Perceived Severity, Anxiety, and Protection Motivation in Shaping Protection Insurance Product Purchase Intentions: Evidence from the COVID-19 Public Health Crises
Bibliographic record
Abstract
This study examines how consumers’ perceptions of threat severity and anxiety during public health crises influence their motivation to protect themselves and, subsequently, their intentions to purchase protection insurance products. Drawing on Protection Motivation Theory (PMT), we develop an integrated framework that links cognitive risk assessments and emotional responses to financial protection decisions. Using survey data collected from 437 respondents in Taiwan during the COVID-19 pandemic, the research model is tested through partial least squares structural equation modeling (PLS-SEM). The empirical results indicate that both perceived severity and anxiety significantly enhance protection motivation, with perceived severity exerting a stronger effect. These two antecedents also directly strengthen consumers’ intentions to purchase protection insurance. Furthermore, protection motivation partially mediates the effects of perceived severity and anxiety on purchase intention. These findings extend the application of PMT to the financial and insurance domains by demonstrating how cognitive and affective factors jointly shape demand for protection insurance in high-risk environments. The practical implications of these results for insurers include risk communication strategies, product positioning, and the development of crisis-responsive insurance solutions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".