The impacts of industry environment on software insourcing, outsourcing, and buying
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Nowadays, an increasing number of firms choose to develop proprietary software, instead of buying packaged software. What factors will affect different types of software investments? According to the environment-strategy alignment research, environment should be an influential factor. However, environment's role has received scarce attention in the literature. The authors' study addresses this research gap by investigating how industry environment affects different types of software investments. The study identifies three types of software investments (software insourcing, outsourcing, and buying) and examines how the characteristics of the industry environment (including industry munificence, dynamism, and concentration) influence each software investment. Design/methodology/approach The generalized least squares (GLS) model and the ordinary least squares with panel-corrected standard errors (OLS-PCSE) model are applied to test the hypotheses, based on industry-level panel data from the US Bureau of Economic Analysis (BEA). Findings The analysis shows that industry munificence, dynamism, and concentration have different impacts on software insourcing, outsourcing, and buying, respectively. Originality/value This study classifies software investment into three types – software insourcing, outsourcing, and buying and investigates how the industry environment affects them. The findings suggest that research should distinguish among software insourcing, outsourcing, and buying due to their different characteristics.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it