SOCIAL COGNITIVE DETERMINANTS OF NON-MALICIOUS, COUNTERPRODUCTIVE COMPUTER SECURITY BEHAVIORS (CCSB): AN EMPIRICAL ANALYSIS
Bibliographic record
Abstract
This study used a cross-sectional survey to test the relationships among social cognitive variables and employees’ counterproductive computer security behaviors (CCSB). We used data collected from 201 professionals in Canadian organizations. Components from social cognitive theory (SCT) including self-efficacy, observational learning, outcome expectations (organizational and personal), self-regulation, and organizational facilitators could diminish employees’ CCSB. No prior research has examined this phenomenon using SCT. A total of 16 hypotheses were formulated and tested with the partial least squares (PLS) technique; 10 were confirmed. Notably, two SCT variables, i.e. outcome expectations (organizational) and self-regulation had direct negative effects on CCSB. The others did not have direct effects on CCSB; however, outcome expectations (personal) had indirect effect on CCSB through self-regulation. Self-efficacy indirectly impacted CCSB through outcome expectations (organizational). In addition, observational learning and outcome expectations (organizational) had indirect effects on CCSB through self-regulation. The results confirmed that organizational facilitator, i.e. training, have positive effects on self-efficacy. The data showed that intention to engage in CCSB is positively associated with indulgence in the behavior, in this instance, self-reported engagement in CCSB. The social cognitive variables in our research model explained 18% of the variance observed in the intention to engage in CCSB
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".