Factors Affecting Employees’ Intention to Use Telework in the Qazvin Province Tax Administration: The Mediating Roles of Attitude and Normative Influence and the Moderating Role of Perceived Susceptibility

Authors

Keywords:

Telework, Behavioral Intention, Perceived Usefulness, Perceived Ease of Use, Task–Technology Fit, Attitude, Normative Influence, Perceived Susceptibility

Abstract

This study aimed to investigate the effects of perceived ease of use, perceived usefulness, and task–technology fit on employees’ intention to use telework, considering attitude and normative influence as mediating variables and perceived susceptibility as a moderating variable. This applied, quantitative, cross-sectional, correlational study was conducted among employees of the Qazvin Province Tax Administration. The statistical population consisted of 300 employees, from whom 200 were selected using simple random sampling; 188 valid questionnaires were returned and analyzed. Data were collected using standardized measures of perceived ease of use, perceived usefulness, task–technology fit, attitude, normative influence, intention to use telework, and perceived susceptibility. All items were rated on a five-point Likert scale. The measurement and structural models were assessed using partial least squares structural equation modeling in SmartPLS, including reliability, convergent and discriminant validity, bootstrapping, mediation, moderation, R², predictive quality, and model-fit indices. Perceived ease of use significantly predicted attitude (β=0.316, p<0.001) and normative influence (β=0.244, p<0.001), but not intention to use telework (β=0.065, p=0.208). Perceived usefulness significantly predicted attitude (β=0.279, p<0.001), normative influence (β=0.277, p<0.001), and intention (β=0.171, p=0.010). Task–technology fit significantly predicted attitude (β=0.194, p<0.001), normative influence (β=0.352, p<0.001), and intention (β=0.148, p=0.016). Attitude (β=0.180, p=0.017) and normative influence (β=0.370, p<0.001) significantly predicted intention. All six indirect effects through attitude and normative influence were significant. Perceived susceptibility significantly moderated the attitude–intention relationship (β=0.122, t=2.167) but not the normative influence–intention relationship (β=0.054, t=0.945). The model explained 56.4% of the variance in intention to use telework. Employees’ intention to use telework is shaped by perceived usefulness, task–technology fit, favorable attitudes, and normative influence, while perceived ease of use operates mainly through indirect pathways. Perceived susceptibility strengthens the effect of attitude on telework intention, highlighting the importance of integrating technological, social, and psychological factors in telework adoption.

Downloads

Download data is not yet available.

References

1. Ding X. Remote Work Evolution: Contrasting Realities in China and US. Highlights in Business, Economics and Management. 2024;30:59-64. doi: 10.54097/hg75z287.

2. Tunca MV, Frățică Dragomir AM, Rădulescu RM. The Implementation and Benefits of Telework in Various Sectors: Reality or Rhetoric? Cunoașterea Științifică. 2024;3(4):114-49. doi: 10.58679/cs52601.

3. Sava Z. The Global Shift towards Remote Work and Its Legal Implications. Interdisciplinary Journal of Research and Development. 2025;12(1 S1):279. doi: 10.56345/ijrdv12n1s137.

4. Llados-Masllorens J, Meseguer-Artola A, Rimbau-Gilabert E, Sabadell i Bosch M. Telework and New Work Practices: The Role of Managers. Human Resources Management and Services. 2024;6(2). doi: 10.18282/hrms.v6i2.3454.

5. Salehi A, Tremblay DG, Dubouloz S, Desmarais C. Exploring the Influence of Managers' Perceived Innovation Compatibility and Experience on Telework Decisions: The Mediating Role of Satisfaction. Journal of HRM. 2026;29(1):1. doi: 10.46287/ZSOE4991.

6. Bracho E, Gisle L, Hermans L, Duveau C, Cagnie B, De Pauw R, et al. The Impact of Telework on Mental Health: A Secondary Analysis of the 2023 BelHealth. European Journal of Public Health. 2024;34(Supplement_3):ckae144.2237. doi: 10.1093/eurpub/ckae144.2237.

7. Ghamkhari SM, Pourali Ganji L. Examining the Effects of Perceived Usefulness, Ease of Use, Enjoyment, Trust, and Social Influence on Intention to Use with the Mediating Role of Consumer Attitude: A Study of Residential Solar Electricity Customers in Yazd. Intelligent Marketing Management. 2023;4(2):66-78.

8. Giac CC, Van Giang CT, Hoang LH, Ngan TTT. A Study on Teachers' Acceptance of Digital Technology in Vietnamese Secondary Education: An Assessment Using the Technology Acceptance Model. International Journal of Learning, Teaching and Educational Research. 2025;24(2):38-62. doi: 10.26803/ijlter.24.2.3.

9. Susilawati E, Ekalestari S, Khaira I, Kesuma S, Mardiana N. Analysis of the Technology Acceptance Model (TAM) in the Implementation of the Curriculum Review Course: A Study on Students' Understanding and Acceptance of Technology at Universitas Islam Sumatera Utara. International Journal on E-Learning and Higher Education. 2025;20(2):19-39. doi: 10.24191/ijelhe.v20n2.2022.

10. Husna NL, Cahyaningsih DS, Kumar AM. Perceived Usefulness dan Perceived Ease of Use: Pengaruh Perceived Usefulness dan Perceived Ease of Use terhadap Actual Usage melalui Attitude toward Using dalam Digitalisasi Sistem Perpajakan UMKM. Jurnal Ilmiah Fokus Ekonomi, Manajemen, Bisnis & Akuntansi (EMBA). 2025;4(2):257-74. doi: 10.34152/emba.v4i2.1449.

11. Kiat LS, Hoo WC, Cheng AY, Prompanyo M, Hossain SFA. Factors Influencing Intention to Use 5G Mobile Technology and Adoption Onwards in Malaysia. WSEAS Transactions on Business and Economics. 2025;22:773-87. doi: 10.37394/23207.2025.22.68.

12. Khodami S, Chavoshi SK, Rahimizadeh N. Investigating Factors Affecting Customers' Behavioral Intention to Use Virtual Reality Based on Diffusion of Innovation Theory, Uses and Gratifications Theory, and Perceived Value Foundations. Information Management Sciences and Techniques. 2023;9(4):119-48.

13. Abdollahzadeh M, Abdollahzadeh M, Abdollahzadeh S, editors. Using Metaheuristic Models to Optimize Factors Affecting Consumers' Behavioral Intention toward Banking Applications. Seventh International Conference on New Developments in Management, Economics, and Accounting; 2024; Tehran, Iran.

14. Bazine ID, Salahddine MA. Understanding the Task-Technology Fit (TTF) Model: Genesis and Conceptual Developments. International Journal of Latest Technology in Engineering, Management & Applied Science. 2025;14:241-7. doi: 10.51583/IJLTEMAS.2025.1408000030.

15. Chavarnakul T, Lin YC, Khan A, Chen SC. Exploring the Determinants and Consequences of Task-Technology Fit: A Meta-Analytic Structural Equation Modeling Perspective. Emerging Science Journal. 2024;8(1):77-94. doi: 10.28991/ESJ-2024-08-01-06.

16. Howard MC, Hair JF, Jr. Integrating the Expanded Task-Technology Fit Theory and the Technology Acceptance Model: A Multi-Wave Empirical Analysis. AIS Transactions on Human-Computer Interaction. 2023;15(1):83-110. doi: 10.17705/1thci.00184.

17. Kamdjoug JRK, Tchana PBT, Wamba SF, Teutio AON. Task-Technology Fit and ICT Use in Remote Work Practice during the COVID-19 Pandemic. Journal of Global Information Management. 2023;31(1):1-24. doi: 10.4018/JGIM.324097.

18. Kelanajaya JW, Darmanto JA, Ananda MX, Putra OPB, editors. When Organizational Technology Falls Short: Task-Technology Fit, Unauthorized AI Usage, and Employee Performance. 2026 6th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET); 2026: IEEE.

19. Aji RF, Utami NP, Putri KK, Efendi MMM, Zahra RSH, Astrianty RA, editors. Investigating Users' Intention Behaviour in Live Streaming E-Commerce: How Task-Technology Fit, Technological Features, and Social Motivation Influence Purchase Intention. 2025 16th International Conference on E-Education, E-Business, E-Management and E-Learning (IC4E); 2025: IEEE.

20. Bobby AR, III, de Loyola RC, Escototo ER, Grecia JN, Duazo DS, De Paula EG, editors. Assessing the Determinants of GCash Payment Adoption among College Students: A Combined Approach Using Task-Technology Fit and Technology Acceptance Models. 2025 International Conference on Computational, Communication and Information Technology (ICCCIT); 2025: IEEE.

21. Ramdani D, Purnomo A, Mulyati E. The Influence of Order Complexity and Logistics System Capability on Task-Technology Fit and Fulfillment Center Logistics System Capability: A Study on E-Commerce Fulfillment Centers. Dinasti International Journal of Education Management and Social Science. 2025;6:4611-22. doi: 10.38035/dijemss.v6i6.4859.

22. Alistyaningrum AD, Aligarh F, Prasetyanto D, Nugroho A, Purwantini AH, Arifin MR, et al. Explaining ChatGPT Use among Accounting Students in Higher Education: An Integrated Task-Technology Fit and DeLone-McLean Perspective. Higher Education, Skills and Work-Based Learning. 2026:1-19. doi: 10.1108/HESWBL-10-2025-0448.

23. Kim SK, Bae JK. A Study on the Factors Influencing the Intention to Continue Using Generative AI Services in Domestic Digital Finance: An Integrated Approach of TTF and PAM. Smart Media Journal. 2026;15(5):86-96. doi: 10.30693/SMJ.2026.15.5.86.

24. Suharto M, Simatupang BM, Budhijana RB, Pracoyo A. Integrating ISSM, TAM, and Task-Technology Fit to Explain Trust Formation in Mobile Banking: Evidence from Islamic Banking. Jurnal Multidisiplin Indonesia. 2026. doi: 10.58344/jmi.v5i4.2676.

25. Yang B, Sun Y. Improving Learning Performance through AI-Enabled Task Completing and Network Building: Integrating Task-Technology Fit and Social-Technology Fit Perspectives. Interactive Learning Environments. 2026;34(5):3468-88. doi: 10.1080/10494820.2025.2569777.

26. Spilbergs A. The Role of Social Influence as a Moderator in Evaluating Factors Affecting the Intention to Use Digital Wallets. Businesses. 2025;5(3):34. doi: 10.3390/businesses5030034.

27. Sadeghi Y, Jalalzadeh SR. Factors Affecting Customers' Intention to Use a Cashback Loyalty Program Based on the Unified Theory of Acceptance and Use of Technology in Online Shopping: The Case of Digikala. Innovative Strategies for Business Development. 2025;1(1):110-38.

28. Marjerison RK, Jun JY, Xu X, Kim JM. Beyond Incentives: Linking Extrinsic Rewards, Socio-Technical Systems, and Employee Satisfaction in Context. SAGE Open. 2026;16(1):21582440261421792. doi: 10.1177/21582440261421792.

29. Aplin-Houtz MJ, Lane E, Sharma S, Murray J, Leahy S, Pines E, et al. Remotely Crafting Technology Acceptance: An Exploratory Study of How Job Crafting Behaviours and Remote Working Influence the Perception of Technology. Human Resource Development International. 2026;29(4):701-30. doi: 10.1080/13678868.2025.2517008.

30. Ozer M, Kose Y, Bastug M, Kucukkaya G, Varlioglu ER. The Shifting Landscape of Cybersecurity: The Impact of Remote Work and COVID-19 on Data Breach Trends. World Congress in Computer Science, Computer Engineering & Applied Computing: Springer Nature Switzerland; 2024. p. 147-56.

31. Al-Okaily M, Shiyyab FS, Salim NA, Al-Rahayfeh A. The Role of AI-Enabled Health Systems in the Jordanian Context: The Case of Digital Contact Tracing Apps. Journal of Health Organization and Management. 2025;39(4):463-83. doi: 10.1108/JHOM-06-2024-0252.

Downloads

Publication Timeline

Published
Submitted
Revised
Accepted

How to Cite

Sadeghian Gharaghieh, S. ., & Sharifi, R. . (2026). Factors Affecting Employees’ Intention to Use Telework in the Qazvin Province Tax Administration: The Mediating Roles of Attitude and Normative Influence and the Moderating Role of Perceived Susceptibility. Journal of Management and Business Solutions, 4(2), 1-22. https://journalmbs.com/index.php/jmbs/article/view/476