A Bibliometric Analysis of Articles Citing the Unified Theory of Acceptance and Use of Technology

  • Michael D. Williams
  • Nripendra P. Rana
  • Yogesh K. Dwivedi
Chapter
Part of the Integrated Series in Information Systems book series (ISIS, volume 28)

Abstract

Despite the relatively recent emergence of the Unified Theory of Acceptance and Use of Technology (UTAUT), the originating article has already been cited by a large number of studies, and hence it appears to have become a popular theoretical choice within the field of information system (IS)/information technology (IT) adoption and diffusion. However, as yet there have been no attempts to analyse the reasons for citing the originating article. Such a systematic review of citations may inform researchers and guide appropriate future use of the theory. This chapter therefore presents the results of a bibliometric analysis and systematic review of 450 citations of the originating article in an attempt to better understand the reasons for citation, and use and adaptations of the theory. Findings revealed that although a large number of studies have cited the originating article since its appearance, only 43 actually utilized the theory or its constructs in their empirical research for examining IS/IT-related issues. This chapter also classifies and discusses these citations and explores the limitations of UTAUT use in existing research.

Keywords

Adoption Diffusion Bibliometric Analysis Systematic Review UTAUT TAM Information Systems Information Technology 

Acronyms

IT

Information technology

IS

Information systems

UTAUT

Unified theory of acceptance and use of technology

TAM

Technology acceptance model

TPB

Theory of planned behaviour

TRA

Theory of reasoned action

MPCU

Model of PC utilization

IDT

Innovation diffusion theory

SCT

Social cognitive theory

References

  1. Abu-Shanab, E., & Pearson, M. (2009). Internet banking in Jordan: An Arabic instrument validation process. International Arab Journal of Information Technology, 6(3), 235–244.Google Scholar
  2. Adriaanse, A., Voordijk, H., & Dewulf, G. (2010). The use of inter organizational ICT in United States construction projects. Automation in Construction, 19(1), 73–83.CrossRefGoogle Scholar
  3. Aggelidis, V. P., & Chatzoglou, P. D. (2009). Using a modified technology acceptance model in hospitals. International Journal of Medical Informatics, 78(2), 115–126.CrossRefGoogle Scholar
  4. Ahmad, T. B. T., Madarsha, K. B., Zainuddin, A. M., Ismail, N. A. H., & Nordin, M. S. (2010). Faculty’s acceptance of computer based technology: Cross-validation of an extended model. Australasian Journal of Educational Technology, 26(2), 268–279.Google Scholar
  5. Akesson, M., & Eriksson, C. I. (2007). The vision of ubiquitous media services: How close are we? In M. J. Smith & G. Salvendy (Eds.), Human interface and the management of information: Interacting in information environments, Pt 2, Proceedings (Vol. 4558, pp. 222–232). Berlin: Springer.CrossRefGoogle Scholar
  6. Alapetite, A., Andersen, H. B., & Hertzum, M. (2009). Acceptance of speech recognition by physicians: A survey of expectations, experiences, and social influence. International Journal of Human Computer Studies, 67(1), 36–49.CrossRefGoogle Scholar
  7. 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.CrossRefGoogle Scholar
  8. Al-Natour, S., & Benbasat, I. (2009). The adoption and use of IT artefacts: A new interaction-centric model for the study of user-artefact relationships. Journal of the Association for Information Systems, 10(9), 661–685.Google Scholar
  9. Al-Senaidi, S., Lin, L., & Poirot, J. (2009). Barriers to adopting technology for teaching and learning in Oman. Computers & Education, 53(3), 575–590.CrossRefGoogle Scholar
  10. Andreatta, P. B., Hillard, M., & Krain, L. P. (2010). The impact of stress factors in simulation-based laparoscopic training. Surgery, 147(5), 631–639.CrossRefGoogle Scholar
  11. 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.Google Scholar
  12. Arbaugh, J. B., Godfrey, M. R., Johnson, M., Pollack, B. L., Niendorf, B., & Wresch, W. (2009). Research in online and blended learning in the business disciplines: Key findings and possible future directions. Internet and Higher Education, 12(2), 71–87.CrossRefGoogle Scholar
  13. Baron, S., Patterson, A., & Harris, K. (2006). Beyond technology acceptance: Understanding consumer practice. International Journal of Service Industry Management, 17(2), 111–135.CrossRefGoogle Scholar
  14. Baumgartner, I., & Green, P. (2008). Adoption of service oriented computing from the IT professionals’ perspective: An e-Government case study. In D. Avison, G. M. Kasper, B. Pernici, I. Ramos, & D. Roode (Eds.), Advances in Information Systems Research, education and practice (Vol. 274, pp. 189–201). New York: Springer.CrossRefGoogle Scholar
  15. Benbasat, I., & Barki, H. (2007). Quo Vadis, TAM. Journal of the Association for Information Systems, 8(4), 211–218 (Article 3).Google Scholar
  16. Brandtzaeg, P. B., & Heim, J. (2009). Why people use social networking sites. In A. A. Ozok & P. Zaphiris (Eds.), Online communities and social computing, proceedings (Vol. 5621, pp. 143–152). Berlin: Springer.CrossRefGoogle Scholar
  17. Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 29(3), 399–426.Google Scholar
  18. Carter, L., & Bélanger, F. (2005). The utilization of e-government services: Citizen trust, innovation and acceptance factors. Information Systems Journal, 15(1), 2–25.CrossRefGoogle Scholar
  19. Carter, L., & Weerakkody, V. (2008). E-government adoption: A cultural comparison. Information Systems Frontiers, 10(4), 473–482.CrossRefGoogle Scholar
  20. Chang, H. H. (2008). Intelligent agent’s technology characteristics applied to online auctions’ task: A combined model of TTF and TAM. Technovation, 28(9), 564–577.CrossRefGoogle Scholar
  21. Chang, I. C., Hwang, H. G., Hung, W. F., & Li, Y. C. (2007). Physicians’ acceptance of pharmacokinetics-based clinical decision support systems. Expert Systems with Applications, 33(2), 296–303.CrossRefGoogle Scholar
  22. Chiu, C. M., Huang, H. Y., & Yen, C. H. (2010). Antecedents of trust in online auctions. Electronic Commerce Research and Applications, 9(2), 148–159.CrossRefGoogle Scholar
  23. Chiu, C. M., & Wang, E. T. G. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information Management, 45(3), 194–201.CrossRefGoogle Scholar
  24. Curtis, L., Edwards, C., Fraser, K. L., Gudelsky, S., Holmquist, J., & Thornton, K. (2010). Adoption of social media for public relations by non-profit organizations. Public Relations Review, 36(1), 90–92.CrossRefGoogle Scholar
  25. Dadayan, L., & Ferro, E. (2005). When technology meets the mind: A comparative study of the technology acceptance model. In M. A. Wimmer, R. Traunmuller, A. Gronlund, & K. V. Andersen (Eds.), Electronic government, proceedings (Vol. 3591, pp. 137–144). Springer-Verlag Berlin Heidelberg.Google Scholar
  26. Debuse, J. C. W., Lawley, M., & Shibl, R. (2008). Educators’ perceptions of automated feedback systems. Australasian Journal of Educational Technology, 24(4), 374–386.Google Scholar
  27. Dinev, T., Hart, P., & Mullen, M. R. (2008). Internet privacy concerns and beliefs about government surveillance – An empirical investigation. The Journal of Strategic Information Systems, 17(3), 214–233.CrossRefGoogle Scholar
  28. Duyck, P., Pynoo, B., Devolder, P., Voet, T., Adang, L., & Ovaere, D. (2010). Monitoring the PACS implementation process in a large University Hospital – discrepancies between radiologists and physicians. Journal of Digital Imaging, 23(1), 73–80.CrossRefGoogle Scholar
  29. Dwivedi, Y. K., Lal, B., Mustafee, N., & Williams, M. D. (2009). Profiling a decade of Information Systems Frontiers’ research. Information Systems Frontiers, 7(4–5), 337–358.Google Scholar
  30. Dwivedi, Y. K., Williams, M. D., & Lal, B. (2008). The diffusion of research on the adoption and diffusion of information technology. In L. Gonzalo, A. Bernardos, J. Casar, K. Kautz, & J. DeGross (Eds.), Proceedings of the IFIP8.6 Conference. Boston: Springer.Google Scholar
  31. Green, G. C., Hevner, A. R., & Collins, R. W. (2005). The impacts of quality and productivity perceptions on the use of software process improvement innovations. Information and Software Technology, 47(8), 543–553.CrossRefGoogle Scholar
  32. Gu, J. C., Lee, S. C., & Suh, Y. H. (2009). Determinants of behavioral intention to mobile banking. Expert Systems with Applications, 36(9), 11605–11616.CrossRefGoogle Scholar
  33. Gumussoy, C. A., & Calisir, F. (2009). Understanding factors affecting e-reverse auction use: An integrative approach. Computers in Human Behavior, 25(4), 975–988.CrossRefGoogle Scholar
  34. Gupta, B., Dasgupta, S., & Gupta, A. (2008). Adoption of ICT in a government organization in a developing country: An empirical study. The Journal of Strategic Information Systems, 17(2), 140–154.CrossRefGoogle Scholar
  35. Hardeman, W., Johnston, M., Johnston, D., Bonetti, D., Wareham, N., & Kinmonth, A. L. (2002). Application of the theory of planned behaviour in behaviour change interventions: A systematic review. Psychology & Health, 17(2), 123–158.CrossRefGoogle Scholar
  36. He, D. H., Lu, Y. B., & Alfred, U. (2007). An integrated framework for mobile business acceptance. Alfred: Alfred University.Google Scholar
  37. Hung, Y. H., Wang, Y. S., & Chou, S. C. T. (2007). User acceptance of E-Government services. Kaohsiung: Natl Sun Yat-Sen University.Google Scholar
  38. Huser, V., Narus, S. P., & Rocha, R. A. (2010). Evaluation of a flowchart-based EHR query system: A case study of RetroGuide. Journal of Biomedical Informatics, 43(1), 41–50.CrossRefGoogle Scholar
  39. Jasperson, J., Carter, P. E., & Zmud, R. W. (2005). A comprehensive conceptualization of post-adoptive behavior associated with information technology enabled work systems. MIS Quarterly, 29(3), 525–557.Google Scholar
  40. Jong, D., & Wang, T. S. (2009). Student acceptance of Web-based learning system. Oulu: Acad Publ.Google Scholar
  41. Ke, W. L., Xue, Y. J., Liang, H. G., & Wei, K. K. (2006). Understanding team influence on professionals’ acceptance of large-scale systems. Kaohsiung: Natl Sun Yat-Sen University.Google Scholar
  42. Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand’s community health centers: Applying the UTAUT model. International Journal of Medical Informatics, 78(6), 404–416.CrossRefGoogle Scholar
  43. Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310–322.CrossRefGoogle Scholar
  44. Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management Science, 51(5), 741–755.Google Scholar
  45. King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information Management, 43, 740–755.CrossRefGoogle Scholar
  46. Koivumaki, T., Ristola, A., & Kesti, M. (2008). The perceptions towards mobile services: An empirical analysis of the role of use facilitators. Personal and Ubiquitous Computing, 12(1), 67–75.CrossRefGoogle Scholar
  47. Komiak, S. Y. X., & Benbasat, I. (2006). The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS Quarterly, 30(4), 941–960.Google Scholar
  48. Kwon, O., & Wen, Y. (2010). An empirical study of the factors affecting social network service use. Computers in Human Behavior, 26(2), 254–263.CrossRefGoogle Scholar
  49. Lai, J. Y., & Chen, W. H. (2009). Measuring e-business dependability: The employee perspective. The Journal of Systems and Software, 82(6), 1046–1055.CrossRefGoogle Scholar
  50. Lai, C. S. K., & Pires, G. (2009). Testing of a model evaluating e-Government portal acceptance and satisfaction. Nr Reading: Academic Conferences Ltd.Google Scholar
  51. Laumer, S., Eckhardt, A., & Trunk, N. (2010). Do as your parents say? Analyzing IT adoption influencing factors for full and under age applicants. Information Systems Frontiers, 12(2), 169–183.CrossRefGoogle Scholar
  52. Lean, O. K., Zailani, S., Ramayah, T., & Fernando, Y. (2009). Factors influencing intention to use e-government services among citizens in Malaysia. International Journal of Information Management, 29(6), 458–475.CrossRefGoogle Scholar
  53. Lee, W. Y., Goodwin, P., Fildes, R., Nikolopoulos, K., & Lawrence, M. (2007). Providing support for the use of analogies in demand forecasting tasks. International Journal of Forecasting, 23(3), 377–390.CrossRefGoogle Scholar
  54. Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for Information System, 12(50), 752–780.Google Scholar
  55. Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information Management, 40(3), 191–204.CrossRefGoogle Scholar
  56. Li, W. (2010). Virtual knowledge sharing in a cross-cultural context. Journal of Knowledge Management, 14(1), 38–50.CrossRefGoogle Scholar
  57. Lin, C. P., & Anol, B. (2008). Learning online social support: An investigation of network information technology based on UTAUT. Cyber psychology & Behavior, 11(3), 268–272.Google Scholar
  58. Liu, D. S., & Chen, W. (2009). An empirical research on the determinants of user M-commerce acceptance. In R. Lee & N. Ishii (Eds.), Software engineering, artificial intelligence, networking and parallel/distributed computing (Vol. 209, pp. 93–104). Springer-Verlag Berlin Heidelberg.Google Scholar
  59. Loo, W. H., Yeow, P. H. P., & Chong, S. C. (2009). User acceptance of Malaysian government multipurpose smartcard applications. Government Information Quarterly, 26(2), 358–367.CrossRefGoogle Scholar
  60. 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.Google Scholar
  61. Luo, X., Li, H., Zhang, J., & Shim, J. P. (2010). Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services. Decision Support Systems, 49(2), 222–234.CrossRefGoogle Scholar
  62. Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common method variance in IS research: A comparison of alternative approaches and a reanalysis of past research. Management Science, 52(12), 1865–1883.Google Scholar
  63. Nov, O., & Ye, C. (2009). Resistance to change and the adoption of digital libraries: An integrative model. Journal of the American Society for Information Science and Technology, 60(8), 1702–1708.CrossRefGoogle Scholar
  64. Pappas, F. C., & Volk, F. (2007). Audience counts and reporting system: Establishing a cyber-infrastructure for museum educators. Journal of Computer-Mediated Communication, 12(2), 17.CrossRefGoogle Scholar
  65. Petter, S., Straub, D., & Rai, A. (2007). Specifying formative constructs in information systems research. Mis Quarterly, 31(4), 623–656.Google Scholar
  66. Richard, J. E., Thirkell, P. C., & Huff, S. L. (2007). An examination of customer relationship management (CRM) technology adoption and its impact on business-to-business customer relationships. Total Quality Management & Business Excellence, 18(8), 927–945.CrossRefGoogle Scholar
  67. Sapio, B., Turk, T., Cornacchia, M., Papa, F., Nicolo, E., & Livi, S. (2010). Building scenarios of digital television adoption: A pilot study. Technology Analysis & Strategic Management, 22(1), 43–63.CrossRefGoogle Scholar
  68. 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.CrossRefGoogle Scholar
  69. Shin, D. H. (2009). Towards an understanding of the consumer acceptance of mobile wallet. Computers in Human Behavior, 25(6), 1343–1354.CrossRefGoogle Scholar
  70. Sun, H. S., & Zhang, P. (2006). The role of moderating factors in user technology acceptance. International Journal of Human-Computer Studies, 64(2), 53–78.Google Scholar
  71. Tsai, Y. H., Lin, C. P., Chiu, C. K., & Joe, S. W. (2009). Understanding learning behavior using location and prior performance as moderators. Social Science Journal, 46(4), 787–799.CrossRefGoogle Scholar
  72. van Biljon, J., & Kotze, P. (2008). Cultural factors in a mobile phone adoption and usage model. Journal of Universal Computer Science, 14(16), 2650–2679.Google Scholar
  73. van Biljon, J., & Renaud, K. (2008). A qualitative study of the applicability of technology acceptance models to senior mobile phone users. In I. Y. Song (Ed.), Advances in conceptual modeling – Challenges and opportunities (Vol. 5232, pp. 228–237). Berlin: Springer.CrossRefGoogle Scholar
  74. van Dijk, J., Peters, O., & Ebbers, W. (2008). Explaining the acceptance and use of government Internet services: A multivariate analysis of 2006 survey data in the Netherlands. Government Information Quarterly, 25(3), 379–399.CrossRefGoogle Scholar
  75. van Setten, M., Veenstra, M., Nijholt, A., & van Dijk, B. (2006). Goal-based structuring in recommender systems. Interacting with Computers, 18(3), 432–456.CrossRefGoogle Scholar
  76. 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.Google Scholar
  77. Wang, Y. S., & Shih, Y. W. (2009). Why do people use information kiosks? A validation of the unified theory of acceptance and use of technology. Government Information Quarterly, 26(1), 158–165.CrossRefGoogle Scholar
  78. Williams, M. D., Dwivedi, Y. K., Lal, B., & Schwarz, A. (2009). Contemporary trends and issues in IT adoption and diffusion research. Journal of Information Technology, 24(1), 1–10.CrossRefGoogle Scholar
  79. Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85–102.CrossRefGoogle Scholar
  80. Yang, D., Wang, Q., Li, M. S., Yang, Y., Ye, K., & Du, J. (2008). A survey on software cost estimation in the Chinese software industry. New York: Assoc Computing Machinery.Google Scholar
  81. Ye, C., Seo, D., Desouza, K. C., Sangareddy, S. P., & Jha, S. (2008). Influences of IT substitutes and user experience on post-adoption user switching: An empirical investigation. Journal of the American Society for Information Science and Technology, 59(13), 2115–2132.CrossRefGoogle Scholar
  82. YenYuen, Y., & Yeow, P. H. P. (2009). User acceptance of internet banking service in Malaysia. In J. Cordeiro, S. Hammoudi, & J. Filipe (Eds.), Web information systems and technologies (Vol. 18, pp. 295–306). Berlin: Springer.CrossRefGoogle Scholar
  83. Yeow, P. H. P., Yuen, Y. Y., Tong, D. Y. K., & Lim, N. (2008). User acceptance of online banking service in Australia. Norristown: Int. Business Information Management Assoc-Ibima.Google Scholar
  84. Zhou, T., Lu, Y. B., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760–767.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Michael D. Williams
    • 1
  • Nripendra P. Rana
  • Yogesh K. Dwivedi
  1. 1.Technology and Innovation Management Group, School of Business & EconomicsSwansea UniversitySwanseaUK

Personalised recommendations