Examining fan engagement through social networking sites
Bibliographic record
Abstract
Purpose The purpose of this paper is to conceptualise and measure the construct of fan engagement through social networking sites (SNS). Design/methodology/approach A multi-stage procedure was completed to validate the proposed fan engagement through SNS model with three first-order constructs (fan-to-fan relationships, team-to-fan relationships and fan co-creation). First, a preliminary analysis of the proposed items to capture fan engagement through SNS was conducted through expert review. Second, an assessment of item reliability and construct validity was completed using confirmatory factor analysis (CFA). Finally, CFA and subsequent structural equation model were conducted to review the psychometric properties and to test the relationships between the proposed construct with online and offline behavioural intentions. Findings The results indicate good psychometric properties of the constructs of fan-to-fan relationships, team-to-fan relationships and fan co-creation, and these three constructs were significantly related with the second-order construct of fan engagement through SNS. Additionally, the construct of fan engagement through SNS was significantly related to both online and offline behavioural intentions. Practical implications These findings suggest that teams should use SNS to interact with fans, to allow fans to share experiences and to involve fans in co-creation processes aimed at increasing engagement and subsequent positive behavioural intentions towards the team. Originality/value This study extends previous research by measuring fan engagement through SNS as a multidimensional construct, and testing its predictive effect on fans’ online and offline behavioural intentions. Several suggestions for future studies and strategies for increasing fan engagement can be drawn from this study.
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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 | 0.016 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".