The Impact of Social Capital and Dynamic Capabilities on New Product Development: An Investigation of the Entertainment Software Industry
Bibliographic record
Abstract
Businesses today face intense international competition, a heightened pace of development and shortened product life cycles. As a result, many researchers recommend firms collaborate and partner with other firms to succeed. With over a decade of research examining alliances and inter-firm collaboration, we know a great deal about the benefits and outcomes firms realize through collaboration. An important gap exists, however, in our understanding of the effect of partnering firms on collaborative outputs. This study attempts to address this gap by examining the success of collaborative new product development outputs. The study was a quasi-experimental study using archival, time-series data. Hypotheses were tested at the project level, defined as the product output from the collaborative development effort. Predictors were developed at both the firm and dyadic levels. Several findings emerged from this research. The primary finding is that roles of alliance partners impact which capability and capital benefits accrue. Firms functioning as a publisher benefit from increases in relevant experience. Firms functioning as a developer benefit from working in areas in which they have experience, but largely to the extent that the developer also generalizes their capabilities. One implication emerging from the capability findings suggests a need for configurational capability research. From a social capital conception, developers with high network centrality have a negative impact on the perceived quality of the final software product. Developers also benefit from embeddedness, products developed by developers in constrained networks outperformed products developed by developers in brokered networks.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".