Building customer trust in mobile commerce
Open accessAbstract
Gaining customer trust in m-commerce is a daunting process, extending from initial trust formation to continuous trust development---but it can be done.
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Security and privacy risk are key unfavorable factors contributing to the slow growth rate of user acceptance of m-commerce.
“As mentioned by Siau and Shen (2003), security and privacy risk are some of the key unfavorable factors contributing to the slow growth rate of user acceptance of m-commerce.”
From Mobile shopping apps adoption and perceived risks: A cross-country perspective utilizing the Unified Theory of Acceptance and Use of Technology (2018) · cites this paper as Siau and Shen, 2003
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- Authors
- Siau, K., & Shen, Z.
- Affiliations
- University of Nebraska–Lincoln (US)
- Venue
- Communications of the ACM
- Published
- 2003-04
- Volume
- 46
- Issue
- 4
- Pages
- 91-94
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2012299124
- Cited by
- 638
- References
- 0
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 9 · 2023: 19 · 2022: 41 · 2021: 32 · 2020: 38 · 2019: 35 · 2018: 28 · 2017: 36 · 2016: 28 · 2015: 38 · 2014: 43 · 2013: 36 · 2012: 35
- FWCI
- 41.24
- Type
- journal article
- Language
- en
- Open access
- Locations
- Communications of the ACM (publishedVersion)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion, other-oa)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
- Keywords
- consumer trust, mobile commerce, trust formation, trust building, online trust
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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.
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