AN INVESTIGATION OF THE INTERPLAY BETWEEN EMOTIONAL CONGRUENCE AND REGULATORY FIT IN PHISHING SUSCEPTIBILITY
Bibliographic record
Abstract
Phishing messages are designed to deceive individuals into divulging sensitive information. This study aims to understand under what circumstances an individual is more susceptible to phishing messages. By drawing on perspectives of emotional congruence and regulatory fit, we propose two mechanisms that independently and synergistically influence individuals’ phishing susceptibility: (1) the congruence between the emotional framing of the phishing message and individuals’ emotional state (i.e., emotional congruence), and (2) the fit between the motivational framing of the phishing message and individuals’ regulatory focus (i.e., regulatory fit). We propose an online experiment approach to investigate the above relationships. We will manipulate the phishing message framing and conduct priming tasks to induce participants’ emotional states and their regulatory focus. This study will uncover why and how the design of a phishing message may make individuals more vulnerable, which will further contribute to designing measures to mitigate phishing risks.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".