Management Information Systems and Correlation Between E-Business and Information Security from a Business Intelligence Perspective
Bibliographic record
Abstract
The research focus was motivated by the emergence of electronic business which increased during and after the COVID-19 pandemic. The research is a literature review and its purpose is to build awareness about the importance of information security and to analyze the correlation between information security and electronic business in an environment where electronic business is emerging and the gap between electronic business and information security is enlarging. The research is a review of peer-reviewed articles retrieved from case studies, empirical research, case analysis, literature reviews, comparative studies, systematic reviews, and conceptual analysis dating from the years of 2018 to 2023. This literature review defines four business intelligence concepts: management information systems, value driven business, electronic business, and information security. This literature review also reveals the effects of all four business intelligence concepts on organizations’ decision-making and financial objectives. Findings of this literature review revealed that electronic businesses need a stronger risk management approach regarding information security. The conclusion shows that the current technological approach and information security tools such as encryption key management, mantraps, and network intrusion detection systems do not ensure trust and/or eliminate digital security risks.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| 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".