Space Norms for Constructing Quality Reviews on Online Consumer Review Sites

2022 | Information Systems Research | Citations: 0

Authors: Hou, Jinghui (Jove); Ma, Xiao

Abstract: High-quality online consumer reviews are valuable to e-commerce stakeholders. Ex ... Expand

Abstract: High-quality online consumer reviews are valuable to e-commerce stakeholders. Extant research mainly focuses on what drives consumers to submit reviews, whereas little has been done to specifically theorize why review writers construct high-quality reviews versus post poor review content. To advance current theorizing on the behavior of writing quality reviews, we propose the concept of space norms, a distinct instantiation of social norms inferred from locally observing how others write reviews on the local digital space (i.e., the review site). Specifically, we theorize that the focal behavior is largely directed by space norms; space norms beget a sense of how one should do and induce conformity in the given social space. We tested the theory of space norms in a series of empirical studies, and the findings provide converging evidence. Study 1 used experimental methods and demonstrates—with large effect sizes—that review writers tend to act in accordance with the space norm of the local site. Post hoc text analyses rule out an alternative explanation and corroborate the findings. Study 2 used field data collected from two e-commerce websites to further triangulate the space norms proposition. Another post hoc study confirms the robustness of the results. This paper contributes to information systems research by developing and empirically validating a theory of space norms that pinpoints the role of local digital spaces in constructing the focal normative phenomenon. It also lends insights to digital platforms and the information technology practitioners. Managers looking to elicit quality reviewing behavior should devise the local digital space to nurture the space norms for the desired behavior. Collapse

Topics: social presence online review social norm website electronic commerce

Methods: theory development natural language processing computational algorithm statistical hypothesis test design artifact

Fraudulent review detection model focusing on emotional expressions and explicit aspects: investigating the potential of feature engineering

2022 | Decision Support Systems | Citations: 5

Authors: Kumar, Ajay; Gopal, Ram D.; Shankar, Ravi; Tan, Kim Hua

Abstract: Reading customer reviews before purchasing items online has become a common prac ... Expand

Abstract: Reading customer reviews before purchasing items online has become a common practice; however, some companies use machine learning (ML) algorithms to generate false reviews in order to create positive brand images of their own products and negative images of competitors' offerings. Existing techniques use review content to identify fraudulent reviewers; however, spammers become more intelligent, started to learn from their mistakes, and changed their tactics in order to avoid detection techniques. Thus, investigating fraudulent ac­ counts' behaviour of generating fake negative or positive reviews for competitors or themselves and the necessity of ML classifiers to identify fraudulent reviews, is more important than ever. In this research, we present a novel feature engineering approach in which we (1) extract several “review-centric” and “reviewer-centric” features from a dataset; (2) combine the cumulative effects of features distributions into a unified model that represents overall behavior of the fraudulent reviewers; (3) investigate the role of effective data pre-processing to improve detection accuracy; and (4) develop a probabilistic approach to detect fraudulent reviewers by learning a novel M-SMOTE model over a derived balanced dataset and feature distributions, which outperforms other ML models. Our study contributes to the literature on digital platforms and fraudulent review detection with significant managerial and theoretical implications through these novel findings. Collapse

Topics: online review electronic commerce website marketing management logistics management

Methods: feature engineering machine learning computational algorithm XGBoost product review

Understanding the Role of Two-Sided Argumentation in Online Consumer Reviews: A Language-Based Perspective

2018 | International Conference on Information Systems | Citations: 0

Authors: Lutz, Bernhard; Pröllochs, Nicolas; Neumann, Dirk

Abstract: This paper examines the effect of two-sided argumentation on the perceived helpf ... Expand

Abstract: This paper examines the effect of two-sided argumentation on the perceived helpfulness of online consumer reviews. In contrast to previous works, our analysis thereby sheds light on the reception of reviews from a language-based perspective. For this purpose, we propose an intriguing text analysis approach based on distributed text representations and multi-instance learning to operationalize the two-sidedness of argumentation in review texts. A subsequent empirical analysis using a large corpus of Amazon reviews suggest that two-sided argumentation in reviews significantly increases their helpfulness. We find this effect to be stronger for positive reviews than for negative reviews, whereas a higher degree of emotional language weakens the effect. Our findings have immediate implications for retailer platforms, which can utilize our results to optimize their customer feedback system and to present more useful product reviews. Collapse

Topics: mobile phone marketing management logistics management word of mouth electronic commerce

Methods: Amazon review regression analysis method qualitative content analysis product review doc2vec

Sources and impacts of social influence from online anonymous user reviews

2018 | Information & Management | Citations: 52

Authors: Zhao, Kexin; Stylianou, Antonis C.; Zheng, Yiming

Abstract: Consumers increasingly rely on online reviews to make purchase decisions. Howeve ... Expand

Abstract: Consumers increasingly rely on online reviews to make purchase decisions. However, the process through which consumers are influenced by online reviews is not well understood. To fill the gap, we apply the social influence theory to theoretically explain and analyze this opinion change process. Specifically, we identify antecedents and consequences of two types of social influence from online user reviews: informational and value-expressive influence. From a survey conducted in a controlled laboratory environment, we found that perceived review quality positively impacts informational influence, while perceived review quality, consistency, and social presence jointly impact value-expressive influence. Interestingly, informational influence impacts both perceived decision quality and perceived usefulness of the website, while value-expressive influence only impacts perceived usefulness of the website. Surprisingly, consumer characteristics, including prior product expertise and self-monitoring, do not have significant effects on the formation of social influence online. Collapse

Topics: social influence perceived usefulness decision quality social presence website

Methods: survey PLS tool mediation analysis Amazon review conceptual modelling

Theories: social influence theory social exchange theory theory of reasoned action elaboration likelihood model social comparison theory

The Impact of External Word-of-Mouth Sources on Retailer Sales of High-Involvement Products

2012 | Information Systems Research | Citations: 0

Authors: Gu, Bin; Park, Jaehong; Konana, Prabhudev

Abstract: Online word-of-mouth (WOM) such as consumer opinions, user experiences, and prod ... Expand

Abstract: Online word-of-mouth (WOM) such as consumer opinions, user experiences, and product reviews has become a major information source in consumer purchase decisions. Prior research on online WOM effect has focused mostly on low-involvement products such as books or CDs. For these products, retailer-hosted (internal) WOM is shown to influence sales overwhelmingly. Numerous surveys, however, suggest consumers often conduct pre-purchase searches for high-involvement products (e.g., digital cameras) and visit external WOM websites during the search process. In this study, we analyze the relative impact of external and internal WOMs on retailer sales for high-involvement products using a panel of sales and WOM data for 148 digital cameras from Amazon.com and three external WOM websites (Cnet, DpReview, and Epinions) over a four-month period. The results suggest that a retailer's internal WOM has a limited influence on its sales of high-involvement products, while external WOM sources have a significant impact on the retailer's sales. The findings imply that external WOM sources play an important role in the information search process. Collapse

Topics: information storage and retrieval system Google+ electronic commerce online word of mouth website

Methods: longitudinal research autocorrelation analysis regression analysis method product review Poisson regression

Impact of Multiple Word of Mouth Sources on Retail Sales

2009 | International Conference on Information Systems | Citations: 2

Authors: Park, JaeHong; Gu, Bin; Konana, Prabhudev

Abstract: Consumer opinions, experiences, and product recommendations posted on online WOM ... Expand

Abstract: Consumer opinions, experiences, and product recommendations posted on online WOM sites have become a major information source in consumer purchase decisions. However, prior research has largely focused on the influence of a single source of WOM – particularly retailer-hosted WOM. This study investigates the relative impact of multiple WOM information on retail sales. We collected a panel of sales data of 148 camera products from Amazon and WOM data from both Amazon and well-known third-party websites for four months. Our analysis reveals that thirdparty hosted WOM has a significant impact on retail sales while retailer-hosted WOM has much less influence on its own sales. Our finding suggests that third-party hosted WOM is an important source of information for online retail customers. We raise questions about the value and influence of retailer-hosted WOM in the presence of reputable third-party hosted WOM sources. Collapse

Topics: online word of mouth website electronic commerce online review

Methods: longitudinal research product review Poisson regression Amazon review regression analysis method