Determinants of Intention to Participate in Corporate BYOD-Programs: The Case of Digital Natives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Corporations continue to see a growing demand for Bring-Your-Own-Device (BYOD) programs which allow employees to use their own computing devices for business purposes. This study analyses the demand of digital natives for such programs when entering the workforce and how they perceive the benefits and risk associated with BYOD. A theoretical model building on net valence considerations, technology adoption theories and perceived risk theory is proposed and tested. International students from five countries in their final year and with relevant work experience were surveyed. The results show that the intention to enroll in a BYOD program is primarily a function of perceived benefits while risks are widely ignored. Only safety and performance risks proved to contribute significantly to the overall perceived risk. The knowledge acquired from this study is particularly beneficial to IT executives as a guide to deciding whether and how to set up or adjust corporate BYOD initiatives.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it