Using supplementary reviews to improve customer requirement identification and product design development

Zhang, Min; Sun, Lin; Li, Yunwei; Wang, G. Alan; He, Zhen

doi:10.1016/j.jmse.2023.03.001

Cited by 5 publications

(9 citation statements)

References 49 publications

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“…This study combines both cognitive and emotional perspectives to suggest a dual-path mechanism for consumers to process shifts in the emotional valence of composite reviews. This mechanism involves the dual mediation of perceived empathy and motivated suspicion, addressing the previous limitation of viewing consumers' interpretation of composite reviews solely from a cognitive perspective (Wang et al, 2021;Chen et al, 2023;Zhang et al, 2023). At the same time, this comprehensive perspective broadens the use of EASI theory to study composite reviews, and provides a more thorough explanation of the inherent processing of consumers' shifts in the emotional valence of composite reviews.…”

Section: Theoretical Contributionsmentioning

confidence: 97%

“…Most studies have focused on the effects of these additional reviews within composite reviews and their interaction with initial reviews. There is a consensus that additional reviews are perceived as more beneficial and elicit more positive consumer attitudes than initial reviews do (Shen et al, 2015;Chen et al, 2019;Zhang et al, 2023). Some research has investigated the consistency and ambivalence between initial and additional reviews to understand their interplay and effects on purchase intentions (Zhou and Li, 2017;Wang et al, 2021;Chen et al, 2023).…”

Section: Theoretical Background 21 Changes In Emotional Valence Of On...mentioning

confidence: 99%

“…When emotions gradually escalate, the negative emotions in additional reviews for positive-negative composite reviews have a stronger effect. Consumers perceive these negative emotions as progressively intensifying, leading them to identify the product as potentially defective after use (Zhang et al, 2023). They are also more likely to understand the reviewer's negative emotions resulting from a disappointing purchase, thereby enhancing empathy.…”

Section: Moderating Effects Of Dynamic Changes In Emotional Intensitymentioning

confidence: 99%

“…, 2015; Wang et al. , 2020; Zhang et al. , 2023) and the interaction between the initial review and the additional review (Zhou and Li, 2017; Wang et al.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies on the impact of changes in the emotional valence of online reviews on consumer adoption intentions have mostly focused on the impact of the order of presentation of the emotional valence of a single review on consumer adoption intentions (Kim and Lee, 2015;Ruiz-Mafe et al, 2018;Bignea et al, 2022). Meanwhile, there has been a great deal of discussion surrounding the emergence of composite reviews, with particular attention paid to two main points: the impact of additional reviews on their own (Shen et al, 2015;Wang et al, 2020;Zhang et al, 2023) and the interaction between the initial review and the additional review (Zhou and Li, 2017;Wang et al, 2021;Chen et al, 2023). While few research have looked at how consumers' purchase intentions are affected by sequential changes in the emotional valence of composite reviews, and it is difficult to distinguish between the different effects of two changes in emotional valence due to the primacy and proximity effects (Chen et al, 2019).…”

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confidence: 99%

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The effect of emotional changes in composite reviews on consumers' information adoption from a dual perspective

Zhang,

Ma,

2024

IMDS

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PurposeWith the appearance of additional review functionality on e-commerce platforms emotional changes in composite reviews have become more diverse. How consumers process the emotional changes in composite reviews is an important concern for companies. This study investigates the impact of explores how changes in the emotional valence and emotional intensity of composite reviews on consumers' information adoption.Design/methodology/approachBased on emotion as social information theory, this study constructs a double mediation model of how the change in emotional valence of composite reviews affects consumers' adoption intention and examines the moderating effect of the dynamic change of emotional intensity. One field and three online experiments were conducted to test the proposed hypotheses.FindingsConsumers were more likely to adopt positive–negative composite reviews than negative–positive composite reviews. Compared to negative–positive composite reviews, positive–negative composite reviews led to higher perceived empathy and lower motivational suspicion, which, in turn, led to higher information adoption. Moreover, dynamic changes in emotional intensity played a moderating role in this effect. Interestingly, the amount of attribute difference changed the differences in perceived empathy and motivated consumer suspicion generated by the composite review when considering the reviewer’s attribute difference description.Originality/valueThe findings have important theoretical contributions that deepen business and consumer understanding of the impact of composite reviews and have practical implications for improving the management of composite reviews by businesses.

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Section: Theoretical Contributionsmentioning

confidence: 97%

Section: Theoretical Background 21 Changes In Emotional Valence Of On...mentioning

confidence: 99%

Section: Moderating Effects Of Dynamic Changes In Emotional Intensitymentioning

confidence: 99%

“…, 2015; Wang et al. , 2020; Zhang et al. , 2023) and the interaction between the initial review and the additional review (Zhou and Li, 2017; Wang et al.…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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The effect of emotional changes in composite reviews on consumers' information adoption from a dual perspective

Zhang,

Ma,

2024

IMDS

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Kano Model for Enhancing Business Strategy Through Customer Preference Analysis: A Case Study of Packaged Mineral Water Bottles

Shravan,

2024

IEEE Eng. Manag. Rev.

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Automatic Suggestion Detection from Online User Reviews with Information Retrieval and Deep Learning

Hadizadeh,

Nazari,

Mansoorizadeh

2023

Preprint

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After the outbreak of COVID-19, the use of the web platform as a means of communication between users and decision-making in online shopping has been accepted in e-commerce. Comments of users express substantially positive and negative emotions about a particular item, which offers suggestions for the improvement of products and organizations. Therefore, mining opinions to extract suggestions can increase the utility of companies and organizations. A good deal of research on suggestion mining employs rule-based approaches and statistical classifiers using manually identified features, while recently, several researchers have considered solutions based on deep learning tools and techniques. The purpose of this study is to use information retrieval techniques for the automatic detection of suggestions. Therefore, distance measurement approaches, multilayer perceptron neural networks, support vector machines, regression logistics, convolutional neural networks with TF-IDF, BOW, and Word2Vec vectors, and keyword extraction were adopted. The proposed approach is presented on the SemEval2019 dataset for extracting suggestions from the text of online user reviews. The results show that the F1Score has improved, as compared to the previous work, and has reached 0.87. The experiments indicate that information retrieval-based approaches tend to be promising for this task.

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Using supplementary reviews to improve customer requirement identification and product design development

Cited by 5 publications

References 49 publications

The effect of emotional changes in composite reviews on consumers' information adoption from a dual perspective

The effect of emotional changes in composite reviews on consumers' information adoption from a dual perspective

Kano Model for Enhancing Business Strategy Through Customer Preference Analysis: A Case Study of Packaged Mineral Water Bottles

Automatic Suggestion Detection from Online User Reviews with Information Retrieval and Deep Learning

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