The effect of service innovation, corporate image, human capital strategy and customer loyalty on performance: Evidence from rice industry
Bibliographic record
Abstract
The current study purpose is to investigate the effect of service innovation, corporate image, and human capital strategy and customer loyalty on performance for small and micro community enterprise of rice products in central northeast Thailand. For this objective, researchers applied the quantitative research approach and used the cross sectional research design. To obtain data, the researchers distributed questionnaires directly and online to respondents which were small and micro community enterprise of rice products in central northeast Thailand for 1 month from the beginning of September and obtained valid answers totaling 320 responses, which then we chose to be the sample in this study. The Structural Equation Modeling (SEM) results show that corporate image had a positive and significant relationship with the human capital strategy. In the same vein, the customer loyalty had also a positive and significant relationship with the humane capital strategy. Further findings show that service innovation had also a positive and significant relationship with the human capital strategy. Moreover, service innovation, corporate image, customer loyalty had also a positive and significant relationship with the business performance. On the other hand, human capital strategy had also a positive and significant relationship with the business performance. Based on these findings, the current study could provide researchers and policy makers to know about the importance of all predictors to increase their business performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".