Generative Artificial Intelligence in Applied Business Contexts: A Systematic Review, Lexical Analysis, and Research Framework
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it