Energy efficiency and norm compliance drivers amongst industry decision-makers: evidence of intersectionality and the role of morality
Bibliographic record
Abstract
Purpose This paper aims to explore industry decision-makers’ motivation for energy efficiency (EE) actions. The research question is: why do industry decision-makers feel obligated to comply with norms or engage in EE actions? More specifically, what types of norm compliance drivers are they responding to? Design/methodology/approach This study uses a two-country (UK and Canada) survey of managers and executives in three key sectors – building and construction, hospitality and utilities to explore the presence of norm compliance driver typologies that motivate EE actions. Findings Drawing on existing theoretical frameworks, this study defines four types of norm compliance drivers related to industry action: custom, third-party, moral and social. The results show evidence of all four, with moral as the most common norm compliance driver. The findings also point to intersectionality: the presence of more than one type of norm compliance driver in reasoning for action. Practical implications Many of the responses related to moral norm compliance drivers are tied to larger environmental issues, such as climate change, which contributes to understanding how to trigger industry action on large global issues. Social implications The finding that moral drivers are a significant proportion of the underlying force behind norm compliance, coupled with the understanding that many of these statements point to larger sustainability goals, suggests policymakers need to take a closer look at how they motivate industry. Originality/value The emphasis on the underlying drivers of norm obligations as a motivation for decision-makers within industry related to EE action makes this paper novel. Doing so from the perspective of industry actors is also original.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".