Do Strategic Orientations and CSR Disclosures Affect Investment Efficiency? Evidence from Textual Analysis in Emerging Markets
Bibliographic record
Abstract
This study explores how firms’ strategic orientations—operational efficiency, customer intimacy, and product innovation—along with corporate social responsibility (CSR) disclosure, influence investment efficiency in emerging markets. Using 1594 firm-year observations from companies listed on the Tehran Stock Exchange (TSE) between 2015 and 2024, we combine quantitative analysis with textual evidence from Management Discussion and Analysis (MD&A) reports. The findings show that operational efficiency and customer intimacy are generally linked to lower investment efficiency, reflecting possible resource misallocation and short-term priorities. In contrast, product innovation has a more nuanced impact: it improves investment efficiency in R&D-intensive sectors and during stable economic periods. CSR disclosure is also negatively associated with investment efficiency, suggesting that while CSR reporting enhances legitimacy and stakeholder trust, it may shift managerial attention and resources away from core investments. Robustness checks—including firm fixed effects, alternative keyword dictionaries, placebo tests, and endogeneity controls—support these results. Additional sub-sample analyses indicate that strategic orientations and CSR disclosure also function as channels of financial innovation: operational efficiency fosters disciplined resource allocation, product innovation supports sustainable growth, and customer intimacy strengthens transparency and stakeholder engagement.
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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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".