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.
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarCross Ref
- 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 Scholar
- Ajzen, I. (1991). "The Theory of Planned Behavior," Organizational Behavior and Human Decision Processes, Vol. 50, pp. 179--211.Google ScholarCross Ref
- Ajzen, I. (2001). "Nature and Operation of Attitudes," Annual Review of Psychology, Vol. 52, pp. 27--58.Google ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- Ashcraft, M.H. (2002). Cognition, Upper Saddle River, NJ: Prentice Hall.Google Scholar
- Bandura, A. (1977). Social learning theory, Englewood Cliffs, NJ: Prentice Hall.Google Scholar
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- Fishbein, M. and Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Reading, MA: Addison-Wesley.Google Scholar
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- Goodhue, D.L. (1995). "Understanding User Evaluations of Information Systems," Management Science, Vol. 41, No. 12, pp. 1827--1844. Google ScholarDigital Library
- Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998). Multivariate Data Analysis, Upper Saddle River, NJ: Prentice Hall. Google ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- Markus, M.L. (1994). "Electronic Mail as the Medium of Managerial Choice," Organization Science, Vol. 5, No. 4, pp. 502--527.Google ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- Meister, D.B. and Compeau, D.R. (2002). "Infusion of Innovation Adoption: An Individual Perspective," Proceedings of the ASAC, Winnipeg, Manitoba.Google Scholar
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- Nickel, G.S. and Pinto, J.N. (1986). "The computer attitude scale," Computers in Human Behavior, Vol.2, pp. 301--306.Google ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- Skinner, B.F. (1969). Contingencies of reinforcement: A theoretical analysis, Englewood Cliffs, NJ: Prentice Hall.Google Scholar
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- Szajna, B. (1996). "Empirical Evaluation of the Revised Technology Acceptance Model," Management Science, Vol. 42, No. 1, pp. 85--92. Google ScholarDigital Library
- 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 ScholarDigital Library
- 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 Scholar
- Venkatesh, V. (1999). "Creation of Favorable User Perceptions: Exploring the Role of Intrinsic Motivation," MIS Quarterly, Vol. 23, No. 2, pp. 239--260. Google ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
Index Terms
- Individual differences and usage behavior: revisiting a technology acceptance model assumption
Recommendations
The effects of individual differences on e-learning users' behaviour in developing countries
We extended the TAM in the context of e-learning in developing countries (Lebanon).We examined if social influence affect the user perceptions towards using e-learning.Examined the moderating effect of gender, age and experience on the key factors.The ...
The influence of individual differences on continuance intentions of enterprise resource planning (ERP)
This study aims to investigate whether individual differences affect enterprise resource planning (ERP) users' continuance intention. In the initial stage ERP users usually lack the complete will to determine whether or not they use ERP, but their ...
The mediation of external variables in the technology acceptance model
TAM specifies a pathway of technology acceptance, from external variables to beliefs, attitudes, and system usage. We tested one of its assumptions that the 'perceived ease-of-use' and 'perceived usefulness' constructs fully mediate the influence of ...
Comments