What Users Do Besides Problem-Focused Coping When Facing It Security Threats: An Emotionfocused Coping Perspective1
Bibliographic record
Abstract
This paper investigates how individuals cope with IT security threats by taking into account both problem-focused and emotion-focused coping. While problem-focused coping (PFC) has been extensively studied in the IT security literature, little is known about emotion-focused coping (EFC). We propose that individuals employ both PFC and EFC to volitionally cope with IT security threats, and conceptually classify EFC into two categories: inward and outward. Our research model is tested by two studies: an experiment with 140 individuals and a survey of 934 respondents. Our results indicate that both inward EFC and outward EFC are stimulated by perceived threat, but that only inward EFC is reduced by perceived avoidability. Interestingly, inward EFC and outward EFC are found to have opposite effects on PFC. While inward EFC impedes PFC, outward EFC facilitates PFC. By integrating both EFC and PFC in a single model, we provide a more complete understanding of individual behavior under IT security threats. Moreover, by theorizing two categories of EFC and showing their opposing effects on users’ security behaviors, we further examine the paradoxical relationship between EFC and PFC, thus making an important contribution to IT security research and practice.
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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 | 0.003 |
| 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 | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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 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".