Economic impacts of in-house and packaged software investments: the influence of software investment opportunities
Bibliographic record
Abstract
Purpose Software has become increasingly important in business. However, the value of aggregate in-house and packaged software investments and the influence of an industry's software investment opportunities (SIOs) are poorly understood in the literature. This study addresses this research gap and proposes that an industry's SIOs play an essential role in the economic impacts of industry in-house and packaged software investments. Design/methodology/approach A model of the economic impacts of in-house and packaged software investments at the industry level under different SIOs is developed and empirically tested based on a panel dataset of private industries in the USA between 1998 and 2020. Findings The results show that with the increase in the number of SIOs in an industry, the economic performance of in-house software investments increases, while that of packaged software investments decreases. Originality/value By highlighting the role of SIOs in moderating the economic performance of in-house and packaged software, this study shows the critical role of the information technology (IT) environment in understanding software's economic value.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".