When products receive reviews across platforms: Studying the platform concentration of electronic word-of-mouth

https://doi.org/10.1016/j.im.2021.103532Get rights and content

Highlights

  • The platform concentration of eWOM is associated with online user decisions.

  • Volume equality and valence equality are used to quantify platform concentration.

  • Greater volume equality positively impacts online user's product adoption decisions.

  • Greater valence equality positively impacts online user's product adoption decisions.

  • Volume equality and valence equality have a positive interaction effect.

Abstract

A product can receive Electronic Word-of-Mouth (eWOM), e.g., user reviews, at both retail and review platforms, with considerable variance in volume and valence. We define such eWOM variation across platforms as the platform concentration of eWOM. A lower platform concentration is measured by a higher level of volume equality and valence equality, i.e., a more comparable number of user reviews and more consistent average ratings among multiple platforms, respectively. Results from multiple models over software data from retail and review platforms shows that volume equality and valence equality positively interact to affect online users’ product adoption decisions.

Introduction

The Internet and electronic commerce have accumulated and distributed electronic word-of-mouth (eWOM) information at an unprecedented rate. Most of the retail platforms provide online user review systems to encourage users and consumers to share their experiences, while many review platforms, as a relatively more independent source, often host both user reviews and expert opinions. Accordingly, marketers in a variety of industries have widely embraced the eWOM marketing strategy as an alternative to traditional advertising [10, 15, 35, 38], such as building online user communities and offering user monetary and non-monetary incentives to contribute to eWOM. To quantify the market outcome of the eWOM strategy, significant attention has been paid toward understanding the eWOM effect [51]. Among eWOM literature, the two most widely discussed quantitative metrics are review volume which measures the total amount of user conversation and review valence which measures average customer evaluation [34, 44]. Review volume and review valence are shown to influence user awareness of the product and imply product quality, respectively [27], thereby playing different roles in online user's decision-making [3, 6, 9, 14, 28, 32, 33, 37].

Presently, with the growing prosperity of e-commerce and the digital world, it is common for a product to simultaneously receive hundreds of user feedbacks and product reviews from multiple platforms. For example, the software program Norton Security Deluxe receives approximately 3800 customer reviews at Amazon and 700 at CNET download (download.cnet.com; CNETD). Just like Norton software, products may receive a disproportionately high or low volume of reviews and/or more favorable reviews at one platform than at the others. This phenomenon is defined as the platform concentration of eWOM. For example, a low platform concentration describes that a product receives comparable eWOM in volume and valence across multiple platforms.

It is natural to ask the question of how such platform concentration of eWOM affects a user's decision of adopting products. Does it make a difference to retail sales if one platform reviewed a product more heavily and more positively than other platforms, all else being equal? The answer can provide marketers a more holistic view of designing their eWOM marketing strategy beyond a single platform. It also suggests to marketers that the return of eWOM investment on a single platform can vary, depending on eWOM hosted by other platforms. The literature on eWOM hosted by multiple platforms is not able to offer a direct answer. The extant literature shows a common theme of comparing the relative importance of eWOM volume and valence among different platforms. The underlying rationale is that the source identity of eWOM varies with different types of platforms, such as retailer hosted vs. third-party hosted eWOM [22]. Differentiating from previous research, the current work does not attempt to add to those discussions. Instead, we introduce a new perspective of platform concentration to this research line regarding eWOM hosted by different types of platforms.

We propose two new quantitative metrics to measure platform concentration: volume equality and valence equality, across the retail and review platforms. Retail and review platforms are two distinct types of platforms with different service purposes and serving different user populations [44]. Online consumers purchase products on a retail platform, but people mainly visit a review platform to look for product information and interact with others without directly placing orders. Among a variety of available eWOM metrics, we choose to focus on volume and valence, given that they are most widely available at nearly every platform and are found to influence user's decision-making [14, 34]. Our proposed metrics are built upon them and are thus easy to implement in the practice.

We define volume equality of eWOM as the extent to which distinct platforms host online user-generated conversations equally in volume [19]. Greater volume equality implies a more uniform volume of eWOM dispersed across several platforms. Total volume is generally believed to reflect the overall size of the user population being aware of the product [34]. We inherit the spirit of Godes and Mayzlin's work (2004) to further propose the informational role of volume equality as the heterogeneity of online users being aware of the product: Greater volume equality captures more diverse user populations discussing and becoming aware of the product. Valence equality captures the consistency of average product evaluations among different platforms. Greater valence equality indicates a more consistent valence of eWOM across platforms. And valence equality is related to cognitive costs for online users to reconcile inconsistent user opinions and thus can potentially affect user decisions.

To examine the effects of these two platform concentration metrics, we constructed a panel dataset on software programs at both Amazon and CNETD over 33 weeks. To the best of our knowledge, the current study is the first to uncover that the platform concentration of eWOM across different types of platforms, in both volume and valence, has a significant impact on online user decisions of adopting products. In our specific context, we operationalize online users’ software adoption decisions at Amazon as software sales and at CNETD as software downloads. We find that greater volume equality of eWOM across retail and review platforms is associated with greater Amazon sales and more CNETD software downloads. In addition, we also find evidence that greater valence equality of eWOM (i.e., more consistent user ratings) across platforms is positively associated with online retail sales and software downloads. Volume equality is also shown to further magnify such impact of valence equality. When a product gets adversely impacted by small valence equality, greater volume equality can make it more detrimental.

The rest of this paper is organized as follows. We discuss the difference of our work from relevant literature in the next section, followed by our proposed research hypotheses. We then describe the research context and variables. Afterward, we present our empirical models and discuss the estimations and robustness tests. Finally, we make conclusions and discuss the theoretical and practical implications of the current study, as well as identify areas for future research.

Section snippets

Related Literature

Recently a growing stream of eWOM literature has been trying to understand the differential impact of eWOM information from multiple sources on user choices [2, 4, 7, 20, 22, 36, 40, 44, 45, 54]. Overall, there are two approaches to differentiate eWOM sources. The more common approach, which is remotely related to ours, is to differentiate the reviewer identity and study the eWOM hosted by a single platform. In essence, this approach compares the trustworthiness and information quality of eWOM

Research Context

We conducted our empirical analysis in the online software market by using data from Amazon and CNETD. In recent years, the product variety of software programs offered through the online channel has been increasing tremendously [53]. Consumers often face difficulties with evaluating software quality before consumption. Meanwhile, consumers who intend to adopt software programs generally have technical knowledge and experience in accessing multiple platforms. This makes the online software

Empirical Analysis

To test our proposed hypotheses, we develop a series of models, each of which will be presented below.

Base Model Estimation

Table 4 summarizes the estimation results of the Base Model in Eq. (3). To highlight the contribution of eWOM platform concentration to online retail sales, we compare two specifications. As shown in the first column of Table 4, we estimate a model that removes metrics related to platform concentration from the Base Model. Specifically, the model in the first column only includes three commonly used eWOM measures: the total volume of eWOM from Amazon and CNETD, whether Amazon receives more

Discussion and Conclusions

In this paper, we study how the platform concentration of eWOM across retail and review platforms influences online product adoption decisions by focusing on two metrics: volume equality and valence equality. We find that it is beneficial for a product to receive a comparable number of consistent user feedbacks from platforms of different types. In addition, receiving inconsistent feedback across platforms becomes more harmful, when the volume of user feedbacks received at each platform is more

CRediT authorship contribution statement

Hong Chen: Data curation, Methodology, Formal analysis, Writing – review & editing. Wenjing Duan: Conceptualization, Methodology, Supervision, Writing – review & editing. Wenqi Zhou: Conceptualization, Formal analysis, Writing – original draft, Writing – review & editing.

Declarations of Competing Interest

None

Acknowledgements

The authors thank participants at the Workshop on e-Business and the International Conference on Information Systems reviewer team for valuable comments on this research. All errors are our own.

Hong Chen is currently an Assistant Professor of Information Sciences and Technology at Penn State University of New Kensington. He received the PhD degree from the University of Rhode Island in 2012. Prior to his current position, Dr. Chen was Visiting Assistant Professor of Information Systems Management in Palumbo-Donahue School of Business at Duquesne University, and, subsequently, Assistant Professor of Computer and Information Science at Siena Heights University. His research is

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    Hong Chen is currently an Assistant Professor of Information Sciences and Technology at Penn State University of New Kensington. He received the PhD degree from the University of Rhode Island in 2012. Prior to his current position, Dr. Chen was Visiting Assistant Professor of Information Systems Management in Palumbo-Donahue School of Business at Duquesne University, and, subsequently, Assistant Professor of Computer and Information Science at Siena Heights University. His research is interdisciplinary, and spans Information Systems and Analytics. His recent research examines the impact of online User Generated Content and cyber fraud on Internet market outcome.

    Wenjing Duan is currently an Associate Professor of Information Systems & Technology Management at School of Business, The George Washington University. She received her Ph.D. in Information Systems from University of Texas at Austin in 2006. Wenjing's research interests glide the intersections between Information Systems, Economics, and Marketing. Among her primary research interests are the economics of e-commerce, online communities and social networks, the Internet marketing, and online Intermediaries. Wenjing has published in MIS Quarterly, Information Systems Research, Journal of Management Information Systems, Communications of ACM, Journal of Retailing, and Decision Support Systems. She received Emerald Management Reviews Citations of Excellence Awards in 2012 and 2014. She is also the recipient of the NET Institute Research Grant and serves on the Editorial Board of the Decision Support Systems.

    Wenqi Zhou is currently David Warco Faculty Fellow and an Associate Professor in Information Systems & Technology at Palumbo-Donahue School of Business, Duquesne University. She received her Ph.D. in Information Systems from The George Washington University in 2013. Her research primarily focuses on understanding social, economic and managerial aspects of information technology and the Internet by analyzing large-scale online data. Her works have been published in Journal of Management Information Systems, Decision Support Systems, IEEE Computer, Electronic Commerce Research and Applications, and ICIS proceedings, among others. She received several Best Paper Award at various conferences, including Workshop on e-Business and Academy of Management Annual Meeting.

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    All authors contribute to this work equally.

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