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Record W2967229898

Reporting Information Security Policy Violations - An Exploratory Study.

2019· article· en· W2967229898 on OpenAlexaff
Tianjie Deng, Hyung Koo Lee, Sumantra Sarkar

Bibliographic record

VenueJournal of the Association for Information Systems · 2019
Typearticle
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsComputer scienceComputer securityInformation securityExploratory researchBusinessInternet privacy
DOInot available

Abstract

fetched live from OpenAlex

Information security policy (ISP) violations have become a serious concern in organizations. Although prevention is the best option since it prevents ISP violations from occurring, incidents still occur. Such violations should be reported so that organizations can take immediate actions and reduce the negative impact. However, the current literature mainly focuses on factors that lead to violations of ISPs and our current understanding of what influences employees’ intention to report others’ ISP violations is limited. In this study, we attempt to fill this gap by conducting an explorative study to investigate the ISP violation reporting phenomenon. Six pilot interviews are conducted to investigate why or why not individuals report others’ ISP violations. Guided by literature on ISP violations and organizational citizenship behavior, our preliminary findings suggest that employees’ intention of reporting is motivated by the purpose and the consequence of the violation, as well as the severity of such consequences.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.026
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.274
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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