The Role of Emotional Advertising in Building Emotional Connections with Customers
Bibliographic record
Abstract
This bibliometric analysis explores the role of emotional advertising in building deep emotional connections between brands and consumers. The research identifies key themes such as advertising, marketing, and consumer behavior, which are central to understanding how emotional appeals influence consumer engagement. By tapping into emotions, brands can foster customer loyalty, enhance brand differentiation, and influence purchasing decisions. The study highlights influential sources and authors in the field, with the Journal of Advertising leading with 44 documents, and Septianto F being the most relevant author with 19 publications. Additionally, the relationship between a country’s total research output and the average citation impact is analyzed, revealing that while the USA leads in total citations, Canada and Austria are noted for their high citation impact, demonstrating the importance of research quality over sheer volume. The findings emphasize the effectiveness of emotional advertising in creating lasting consumer-brand connections, and the increasing amount of study in this field indicates how important it is to contemporary marketing tactics. For brands, leveraging emotional advertising is a powerful way to differentiate themselves and build strong, loyal customer relationships. The study also offers a clear roadmap for future researchers to identify key trends and influential sources, thereby enhancing the existing literature on emotional advertising. A direction for future research includes investigating the role of emerging digital platforms, such as social media and influencers, in shaping emotional advertising strategies. This study's primary focus on bibliometric data may limit its ability to properly capture the qualitative elements of emotional advertising's efficacy in various cultural and commercial contexts.
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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 | 0.007 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.044 | 0.059 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".