Does Service Innovation Act as a Mediator in Differentiation Strategy and Organizational Performance Nexus? An Empirical Study
Bibliographic record
Abstract
The study determines how service innovation impacts differentiation strategy and the impact on organizational performance. The target population of this research was 475 hotels, which are three to five star hotels in Malaysia. Due to the small population and the nature of the research, questionnaires were sent by mail and email to all the targeted three to five star hotels’ managers. Regression was used to analyse the relationship of differentiation strategy, service innovation and organizational performance. The result shows that differentiation strategy has a significant effect on organizational performance and service innovation has a significant effect on organizational performance. Remarkably, this study found that service innovation partially mediates the relationship of differentiation strategy and organizational performance. This study found that hoteliers that pursuing a differentiation strategy should simultaneously employ service innovation to attain better organizational performance. Thus, this study contributes a significant knowledge to the Malaysia hotel industry. This study fills in some of the gap and showing the significance of differentiation strategy and service innovation in the hotel industry which has received little empirical attention in current strategic management literatures. It also offers some practical contributions to the development of service innovation in relation to differentiation strategy and organizational 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".