The evolution of international marketing management: AI applications and emerging markets driving international selling in the digital era
Bibliographic record
Abstract
Purpose This research systematically investigates the main themes in international marketing and management (IMM) literature published between 1983 and March 2025. Drawing on a comprehensive review of academic publications, it uncovers prevailing research trends and outlines a future agenda, with special attention to the role of artificial intelligence (AI) in shaping the field. A distinctive feature of this review is its focus on international selling, which serves as the lens for examining how AI is reshaping IMM theory, practice and research development. Design/methodology/approach The analysis is based on a dataset of 450 peer-reviewed articles sourced from 38 top-tier (Q1) marketing journals. These articles were selected through a keyword-based search strategy targeting titles and keywords explicitly related to IMM. The study employed a hybrid systematic review combining bibliometric analysis and qualitative content analysis, utilizing KH Coder 3.0. Techniques such as multidimensional scaling and co-occurrence network analysis were applied to extract thematic structures and research patterns. Findings The findings indicate a notable shift in IMM research over time, reflected in evolving topics, methods and theoretical approaches. Four main themes and eleven sub-themes were identified. These include (1) international management strategies; (2) market entry strategies – covering cultural distance, strategic alliances, Chinese market contexts, international joint ventures and cross-border acquisitions; (3) digital marketing strategies, with emphasis on platform-based sales and online retail and (4) internationalization and global expansion, informed by transaction cost theory, institutional environments and the roles of small and medium-sized enterprises and multinational corporations. Five emerging themes in the application of AI contribute to enhanced customer satisfaction, stronger engagement and loyalty, more effective co-creation processes, refined market entry strategies, deeper insights into consumer behavior and technological trends and improved performance in omnichannel retailing. Originality/value Unlike prior reviews that examine digitalization and international marketing in isolation, this study uniquely positions international selling as the central lens to explore how AI is transforming research themes, theoretical foundations and managerial practices in IMM.
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How this classification was reachedexpand
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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".