The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
The Measurement of End-User Computing Satisfaction
Abstract
This article contrasts traditional versus end-user computing environments and reports on the development of an instrument which merges ease of use and information product items to measure the satisfaction of users who directly interact with the computer for a specific application. Using a survey of 618 end users, the researchers conducted a factor analysis and modified the instrument. The results suggest a 12-item instrument that measures five components of end-user satisfaction — content, accuracy, format, ease of use, and timeliness. Evidence of the instrument’s discriminant validity is presented. Reliability and validity is assessed by nature and type of application. Finally, standards for evaluating end-user applications are presented, and the instrument’s usefulness for achieving more precision in research questions is explored.
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
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
User Involvement and MIS Success: A Review of Research
Ives, B., & Olson, M. H. (1984) · Management Science · doi:10.1287/mnsc.30.5.586 · Cited by 1,318
Management Information Systems: Appreciation and Involvement
Swanson, E. B. (1974) · Management Science · doi:10.1287/mnsc.21.2.178 · Cited by 529
Goodwin, N. C. (1987) · Communications of the ACM · doi:10.1145/214748.214758 · Cited by 277
Cited by
Moore, G. C., & Benbasat, I. (1991) · Information Systems Research · doi:10.1287/isre.2.3.192 · Cited by 9,150
Understanding Information Systems Continuance: An Expectation-Confirmation Model1
Bhattacherjee, A. (2001) · MIS Quarterly · doi:10.2307/3250921 · Cited by 8,767
Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008) · Decision Support Systems · doi:10.1016/j.dss.2007.07.001 · Cited by 3,613
“759 [49] Privacy protection (PPP) Reflective 3.”
MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011) · MIS Quarterly · doi:10.2307/23044045 · Cited by 3,165
A Theoretical Integration of User Satisfaction and Technology Acceptance
Wixom, B. H., & Todd, P. A. (2005) · Information Systems Research · doi:10.1287/isre.1050.0042 · Cited by 2,835
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
Assessing 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
Understanding e-learning continuance intention: An extension of the Technology Acceptance Model
Roca, J. C., Chiu, C.-M., & Martínez, F. J. (2006) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2006.01.003 · Cited by 1,399
Evaluating E-learning systems success: An empirical study
Al-Fraihat, D., Joy, M., Masa'deh, R., & Sinclair, J. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2019.08.004 · Cited by 1,350
NELSON, R. R., TODD, P. A., & WIXOM, B. H. (2005) · Journal of Management Information Systems · doi:10.1080/07421222.2005.11045823 · Cited by 998
Technology acceptance model (TAM) and social media usage: an empirical study on Facebook
Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014) · Journal of Enterprise Information Management · doi:10.1108/jeim-04-2012-0011 · Cited by 937
Gender differences in perceptions and relationships among dominants of e-learning acceptance
Ong, C.-S., & Lai, J.-Y. (2006) · Computers in Human Behavior · doi:10.1016/j.chb.2004.03.006 · Cited by 863
Chaudoir, S. R., Dugan, A. G., & Barr, C. H. (2013) · Implementation Science · doi:10.1186/1748-5908-8-22 · Cited by 800
Information System Success: Individual and Organizational Determinants
Sabherwal, R., Jeyaraj, A., & Chowa, C. (2006) · Management Science · doi:10.1287/mnsc.1060.0583 · Cited by 685
Perceived information quality in data exchanges: Effects on risk, trust, and intention to use
Nicolaou, A. I., & McKnight, D. H. (2006) · Information Systems Research · doi:10.1287/isre.1060.0103 · Cited by 682
A Framework and Guidelines for Context-Specific Theorizing in Information Systems Research
Hong, W., Chan, F. K. Y., Thong, J. Y. L., Chasalow, L. C., & Dhillon, G. (2014) · Information Systems Research · doi:10.1287/isre.2013.0501 · Cited by 609
Liaw, S.-S., & Huang, H.-M. (2013) · Computers & Education · doi:10.1016/j.compedu.2012.07.015 · Cited by 571
User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model
Lee, D. Y., & Lehto, M. R. (2013) · Computers & Education · doi:10.1016/j.compedu.2012.10.001 · Cited by 565
Al-Gahtani, S. S., & King, M. (1999) · Behaviour & Information Technology · doi:10.1080/014492999119020 · Cited by 519
(2000) · Cited by 403
Decisional factors driving organic food consumption: Generation of consumer purchase intentions
Teng, C.-C., & Wang, Y.-M. (2015) · British Food Journal · doi:10.1108/bfj-12-2013-0361 · Cited by 391
Ganguli, S., & Roy, S. K. (2011) · International Journal of Bank Marketing · doi:10.1108/02652321111107648 · Cited by 357
An Assessment of the Contingency Theory of Management Information Systems
Weill, P., & Olson, M. H. (1989) · Journal of Management Information Systems · doi:10.1080/07421222.1989.11517849 · Cited by 351
Critical success factors of enterprise resource planning systems implementation success in China
Zhang, L., Lee, M. K. O., Zhang, Z., & Banerjee, P. (2003) · 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the · doi:10.1109/hicss.2003.1174613 · Cited by 335
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
Self-citationAl-Rahmi, W. M., Alias, N., Othman, M. S., Alzahrani, A. I., Alfarraj, O., Saged, A. A., & Abdul Rahman, N. S. (2018) · IEEE Access · doi:10.1109/access.2018.2802325 · Cited by 204
Wu, J.-H., & Wang, Y.-M. (2007) · Computers in Human Behavior · doi:10.1016/j.chb.2005.07.005 · Cited by 203
Predictive factors of physicians’ satisfaction with telemedicine services acceptance
Kissi, J., Dai, B., Dogbe, C. S., Banahene, J., & Ernest, O. (2020) · Health Informatics Journal · doi:10.1177/1460458219892162 · Cited by 157
The impact of individual differences on e‐learning system satisfaction: A contingency approach
Lu, H., & Chiou, M. (2010) · British Journal of Educational Technology · doi:10.1111/j.1467-8535.2009.00937.x · Cited by 150
The role of quality in e‐procurement performance: An empirical analysis
Vaidyanathan, G., & Devaraj, S. (2008) · Journal of Operations Management · doi:10.1016/j.jom.2007.08.004 · Cited by 133
Najmul Islam, A. K. M. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2013.09.010 · Cited by 107
Practitioners’ views on computerized drug-drug interaction alerts in the VA system
Ko, Y., Abarca, J., Malone, D. C., Dare, D. C., Geraets, D., Houranieh, A., Jones, W. N., Nichol, W. P., Schepers, G. P., & Wilhardt, M. (2007) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m2224 · Cited by 102
Kossyva, D., Theriou, G., Aggelidis, V., & Sarigiannidis, L. (2024) · Journal of Knowledge Management · doi:10.1108/jkm-03-2022-0174 · Cited by 101
Perceived system performance: a test of an extended technology acceptance model
Liu, L., & Ma, Q. (2006) · ACM SIGMIS Database: the DATABASE for Advances in Information Systems · doi:10.1145/1161345.1161354 · Cited by 80
The impact of service level on the acceptance of application service oriented medical records
Liu, L., & Ma, Q. (2005) · Information & Management · doi:10.1016/j.im.2004.12.004 · Cited by 59
The impact of user‐perceived e‐procurement quality on system and contract compliance
Brandon‐Jones, A., & Carey, S. (2011) · International Journal of Operations & Production Management · doi:10.1108/01443571111111928 · Cited by 58
Identifying issues in customer relationship management at merck-medco
Torkzadeh, G., Chang, J. C.-J., & Hansen, G. W. (2006) · Decision Support Systems · doi:10.1016/j.dss.2005.10.003 · Cited by 56
Self-citationRamayah, T., Roy, M. H., Arokiasamy, S., Zbib, I., & Ahmed, Z. U. (2007) · International Journal of Business Information Systems · doi:10.1504/ijbis.2007.011980 · Cited by 53
Brandon-Jones, A. (2017) · International Journal of Operations & Production Management · doi:10.1108/ijopm-08-2016-0480 · Cited by 30
Creating and Validating an Information Quality Scale for E-Commerce Platforms
Liu, C.-T., Guo, Y. M., & Hsu, J.-L. (2023) · Journal of Electronic Commerce in Organizations · doi:10.4018/jeco.327350 · Cited by 10
Upadhyay, P., & Dan, P. K. (2009) · Communications in Computer and Information Science · doi:10.1007/978-3-642-00405-6_22 · Cited by 6
Metadata
- Authors
- Doll, W. J., & Torkzadeh, G.
- Venue
- MIS Quarterly
- Publisher
- MIS Quarterly
- Published
- 1988-06-01
- Volume
- 12
- Issue
- 2
- Pages
- 259-274
- ISSN
- 0276-7783 (ISSN-L)
- 2162-9730
- OpenAlex
- W1954663826
- Cited by
- 2,412
- References
- 4
- FWCI
- 67.05
- 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.