An activity theory approach to information security non-compliance
Bibliographic record
Abstract
Purpose The purpose of this paper is to introduce activity theory (AT) as a new theoretical lens to the field of information security non-compliance by explaining how research in that field can benefit from AT and to suggest eight propositions for future research. Design/methodology/approach Based on AT, the paper suggests that employees, IT systems, task characteristics, information security policies (ISPs), community and division of labor can be viewed to form an ensemble that is labeled activity. Their characteristics and/or the relationships that exist between them in organizational contexts are hypothesized to influence non-compliance behaviors. Findings The paper suggests that AT provides a broad lens that can be useful for explaining a large variety of non-compliant behaviors related to information security. Research limitations/implications The paper focuses only on non-compliant behaviors that employees undertake with non-malicious intentions and offers avenues for future research based on the propositions that are developed in the paper. Originality/value The paper provides a useful step toward a better understanding of non-compliant ISP behaviors. In addition, it proposes and explains new research areas in the non-compliance field.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".