Technological Entitlement: It’s My Technology and I’ll (Ab)Use It How I Want To
Bibliographic record
Abstract
Entitlement has been identified as a potentially valuable employee characteristic in the prediction of computer abuse but has not been studied systematically in the IS domain. We introduce the construct of technological entitlement as the persistent sense of being more deserving of technological resources, uses, and privileges compared to other employees. Adapting a model of general entitlement to the work technology context, we theorize that technological entitlement predicts computer abuse and that this relationship is amplified by perceptions of technology restriction. After developing and validating a scale to measure technological entitlement, we conduct three studies with working adult samples to test our hypotheses. In Study 1 (n = 187), using a behavioral design, we find that technological entitlement predicts computer abuse behavior (beyond general entitlement) and that this relationship is stronger when employees perceive organizational restrictions on technology usage. We replicate these findings in Study 2 (n = 339) with an experiment. In Study 3 (n = 156), we manipulate the context of restrictiveness within our experimental vignette to establish the generalizability of our moderator. We discuss how technological entitlement helps explain existing inconsistencies in the effectiveness of deterrence measures as well as other theoretical and practical implications of our work.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".