Abstract

In today’s dynamic global business economy, the use of information technology has become an essential and pervasive technique for organizational success. In this essence, the present study extends the unified theory of acceptance and use of technology with task technology fit to see how underpinned factors impact on employee intention to adopt information technology and enhance employee job performance. In order to test the proposed research model, the respondent’s observations are required. Therefore, an administrative survey was conducted towards Saudi public organizations. A survey questionnaire was distributed among middle-level managers working in HR departments of Saudi Public organizations. In response to administrative survey 398 questionnaires were returned with a response rate of 79.6%. Among 398 questionnaires 38 were discarded due to inappropriate answers and 358 questionnaires were finally used for structural equation modelling. The inclusion criterion was that HR managers must have knowledge about online services offering by respective organizations to employees. For data analysis, structural equation modelling approach was used. Results indicate that the extended the unified theory of acceptance and use of technology model has substantial power and explained 77.0% variance in employee intention to adopt the technology. The effect size analysis  showed that within extended model effort expectancy was the most important factor. The predictive relevance  of the model was also adequate. Finally, importance of performance matrix analysis suggested that managers and policymakers should focus on effort expectancy, task characteristics, technology characteristics and supervisor support to boost employee intention to adopt technology and employee job performance.

Keywords: employee job performance, innovation valance, intention to adopt technology, moderating analysis, structural equation modelling, supervisory support

JEL Classification: J50, M19.

Cite as: Alyoubi, B. A., Yamin, M. A. Y. (2019). The impact of task technology fit on employee job performance. Marketing and Management of Innovations, 4, 140-159. https://doi.org/10.21272/mmi.2019.4-12

This work is licensed under a Creative Commons Attribution 4.0 International License

References

1. Ahmad, A. R., & Marinah, A. (2013). Learning organization and organizational commitment in primary school. International Proceedings of Economics Development and Research, 60, 55.
2. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
3. Al-Alawi, A. I., & Al-Bassam, S. A. (2019). Evaluation of telecommunications regulatory practice in the Kingdom of Bahrain: development and challenges. International Journal of Business Information Systems, 31(2), 282-303.
4. Aldholay, A., Isaac, O., Abdullah, Z., Abdulsalam, R., & Al-Shibami, A. H. (2018). An extension of Delone and McLean IS success model with self-efficacy: Online learning usage in Yemen. The International Journal of Information and Learning Technology, 35(4), 285-304.
5. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.
6. Armenakis, A. A., Bernerth, J. B., Pitts, J. P., & Walker, H. J. (2007). Organizational change recipients’ beliefs scale: Development of an assessment instrument. The Journal of applied behavioral science, 43(4), 481-505.
7. Bandura, A., Freeman, W., & Lightsey, R. (1999). Self-efficacy: The exercise of control: Springer.
8. Bergiel, E. B., Nguyen, V. Q., Clenney, B. F., & Stephen Taylor, G. (2009). Human resource practices, job embeddedness and intention to quit. Management Research News, 32(3), 205-219.
9. Brown, S. A., Dennis, A. R., & Venkatesh, V. (2010). Predicting collaboration technology use: Integrating technology adoption and collaboration research. Journal of management information systems, 27(2), 9-54.
10. Choi, J. N. (2004). Individual and contextual predictors of creative performance: The mediating role of psychological processes. Creativity Research Journal, 16(2-3), 187-199.
11. Cohen, J. (1988). Statistical power analysis for the behavioural sciences. Hillside. NJ: Lawrence Earlbaum Associates.
12. Daud Norzaidi, M., Choy Chong, S., Murali, R., & Intan Salwani, M. (2007). Intranet usage and managers’ performance in the port industry. Industrial Management & Data Systems, 107(8), 1227-1250.
13. 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.
14. Dewett, T. (2007). Linking intrinsic motivation, risk taking, and employee creativity in an R&D environment. R&D Management, 37(3), 197-208.
15. Fishbein, M., & Ajzen, I. (1975). Belief. Attitude, Intention and Behavior: An Introduction to Theory and Research Reading, MA: Addison-Wesley, 6.
16. Fornell, C., & Larcker, D. F. (1981a). Structural Equation Models With Unobservable Variables and Measurement Error: Algebra and Statistics. Journal of marketing Research, 18(3), 382.
17. Fornell, C., & Larcker, D. F. (1981b). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 382-388.
18. Gold, A. H., & Arvind Malhotra, A. H. S. (2001). Knowledge management: An organizational capabilities perspective. Journal of Management Information Systems, 18(1), 185-214.
19. Goodhue, D. L. (1995). Understanding user evaluations of information systems. Management science, 41(12), 1827-1844.
20. Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS quarterly, 213-236.
21. Ha, S. T., & Lo, M. C. (2018). An empirical examination of knowledge management and organisational performance among Malaysian manufacturing SMEs. International Journal of Business Innovation and Research, 17(1), 23-37.
22. Hair, J., Anderson, R., Black, B., & Babin, B. (2016). Multivariate Data Analysis: Pearson Education.
23. Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM): Sage Publications.
24. Hanaysha, J. (2016). Testing the effects of employee engagement, work environment, and organizational learning on organizational commitment. Procedia-Social and Behavioral Sciences, 229, 289-297.
25. Henseler, J., & Fassott, G. (2010). Testing moderating effects in PLS path models: An illustration of available procedures Handbook of partial least squares (pp. 713-735): Springer.
26. Hirst, G., Van Dick, R., & Van Knippenberg, D. (2009). A social identity perspective on leadership and employee creativity. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 30(7), 963-982.
27. Hung, C.-Y., Sun, J. C.-Y., & Yu, P.-T. (2015). The benefits of a challenge: student motivation and flow experience in tablet-PC-game-based learning. Interactive Learning Environments, 23(2), 172-190.
28. Ioimo, R. E., & Aronson, J. E. (2003). The benefits of police field mobile computing realized by non-patrol sections of a police department. International Journal of Police Science & Management, 5(3), 195-206.
29. Isaac, O., Abdullah, Z., Aldholay, A. H., & Ameen, A. A. (2019). Antecedents and outcomes of internet usage within organisations in Yemen: An extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Asia Pacific Management Review.
30. Kline, R. (2011). Principles and Practice of Structural Equation Modeling, 3rd edn Guilford Press. New York.
31. Ling Keong, M., Ramayah, T., Kurnia, S., & May Chiun, L. (2012). Explaining intention to use an enterprise resource planning (ERP) system: an extension of the UTAUT model. Business Strategy Series, 13(4), 173-180.
32. Lockwood, N. R. (2007). Leveraging employee engagement for competitive advantage. Society for Human Resource Management Research Quarterly, 1(1), 1-12.
33. Lokuge, S., Sedera, D., Grover, V., & Dongming, X. (2019). Organizational readiness for digital innovation: Development and empirical calibration of a construct. Information & management, 56(3), 445-461.
34. Lokuge, S., Sedera, D., & Nanayakkara, K. S. (2018). Innovate or copy: a Qualitative Document Analysis to Entrepreneurship in Developing Countries. Paper presented at the ECIS.
35. Makokha, M. W., & Ochieng, D. O. (2014). Assessing the Success of ICT’s from a User Perspective: Case Study of Coffee Research Foundation, Kenya. Journal of Management and Strategy, 5(4), 46.
36. Marsick, V. J., & Watkins, K. E. (1999). Facilitating learning organizations: Making learning count: Gower Publishing, Ltd.
37. Martins, C., Oliveira, T., & Popoviс, A. (2014). Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1-13.
38. Mohammad Ali, Y. (2018). The mediating role of ethical organizational climate between HRM practices and HR outcomes in Public Sector of Saudi Arabia. International Journal of Business Excellence, 0(0). doi: 10.1504/IJBEX.2019.10016985
39. Mohammad Ali Yousef Yamin, & Swaiess, M. (2019). Investigating Employee creative performance with integration of DeLone and McLean Information system success model and Technology acceptance model: The moderating role of Creative self-efficacy. International Journal of Business Excellence, 0(0). doi: 10.1504/IJBEX.2019.10024168
40. Oliveira, T., Faria, M., Thomas, M. A., & Popoviс, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34(5), 689-703.
41. Ozturk, A. B., Bilgihan, A., Salehi-Esfahani, S., & Hua, N. (2017). Understanding the mobile payment technology acceptance based on valence theory: A case of restaurant transactions. International Journal of Contemporary Hospitality Management, 29(8), 2027-2049.
42. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879.
43. Rahi, S. (2015). Moderating Role of Brand Image With Relation to Internet Banking and Customer Loyalty: A Case of Branchless Banking. The Journal of Internet Banking and Commerce, 20(3).
44. Rahi, S. (2016). Impact of Customer Perceived Value and Customers Perception of Public Relation on Customer Loyalty with Moderating Role of Brand Image. The Journal of Internet Banking and Commerce, 21(2).
45. Rahi, S. (2017). Research design and methods: A systematic review of research paradigms, sampling issues and instruments development. International Journal of Economics & Management Sciences, 6(2), 1-5.
46. Rahi, S. (2017). Structural Equation Modeling Using SmartPLS: CreateSpace Independent Publishing Platform.
47. Rahi, S. (2018). Research Design and Methods: CreateSpace Independent Publishing Platform.
48. Rahi, S. (2019). Integration of UTAUT model in internet banking adoption context. Journal of Research in Interactive Marketing, 13(3), 411-435. doi: 10.1108/jrim-02-2018-0032.
49. Rahi, S., & Abd. Ghani, M. (2018a). Does gamified elements influence on user’s intention to adopt and intention to recommend internet banking? International Journal of Information and Learning Technology, 0(0), null. doi: doi:10.1108/IJILT-05-2018-0045.
50. Rahi, S., & Abd. Ghani, M. (2018b). The role of UTAUT, DOI, perceived technology security and game elements in internet banking adoption. World Journal of Science, Technology and Sustainable Development, 15(4), 338-356. doi: doi:10.1108/WJSTSD-05-2018-0040.
51. Rahi, S., & Abd. Ghani, M. (2019a). Does gamified elements influence on user’s intention to adopt and intention to recommend internet banking? International Journal of Information and Learning Technology, 36(1), 2-20. doi: doi:10.1108/IJILT-05-2018-0045.
52. Rahi, S., & Abd. Ghani, M. (2019b). Integration of expectation confirmation theory and self-determination theory in internet banking continuance intention. Journal of Science and Technology Policy Management, 0(0), null. doi: doi:10.1108/JSTPM-06-2018-0057
53. Rahi, S., & Abd. Ghani, M. (2019c). Investigating the role of UTAUT and e-service quality in internet banking adoption setting. The TQM Journal, 31(3), 491-506.
54. Rahi, S., Abd.Ghani, M., & Hafaz Ngah, A. (2019). Integration of unified theory of acceptance and use of technology in internet banking adoption setting: Evidence from Pakistan. Technology in Society, 58, 101120. doi: https://doi.org/10.1016/j.techsoc.2019.03.003.
55. Rahi, S., Alnaser, F. M., & Ghani, M. A. (2019). Designing survey research: recommendation for questionnaire development, calculating sample size and selecting research paradigms. Economic and Social Development: Book of Proceedings, 1157-1169.
56. Rahi, S., Ghani, M., Alnaser, F., & Ngah, A. (2018). Investigating the role of unified theory of acceptance and use of technology (UTAUT) in internet banking adoption context. Management Science Letters, 8(3), 173-186.
57. Rahi, S., Ghani, M., & Ngah, A. (2018). A structural equation model for evaluating user’s intention to adopt internet banking and intention to recommend technology. Accounting, 4(4), 139-152.
58. Rahi, S., & Ghani, M. A. (2016). Customer’s Perception of Public Relation in E-Commerce and its Impact on E-Loyalty with Brand Image and Switching Cost. Journal of Internet Banking and Commerce, 21(3).
59. Rahi, S., & Ghani, M. A. (2018). A Structural Equation Modeling (SEM-AMOS) for Investigating Brand Loyalty and Customer’s Intention towards Adoption of Internet Banking Paper presented at the Economic and Social Development (Book of Proceedings), 29th International Scientific Conference on Economic and Social.
60. 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 System 0(0). doi: 10.1504/IJBIS.2020.10020858.
61. Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH.
62. Samar, R., & Mazuri, A. G. (2019a). Does gamified elements influence on user’s intention to adopt internet banking with integration of UTAUT and General Self-Confidence? International Journal of Business Excellence, 0(0). doi: 10.1504/IJBEX.2019.10016706.
63. Samar, R., & Mazuri, A. G. (2019b). Integration of DeLone & McLean and Self-Determination Theory in internet banking continuance intention context. International Journal of Accounting and Information Management, 27(3).
64. Samar, S., Ghani, M., & Alnaser, F. (2017). Predicting customer’s intentions to use internet banking: the role of technology acceptance model (TAM) in e-banking. Management Science Letters, 7(11), 513-524.
65. Sarker, S., Valacich, J. S., & Sarker, S. (2005). Technology adoption by groups: A valence perspective. Journal of the Association for Information Systems, 6(2), 3.
66. 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.
67. Tierney, P., & Farmer, S. M. (2002). Creative self-efficacy: Its potential antecedents and relationship to creative performance. Academy of management journal, 45(6), 1137-1148.
68. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
69. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003a). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
70. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003b). User acceptance of information technology: Toward a unified view1. MIS quarterly, 27(3), 425.
71. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology.
72. Virdyananto, A. L., Dewi, M. A. A., Hidayanto, A. N., & Hanief, S. (2016). User acceptance of human resource information system: An integration model of Unified Theory of Acceptance and Use of Technology (UTAUT), Task Technology Fit (TTF), and Symbolic Adoption. Paper presented at the 2016 International Conference on Information Technology Systems and Innovation (ICITSI).
73. Wang, S., & Noe, R. A. (2010). Knowledge sharing: A review and directions for future research. Human resource management review, 20(2), 115-131.
74. Wang, Y.-S., & Liao, Y.-W. (2008). Assessing eGovernment systems success: A validation of the DeLone and McLean model of information systems success. Government Information Quarterly, 25(4), 717-733.
75. 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.
76. Yu, C.-S. (2012). Factors affecting individuals to adopt mobile banking: Empirical evidence from the UTAUT model. Journal of Electronic Commerce Research, 13(2), 104.
77. 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.