How does dynamic network capability operate? A moderated mediation analysis with NPD speed and firm age
Bibliographic record
Abstract
Purpose Systematic research examining the mechanisms that mediate the dynamic capability–performance relationship remains scarce. So too is research on the conditions under which these mechanisms might be influential. Accordingly, this study aims to build upon business network research to examine how a firm’s dynamic network capability (DNC) impacts firm performance, mediated by the speed of product reconfiguration (i.e. new product development [NPD] speed) and bounded by firm age. Design/methodology/approach The authors conduct moderated mediation analysis on survey data from small- and medium-sized manufacturing and technology firms in the USA. This study uses an initial survey and then a follow-up survey. Findings The findings support the general view that DNC is instrumental to firm performance, regardless of firm age. However, DNC operates differently for younger vs older firms. That is, DNC’s impact on the performance of younger firms is enabled by speeding up NPD, while much of the performance impact for older firms appears to be through alternative resource reconfiguration route(s). This study identifies the need to include a mediating variable such as resource reconfiguration to detect how DNC impacts performance. Research limitations/implications The model could include different dimensions of mediating resource reconfigurations, alternative boundary conditions and longer-term data. Practical implications This study provides managers with insight on how speed of product reconfiguration (in terms of NPD) operates in the DNC–performance relationship. It also helps them understand how this relationship changes in younger vs older firms. Originality/value To the best of the authors’ knowledge, this study is the first to provide empirical evidence on how DNC operates to influence performance in firms that are younger vs older.
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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 | 0.023 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".