Digital marketing ecosystems and global market expansion: current state and future research agenda
Bibliographic record
Abstract
Purpose This article aims to provide greater clarity regarding the conceptualization and critical role of digital marketing ecosystems for the global expansion of multinational enterprises (MNEs) and offer novel research directions to prompt future research. Design/methodology/approach The authors first review the marketing literature related to marketing ecosystems, highlighting the evolution of this body of work across a range of domains such as services, innovation and new product development, communications and marketing strategy more broadly. Next, two case examples of MNEs whose global expansion efforts have been supported by their marketing ecosystems are used to highlight the role of marketing ecosystems in global market expansion. Finally, novel research directions are offered to prompt future research and provide greater insight into this emerging area. Findings The case examples we examine yield important insights into the role of marketing ecosystems for MNEs expanding from emerging markets (EMs) to developed markets (DMs). EM-MNEs such as TEMU face more communication and payment ecosystem challenges while opening their supply chain to DMs. Contrary to EM-MNEs, DM-MNEs face institutional and sociocultural challenges that require different marketing ecosystem orchestration approaches. Originality/value Marketing ecosystems can provide MNEs with greater multinational flexibility, enabling them to adapt their global strategies to navigate increasing complexities in global markets, such as trends toward increased protectionism and geopolitical disruptions. However, there is surprisingly little research addressing this issue.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".