Social media, s-commerce and social capital: a netnography of football fans and organisations
Bibliographic record
Abstract
Social media channels allow brands to establish meaningful social relations with customers. This paper evaluates the role of social capital in building these online relationships for the benefit of commercial value and s-commerce for brands. Extensive empirical data was collected over a two-year netnography study using the social media channels of a football club in the UK as a vehicle for the study. A blended methods netnography (Fenton and Procter, 2019) was employed that included online participant observation, social network analysis, and semi-structured interviews with football fans and social media managers. The majority of brand social media followers are often found to be lurkers. These are weakly connected, social media followers - listening but not interacting. Finding ways to strengthen social capital with social media followers have significant brand and commercial implications. Positive interactions are critical to building social capital to strengthen and sustain the brand. Social capital can be successfully built and nurtured through engaging content and positive interactions through social media channels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".