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Record W4283379311 · doi:10.2308/jeta-2020-081

Cybersecurity Research in Accounting Information Systems: A Review and Framework

2022· review· en· W4283379311 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Emerging Technologies in Accounting · 2022
Typereview
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsInformation assuranceComputer securityComputer scienceFrame (networking)Information systemInformation securityData sciencePolitical science

Abstract

fetched live from OpenAlex

ABSTRACT The study of cybersecurity issues plays a fundamental role in accounting information systems (AIS) research. However, as the importance of cybersecurity has continued to grow in other disciplines, such as computer science and management information systems, it has become less clear what is distinct about AIS-based cybersecurity research, what unique insights AIS research has contributed to the study of cybersecurity, and what promising directions for AIS research into cybersecurity remain untapped. In order to answer these questions, we perform a literature review covering 56 articles published in 11 AIS-oriented journals. We find four distinct, yet related, categories of research inquiry: cybersecurity risks and threats, cybersecurity controls, cybersecurity-related assurance, and cybersecurity breaches. In highlighting the key insights uncovered from these four areas, we frame “what we know,” as well as “what remains to be learned,” by outlining a detailed proposal of future research opportunities for AIS researchers.

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.

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)0.014
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.943
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0040.005
Science and technology studies0.0000.000
Scholarly communication0.0010.005
Open science0.0030.003
Research integrity0.0000.005
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 head0.069
GPT teacher head0.382
Teacher spread0.313 · 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