The Influence of Environmental, Social, and Governance (ESG) Perception on Investor Trust and Brand Relationship Quality: A Study Among Retail Investors in Hong Kong
Bibliographic record
Abstract
Background/Introduction: Investor trust and brand relationship quality, along with initiatives for environmental, social, and governance (ESG), have become highly important. Despite their relevance, limited research has been conducted on how ESG initiatives influence investors’ perceptions in financial markets. Objectives/Aims: This work conducts a cross-sectional analysis to examine the relationship between perceived ESG initiatives and investor trust and brand relationship quality among retail investors in Hong Kong, one of the world’s leading financial markets. Methods: This study involved 479 retail investors. Three instruments were administered in the questionnaires: (1) the perceived environmental, social, and governance scale, (2) the investor trust scale, and (3) the brand relationship quality scale. Results: The analysis demonstrates that PESG and various aspects of investor trust and brand relationship quality had strong positive correlations. Notably, the environmental and social concerns of PESG were found to be strong predictors of investor trust and brand relationship quality, whereas governance awareness had the least effect. Conclusions: Improving a firm’s ESG image can boost investors’ confidence and the quality of brand relationships, thus aligning with sustainability and business strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".