Green Information Technologies and Systems: Employees’ Perceptions of Organizational Practices
Bibliographic record
Abstract
In this study, we examine the extent to which employees recognize the importance of information technologies and systems (IT/S) in developing and implementing environmental initiatives. To address this question, we first review past research on this topic and draw on a framework for examining environmental motivating forces, strategies, and employee environmental orientations. We then analyze qualitative data based on in-depth interviews with employees in financial services organizations. Our aim is to develop a richer understanding of how employees currently view IT/S issues in relation to environmental sustainability and if similarities exist between different types of financial institutions. We also assess the extent to which these employee perceptions align with both actual organizational practices, as captured in interviews with information technology managers, and practices espoused by organizations, as reflected on their corporate websites. Our findings suggest that organizations are still in the infancy stage of awareness and adoption of “Green” IT/S. As a result, we identify four types of gaps: knowledge gaps, practice gaps, opportunity gaps, and knowing—doing gaps. We suggest that future research should draw on absorptive capacity, organizational learning, and social marketing theories to help align employees’ attitudes, cognitions, and behaviors and to drive environmental changes.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".