INVESTMENT ATTRACTIVENESS: PERAN MODERASI REPUTASI PERUSAHAAN TERHADAP HUBUNGAN ESG PERFORMANCE DAN KEUNGGULAN KOMPETITIF
Bibliographic record
Abstract
This study addresses the issue of investment attractiveness on the Indonesia Stock Exchange (IDX) by highlighting Environmental, Social, and Governance (ESG) performance and corporate competitive advantage. The main problem lies in the low level of ESG strategy integration in Indonesian companies, even though global investors are increasingly emphasising sustainability in their investment decisions. The purpose of this study is to analyse the effect of ESG performance and competitive advantage on investment attractiveness and to examine the role of corporate reputation as a moderating variable. The research method uses a quantitative approach with secondary data from 57 non-financial companies listed on the IDX for the period 2021–2023. The analysis was conducted using Moderated Regression Analysis (MRA) with EViews 10 software. The novelty of this study lies in the use of corporate reputation with CSR Strategy Score measurement as a moderating variable, which has rarely been examined in previous studies. The results show that ESG performance does not have a significant effect on investment attractiveness, while competitive advantage has a significant negative effect. However, when corporate reputation is included as a moderator, the relationship between ESG and competitive advantage on investment attractiveness becomes significant, confirming the importance of reputation as a reinforcing mechanism. These findings are expected to contribute to companies and regulators in designing more effective sustainability strategies.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".