The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Measuring System Usage: Implications for IS Theory Testing
Abstract
There is widespread agreement among researchers that system usage, defined as the utilization of information technology (IT) by individuals, groups, or organizations, is the primary variable through which IT affects white collar performance. Despite the number of studies targeted at explaining system usage, there are crucial differences in the way the variable has been conceptualized and operationalized. This wide variation of system usage measures hinders the efforts of MIS researchers to compare findings across studies, thus impeding the accumulation of knowledge and theory in this area. The purpose of this paper is to address conceptual as well as methodological issues related to measuring system usage. First, via LISREL measurement modeling techniques, we compare subjective and objective measures of system usage, namely, self-reported versus computer-recorded measures. Next, using a modified form of Davis' Technology Acceptance Model (TAM) as a nomological net, we test the nomological validity of these system usage constructs and measures. Results of the LISREL measurement and nomological net analysis suggest that system usage should be factored into self-reported system usage and computer-recorded system usage. Contrary to expectations, these constructs do not appear to be strongly related to each other. Moreover, while self-reported measures of system usage are related to self-reported measures of TAM independent variables, objective, computer-recorded measures show distinctly weaker links. In the face of such counter-evidence, it is tempting to argue that research that has relied on subjective measures of system usage (for example, research confirming TAM) may be artifactual. There are several alternative explanations, though, that maintain the integrity of TAM and studies that measure system usage subjectively. These alternative explanations suggest directions for further research as well as new approaches to measurement.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
Judgment under Uncertainty: Heuristics and Biases
Tversky, A., & Kahneman, D. (1975) · Utility, Probability, and Human Decision Making · doi:10.1007/978-94-010-1834-0_8 · Cited by 22,650
The Framing of Decisions and the Psychology of Choice
Tversky, A., & Kahneman, D. (1981) · Science · doi:10.1126/science.7455683 · Cited by 18,497
Convergent and discriminant validation by the multitrait-multimethod matrix.
Campbell, D. T., & Fiske, D. W. (1959) · Psychological Bulletin · doi:10.1037/h0046016 · Cited by 17,116
Information Systems Success: The Quest for the Dependent Variable
DeLone, W. H., & McLean, E. R. (1992) · Information Systems Research · doi:10.1287/isre.3.1.60 · Cited by 10,697
Moore, G. C., & Benbasat, I. (1991) · Information Systems Research · doi:10.1287/isre.2.3.192 · Cited by 9,150
Bem, D. J. (1972) · Advances in Experimental Social Psychology · doi:10.1016/s0065-2601(08)60024-6 · Cited by 5,241
Mathieson, K. (1991) · Information Systems Research · doi:10.1287/isre.2.3.173 · Cited by 4,216
Determinants of success for computer usage in small business
DeLone, W. H. (1988) · MIS Quarterly · doi:10.2307/248803 · Cited by 703
Management Information Systems: Appreciation and Involvement
Swanson, E. B. (1974) · Management Science · doi:10.1287/mnsc.21.2.178 · Cited by 529
Cited by
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies
Venkatesh, V., & Davis, F. D. (2000) · Management Science · doi:10.1287/mnsc.46.2.186.11926 · Cited by 23,066
The DeLone and McLean Model of Information Systems Success: A Ten-Year Update
(2003) · Journal of Management Information Systems · doi:10.1080/07421222.2003.11045748 · Cited by 11,791
Trust and TAM in Online Shopping: An Integrated Model1
Gefen, D., Karahanna, E., & Straub, D. W. (2003) · MIS Quarterly · doi:10.2307/30036519 · Cited by 8,635
Self-citationVenkatesh, V. (2000) · Information Systems Research · doi:10.1287/isre.11.4.342.11872 · Cited by 6,664
Structural Equation Modeling and Regression: Guidelines for Research Practice
Gefen, D., Straub, D., & Boudreau, M.-C. (2000) · Communications of the Association for Information Systems · doi:10.17705/1cais.00407 · Cited by 6,416
Self-citationA meta-analysis of the technology acceptance model
King, W. R., & He, J. (2006) · Information & Management · doi:10.1016/j.im.2006.05.003 · Cited by 2,906
Validation Guidelines for IS Positivist Research
Straub, D., & Gefen, D. (2004) · Communications of the Association for Information Systems · doi:10.17705/1cais.01324 · Cited by 2,797
Self-citationPavlou, P. A., Liang, H., & Xue, Y. (2007) · MIS Quarterly · doi:10.2307/25148783 · Cited by 2,646
The Technology Acceptance Model: Past, Present, and Future
Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003) · Communications of the Association for Information Systems · doi:10.17705/1cais.01250 · Cited by 2,572
Building Effective Online Marketplaces with Institution-Based Trust
Pavlou, P. A., & Gefen, D. (2004) · Information Systems Research · doi:10.1287/isre.1040.0015 · Cited by 2,516
Information Privacy: Measuring Individuals’ Concerns About Organizational Practices1
Smith, H. J., Milberg, S. J., & Burke, S. J. (1996) · MIS Quarterly · doi:10.2307/249477 · Cited by 2,266
The Relative Importance of Perceived Ease of Use in IS Adoption: A Study of E-Commerce Adoption
Drexel University, Gefen, D., Straub, D., & Georgia State University (2000) · Journal of the Association for Information Systems · doi:10.17705/1jais.00008 · Cited by 1,833
Self-citationAssessing the Validity of IS Success Models: An Empirical Test and Theoretical Analysis
Rai, A., Lang, S. S., & Welker, R. B. (2002) · Information Systems Research · doi:10.1287/isre.13.1.50.96 · Cited by 1,611
Testing the technology acceptance model across cultures: A three country study
Straub, D., Keil, M., & Brenner, W. (1997) · Information & Management · doi:10.1016/s0378-7206(97)00026-8 · Cited by 1,246
Self-citationAntecedents of B2C Channel Satisfaction and Preference: Validating e-Commerce Metrics
Devaraj, S., Fan, M., & Kohli, R. (2002) · Information Systems Research · doi:10.1287/isre.13.3.316.77 · Cited by 1,205
The Role of Espoused National Cultural Values in Technology Acceptance1
Srite, M., & Karahanna, E. (2006) · MIS Quarterly · doi:10.2307/25148745 · Cited by 1,169
Self-citationA Review of the Predictors, Linkages, and Biases in IT Innovation Adoption Research
Jeyaraj, A., Rottman, J. W., & Lacity, M. C. (2006) · Journal of Information Technology · doi:10.1057/palgrave.jit.2000056 · Cited by 1,136
The development of initial trust in an online company by new customers
Koufaris, M., & Hampton-Sosa, W. (2004) · Information & Management · doi:10.1016/j.im.2003.08.004 · Cited by 1,114
Extending the technology acceptance model: the influence of perceived user resources
Mathieson, K., Peacock, E., & Chin, W. W. (2001) · ACM SIGMIS Database: the DATABASE for Advances in Information Systems · doi:10.1145/506724.506730 · Cited by 1,099
Hassanein, K., & Head, M. (2007) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2006.11.018 · Cited by 986
An extension of the technology acceptance model in an ERP implementation environment
Amoako-Gyampah, K., & Salam, A. F. (2004) · Information & Management · doi:10.1016/j.im.2003.08.010 · Cited by 922
Does the technology acceptance model predict actual use? A systematic literature review
Turner, M., Kitchenham, B., Brereton, P., Charters, S., & Budgen, D. (2010) · Information and Software Technology · doi:10.1016/j.infsof.2009.11.005 · Cited by 851
Dead Or Alive? The Development, Trajectory And Future Of Technology Adoption Research.
University of Arkansas, Venkatesh, V., Davis, F., University of Arkansas, Morris, M., & University of Virginia (2007) · Journal of the Association for Information Systems · doi:10.17705/1jais.00120 · Cited by 789
Chau, P. Y. K., & Hu, P. J. (2002) · Journal of Management Information Systems · doi:10.1080/07421222.2002.11045699 · Cited by 707
The Technology Acceptance Model: A Meta-Analysis of Empirical Findings
Ma, Q., & Liu, L. (2004) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.2004010104 · Cited by 660
Technology acceptance: a meta‐analysis of the TAM: Part 1
Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007) · Journal of Modelling in Management · doi:10.1108/17465660710834453 · Cited by 624
Davis, F. D., & Venkatesh, V. (2004) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2003.822468 · Cited by 582
Pavlou, P. A., & Gefen, D. (2005) · Information Systems Research · doi:10.1287/isre.1050.0065 · Cited by 580
It's all about attitude: revisiting the technology acceptance model
Yang, H.-D., & Yoo, Y. (2004) · Decision Support Systems · doi:10.1016/s0167-9236(03)00062-9 · Cited by 578
Technology use and performance: A field study of broker workstations
Lucas, H. C., & Spitler, V. K. (1999) · Decision Sciences · doi:10.1111/j.1540-5915.1999.tb01611.x · Cited by 561
Using a modified technology acceptance model in hospitals
AGGELIDIS, V, & CHATZOGLOU, P. (2009) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2008.06.006 · Cited by 553
Sharma, R., Yetton, P., & Crawford, J. (2009) · MIS Quarterly · doi:10.2307/20650305 · Cited by 496
Understanding the effect of e-learning on individual performance: The role of digital literacy
Mohammadyari, S., & Singh, H. (2015) · Computers & Education · doi:10.1016/j.compedu.2014.10.025 · Cited by 496
Kim, J., & Park, H.-A. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.2143 · Cited by 475
Sharma, R., & Yetton, P. (2003) · MIS Quarterly · doi:10.2307/30036548 · Cited by 470
What drives FinTech adoption? A multi-method evaluation using an adapted technology acceptance model
Singh, S., Sahni, M. M., & Kovid, R. K. (2020) · Management Decision · doi:10.1108/md-09-2019-1318 · Cited by 433
Lee, J., Kim, J., & Choi, J. Y. (2019) · Telematics and Informatics · doi:10.1016/j.tele.2018.12.006 · Cited by 382
Research Note—Two Competing Perspectives on Automatic Use: A Theoretical and Empirical Comparison
Kim, S. S., Malhotra, N. K., & Narasimhan, S. (2005) · Information Systems Research · doi:10.1287/isre.1050.0070 · Cited by 381
Amoako-Gyampah, K. (2007) · Computers in Human Behavior · doi:10.1016/j.chb.2004.12.002 · Cited by 345
Developing a multidimensional measure of system-use in an organizational context
Doll, W. J., & Torkzadeh, G. (1998) · Information & Management · doi:10.1016/s0378-7206(98)00028-7 · Cited by 334
Sharma, R., & Yetton, P. (2007) · MIS Quarterly · doi:10.2307/25148789 · Cited by 285
Five-factor model personality traits as predictors of perceived and actual usage of technology
Barnett, T., Pearson, A. W., Pearson, R., & Kellermanns, F. W. (2015) · European Journal of Information Systems · doi:10.1057/ejis.2014.10 · Cited by 281
Technology acceptance: a meta-analysis of the TAM: part 2
Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007) · Journal of Modelling in Management · doi:10.1108/17465660710834462 · Cited by 257
Raghunathan, S. (1999) · Decision Support Systems · doi:10.1016/s0167-9236(99)00060-3 · Cited by 222
Predicting the continued use of Internet-based learning technologies: the role of habit
Limayem, M., & Cheung, C. M. K. (2011) · Behaviour & Information Technology · doi:10.1080/0144929x.2010.490956 · Cited by 191
Self-citationHan, S., Min, J., & Lee, H. (2015) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2015.04.004 · Cited by 183
Toward a better understanding of behavioral intention and system usage constructs
Wu, J., & Du, H. (2012) · European Journal of Information Systems · doi:10.1057/ejis.2012.15 · Cited by 175
What factors influence auditors' use of computer-assisted audit techniques?
Bierstaker, J., Janvrin, D., & Lowe, D. J. (2014) · Advances in Accounting · doi:10.1016/j.adiac.2013.12.005 · Cited by 165
A comparison of purchase decision calculus between potential and repeat customers of an online store
Kim, H.-W., & Gupta, S. (2009) · Decision Support Systems · doi:10.1016/j.dss.2009.04.014 · Cited by 150
Technology acceptance research: Meta-analysis
Marikyan, D., Papagiannidis, S., & Stewart, G. (2026) · Journal of Information Science · doi:10.1177/01655515231191177 · Cited by 140
The Janus face of Facebook: Positive and negative sides of social networking site use
Mäntymäki, M., & Islam, A. K. M. N. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.02.078 · Cited by 119
PDA usage in healthcare professionals: testing an extended technology acceptance model
Liang, H., Xue, Y., & Byrd, T. A. (2003) · International Journal of Mobile Communications · doi:10.1504/ijmc.2003.003992 · Cited by 98
Effects and acceptance of precision education in an AI-supported smart learning environment
Hu, Y.-H. (2022) · Education and Information Technologies · doi:10.1007/s10639-021-10664-3 · Cited by 68
Metadata
- Authors
- Straub, D., Limayem, M., & Karahanna-Evaristo, E.
- Venue
- Management Science
- Published
- 1995-08
- Volume
- 41
- Issue
- 8
- Pages
- 1328-1342
- ISSN
- 0025-1909 (ISSN-L)
- 1526-5501
- OpenAlex
- W2050075077
- Cited by
- 958
- References
- 10
- FWCI
- 26.24
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.