Collective perceptions of digital privacy in organizations
Bibliographic record
Abstract
This theory development paper addresses the emergence of collective perceptions of digital privacy (CPDP) in organizations, a phenomenon understudied in information systems (IS) literature.Drawing on contextual integrity framework (Nissenbaum 2004), we conceptualize CPDP as a unit-level construct rooted in individual perceptions, shaped by social dynamics and contextual factors such as virtual territories.Using multilevel theory (Kozlowski & Klein, 2000), we propose that CPDP emerges through composition (homogeneous perceptions) or compilation (heterogeneous or clustered perceptions), influenced by peer comparisons, expert reliance, and organizational disparities.Our theory addresses gaps in IS research by moving beyond individuallevel analysis, incorporating the role of IT artifacts into privacy concept, and contextualizing digital privacy within organizational settings.This work offers a foundation for future empirical research on privacy behaviors, security breaches, and organizational productivity, while providing actionable insights for designing effective privacy policies and products.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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 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".