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Individual differences and usage behavior: revisiting a technology acceptance model assumption

Published:07 June 2005Publication History
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Abstract

A critical assumption of the Technology Acceptance Model (TAM) is that its belief constructs - perceived ease of use (PEOU) and perceived usefulness (PU) - fully mediate the influence of external variables on IT usage behavior. If this assumption is true, researchers can effectively "assume away" the effects of broad categories of external variables, those relating to the specific task, the technology, and user differences. One recent study did indeed find that belief constructs fully mediated individual differences, and its authors suggest that further studies with similar results could pave the way for simpler acceptance models that ignore such differences. To test the validity of these authors' results, we conducted a similar study to determine the effect of staff seniority, age, and education level on usage behavior. Our study involved 106 professional and administrative staff in the IT division of a large manufacturing company who voluntarily use email and word processing. We found that these individual user differences have significant direct effects on both the frequency and volume of usage. These effects are beyond the indirect effects as mediated through the TAM belief constructs. Thus, rather than corroborating the recent study, our findings underscore the importance of users' individual differences and suggest that TAM's belief constructs are accurate but incomplete predictors of usage behavior.

References

  1. Adams, D.A., Nelson, R.R. and Todd, P.A. (1992). "Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication," MIS Quarterly, Vol. 16, pp. 227--247. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Agarwal, R. (2000). "Individual Acceptance of Information Technologies," in R. W. Zmud (ed.), Framing the Domains of IT Management: Projecting the Future through the Past, Cincinnati, OH: Pinnaflex Educational Resources, pp. 85--104.Google ScholarGoogle Scholar
  3. Agarwal, R. and Prasad, J. (1999). "Are Individual Differences Germane to the Acceptance of New Information Technologies?," Decision Sciences, Vol. 30 No. 2, pp. 361--391.Google ScholarGoogle ScholarCross RefCross Ref
  4. Ajzen, I. (1985). "From Intentions to Actions: A Theory of Planned Behavior," in J. Kuhi and J. Beckmann (Eds.), Action-Control: From Cognition to Behavior, Heidelberg, Germany: Springer, pp. 11--39.Google ScholarGoogle Scholar
  5. Ajzen, I. (1991). "The Theory of Planned Behavior," Organizational Behavior and Human Decision Processes, Vol. 50, pp. 179--211.Google ScholarGoogle ScholarCross RefCross Ref
  6. Ajzen, I. (2001). "Nature and Operation of Attitudes," Annual Review of Psychology, Vol. 52, pp. 27--58.Google ScholarGoogle ScholarCross RefCross Ref
  7. Ajzen, I. (2002). "Residual Effects of Past on Later Behavior: Habituation and Reasoned Action Perspectives," Personality and Social Psychology Review, Vol. 6, No. 2, pp. 107--122.Google ScholarGoogle ScholarCross RefCross Ref
  8. Alavi, M. and Joachimsthaler, E.A. (1992). "Revisiting DSS implementation research: A meta-analysis of the literature and suggestions for researchers," MIS Quarterly, Vol. 16, No. 1, pp. 95--116. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Ang, S. and Slaughter, S.A. (2001). "Work Outcomes and Job Design for Contract Versus Permanent Information Systems Professionals on Software Development Teams," MIS Quarterly, Vol. 25, No. 3, pp. 321--350. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Armitage, C.J. and Conner, M. (1999). "The Theory of Planned Behavior: Assessment of Predictive Validity and 'Perceived Control'," British Journal of Social Psychology, Vol. 38, pp. 35--54.Google ScholarGoogle ScholarCross RefCross Ref
  11. Ashcraft, M.H. (2002). Cognition, Upper Saddle River, NJ: Prentice Hall.Google ScholarGoogle Scholar
  12. Bandura, A. (1977). Social learning theory, Englewood Cliffs, NJ: Prentice Hall.Google ScholarGoogle Scholar
  13. Barclay, D., Higgins, C.A. and Thompson, R.L. (1995). "The Partial Least Squares (PLS) Approach to Causal Modeling: Personal Computer Adoption and Use as an Illustration," Technology Studies, Vol. 2, No. 2, pp. 285--309.Google ScholarGoogle Scholar
  14. Berthon, P., Pitt, L., Ewing, M. and Carr, C.L. (2002). "Potential Research Space in MIS: A Framework for Envisioning and Evaluating Research Replication, Extension, and Generation," Information Systems Research, Vol. 13, No. 4, pp. 416--427. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Brigman, S. and Cherry, K.E. (2002). "Age and Skilled Performance: Contributions of Working Memory and Processing Speed," Brain and Cognition, Vol. 50, pp. 242--256.Google ScholarGoogle ScholarCross RefCross Ref
  16. Chau, P.Y.K. (1996). "An empirical assessment of a modified technology acceptance model," Journal of Management Information Systems, Vol. 13 No. 2, pp. 185--204. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Chin, W.W. and Marcolin, B.L. (2001). "The Future of Diffusion Research," The DATA BASE for Advances in Information Systems, Vol. 32, No. 3, pp. 8--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Chin, W.W. and Todd, P.A. (1995). "On the Use, Usefulness, and Ease of Use of Structural Equation Modeling in MIS Research: A Note of Caution," MIS Quarterly, Vol. 19, No. 2, pp. 237--246. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Compeau, D., Higgins, C.A. and Huff, S. (1999). "Social Cognitive Theory and Individual Reactions to Computing Technology: A Longitudinal Study," MIS Quarterly, Vol. 23, No. 2, pp. 145--158. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Davis, F. (1989). "Perceived Usefulness, Perceived Ease of Use, and End User Acceptance of Information Technology," MIS Quarterly, Vol. 13, No. 3, pp. 318--339.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Davis, F.D. (1993). "User acceptance of information technology: System characteristics, user perceptions and behavioral impacts," International Journal of Man-Machine Studies, Vol. 38, pp. 475--487. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989). "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, Vol. 35, pp. 982--1003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Davis, L.D. and Davis, F.D. (1990). "The effect of training techniques and personal characteristics on training end users of information systems," Journal of Management Information Systems, Vol. 7, No. 2, pp. 93--110. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. DeLone, W.H. and McLean, E.R. (1992). "Information Systems Success: The Quest for the Dependent Variable," Information Systems Research, Vol. 3, No. 1, pp. 60--95.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. DeLone, W.H. and McLean, E.R. (2003). "The DeLone and McLean Model of Information Systems Success: A Ten-Year Review," Journal of Management Information Systems, Vol. 19, No. 4, pp. 9--30. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Dishaw, M.T. and Strong, D.M. (1999). "Extending the Technology Acceptance Model with Task-Technology fit Constructs," Information & Management, Vol. 36, No. 1, pp. 9--21.Google ScholarGoogle ScholarCross RefCross Ref
  27. Fishbein, M. and Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Reading, MA: Addison-Wesley.Google ScholarGoogle Scholar
  28. Fornell, C. and Larcker, D. (1981). "Evaluating Structural Equation Models with Unobservable Variables and Measurement Error," Journal of Marketing Research, Vol. 18, pp. 39--50.Google ScholarGoogle ScholarCross RefCross Ref
  29. Gefen, D. and Straub, D. (1997). "Gender Differences in the Perception and Use of E-Mail: An Extension to the Technology Acceptance Model," MIS Quarterly, Vol. 21, No. 4, pp. 389--400. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Gefen, D., Straub, D.W. and Boudreau, M.-C. (2000). "Structural Equation Modeling and Regression: Guidelines for Research Practice," Communications of The AIS, Vol. 4 No., 7, pp. 1--77.Google ScholarGoogle Scholar
  31. Gomez, L.M., Egan, D.E. and Bowers, C. (1986), "Learning to use a text editor: Some learner characteristics that predict success," Human Computer Interaction, Vol. 2, pp. 1--23.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Goodhue, D.L. (1995). "Understanding User Evaluations of Information Systems," Management Science, Vol. 41, No. 12, pp. 1827--1844. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998). Multivariate Data Analysis, Upper Saddle River, NJ: Prentice Hall. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Hall, D. and Mansfield, R. (1975). "Relationships of Age and Seniority with Career Variables of Engineers and Scientists," Journal of Applied Psychology, Vol. 60, No. 2, pp. 201--210.Google ScholarGoogle ScholarCross RefCross Ref
  35. Harrison, A.W. and Rainer, R.K. (1992). "The influence of individual differences on skill in end-user computing," Journal of Management Information Systems, Vol. 9, No. 1, pp. 93--111. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Hartwick, J.H. and Barki, H. (1994). "Explaining the Role of User Participation in Information System Use," Management Science, Vol. 40, pp. 440--465. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Hong, W., Thong, J.Y.L., Wong, W.-M. and Tam, K.-Y. (2002). "Determinants of User Acceptance of Digital Libraries: An Empirical Examination of Individual Differences and System Characteristics," Journal of Management Information Systems, Vol. 18, No. 3, pp. 97--124. Google ScholarGoogle ScholarCross RefCross Ref
  38. Igbaria, M., Guimaraes, T. and Davis, G.B. (1995). "Testing the Determinants of Microcomputer Usage via a Structural Equation Model," Journal of Management Information Systems, Vol.11, No. 4, pp. 87--114. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Igbaria, M. and Parsuraman, S. (1989). "A Path Analytic Study of Individual Characteristics, Computer Anxiety, and Attitudes Towards Microcomputers," Journal of Management, Vol. 15, No. 3, pp. 373--388.Google ScholarGoogle ScholarCross RefCross Ref
  40. Igbaria, M., Zinatelli, N., Cragg, P. and Cavaye, A.L.M. (1997). "Personal Computing Acceptance Factors in Small Firms: A Structural Equation Model," MIS Quarterly, Vol. 21, No. 3, pp. 279--305. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Karahanna, E. and Straub, D. (1999). "The Psychological Origins of Perceived Usefulness and Perceived Ease-of-Use," Information & Management, Vol. 35, pp. 237--250. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Karahanna, E., Straub, D.W. and Chervany, N.L. (1999). "Information Technology Adoption Across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs," MIS Quarterly, Vol. 23, No. 2, pp. 183--213. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Legris, P., Ingham, J. and Collerette, P. (2003). "Why Do People Use Information Technology? A Critical Review of the Technology Acceptance Model," Information & Management, Vol. 40, pp. 191--204. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Lucas Jr, H.C. and Spitler, V.K. (1999) "Technology Use and Performance: A Field Study of Broker Workstations," Decision Sciences, Vol. 30, No. 2, pp. 1--21.Google ScholarGoogle ScholarCross RefCross Ref
  45. Majchrzak, A. and Cotton, J. (1988). "A longitudinal study of adjustment to technological change: From mass to computer-automated batch production," Journal of Occupational Psychology, Vol. 61, pp. 43--66.Google ScholarGoogle ScholarCross RefCross Ref
  46. Markus, M.L. (1994). "Electronic Mail as the Medium of Managerial Choice," Organization Science, Vol. 5, No. 4, pp. 502--527.Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Mathieson, K. (1991). "Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior," Information Systems Research, Vol. 2, pp. 173--191.Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Mathieson, K., Peacock, E. and Chin, W.W. (2001). "Extending the Technology Acceptance Model: The Influence of Perceived User Resources," The DATA BASE for Advances in Information Systems, Vol. 32, No. 3, pp. 86--112. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Meister, D.B. and Compeau, D.R. (2002). "Infusion of Innovation Adoption: An Individual Perspective," Proceedings of the ASAC, Winnipeg, Manitoba.Google ScholarGoogle Scholar
  50. Moore, G.C. and Benbasat, I. (1991). "Development of an Instrument to Measure the Perceptions of Adopting and Information Technology Innovation," Information Systems Research, Vol. 2, pp. 192--222.Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Morris, M.G. and Venkatesh, V. (2000). "Age Differences in Technology Adoption Decisions: Implications for a Changing Workforce," Personnel Psychology, Vol. 53, pp. 375--403.Google ScholarGoogle ScholarCross RefCross Ref
  52. Nickel, G.S. and Pinto, J.N. (1986). "The computer attitude scale," Computers in Human Behavior, Vol.2, pp. 301--306.Google ScholarGoogle ScholarCross RefCross Ref
  53. Oullette, J.A. and Wood, W. (1998). "Habit and Intention in Everyday Life: The Multiple Processes by Which Past Behavior Predicts Future Behavior," Psychological Bulletin, Vol. 124, pp. 54--74.Google ScholarGoogle ScholarCross RefCross Ref
  54. Plouffe, C.R., Hulland, J.S. and Vandenbosch, M. (2001). "Research Report: Richness Versus Parsimony in Modeling Technology Adoption Decisions--Understanding Merchant Adoption of a Smart Card-Based Payment System," Information Systems Research, Vol. 12, No. 2, pp. 208--222. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Ravichandran, T. and Rai, A. (2000) "Quality Management in Systems Development: An Organizational System Perspective," MIS Quarterly, Vol. 24, No. 3, pp. 381--415. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Rigdon, E.E. (1998). "Structural Equation Modeling," in G.A. Marcoulides (Ed.), Modern Methods for Business Research, Mahwah, NJ: Lawrence Erlbaum Associates, pp. 251--294.Google ScholarGoogle Scholar
  57. Robey, D. and Sahay, S. (1996). "Transforming Work through Information Technology: A Comparative Case Study of Geographic Information Systems in County Government," Information Systems Research, Vol. 7, No. 1, pp. 93--110.Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Ronis, D.L., Yates, J.F. and Kirscht, J.P. (1989). "Attitudes, Decisions, and Habits as Determinants of Repeated Behavior," in A. R. Pratkanis, S. J. Breckler and A. G. Greenwald (Eds.), Attitude Structure and Function, NJ: Lawrence Erlbaum Associates, pp. 213--239.Google ScholarGoogle Scholar
  59. Segars, A.H. and Grover, V. (1993). "Re-Examining Perceived Ease of Use and Usefulness: A Confirmatory Factor Analysis," MIS Quarterly, Vol. 18, No. 4, pp. 517--525.Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. Sharma, S. and Rai, A. (2003). "An Assessment of the Relationship Between ISD Leadership Characteristics and IS Innovation Adoption in Organizations," Information & Management, Vol. 40, pp. 391--401. Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Skinner, B.F. (1969). Contingencies of reinforcement: A theoretical analysis, Englewood Cliffs, NJ: Prentice Hall.Google ScholarGoogle Scholar
  62. Sparks, P. and Guthrie, C.A. (1998). "Self-Identity and the Theory of Planned Behavior: A Useful Addition or an Unhelpful Artifice," Journal of Applied Social Psychology, Vol. 28, pp. 1393--1410.Google ScholarGoogle ScholarCross RefCross Ref
  63. Sparks, P. and Shepherd, R. (1992). "Self-Identity and the Theory of Planned Behavior: Assessing the Role of Identification with "Green Consumerism"," Social Psychological Quarterly, Vol. 55, No. 4, pp. 388--399.Google ScholarGoogle ScholarCross RefCross Ref
  64. Speier, C. and Venkatesh, V. (2002). "The Hidden Minefields in the Adoption of Sales Force Automation Technologies," Journal of Marketing, Vol. 66, No. 3, pp. 98--111.Google ScholarGoogle ScholarCross RefCross Ref
  65. Straub, D., Limayem, M. and Karahanna-Evaristo, E. (1995). "Measuring System Usage: Implications for IS Theory Testing," Management Science, Vol.41, pp. 1328--1342. Google ScholarGoogle ScholarDigital LibraryDigital Library
  66. Szajna, B. (1996). "Empirical Evaluation of the Revised Technology Acceptance Model," Management Science, Vol. 42, No. 1, pp. 85--92. Google ScholarGoogle ScholarDigital LibraryDigital Library
  67. Taylor, S. and Todd, P.A. (1995). "Understanding Information Technology Usage: A Test of Competing Models," Information Systems Research, Vol. 6, No. 2, pp. 144--176.Google ScholarGoogle ScholarDigital LibraryDigital Library
  68. Trice, A.W. and Treacy, M.E. (1986). "Utilization as a Dependent Variable in MIS Research," Proceedings of the Proceedings of the Seventh International Conference on Information Systems, San Diego, CA, 1986, pp. 227--239.Google ScholarGoogle Scholar
  69. Venkatesh, V. (1999). "Creation of Favorable User Perceptions: Exploring the Role of Intrinsic Motivation," MIS Quarterly, Vol. 23, No. 2, pp. 239--260. Google ScholarGoogle ScholarDigital LibraryDigital Library
  70. Venkatesh, V. (2000). "Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model," Information Systems Research, Vol. 11, No. 4, pp. 342--365. Google ScholarGoogle ScholarDigital LibraryDigital Library
  71. Venkatesh, V. and Davis, F.D. (2000). "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, Vol. 46, No. 2, pp. 186--204. Google ScholarGoogle ScholarDigital LibraryDigital Library
  72. Venkatesh, V. and 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, Vol. 24, No. 1, pp. 115--139. Google ScholarGoogle ScholarDigital LibraryDigital Library
  73. Venkatesh, V., Morris, M.G. and Ackerman, P.L. (2000). "A Longitudinal Field Investigation of Gender Differences in Individual Technology Adoption Decision-Making Processes," Organizational Behavior and Human Decision Processes, Vol. 83, No. 1, pp. 33--60.Google ScholarGoogle ScholarCross RefCross Ref
  74. Venkatesh, V., Morris, M.G., Davis, G.B. and Davis, F.D. (2003). "User Acceptance of Information Technology: Toward a Unified View," MIS Quarterly, Vol. 27, No. 3, pp. 425--478. Google ScholarGoogle ScholarDigital LibraryDigital Library
  75. Verplanken, A., Aarts, H., van Knippenberg, A. and Moonen, A. (1998). "Habit Versus Planned Behavior: A Field Experiment," British Journal of Social Psychology, Vol. 27, pp. 539--560.Google ScholarGoogle Scholar
  76. Yoo, Y. and Alavi, M. (2001). "Media and Group Cohesion: Relative Influences on Social Presence, Task Participation, and Group Consensus," MIS Quarterly, Vol. 25, No. 3, pp. 371--390. Google ScholarGoogle ScholarDigital LibraryDigital Library
  77. Zmud, R.W. (1979). "Individual Differences and MIS Success: A Review of the Empirical Literature," Management Science, Vol. 25, No. 10, pp. 966--979.Google ScholarGoogle ScholarDigital LibraryDigital Library

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