The DOI matches its Crossref record; 91 of the 91 references it cites were found in a registry or an index.
An empirical investigation of the key factors for success in software process improvement
Abstract
Understanding how to implement software process improvement (SPI) successfully is arguably the most challenging issue facing the SPI field today. The SPI literature contains many case studies of successful companies and descriptions of their SPI programs. However, the research efforts to date are limited and inconclusive and without adequate theoretical and psychometric justification. This paper extends and integrates models from prior research by performing an empirical investigation of the key factors for success in SPI. A quantitative survey of 120 software organizations was designed to test the conceptual model and hypotheses of the study. The results indicate that success depends critically on six organizational factors, which explained more than 50 percent of the variance in the outcome variable. The main contribution of the paper is to increase the understanding of the influence of organizational issues by empirically showing that they are at least as important as technology for succeeding with SPI and, thus, to provide researchers and practitioners with important new insights regarding the critical factors of success in SPI.
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Cited by
Lee, J.-C., Shiue, Y.-C., & Chen, C.-Y. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2015.08.030 · Cited by 332
Software process improvement in small and medium software enterprises: a systematic review
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Metadata
- Authors
- Dyba, T.
- Venue
- IEEE Transactions on Software Engineering
- Published
- 2005-05
- Volume
- 31
- Issue
- 5
- Pages
- 410-424
- ISSN
- 0098-5589 (ISSN-L)
- OpenAlex
- W2107213026
- Cited by
- 256
- References
- 91
- FWCI
- 39.65
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
