Identification of critical brand community variables and constructs using importance-performance analysis and neural networks
Bibliographic record
Abstract
Purpose This paper aims to use a unique statistical analysis tool to examine the importance and performance of critical brand community constructs and indicators to make concrete recommendations for brand community managers going forward. Design/methodology/approach An online survey was used to gather 501 responses from North American members of the Qualtrics panel. The data was analyzed with partial least squares (PLS) modeling software SmartPLS and neural networks available in statistical software JMP by SAS. Findings Using the brand community motives by Madupy and Cooley (2010), the results of this paper indicated that there was significant room for improvement in customer engagement. Based on further analysis, entertainment and identification with the brand community were the most important constructs in driving community engagement so that the identification construct received a “do better” ruling meaning that the improvement of the indentification construct score would enhance significantly the score of the target construct engagement score. Originality/value For brand community managers, it is important to know the true importance of the critical brand community constructs and indicators, along with an assessment of current performance. This helps to increase satisfaction and relationship quality among brand community members. The current study uses unique statistical analysis tools to make such concrete recommendations.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".