Investor Perception of ESG Performance: Examining Investment Intentions in the Chinese Stock Market with Social Self-Efficacy Moderation
Bibliographic record
Abstract
The increasing importance of environmental, social, and governance (ESG) factors has sparked scholarly interest in how company reputation influences stock market investment decisions. Most ESG research has focused on secondary data from public firms, ignoring the potential of surveys as a research tool. Addressing this gap, our study investigates the relationship between retail investors’ perceptions of corporate ESG performance and their investment attitude, as well as the impact on intention, with social self-efficacy serving as a moderator. The theoretical framework of this research was adopted from the theory of planned behavior (TPB) and previous studies that used TPB to measure intention reveal a range of explanations for the connection between the factors influencing intention through attitude. Structural Equation Modeling (SEM) analysis was used in this study, and the new findings show that Chinese investors’ perceptions of corporate ESG performance positively influence their investment attitudes and intentions. Furthermore, social self-efficacy moderates the relationship between the corporate environment and governance performance, attitudes, and intentions. Accordingly, this study identifies the contribution of explaining how investment intentions are related to corporate ESG performance, which has been based on past ESG studies, to lay a platform for sustainable corporate practices in the Chinese stock market.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".