The impact of ignorance and bias on information security protection motivation: a case of e-waste handling
Bibliographic record
Abstract
Purpose Protection motivation theory (PMT) explains that the intention to cope with information security risks is based on informed threat and coping appraisals. However, people cannot always make appropriate assessments due to possible ignorance and cognitive biases. This study proposes a research model that introduces four antecedent factors from ignorance and bias perspectives into the PMT model and empirically tests this model with data from a survey of electronic waste (e-waste) handling. Design/methodology/approach The data collected from 356 Chinese samples are analyzed via structural equation modeling (SEM). Findings The results revealed that for threat appraisal, optimistic bias leads to a lower perception of risks. However, factual ignorance (lack of knowledge of risks) does not significantly affect the perceived threat. For coping appraisal, practical ignorance (lack of knowledge of coping with risks) leads to low response efficacy and self-efficacy and high perceptions of coping cost, but the illusion of control overestimates response efficacy and self-efficacy. Originality/value First, this study addresses a new type of information security problem in e-waste handling. Second, this study extends the PMT model by exploring the roles of ignorance and bias as antecedents. Finally, the authors reinvestigate the basic constructs of PMT to identify how rational threat and coping assessments affect user intentions to cope with data security 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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".