The Application of Artificial Intelligence in Marketing: A Review of Research
Bibliographic record
Abstract
This study primarily reviews the research progress of artificial intelligence (AI) in the field of marketing as reported in academic journals. It begins by examining core mechanisms such as anthropomorphism and emotional interaction, as well as mental perception and trust building, to analyze how AI satisfies consumers' needs for self-definition and identity through the "computer as a social participant" paradigm and data-driven algorithms. It then reviews the application of AI in typical marketing scenarios such as personalized recommendations, brand communication and content generation, intelligent customer service, and service remediation, and summarizes its impact on consumers' intertemporal choices, price discrimination responses, and word-of-mouth behavior. It further explores AI's unique value in empowering vulnerable groups (such as visually impaired individuals and those with psychological distress) and stigmatized groups. Finally, it proposes future research directions, including AI ethics and regulations, algorithmic fairness, multi-dimensional identity mechanisms, cross-cultural anthropomorphism design, and AI-enabled sustainable and socially valuable marketing.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".