The Impact of Brand Equity on Employee’s Opportunistic Behavior: A Case Study on Enterprises in Vietnam
Bibliographic record
Abstract
Brand is considered a valuable asset that a business wants to create and maintain growth throughout its business cycle. This paper examines the impact of corporate brand equity on employees’ opportunistic behavior. The paper uses quantitative research methods, through linear SEM (Structural Equation Modelling) analysis of structural model with a scale of 609 samples of employees of enterprises in Vietnam. The research results show that corporate brand equity has a negative impact on employees’ opportunistic behavior. In the relationship between these two factors, trust and emotional engagement act as intermediate factors. Additionally, the research demonstrates that trust has a positive effect on all three components of employee engagement, including emotional engagement, computational engagement, and standards-based engagement. On that basis, the research suggests a number of recommendations to minimize the opportunistic behavior of employees in the enterprise. The findings of this study have shown the importance and impact of brand equity on employee opportunistic behavior. These are meaningful contributions in both theory and practice to help businesses gain deeper insight into brand equity and the need to pay attention to building and developing durable brand equity for businesses. At the same time, it is an important basis for the next research projects.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".