The Role of Personal Values and Perceived Social Support in Developing Socially Responsible Consumer Behavior
Bibliographic record
Abstract
Corporate social responsibility (CSR) is a longstanding theme in marketing research. Although plenty of research have been done to examine the influence of CSR activities on consumer behavior, less attention is paid to explore the psychological factors that shape consumer’s socially responsible behavior (SRB). The current research addresses this gap by reviewing and comparing the literature from psychology and marketing streams that moves towards a degree of convergence. It examines the psychological role of personal values and external factors like perceived social support to build pro-social behavior among consumers. The personal values include; self-transcendence and self-enhancement values and perceived social support by social network in shaping consumer’s socially responsible behavior. The study proposed and tested the theoretical model using Structural Equation Model (SEM) technique. Data is collected through self-administered survey from 450 consumers in Pakistan. The study found that higher self-transcendence values leads to higher level of socially responsible behavior among consumers. Whereas self-enhancement values has negative influence on consumers in adopting socially responsible behavior. The social networks including; parents, friends/peers, teachers’ play important role in development of socially responsible behavior among consumers as individuals tend to pay focus on the recommendations of their members in their social networks. The findings of this study provide important recommendations to the corporate policy makers to ensure sustainable organizational performance in today’s competitive business environment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".