Social responsibility or smoke screening: evidence from India
Bibliographic record
Abstract
Purpose The purpose of this study is to examine whether the choice of a firm to spend resources on corporate social responsibility (CSR) activities is associated with its actual social impacts as measured by its energy consumption and the quality of its financial reporting. Based on legitimacy theory, the authors argue firms in India use CSR expenditures as mere smoke screens to build a positive public image. Design/methodology/approach By using energy consumption per unit of sale as a measure of real environmental impact, the authors model firms' CSR investment behavior. Additionally, the authors use earnings management measures to examine whether CSR spenders engage in manipulating reported earnings, a practice socially responsible firms would not engage in. These hypotheses are tested using a panel data set of Indian firms for the period 2012–2014. Findings Consistent with legitimacy theory, the authors show firms that participate in socially undesirable activities such as heavy energy consumption and accounting manipulation are more likely to pursue CSR voluntarily. Additionally, the authors find evidence suggesting firms that voluntarily engage in CSR tend to have lower firm values. Originality/value This study examines the social and environmental concerns of firms that invest in CSR, especially in an emerging market context. The findings help understand the motivation for CSR behavior of corporate firms and may well explain the observed negative relationship between firm value and voluntary CSR spending observed in many emerging market contexts, especially in India.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".