Exploring the nexus of digital technology and organizational change in non-profit sport organizations
Bibliographic record
Abstract
The sport industry is not immune to the force digital technology brings in facilitating organizational change in sport organizations. Consequently, the purpose of the study was to explore the nexus of digital technology and organizational change in sport organizations, specifically identifying the presence of smaller digital changes and larger digital transformations. A collective case study of five Canadian national sport organizations undergoing organizational change was conducted; data derived from 49 semi-structured interviews and 151 documents. Results indicated these organizations engaged in a variety of digital changes like incorporating Microsoft 365, Zoom, and Samepage, and developing their own digital tools. Although these technologies fostered some larger digital transformations insofar as they enabled greater stakeholder communication and engagement, in the context of organizational change, digital technology remained predominantly at a small-scale, digital change level. The findings make it imperative for executives to reflect upon whether digital technology is being (un)intentionally disregarded from a strategic standpoint.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".