The effect of market orientation on innovation speed and new product performance
Bibliographic record
Abstract
Purpose It has been argued that innovation speed has been inappropriately absent in models of market orientation. The present study seeks to provide new insights into whether and how market orientation's three main components: intelligence generation, intelligence dissemination, and responsiveness affect innovation speed and new product performance, and about the mediating role of innovation speed. Design/methodology/approach Data were collected from a sample of 247 firms in a variety of manufacturing industries. A mail survey was developed to collect the data. Findings The results indicate that intelligence generation has an indirect positive effect on innovation speed via intelligence dissemination and responsiveness. Intelligence dissemination influences innovation speed positively, both directly and indirectly through responsiveness. Findings report a curvilinear ( J ‐shaped) relationship between responsiveness and innovation speed. With regard to the effect of the market orientation's components on new product performance, the findings indicate a positive relationship between responsiveness and new product performance. The parameter estimates for the direct paths linking intelligence generation and intelligence dissemination with new product performance were found to be not significant. Instead, the findings show that intelligence generation and intelligence dissemination influence new product performance indirectly through responsiveness. Finally, a positive relationship was found between innovation speed and new product performance. Originality/value The research makes three important contributions to the marketing strategy and new product development literatures. First, by splitting market orientation into the components of intelligence generation, intelligence dissemination and responsiveness, the study provides a closer examination into the effect of market orientation on innovation speed and new product performance. Second, the results indicate that the effects of intelligence generation and intelligence dissemination on innovation speed and new product performance are mediated by responsiveness to market intelligence. Third, findings support the argument that innovation speed partially mediates the effect of market orientation's three main components on new product performance.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".