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Why Don’t Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior1
Abstract
Using the Technology Acceptance Model (TAM), this research investigated gender differences in the overlooked context of individual adoption and sustained usage of technology in the workplace. User reactions and technology usage behavior were studied over a five-month period among 342 workers being introduced to a new software system. At all three points of measurement, compared to women, men's technology usage decisions were more strongly influenced by their perceptions of usefulness. In contrast, women were more strongly influenced by perceptions of ease of use and subjective norm, although the effect of subjective norm diminished over time. These findings were robust even after statistically controlling for key confounding variables identified in prior organizational behavior research (i.e., income, occupation, and education levels), and another possible confound from technology research, prior experience with computers in general. Thus, in addition to identifying key boundary conditions in the role of the original TAM constructs (perceived usefulness and perceived ease of use), this research provides the basis for the integration of subjective norm into the model. In light of these findings, implications for theory and practice are discussed.
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The greater the positive social influence a user perceives over algorithms, the higher the probability of building trust in AI algorithms over time.
“Proposition 4. The greater the positive social influence a user perceives over algorithms, the higher the probability of building trust in AI algorithms over time.”
From Why do users trust algorithms? A review and conceptualization of initial trust and trust over time (2022) · cites this paper as Venkatesh & Morris, 2000
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“…gain more experience with technology, the influence of significant others is expected to diminish, with individuals relying more on their own past experiences to shape their perceptions of technology, rather than the opinions of others (Venkatesh & Davis, 2000; Venkatesh & Morris, 2000).”
BackgroundPost-positivist Review of Technology Acceptance Model.
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Metadata
- Authors
- Venkatesh, V., & Morris, M. G.
- Affiliations
- Robert H. Smith School of Business, University of Maryland, College Park, College Park, MD, U.S.A.
- Air Force Institute of Technology, Wright-Patterson AFB, OH, U.S.A.
- Venue
- MIS Quarterly
- Publisher
- MIS Quarterly
- Published
- 2000-03-01
- Volume
- 24
- Issue
- 1
- Pages
- 115-139
- ISSN
- 0276-7783
- 2162-9730
- Cited by
- 2,780
- References
- 17
- Language
- en
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 ›
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