Relationships between Information Security Concerns and National Cultural Dimensions
Bibliographic record
Abstract
This study investigates the relationships between the contextual factor of national culture and information security concerns in the global financial services industry (GFSI). Essentially, this study attempts to expand the breath of information provided in the recent 2009 Deloitte Touche Tohmatsu (DTT) survey, which reported such issues in the financial services industry. The inference from the 2009 DTT survey was that information security concerns across GFSI are being informed solely by industry-related standards or imperatives. As such, perceptions and attitudes towards such issues were thought to remain unchanged in differing contexts. Results from this study’s analysis showed that the perceptions of information security concerns in GFSI compared reasonably well, but also varied by some national cultural attributes to debunk such a claim. Corporate managers in the industry may benefit from this research’s findings as they formulate country-wide information security policies and strategies. As well, insights from this current effort indicate that it would be erroneous for practitioners to accept that entities in the financial services hold exactly the same view on information security issues in their industry. Future research avenues are discussed.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".