Skip to main content
SpringerLink
Log in

Technology adoption at individual level: toward an integrated overview

  • Long Paper
  • Published:
Universal Access in the Information Society Aims and scope Submit manuscript

Abstract

In view of technology adoption at individual level (TechA@IL), numerous theories and models have been put forward to predict and explain human behavior toward technology adoption and actual use. However, while some of these models have enjoyed widespread use, a study that offers a systematic presentation of the progress made in the field of TechA@IL aiming to track models’ evolvement, mutual influence and inter-dependence remains lacking. Consequently, instead of intending to compare theoretical perspectives, including their applicability and explanatory power to acclaim which model is more valid or powerful, this study brings a chronological account of more than half a century of research presenting an overview of relational linkages among seventeen most influential models of TechA@IL. Models’ core independent constructs (i.e., predictors), which affect behavioral intention and influence actual use, are thematically grouped into individual aspects, task and technological aspects, and social and environmental aspects. Thus, based on Eason’s framework, the Triad of Predictors explaining the key dependent constructs of interest, i.e., behavioral intention to use particular technology and its actual use, is proposed. Besides, to provide a comprehensive insight into current state of knowledge with respect to TechA@IL, a succinct account of several hybrid theoretical perspectives is offered. Limitations of the study and potential directions of future research are discussed as well. This research is believed to enhance understanding and appreciation of the progress made in the field of TechA@IL, and should be of interest to both, researchers and practitioners interested in technology adoption.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Data availability

The data of the manuscript consist of articles published in journals and conferences. Many of these are freely available online, others can be accessed for a fee or through subscription.

References

  1. Abdullah, F., Ward, R.: Developing a general extended technology acceptance model for e learning (GETAMEL) by analysing commonly used external factors. Comput. Hum. Behav. 56, 238–256 (2016). https://doi.org/10.1016/j.chb.2015.11.036

    Article  Google Scholar 

  2. Ajzen, I.: From intentions to actions: a theory of planned behavior. In: Kuhl, J., Beckmann, J. (eds.) Action Control SSSP Springer Series in Social Psychology, pp. 11–39. Springer, Berlin (1985). https://doi.org/10.1007/978-3-642-69746-3_2

    Chapter  Google Scholar 

  3. Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2), 179–211 (1991). https://doi.org/10.1016/0749-5978(91)90020-T

    Article  Google Scholar 

  4. Al-Emran, M., Granić, A.: Is it still valid or outdated? A bibliometric analysis of the technology acceptance model and its applications from 2010 to 2020. In: Al-Emran, M., Shaalan, K. (eds.) Recent Advances in Technology Acceptance Models and Theories, pp. 1–12. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-64987-6_1

    Chapter  Google Scholar 

  5. Al-Emran, M., Granić, A., Al-Sharafi, M., Nisreen, A., Sarrab, M.: Examining the roles of students’ beliefs and security concerns for using smartwatches in higher education. J. Enterp. Inf. Manag. 34(4), 1229–1251 (2021). https://doi.org/10.1108/JEIM-02-2020-0052

    Article  Google Scholar 

  6. Alowayr, A., Al-Azawei, A.: Predicting mobile learning acceptance: an integrated model and empirical study based on higher education students’ perceptions. Australas. J. Educ. Technol. 37(3), 38–55 (2021). https://doi.org/10.14742/ajet.6154

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Bagozzi, R.P.: The lagacy of the technology acceptance model and a proposal for a paradigm shift. J. Assoc. Inf. Syst. 8(4), 244–254 (2007). https://doi.org/10.17705/1jais.00122

    Article  Google Scholar 

  9. Bagozzi, R.P., Warshaw, P.R.: Trying to consume. J. Consum. Res. 17(2), 127–140 (1990). https://doi.org/10.1086/208543

    Article  Google Scholar 

  10. Bandura, A.: Social Foundations of Thought and Action: a Social Cognitive Theory. Prentice- Hall Inc, Englewood Cliffs, NJ (1986)

    Google Scholar 

  11. Bhattacherjee, A.: Understanding information systems continuance: an expectation-confirmation model. MIS Q. 25(3), 351–370 (2001). https://doi.org/10.2307/3250921

    Article  Google Scholar 

  12. Bhattacherjee, A., Barfar, A.: Information technology continuance research: current state and future directions. Asia Pac. J. Inf. Syst. 21(2), 1–18 (2011). https://doi.org/10.2307/3250921

    Article  Google Scholar 

  13. Bhattacherjee, A., Perols, J., Sanford, C.: Information technology continuance: a theoretic extension and empirical test. J. Comput. Inf. Syst. 49(1), 17–26 (2008). https://doi.org/10.1080/08874417.2008.11645302

    Article  Google Scholar 

  14. Buabeng-Andoh, C., Baah, C.: Pre-service teachers’ intention to use learning management system: an integration of UTAUT and TAM. Interact. Technol. Smart Educ. 17(4), 455–474 (2020). https://doi.org/10.1108/ITSE-02-2020-0028

    Article  Google Scholar 

  15. Compeau, D.R., Higgins, C.A.: Computer self-efficacy: development of a measure and initial test. MIS Q. 19(2), 189–211 (1995). https://doi.org/10.2307/249688

    Article  Google Scholar 

  16. Cooper, R.B., Zmud, R.W.: Information technology implementation research: a technological diffusion approach. Manag. Sci. 36(2), 123–139 (1990)

    Article  Google Scholar 

  17. Csikszentmihalyi, M.: Beyond Boredom and Anxiety. Jossey-Bass, San Francisco (1975)

    Google Scholar 

  18. Davis, F.D.: A technology acceptance model for empirically testing new end-user information systems: theory and results. Doctoral dissertation. MIT Sloan School of Management, Cambridge, MA (1986)

  19. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989). https://doi.org/10.2307/249008

    Article  Google Scholar 

  20. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35(8), 982–1003 (1989). https://doi.org/10.1287/mnsc.35.8.982

    Article  Google Scholar 

  21. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: Extrinsic and intrinsic motivation to use computers in the workplace. J. Appl. Soc. Psychol. 22, 1111–1132 (1992). https://doi.org/10.1111/j.1559-1816.1992.tb00945.x

    Article  Google Scholar 

  22. DeLone, W.H., McLean, E.R.: Information systems success: the quest for the dependent variable. Inf. Sys. Res. 3(1), 60–95 (1992). https://doi.org/10.1287/isre.3.1.60

    Article  Google Scholar 

  23. DeLone, W.H., McLean, E.R.: The DeLone and McLean model of information systems success: a ten-year update. J. Manag. Inf. Syst. 19(4), 9–30 (2003). https://doi.org/10.1080/07421222.2003.11045748

    Article  Google Scholar 

  24. Eason, K.D.: Ergonomic perspectives on advances in human–computer interaction. Ergonomics 34(6), 721–741 (1991). https://doi.org/10.1080/00140139108967347

    Article  Google Scholar 

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

    Google Scholar 

  26. Gómez-Ramirez, I., Valencia-Arias, A., Duque, L.: Approach to M-Learning acceptance among university students: an integrated model of TPB and TAM. Int. Rev. Res. Open Distrib. Learn. 20(3), 141–164 (2019). https://doi.org/10.19173/irrodl.v20i4.4061

    Article  Google Scholar 

  27. Goodhue, D.L., Thompson, R.L.: Task-technology fit and individual performance. MIS Q. 19(2), 213–236 (1995). https://doi.org/10.2307/249689

    Article  Google Scholar 

  28. Granić, A.: Educational technology adoption: a systematic review. Educ. Inf. Technol. (2022). https://doi.org/10.1007/s10639-022-10951-7

    Article  Google Scholar 

  29. Granić, A., Marangunić, N.: Technology acceptance model in educational context: a systematic literature review. Br. J. Edu. Technol. 50(5), 2572–2593 (2019). https://doi.org/10.1111/bjet.12864

    Article  Google Scholar 

  30. Gurjar, N.: Understanding technology adoption: theory and critical review of seminal research. In: Langran, E., Borup, J. (eds.) Proceedings of Society for Information Technology & Teacher Education International Conference, pp. 846–858. Washington, D.C., United States (2018).

  31. Hartman, J.B., Gehrt, K.C., Watchravesringkan, K.: Re-examination of the concept of innovativeness in the context of the adolescent segment: development of a measurement scale. J. Target Meas. Anal. Mark. 12(4), 353–365 (2004). https://doi.org/10.1057/palgrave.jt.5740122

    Article  Google Scholar 

  32. Hirschman, E.C.: Innovativeness, novelty seeking, and consumer creativity. J. Consum. Res. 7(3), 283–295 (1980). https://www.jstor.org/stable/2489013

  33. Huang, C.-Y., Wang, H.-Y., Yang, C.-L., Shiau, S.: A derivation of factors influencing the diffusion and adoption of an open-source learning platform. Sustainability 12(18), 7532 (2020). https://doi.org/10.3390/su12187532

    Article  Google Scholar 

  34. Iacovou, C.L., Benbasat, I., Dexter, A.S.: Electronic data interchange and small organizations: adoption and impact of technology. MIS Q. 19(4), 465–485 (1995). https://doi.org/10.2307/249629

    Article  Google Scholar 

  35. Igbaria, M., Parasuraman, S., Baroudi, J.: Motivational model of microcomputer usage. J. Manag. Inf. Syst. 13(1), 127–143 (1996). https://doi.org/10.1080/07421222.1996.11518115

    Article  Google Scholar 

  36. Jen, W., Lu, T., Liu, P.-T.: An integrated analysis of technology acceptance. Behaviour models: comparison of three major models. MIS Review 15(1), 89–121 (2009)

    Google Scholar 

  37. Karahanna, E., Straub, D.W., Chervany, N.L.: Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Q. 23(2), 183–213 (1999). https://doi.org/10.2307/249751

    Article  Google Scholar 

  38. Karahanna, E., Agarwal, R., Angst, C.M.: Reconceptualizing compatibility beliefs in technology acceptance research. MIS Q. 30(4), 781–804 (2006). https://doi.org/10.2307/25148754

    Article  Google Scholar 

  39. Kim, H.W., Chan, H.C., Gupta, S.: Value-based adoption of mobile internet: an empirical investigation. Decis. Support Syst. 43(1), 111–126 (2007). https://doi.org/10.1016/j.dss.2005.05.009

    Article  Google Scholar 

  40. Kim, Y., Crowston, K.: Technology adoption and use theory review for studying scientists’ continued use of cyber-infrastructure. In: Proceedings of the ASIST Annual Meeting, 48 (2011). https://doi.org/10.1002/meet.2011.14504801197

  41. Lai, P.C.: The literature review of technology adoption models and theories for the novelty technology. J. Inf. Syst. Technol. Manag. 14(1), 21–38 (2017). https://doi.org/10.4301/S1807-17752017000100002

    Article  Google Scholar 

  42. Limayem, M., Hirt, S.G., Cheung, C.M.K., Hirt, S.G.: How habit limits the predictive power of intention: the case of information systems continuance. MIS Q. 31(4), 705–737 (2007). https://doi.org/10.2307/25148817

    Article  Google Scholar 

  43. Marangunić, N., Granić, A.: Technology acceptance model: a literature review from 1986 to 2013. Univ. Access Inf. Soc. 14(1), 81–95 (2015). https://doi.org/10.1007/s10209-014-0348-1

    Article  Google Scholar 

  44. Momani, A.M., Jamous, M.M., Hilles, S.M.S.: Technology acceptance theories: review and classification. In: Technology Adoption and Social Issues: Concepts, Methodologies, Tools, and Applications Information Resources Management Association, pp. 1–16. IGI Global, Hershey (2018). https://doi.org/10.4018/978-1-5225-5201-7.ch001

    Chapter  Google Scholar 

  45. Moore, G.C., Benbasat, I.: Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf. Syst. Res. 2(3), 173–191 (1991). https://doi.org/10.1287/isre.2.3.192

    Article  Google Scholar 

  46. Oliver, R.L.: A cognitive model of the antecedents and consequences of satisfaction decisions. J. Mark. Res. 17(4), 460–469 (1980). https://doi.org/10.1177/002224378001700405

    Article  Google Scholar 

  47. Parasuraman, A.: Technology Readiness Index (TRI): a multiple-item scale to measure readiness to embrace new technologies. J. Serv. Res. 2, 307–320 (2000). https://doi.org/10.1177/109467050024001

    Article  Google Scholar 

  48. Roca, J.C., Chiu, C.M., Martinez, F.J.: Understanding e-learning continuance intention: an extension of the Technology Acceptance Model. Int. J. Hum. Comput. Stud. 64(8), 683–696 (2006). https://doi.org/10.1016/j.ijhcs.2006.01.003

    Article  Google Scholar 

  49. Rogers, E.: Diffusion of Innovations. The Free Press, New York (1962)

    Google Scholar 

  50. Rogers, E.: Diffusion of Innovations, 4th edn. The Free Press, New York (1995)

    Google Scholar 

  51. Rogers, R.W.: A protection motivation theory of fear appeals and attitude change. J. Psychol. 91(1), 93–114 (1975). https://doi.org/10.1080/00223980.1975.9915803

    Article  Google Scholar 

  52. Salahshour Rad, M., Nilashi, M., Mohamed Dahlan, H.: Information technology adoption: a review of the literature and classification. Univ. Access Inf. Soc. 17, 361–390 (2018). https://doi.org/10.1007/s10209-017-0534-z

    Article  Google Scholar 

  53. Sheppard, M., Vibert, C.: Re-examining the relationship between ease of use and usefulness for the net generation. Educ. Inf. Technol. 24(5), 3205–3218 (2019). https://doi.org/10.1007/s10639-019-09916-0

    Article  Google Scholar 

  54. Sykes, T.A., Venkatesh, V., Gosain, S.: Model of acceptance with peer support: a social network perspective to understand employees’ system use. MIS Q. 33(2), 371–393 (2009). https://doi.org/10.2307/20650296

    Article  Google Scholar 

  55. Tamilmani, K., Rana, N.P., Wamba, S.F., Dwivedi, R.: The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): a systematic literature review and theory evaluation. Int. J. Inf. Manag. 57, 102269 (2021). https://doi.org/10.1016/j.ijinfomgt.2020.102269

    Article  Google Scholar 

  56. Tarhini, A., Arachchilage, N.A.G., Masa’deh, R., Abbasi, M.S.: A critical review of theories and models of technology adoption and acceptance in information system research. Int. J. Technol. Diffus. 6(4), 58–77 (2015). https://doi.org/10.4018/IJTD.2015100104

    Article  Google Scholar 

  57. Taylor, S., Todd, P.A.: Understanding information technology usage: a test of competing models. Inf. Syst. Res. 6(2), 144–176 (1995). https://doi.org/10.1287/isre.6.2.144

    Article  Google Scholar 

  58. Taylor, S., Todd, P.: Assessing IT usage: the role of prior experience. MIS Q. 19(4), 561 (1995). https://doi.org/10.2307/249633

    Article  Google Scholar 

  59. Thompson, R.L., Higgins, C.A., Howell, J.M.: Personal computing: toward a conceptual model of utilization. MIS Q. 15(1), 124–143 (1991). https://doi.org/10.2307/249443

    Article  Google Scholar 

  60. Tornatzky, L., Fleischer, M.: The Process of Technology Innovation. Lexington Books, Lexington (1990)

    Google Scholar 

  61. Triandis, H.C.: Values, attitudes, and interpersonal behavior. In: Nebraska Symposium on Motivation, 1979: Beliefs, Attitudes, and Values, pp. 195–259. University of Nebraska Press, Lincoln, NE, (1980)

  62. Venkatesh, V., Thong, J.Y., Xu, X.: Consumer acceptance and use of information technology: extending the Unified Theory of Acceptance and Use of Technology. MIS Q. 36, 157–178 (2012). https://doi.org/10.2307/41410412

    Article  Google Scholar 

  63. Venkatesh, V.: Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf. Syst. Res. 11(4), 342–365 (2000)

    Article  Google Scholar 

  64. Venkatesh, V., Bala, H.: Technology acceptance model 3 and a research Agenda on interventions. Decis. Sci. 39(2), 273–315 (2008). https://doi.org/10.1111/j.1540-5915.2008.00192.x

    Article  Google Scholar 

  65. Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46(2), 186–204 (2000). https://doi.org/10.1287/mnsc.46.2.186.11926

    Article  Google Scholar 

  66. Venkatesh, V., Brown, S.A., Maruping, L.M., Bala, H.: Predicting different conceptualizations of system use: the competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Q. 32(3), 483–502 (2008)

    Article  Google Scholar 

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

    Article  Google Scholar 

  68. Williams, M., Rana, N., Dwivedi, Y.: The unified theory of acceptance and use of technology (UTAUT): a literature review. J. Enterp. Inf. Manag. 28(3), 443–488 (2015). https://doi.org/10.1108/JEIM-09-2014-0088

    Article  Google Scholar 

  69. Wu, B., Chen, X.: Continuance intention to use MOOCs: integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Comput. Hum. Behav. 67, 1–12 (2017). https://doi.org/10.1016/j.chb.2016.10.028

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Faculty of Science, University of Split, Ruđera Boškovića 33, 21000, Split, Croatia

    Andrina Granić

Authors
  1. Andrina Granić
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrina Granić.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest.

Ethical approval

No ethics review was required to undertake this research.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Granić, A. Technology adoption at individual level: toward an integrated overview. Univ Access Inf Soc (2023). https://doi.org/10.1007/s10209-023-00974-3

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10209-023-00974-3

Keywords

Search

Navigation