E-waste information security protection motivation: the role of optimism bias
Bibliographic record
Abstract
Purpose Electronic waste (e-waste) such as discarded computers and smartphones may contain large amounts of confidential data. Improper handling of remaining information in e-waste can, therefore, drive information security risk. This risk, however, is not always properly assessed and managed. The authors take the protection motivation theory (PMT) lens of analysis to understand intentions to protect one's discarded electronic assets. Design/methodology/approach By applying structural equation modeling, the authors empirically tested the proposed model with survey data from 348 e-waste handling users. Findings Results highlight that (1) protection intention is influenced by the perceived threat of discarding untreated e-waste (a threat appraisal) and self-efficacy to treat the discarded e-waste (a coping appraisal) and (2) optimism bias plays a dual-role in a direct and moderating way to reduce the perceived threat of untreated e-waste and its effect on protection intentions. Originality/value Results support the assertions and portray a unique theoretical account of the processes that underline people's motivation to protect their data when discarding e-waste. As such, this study explains a relatively understudied information security risk behavior in the e-waste context, points to the role of optimism bias in such decisions and highlights potential interventions that can help to alleviate this information security risk behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.022 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".