The ties that bind? Online musicians and their fans
Bibliographic record
Abstract
Purpose – The purpose of this study is to examine the relationships between musician’s social network sites (SNS), the tie that fans may develop via these sites, and music acquisition, via legal and illegal means. Design/methodology/approach – A quantitative approach was taken, gathering 352 responses from young adults via an online survey. Findings – Perceptions of interactivity and sincerity on musicians’ SNS are found to lead to stronger ties, enhancing the fan’s feeling of closeness to the musician, the fan’s inclination to spread positive word-of-mouth, and the time a fan spends on the site. Pathways are found between the fan activity, sense of closeness and time spent on the SNS. In terms of acquisition, the tie strength indicator of time spent on the SNS holds a positive relationship with purchase intent. While a sense of closeness holds a negative relationship to illegal downloading activity, the fan’s activity recommending the musician has a positive influence on illegal downloading. Research limitations/implications – Limitations of this study include a limited amount of information on the musician and extent of fandom, suggesting future research to tease out the effects of SNS on fans with varying levels of existing commitment to musicians. Practical implications – Stronger ties between fans and musicians may be developed via interactive and sincere SNS. Activities which encourage the fan to give recommendations and spread positive word-of-mouth are especially influential in driving purchase intent. Originality/value – These results provide theoretical and practical implications in relation to how SNS may influence the online fan-celebrity “tie” and music acquisition – three elements which have not to date been examined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".