Social influence on technology acceptance behavior: self-identity theory perspective
Abstract
Social influence on technology acceptance behavior has been acknowledged but needs to be further articulated. While Subjective Norm (SN) has been dominantly used to capture the essence of social influence, the findings to date has led some researchers to question whether it captures the full extent of social influence. Recently, social psychologists have examined Self-Identity as a construct reflecting social influence on behavior. In particular, Self-Identity has been shown to have significant influence on voluntary behavior and have enduring effects, situations where the Subjective Norm had little effect. This study examines the effect of Self-Identity on technology acceptance decision in the context of a web-based class support system under the Technology Acceptance Model (TAM). The result demonstrates a significant direct and indirect effect of Self-Identity on technology acceptance. The result also confirms that Self-Identity has significant direct effect on the acceptance in voluntary and experienced situations, while Subjective Norm has no significant effect in both situations. Key implications for theory and practice are discussed.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Users who had their Competence Need more fulfilled after using the Banking Bot perceived the use as more in line with their self-image.
“it can be derived that users who had their Competence Need more fulfilled after using the Banking Bot, perceived the use as more in line with their self-image.”
From Development and Validation of a Basic Psychological Needs Scale for Technology Use (2024) · cites this paper as Lee et al., 2006
Cites
Understanding information technology usage: a test of competing models
Taylor, S., & Todd, P. A. (1995) · Information Systems Research · doi:10.1287/isre.6.2.144 · Cited by 8,815 · see citing works
Venkatesh, V. (2000) · Information Systems Research · doi:10.1287/isre.11.4.342.11872 · Cited by 6,664 · see citing works
Assessing IT Usage: The Role of Prior Experience
Taylor, S., & Todd, P. (1995) · MIS Quarterly · doi:10.2307/249633 · Cited by 2,891 · see citing works
The theory of planned behaviour: self identity, social identity and group norms.
Terry, D. J., Hogg, M. A., & White, K. M. (1999) · British Journal of Social Psychology · doi:10.1348/014466699164149 · Cited by 1,126 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Kulviwat, S., Bruner, G. C., & Al-Shuridah, O. (2009) · Journal of Business Research · doi:10.1016/j.jbusres.2007.04.014 · Cited by 406 · see citing works
The effects of gratifications, flow and satisfaction on the usage of livestreaming services
Hsu, C. L., & Lin, J. C.-C. (2023) · Library Hi Tech · doi:10.1108/lht-02-2021-0069 · Cited by 68 · see citing works
Development and Validation of a Basic Psychological Needs Scale for Technology Use
Moradbakhti, L., Leichtmann, B., & Mara, M. (2024) · Psychological Test Adaptation and Development · doi:10.1027/2698-1866/a000062 · Cited by 41 · see citing works
Nævdal, R., Vis, C., & Kenter, R. M. F. (2025) · Internet Interventions · doi:10.1016/j.invent.2025.100881 · Cited by 6 · see citing works
“A second reason may be that iCBT conflict with important professional identities (Cresswell and Sheikh, 2013; Lee et al., 2006).”
- Authors
- Lee, Y., Lee, J., & Lee, Z.
- Affiliations
- University of Kansas (US)
- University of Colorado
- Yonsei University (KR)
- ORCID
- Lee, Zoonky
- Venue
- ACM SIGMIS Database: the DATABASE for Advances in Information Systems
- Published
- 2006-09-19
- Volume
- 37
- Issue
- 2-3
- Pages
- 60-75
- ISSN
- 0095-0033 (ISSN-L)
- 1532-0936
- OpenAlex
- W2132732146
- Cited by
- 166
- References
- 4
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 9 · 2023: 11 · 2022: 7 · 2021: 15 · 2020: 8 · 2019: 2 · 2018: 8 · 2017: 8 · 2016: 4 · 2015: 6 · 2014: 12 · 2013: 8 · 2012: 9
- FWCI
- 14.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM SIGMIS Database the DATABASE for Advances in Information Systems (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Life Sciences › Agricultural and Biological Sciences › General Agricultural and Biological Sciences › Agricultural Innovations and Practices
- Keywords
- social influence, technology acceptance, self-identity, subjective norms, technology acceptance model, voluntary behavior, experienced users, online learning platforms
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