Effects of market pressure on organisational innovation performance: mediating roles of commitment to innovate, resource to innovate and manager innovation capability
Bibliographic record
Abstract
Purpose The purpose of this research is to investigate the effects of market pressure on organisational innovation performance through commitment and resource to innovate and manager innovation capability. Design/methodology/approach A quantitative study was conducted with 273 participants working in different organisations. These participants were drawn from the LinkedIn network of one of the researchers. The data were collected using a survey questionnaire uploaded onto www.surveymonkey.com. The data were analysed using SPSS and AMOS, and structural equation modelling techniques were used to test the hypotheses. Findings We found that market pressure has a positive effect on both commitment to innovate and resource to innovate. In turn, both commitment to innovate and resource to innovate influence manager innovation capability. We also found that manager innovation capability positively affects organisational innovation performance. Furthermore, manager innovation capability is a mediator in the relationship between commitment to innovate and organisational innovation performance as well as in the relationship between resource to innovate and organisational innovation performance. Finally, the relationship between manager innovation capability and organisational innovation performance is moderated by both extrinsic and intrinsic motivations to innovate. Practical implications Our study provides empirical evidence of the roles of commitment to innovate, resource to innovate and manager innovation capability in enhancing the innovation performance of organisations. Therefore, organisations should show their commitment to innovate, provide resources to innovate, develop managers’ capabilities to innovate and use a mix of intrinsic and extrinsic motivators to boost their innovation performance. Originality/value This study offers new insights into the dynamics of how market pressure leads to innovation within organisations.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".