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A review on objective measurement of usage in technology acceptance studies

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Abstract

This paper reviews objective measurement of usage in user acceptance studies using the technology acceptance model (TAM). The use of objective measurement is quite uncommon in TAM. In addition to its low frequency, another striking phenomenon is the way objective measurement is used. Namely, only a minor potential of the information available is typically used, and, for example, the temporal aspect (changes in time) is almost always neglected. The paper describes the TAM studies where objective measurement of usage has been utilized and ponders the way objective measurements are taken. The ultimate goal of the paper is to improve objective measures used in TAM studies. To this end, several suggestions are given.

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References

  1. Benbunan-Fich, R., Adler, R.F., Mavlanova, T.: Measuring multitasking behavior with activity-based metrics. ACM Trans. Comput. Hum. Interact. 18(2), 7 (2011)

    Article  Google Scholar 

  2. Brown, S.A., Venkatesh, V., Goyal, S.: Expectation confirmation in technology use. Inf. Syst. Res. 23(2), 474–487 (2012)

    Article  Google Scholar 

  3. Burton-Jones, A.: New perspectives on the system usage construct. Georgia State University Computer Information Systems Dissertations. Paper 3 (2005)

  4. Carrell, D., Ralston, J.D.: Variation in adoption rates of a patient web portal with a shared medical record by age, gender, and morbidity level. In: AMIA Annual Symposium Proceedings, pp. 871. (2006)

  5. Cheung, E.Y.M., Sachs, J.: Test of the technology acceptance model for a web-based information system in a Hong Kong Chinese sample 1. Psychol. Rep. 99(3), 691–703 (2006)

    Article  Google Scholar 

  6. Chuttur, M.: Overview of the technology acceptance model: origins, developments, and future directions. Sprouts: Working Papers on Information Systems, 9(37) http://sprouts.aisnet.org/9-37 (2009)

  7. Collopy, F.: Biases in retrospective self-reports of time use: an empirical study of computer users. Manag. Sci. 42, 758–767 (1996)

    Article  MATH  Google Scholar 

  8. Dasgupta, S., Granger, M., McGarry, N.: User acceptance of e-collaboration technology: an extension of the technology acceptance model. Group Decis. Negot. 11(2), 87–100 (2002)

    Article  Google Scholar 

  9. Davis, F.D.: A technology acceptance model for empirically testing new end-user information systems: theory and results. PhD Thesis, Sloan School of Management, Massachusetts Institute of Technology (1986)

  10. Davis, F.D., Venkatesh, V.: Toward pre-prototype user acceptance testing of new information systems: implications for software project management. IEEE Trans. Eng. Manag. 51(1), 31–46 (2004)

    Article  Google Scholar 

  11. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35, 982–1003 (1989)

    Article  Google Scholar 

  12. de Reuver, M., Bouwman, H.: Dealing with self-report bias in mobile Internet acceptance and usage studies. Inf. Manag. 5(3), 287–294 (2015)

    Article  Google Scholar 

  13. De Vos, H., Ter Hofte, G.H., De Poot, H.: IM@Work adoption of instant messaging in a knowledge worker organisation. In: Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS’04), IEEE Computer Society Press (2004)

  14. Devaraj, S., Easley, R.F., Crant, J.M.: Research note-how does personality matter? Relating the five-factor model to technology acceptance and use. Inf. Syst. Res. 19(1), 93–105 (2008)

    Article  Google Scholar 

  15. Dillon, A., Morris, M.G.: User acceptance of information technology: theories and models. In: Williams, M. (ed.) Annual Review of Information Science and Technology, vol. 31, pp. 3–32. Information Today, Medford, NJ (1996)

  16. Dybå, T., Moe, N.B., Arisholm, E.: Measuring software methodology usage: challenges of conceptualization and operationalization. In: Proceedings of the International Symposium on Empirical Software Engineering (ISESE’05), IEEE Computer Society Press, pp. 447–457. (2005)

  17. Eckhardt, A., Maier, C., Hsieh, J., Chuk, T., Chan, A., Hsiao, J., Buettner, R.: Objective measures of IS usage behavior under conditions of experience and pressure using eye fixation data. In: Proceedings of the Thirty Fourth International Conference on Information Systems (ICIS 2013). (2013)

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

    Google Scholar 

  19. Gebauer, L., Söllner, M., Leimeister, J.M.: Towards understanding the formation of continuous IT use http://pubs.wi-kassel.de/wp-content/uploads/2013/10/JML_440.pdf. Accessed 2 Jun 2015

  20. Giesbers, B., Rienties, B., Tempelaar, D., Gijselaers, W.: Investigating the relations between motivation, tool use, participation, and performance in an e-learning course using web-videoconferencing. Comput. Hum. Behav. 29(1), 285–292 (2013)

    Article  Google Scholar 

  21. Ginzberg, M.J.: Early diagnosis of MIS implementation failure: promising results and unanswered questions. Manag. Sci. 27(4), 459–478 (1981)

    Article  Google Scholar 

  22. Henderson, R., Divett, M.J.: Perceived usefulness, ease of use and electronic supermarket use. Int. J. Hum Comput Stud. 59(3), 383–395 (2003)

    Article  Google Scholar 

  23. Holden, R.J., Karsh, B.T.: The technology acceptance model: its past and its future in health care. J. Biomed. Inform. 43(1), 159–172 (2010)

    Article  Google Scholar 

  24. Horton, R.P., Buck, T., Waterson, P.E., Clegg, C.W.: Explaining intranet use with the technology acceptance model. J. Inf. Technol. 16(4), 37–249 (2001)

    Article  Google Scholar 

  25. Hsiao, C.H., Yang, C.: The intellectual development of the technology acceptance model: a co-citation analysis. Int. J. Inf. Manag. 31(2), 128–136 (2011)

    Article  MathSciNet  Google Scholar 

  26. Joo, Y.J., Lim, K.Y., Lim, E.: Investigating the structural relationship among perceived innovation attributes, intention to use and actual use of mobile learning in an online university in South Korea. Australian J. Educ. Technol. 30(4) (2014)

  27. Kaasinen, E., Roto, V., Roloff, K., Väänänen-Vainio-Mattila, K., Vainio, T., Maehr, W., Shrestha, S.: User experience of mobile internet: analysis and recommendations. Int. J. Mob. Hum. Comput. Interact. 1(4), 4–23 (2009)

    Article  Google Scholar 

  28. Kay, J., Thomas, R.C.: Studying long-term system use. Commun. ACM 38(7), 61–69 (1995)

    Article  Google Scholar 

  29. Kim, S., Garrison, G.: Investigating mobile wireless technology adoption: an extension of the technology acceptance model. Inf. Syst. Front. 11(3), 323–333 (2009)

    Article  Google Scholar 

  30. King, W.R., He, J.: A meta-analysis of the technology acceptance model. Inf. Manag. 43(6), 740–755 (2006)

    Article  Google Scholar 

  31. King, W.R., Rodriguez, J.I.: Note—participative design of strategic decision support systems: an empirical assessment. Manag. Sci. 27(6), 717–726 (1981)

    Article  Google Scholar 

  32. Klein, R.: Internet-based patient-physician electronic communication applications: patient acceptance and trust. E-Serv. J. 5(2), 27–51 (2006)

    Article  Google Scholar 

  33. Kuniavsky, M.: Observing the User Experience: A Practitioner’s Guide to User Research. Morgan Kaufmann, San Francisco, CA (2003)

    Google Scholar 

  34. Law, E.L.-C., van Schaik, P.: Modelling user experience—an agenda for research and practice. Interact. Comput. 22, 313–322 (2010)

    Article  Google Scholar 

  35. Lee, Y., Kozar, K.A., Larsen, K.R.T.: The technology acceptance model: past, present, and future. Commun. AIS 12(50), 752–780 (2003)

    Google Scholar 

  36. Legris, P., Ingham, J., Collerette, P.: Why do people use information technology? A critical review of the technology acceptance model. Inf. Manag. 40(3), 191–204 (2003)

    Article  Google Scholar 

  37. Lober, W.B., Zierler, B., Herbaugh, A., Shinstrom, S.E., Stolyar, A., Kim, E.H., Kim, Y.: Barriers to the use of a personal health record by an elderly population. In: AMIA Annual Symposium Proceedings, pp. 514–518. (2006)

  38. Lu, H.-P., Gustafson, D.H.: An empirical study of perceived usefulness and perceived ease of use on computerized support system use over time. Int. J. Inf. Manag. 14, 317–329 (1994)

    Article  Google Scholar 

  39. Luo, N., Guo, X., Zhang, J., Chen, G., Zhang, N.: Understanding the continued use of intra-organizational blogs: an adaptive habituation model. Comput. Hum. Behav. 50, 57–65 (2015)

    Article  Google Scholar 

  40. Ma, Q., Liu, L.: The technology acceptance model: a meta-analysis of empirical findings. J. Organ. End User Comput. 16(1), 59–72 (2004)

    Article  Google Scholar 

  41. Ma, W., Yuen, A.: E-learning system acceptance and usage pattern. In Technology Acceptance in Education, pp. 201–216. SensePublishers, Dordrecht (2011)

    Book  Google Scholar 

  42. Marangunić, N., Granić, A.: Technology acceptance model: a literature review from 1986 to 2013. Univ. Access Inf. Soc. 14, 81–95 (2015)

    Article  Google Scholar 

  43. Möller, A., Kranz, M., Schmid, B., Roalter, L., Diewald, S.: Investigating self-reporting behavior in long-term studies. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2931–2940. (2013)

  44. Or, C.K., Karsh, B.T.: A systematic review of patient acceptance of consumer health information technology. J. Am. Med. Inform. Assoc. 16(4), 550–555 (2009)

    Article  Google Scholar 

  45. Petter, S., DeLone, W., McLean, E.A.: Information systems success: the quest for the independent variables. J. Manag. Inf. Syst. 29(4), 7–62 (2013)

    Article  Google Scholar 

  46. Robey, D.: User attitudes and management information system use. Acad. Manag. J. 22(3), 527–538 (1979)

    Article  Google Scholar 

  47. Rowe, F.: What literature review is not: diversity, boundaries and recommendations. Eur. J. Inf. Syst. 23(3), 241–255 (2014)

    Article  Google Scholar 

  48. Salovaara, A., Öörni, A., Sokura, B.: Heterogeneous use for multiple purposes: a point of concern to IS use models’ validity. In: Proceedings of the Thirty Fourth International Conference on Information Systems (ICIS 2013). (2013)

  49. Schewe, C.D.: The management information system user: an exploratory behavioral analysis. Acad. Manag. J. 19(4), 577–590 (1976)

    Article  MathSciNet  Google Scholar 

  50. Sharp, J.H.: Development, extension, and application: a review of the technology acceptance model. Information Systems. Educ. J. 5(9) (2006)

  51. Shin, D.H.: Ubiquitous computing acceptance model: end user concern about security, privacy and risk. Int. J. Mobile Commun. 8, 169–186 (2010)

    Article  Google Scholar 

  52. Srinivasan, A.: Alternative measures of system effectiveness: associations and implications. MIS Q. 9(3), 243–253 (1985)

    Article  Google Scholar 

  53. Stoel, L., Lee, K.H.: Modeling the effect of experience on student acceptance of Web-based courseware. Internet Res. 13(5), 364–374 (2003)

    Article  Google Scholar 

  54. Straub, D., Limayem, M., Karahanna-Evaristo, E.: Measuring system usage: implications for IS theory testing. Manag. Sci. 41(8), 1328–1342 (1995)

    Article  Google Scholar 

  55. Szajna, B.: Empirical evaluation of the revised technology acceptance model. Manag. Sci. 42(1), 85–92 (1996)

    Article  Google Scholar 

  56. Taylor, S., Todd, P.A.: Understanding information technology usage: a test of competing models. Inf. Syst. Res. 6(2), 144–176 (1995)

    Article  Google Scholar 

  57. Townsend, A.M., Demarie, S.M., Hendrickson, A.R.: Desktop video conferencing in virtual workgroups: anticipation, system evaluation and performance. Inf. Syst. J. 11(3), 213–222 (2001)

    Article  Google Scholar 

  58. Turner, M., Kitchenham, B., Brereton, P., Charters, S., Budgen, D.: Does the technology acceptance model predict actual use? A systematic literature review. Inf. Softw. Technol. 52, 463–479 (2010)

    Article  Google Scholar 

  59. Venkatesh, V., Bala, H.: Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 39(2), 273–315 (2008)

    Article  Google Scholar 

  60. Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46, 186–204 (2000)

    Article  Google Scholar 

  61. Venkatesh, V., Morris, M.G.: Why don’t men ever stop to ask for directions? Gender, social influence, ant their role in technology acceptance and usage behaviour. MIS Q. 24(1), 115–139 (2000)

    Article  Google Scholar 

  62. Venkatesh, V., Speier, C., Morris, M.G.: User acceptance enablers in individual decision making about technology: toward an integrated model. Decis. Sci. 33(2), 297–316 (2002)

    Article  Google Scholar 

  63. 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)

    Google Scholar 

  64. Walldén, S., Mäkinen, E.: Measuring the long-term voluntary use of an information system. Inf. Syst. Front. (2014, submitted for publication)

  65. Walsh, I.: A strategic path to study IT use through users’ IT culture and IT needs: a mixed-method grounded theory. J. Strateg. Inf. Syst. 23(2), 146–173 (2013)

    Article  Google Scholar 

  66. Wu, J., Du, H.: Toward a better understanding of behavioural intention and system usage constructs. Eur. J. Inf. Syst. 21, 680–698 (2012)

    Article  Google Scholar 

  67. Yi, M.Y., Hwang, Y.J.: Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. Int. J. Hum Comput Stud. 59(4), 431–449 (2003)

    Article  Google Scholar 

  68. Yousafzai, S.Y., Foxall, G.R., Pallister, J.G.: Technology acceptance: a meta-analysis of the TAM: part 1. J. Model. Manag. 2(3), 251–280 (2007)

    Article  Google Scholar 

  69. Zhang, X., Yu, P., Yan, J., Spil, I.T.A.: Using diffusion of innovation theory to understand the factors impacting patient acceptance and use of consumer e-health innovations: a case study in a primary care clinic. BMC Health Serv. Res. 15(1), 71 (2015)

    Article  Google Scholar 

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Acknowledgments

The work of the first author was supported by the Foundation for Economic Education (Liikesivistys-rahasto). The authors wish to thank Prof. emer. Pertti Järvinen and the anonymous referees for their constructive comments.

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  1. School of Information Sciences, University of Tampere, 33014, Tampere, Finland

    Sari Walldén, Erkki Mäkinen & Roope Raisamo

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  1. Sari Walldén
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Correspondence to Sari Walldén.

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Walldén, S., Mäkinen, E. & Raisamo, R. A review on objective measurement of usage in technology acceptance studies. Univ Access Inf Soc 15, 713–726 (2016). https://doi.org/10.1007/s10209-015-0443-y

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