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Evolution and organizational information systems: an assessment of Nolan's stage model
Open accessAbstract
Richard Nolan's stage model is the best known and most widely cited model of computing evolution in organizations. The model's development over a decade demonstrates its own evolution from a simple theory, based on the factoring of change states indicated by changes in computing budgets, to an elaborate account of the characteristics of six stages of computing growth. An analysis of the model's logical and empirical structure reveals a number of problems in its formulation that help to account for the fact that its principal tenets have not been independently validated. The model is shown to be an “evolutionistic” theory within the theories of evolution in the social sciences, focusing on assumed directions of growth and an implied end state toward which growth proceeds, and suffering from problems inherent in such theories. Further research based on an “evolutionary” view of computing growth is suggested as a means of improving theories of computing in organizations.
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Citations
Cites
The Discovery of Grounded Theory: Strategies for Qualitative Research
Glaser, B. G., & Strauss, A. L. (2017) · Aldine (Routledge reprint) · doi:10.4324/9780203793206 · Cited by 25,789
Metadata
- Authors
- King, J. L., & Kraemer, K. L.
- Venue
- Communications of the ACM
- Published
- 1984-05
- Volume
- 27
- Issue
- 5
- Pages
- 466-475
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2037027277
- Cited by
- 281
- References
- 1
- FWCI
- 53.62
- Type
- journal article
- Language
- en
- Open access
- bronze
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- King, J. L., & Kraemer, K. L. (1984). Evolution and organizational information systems: an assessment of Nolan's stage model. Communications of the ACM. https://doi.org/10.1145/358189.358074
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
