Generative AI in Marketing: Promises, Perils, and Public Policy Implications
Bibliographic record
Abstract
By evaluating the pattern of generative AI (GAI) use by businesses in marketing, this study aims to understand the subsequent impact on society and develop policy implications that promote its beneficial use. To this end, the authors develop an organizing framework that contends that the usage of GAI models by businesses for marketing purposes creates promises and perils for society through a specific business process. This business process is represented by the action → capabilities → transformation → impact link in the proposed framework. Additionally, the authors find that the level of technology infrastructure, skilled personnel, and data access moderates the influence of GAI on businesses’ ability to develop technology-driven capabilities. Furthermore, adaptive leadership and management strategies moderate the impact of these capabilities on technology-enabled business transformations. This research is the first study to critically evaluate the use of GAI in marketing from a public policy perspective. The study concludes with an agenda for future research.
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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 | 0.025 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".