Evidence of Workplace Politics Undermining Knowledge Sharing and Sustainability
Bibliographic record
Abstract
The present research examines how employees perceive their firms’ CSR initiatives that ultimately translate into desired attitudes and behaviors, i.e., employee environmental commitment (EEC) and knowledge sharing (KS) at the workplace, by underpinning social identity theory. However, when do undesired working conditions, i.e., Perception of Politics (POP), adversely influence these desired outcomes? We deliberately selected 45 firms in the services and manufacturing sectors of Pakistan operating in larger metropolitan cities and prevalent tourist destinations, and actively participating in CSR activities. Thereafter, three self-administered surveys were conducted by employing a time-lagged design with two temporal breaks. A total of 655 surveys were distributed among middle managers across selected firms. Accordingly, it is found that employees who strongly identify with their organizations tend to align their personal values with organizational sustainability efforts and actively participate in environmentally responsible practices. They also demonstrate a greater willingness to share knowledge and enhance the organization’s collective intelligence. However, when employees perceive a high level of political behavior within the organization, their trust in its ethical standards diminishes, leading to various negative attitudes and behaviors in the workplace. This research contributed in two ways to the existing literature: (a) by examining the employees’ understandings of firms’ CSR engagements and their trickle-down effect on EEC and KS, (b) and studying when POP adversely effects the above relationship.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".