Surveying Employee Attitudes on Corporate Social Responsibility at the Frontline Level of an Energy Transportation Company
Bibliographic record
Abstract
Abstract As large companies embrace and integrate the principles of corporate social responsibility (CSR) into their business practices, company personnel are expected to show actions that are connected to communicated corporate values and related policies. To enhance the likelihood that employees at the frontline level will accept these principles and become engaged with these values, it is in the firm's best interests to quantify and understand employee attitudes toward the social responsibility construct itself. The present work considers whether the variables of work climate perception, education level, and age directly influence one's social responsibility perspective at work, extending the Marz model to understand what may impact frontline CSR attitudes. A case study is presented, based on a survey of frontline personnel employed by a North American energy transportation company. This investigation uses an updated survey tool and method for polling a sample population. Survey development is described, analysis methods are explained, and results are presented with statistical measures to verify hypotheses related to employee engagement in CSR. Some potential implications of the results for corporate strategy are discussed. Copyright © 2012 John Wiley & Sons, Ltd and ERP 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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".