.
[15]
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to Use and How to Report the Results of PLS-SEM. EuropeanBusinessReview,31, 2-24. https://doi.org/10.1108/ebr-11-2018-0203
[16]
Hair, Jr., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). MultivariateDataAnalysis (6th ed.). Pearson-Prentice Hall.
[17]
Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W. et al. (2014). Common Beliefs and Reality about PLS. OrganizationalResearchMethods,17, 182-209. https://doi.org/10.1177/1094428114526928
[18]
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS Path Modeling in New Technology Research: Updated Guidelines. IndustrialManagement&DataSystems,116, 2-20. https://doi.org/10.1108/imds-09-2015-0382
[19]
Henseler, J., Ringle, C. M., & Sarstedt, M. (2012). Using Partial Least Squares Path Modeling in Advertising Research: Basic Concepts and Recent Issues. In HandbookofResearchonInternationalAdvertising. Edward Elgar Publishing. https://doi.org/10.4337/9781781001042.00023
[20]
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.), NewChallengestoInternationalMarketing (pp. 277-319). Emerald Group Publishing Limited.
[21]
Hoelter, J. W. (1983). The Analysis of Covariance Structures. SociologicalMethods&Research,11, 325-344. https://doi.org/10.1177/0049124183011003003
[22]
Huang, T., & Liao, S. (2015). A Model of Acceptance of Augmented-Reality Interactive Technology: The Moderating Role of Cognitive Innovativeness. ElectronicCommerceResearch,15, 269-295. https://doi.org/10.1007/s10660-014-9163-2
[23]
Iranmanesh, M., Zailani, S., & Nikbin, D. (2017). RFID Continuance Usage Intention in Health Care Industry. QualityManagementinHealthCare,26, 116-123. https://doi.org/10.1097/qmh.0000000000000134
[24]
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.
[25]
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. HasanuddinJournalofBusinessStrategy,3, 52-64. https://doi.org/10.26487/hjbs.v3i1.410
[26]
Jaafreh, A. B. (2017). Evaluation Information System Success: Applied DeLone and McLean Information System Success Model in Context Banking System in KSA. InternationalReviewofManagementandBusinessResearch, 6, 829-845.
[27]
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. JournalofServiceScienceandManagement,8, 291-305. https://doi.org/10.4236/jssm.2015.83032
[28]
Khayer, A., & Bao, Y. (2019). The Continuance Usage Intention of Alipay. TheBottomLine,32, 211-229. https://doi.org/10.1108/bl-07-2019-0097
[29]
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. TechnologyinSociety,60, Article 101225. https://doi.org/10.1016/j.techsoc.2019.101225
[30]
Kumar, A., & Lata, S. (2021). The System Quality and Customer Satisfaction with Website Quality as Mediator in Online Purchasing: A Developing Country Perspectives. JournalofOperationsandStrategicPlanning,4, 7-26. https://doi.org/10.1177/2516600x21991945
[31]
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. ComputersinHumanBehavior,35, 464-478. https://doi.org/10.1016/j.chb.2014.03.022
[32]
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. InternationalJournalofHospitalityManagement,87, Article 102381. https://doi.org/10.1016/j.ijhm.2019.102381
[33]
Lutfi, A. (2023). Factors Affecting the Success of Accounting Information System from the Lens of Delone and Mclean IS Model. InternationalJournalofInformationManagementDataInsights,3, Article 100202. https://doi.org/10.1016/j.jjimei.2023.100202
[34]
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
[35]
McQuitty, S. (2004). Statistical Power and Structural Equation Models in Business Research. JournalofBusinessResearch,57, 175-183. https://doi.org/10.1016/s0148-2963(01)00301-0
[36]
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
[37]
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. InternationalJournalofAcademicResearchinBusinessandSocialSciences,13, 1308-1323. https://doi.org/10.6007/ijarbss/v13-i1/15570
[38]
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. AfricanJournalofEmpiricalResearch,5, 617-626. https://doi.org/10.51867/ajernet.5.4.50
[39]
Nascimento, B., Oliveira, T., & Tam, C. (2018). Wearable Technology: What Explains Continuance Intention in Smartwatches? JournalofRetailingandConsumerServices,43, 157-169. https://doi.org/10.1016/j.jretconser.2018.03.017
[40]
Noerman, T., Erlando, A., & Riyanto, F. D. (2021). Factors Determining Intention to Continue Using E-HRM. The Journal of Asian Finance, Economics and Business,8, 1079-1089.
[41]
Obeidat, S. M. (2016). The Link between E-HRM Use and HRM Effectiveness: An Empirical Study. PersonnelReview,45, 1281-1301. https://doi.org/10.1108/pr-04-2015-0111
[42]
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.
[43]
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. JournalofAppliedPsychology,88, 879-903. https://doi.org/10.1037/0021-9010.88.5.879
[44]
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. InternationalJournalofBusinessInformationSystems,33, 549-569. https://doi.org/10.1504/ijbis.2020.105870
[45]
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. InternationalJournalofQuality&ReliabilityManagement,38, 986-1004. https://doi.org/10.1108/ijqrm-03-2020-0074
[46]
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. EnterpriseInformationSystems,15, 769-793. https://doi.org/10.1080/17517575.2020.1850872
[47]
Rahman, M., Mordi, C., & Nwagbara, U. (2018). Factors Influencing E-HRM Implementation in Government Organisations. JournalofEnterpriseInformationManagement,31, 247-275. https://doi.org/10.1108/jeim-05-2017-0066
[48]
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. InternationalJournalforQualityResearch,15, 679-696. https://doi.org/10.24874/ijqr15.02-20
[49]
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
[50]
Sareen, P. (2015). Study of Employee Satisfaction towards E-HRM System. EuropeanJournalofAppliedBusinessandManagement, 1, 1-18.
[51]
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
[52]
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. TheJournalofEducationalResearch,99, 323-338. https://doi.org/10.3200/joer.99.6.323-338
[53]
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.
[54]
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. TheJournalofExperimentalEducation,74, 267-288. https://doi.org/10.3200/jexe.74.3.267-288
[55]
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
[56]
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. JournalofRetailingandConsumerServices,55, Article 102086. https://doi.org/10.1016/j.jretconser.2020.102086
[57]
Taylor, S., & Todd, P. A. (1995). Understanding Information Technology Usage: A Test of Competing Models. InformationSystemsResearch,6, 144-176. https://doi.org/10.1287/isre.6.2.144
[58]
Upadhyay, A. K., & Khandelwal, K. (2018). Applying Artificial Intelligence: Implications for Recruitment. StrategicHRReview,17, 255-258. https://doi.org/10.1108/shr-07-2018-0051
[59]
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. ManagementScience,46, 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
[60]
Voermans, M., & Van Veldhoven, M. J. P. M. (2007). Attitude towards E‐HRM: An Empirical Study at Philips. PersonnelReview, 36, 887-902.
[61]
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.
[62]
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.
[63]
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
[64]
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.
[65]
Yang, Y., Asaad, Y., & Dwivedi, Y. (2017). Examining the Impact of Gamification on Intention of Engagement and Brand Attitude in the Marketing Context. ComputersinHumanBehavior,73, 459-469. https://doi.org/10.1016/j.chb.2017.03.066
[66]
Yusliza, M. Y., & Ramayah, T. (2011). Explaining the Intention to Use Electronic HRM among HR Professionals: Results from a Pilot Study. AustralianJournalofBasicandAppliedSciences, 5, 489-497.
[67]
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. JournalofMedicalInternetResearch,21, e15023. https://doi.org/10.2196/15023
