Generative Artificial Intelligence in Applied Business Contexts: A Systematic Review, Lexical Analysis, and Research Framework
Bibliographic record
Abstract
Generative Artificial Intelligence (GenAI) is transforming business practices with potential applications in customer service, code generation, risk analysis, and HR functions. GenAI may simultaneously create or exacerbate ethical, legal, and security concerns in the business context despite its promise. Thus, researchers should be interested in its role and impact, especially in the applied business context. This multi-method systematic review examines GenAI literature in applied business research, revealing dominant themes like ChatGPT and language models but noting a scarcity of business-based studies. Analysis of GenAI research features in applied business studies identifies a limited focus on theoretical frameworks, data collection methods, and data analysis processes. We suggest frameworks for future research to assess GenAI’s impact on system and information quality, user satisfaction, and organizational outcomes based on our findings. This review provides a vital foundation for understanding and advancing GenAI in applied business research contexts.
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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 | 0.023 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.031 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".