The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Using Davis's Perceived Usefulness and Ease‐of‐use Instruments for Decision Making: A Confirmatory and Multigroup Invariance Analysis
Abstract
As key components of Davis's technology acceptance model (TAM), the perceived usefulness and perceived ease‐of‐use instruments are widely accepted among the MIS research community as tools for evaluating information system applications and predicting usage. Despite this wide acceptance, a series of incremental cross‐validation studies have produced conflicting and equivocal results that do not provide guidance for researchers or practitioners who might use the TAM for decision making. Using a sample of 902 “initial exposure” responses, this research conducts: (1) a confirmatory factor analysis to assess the validity and reliability of the original instruments proposed by Davis, and (2) a multigroup invariance analysis to assess the equivalence of these instruments across subgroups based on type of application, experience with computing, and gender. In contrast to the mixed results of prior cross‐validation efforts, the results of this confirmatory study provide strong support for the validity and reliability of Davis's sixitem perceived usefulness and six‐item ease‐of‐use instruments. The multigroup invariance analysis suggests the usefulness and ease‐of‐use instruments have invariant true scores across most, but not all, subgroups. With notable exemptions for word processing applications and users with no prior computing experience, this research provides evidence that the item‐factor loadings (true scores) are invariant across spread sheet, database, and graphic applications. The implications of the results for managerial decision making are discussed.
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
Self-efficacy: Toward a unifying theory of behavioral change
Bandura, A. (1977) · Psychological Review · doi:10.1037/0033-295x.84.2.191 · Cited by 43,925
Comparative fit indexes in structural models.
Bentler, P. M. (1990) · Psychological Bulletin · doi:10.1037/0033-2909.107.2.238 · Cited by 25,266
Self-efficacy mechanism in human agency
Bandura, A. (1982) · American Psychologist · doi:10.1037/0003-066x.37.2.122 · Cited by 15,975
Mathieson, K. (1991) · Information Systems Research · doi:10.1287/isre.2.3.173 · Cited by 4,216
Computer anxiety: sex, race and age
Gilroy, F. D., & Desai, H. B. (1986) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(86)80084-0 · Cited by 206
Cited by
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
Extending the TAM for a World-Wide-Web context
Moon, J.-W., & Kim, Y.-G. (2001) · Information & Management · doi:10.1016/s0378-7206(00)00061-6 · Cited by 3,802
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
The utilization of e-government services: citizen trust, innovation and acceptance factors
Carter, L., & Bélanger, F. (2005) · Information Systems Journal · doi:10.1111/j.1365-2575.2005.00183.x · Cited by 2,207
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
Hong, W., Thong, J. Y. L., Wong, W.-M., & Tam, K.-Y. (2002) · Journal of Management Information Systems · doi:10.1080/07421222.2002.11045692 · Cited by 827
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
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
TUNG, F., CHANG, S., & CHOU, C. (2008) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2007.06.006 · Cited by 476
The adoption of information technology in the sales force
Schillewaert, N., Ahearne, M. J., Frambach, R. T., & Moenaert, R. K. (2005) · Industrial Marketing Management · doi:10.1016/j.indmarman.2004.09.013 · Cited by 362
Yen, D. C., Wu, C.-S., Cheng, F.-F., & Huang, Y.-W. (2010) · Computers in Human Behavior · doi:10.1016/j.chb.2010.02.005 · Cited by 319
Pan, S., & Jordan-Marsh, M. (2010) · Computers in Human Behavior · doi:10.1016/j.chb.2010.03.015 · Cited by 300
Park, N. (2010) · Journal of Communication · doi:10.1111/j.1460-2466.2009.01440.x · Cited by 141
Mobile Banking Adoption: Examining the Role of Personality Traits
Agyei, J., Sun, S., Abrokwah, E., Penney, E. K., & Ofori-Boafo, R. (2020) · Sage Open · doi:10.1177/2158244020932918 · Cited by 69
Chen, Y., Zahedi, F. M., Abbasi, A., & Dobolyi, D. (2021) · Information & Management · doi:10.1016/j.im.2020.103394 · Cited by 53
A new hybrid model for exploring the adoption of online nursing courses
Tung, F.-C., & Chang, S.-C. (2008) · Nurse Education Today · doi:10.1016/j.nedt.2007.06.003 · Cited by 53
Lim, J. S., & Hong, N. (2025) · Health & New Media Research · doi:10.22720/hnmr.2025.00115 · Cited by 6
Metadata
- Authors
- Doll, W. J., Hendrickson, A., & Deng, X.
- Venue
- Decision Sciences
- Publisher
- Wiley
- Published
- 1998-09
- Volume
- 29
- Issue
- 4
- Pages
- 839-869
- ISSN
- 0011-7315 (ISSN-L)
- 1540-5915
- OpenAlex
- W2059549509
- Cited by
- 458
- References
- 6
- FWCI
- 24.33
- 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.