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Cited by 648

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Executive Decisions About Adoption of Information Technology in Small Business: Theory and Empirical Tests

Harrison, David A., Mykytyn, Peter P., Riemenschneider, Cynthia K.·1997·Information Systems Research

Abstract

The Theory of Planned Behavior (TPB) was used to explain and predict small business executives’ decisions to adopt information technology (IT). These theories were tested in a multiphase field study involving 162 small businesses (25 ≤ n ≤ 200 employees) from a broad set of industries considering a variety of ITs. Results indicate strong support for a decision process based on attitude (perceived positive and negative consequences for the firm), subjective norm (social expectations), and perceived control (resources to overcome obstacles) regarding IT adoption. Additional variables such as firm and individual executive characteristics had no unique effect on adoption decisions. However, as business size increased, so did the importance of expectations from the (social) environment, while the importance of intra-firm consequences and control over potential adoption barriers declined.

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Metadata

Authors
Harrison, D. A., Mykytyn, P. P., & Riemenschneider, C. K.
Venue
Information Systems Research
Published
1997-06
Volume
8
Issue
2
Pages
171-195
ISSN
  • 1047-7047 (ISSN-L)
  • 1526-5536
OpenAlex
W2016215833
Cited by
648
References
9
FWCI
25.40
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Executive Decisions About Adoption of Information Technology in Small Business: Theory and Empirical Tests. https://app.cochat.ai/papers/10.1287/isre.8.2.171
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Harrison, D. A., Mykytyn, P. P., & Riemenschneider, C. K. (1997). Executive Decisions About Adoption of Information Technology in Small Business: Theory and Empirical Tests. Information Systems Research. https://doi.org/10.1287/isre.8.2.171

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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.