From Lebanon to Canada - The role of Emotional Intelligence in Online Shopping Feedback
Bibliographic record
Abstract
Background & Motivation: As a frequent online shopper, looking for the best deals, and checking reviews myself for opinions and choices, my research is deeply entrenched in how true those reviews are, how genuine, relatable, and how the tone of those reviews impact brand image and purchase decisions.Managing a household abroad, juggling family and work obligations, and leading a life mediated by screens, emotional intelligence (EI) is crucial in digital communication. Some companies manage to empathize with customers by tackling their unpleasant relationships and turning them to loyal fans. This study tackles academic pursuit of emotional awareness for online platform success and personal reflection on a real-world phenomenon that impacts every online purchase. I am intrigued to look at the technical background of e-commerce and the human aspect for long vision success.Research Objectives: This paper critically looks at how emotional intelligence from consumers perspective and from e-retailers perspective has implication on ideas, taking decisions, and having faith in the reviews. The central research objectives are:· Analyze the role of emotional intelligence while understanding and answering reviews· Examine how feedback rooted in emotional intelligence impacts loyalty and satisfaction· Assess if such responses from sellers can manage negative comments and foster positive onesMethodology: The study adopts a mixed-methods approach, combining:· A systematic literature review of EI and E-commers reviews impact· Case studies of EI-driven replies· Quantitative survey to bridge the gap between theory and lived experience.Key Findings:The results indicate that those online consumers who have higher emotional intelligence have greater sensitivity when it comes to assessing reviews thus creating distinction between negative comments that are criticizing versus those who have given emotionally unhelpful feedback. Such consumers are highly likely to add value in their reviews and are on the lookout for responses in a timely manner from the sellers. For the vendors, businesses and websites that adopt a strategy that is emotionally intelligent with feedback that acknowledges emotions and share personalized feedback have higher customer retention and better reviews and ratings.Personal Reflections: This research journey showcased the commonality of results between technology, emotion, and commerce. It is clearer now that emotional intelligence is not only a personal characteristic but a strategic tool for any encounter online between a consumer and a brand. Looking at emotional cues in reviews allows for a leeway to connect on a deep level and to change feedback into loyalty. Consumers are expecting more emotional communication in online transactions.Conclusion & Contribution: This study enables better comprehension of emotional intelligence in e-commerce and customer experience management. As psychological insights have an impact on consumer behavior that should have an impact on practical strategies, the research closes a gap between academic inquiry and practical application. The framework enables emotionally intelligent feedback replies and staff to answer with empathy. Integrating emotional intelligence into online shopping leads to trust, and a more authentic online experience.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".