The Effects of Technological Innovation, Organizational Innovation and Absorptive Capacity on Product Innovation: A Structural Equation Modeling Approach
Bibliographic record
Abstract
<p>This study investigates the impacts of organizational innovation, technological innovation and absorptive capacity on product innovation as well as examines the antecedents of technological innovation and organizational innovation in one of the Tertiary Education Institutions in Malaysia. A total of 600 samples were distributed to the tertiary students. A questionnaire survey was adopted as the main method of data collection and structural equation modeling was applied as a data analysis tool. The findings indicate that: (1) Organizational innovation, technological innovation and absorptive capacity are positively related to product innovation respectively; (2) Technological innovation positively mediates the relationship between organizational innovation and product innovation; (3) Technological innovation positively mediates the relationship between absorptive capacity and product innovation; (4) Organizational innovation positively mediates the relationship between absorptive capacity and technological innovation; (5) Organizational innovation positively mediates the relationship between absorptive capacity and product innovation. The findings of this study do provide relevant theoretical, managerial and policy contributions in the literature.</p>
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".