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Companies are increasingly drawing on their user communities to generate promising ideas for new products, which are then marketed as “user-designed” products to the broader consumer market. We demonstrate that nonparticipating, observing consumers prefer to buy from user- rather than designer-driven firms because of an enhanced identification with the firm that has adopted this user-driven philosophy. Three experimental studies validate a newly proposed social identification account underlying this effect. Because consumers are also users, their social identities connect to the user-designers, and they feel empowerment by vicariously being involved in the design process. This formed connection leads to preference for the firm’s products. Importantly, this social identification account also effectively predicts when the effect does not materialize. First, we find that if consumers feel dissimilar to participating users, the effects are attenuated. We demonstrate that this happens when the community differs from consumers along important demographics (i.e., gender) or when consumers are nonexperts in the focal domain (i.e., they feel that they do not belong to the social group of participating users). Second, the effects are attenuated if the user-driven firm is only selectively rather than fully open to participation from all users (observing consumers do not feel socially included). These findings advance the emerging theory on user involvement and offer practical implications for firms interested in pursuing a user-driven philosophy. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.1999 . This paper was accepted by Pradeep Chintagunta, marketing .

Volunteer users employ collaborative Internet technologies to develop open source products, a form of user-generated content, where time to product release is a crucial measure of project success. The open source community features two separate but related subcommunities: developer users who contribute time and effort to develop products and end users who act as collaborative testers and provide feedback. We develop hypotheses concerning how the location of the project's founders in the social network of developer users, the interplay of developer users and end users, and project and product characteristics affect time to product release. We use data on 817 development projects from SourceForge, a large open source community forum, to calibrate a split hazard model to test the hypotheses. That model supports the two-community conceptualization and most of the related hypotheses. The results have theoretical and managerial implications; for example, a pivotal position of founders in the developer user community can reduce time to product release by up to 31 and projects in which users are more engaged can experience an 11 time to product release compared with those projects in which they are not.

User-generated product questions and answers (Q&As) are a new way for consumers to exchange product knowledge that can provide important support for consumer decisions. Research on the influencing factors of consumers’ judgment of the helpfulness of user-generated product Q&As can help optimize Q&A systems. Although research has extensively examined the impacts of answers and answerers on user-generated Q&As, the underlying mechanism of how the characteristics of questions and Q&As affect the helpfulness of user-generated Q&As has rarely been explored. Based on the elaboration likelihood model (ELM), a research model reflecting the impacts of the characteristics of questions and Q&As on the helpfulness of user-generated product Q&As is developed and empirically examined by using data collected from 4,814 product questions and 10,573 corresponding answers on Amazon.com. Specifically, the moderating effects of the question type and product type on the relationship between Q&A cues and the helpfulness of user-generated product Q&As are investigated. The results show that the helpfulness of user-generated product Q&As is positively affected by answer professionalism, Q&A topic consistency, answer opinion consistency, and answer valence, while it is negatively influenced by answer knowledge stickiness. As expected, the relationship between answer knowledge stickiness, answer opinion consistency, and the helpfulness of user-generated product Q&As is weakened when products are experiential. The relationship between answer knowledge stickiness, Q&A topic consistency, and the helpfulness of user-generated product Q&As is weakened when consumers’ questions are correlated with product attributes. Plain Language Summary Exploring the helpfulness of User-Generated Product Q&As Based on the elaboration likelihood model (ELM), a research model reflecting the impacts of the characteristics of questions and Q&As on the helpfulness of user-generated product Q&As is developed and empirically examined by using data collected from 4,814 product questions and 10,573 corresponding answers on Amazon.com. Specifically, the moderating effects of the question type and product type on the relationship between Q&A cues and the helpfulness of user-generated product Q&As are investigated. The results show that the helpfulness of user-generated product Q&As is positively affected by answer professionalism, Q&A topic consistency, answer opinion consistency, and answer valence, while it is negatively influenced by answer knowledge stickiness. As expected, the relationship between answer knowledge stickiness, answer opinion consistency and the helpfulness of user-generated product Q&As will be weakened when products are experiential. The relationship between answer knowledge stickiness, Q&A topic consistency and the helpfulness of user-generated product Q&As will be weakened when consumers’ questions are correlated with product attributes.

Recommender systems are an integral part of many commercial platforms. A subset of recommended products is the result of the aggregated behavior of users who also purchased those products, known as the user-generated network. User-generated product reviews, images, and hashtags are increasingly valuable sources of information for customers to make product decisions online. A recent work stream addressed the economic impact of the review. Typically, the influence of product reviews is explained by numerical variables representing the value and number of reviews. On the other hand, the platform itself provides some products related to these products in a more generic way, for example: B. Product categories. Product recommendations are basically a filtering system that tries to predict and display items that a user might want to buy. It might not be completely accurate, but if it's showing you something you like, then it's working. This set of recommended products is called a system-generated network. Our goal is to capture the evolution of product networks in terms of link formation and removal as a consequence of any of the above suggestions by taking into account the strength of nodes in the network. Our results show that while user-generated recommendations have a strong impact on link formation and rank distribution in product networks, system-generated networks have no significant effect.

This article aims to specify the performance implications of neutral user-generated content (UGC) on product sales by differentiating mixed-neutral UGC, which contains an equal amount of positive and negative claims, from indifferent-neutral UGC, which includes neither positive nor negative claims. The authors propose that positive and negative UGC only provide opportunities for consumers to process product-related information, whereas both mixed- and indifferent-neutral UGC affect consumers’ motivation and ability to process positive and negative UGC. The results of three studies using multiple measures (text and numerical UGC), contexts (automobiles, movies, and tablets), and methods (empirical and behavioral experiment) indicate contrasting premium and discount effects such that mixed-neutral UGC amplifies the effects of positive and negative UGC, whereas indifferent-neutral UGC attenuates them. Empirical evidence further indicates that ignoring mixed- or indifferent-neutral UGC leads to substantial under- or overestimates of the effects of positive and negative UGC. The effects of neutral UGC on product sales thus are not truly neutral, and the direction of the bias depends on both the type of UGC and the distribution of positive and negative UGC.

Online chatter, or user-generated content, constitutes an excellent emerging source for marketers to mine meaning at a high temporal frequency. This article posits that this meaning consists of extracting the key latent dimensions of consumer satisfaction with quality and ascertaining the valence, labels, validity, importance, dynamics, and heterogeneity of those dimensions. The authors propose a unified framework for this purpose using unsupervised latent Dirichlet allocation. The sample of user-generated content consists of rich data on product reviews across 15 firms in five markets over four years. The results suggest that a few dimensions with good face validity and external validity are enough to capture quality. Dynamic analysis enables marketers to track dimensions' importance over time and allows for dynamic mapping of competitive brand positions on those dimensions over time. For vertically differentiated markets (e.g., mobile phones, computers), objective dimensions dominate and are similar across markets, heterogeneity is low across dimensions, and stability is high over time. For horizontally differentiated markets (e.g., shoes, toys), subjective dimensions dominate but vary across markets, heterogeneity is high across dimensions, and stability is low over time.

The creation and sharing of user-generated content such as product reviews has become increasingly “social,” particularly in online communities where members are connected. While some online communities have used monetary rewards to motivate product review contributions, empirical evidence regarding the effectiveness of such rewards remains limited. We examine the possible moderating effect of social connectedness (measured as the number of friends) on publicly offered monetary rewards using field data from an online review community. This community saw an (unexpected) overall decrease in total contributions after introducing monetary rewards for posting reviews. Further examination across members finds a strong moderating effect of social connectedness. Specifically, contributions from less-connected members increased by 1,400%, while contributions from more-connected members declined by 90%. To corroborate this effect, we rule out multiple alternative explanations and conduct robustness checks. Our findings suggest that token-sized monetary rewards, when offered publicly, can undermine contribution rates among the most connected community members. Data and the online appendix are available at https://doi.org/10.1287/mksc.2016.1022

This paper examines the informational role of product ratings. We build a theoretical model in which ratings can help consumers figure out how much they would enjoy the product. In our model, a high average rating indicates a high product quality, whereas a high variance of ratings is associated with a niche product, one that some consumers love and others hate. Based on its informational role, a higher variance would correspond to a higher subsequent demand if and only if the average rating is low. We find empirical evidence that is consistent with the theoretical predictions with book data from Amazon.com and BN.com. A higher standard deviation of ratings on Amazon improves a book's relative sales rank when the average rating is lower than 4.1 stars, which is true for 35% of all the books in our sample. This paper was accepted by Pradeep Chintagunta, marketing.

Online product reviews are increasingly important for consumer decisions, yet we still know little about how reviews are generated in the first place. In an effort to gather more reviews, many websites encourage user interactions such as allowing one user to subscribe to another. Do these interactions actually facilitate the generation of product reviews? More importantly, what kind of reviews do such interactions induce? We study these questions using data from one of the largest product review websites where users can subscribe to one another. By applying both panel data and a flexible matching method, we find that as users become more popular, they produce more reviews and more objective reviews; however, their numeric ratings also systematically change and become more negative and more varied. Such trade-off has not been previously documented and has important implications for both product review and other user-generated content websites.

. [Display omitted] ► Product review valence and perceived helpfulness have a positive relationship. ► Product review length and perceived helpfulness have a positive relationship. ► All else the same, experiential reviews are less helpful than utilitarian reviews. ► The effects of review valence and length are moderated by product type. ► Reviewer innovativeness has an inverted-U-shaped effect on review helpfulness. Online user-generated product reviews have become an indispensible tool for consumers and thus for retailers who want to attract and retain consumers. Yet, relatively little is known about what causes consumers to find an online peer review helpful to their shopping tasks. Prior research examines mostly the effects of product reviews on consumer product attitude, product choice, and product sales. This paper, however, provides an analysis of the determinants of review helpfulness. In two studies, we examine the effects of review characteristics, product type, and reviewer characteristics on perceived review helpfulness. With data collected from a real online retailer, we provide empirical evidence to support our conceptual predictions. Specifically, both review valence and length have positive effects on review helpfulness, but the product type (i.e., experiential vs. utilitarian product) moderates these effects. Using content analysis of reviews, we develop a measure of expressed reviewer innovativeness (i.e., the predisposition toward new products as revealed in review content). A curvilinear relationship exists between expressed reviewer innovativeness and review helpfulness. These findings lead to pertinent managerial implications.