Geng S, Li W, Qu X, Chen L, (2017), Design for the pricing strategy of return-freight insurance based on online product reviews, Electronic Commerce Research and ApplicationsVolume 25, September–October 2017, Pages 16-28

https://www.sciencedirect.com/science/article/pii/S156742231730039X

Mots clés : Assurance retour de fret, Avis de produit en ligne, Incertitude sur l’ajustement du produit, Politique de retour

Résumé : Pour résoudre les litiges liés aux achats en ligne avec le retour des produits, certaines compagnies d’assurance ont développé un nouveau type d’assurance appelé assurance retour-fret pour compenser la perte de frais de retour par les consommateurs. La détermination traditionnelle des primes d’assurance n’a pas complètement fusionné tous les facteurs de commerce électronique, tels que l’incertitude de l’ajustement des produits des achats en ligne, qui pourrait affecter à la fois la demande d’assurance et la quantité de retour. Sur la base de ces caractéristiques, nous développons un modèle de maximisation des bénéfices en termes de certains paramètres de réaction du marché, en particulier l’incertitude de l’ajustement du produit, et calculons la stratégie de tarification optimale, y compris la prime d’assurance et la compensation. En utilisant la théorie des perspectives, nous expliquons pourquoi les avis de produits en ligne pourrait influencer le comportement d’aversion au risque des demandeurs d’assurance dans la situation du commerce électronique. Ensuite, nous résolvons pour la prime et la compensation raisonnables accordées aux avis de produits en ligne du point de vue de la compagnie d’assurance et obtenons un certain nombre de directives managériales pour utiliser les variables de marketing et de stratégie opérationnelle pour influencer ces paramètres de réaction afin d’obtenir le maximum d’avantages du marché. Il est intéressant de noter que lorsque la sensibilité des consommateurs à l’incertitude sur l’ajustement du produit est modérée, une augmentation de l’incertitude sur l’ajustement du produit déplace la prime d’assurance et la compensation dans la direction opposée.

Grandes lignes :

• Assurance de retour de fret, qui compense la perte de frais de retour de produit en ligne.
• L’incertitude liée à l’ajustement du produit doit être prise en compte lors de l’analyse de la stratégie de tarification de l’assurance retour-fret.
• La relation entre la prime d’assurance retour-fret et l’incertitude sur l’ajustement du produit n’est pas une corrélation positive.
• Le bénéfice de l’assureur n’augmente pas toujours à mesure que la prime d’assurance retour-fret augmente.
• L’aléa moral doit être considéré comme une contrainte importante dans la conception d’une stratégie de tarification d’assurance.

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Moon S, Kamakura W, (2017), A picture is worth a thousand words: Translating product reviews into a product positioning map, International Journal of Research in Marketing, Volume 34, Issue 1, March 2017, Pages 265-285

https://www.sciencedirect.com/science/article/abs/pii/S016781161630074X

Mots clés : Avis de produits en ligne, Carte de positionnement du produit, Exploration de texte, Psychométrie, Découvrir les produits, Segmentation des consommateurs

Résumé : Les avis sur les produits deviennent omniprésents sur le Web, ce qui représente une riche source d’informations pour les consommateurs sur un large éventail de catégories de produits. Surtout, un avis de produit reflète non seulement la perception et la préférence pour un produit, mais aussi l’acuité, le parti pris et le style d’écriture de la critique. Cet aspect a été négligé dans des études antérieures qui ont tiré des conclusions sur les marques à partir des critiques de produits en ligne. Notre cadre combine des techniques d’exploration de texte basées sur l’apprentissage d’ontologie et des techniques psychométriques pour traduire les critiques de produits en ligne en une carte de positionnement de produit, tout en tenant compte des réponses idiosyncrasiques et des styles d’écriture des critiques individuels ou d’un nombre gérable de groupes de critiques (c.-à-d., Les méta-critiques). Nos illustrations empiriques utilisant des critiques de vins et d’hôtels montrent qu’une revue de produit révèle des informations sur le critique (pour l’exemple de vin avec un petit nombre de critiques d’experts) ou le méta-critique (pour l’exemple d’hôtel avec un nombre énorme de critiques réduit à un nombre gérable de méta-évaluateurs), ainsi que sur le produit examiné.

Grandes lignes :

• Les avis sur les produits deviennent omniprésents sur le Web.
• Un avis de produit reflète non seulement la perception et la préférence pour un produit, mais aussi l’acuité, le parti pris et le style d’écriture de la critique.

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Singh A, Tucker C (2017) A machine learning approach to product review disambiguation based on function, form and behavior classification, Decision Support Systems, Volume 97, May 2017, Pages 81-91

https://www.sciencedirect.com/science/article/pii/S0167923617300477

Mots clés : Apprentissage automatique, Extraction d’attribut de produit, Exploration de texte, Avis sur les produits, La conception des produits

Résumé : Les évaluations de produits en ligne se sont révélées être une source d’informations viable pour aider les clients à prendre des décisions d’achat éclairées. Dans de nombreux cas, les utilisateurs de plateformes d’achat en ligne ont la possibilité d’évaluer les produits sur une échelle numérique et de fournir également des commentaires textuels concernant un produit acheté. Au-delà de l’utilisation de plateformes de revue de produits en ligne comme systèmes d’aide à la décision client, cette source de données riche en informations pourrait également aider les concepteurs qui cherchent à augmenter les chances de succès de leurs produits sur le marché grâce à une meilleure compréhension des besoins du marché. Cependant, la taille et la complexité croissantes des produits sur le marché rendent difficile l’analyse manuelle de ces données. Les informations obtenues à partir de ces sources, si elles ne sont pas exploitées correctement, risquent de déformer le véritable succès / échec d’un. L’objectif de cet article est triple: i) proposer une approche d’apprentissage automatique qui dissipe les commentaires des clients en ligne en les classant en l’une des trois caractéristiques directes du produit (c.-à-d. La forme ,fonction ou comportement ) et deux caractéristiques indirectes du produit (c.-à-d. service et autre ), ii) pour découvrir l’ algorithme d’apprentissage automatique qui donne les résultats les plus élevés et les plus généralisables pour atteindre l’objectif i) et iii) pour quantifier la corrélation entre les évaluations de produits et les évaluations directes et les caractéristiques indirectes du produit. Une étude de cas impliquant des données d’examen pour des produits extraits de sites Web de commerce électronique est présentée pour démontrer la validité de la méthode proposée. Une approche de validation multicouche est présentée pour explorer la généralisation de la méthode proposée. Les contributions scientifiques de ce travail ont le potentiel de transformer la manière dont les concepteurs de produits et les clients intègrent les avis sur les produits dans leurs processus décisionnels en quantifiant la relation entre les avis sur les produits et les caractéristiques des produits.

Grandes lignes :

• Les évaluations de produits en ligne sont une source d’informations viable pour aider les clients à prendre des décisions d’achat.
• Elles peuvent aider les concepteurs qui cherchent à augmenter les chances de succès de leurs produits.
• Classification par apprentissage automatique de la forme, de la fonction et du comportement du produit
• Précisions de classification d’apprentissage automatique de plus de 82% pour le modèle de base
• Corrélation entre les évaluations de produits et la forme, la fonction et le comportement des produits
• La corrélation entre la forme des produits et les notes des produits a donné une valeur de 0,934

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Zhuang W, Cui G, Peng L, (2018), Manufactured opinions: The effect of manipulating online product reviews, Journal of Business Research, Volume 87, June 2018, Pages 24-35

https://www.sciencedirect.com/science/article/abs/pii/S0148296318300912

Mots clés : Avis de produits en ligne, Manipulation, Soupçon, Persuasion, Commerce électronique

Résumé : Des recherches antérieures supposent que les consommateurs peuvent détecter et ignorer la manipulation des avis sur les produits en ligne ou sont inconscients de telles pratiques. Nous supposons que l’équilibre se produit en raison des signaux de manipulation, de la suspicion des consommateurs et de leur expertise. Notre analyse des données sur l’occupation des hôtels montre que l’effet de l’ajout d’avis positifs et de la suppression d’avis négatifs sur les ventes présente une courbe en U inversée. De plus, les marques faibles souffrent davantage d’un ajout excessif. Nos expériences en laboratoire montrent que l’ajout affecte l’intention d’achat du consommateur, mais il suscite également des soupçons, ce qui exerce un effet médiateur négatif. La suppression est plus déguisée et difficile à soupçonner. Les novices sont plus influencés par les manipulations que leurs homologues expérimentés. Un ajout excessif entraîne la méfiance des consommateurs et peut se retourner contre eux. La suppression exacerbe l’asymétrie d’information et entraîne une sélection défavorable, ce qui justifie la retenue et la réglementation.

Grandes lignes :

• Un ajout excessif entraîne la méfiance des consommateurs et peut se retourner contre eux. 
• La suppression est plus déguisée et difficile à soupçonner. 
• La suppression exacerbe l’asymétrie d’information et entraîne une sélection défavorable, ce qui justifie la retenue et la réglementation.

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Lee H, Hae Rim C, Lee D, (2019), Learning to rank products based on online product reviews using a hierarchical deep neural network, Electronic Commerce Research and Applications, 36.

https://www.sciencedirect.com/science/article/pii/S1567422319300511

Mots clés : Classement des produits, Avis de produits en ligne, Réseau neuronal profond hiérarchique

Résumé : Le classement des produits basé sur les évaluations de produits en ligne consiste à déduire les préférences relatives des utilisateurs entre différents produits en tant que variante de l’analyse des sentiments au niveau de l’entité. Malgré la relation complexe entre la préférence globale de l’utilisateur et les opinions diverses et individuelles, les approches existantes utilisent généralement des hypothèses empiriques sur les caractéristiques de sentiment des produits d’intérêt. Dans cet article, nous proposons une nouvelle approche unifiée pour apprendre à classer les produits en fonction des critiques de produits en ligne. Contrairement aux approches existantes, il utilise des techniques d’apprentissage approfondi pour extraire la représentation de révision latente de haut niveau qui contient les informations les plus sémantiques dans le processus d’apprentissage. Pour cette approche, nous étendons le réseau d’attention hiérarchique récemment proposé pour opérer dans le domaine du classement. Ce réseau apprend de manière hiérarchique les représentations optimales des fonctionnalités des produits et leurs avis grâce à l’utilisation d’encodeurs à deux niveaux basés sur l’attention. Pour construire un modèle de classement plus avancé, plusieurs fonctionnalités ont été ajoutées pour donner suffisamment d’informations sur les préférences relatives des utilisateurs, et deux fonctions représentatives de perte de classement, RankNet et ListNet, ont été appliquées. De plus, nous démontrons que ce réseau surpasse les méthodes existantes de prédiction du classement des ventes sur la base des évaluations de produits en ligne.

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https://www.sciencedirect.com/science/article/pii/S1567422319300778

Mots clés : Coopétition, Avis de produits en ligne, Effet plateforme, Vendeurs tiers, Plateforme de vente au détail

Résumé : Les détaillants en ligne ouvrent leurs plateformes pour inviter des vendeurs tiers à vendre directement sur leurs plateformes pour des commissions. Étant donné que la plupart des consommateurs prennent des décisions d’achat en utilisant les avis de produits en ligne comme référence, nous développons un modèle de théorie des jeux pour explorer l’impact des avis sur la décision d’un vendeur tiers de vendre sur une plateforme de vente au détail ouverte et sur le profit du détaillant de la plateforme. Sur la base de deux attributs de produit – qualité et ajustement – nous distinguons deux cas, la qualité domine l’ajustement et l’ajustement domine la qualité. Nous constatons que, dans le cadre d’avis symétriques (c.-à-d. D’avis ne favorisant ni le produit du détaillant ni celui d’un tiers), des avis plus élevés homogénéisent les produits, intensifiant ainsi la concurrence entre le détaillant et le tiers dans les domaines où la qualité domine. Cependant, même dans le cadre d’avis asymétriques, le tiers ne peut être incité à vendre sur la plateforme de vente au détail que si l’on considère la qualité des systèmes de révision du détaillant, ce qui peut entraîner un effet de plateforme sur les avis. L’effet de plateforme renforce l’importance des avis, de sorte qu’il puisse élargir la différenciation des produits créée par les avis asymétriques. En particulier, lorsque l’effet de plateforme et l’asymétrie des avis sont suffisamment élevés, le détaillant peut non seulement inciter le tiers à vendre sur sa plateforme, mais également bénéficier à la fois de la qualité domine l’ajustement et de l’ajustement domine la qualité.

Grandes lignes :

• Dans le cadre d’avis symétriques, des avis plus élevés homogénéisent les produits, intensifiant ainsi la concurrence entre le détaillant et le tiers
• L’effet de plateforme renforce l’importance des avis

La qualité domine l’ajustement et l’ajustement domine la qualité

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Gallo I, Townsend I, Alegre I, (2019), Experiential product framing and its influence on the creation of consumer reviews, Journal of Business Research, 98, p177-p190.

https://www.sciencedirect.com/science/article/abs/pii/S0148296319300074

Mots clés : Marketing expérimental, Encadrement expérentiel, Critiques de produits, Le bouche à oreille

Résumé : Nous examinons comment le cadrage expérientiel, une tactique de marketing de plus en plus populaire, influence le comportement de révision des consommateurs. Le cadrage expérientiel est une stratégie de communication par laquelle les spécialistes du marketing décrivent un produit matériel comme s’il s’agissait d’une expérience, quelque chose que le consommateur vit, plutôt que de se concentrer sur les fonctionnalités et les attributs du produit. Sur la base de travaux antérieurs comparant la relation des consommateurs avec les produits à celle des expériences ainsi que des travaux antérieurs sur le comportement de révision des produits et le marketing expérientiel, nous émettons l’hypothèse que le cadrage expérientiel augmente la probabilité des consommateurs de revoir un produit. En effet, un examen des données ainsi que deux études en laboratoire montrent que le cadrage d’un produit comme une expérience est associé à une augmentation du bouche à oreille. Nos résultats soutiennent également notre processus proposé ; lorsque les produits sont conçus comme des expériences, les consommateurs les perçoivent comme plus personnels et auto-définis; cela augmente alors la probabilité pour le consommateur de s’engager dans l’activité d’auto-démonstration de l’examen des produits.

Grandes lignes :

• Le cadrage expérientiel augmente la probabilité des consommateurs de revoir un produit.
• Le cadrage d’un produit comme une expérience est associé à une augmentation du bouche à oreille
• Lorsque les produits sont conçus comme des expériences, les consommateurs les perçoivent comme plus personnels

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https://www.sciencedirect.com/science/article/abs/pii/S1094996820300785

Mots clés : Examens en ligne, Expertise des sources, Expérience des produits, Certitude des attitudes, Exhaustivité des informations

Résumé : Cette recherche explore les effets de la tentation dans les critiques de produits en ligne sur la certitude de l’attitude du produit des consommateurs et les intentions comportementales. En s’appuyant sur la théorie de la saillance, la théorie de l’attribution et le travail dans la certitude d’attitude, on voit que, lorsque les consommateurs qui ont vu des critiques positives d’un produit sont exposés à une évaluation provisoire, leur certitude et leur volonté d’acheter sont réduites. On prédit également que les réactions des consommateurs diffèrent en fonction de l’expertise de la source d’avis ainsi que de l’expérience produit du consommateur.

Est abordée également la confiance dans l’exhaustivité des informations en tant que mécanisme métacognitif qui explique l’effet de révision provisoire. Plus précisément, on soutient que les consommateurs qui voient un examen provisoire sont sensibilisés aux informations potentiellement manquantes, ce qui réduit leur certitude et leur volonté d’achat.

Les hypothèses sont testées dans une série d’expériences qui démontrent que la timidité réduit la certitude et la volonté d’achat, mais que l’effet est atténué lorsque l’évaluateur est un novice et lorsque le consommateur a un niveau élevé d’expérience du produit.

Grandes lignes :

• Un examen provisoire se traduit par une certitude et des intentions d’achat inférieures à un examen positif, négatif ou mixte
• Le fait que ce soit provisoire se traduit par une diminution de la confiance des consommateurs dans l’exhaustivité des informations.
• L’effet d’un avis provisoire est diminué lorsque l’examinateur provisoire est perçu comme un novice.

Les consommateurs qui ont une faible expertise produit sont davantage influencés par un examen provisoire que ceux qui ont une expertise produit plus élevée

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