Exploring the Determinants and Consequences of Task-Technology Fit: A Meta-Analytic Structural Equation Modeling Perspective

Thira Chavarnakul, Yu-Chun Lin, Asif Khan, Shih-Chih Chen


Objectives: Task-Technology Fit (TTF) is mainly used to determine the users’ performance based on the tasks and technological attributes. This study integrated and evaluated TTF with the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). This paper aims to compile and analyze the literature on task-technology fit (TTF) since 2000. Method: Through the meta-analytic structural equation modeling (MASEM) approach, understand the application of TTF in the last 20 years and explore future research directions. In addition, this paper employs subgroup analysis and sample sub-grouping to better understand the differences between these studies. The samples were divided into two categories: identity groups (employee, individual, and student) and voluntary groups (voluntary and non-voluntary). Findings: The relationship between the variables belonging to the original TTF model (including TASK, TEC, TTF, PI, and UT) was found to be relatively stable. After combining the variables of UTAUT2 (including PEOU, BI, and PE) and IC, all paths were also found to have a medium or high effect. The TTF-BI path was significant in the identity-based subgroup analysis, and the IC-TTF path was significant in the voluntary-based subgroup analysis. Novelty:Given that the traditional TTF literature is too subjective, this paper adopts MASEM as applied in management research. There are few similar studies so far. Therefore, this paper not only analyzes TTF objectively through MASEM but also provides some directions and suggestions for expanding the TTF model and hopes to give a stronger explanation for future research.


Doi: 10.28991/ESJ-2024-08-01-06

Full Text: PDF


Task-Technology Fit; Meta-Analytic Structural Equation Modeling; Performance Expectancy; Perceived Ease-of-Use; Individual Characteristics.


Wang, H., Tao, D., Yu, N., & Qu, X. (2020). Understanding consumer acceptance of healthcare wearable devices: An integrated model of UTAUT and TTF. International Journal of Medical Informatics, 139, 104156. doi:10.1016/j.ijmedinf.2020.104156.

Awad, H. A. H. (2020). Investigating employee performance impact with integration of task technology fit and technology acceptance model: The moderating role of self-efficacy. International Journal of Business Excellence, 21(2), 231–249. doi:10.1504/IJBEX.2020.107594.

Pillai, R., & Sivathanu, B. (2020). Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations. Benchmarking, 27(9), 2599–2629. doi:10.1108/BIJ-04-2020-0186.

McGill, T. J., & Klobas, J. E. (2009). A task-technology fit view of learning management system impact. Computers and Education, 52(2), 496–508. doi:10.1016/j.compedu.2008.10.002.

D’Ambra, J., Wilson, C. S., & Akter, S. (2013). Application of the task-technology fit model to structure and evaluate the adoption of E-books by academics. Journal of the American Society for Information Science and Technology, 64(1), 48–64. doi:10.1002/asi.22757.

Gebauer, J., Shaw, M. J., & Gribbins, M. L. (2010). Task-technology fit for mobile information systems. Journal of Information Technology, 25(3), 259–272. doi:10.1057/jit.2010.10.

Kim, M. J., Chung, N., Lee, C. K., & Preis, M. W. (2015). Motivations and use context in mobile tourism shopping: Applying contingency and task-technology fit theories. International Journal of Tourism Research, 17(1), 13–24. doi:10.1002/jtr.1957.

Lee, C. C., Cheng, H. K., & Cheng, H. H. (2007). An empirical study of mobile commerce in insurance industry: Task-technology fit and individual differences. Decision Support Systems, 43(1), 95–110. doi:10.1016/j.dss.2005.05.008.

Vanduhe, V. Z., Nat, M., & Hasan, H. F. (2020). Continuance Intentions to Use Gamification for Training in Higher Education: Integrating the Technology Acceptance Model (TAM), Social Motivation, and Task Technology Fit (TTF). IEEE Access, 8, 21473–21484. doi:10.1109/ACCESS.2020.2966179.

Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information and Management, 36(1), 9–21. doi:10.1016/S0378-7206(98)00101-3.

Alturki, U., & Aldraiweesh, A. (2022). Adoption of Google Meet by Postgraduate Students: The Role of Task Technology Fit and the TAM Model. Sustainability (Switzerland), 14(23), 15765. doi:10.3390/su142315765.

Faqih, K. M. S., & Jaradat, M. I. R. M. (2021). Integrating TTF and UTAUT2 theories to investigate the adoption of augmented reality technology in education: Perspective from a developing country. Technology in Society, 67, 101787. doi:10.1016/j.techsoc.2021.101787.

Alkhwaldi, A.F., Abdulmuhsin, A.A. (2022). Understanding User Acceptance of IoT Based Healthcare in Jordan: Integration of the TTF and TAM. Digital Economy, Business Analytics, and Big Data Analytics Applications. Studies in Computational Intelligence, 1010. Springer, Cham, Switzerland. doi:10.1007/978-3-031-05258-3_17.

Mustafa, A. S., Alkawsi, G. A., Ofosu-Ampong, K., Vanduhe, V. Z., Garcia, M. B., & Baashar, Y. (2022). Gamification of E-Learning in African Universities. Next-Generation Applications and Implementations of Gamification Systems, 73–96, IGI Global, Pennsylvania, United States. doi:10.4018/978-1-7998-8089-9.ch005.

Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232. doi:10.1016/j.chb.2016.10.028.

Kim, R., & Song, H. D. (2022). Examining the Influence of Teaching Presence and Task-Technology Fit on Continuance Intention to Use MOOCs. Asia-Pacific Education Researcher, 31(4), 395–408. doi:10.1007/s40299-021-00581-x.

Shanshan, S., & Wenfei, L. (2022). Understanding the impact of quality elements on MOOCs continuance intention. Education and Information Technologies, 27(8), 10949–10976. doi:10.1007/s10639-022-11063-y.

Cheng, Y. M. (2022). Can tasks and learning be balanced? A dual-pathway model of cloud-based e-learning continuance intention and performance outcomes. Kybernetes, 51(1), 210–240. doi:10.1108/K-07-2020-0440.

Khazanchi, D. (2005). Information technology (IT) appropriateness: The contingency theory of “FIT” and IT implementation in small and medium enterprises. Journal of Computer Information Systems, 45(3), 88–95.

Al-Rahmi, A. M., Shamsuddin, A., Alturki, U., Aldraiweesh, A., Yusof, F. M., Al-Rahmi, W. M., & Aljeraiwi, A. A. (2021). The influence of information system success and technology acceptance model on social media factors in education. Sustainability (Switzerland), 13(14), 7770. doi:10.3390/su13147770.

Al-Rahmi, A. M., Shamsuddin, A., Wahab, E., Al-Rahmi, W. M., Alturki, U., Aldraiweesh, A., & Almutairy, S. (2022). Integrating the Role of UTAUT and TTF Model to Evaluate Social Media Use for Teaching and Learning in Higher Education. Frontiers in Public Health, 10, 905968. doi:10.3389/fpubh.2022.905968.

Viswesvaran, C., & Ones, D. S. (1995). Theory Testing: Combining Psychometric Meta-Analysis and Structural Equations Modeling. Personnel Psychology, 48(4), 865–885. doi:10.1111/j.1744-6570.1995.tb01784.x.

Cooper, H., Hedges, L. V., & Valentine, J. C. (2019). The handbook of research synthesis and meta-analysis. Russell Sage Foundation, New York, United Satates. doi:10.7758/9781610448864.

Zaremohzzabieh, Z., Roslan, S., Mohamad, Z., Ismail, I. A., Jalil, H. A., & Ahrari, S. (2022). Influencing Factors in MOOCs Adoption in Higher Education: A Meta-Analytic Path Analysis. Sustainability (Switzerland), 14(14), 8268. doi:10.3390/su14148268.

Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly: Management Information Systems, 19(2), 213–233. doi:10.2307/249689.

Tam, C., & Oliveira, T. (2016). Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective. Computers in Human Behavior, 61, 233–244. doi:10.1016/j.chb.2016.03.016.

Yen, D. C., Wu, C. S., Cheng, F. F., & Huang, Y. W. (2010). Determinants of users’ intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Computers in Human Behavior, 26(5), 906–915. doi:10.1016/j.chb.2010.02.005.

Aljukhadar, M., Senecal, S., & Nantel, J. (2014). Is more always better? Investigating the task-technology fit theory in an online user context. Information and Management, 51(4), 391–397. doi:10.1016/j.im.2013.10.003.

Yu, T. K., & Yu, T. Y. (2010). Modelling the factors that affect individuals- utilisation of online learning systems: An empirical study combining the task technology fit model with the theory of planned behaviour. British Journal of Educational Technology, 41(6), 1003–1017. doi:10.1111/j.1467-8535.2010.01054.x.

Pal, D., & Patra, S. (2020). University Students’ Perception of Video-Based Learning in Times of COVID-19: A TAM/TTF Perspective. International Journal of Human–Computer Interaction, 37(10), 903–921. doi:10.1080/10447318.2020.1848164.

Wong, T. K. M., Man, S. S., & Chan, A. H. S. (2021). Exploring the acceptance of PPE by construction workers: An extension of the technology acceptance model with safety management practices and safety consciousness. Safety Science, 139, 105239. doi:10.1016/j.ssci.2021.105239.

Huang, C.-K., Chen, C.-D., & Liu, Y.-T. (2019). To stay or not to stay? Discontinuance intention of gamification apps. Information Technology & People, 32(6), 1423–1445. doi:10.1108/itp-08-2017-0271.

Xu, Y., Wang, Y., Khan, A., & Zhao, R. (2021). Consumer Flow Experience of Senior Citizens in Using Social Media for Online Shopping. Frontiers in Psychology, 12, 12. doi:10.3389/fpsyg.2021.732104.

Zhao, H., & Khan, A. (2022). The Students’ Flow Experience with the Continuous Intention of Using Online English Platforms. Frontiers in Psychology, 12, 807084–807084. doi:10.3389/fpsyg.2021.807084.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. doi:10.1287/mnsc.35.8.982.

Chen, J. K. C., Batchuluun, A., & Batnasan, J. (2015). Services innovation impact to customer satisfaction and customer value enhancement in airport. Technology in Society, 43, 219–230. doi:10.1016/j.techsoc.2015.05.010.

Amin, M., Rezaei, S., & Abolghasemi, M. (2014). User satisfaction with mobile websites: the impact of perceived usefulness (PU), perceived ease of use (PEOU) and trust. Nankai Business Review International, 5(3), 258–274. doi:10.1108/NBRI-01-2014-0005.

Gefen, D., & Straub, D. (2000). The Relative Importance of Perceived Ease of Use in IS Adoption: A Study of E-Commerce Adoption. Journal of the Association for Information Systems, 1(1), 1–30. doi:10.17705/1jais.00008.

Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information and Management, 40(3), 191–204. doi:10.1016/S0378-7206(01)00143-4.

Tam, C., & Oliveira, T. (2019). Does culture influence m-banking use and individual performance? Information and Management, 56(3), 356–363. doi:10.1016/j.im.2018.07.009.

Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760–767. doi:10.1016/j.chb.2010.01.013.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. doi:10.2307/30036540.

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly: Management Information Systems, 36(1), 157–178. doi:10.2307/41410412.

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376. doi:10.17705/1jais.00428.

Wu, R. Z., & Lee, J. H. (2017). The comparative study on third party mobile payment between UTAUT2 and TTF. Journal of Distribution Science, 15(11), 5–19. doi:10.15722/jds.15.11.201711.5.

Bugembe, J. (2010). Perceived usefulness, perceived ease of use, attitude and actual usage of anew financial management system: A case of Uganda National Examinations Board. Master Thesis, Makerere University, Kampala, Uganda.

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. doi:10.1016/j.chb.2017.01.001.

Khayati, S., & Zouaoui, S. K. (2013). Perceived usefulness and use of information technology: The moderating influences of the dependence of a subcontractor towards his contractor. Journal of Knowledge Management, Economics and Information Technology, 3(6), 68-77.

Min, Q., Ji, S., & Qu, G. (2008). Mobile Commerce User Acceptance Study in China: A Revised UTAUT Model. Tsinghua Science and Technology, 13(3), 257–264. doi:10.1016/S1007-0214(08)70042-7.

Tossy, T. (2014). Modelling the Adoption of Mobile Payment System for Paying Examination Fees in Tanzanian Major Cities. International Journal of Computing & ICT Research, 8(2), 83-98.

Fishbein, M., Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research; Addison-Wesley: Reading, Massachusetts, United States, 1975.

Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research findings. Sage, New York, United States.

Cheung, M. W.-L., & Chan, W. (2005). Meta-analytic structural equation modeling: A two-stage approach. Psychological Methods, 10(1), 40–64. doi:10.1037/1082-989x.10.1.40.

Cohen, A. (1993). Organizational Commitment and Turnover: A Met A-Analysis. Academy of Management Journal, 36(5), 1140–1157. doi:10.5465/256650.

Williams, C. R., & Livingstone, L. P. (1994). Another Look at The Relationship Between Performance and Voluntary Turnover. Academy of Management Journal, 37(2), 269–298. doi:10.5465/256830.

Freeman, P. R., Hedges, L. V., & Olkin, I. (1986). Statistical Methods for Meta-Analysis. Biometrics, 42(2), 454. doi:10.2307/2531069.

Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003). Measuring inconsistency in meta-analyses. British Medical Journal, 327(7414), 557–560. doi:10.1136/bmj.327.7414.557.

Huedo-Medina, T. B., Sánchez-Meca, J., Marín-Martínez, F., & Botella, J. (2006). Assessing heterogeneity in meta-analysis: Q statistic or I 2 Index? Psychological Methods, 11(2), 193–206. doi:10.1037/1082-989X.11.2.193.

Mervis, J. (2014). Why null results rarely see the light of day. Science, 345(6200), 992–992. doi:10.1126/science.345.6200.992.

Rothstein, H. R., Sutton, A. J., & Borenstein, M. (Eds.). (2005). Publication Bias in Meta‐Analysis. John Wiley & Sons, Hoboken, United States. doi:10.1002/0470870168.

Sterne, J. A. C., Gavaghan, D., & Egger, M. (2000). Publication and related bias in meta-analysis: Power of statistical tests and prevalence in the literature. Journal of Clinical Epidemiology, 53(11), 1119–1129. doi:10.1016/S0895-4356(00)00242-0.

E. Hunter, J., L. Schmidt, F., & B. Jackson, G. (1986). Meta-Analysis: Cumulating Research Findings across Studies Sage Publications: Beverly Hills, 1982, 176 pp. Educational Researcher, 15(8), 20-21. doi:10.3102/0013189x015008020.

Torgerson, C. J. (2006). Publication Bias: The Achilles’ Heel of Systematic Reviews? British Journal of Educational Studies, 54(1), 89–102. doi:10.1111/j.1467-8527.2006.00332.x.

Sterne, J. A. C., Sutton, A. J., Ioannidis, J. P. A., Terrin, N., Jones, D. R., Lau, J., Carpenter, J., Rucker, G., Harbord, R. M., Schmid, C. H., Tetzlaff, J., Deeks, J. J., Peters, J., Macaskill, P., Schwarzer, G., Duval, S., Altman, D. G., Moher, D., & Higgins, J. P. T. (2011). Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomized controlled trials. BMJ, 343(jul22 1), d4002–d4002. doi:10.1136/bmj.d4002.

Rosenthal, R. (1986). Meta-Analytic Procedures for Social Science Research Sage Publications: Beverly Hills, 1984, 148 pp. Educational Researcher, 15(8), 18–20. doi:10.3102/0013189x015008018.

Orwin, R. G. (1983). A Fail-Safe N for Effect Size in Meta-Analysis. Journal of Educational Statistics, 8(2), 157–159. doi:10.3102/10769986008002157.

Nayanajith, G., Damunupola, K. A., & Ventayen, R. J. (2019). Relationship of perceived Trust and perceived ease of use on adoption of computer aided learning in the context of Sri Lankan International Schools. Southeast Asian Journal of Science and Technology, 4(1), 64-74.

Omotayo, F. O., & Haliru, A. R. (2020). Perception of task-technology fit of digital library among undergraduates in selected universities in Nigeria. Journal of Academic Librarianship, 46(1), 102097. doi:10.1016/j.acalib.2019.102097.

Yamin, M. A. Y., & Alyoubi, B. A. (2020). Adoption of telemedicine applications among Saudi citizens during COVID-19 pandemic: An alternative health delivery system. Journal of Infection and Public Health, 13(12), 1845–1855. doi:10.1016/j.jiph.2020.10.017.

Hsieh, P.-J., & Lin, W.-S. (2019). Understanding the performance impact of the epidemic prevention cloud: an integrative model of the task-technology fit and status quo bias. Behaviour & Information Technology, 39(8), 899–916. doi:10.1080/0144929x.2019.1624826.

Yi, Y. J., You, S., & Bae, B. J. (2016). The influence of smartphones on academic performance. Library Hi Tech, 34(3), 480–499. doi:10.1108/lht-04-2016-0038.

Tripathi, S., & Jigeesh, N. (2015). Task-technology fit (TTF) model to evaluate adoption of cloud computing: a multi-case study. International Journal of Applied Engineering Research, 10(3), 9185-9200.

Ong, A. K. S., Prasetyo, Y. T., Roque, R. A. C., Garbo, J. G. I., Robas, K. P. E., Persada, S. F., & Nadlifatin, R. (2022). Determining the Factors Affecting a Career Shifter’s Use of Software Testing Tools amidst the COVID-19 Crisis in the Philippines: TTF-TAM Approach. Sustainability (Switzerland), 14(17), 11084. doi:10.3390/su141711084.

Roth, T., Stohr, A., Amend, J., Fridgen, G., & Rieger, A. (2023). Blockchain as a driving force for federalism: A theory of cross-organizational task-technology fit. International Journal of Information Management, 68, 102476. doi:10.1016/j.ijinfomgt.2022.102476.

Zigurs, I., & Buckland, B. K. (1998). A theory of task/technology fit and group support systems effectiveness. MIS Quarterly, 22(3), 313–334. doi:10.2307/249668.

Zigurs, I., & Khazanchi, D. (2008). From profiles to patterns: A new view of task-technology fit. Information Systems Management, 25(1), 8–13. doi:10.1080/10580530701777107.

Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the Acceptance of Mobile Library Applications with an Extended Technology Acceptance Model (TAM). Computers and Education, 145, 103732. doi:10.1016/j.compedu.2019.103732.

Howard, M. C., & Hair, J. F. (2023). Integrating the Expanded Task-technology Fit Theory and the Technology Acceptance Model: A Multi-wave Empirical Analysis. AIS Transactions on Human-Computer Interaction, 15(1), 83–110. doi:10.17705/1thci.00084.

Breidbach, C. F., & Maglio, P. P. (2016). Technology-enabled value co-creation: An empirical analysis of actors, resources, and practices. Industrial Marketing Management, 56, 73–85. doi:10.1016/j.indmarman.2016.03.011.

Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22, 960–967. doi:10.1016/j.promfg.2018.03.137.

Full Text: PDF

DOI: 10.28991/ESJ-2024-08-01-06


  • There are currently no refbacks.

Copyright (c) 2024 Thira Chavarnakul, Yu-Chun Lin, Asif Khan, Shih-Chih Chen