(Dis)Innovative digital strategy in professional sport: examining sponsor leveraging through social media
Bibliographic record
Abstract
Purpose The purpose of this study was to examine the effectiveness of social media strategies of sport organizations when an unexpected absence of relevant content occurs. The study explored the typologies of Instagram posts of NHL teams and measured engagement of social media content that was not planned in advance. Design/methodology/approach A mixed methods approach was utilized through a content analysis of 12 NHL team social media feeds. 502 ( n = 502) posts were examined from the period of March 12 – May 26 during which the NHL season was suddenly paused due to the COVID-19 pandemic. Typologies of posts were identified through a qualitative coding process and ANOVA tests were conducted to examine the effectiveness of each typology in engaging consumers. Findings This study found that social media strategies of the sampled NHL teams is evidence of disinnovation with digital, as opposed to the previously conceptualized innovative properties that these activities bear. Therefore, in order to achieve the consumer engagement outcomes sought to build stronger relationships with fans and deliver on the expected leveraging capabilities for sponsors, sport marketers must reconsider their current, imbalanced approach and whether the more inherently interactive content should be balanced with entertaining content that requires organic consumer engagement. Originality/value This study offers a unique application of UGT, highlighting that social media in a sport context is not just about gratifying consumers, but preventing diminishing engagement and exploitation of users through overuse of sponsorship-laced content.
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 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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".