The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Who Influences Whom? Analyzing Workplace Referents’ Social Influence on it Adoption and Non-Adoption
Abstract
Technology adoption research has long struggled to incorporate normative beliefs from sources in the social environment of adopters into adoption models. We study the role of social influence from different workplace referent groups, like superiors and colleagues from the same or the IT department, on the intention to adopt. An empirical analysis, using data from 152 firms, based on the Unified Theory of Acceptance and Use of Technology and related approaches reveals that social influence on adoption significantly differs with regard to both source (peer groups) and sink (adopters and non-adopters) of the influence. The results imply that a single cumulative subjective norm measure might be too naive and that future research might considerably improve our understanding of IT adoption and non-adoption by revealing the differential impact of various peer groups on adoption intention, and also on its antecedents.
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Cites
The theory of planned behavior
Ajzen, I. (1991) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(91)90020-t · Cited by 90,587
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194
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
On the Evaluation of Structural Equation Models
Bagozzi, R. P., & Yi, Y. (1988) · Journal of the Academy of Marketing Science · doi:10.1007/bf02723327 · Cited by 26,163
A theory of social comparison processes
Festinger, L. (1954) · Human Relations · doi:10.1177/001872675400700202 · Cited by 20,926
From Intentions to Actions: A Theory of Planned Behavior
Ajzen, I. (1985) · Action Control · doi:10.1007/978-3-642-69746-3_2 · Cited by 19,057
A Paradigm for Developing Better Measures of Marketing Constructs
Churchill, G. A. (1979) · Journal of Marketing Research · doi:10.1177/002224377901600110 · Cited by 16,549
Back-translation for cross-cultural research.
Brislin, R. W. (1970) · Journal of Cross-Cultural Psychology · doi:10.1177/135910457000100301 · Cited by 14,091
Structural equation models with unobservable variables and measurement error: Algebra and statistics
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800313 · Cited by 13,287
Moore, G. C., & Benbasat, I. (1991) · Information Systems Research · doi:10.1287/isre.2.3.192 · Cited by 9,150
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
Modern Methods for Business Research
(1998) · doi:10.4324/9781410604385 · Cited by 6,534
Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003) · Information Systems Research · doi:10.1287/isre.14.2.189.16018 · Cited by 6,479
Mathieson, K. (1991) · Information Systems Research · doi:10.1287/isre.2.3.173 · Cited by 4,216
Conventional wisdom on measurement: A structural equation perspective.
Bollen, K., & Lennox, R. (1991) · Psychological Bulletin · doi:10.1037/0033-2909.110.2.305 · Cited by 3,618
Diamantopoulos, A., & Siguaw, J. A. (2006) · British Journal of Management · doi:10.1111/j.1467-8551.2006.00500.x · Cited by 3,410 · cited 1×
“For formative measures, (1) content validity (Straub et al ., 2004), (2) weights (Chin, 1998) and (3) multicollinearity (Diamantopoulos and Siguaw, 2006) should be evaluated.”
BackgroundEdgington, E., Edgington, E., & Onghena, P. (2007) · doi:10.1201/9781420011814 · Cited by 2,030
Frambach, R. T., & Schillewaert, N. (2002) · Journal of Business Research · doi:10.1016/s0148-2963(00)00152-1 · Cited by 1,270
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
The acceptance and use of a virtual learning environment in China
van Raaij, E. M., & Schepers, J. J. L. (2008) · Computers & Education · doi:10.1016/j.compedu.2006.09.001 · Cited by 940
A New Model for Predicting Behavioral Intentions: An Alternative to Fishbein
Warshaw, P. R. (1980) · Journal of Marketing Research · doi:10.1177/002224378001700201 · Cited by 340
Social influence on technology acceptance behavior: self-identity theory perspective
Lee, Y., Lee, J., & Lee, Z. (2006) · ACM SIGMIS Database: the DATABASE for Advances in Information Systems · doi:10.1145/1161345.1161355 · Cited by 166
Specifying Formative Constructs in Information Systems Research1
Petter, S., Straub, D., & Rai, A. (2007) · MIS Quarterly · doi:10.2307/25148814 · Cited by 32
Cited by
Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead
University of Arkansas, Venkatesh, V., Thong, J., Hong Kong University of Science and Technology, Xu, X., & The Hong Kong Polytechnic University (2016) · Journal of the Association for Information Systems · doi:10.17705/1jais.00428 · Cited by 2,513
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019) · Information Systems Frontiers · doi:10.1007/s10796-017-9774-y · Cited by 2,137
“This is based on appropriate theoretical foundations (Ajzen 1991; Taylor and Todd 1995b) and empirical findings (e.g., Eckhardt et al. 2009; Foon and Fah 2011; Yeow and Loo 2009) that support the effect of facilitating conditions and behavioural intention even in the presence of effort expectancy —…”
BackgroundAn empirical validation of a unified model of electronic government adoption (UMEGA)
Dwivedi, Y. K., Rana, N. P., Janssen, M., Lal, B., Williams, M. D., & Clement, M. (2017) · Government Information Quarterly · doi:10.1016/j.giq.2017.03.001 · Cited by 622
Escobar-Rodríguez, T., & Carvajal-Trujillo, E. (2014) · Tourism Management · doi:10.1016/j.tourman.2014.01.017 · Cited by 590
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
Metadata
- Authors
- Eckhardt, A., Laumer, S., & Weitzel, T.
- Venue
- Journal of Information Technology
- Publisher
- SAGE Publications
- Published
- 2009-03
- Volume
- 24
- Issue
- 1
- Pages
- 11-24
- ISSN
- 0268-3962 (ISSN-L)
- 1466-4437
- OpenAlex
- W2082223960
- Cited by
- 287
- References
- 23
- FWCI
- 29.58
- Type
- journal article
- Language
- en
- Open access
- closed
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