A Structural Equation Model of CSR and Performance: Mediation by Innovation and Productivity
Bibliographic record
Abstract
This study has explored the role of Innovation and productivity as mediating factors between Corporate Social Responsibility (CSR) and firm’s Performance relationship. The objectives of this study are to build and test a theoretical model to identify the mediating factors in the linkage between CSR and performance.The theoretical model (CIPP Model) is deduced using strategic paradigm of literature. It consists of CSR, Innovation, Productivity, and Performance dimensions. Furthermore, several hypotheses were generated to examine the model. Structural Equation Modeling (SEM) is employed to test the model using data from CSR practicing firms 197 from Saudi Arabia. Confirmatory Factor Analysis (CFA) is performed followed by Structural Equation Modeling to examine the model. The results generally support the hypothesized model with research and managerial implications. The mediation of Innovation along with productivity in CSR linkage to performance isa major contribution to the literature, which may help to explore the black box in the relationship of CSR with performance. Besides proving the business case for the CSR in general, it also advocates the adoption of CSR practices by firms and MNEs operating in the developing countries, as it may enhance innovation and productivity leading to their increased financial performance. This model would be practical and useful for business managers that seek a competitive solution for succeeding in a business crisis and work in the settings of international business.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".