Multi-stakeholder approach in MNEs’ product advertising: evolutionary paths of content and language
Bibliographic record
Abstract
Purpose Despite growing calls for practical insights on the multi-stakeholder approach in marketing and its communications, there is no empirical evidence of such kind in international advertising. This study aims to examine whether and how multinational enterprises (MNEs) have redesigned international advertising to reflect multi-stakeholder principles in language and content over time. Design/methodology/approach We adopt a mixed methodological approach based on literature review, the Delphi method and content analysis of 258 international advertising campaigns from 86 Global Reptrak® most reputed MNEs in 2016, 2019 and 2022. Furthermore, we use the Apriori algorithm, which analyzes advertising campaigns’ keyword complementarity and substitution trends. Findings From 2016 to 2022, MNEs are gradually shifting from focusing solely on product-centered content and language to integrating broader values, such as responsibility, sustainability, humanity and ethics, which address the interests of diverse stakeholder groups in their international advertising campaigns. Research limitations/implications Our study offers a homogeneous picture of digital international advertisements spread mainly in Southern Europe, Turkey, Canada, the United Kingdom, the United States of America and India, with no relevant differences detected in the contents and language. Future studies could replicate our analysis by including advertisements spread in other emerging countries, which we did not cover for methodological reasons. Practical implications Our study guides marketers on integrating multiple stakeholder values into their advertisements. Originality/value Our research provides a novel contribution to the evolution of traditional marketing communications towards a multi-stakeholder approach by identifying three main evolutionary paths for international advertising: unimodal, bimodal and multimodal communication in 2016, 2019 and 2022, respectively.
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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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".