From Lebanon to Canada - The role of Emotional Intelligence in Online Shopping Feedback
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,006 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».