Brand community motives and their impact on brand community engagement: variations between diverse audiences
Bibliographic record
Abstract
Purpose The purpose of this research is to compare two different sample populations (student and general) to determine the impact of brand community motives on brand community engagement. Design/methodology/approach Two samples were drawn for the purpose of the current research. The first sample was drawn among the members of various brand communities from a general North American population sample (N = 503). The second sample was drawn purely from students, belonging to a variety of brand communities, from a middle-sized Canadian university (N = 195). Partial least squares structural equation modelling was used to analyse the strength, significance and effect sizes of the relationships between brand community motive and engagement constructs. Findings The findings indicate that the impact of brand community motives varied by sample population. The information and entertainment motives were significantly related to brand community engagement in both sample populations with roughly equal effect sizes. The social integration motive was again significantly related to the brand community engagement construct in the student sample population – but not for the general North American general population sample. Further, the self-discovery motive and status enhancement motives were significantly related to brand community engagement in the North American sample, but not for the student sample. This indicates significant differences between the two sample populations. Originality/value The results of the current research demonstrate that student populations are significantly different from the general population regarding their motives towards brand communities. This indicates that brand community managers need to be aware of the motives of different brand community members and also that they need to exercise caution about utilizing purely student data to make decisions about brand community management.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".