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1 – 10 of over 12000Ngoc Minh Nguyen, Hoang Huong Giang, Ngoc Thi Minh Vu and Son Anh Ta
This paper examines the moderating effects of online reviews on the relationship between country image, product image, and purchase intention of products from two developed…
Abstract
Purpose
This paper examines the moderating effects of online reviews on the relationship between country image, product image, and purchase intention of products from two developed countries in Vietnam.
Design/methodology/approach
This current research used a cross-sectional design. Data was collected via questionnaires, and 305 responses were left after refining. The collected data were analyzed using exploratory factor analysis, confirmatory factor analysis, structural equation modeling, and multi-group analysis methods.
Findings
Affective country images do not directly affect purchase intention when online review quality and positivity are high. Cognitive country images still directly affect purchase intention when online review positiveness is low. However, online review quantity does not moderate the effects of country images on product images and purchase intention.
Research limitations/implications
Cognitive country image consistently affects purchase intention through the central route independent of online reviews. In contrast, the affective country image will likely affect purchase intention through the peripheral route when online reviews are insufficient for customers.
Practical implications
Firms can mitigate the adverse effects of country image, especially cognitive country image, in foreign markets by improving online review quality and positiveness.
Originality/value
Our study extended existing literature by providing a better understanding of the nature of country image and the roles of country image dimensions in shaping product image and purchase intention in the context of the increasing popularity of online reviews.
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Yong Liu, Chang-Xue Lin and Gang Zhao
The paper attempts to discuss the optimal pricing decisions under the decentralized and centralized decision and analyze the influence of online reviews and in-sale service on…
Abstract
Purpose
The paper attempts to discuss the optimal pricing decisions under the decentralized and centralized decision and analyze the influence of online reviews and in-sale service on dual-channel supply chain. Finally, the authors design a two-part tariff coordination mechanism.
Design/methodology/approach
To deal with this pricing conflict problems of dual-channel supply chain consisting of dominant manufacturer and a retailer, considering the fact that online reviews and in-sale service are important factors on consumers’ purchase decisions, the authors establish some basic models and exploit them to discuss the optimal pricing decisions under the decentralized and centralized decision and analyze the influence of online reviews and in-sale service on dual-channel supply chain. Finally, the authors design a profit-sharing coordination mechanism.
Findings
The results show that the optimal online direct selling price is positively correlated with product perceived quality obtained from online reviews and negatively correlated with the in-sale service. The traditional retail price is positively correlated with the in-sale service and weakly correlated with online reviews. For the manufacturer and retailer, whether decentralized decision or coordination contract, their profits increase with the increase of the in-sale service in a certain range and quality perceived from spontaneous online reviews. Online reviews and in-sale service are important factors on consumers’ purchase decisions. Positive in-sale services and online reviews can provide consumers with a better shopping experience, thereby promoting their enthusiasm for shopping and improving their quality of life. The two-part tariff coordination mechanism improves the profits of the manufacturer and the traditional retailer, respectively, through the transfer fee.
Originality/value
The proposed approach can well analyze the channel conflicts and pricing problems between retailers and manufacturers with respect to product offline price and online price. The analysis and results can inform decision-making for manufacturers and retailers.
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Abstract
Purpose
Drawing on the pleasure-arousal-dominance (PAD) emotion model, the emotional states of consumers embedded in online reviews can be described through three dimensions, that is, pleasure, arousal and dominance, rather than only the one-dimensional positive and negative polarity, as in previous studies. Therefore, this study aims to explore the effect of online review emotion on perceived review helpfulness based on these three basic emotional dimensions.
Design/methodology/approach
A lexicon-based method is developed to analyze PAD emotions of online reviews from JD.com. The zero-inflated negative binomial regression is utilized to empirically validate the study hypothesis. The authors examine the influence of pleasure, arousal, dominance, emotion diversity and emotion deviation on review helpfulness, as well as the moderating effect of product type on the relationship between all independent variables and online review helpfulness.
Findings
The study results show that the pleasure emotion impairs the helpfulness of online reviews, while the arousal and dominance emotions have a positive impact. Moreover, the authors find that compared with search products, the effects of pleasure, arousal and dominance on perceived helpfulness are strengthened for experience products. However, the emotional diversity and emotional deviation have opposite effects on the helpfulness of search products and experience products. Additionally, the results show that dominance emotion plays a more important role in the interaction effect.
Originality/value
The empirical findings confirm the applicability of PAD in the online review context and extend the existing knowledge of the influence of review emotion on helpfulness. A feasible scheme for extracting PAD variables from Chinese text is developed. The study findings also have significant implications for reviewers, merchants and platform managers of e-commerce websites.
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Tri Lam, Jon Heales and Nicole Hartley
The continuing development of digital technologies creates expanding opportunities for information transparency. Consumers use social media to provide online reviews that are…
Abstract
Purpose
The continuing development of digital technologies creates expanding opportunities for information transparency. Consumers use social media to provide online reviews that are focused on changing levels of consumer trust. This study examines the effect of perceived risk that prompts consumers to search for online reviews in the context of food safety.
Design/methodology/approach
Commitment-trust theory forms the theoretical lens to model changes in consumer trust resulting from online reviews. Consumer-based questionnaire surveys collected data to test the structural model, using structural equation modelling (SEM).
Findings
The findings show when consumers perceive high levels of risk, they use social media to obtain additional product-related information. The objective, unanimous, evidential and noticeable online reviews are perceived as informative to consumers. Perceived informativeness of positive online reviews is found to increase consumers trust and, in turn, increase their purchase intentions.
Originality/value
The findings contribute to the knowledge of online review-based trust literature and provide far-reaching implications for information system (IS)-practitioners in business.
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Kuoyi Lin, Xiaoyang Kan and Meilian Liu
This study develops and validates an innovative approach for extracting knowledge from online user reviews by integrating textual content and emojis. Recognizing the pivotal role…
Abstract
Purpose
This study develops and validates an innovative approach for extracting knowledge from online user reviews by integrating textual content and emojis. Recognizing the pivotal role emojis play in enhancing the expressiveness and emotional depth of digital communication, this study aims to address the significant gap in existing sentiment analysis models, which have largely overlooked the contribution of emojis in interpreting user preferences and sentiments. By constructing a comprehensive model that synergizes emotional and semantic information conveyed through emojis and text, this study seeks to provide a more nuanced understanding of user preferences, thereby enhancing the accuracy and depth of knowledge extraction from online reviews. The goal is to offer a robust framework that enables more effective and empathetic engagement with user-generated content on digital platforms, paving the way for improved service delivery, product development and customer satisfaction through informed insights into consumer behavior and sentiments.
Design/methodology/approach
This study uses a structured methodology to integrate and analyze text and emojis from online reviews for effective knowledge extraction, focusing on user preferences and sentiments. This methodology consists of four key stages. First, this study leverages high-frequency noun analysis to identify and extract product attributes mentioned in online user reviews. By focusing on nouns that appear frequently, the authors can systematically discern the primary features or aspects of products that users discuss, thereby providing a foundation for a more detailed sentiment and preference analysis. Second, a foundational sentiment dictionary is established that incorporates sentiment-bearing words, intensifiers and negation terms to analyze the textual part of the reviews. This dictionary is used to assign sentiment scores to phrases and sentences within reviews, allowing the quantification of textual sentiments based on the presence and combination of these predefined lexical items. Third, an emoticon sentiment dictionary is developed to address the emotional content conveyed through emojis. This dictionary categorizes emojis based on their associated sentiments, thus enabling the quantification of emotional expressions in reviews. The sentiment scores derived from the emojis are then integrated with those from the textual analysis. This integration considers the weights of text- and emoji-based emotions to compute a comprehensive attribute sentiment score that reflects a nuanced understanding of user sentiments and preferences. Finally, the authors conduct an empirical study to validate the effectiveness of the proposed methodology in mining user preferences from online reviews by applying the approach to a data set of online reviews and evaluating its ability to accurately identify product attributes and user sentiments. The validation process assessed the reliability and accuracy of the methodology in extracting meaningful insights from the complex interplay between text and emojis. This study offers a holistic and nuanced framework for knowledge extraction from online reviews, capturing both explicit and implicit sentiments expressed by users through text and emojis. By integrating these elements, this study seeks to provide a comprehensive understanding of user preferences, contributing to improved consumer insight and strategic decision-making for businesses and researchers.
Findings
The application of the proposed methodology for integrating emojis with text in online reviews yields significant findings that underscore the feasibility and value of extracting realistic user knowledge to gain insights from user-generated content. The analysis successfully captured consumer preferences, which are instrumental in informing service decisions and driving innovation. This achievement is largely attributed to the development and utilization of a comprehensive emotion-sentiment dictionary tailored to interpret the complex interplay between textual and emoji-based expressions in online reviews. By implementing a sentiment calculation model that intricately combines textual sentiment analysis with emoji sentiment analysis, this study was able to accurately determine the final attribute emotion for various product features discussed in the reviews. This model effectively characterized the emotional knowledge of online users and provided a nuanced understanding of their sentiments and preferences. The emotional knowledge extracted is not only quantifiable but also rich in context, offering deeper insights into consumer behavior and attitudes. Furthermore, a case analysis is conducted to rigorously test the validity of the proposed model in a real-world scenario. This practical examination revealed that the model is not only capable of accurately extracting and analyzing user preferences but is also adaptable to different contexts and product categories. The case analysis highlights the robustness and flexibility of the model, demonstrating its potential to enhance the precision of knowledge extraction processes significantly. Overall, the results confirm the effectiveness of the proposed approach in integrating text and emojis for comprehensive knowledge extraction from online reviews. The findings validate the model’s capability to offer actionable insights into consumer preferences, thereby supporting more informed and strategic decision-making by businesses. This study contributes to the broader field of sentiment analysis by showcasing the untapped potential of emojis as valuable indicators of user sentiments, opening new avenues for research and applications in digital marketing and consumer behavior analysis.
Originality/value
This study introduces a pioneering approach to extract knowledge from Web user interactions, notably through the integration of online reviews that incorporate both textual content and emoticons. This innovative methodology stands out because it holistically considers the dual channels of communication, text and emojis, to comprehensively mine Web user preferences. The key contribution of this study lies in its novel insights into the extraction of consumer preferences, advancing beyond traditional text-based analysis to embrace nuanced expressions conveyed through emoticons. The originality of this study is underpinned by its acknowledgment of emoticons as a significant and untapped source of sentiment and preference indicators in online reviews. By effectively merging emoticon analysis and emoji emotion scoring with textual sentiment analysis, this study enriches the understanding of Web user preferences and enhances the accuracy and depth of consumer preference insights. This dual-analysis approach represents a significant leap forward in sentiment analysis, setting a new standard for how digital communication can be leveraged to derive meaningful insights into consumer behavior. Furthermore, the results have practical implications to businesses and marketers. The insights gained from this integrated analytical approach offer a more granular and emotionally nuanced view of customer feedback, which can inform more effective marketing strategies, product development and customer service practices. By pioneering this comprehensive method of knowledge extraction, this study paves the way for future research and practice to interpret and respond more accurately to the complex landscape of online consumer expressions. This study’s originality and value lie in its innovative method of capturing and analyzing the rich tapestry of Web user communication, offering a ground-breaking perspective on consumer preference extraction that promises to enhance both academic research and practical applications in the digital era.
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Huan-huan Zhao, Yong Liu and Wen-wen Ren
We attempt to analyze the impact of retailer’s rebate strategy on consumer reviews and retailer’s profits.
Abstract
Purpose
We attempt to analyze the impact of retailer’s rebate strategy on consumer reviews and retailer’s profits.
Design/methodology/approach
Retailers' rebates have a chance to affect sales and their profits by encouraging customers to submit product reviews. To investigate the impact of retailer’s rebate strategy on consumer reviews and retailer’s profits, we describe the consumer’s utility function and the number of consumer-written reviews by introducing the concepts of product demand mismatch and consumer review effort, then develop a two-stage model of the retailer’s rebate strategy and examine how the retailer’s rebate affects online reviews, the consumer’s perceived utility and the retailer’s profit. Finally, a number case verifies the validity and rationality of the proposed model.
Findings
The results show that the rebate strategy can effectively reduce consumer dissatisfaction caused by excessive product demand mismatch, improve the consumer utility, prompt more positive comments, and thus increase product sales.
Originality/value
In this paper, we focus on the impact of retailers' rebate strategy on consumer purchase decisions. The research can accurately reflect the influence of online reviews on consumers and retailers, assisting merchants in making the best selections. The analysis indicates that the retailer’s rebate strategy can have a direct impact on consumers' evaluation choices and product sales.
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Xiao Peng, Hessam Vali, Xixian Peng, Jingjun (David) Xu and Mehmet Bayram Yildirim
The study examines the potential moderating effects of repeating purchase cues and product knowledge on the relationship between the varying consistency of the review set and…
Abstract
Purpose
The study examines the potential moderating effects of repeating purchase cues and product knowledge on the relationship between the varying consistency of the review set and causal attribution. This study also investigates how causal attribution correlates with the perceived misleadingness of the review set.
Design/methodology/approach
A scenario-based experiment was conducted with 170 participants to explore the relationship between the consistency of the review set and causal attribution and how repeating purchase cues and product knowledge moderates this relationship.
Findings
Findings suggest that inconsistent review sets lead to more product (vs reviewer) attribution than consistent review sets. The repeating purchase cues mitigate the negative relationship between the consistency of the review set and product attribution, whereas product knowledge mitigates the positive relationship between the consistency of the review set and reviewer attribution. Furthermore, the results indicate that high product attribution and low reviewer attribution are associated with low perceived misleadingness.
Originality/value
This study is novel because it examines the moderating effects of repeating purchase cues and product knowledge on the relationship between the consistency of the review set and causal attribution. It adds to the literature by shedding light on the causal attribution process underlying the formation of perceived misleadingness of online reviews. The findings of this study provide valuable insights for managers on how to enhance the positive effects of consistent review sets and mitigate the negative effects of inconsistent review sets.
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Xiaoguang Wang, Yue Cheng, Tao Lv and Rongjiang Cai
The authors hope to filter valuable information from online reviews, obtain objective and accurate information about the demands of auto consumers and help auto companies develop…
Abstract
Purpose
The authors hope to filter valuable information from online reviews, obtain objective and accurate information about the demands of auto consumers and help auto companies develop more reasonable production and marketing strategies for healthy and sustainable development. This paper aims to discuss the aforementioned objectives.
Design/methodology/approach
The authors collected review data from online automotive forums and generated a corpus after pre-processing. Then, the authors extracted consumer demands and topics using the LDA model. Finally, the authors used a trained Word2vec tool to extend the consumer demand topics.
Findings
Different types of vehicle consumers have the same demands, such as “Space,” “Power Performance,” and “Brand Comparison,” and distinct demands, such as “Appearance,” “Safety,” “Service,” and “New Energy Features”; consumers who buy new energy vehicles are still accustomed to comparing with the brands or models of fuel vehicles; new energy vehicles consumers pay more attention to services and service quality during the purchasing and using process.
Research limitations/implications
The development time of new energy vehicles is relatively short, with some models being available for only one year or even six months. The smaller amount of available data may impact the applicability of topic models. The sample size, especially for new energy vehicles, needs to be increased to improve the general applicability of topic models further.
Practical implications
First, this measure helps online review websites improve their existing review publication mechanisms, enhance the overall quality of online review content, increase user traffic and promote the healthy development of online review websites. Second, this allows for timely adjustments in future product production and sales plans and further enhances automotive companies' ability to leverage online reviews for Internet marketing.
Originality/value
The authors have improved the accuracy and stability of the fused topic model, providing a scientific and efficient research tool for multi-dimensional topic mining of online reviews. With the help of research results, consumers can more easily understand the discussion topics and thus filter out valuable reference information. As a result, automotive companies may gain information about consumer demands and product quality feedback and thus quickly adjust production and marketing strategies to increase sales and market share.
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Madhuri Prabhala and Indranil Bose
While there has been extensive research on understanding the effects of online reviews on product sales, there is not enough investigation of the inter-relationships between…
Abstract
Purpose
While there has been extensive research on understanding the effects of online reviews on product sales, there is not enough investigation of the inter-relationships between online reviews, online search and product sales. The study attempts to address this gap in the context of the Indian car market.
Design/methodology/approach
The research uses text mining and considers six important review features volume, valence, length, deviation of valence, sentiment and readability within the heuristic and systematic model of information processing. Panel data regression is used along with mediation analysis to study the inter-relationships between features of reviews, online search and sales.
Findings
The study finds that numerical heuristic features significantly affect sales and online search, numerical systematic feature affects sales and the textual heuristic and systematic features do not affect sales or online search in the Indian car market. Further, online search mediates the association between features of reviews and sales of cars.
Research limitations/implications
Although only car sales data from India is considered in this research, similar relationships between review features, online search and sales could exist for the car market of other countries as well.
Originality/value
This research uncovers the unique role of online search as a mediator between review features and sales, whereas prior literature has considered review features and online search as independent variables that affect sales.
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Zhangxiang Zhu, Yaxin Zhao and Jing Wang
This study aims to explore the relationship between the content characteristics of destination online reviews and travel intention under three individual circumstances: temporal…
Abstract
Purpose
This study aims to explore the relationship between the content characteristics of destination online reviews and travel intention under three individual circumstances: temporal distance, social distance and experiential distance.
Design/methodology/approach
Based on construal-level theory (CLT), this study divides online travel reviews into concrete and abstract reviews. Three experiments were conducted to test the moderating effects of temporal distance, social distance and experiential distance on the influence of review content characteristics on tourists' travel intentions.
Findings
The results show that abstract reviews would lead to higher travel intentions than concrete reviews. Furthermore, tourists' travel intentions differed depending on social distance and were significantly affected by reviews posted by reviewers similar to review recipients. In addition, the study contributes by discovering that the moderating effects of temporal distance, social distance and experiential distance were not significant, which differs from most of the previous research conclusions.
Originality/value
This study focused on review content characteristics, which provided a novel perspective for constructing online travel reviews. Furthermore, this research defined the concept of experiential distance in the context of online travel and expanded the research on psychological distance.
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