The impact of Software Process Maturity on Software Project Performance: The Contingent Role of Software Development Risk
Bibliographic record
Abstract
Despite growing efforts to improve software development processes, recurring concerns about software project performance remain largely present. The rate of software development project failure rate has been routinely documented in information systems (IS) research (Wallace, 2004; El-Masry and Rivard, 2010). The management of software development projects is often marked by inadequate planning, a poor grasp of the overall development process, and no clear management framework, even as the focus in software development shifts from a technology perspective to a more process-centric view (Slaughter, 2006). To address such concerns few CMM-based studies have examined the benefits and direct impact of software process maturity on software project performance but with mixed results. The present paper attempts to systematically examine the contingent role of software development risk on the impact of software process maturity level on software project performance. Guided by risk-based perspective in Software Engineering and CMM-based framework, an exploratory model was developed and tested. The premise of this paper is that software development risk plays a contingent role in the relationship between software process maturity and software project performance. Drawing on a sample of 107 organizations that have undergone official CMM appraisals, the results of partial least squares analysis of the data reveal initial evidence that (1) a positive effect of software process maturity level on software project performance while underscoring the negative effect of software development risk on software project performance, and (2) more importantly, the findings show that software development risk plays a contingent role software process maturity level on software project performance. For researchers, the integration of software development risk can provide a much needed linkage in the three fundamental constructs of CMM. From a managerial perspective, in order to foster a better software project performance, IS project leaders and managers should strongly emphasize devising effective software development risk assessment since a variation of this construct’s level may strengthen or weaken the relationship between software development process maturity and software project performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".