Mobile Technology and Boundary Permeability
Bibliographic record
Abstract
An extensive review of the literature reveals a lack of insight into why some employees and their families benefit from the adoption of mobile technology while others do not. The paper summarizes the authors' efforts to answer this question. The authors undertook a longitudinal case study of the adoption and use of a B lack B erry S martphone by 25 professional knowledge workers. Four theoretical lenses were used to help with the data analysis process: boundary theory, the social constructivist view of technology, sensemaking and attribution theory. Analysis of the Time 2 data identified three groups. Segmentors (n = 4) did not use their smartphones outside work hours. Integrators (n = 8), used their smartphones to connect to both work and family anywhere, but not any time (temporally separated work and family roles). Struggling segmentors (n = 13) felt pressured by their organization to use their device 24/7 and did so. The analysis indicates that the relationship between the use of mobile technology and successful boundary management depends on the development of a strategy to manage the device prior to adoption, the ability to change one's strategy to respond to concerns at home, and self‐control.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".