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Article
Publication date: 23 November 2021

Zhangxiang Zhu, Jiapei Liu and Wei Dong

The conclusions of studies on the factors correlated with the perceived usefulness of online reviews are inconsistent due to differences in research perspectives, research…

Abstract

Purpose

The conclusions of studies on the factors correlated with the perceived usefulness of online reviews are inconsistent due to differences in research perspectives, research objects, research methods and data types. This study conducted a meta-analysis to verify a proposed model of perceived usefulness to obtain general conclusions.

Design/methodology/approach

A meta-analysis was conducted to study the factors correlated with the perceived usefulness of online reviews based on 51 studies.

Findings

The results indicate that, with the exception of negative reviews, the order of relevance for the perceived usefulness of online reviews is as follows: the trust tendency of review readers, review replies, review depth, review pictures, reviewer trustworthiness, positive reviews, reviewer expertise, review time and reviewer information disclosure. Perceived usefulness was significantly positively correlated with purchase intention. Review time, positive reviews and negative reviews were also more significantly correlated with perceived usefulness for search products than for experiential products. Review depth, reviewer trustworthiness, reviewer expertise and purchase intention had greater positive correlations with perceived usefulness for experiential products than for search products.

Originality/value

This study proposes an extended information adoption model based on argument quality and source credibility. The model includes personal factors such as the trust tendency of review readers, constructs a theoretical model of the factors correlated with the perceived usefulness of online reviews and considers the moderating effects of product type.

Details

Aslib Journal of Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-3806

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Article
Publication date: 23 November 2021

Omer Cem Kutlubay, Mesut Cicek and Serdar Yayla

The ongoing COVID-19 pandemic has led to drastic changes in the lives of customers. Social isolation, financial difficulties, fear of being infected and many other factors…

Abstract

Purpose

The ongoing COVID-19 pandemic has led to drastic changes in the lives of customers. Social isolation, financial difficulties, fear of being infected and many other factors have caused the psychological well-being of customers to deteriorate. By taking up the role of online reviews in the regulation of consumers’ moods, this study aims to examine the changes that have occurred in online product ratings, as well as the negative tone and word counts of product reviews during the COVID-19 pandemic.

Design/methodology/approach

This study examines the online reviews of 321 products in the pre-COVID, immediate COVID and extended COVID periods. This paper compares the changes that have taken place in product evaluations via various analysis of variance analyses. The authors also test the effect of COVID-related deaths on product evaluations via regression analyses.

Findings

The results indicate that online product ratings decreased sharply just after the outbreak of COVID-19. The study also found that the tone of reviews was found to be more negative and the length of reviews appeared to be longer in comparison to the pre-COVID-19 period. The results also revealed that the product type (experience vs search) moderated the effect of the pandemic in online reviews and the impact of COVID-19 on online product reviews diminished in the later stages of the ongoing pandemic.

Practical implications

Managers should be aware of the detrimental impact of pandemics on online product reviews and be more responsive to customer problems during the early stages of pandemics.

Originality/value

To the best of the authors’ knowledge, this is the first study that analyzes the effects of a pandemic on online product ratings and review content. As such, this study offers a timely contribution to the marketing literature.

Details

Journal of Product & Brand Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1061-0421

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Article
Publication date: 5 October 2021

Chenglei Qin, Chengzhi Zhang and Yi Bu

To better understand the online reviews and help potential consumers, businessmen and product manufacturers effectively obtain users’ evaluation on product aspects, this…

Abstract

Purpose

To better understand the online reviews and help potential consumers, businessmen and product manufacturers effectively obtain users’ evaluation on product aspects, this paper aims to explore the distribution regularities of users’ attention and sentiment on product aspects from the temporal perspective of online reviews.

Design/methodology/approach

Temporal characteristics of online reviews (purchase time, review time and time intervals between purchase time and review time), similar attributes clustering and attribute-level sentiment computing technologies are used based on more than 340k smartphone reviews of three products from JD.COM (a famous online shopping platform in China) to explore the distribution regularities of users’ attention and sentiment on product aspects in this paper.

Findings

The empirical results show that a power-law distribution can fit users’ attention on product aspects, and the reviews posted in short time intervals contain more product aspects. Besides, the results show that the values of users’ sentiment on product aspects are significantly higher/lower in short time intervals which contribute to judging the advantages and weaknesses of a product.

Research limitations/implications

This paper cannot acquire online reviews for more products with temporal characteristics to verify the findings because of the restriction on reviews crawling by the shopping platforms.

Originality/value

This work reveals the distribution regularities of users’ attention and sentiment on product aspects, which is of great significance in assisting decision-making, optimizing review presentation and improving the shopping experience.

Details

The Electronic Library , vol. 39 no. 4
Type: Research Article
ISSN: 0264-0473

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Article
Publication date: 28 July 2021

Yong-Hai Li, Jin Zheng, Shan-Tao Yue and Zhi-Ping Fan

In recent years, electronic word-of-mouth (e-WOM) concerning travel products reflected in online review information has become an important reference for tourists to make…

Abstract

Purpose

In recent years, electronic word-of-mouth (e-WOM) concerning travel products reflected in online review information has become an important reference for tourists to make their product purchase decisions, while for travel service providers (TSPs), monitoring and improving the e-WOM of their travel products is always an important task. Therefore, based on the online review information, how to capture e-WOM of travel products and find out specific ways to improve the e-WOM is a noteworthy research problem. The purpose of this paper is to develop a method for capturing and analyzing e-WOM toward travel products based on sentiment analysis and stochastic dominance.

Design/methodology/approach

Specifically, online review information of travel products is first crawled and preprocessed. Second, sentiment strengths of online review information toward travel products concerning each feature are judged. Then, the matrix of structured online review information toward travel products is formed. Further, the matrix of e-WOM comparisons between any two travel products is constructed, and e-WOM ranking concerning each travel product is determined. Finally, trade-off chart models are constructed to conduct the e-WOM improvement analyses concerning the travel products.

Findings

An empirical study based on the online review information toward six travel products crawled from the Tuniu.com website is given to illustrate the use of the proposed method.

Originality/value

The proposed method can not only realize the real-time e-WOM monitoring to travel products but also be useful for TSPs to improve the e-WOM of their travel products.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 22 January 2021

Xiaofei Li, Baolong Ma and Hongrui Chu

The value of online reviews has been well documented by academics and practitioners. However, to maximise the benefits of consumer reviews, online sellers must avoid the…

Abstract

Purpose

The value of online reviews has been well documented by academics and practitioners. However, to maximise the benefits of consumer reviews, online sellers must avoid the negative consequences associated with customer feedback, such as reputation loss, or product returns after purchase. In developing a better understanding of the relationships between online reviews and their potential for negative impacts, this research aims to explore product returns. Through a quantitative model, this research demonstrates why online reviews can result in product return behaviours.

Design/methodology/approach

The hypotheses were tested via two studies. In Study 1, the authors examine the direct effects of review valence and review volume on product returns by analysing secondary data on 4,995 stores on China's Taobao.com. Study 2 further extends and validates the findings of Study 1 with a survey sample of 795 participants across several online shopping platforms. This analysis examines the mechanics and conditions that influence the relationships between online reviews and product returns through partial least squares-structural equation modelling (PLS-SEM).

Findings

The results show that both review valence (i.e. average star ratings) and the number of reviews can increase the probability of product returns due to the high expectations that result from positive online reviews. Further, the effect of review valence on product returns is stronger for first-time purchasers at a store. In terms of mitigation, the analysis shows that bilateral communications between sellers and buyers can temper the unrealistic expectations set by positive reviews, leading to fewer product returns.

Originality/value

This research adds to the literature on online reviews by exploring the negative consequences of online reviews and the role they play in online purchasing decisions. The findings also provide direct evidence as to why online reviews can result in more product returns, adding clarity to extant research which contains conflicting conclusions as to how online reviews affect product return behaviours.

Details

Asia Pacific Journal of Marketing and Logistics, vol. 33 no. 8
Type: Research Article
ISSN: 1355-5855

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Article
Publication date: 31 March 2021

Li-Chun Hsu

This study developed a new interpretation of the attitude contagion theory, with the information adoption model (IAM) as the theoretical basis. A review of electronic…

Abstract

Purpose

This study developed a new interpretation of the attitude contagion theory, with the information adoption model (IAM) as the theoretical basis. A review of electronic word-of-mouth studies was conducted by using informational and individual determinants to develop an integrated empirical model that identified the antecedents and consequences of consumer attitude toward online reviews.

Design/methodology/approach

This study recruited 750 members of Facebook beauty fan pages in Taiwan and used the structural equation model to test research hypotheses.

Findings

Results revealed that perceived “ electronic word-of mouth (eWOM) credibility of online reviews” and “product involvement” could be used to explain the effects of attitude toward online reviews. Regarding the attitude contagion effect, the effect of “attitude toward online review” on both “attitude toward a product” and “attitude toward a brand” is stronger than that on “eWOM adoption.”

Originality/value

This paper provides valuable insights into the antecedents, consequences and mediating mechanisms that determine consumer attitude toward online reviews.

Details

Journal of Enterprise Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0398

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Article
Publication date: 2 April 2021

Huiliang Zhao, Qin Yang and Zhenghong Liu

The customer enables online reviews, discusses product features and enhances the user's experiences in online activities. Users generated product innovation and product

Abstract

Purpose

The customer enables online reviews, discusses product features and enhances the user's experiences in online activities. Users generated product innovation and product reviews effect as market competition. This research study explains deep learning, online reviews and product innovation empirical evidence used by mobile apps.

Design/methodology/approach

Online reviews and product innovation are very important for every organization and firms to achieve a competitive advantage in a large business environment. When the authors see past traditional history, customers are not involved in product creating and innovating processes. Due to new technology changes, online systems and web 2.0 increase this ability.

Findings

For this research purpose, the authors use different analytical software to measure the impact among variables. This study is established on primary data; this study collected data from online customers and its users. For data collection, the authors use some questionnaires, and these questions are filled from 200 respondents.

Research limitations/implications

This research study used data from the Google app store – Google product selling application – and gathered customers' online reviews. Research found that customers' online reviews and deep learning positively and significantly influence product innovation through networking technology. This research-based online mobile application and its research reviews found that organizations convert their own business online and effectively and efficiently enhance creditability.

Originality/value

This research study used data from the Google app store Google product selling application and gathered customers' online reviews. Research founded that customers' online reviews and deep learning are positively and significantly influence product innovation through networking technology. This research-based online mobile application and its research reviews found that organizations convert their own business online and effectively and efficiently enhance creditability.

Details

Business Process Management Journal, vol. 27 no. 6
Type: Research Article
ISSN: 1463-7154

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Article
Publication date: 24 July 2020

Elika Kordrostami, Yuping Liu-Thompkins and Vahid Rahmani

Valence and volume of online reviews are generally considered to influence sales positively. However, existing findings regarding the relative influence of these two…

Abstract

Purpose

Valence and volume of online reviews are generally considered to influence sales positively. However, existing findings regarding the relative influence of these two components have been inconclusive. This paper aims to explain some of these inconsistencies by examining the moderating role of regulatory focus (both as a chronic disposition and as a situational focus induced by the product category) in the relationship between online review volume/valence and consumers purchase decisions.

Design/methodology/approach

Two studies were conducted. Study 1 used a 2 (Volume: high/ low) * 3 (Valence: high/medium/low) within-subject experimental design. Study 2 analyzed real-world data from Amazon.com. Logistic and panel regression analyses were used to test the research hypotheses.

Findings

The studies confirmed the hypothesized effect of regulatory focus on online review valence and volume effects. Specifically, Study 1 showed that online review valence was more impactful for consumers with a promotion focus than for consumers with a prevention focus. The opposite was true for online review volume effects, where consumers with a prevention focus were influenced more by volume in their decision-making compared to consumers with a promotion focus. Study 2 showed that the pattern of results we found in Study 1 also applied to situational regulatory focus induced by the product category. The effect of review volume on sales rank was stronger for prevention-oriented products, whereas the effect of valence was stronger for promotion-oriented products.

Research limitations/implications

In Study 1, one product category was involved in the study (Digital camera). Involving more different product categories will add reliability to the results of current research. Also, it can offer external validity to current research results. In Study 2, there was no exact measurement for sales, as Amazon.com does not share that kind of information. Instead, Sales Rank was used as a proxy variable. Future research could look into the websites that offer access to the exact sales information.

Practical implications

The current research findings suggest the need for companies to adapt their consumer review management strategy to the regulatory orientation of their target market and products. When a promotion-focused mindset is targeted, strategies for increasing the favorability of product reviews should be used, in contrast, tactics for increasing the quantity of reviews may be more suitable when a prevention-focused mindset is involved.

Originality/value

To the best of the authors' knowledge, this research is the first to investigate the interaction between regulatory focus of consumers and products and online review components.

Details

European Journal of Marketing, vol. 55 no. 1
Type: Research Article
ISSN: 0309-0566

Keywords

Open Access
Article
Publication date: 21 December 2020

R. Venkatesakumar, Sudhakar Vijayakumar, S. Riasudeen, S. Madhavan and B. Rajeswari

The star rating summarises the review content and conveys the message faster than other review components. Star ratings influence helpfulness of the reviews, and extreme…

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Abstract

Purpose

The star rating summarises the review content and conveys the message faster than other review components. Star ratings influence helpfulness of the reviews, and extreme reviews are considered as less helpful in the decision process. However, literature has rarely addressed variations in star ratings across product categories and variations between two online retailers. In this paper, the authors have compared the distribution of star ratings across 11 products and among the retailers.

Design/methodology/approach

Online reviews for 11 product categories have collected, and the authors compared the distribution of star ratings across 11 products and retailers. Correspondence analysis has been applied to show the association between star ratings and product categories for the e-retail firms.

Findings

The Amazon site contains proportionately more number of 1-star rated reviews than Flipkart. In Amazon reviews, few product categories are closely associated with 1-star and 2-star reviews, whereas no product categories are closely associated with 1-star and 2-star reviews in Flipkart reviews. The results indicate two distinct communication strategies followed by the firms in managing online consumer reviews.

Research limitations/implications

The authors did not analyse data across demographic details because of access restriction policies of the websites.

Practical implications

Understanding the distribution of review characteristics will improve the consumer’s decision-making ability and using online review content judiciously.

Social implications

This study’s results show significant insights on online retailing by providing cues in using shopping sites and online review characteristics of two prominent retailers.

Originality/value

This paper has brought out a distinct distribution pattern of online review between Amazon and Flipkart. Amazon allows a higher degree of negative contents, whereas Flipkart allows more number of positive reviews.

Details

Vilakshan - XIMB Journal of Management, vol. 18 no. 2
Type: Research Article
ISSN: 0973-1954

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Article
Publication date: 30 December 2020

Ming-Yang Li, Xiao-Jie Zhao, Lei Zhang, Xin Ye and Bo Li

In recent years, the updating speed of products has been significantly accelerated, which not only provides diversified styles for consumers to select from but also makes…

Abstract

Purpose

In recent years, the updating speed of products has been significantly accelerated, which not only provides diversified styles for consumers to select from but also makes consumers face selection problems sometimes. In addition, a large number of online reviews for products emerge on many e-commerce websites and influence consumers’ purchasing decisions. The purpose of this study is to propose a method for product selection considering consumer’s expectations and online reviews to support consumers’ purchasing decisions.

Design/methodology/approach

The product attributes are divided into two categories, i.e. demand attributes and word-of-mouth (WOM) attributes. For the demand attributes, for which the consumers can give specific quantified expectations, the value function of prospect theory is used to determine the consumer’s perceived values to the alternative products according to consumers’ expectations for these attributes and products’ specifications. For the WOM attributes, for which the consumers cannot give specific quantified expectations, the sentiment analysis method is used to identify the sentiment strengths for these attributes in the online reviews, and then the consumer’s perceived values to the alternative products are determined. On this basis, the product selection methods for single consumers and group consumers are given respectively.

Findings

Finally, taking the data of JD.com (https://www.jd.com/) as an example, the practicability and rationality of the method proposed in this paper is validated.

Originality/value

First, a new product selection problem considering consumer’s expectations and online reviews is extracted. Second, the product attributes are considered more comprehensively and are classified into two main categories. Third, the bounded rationality of the consumers in the decision-making process is described more reasonably. Fourth, the sentiment dictionaries for each WOM attribute are constructed and the algorithm step of identifying the sentiment strengths is designed, which can help to identify the sentiment strengths in the online reviews more accurately. Fifth, the situation that a group plans to purchase the same products and the members have inconsistent expectations for the product attributes is considered.

Details

Kybernetes, vol. 50 no. 9
Type: Research Article
ISSN: 0368-492X

Keywords

1 – 10 of over 54000