Information technology security management concerns in global financial services institutions
Bibliographic record
Abstract
Purpose The purpose of this paper is to add a layer of understanding to a previous survey of information technology (IT) security concerns and issues in global financial services institutions (GFSI). Design/methodology/approach This paper uses data obtained from a secondary source. The dimensions of national culture used in this paper come from Hofstede's work. Two analyses are performed on the data. First, a non‐parametric test is conducted to determine whether there are significant differences on the 13 IT security concerns when the dimensions of national culture are used to group responses. Second, a correlation analysis is carried out between the study's variables. Findings First, the results indicate that the dimensions of national culture are not statistically important in differentiating responses and perceptions of IT security concerns across GFSI. Second, some of the dimensions of national culture are found to have significant correlations with a few of the IT security concerns investigated. Research limitations/implications The use of a secondary data source introduces some limitations. The views captured in the survey are those of management team, it is likely that end‐users' perceptions may vary considerably. Nonetheless, the main finding of the paper for corporate managers in the financial services industry is that IT security concerns appear to be uniform across cultures. Further, the data show that the dimension of uncertainty avoidance deserves further attention with regard to the assessment of security concerns in GFSI. This information may be useful for decision making and planning purposes in the financial services industry. Originality/value This paper is believed to be among the first to examine the impacts of national culture on IT security concerns in GFSI. The paper's conclusions may offer useful insights to corporate managers in the industry.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".