Perception of corporate social responsibility in a morally contentious industry: the roles of consumption motives and ethical perspectives
Bibliographic record
Abstract
Purpose This study investigates the roles of consumption motives and ethical perspectives in explaining individuals’ perceptions of corporate social responsibility (CSR) within the context of the recreational marijuana industry, often characterized as morally contentious. Design/methodology/approach The research was conducted in Canada, a country where recreational marijuana is legally permitted. Through an online survey, 411 participants were recruited, and the data were analyzed using Statistical Package for the Social Sciences (SPSS) and SmartPLS4, employing ANOVA and structural equation modeling (SEM) techniques. Findings ANOVA analyses reveal significant differences across four ethical perspectives: absolutism, subjectivism, situationism and exceptionism. Conformity motives are most prominent in the exceptionism group, while expansion motives are more common in the subjectivism group. CSR perceptions vary among these groups, with situationism showing the most favorable views. In the absolutism group, expansion and social motives positively influence CSR perception, whereas conformity motives negatively impact it. Conversely, in the exceptionism and situationism groups, only expansion motives positively affect CSR perception. Unexpectedly, within the subjectivism group, only conformity motives have a significant negative effect on CSR perception. Originality/value This study examines a controversial industry and contributes to research on recreational marijuana by comparing consumer motives from ethical perspectives. Unlike previous research focused on consumption behaviors (e.g. use frequency), this study investigates how CSR perceptions are shaped by consumption motives and vary with ethical viewpoints.
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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 | 0.003 | 0.007 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".