Technological Entitlement: It’s My Technology and I’ll (Ab)Use It How I Want To
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it