User behaviour in sustainable technology: an exploration of endorsement strategy
Bibliographic record
Abstract
Given the lethargic acceptance behaviour of sustainable technologies (STs) by the public, the most crucial factor to contemplate is the influential form of organization endorsement strategy that can effectively enhance user intentions and behaviour. Using the social identity theory (SIT) and source credibility theory (SCT), this research examines the moderating potential of an attractive celebrity (CET) and social media influencer (IET) on user intention and behaviour in low- and high-involvement sustainable technologies (LISPs and HISPs). Based on an SEM-ANN analysis of data from 605 Chinese respondents, our empirical findings show that in the LISPs context, IETs moderate the intention-behaviour relationship positively whereas CETs have the opposite impact. In contrast, the moderating effect of CETs on HISPs is positive, whereas the effect of IETs is negative. Furthermore, it was shown that HISP users’ income level significantly influenced their behaviour, while education level had no significant impact on either category. These outcomes have both theoretical and practical implications in developing resilient strategies to retain users and provide guidance on how to efficiently optimise, integrate, and evaluate the STs.
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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 | 0.015 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".