Comportements stratégiques autonomes et pressions institutionnelles : le cas du BYOD
Bibliographic record
Abstract
The Bring Your Own Device phenomenon (BYOD) represents a major trend on the job market. Many employees demand to use the devices and software of their choice: mobile phones, tablets, online data storage and data sharing sites (Dropbox, iCloud), videoconferencing systems (Facetime, Skype) among others. This flexibility can be key when choosing an employer or for the purpose of talent retention. Even when these practices are not allowed, many employees, anxious to do their job better, easily find a way around. Conversely, some employers expect their employees to use their own smartphone for some tasks, thus saving on costs. Most of the research published to date on this topic focusses on security (of organizational systems and data), risks, privacy, and in specific contexts (medical settings). Our research focusses on contexts where employees want to use their own device; it tries to answer the following question: what factors and mechanisms enable the implementation of BYOD in professional spheres? We analyse this phenomenon through the lens of institutional theory (more specifically institutional pressures) and by identifying autonomous strategic behaviours of key actors; we suggest that the interplay of institutional pressures and autonomous behaviours leads to BYOD, an emergent phenomenon, that was not planned by management, and then, in turn, possibly to emerging strategies. Our methodology is a case study.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".