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Meta-Analysis of the Unified Theory of Acceptance and Use of Technology (UTAUT): Challenging its Validity and Charting a Research Agenda in the Red Ocean

Blut, Markus, Durham University Business School, UK, Chong, Alain Yee Loong, Nottingham University Business School–Ningbo, China, Tsigna, Zayyad, Nottingham University Business School–Ningbo, China, Venkatesh, Viswanath, Pamplin College of Business, Virginia Tech, USA·2022·Journal of the Association for Information Systems
DOI 10.17705/1jais.00719
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Abstract

There are both formal and informal cries that UTAUT and, by association, the stream of research on technology adoption has reached its limit, with little or no opportunities for new knowledge creation. Such a conclusion is ironic because the theory has not been sufficiently and suitably replicated. It is possible that misspecifications in the various replications, applications, and extensions have led to the incorrect conclusion that UTAUT is more robust than it really is, leaving limited opportunities for future work. Although work on UTAUT has included important variables, predictors, and moderators, absent a faithful use of the original specification, it is impossible to assess the true nature of the effects of the original and additional variables. The present meta-analysis uses 25,619 effect sizes reported by 737,112 users in 1,935 independent samples to address this issue. Consequently, we develop a clear current state-of-the-art and revised UTAUT that extends the original theory with new endogenous mechanisms from different, other theories (i.e., technology compatibility, user education, personal innovativeness, and costs of technology) and new moderating mechanisms to examine the generalizability of UTAUT in different contexts (e.g., technology type and national culture). Based on this revised UTAUT, we present a research agenda that can guide future research on the topic of technology adoption in general and UTAUT in particular.

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  1. AbuShanab, E., Pearson, J. M., & Setterstrom, A. J. (2010). Internet banking and customers' acceptance in Jordan: The unified model's perspective. Communications of the Association for Information Systems, 26(1), 493-524.
  2. Agarwal, S., & Teas, R. K. (2002). Cross-national applicability of a perceived quality model. Journal of Product & Brand Management, 11(4), 213-236.
  3. Aguinis, H., Dalton, D. R., Bosco, F. A., Pierce, C. A., & Dalton, C. M. (2011). Meta-analytic choices and judgment calls: Implications for theory building and testing, obtained effect sizes, and scholarly impact. Journal of Management, 37(1), 5-38.
  4. Alaiad, A., Zhou, L., & Koru, G. (2013). An empirical study of home healthcare robots adoption using the UTAUT model. Transactions of the International Conference on Health Information Technology Advancement, 2(1), 185-198.
  5. Al-Gahtani, S. S., Hubona, G. S., & Wang, J. (2007). Information technology (IT) in Saudi Arabia: Culture and the acceptance and use of it. Information & Management, 44(8), 681- 691.
  6. Ali, F., Nair, P. K., & Hussain, K. (2016). An assessment of students' acceptance and usage of computer supported collaborative classrooms in hospitality and tourism schools. Journal of Hospitality, Leisure, Sport & Tourism Education, 18, 51-60.
  7. Alvesson, M., & Kärreman, D. (2007). Constructing mystery: Empirical matters in theory development. Academy of Management Review, 32(4), 1265-1281.
  8. Bagozzi, R. P. (2007). The legacy of the Technology Acceptance Model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8(4), 243-254.
  9. Bain, C. D., & Rice, M.L. (2006). The influence of gender on attitudes, perceptions, and uses of technology. Journal of Research on Technology in Education, 39(2), 119-132.
  10. Bala, H., & Venkatesh, V. (2013). Changes in employees' job characteristics during an enterprise system implementation: A latent growth modeling perspective. MIS quarterly, 37(4), 1113-1140.
  11. Balasubramanian, S., Peterson, R. A., & Jarvenpaa, S. L. (2002). Exploring the implications of m-commerce for markets and marketing. Journal of the Academy of Marketing Science, 30(4), 348-361.
  12. Barnett, T., Pearson, A. W., Pearson, R., & Kellermanns, F. W. (2015). Five-factor model personality traits as predictors of perceived and actual usage of technology. European Journal of Information Systems, 24(4), 374-390.
  13. Benbasat, I., & Barki, H. (2007). Quo vadis TAM?. Journal of the Association for Information Systems, 8(4), 212-218.
  14. Bergh, D. D., Aguinis, H., Heavey, C., Ketchen, D. J., Boyd, B. K., Su, P., Lau, C. L., & Joo, H. (2016). Using meta-analytic structural equation modeling to advance strategic management research: Guidelines and an empirical illustration via the strategic leadership-performance relationship. Strategic Management Journal, 37(3), 477-497.
  15. Berry, H., Guillén, M. F., & Zhou, N. (2010). An institutional approach to cross-national distance. Journal of International Business Studies, 41(9), 1460-1480.
  16. Blut, M., Wang, C., & Schoefer, K. (2016). Factors influencing the acceptance of self-service technologies: A meta-analysis. Journal of Service Research, 19(4), 396-416.
  17. Brakus, J. J., Schmitt, B. H., & Zarantonello, L. (2009). Brand experience: What is it? How is it measured? Does it affect loyalty?. Journal of Marketing, 73(3), 52-68.
  18. Brown, S. A., Venkatesh, V., & Goyal, S. (2012). Expectation confirmation in technology use. Information Systems Research, 23(2), 474-487.
  19. Brown, S. A., Venkatesh, V., & Goyal, S. (2014). Expectation confirmation in information systems research. MIS Quarterly, 38(3), 729-756.
  20. Carney, M., Gedajlovic, E. R., Heugens, P. P., Van Essen, M., & Van Oosterhout, J. (2011). Business group affiliation, performance, context, and strategy: A meta-analysis. Academy of Management Journal, 54(3), 437-460.
  21. Chaiken, S. (1980). Heuristic versus systematic information processing and the use of source versus message cues in persuasion. Journal of Personality and Social Psychology, 39(5), 752-766.
  22. Chen, Y. F., & Chen, H. J. (2015). The influence on behaviors of teachers using the interactive electronic whiteboard for teaching at primary schools. International Journal of Humanities Social Sciences and Education, 2(7), 105-111.
  23. Chong, A. Y. L. (2013). Mobile commerce usage activities: The roles of demographic and motivation variables. Technological Forecasting and Social Change, 80(7), 1350-1359.
  24. Chong, A. Y. L., Chan, F. T., & Ooi, K. B. (2012). Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia. Decision Support Systems, 53(1), 34-43.
  25. Colquitt, J. A., LePine, J. A., & Noe, R. A. (2000). Toward an integrative theory of training motivation: A meta-analytic path analysis of 20 years of research. Journal of Applied Psychology, 85(5), 678-707.
  26. Creswell, J. (2002). Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research, Upper Saddle River, NJ: Merrill Prentice Hall.
  27. Dennis, A. R., & Valacich, J. S. (2014). A replication manifesto. AIS Transactions on Replication Research, 1(1), 1-4.
  28. Dinev, T., Goo, J., Hu, Q., & Nam, K. (2009). User behaviour towards protective information technologies: The role of national cultural differences. Information Systems Journal, 19(4), 391-412.
  29. Drees, J. M., & Heugens, P. P. (2013). Synthesizing and extending resource dependence theory: A meta-analysis. Journal of Management, 39(6), 1666-1698.
  30. Eckhardt, A., Laumer, S., & Weitzel, T. (2009). Who influences whom? Analyzing workplace referents' social influence on IT adoption and non-adoption. Journal of Information Technology, 24(1), 11-24.
  31. Franke, G. R., & Richey, R. G. (2010). Improving generalizations from multi-country comparisons in international business research. Journal of International Business Studies, 41(8), 1275-1293.
  32. Gao, Y., Li, H., & Luo, Y. (2015). An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems, 115(9), 1704-1723.
  33. Gerow, J. E., Grover, V., Thatcher, J., & Roth, P. L. (2014). Looking toward the future of IT- business strategic alignment through the past. MIS Quarterly, 38(4), 1159-1186.
  34. Gerstner, C. R., & Day, D. V. (1997). Meta-analytic review of leader-member exchange theory: Correlates and construct issues. Journal of Applied Psychology, 82(6), 827-844.
  35. Geyskens, I., Krishnan, R., Steenkamp, J. B. E., & Cunha, P. V. (2009). A review and evaluation of meta-analysis practices in management research. Journal of Management, 35(2), 393-419.
  36. Geyskens, I., Steenkamp, J. B. E., & Kumar, N. (2006). Make, buy, or ally: A transaction cost theory meta-analysis. Academy of Management Journal, 49(3), 519-543.
  37. Goodhue, D. L. (1995). Understanding user evaluations of information systems. Management Science, 41(12), 1827-1844.
  38. Grewal, D., Puccinelli, N., & Monroe, K. B. (2018). Meta-analysis: Integrating accumulated knowledge. Journal of the Academy of Marketing Science, 46(1), 9-30.
  39. Haythornthwaite, C., & Wellman, B. (2002). The Internet in Everyday Life (pp. 3-41). Oxford: Blackwell.
  40. He, X., Inman, J. J., & Mittal, V. (2008). Gender jeopardy in financial risk taking. Journal of Marketing Research, 45(4), 414-424.
  41. Hennington, A., & Janz, B. D. (2007). Information systems and healthcare XVI: Physician adoption of electronic medical records: Applying the UTAUT model in a healthcare context. Communications of the Association for Information Systems, 19(1), 60-80.
  42. Hew, J. J., Lee, V. H., Ooi, K. B., & Wei, J. (2015). What catalyses mobile apps usage intention: An empirical analysis. Industrial Management & Data Systems, 115(7), 1269- 1291.
  43. Hill, C. W., & Rothaermel, F. T. (2003). The performance of incumbent firms in the face of radical technological innovation. Academy of Management Review, 28(2), 257-274.
  44. Hoehle, H., Zhang, X., & Venkatesh, V. (2015). An espoused cultural perspective to understand continued intention to use mobile applications: A four-country study of mobile social media application usability. European Journal of Information Systems, 24(3), 337- 359.
  45. Hofstede, G. (1980). Motivation, leadership, and organization: Do American theories apply abroad? Organizational Dynamics, 9(1), 42-63.
  46. Hofstede, G. (1991). Cultures and Organizations. Intercultural Cooperation and its Importance for Survival. Software of the Mind. London: McGraw-Hill.
  47. Hofstede, G. (2001). Culture's Consequences: Comparing Values, Behaviors, Institutions and Organizations Across Nations, London: Sage Publications.
  48. Hofstede, G., Hofstede, G. J. & Minkov, M. (2010). Cultures and Organizations: Software of the Mind: Intercultural Cooperation and Its Importance for Survival (3rd Edition). New York, NY: McGraw Hill.
  49. Hollebeek, L. D., Sprott, D. E., Andreassen, T. W., Costley, C., Klaus, P., Kuppelwieser, V., Karahasanovic, A., Taguchi, T., Islam, J. U., & Rather, R. A. (2019). Customer engagement in evolving technological environments: Synopsis and guiding propositions. European Journal of Marketing, 53(9), 2018-2023.
  50. Huang, K. Y., Choi, N., & Chengalur-Smith, I. N. (2010). Cultural dimensions as moderators of the UTAUT model: A research proposal in a healthcare context. Proceedings of the Sixteenth Americas Conference on Information Systems.
  51. Hunter, J. E., & Schmidt, F. L. (2004). Methods of Meta-analysis: Correcting Error and Bias in Research Findings. London: Sage Publications.
  52. Igbaria, M., Greenhaus, J. H., & Parasuraman, S. (1991). Career orientations of MIS employees: An empirical analysis. MIS Quarterly, 15(2), 151-169.
  53. Im, I., Hong, S., & Kang, M. S. (2011). An international comparison of technology adoption: Testing the UTAUT model. Information & Management, 48(1), 1-8.
  54. Im, I., Kim, Y., & Han, H. J. (2008). The effects of perceived risk and technology type on users' acceptance of technologies. Information & Management, 45(1), 1-9.
  55. Iyer, G. R., Blut, M., Xiao, S. H., & Grewal, D. (2020). Impulse buying: A meta-analytic review. Journal of the Academy of Marketing Science, 48(3), 384-404.
  56. Järvinen, J., Ohtonen, R., & Karjaluoto, H. (2016). Consumer acceptance and use of Instagram. 49th Hawaii International Conference on System.
  57. Johns, G. (2006). The essential impact of context on organizational behavior. Academy of Management Review, 31(2), 386-408.
  58. Judge, T. A., Heller, D., & Mount, M. K. (2002). Five-factor model of personality and job satisfaction: A meta-analysis. Journal of Applied Psychology, 87(3), 530-541.
  59. Judge, T. A., & Piccolo, R. F. (2004). Transformational and transactional leadership: A meta- analytic test of their relative validity. Journal of Applied Psychology, 89(5), 755-768.
  60. Jung, I., & Lee, Y. (2015). YouTube acceptance by university educators and students: A cross-cultural perspective. Innovations in Education and Teaching International, 52(3), 243-253.
  61. Kamakura, W., Kopalle, P. K., & Lehmann, D. R. (2014). Empirical generalizations in retailing. Journal of Retailing, 90(2), 121-124.
  62. Karna, A., Richter, A., & Riesenkampff, E. (2016). Revisiting the role of the environment in the capabilities-financial performance relationship: A meta-analysis. Strategic Management Journal, 37(6), 1154-1173.
  63. Khorasanizadeh, H., Honarpour, A., Park, M. S. A., Parkkinen, J., & Parthiban, R. (2016). Adoption factors of cleaner production technology in a developing country: Energy efficient lighting in Malaysia. Journal of Cleaner Production, 131, 97-106.
  64. King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755.
  65. Klein, K. J., Tosi, H., & Cannella Jr., A. A. (1999). Multilevel theory building: Benefits, barriers, and new developments. Academy of Management Review, 24(2), 248-253.
  66. Koernig, S. K. (2003). Escapes: The electronic physical environment and service tangibility. Psychology & Marketing, 20(2), 151-167.
  67. Krishnaraju, V., Mathew, S. K., & Sugumaran, V. (2016). Web personalization for user acceptance of technology: An empirical investigation of e-government service. Information Systems Frontiers, 18(3), 579-595.
  68. Kurtessis, J. N., Eisenberger, R., Ford, M. T., Buffardi, L. C., Stewart, K. A., & Adis, C. S. (2017). Perceived organizational support: A meta-analytic evaluation of organizational support theory. Journal of Management, 43(6), 1854-1884.
  69. Lee, S. G., Trimi, S., & Kim, C. (2013). The impact of cultural differences on technology adoption. Journal of World Business, 48(1), 20-29.
  70. Lenartowicz, T., & Roth, K. (2001). Does subculture within a country matter? A cross- cultural study of motivational domains and business performance in Brazil. Journal of International Business Studies, 32(2), 305-325.
  71. LePine, J. A., Erez, A., & Johnson, D. E. (2002). The nature and dimensionality of organizational citizenship behavior: A critical review and meta-analysis. Journal of Applied Psychology, 87(1), 52-65.
  72. Lian, J. W. (2015). Critical factors for cloud based e-invoice service adoption in Taiwan: An empirical study. International Journal of Information Management, 35(1), 98-109.
  73. Liew, E. J., Vaithilingam, S., & Nair, M. (2014). Facebook and socio-economic benefits in the developing world. Behaviour & Information Technology, 33(4), 345-360.
  74. Loh, X. M., Lee, V. H., Tan, G. W. H., Ooi, K. B., & Dwivedi, Y. K. (2020). Switching from cash to mobile payment: What's the hold-up?. Internet Research, doi.org/10.1108/INTR- 04-2020-0175. · DOI
  75. Lu, J., Yao, J. E., & Yu, C. S. (2005). Personal innovativeness, social influences and adoption of wireless internet services via mobile technology. Journal of Strategic Information Systems, 14(3), 245-268.
  76. Ma, Y., & Turel, O. (2019). Information technology use for work and technostress: Effects of power distance and masculinity culture dimension. Cognition, Technology & Work, 21(1), 145-157.
  77. MacInnis, D. J. (2011). A framework for conceptual contributions in marketing. Journal of Marketing, 75(4), 136-154.
  78. Madden, T. J., Ellen, P. S., & Ajzen, I. (1992). A comparison of the theory of planned behavior and the theory of reasoned action. Personality and Social Psychology Bulletin, 18(1), 3-9.
  79. Martins, C., Oliveira, T., & Popovič, 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.
  80. Matusitz, J., & Musambira, G. (2013). Power distance, uncertainty avoidance, and technology: Analyzing Hofstede's dimensions and human development indicator. Journal of Technology in Human Services, 31(1), 42-60.
  81. McShane, B. B., & Böckenholt, U. (2017). Single-paper meta-analysis: Benefits for study summary, theory testing, and replicability. Journal of Consumer Research, 43(6), 1048- 1063.
  82. Meuter, M. L., Ostrom, A. L., Roundtree, R. I., & Bitner, M. J. (2000). Self-service technologies: Understanding customer satisfaction with technology-based service encounters. Journal of Marketing, 64(3), 50-64.
  83. Miller, C. C., Glick, W. H., Wang, Y. D., & Huber, G. P. (1991). Understanding technology- structure relationships: Theory development and meta-analytic theory testing. Academy of Management Journal, 34(2), 370-399.
  84. Morosan, C., & DeFranco, A. (2016). It's about time: Revisiting UTAUT2 to examine consumers' intentions to use NFC mobile payments in hotels. International Journal of Hospitality Management, 53, 17-29.
  85. Muncer, S. J., Craigie, M., & Holmes, J. (2003). Meta-analysis and power: Some suggestions for the use of power in research synthesis. Understanding Statistics: Statistical Issues in Psychology, Education, and the Social Sciences, 2(1), 1-12.
  86. Nelson, M. R., & Shavitt, S. (2002). Horizontal and vertical individualism and achievement values: A multimethod examination of Denmark and the United States. Journal of Cross- Cultural Psychology, 33(5), 439-458.
  87. Notani, A. S. (1998). Moderators of perceived behavioral control's predictiveness in the theory of planned behavior: A meta-analysis. Journal of Consumer Psychology, 7(3), 247- 271.
  88. Novak, T. P., Hoffman, D. L., & Duhachek, A. (2003). The influence of goal-directed and experiential activities on online flow experiences. Journal of Consumer Psychology, 13(1- 2), 3-16.
  89. Orsingher, C., Hogreve, J., & Ordanini, A. (2016). Building on the past: Advancing theory in services through meta-analysis. Journal of Service Management, 27(1), 37-42.
  90. Ozturk, A. B., Nusair, K., Okumus, F., & Hua, N. (2016). The role of utilitarian and hedonic values on users' continued usage intention in a mobile hotel booking environment. International Journal of Hospitality Management, 57, 106-115.
  91. Palmatier, R. W., Dant, R. P., Grewal, D., & Evans, K. R. (2006). Factors influencing the effectiveness of relationship marketing: A meta-analysis. Journal of Marketing, 70(4), 136-153.
  92. Pan, Y., & Zinkhan, G. M. (2006). Determinants of retail patronage: A meta-analytical perspective. Journal of Retailing, 82(3), 229-243.
  93. Petter, S., & McLean, E. R. (2009). A meta-analytic assessment of the DeLone and McLean IS success model: An examination of IS success at the individual level. Information & Management, 46(3), 159-166.
  94. Powell, A., Williams, C. K., Bock, D. B., Doellman, T., & Allen, J. (2012). E-voting intent: A comparison of young and elderly voters. Government Information Quarterly, 29(3), 361- 372.
  95. Pramatari, K., & Theotokis, A. (2009). Consumer acceptance of RFID-enabled services: A model of multiple attitudes, perceived system characteristics and individual traits. European Journal of Information Systems, 18(6), 541-552.
  96. Rahi, S., & Ghani, M. A. (2018). The role of UTAUT, DOI, perceived technology security and game elements in internet banking adoption. World Journal of Science, Technology and Sustainable Development, 28(5), 410-432.
  97. Rai, A., Maruping, L. M., & Venkatesh, V. (2009). Offshore information systems project success: The role of social embeddedness and cultural characteristics. MIS Quarterly, 33(3), 617-641.
  98. Ramayah, T., & Ignatius, J. (2005). Impact of perceived usefulness, perceived ease of use and perceived enjoyment on intention to shop online. ICFAI Journal of Systems Management, 3(3), 36-51.
  99. Samaha, S. A., Beck, J. T., & Palmatier, R. W. (2014). The role of culture in international relationship marketing. Journal of Marketing, 78(5), 78-98.
  100. Shaw, J. D., & Ertug, G. (2017). The suitability of simulations and meta-analyses for submissions to Academy of Management Journal. Academy of Management Journal, 60(6), 2045-2049.
  101. Sleesman, D. J., Conlon, D. E., McNamara, G., & Miles, J. E. (2012). Cleaning up the big muddy: A meta-analytic review of the determinants of escalation of commitment. Academy of Management Journal, 55(3), 541-562.
  102. Sorrentino, R. M., Bobocel, D. R., Gitta, M. Z., Olson, J. M., & Hewitt, E. C. (1988). Uncertainty orientation and persuasion: Individual differences in the effects of personal relevance on social judgments. Journal of Personality and Social Psychology, 55(3), 357- 371.
  103. Srite, M., & Karahanna, E. (2006). The role of espoused national cultural values in technology acceptance. MIS Quarterly, 30(3), 679-704.
  104. Steenkamp, J. B. E., & Geyskens, I. (2006). How country characteristics affect the perceived value of web sites. Journal of Marketing, 70(3), 136-150.
  105. Straub, D. W. (2009). Editor's comments: Creating blue oceans of thought via highly citable articles. MIS Quarterly, 33(4), 3-7.
  106. Straub, D., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information & Management, 33(1), 1-11.
  107. Straub, D., Loch, K., Evaristo, R., Karahanna, E., & Srite, M. (2002). Toward a theory-based measurement of culture. Journal of Global Information Management, 10(1), 13-23.
  108. Sun, H., & Zhang, P. (2006). The role of moderating factors in user technology acceptance. International Journal of Human-Computer Studies, 64(2), 53-78.
  109. Swoboda, B., Puchert, C., & Morschett, D. (2016). Explaining the differing effects of corporate reputation across nations: A multilevel analysis. Journal of the Academy of Marketing Science, 44(4), 454-473.
  110. Sykes, T. A., & Venkatesh, V. (2017). Explaining post-implementation employee system use and job performance: Impacts of the content and source of social network ties. MIS Quarterly, 41(3), 917-936.
  111. Taiwo, A. A., & Downe, A. G. (2013). The theory of user acceptance and use of technology (UTAUT): A meta-analytic review of empirical findings. Journal of Theoretical & Applied Information Technology, 49(1), 48-58.
  112. Tan, P. J. B. (2013). Applying the UTAUT to understand factors affecting the use of English e-learning websites in Taiwan. SAGE Open, 3(4), 1-12.
  113. Taylor, M. C., & Hall, J. A. (1982). Psychological androgyny: Theories, methods, and conclusions. Psychological Bulletin, 92(2), 347-366.
  114. Teo, A. C., Tan, G. W. H., Ooi, K. B., Hew, T. S., & Yew, K. T. (2015). The effects of convenience and speed in m-payment. Industrial Management & Data Systems, 115(2), 311-331.
  115. Thatcher, J. B., Loughry, M. L., Lim, J., & McKnight, D. H. (2007). Internet anxiety: An empirical study of the effects of personality, beliefs, and social support. Information & Management, 44(4), 353-363.
  116. Thong, J. Y., Venkatesh, V., Xu, X., Hong, S. J., & Tam, K. Y. (2011). Consumer acceptance of personal information and communication technology services. IEEE Transactions on Engineering Management, 58(4), 613-625.
  117. Triandis, H. C., & Gelfand, M. J. (1998). Converging measurement of horizontal and vertical individualism and collectivism. Journal of Personality and Social Psychology, 74(1), 118- 128.
  118. Tsang, E. W., & Kwan, K. M. (1999). Replication and theory development in organizational science: A critical realist perspective. Academy of Management Review, 24(4), 759-780.
  119. Udo, G., Bagchi, K., & Maity, M. (2016). Exploring factors affecting digital piracy using the norm activation and UTAUT models: the role of national culture. Journal of Business Ethics, 135(3), 517-541.
  120. Van Eerde, W., & Thierry, H. (1996). Vroom's expectancy models and work-related criteria: A meta-analysis. Journal of Applied Psychology, 81(5), 575-586.
  121. Venkatesh, V., Brown, S. A., & Bala, H. (2013). Bridging the qualitative-quantitative divide: Guidelines for conducting mixed methods research in information system. MIS quarterly, 37(1), 21-54.
  122. Venkatesh, V., Brown, S. A., & Sullivan, Y. W. (2016). Guidelines for conducting mixed- methods research: An extension and illustration. Journal of the Association for Information Systems, 17(7), 435-494.
  123. Venkatesh, V., & Goyal, S. (2010). Expectation disconfirmation and technology adoption: Polynomial modeling and response surface analysis. MIS Quarterly, 34(2), 281-303.
  124. Venkatesh, V., Maruping, L. M., & Brown, S. A. (2006). Role of time in self-prediction of behavior. Organizational Behavior and Human Decision Processes, 100(2), 160-176.
  125. Venkatesh, V., & Morris, M. G. (2000). Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115-139.
  126. Venkatesh, V., & Sykes, T. A. (2013). Digital divide initiative success in developing countries: A longitudinal field study in a village in India. Information Systems Research, 24(2), 239-260.
  127. Venkatesh, V., Sykes, T. A., Aljafari, R., & Poole, M. S. (2021). The future is now: Calling for a focus on temporal issues in information system research. Industrial Management & Data Systems, 121(1), 30-47.
  128. Alaiad, A., Zhou, L., & Koru, G. (2013). An empirical study of home healthcare robots adoption using the UTAUT model. Transactions of the International Conference on Health Information Technology Advancement, 2(1), 185-198.
  129. Alshare, K., & Mousa, A. (2014). The moderating effect of espoused cultural dimensions on consumer's intention to use mobile payment devices. Proceedings of the 35 th International Conference on Information Systems.
  130. 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.
  131. Carter, L., & Schaupp, L.C. (2008). Efficacy and acceptance in e-file adoption. Proceedings of the American Conference for Information Systems.
  132. Casey, T., & Wilson-Evered, E. (2012). Predicting uptake of technology innovations in online family dispute resolution services: An application and extension of the UTAUT. Computers in Human Behavior, 2(6), 2034-2045.
  133. Chiu, C. M., & Wang, E. T. (2008). Understanding web-based learning continuance intention: The role of subjective task value. Information & Management, 45(3), 194-201.
  134. Guo, Y., & Barnes, S. J. (2011). Purchase behavior in virtual worlds: An empirical investigation in second life. Information & Management, 48(7), 303-312.
  135. Guo, Y., & Barnes, S. J. (2012). Explaining purchasing behavior within world of warcraft. Journal of Computer Information Systems, 52(3), 18-30.
  136. Hess, T. J., Joshi, K., & McNab, A. L. (2010). An alternative lens for understanding technology acceptance: An equity comparison perspective. Journal of Organizational Computing and Electronic Commerce, 20(2), 123-154.
  137. Loose, M., Weeger, A., & Gewald, H. (2013). BYOD-the next big thing in recruiting? Examining the determinants of BYOD service adoption behavior from the perspective of future employees. Proceedings of the 19th Americas Conference on Information Systems.
  138. Martins, C., Oliveira, T., & Popovič, 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.
  139. McKenna, B., Tuunanen, T., & Gardner, L. (2013). Consumers' adoption of information services. Information & Management, 50(5), 248-257.
  140. McLeod, A., Pippin, S., & Catania, V. (2009). Using technology acceptance theory to model individual differences in tax software use. Proceedings of the American Conference for Information Systems.
  141. Oh, J. C., & Yoon, S. J. (2014). Predicting the use of online information services based on a modified UTAUT model. Behaviour & Information Technology, 33(7), 716-729.
  142. Oliveira, T., Faria, M., Thomas, M. A., & Popovič, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34(5), 689-703.
  143. Saeed, K. A. (2013). Perceived financial control and acceptance of mobile banking services: An empirical assessment. Proceedings of the 19th Americas Conference on Information Systems.
  144. Schaupp, L. C., Carter, L., & McBride, M. E. (2010). E-file adoption: A study of us taxpayers' intentions. Computers in Human Behavior, 26(4), 636-644.
  145. Sun, Y., Liu, L., Peng, X., Dong, Y., & Barnes, S. J. (2014). Understanding Chinese users' continuance intention toward online social networks: An integrative theoretical model. Electronic Markets, 24(1), 57-66.
  146. Venkatesh, V., Thong, J. Y., Chan, F. K., Hu, P. J. H., & Brown, S. A. (2011). Extending the two-stage information systems continuance model: Incorporating UTAUT predictors and the role of context. Information Systems Journal, 21(6), 527-555.
  147. Wang, T., Jung, C. H., Kang, M. H., & Chung, Y. S. (2014). Exploring determinants of adoption intentions towards enterprise 2.0 applications: An empirical study. Behaviour & Information Technology, 33(10), 1048-1064.
  148. Wang, Y. Y., Townsend, A., Luse, A., & Mennecke, B. (2012). The determinants of acceptance of recommender systems: Applying the UTAUT model. Proceedings of the 18th Americas Conference on Information Systems.
  149. Xiong, J., Qureshi, S., & Najjar, L. (2013). Factors that affect information and communication technology adoption by small businesses in China. Proceedings of the 19th Americas Conference on Information Systems.
  150. Yuen, Y. Y., Yeow, P. H., Lim, N., & Saylani, N. (2010). Internet banking adoption: Comparing developed and developing countries. Journal of Computer Information Systems, 51(1), 52-61.
  151. Bourdon, I., & Sandrine, O. (2009). Towards an understanding of knowledge management systems: UTAUT revisited. Proceedings of the 15th Americas Conference on Information Systems.
  152. 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.
  153. Lallmahomed, M. Z., Rahim, N. Z. A., Ibrahim, R., & Rahman, A. A. (2013). Predicting different conceptualizations of system use: Acceptance in hedonic volitional context (Facebook). Computers in Human Behavior, 29(6), 2776-2787.
  154. Liew, E. J., Vaithilingam, S., & Nair, M. (2014). Facebook and socio-economic benefits in the developing world. Behaviour & Information Technology, 33(4), 345-360.
  155. Oliveira, T., Faria, M., Thomas, M. A., & Popovič, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34(5), 689-703.
  156. Alshare, K., & Mousa, A. (2014). The moderating effect of espoused cultural dimensions on consumer's intention to use mobile payment devices. Proceedings of the 35 th International Conference on Information Systems.
  157. Al-Gahtani, S. S., Hubona, G. S., & Wang, J. (2007). Information technology (IT) in Saudi Arabia: Culture and the acceptance and use of it. Information & Management, 44(8), 681-691.
  158. 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.
  159. Dasgupta, S., & Gupta, B. (2012). Impact of organizational culture on technology use in a developing country. Proceedings of the 17th Americas Conference on Information Systems.
  160. Guo, Y., & Barnes, S. J. (2012). Explaining purchasing behavior within world of warcraft. Journal of Computer Information Systems, 52(3), 18-30.
  161. Hess, T. J., Joshi, K., & McNab, A. L. (2010). An alternative lens for understanding technology acceptance: An equity comparison perspective. Journal of Organizational Computing and Electronic Commerce, 20(2), 123-154.
  162. Im, I., Hong, S., & Kang, M. S. (2011). An international comparison of technology adoption: Testing the UTAUT model. Information & Management, 48(1), 1-8.
  163. Liew, E. J., Vaithilingam, S., & Nair, M. (2014). Facebook and socio-economic benefits in the developing world. Behaviour & Information Technology, 33(4), 345-360.
  164. Lu, J., Yu, C. S., & Liu, C. (2009). Mobile data service demographics in urban China. Journal of Computer Information Systems, 50(2), 117-126.
  165. Martins, C., Oliveira, T., & Popovič, 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.
  166. McLeod, A., Pippin, S., & Catania, V. (2009). Using technology acceptance theory to model individual differences in tax software use. Proceedings of the American Conference for Information Systems.
  167. Niehaves, B., & Plattfaut, R. (2010). What is the issue with internet acceptance among elderly citizens? Theory development and policy recommendations for inclusive e-government. Proceedings of The International Conference on Electronic Government.
  168. Oh, J. C., & Yoon, S. J. (2014). Predicting the use of online information services based on a modified UTAUT model. Behaviour & Information Technology, 33(7), 716-729.
  169. Park, S. H., Lee, L., & Yi, M. Y. (2011). Group-level effects of facilitating conditions on individual acceptance of information systems. Information Technology and Management, 12(4), 315-334.
  170. Thong, J. Y., Venkatesh, V., Xu, X., Hong, S. J., & Tam, K. Y. (2011). Consumer acceptance of personal information and communication technology services. IEEE Transactions on Engineering Management, 58(4), 613-625.
  171. Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system use: The competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Quarterly, 32(3), 483-502.
  172. Venkatesh, V., & Zhang, X. (2010). Unified theory of acceptance and use of technology: US vs. China. Journal of Global Information Technology Management, 13(1), 5-27.
  173. Wang, T., Jung, C. H., Kang, M. H., & Chung, Y. S. (2014). Exploring determinants of adoption intentions towards enterprise 2.0 applications: An empirical study. Behaviour & Information Technology, 33(10), 1048-1064.
  174. Wang, Y. Y., Townsend, A., Luse, A., & Mennecke, B. (2012). The determinants of acceptance of recommender systems: Applying the UTAUT model. Proceedings of the 18th Americas Conference on Information Systems.
  175. Yuen, Y. Y., Yeow, P. H., Lim, N., & Saylani, N. (2010). Internet banking adoption: Comparing developed and developing countries. Journal of Computer Information Systems, 51(1), 52-61. References
  176. Academic Journal Guide. (2015). Academic Journal Guide 2015. Chartered Association of Business Schools [available at www.charteredabs.org].
  177. Angst, C. M., & Agarwal, R. (2009). Adoption of electronic health records in the presence of privacy concerns: The elaboration likelihood model and individual persuasion. MIS Quarterly, 33(2), 339-370.
  178. Balasubramanian, S., Peterson, R. A., & Jarvenpaa, S. L. (2002). Exploring the implications of m-commerce for markets and marketing. Journal of the Academy of Marketing Science, 30(4), 348-361.
  179. Benbasat, I., & Barki, H. (2007). Quo vadis TAM?. Journal of the Association for Information Systems, 8(4), 212-218.
  180. Bhatti, H. S., Abareshi, A., & Pittayachawan, S. (2017). Towards the investigation of the effect of customer satisfaction and customer experience on behavioural intention in mobile telecommunication services in Australia. 2017 International Conference on Research and Innovation in Information Systems (ICRIIS).
  181. Blut, M., Chowdhry, N., Mittal, V., & Brock, C. (2015). E-service quality: A meta-analytic review. Journal of Retailing, 91(4), 679-700.
  182. 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.
  183. Debuse, J. C., Lawley, M., & Shibl, R. (2008). Educators' perceptions of automated feedback systems. Australasian Journal of Educational Technology, 24(4), 374-386.
  184. Ellis, P. (2010). The Essential Guide to Effect Sizes: An Introduction to Statistical Power, Meta-Analysis and the Interpretation of Research Results. Cambridge: University Press.
  185. Field, A. P. (2001). Meta-analysis of correlation coefficients: A Monte Carlo comparison of fixed-and random-effects method. Psychological Methods, 6(2), 161-180.
  186. Gerow, J. E., Grover, V., Thatcher, J., & Roth, P. L. (2014). Looking toward the future of IT- business strategic alignment through the past. MIS Quarterly, 38(4), 1159-1186.
  187. Hofstede, G. (2001). Culture's Consequences: Comparing Values, Behaviors, Institutions and Organizations Across Nations, London: Sage Publications.
  188. Hu, T., Zhang, P., Zhang, X., & Dai, H. (2009). Gender differences in internet use: A logistic regression analysis. AMCIS 2009 Proceedings.
  189. Hunter, J. E., & Schmidt, F. L. (2004). Methods of Meta-analysis: Correcting Error and Bias in Research Findings. London: Sage Publications.
  190. Kim, H. W., Chan, H. C., & Gupta, S. (2016). Examining information systems infusion from a user commitment perspective. Information Technology & People, 29(1), 173-199.
  191. Li, W. (2010). Virtual knowledge sharing in a cross-cultural context. Journal of Knowledge Management, 14(1), 38-50.
  192. Meuter, M. L., Ostrom, A. L., Roundtree, R. I., & Bitner, M. J. (2000). Self-service technologies: Understanding customer satisfaction with technology-based service encounters. Journal of Marketing, 64(3), 50-64.
  193. Muncer, S. J., Craigie, M., & Holmes, J. (2003). Meta-analysis and power: Some suggestions for the use of power in research synthesis. Understanding Statistics: Statistical Issues in Psychology, Education, and the Social Sciences, 2(1), 1-12.
  194. Neufeld, D. J., Dong, L., & Higgins, C. (2007). Charismatic leadership and user acceptance of information technology. European Journal of Information Systems, 16(4), 494-510.
  195. Park, J., & Yang, S. (2006). The moderating role of consumer trust and experiences: Value driven usage of mobile technology. International Journal of Mobile Marketing, 1(2), 24- 37.
  196. Pick, D., & Eisend, M. (2016). Customer responses to switching costs: A meta-analytic investigation of the moderating influence of culture. Journal of International Marketing, 24(4), 39-60.
  197. Samaha, S. A., Beck, J. T., & Palmatier, R. W. (2014). The role of culture in international relationship marketing. Journal of Marketing, 78(5), 78-98.
  198. van Setten, M., Veenstra, M., Nijholt, A., & van Dijk, B. (2006). Goal-based structuring in recommender systems. Interacting with Computers, 18(3), 432-456.
  199. 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.
  200. 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. MIS Quarterly, 36(1), 157-178.

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Metadata

Authors
Blut, M., Durham University Business School, U., Chong, A. Y. L., Nottingham University Business School–Ningbo, C., Tsigna, Z., Nottingham University Business School–Ningbo, C., Venkatesh, V., & Pamplin College of Business, V. T.
Venue
Journal of the Association for Information Systems
Publisher
Association for Information Systems
Published
2022
Volume
23
Issue
1
Pages
13-95
ISSN
1536-9323
DOI
10.17705/1jais.00719
Cited by
228
References
24
Language
en

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