A Meta-Analysis of the Role of Environment-Based Voluntariness in Information Technology Acceptance

The technology acceptance model (TAM) asserts that ease of use and usefulness are two primary determinants of behavioral intention and usage. A parallel research stream emphasizes voluntariness, a key social influence and contextual variable, as a critical factor in information technology (IT) adoption, but pays little attention to its role in TAM. This paper addresses this particular absence by investigating the impact of environment-based voluntariness on the relationships among the four primary TAM constructs. A meta-analysis of 71 empirical studies provides strong support for the hypotheses that environment-based voluntariness moderates the effects of ease of use and usefulness on behavioral intention, but not the effect of ease of use on usefulness. Moreover, inconsistent with our expectations, environment-based voluntariness does not moderate the effects of ease of use and usefulness on usage. By further analyzing the data set, we suggest this may be because of the relatively small sample size, the presence of other factors, or the inappropriate measurement of usage in previous studies. The current study contributes not only to the distinction between user-based and environment-based voluntariness but also to a more complete understanding of user acceptance of IT across system-use environments.

[1]  D. Wegner,et al.  Apparent mental causation. Sources of the experience of will. , 1999, The American psychologist.

[2]  Linda Jo Calloway,et al.  An integrated model of technology acceptance for mobile computing , 2004 .

[3]  Chris W. Clegg,et al.  Explaining intranet use with the technology acceptance model , 2001, J. Inf. Technol..

[4]  Robert F. Otondo,et al.  Reexamining Technology Acceptance in Online Task Behaviours , 2006, Electron. Mark..

[5]  Kathleen Burnett,et al.  User acceptance of web-based subscription databases: extending the technology acceptance model , 2005 .

[6]  Akhilesh Bajaj,et al.  A feedback model to understand information system usage , 1998, Inf. Manag..

[7]  Izak Benbasat,et al.  Trust and TAM for Online Recommendation Agents , 2004, AMCIS.

[8]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[9]  Luis L. Martins,et al.  A Model of Business School Students' Acceptance of a Web-Based Course , 2004 .

[10]  Matthew K. O. Lee,et al.  Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation , 2005, Inf. Manag..

[11]  Deborah Compeau,et al.  Social Cognitive Theory and Individual Reactions to Computing Technology: A Longitudinal Study , 1999, MIS Q..

[12]  Chin-Lung Hsu,et al.  Why do people play on-line games? An extended TAM with social influences and flow experience , 2004, Inf. Manag..

[13]  V. Venkatesh,et al.  AGE DIFFERENCES IN TECHNOLOGY ADOPTION DECISIONS: IMPLICATIONS FOR A CHANGING WORK FORCE , 2000 .

[14]  Elena Karahanna,et al.  Time Flies When You're Having Fun: Cognitive Absorption and Beliefs About Information Technology Usage , 2000, MIS Q..

[15]  E. Vance Wilson,et al.  Predicting Continuing Acceptance of IT in Conditions of Sporadic Use , 2005, AMCIS.

[16]  L. Hedges,et al.  The Handbook of Research Synthesis , 1995 .

[17]  Marios Koufaris,et al.  Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior , 2002, Inf. Syst. Res..

[18]  Bernadette Szajna,et al.  Empirical evaluation of the revised technology acceptance model , 1996 .

[19]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[20]  Rajeev Sharma,et al.  The Contingent Effects of Management Support and Task Interdependence on Successful Information Systems Implementation , 2003, MIS Q..

[21]  A. Bandura.  Self-efficacy: toward a unifying theory of behavioral change. , 1977, Psychological review.

[22]  Mun Y. Yi,et al.  Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model , 2003, Int. J. Hum. Comput. Stud..

[23]  Mark Keil,et al.  Usefulness and ease of use: field study evidence regarding task considerations , 1995, Decis. Support Syst..

[24]  Mark Vandenbosch,et al.  Research Report: Richness Versus Parsimony in Modeling Technology Adoption Decisions - Understanding Merchant Adoption of a Smart Card-Based Payment System , 2001, Inf. Syst. Res..

[25]  Alan R. Dennis,et al.  Understanding Fit and Appropriation Effects in Group Support Systems via Meta-Analysis , 2001, MIS Q..

[26]  Detmar W. Straub,et al.  Veni, Vidi, Vici: Breaking the TAM Logjam , 2007, J. Assoc. Inf. Syst..

[27]  Mauricio Featherman,et al.  Evaluative criteria and user acceptance of internet-based financial transaction processing systems , 2002 .

[28]  Magid Igbaria,et al.  Personal Computing Acceptance Factors in Small Firms: A Structural Equation Model , 1997, MIS Q..

[29]  Dahui Li,et al.  The Effect of Affiliation Motivation on the Intention to Use Groupware in an MBA Program , 2004, J. Comput. Inf. Syst..

[30]  William J. Doll,et al.  Developing a multidimensional measure of system-use in an organizational context , 1998, Inf. Manag..

[31]  Younghwa Lee,et al.  The Technology Acceptance Model: Past, Present, and Future , 2003, Commun. Assoc. Inf. Syst..

[32]  Kieran Mathieson,et al.  Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior , 1991, Inf. Syst. Res..

[33]  Ritu Agarwal,et al.  Are Individual Differences Germane to the Acceptance of New Information Technologies , 1999 .

[34]  L. Hedges,et al.  Statistical Methods for Meta-Analysis , 1987 .

[35]  R. Rosenthal.  The file drawer problem and tolerance for null results , 1979 .

[36]  Detmar W. Straub,et al.  Information Technology Adoption Across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs , 1999, MIS Q..

[37]  Diane M. Strong,et al.  Perceived critical mass effect on groupware acceptance , 2000, Eur. J. Inf. Syst..

[38]  P. Pavlou.  INTEGRATING TRUST IN ELECTRONIC COMMERCE WITH THE TECHNOLOGY ACCEPTANCE MODEL: MODEL DEVELOPMENT AND VALIDATION , 2001 .

[39]  Henri Barki,et al.  Explaining the Role of User Participation in Information System Use , 1994 .

[40]  Yi-Shun Wang,et al.  The adoption of electronic tax filing systems: an empirical study , 2003, Gov. Inf. Q..

[41]  Yi-Shun Wang,et al.  DETERMINANTS OF USER ACCEPTANCE OF INTERNET BANKING: AN EMPIRICAL STUDY , 2003 .

[42]  Mark I. H. Hwang,et al.  The use of meta-analysis in MIS research: promises and problems , 1996, DATB.

[43]  Lorne Olfman,et al.  The Importance of Learning Style in End-User Training , 1990, MIS Q..

[44]  Cheng-Kiang Farn,et al.  Acceptance of electronic tax filing: A study of taxpayer intentions , 2006, Inf. Manag..

[45]  Detmar W. Straub,et al.  Measuring System Usage: Implications for IS Theory Testing , 1995 .

[46]  Gregg B. Jackson,et al.  Meta-Analysis: Cumulating Research Findings Across Studies , 1982 .

[47]  I. Ajzen,et al.  Understanding Attitudes and Predicting Social Behavior , 1980 .

[48]  Wynne W. Chin,et al.  The Effects of Group Attitudes Toward Alternative GDSS Designs on the Decision‐making Performance of Computer‐Supported Groups* , 1994 .

[49]  Barbara Stern,et al.  Consumer Bidding Behavior on Internet Auction Sites , 2002, Int. J. Electron. Commer..

[50]  Gordon B. Davis,et al.  Testing the Determinants of Microcomputer Usage via a Structural Equation Model , 1995, J. Manag. Inf. Syst..

[51]  Hock-Hai Teo,et al.  Evaluating information accessibility and community adaptivity features for sustaining virtual learning communities , 2003, Int. J. Hum. Comput. Stud..

[52]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[53]  Thompson S. H. Teo,et al.  Intrinsic and extrinsic motivation in Internet usage , 1999 .

[54]  Glenn Snelbecker,et al.  Factors related to the use of an electronic performance support system (epss) , 2000 .

[55]  Heshan Sun,et al.  The role of moderating factors in user technology acceptance , 2006, Int. J. Hum. Comput. Stud..

[56]  Jennifer J. Argo,et al.  Meta-Analyses of the Effectiveness of Warning Labels , 2004 .

[57]  Vallabh Sambamurthy,et al.  Sources of Influence on Beliefs about Information Technolgoy Use: An Empirical Study of Knowledge Workers , 2003, MIS Q..

[58]  K. N. Polivanova,et al.  Psychological Analysis of the Crises in Mental Development , 2001 .

[59]  Zhan Wu,et al.  How Individual Differences Influence Technology Usage Behavior? Toward an Integrated Framework , 2005, J. Comput. Inf. Syst..

[60]  I. Ajzen.  The theory of planned behavior , 1991 .

[61]  De Liu,et al.  The Effects of Trust and Enjoyment on Intention to Play Online Games , 2007 .

[62]  Peter A. Todd,et al.  Understanding Information Technology Usage: A Test of Competing Models , 1995, Inf. Syst. Res..

[63]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[64]  L. Hedges,et al.  The power of statistical tests in meta-analysis. , 2001, Psychological methods.

[65]  D. Wegner.  The Illusion of Conscious Will , 2018, The MIT Press.

[66]  Susan A. Brown,et al.  Do I really have to? User acceptance of mandated technology , 2002, Eur. J. Inf. Syst..

[67]  Viswanath Venkatesh,et al.  Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model , 2000, Inf. Syst. Res..

[68]  Detmar W. Straub,et al.  Inexperience and experience with online stores: the importance of TAM and trust , 2003, IEEE Trans. Engineering Management.

[69]  John Andy Wood,et al.  Methodology for Dealing With Duplicate Study Effects in a Meta-Analysis , 2008 .

[70]  Cynthia LeRouge,et al.  A Causal Model for Using Collaborative Technologies to Facilitate Student Team Projects , 2004, J. Comput. Inf. Syst..

[71]  D. Rubin,et al.  Comparing Effect Sizes of Independent Studies , 1982 .

[72]  Saonee Sarker.  Examining the "Levels of Analysis" Issue in Understanding Technology Adoption by Groups , 2006, ICIS.

[73]  Izak Benbasat,et al.  Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation , 1991, Inf. Syst. Res..

[74]  Grover S. Kearns,et al.  The Role of Convergence in Information Systems and Business Planning , 2006, Journal of International Technology and Information Management.

[75]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[76]  Daniel M. Wegner,et al.  Voluntary involuntariness: Thought suppression and the regulation of the experience of will , 2003, Consciousness and Cognition.

[77]  David Gefen,et al.  Managing User Trust in B2C e-Services , 2003 .

[78]  Detmar W. Straub,et al.  Reconceptualizing System Usage: An Approach and Empirical Test , 2006, Inf. Syst. Res..

[79]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[80]  Detmar W. Straub,et al.  Trust and TAM in Online Shopping: An Integrated Model , 2003, MIS Q..

[81]  Detmar W. Straub,et al.  Gender Differences in the Perception and Use of E-Mail: An Extension to the Technology Acceptance Model , 1997, MIS Q..

[82]  Kar Yan Tam,et al.  Determinants of User Acceptance of Digital Libraries: An Empirical Examination of Individual Differences and System Characteristics , 2002, J. Manag. Inf. Syst..

[83]  Raafat George Saadé,et al.  The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model , 2005, Inf. Manag..

[84]  Geoffrey S. Hubona,et al.  Individual differences and usage behavior: revisiting a technology acceptance model assumption , 2005, DATB.

[85]  Mark Srite,et al.  The Role of Espoused National Cultural Values in Technology Acceptance , 2006, MIS Q..

[86]  Peter A. Todd,et al.  Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication , 1992, MIS Q..

[87]  Detmar W. Straub,et al.  Structural Equation Modeling and Regression: Guidelines for Research Practice , 2000, Commun. Assoc. Inf. Syst..

[88]  Adel M. Aladwani.  The development of two tools for measuring the easiness and usefulness of transactional Web sites , 2002, Eur. J. Inf. Syst..

[89]  D. Gustafson,et al.  An empirical study of perceived usefulness and perceived ease of use on computerized support system use over time , 1994 .

[90]  Robert L. Heneman.  The relationship between supervisory ratings and results-oriented measures of performance: A meta-analysis. , 1986 .

[91]  Rajeev Sharma,et al.  The Contingent Effects of Training, Technical Complexity, and Task Interdependence on Successful Information Systems Implementation , 2007, MIS Q..

[92]  Ritu Agarwal,et al.  The Role of Innovation Characteristics and Perceived Voluntariness in the Acceptance of Information Technologies , 1997 .

[93]  Paul A. Pavlou,et al.  Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model , 2003, Int. J. Electron. Commer..

[94]  Elena Karahanna,et al.  Reconceptualizing Compatability Beliefs in Technology Acceptance Research , 2006, MIS Q..

[95]  Judy Chuan-Chuan Lin,et al.  Towards an understanding of the behavioural intention to use a web site , 2000, Int. J. Inf. Manag..

[96]  William Lewis,et al.  Research Note - Statistical Power in Analyzing Interaction Effects: Questioning the Advantage of PLS with Product Indicators , 2007, Inf. Syst. Res..

[97]  Linda Parrack Livingstone,et al.  Another Look at The Relationship Between Performance and Voluntary Turnover , 1994 .

[98]  L. Hedges.  Estimation of effect size from a series of independent experiments. , 1982 .

[99]  G. Glass.  Primary, Secondary, and Meta-Analysis of Research1 , 1976 .

[100]  Qingxiong Ma,et al.  The Technology Acceptance Model: A Meta-Analysis of Empirical Findings , 2004, J. Organ. End User Comput..

[101]  Charles Chowa,et al.  Information System Success: Individual and Organizational Determinants , 2006, Manag. Sci..

[102]  John E. Hunter,et al.  Methods of Meta-Analysis: Correcting Error and Bias in Research Findings , 1991 .

[103]  Viswanath Venkatesh,et al.  Why Don't Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior , 2000, MIS Q..