PEER RATINGS AND ASSESSMENT QUALITY IN CROWD-BASED INNOVATION PROCESSES

Social networks – whether public or in enterprises – regularly ask users to rate their peers’ content using different voting techniques. When employed in innovation challenges, these rating procedures are part of an open, interactive, and continuous engagement among customers, employees, or citizens. In this regard, assessment accuracy (i.e., correctly identifying good and bad ideas) in crowdsourced eval-uation processes may be influenced by the display of peer ratings. While it could sometimes be useful for users to follow their peers, it is not entirely clear under which circumstances this actually holds true. Thus, in this research-in-progress article, we propose a study design to systematically investigate the effect of peer ratings on assessment accuracy in crowdsourced idea evaluation processes. Based on the elaboration likelihood model and social psychology, we develop a research model that incorporates the mediating factors extraversion, locus of control, as well as peer rating quality (i.e., the ratings’ corre-lation with the evaluated content’s actual quality). We suggest that the availability of peer ratings de-creases assessment accuracy and that rating quality, extraversion, as well as an internal locus of control mitigate this effect

Online Content Consumption: Social Endorsements, Observational Learning and Word-of-Mouth

The consumption of online content can occur through observational learning (OL) whereby consumers follow previous consumers’ choices or social endorsement (SE) wherein consumers receive content sharing from their social ties. As users consume content, they also generate post-consumption word-of-mouth (WOM) signals. OL, SE and WOM together shape the diffusion of the content. This study examines the drivers of SE and the effect of SE on content consumption and post-consumption WOM. In particular, we compare SE with OL. Using a random sample of 8,945 new videos posted on YouTube, we collected a multi-platform dataset consisting of data on video consumption and WOM from YouTube and data on tweet sharing of the video from Twitter. Applying a panel vector autoregression (PVAR) model, we find that OL increases consumption significantly more than SE in the short run. However, SE has a stronger effect on content consumption in the long run. This can be attributed to the impact of SE on WOM signals, which also increase content consumption. While OL and SE leads to similar amount of positive WOM, SE generates significantly more negative WOM than OL. Our results also show that SE is driven by WOM (i.e., likes and dislikes) but not content popularity. We further confirm the effects of OL vs. SE on content consumption and WOM using a randomized experiment at the individual consumer level. Implications for content providers and social media platforms are derived accordingly

How Online Diaries Persuade Customers — The Role of Narratives

Online diary is a series of reviews in chronological order generated by customers to record their experience over time, which is a new type of online review emerging in the medical beauty industry. This study extends narrative transportation theory to explore the effect of the dynamic structure of online diaries on persuasion. We posit that emotional shift and utilitarian value can positively enhance online diary persuasion through improving transportation, and the relationship between the temporal flow and persuasion is converse U shape. The moderating role of social influence and visual content richness to the main effect is also investigated in this study. We collected real data to test our hypotheses utilizing Natural Language Processing (NLP) method and econometric model. This study is expected to make both theoretical and practical contributions

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the pattern by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game makes a decision among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information

A Cross-Cultural Perspective

This article investigates the quantity- and quality-motivated bandwagon effects on individual movie selection from a cross-cultural perspective. Based on theories of information processing and decision making, we examined how people from different cultural backgrounds (e.g., collective versus individual and vertical versus horizontal cultural orientations) differ in their preferences of aggregate electronic word-of-mouth (e-WOM), which is related to heuristic information processing, or more detailed individual e-WOM, which is related to systematic information processing. Empirically, we relied on an online study using movie selection scenarios that were distributed to participants in Singapore, the United States, and Germany. Results indicate significant cultural differences in the preference for e-WOM and related information processing modes. The individual cultural orientations of the participants offered a better explanation of the variance than a simple country-of-origin differentiation

The Dark Side of Images: Effect of Customer Generated Images on Product Assessment

Customer Generated Image (CGI) on e-commerce platforms has been widely recognized as a marketing tool to persuade customers into purchases. Despite its persuasive power, the effect of CGI on post purchase satisfaction has seldom been examined. This study draws upon Elaboration Likelihood Model and proposes that the affective cues in CGI could distract consumer’s cognitive information processing and lead to unsatisfactory purchases with a larger probability. To empirically test our hypothesis, we employed a difference-in-differences model with propensity score weighting method and deep learning based face detection algorithm and found that CGI could cause subsequent review ratings 0.12 stars lower compared with those not exposed to CGI. Additional analysis indicated that this negative effect could be attenuated if the CGI contains human faces or the image review has a low rating. These findings have important implications for online platforms to better leverage user generated rich media content

The Dynamics of Viral Marketing

We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a 'long tail' where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies communities, product and pricing categories for which viral marketing seems to be very effective

Social Media Effectiveness

Over the last decade, the advent of social media such as online product reviews (e.g., Amazon.com),blogs and other social networking sites (e.g., Facebook.com) has dramatically changed the way consumers obtain and exchange information about products. This dissertation investigates the impact of various types of social media on product performance and compares the effectiveness of social and traditional media under various conditions. Specifically, the first chapter performs a meta-analysis of consumer-generated WOM elasticity in social media to identify the factors that influence the impact of WOM on product sales and to assess the generalizability of the relationship. The second chapter examines how social media may influence product performance in different product contexts as compared with traditional media, which assists managers in making better media decisions. Taken together, this dissertation evaluates the progress in this field, and then takes a step further by applying past findings to understand how social media may perform at various stages in the product lifecycle

Conceptualizing the Electronic Word-of-Mouth Process: What We Know and Need to Know About eWOM Creation, Exposure, and Evaluation

Electronic word of mouth (eWOM) is a prevalent consumer practice that has undeniable effects on the company bottom line, yet it remains an over-labeled and under-theorized concept. Thus, marketers could benefit from a practical, science-based roadmap to maximize its business value. Building on the consumer motivation–opportunity–ability framework, this study conceptualizes three distinct stages in the eWOM process: eWOM creation, eWOM exposure, and eWOM evaluation. For each stage, we adopt a dual lens—from the perspective of the consumer (who sends and receives eWOM) and that of the marketer (who amplifies and manages eWOM for business results)—to synthesize key research insights and propose a research agenda based on a multidisciplinary systematic review of 1050 academic publications on eWOM published between 1996 and 2019. We conclude with a discussion of the future of eWOM research and practice

What Makes a Review Voted? An Empirical Investigation of Review Voting in Online Review Systems

Many online review systems adopt a voluntary voting mechanism to identify helpful reviews to support consumer purchase decisions. While several studies have looked at what makes an online review helpful (review helpfulness), little is known on what makes an online review receive votes (review voting). Drawing on information processing theories and the related literature, we investigated the effects of a select set of review characteristics, including review length and readability, review valence, review extremity, and reviewer credibility on two outcomes—review voting and review helpfulness. We examined and analyzed a large set of review data from Amazon with the sample selection model. Our results indicate that there are systematic differences between voted and non-voted reviews, suggesting that helpful reviews with certain characteristics are more likely to be observed and identified in an online review system than reviews without the characteristics. Furthermore, when review characteristics had opposite effects on the two outcomes (i.e. review voting and review helpfulness), ignoring the selection effects due to review voting would result in the effects on review helpfulness being over-estimated, which increases the risk of committing a type I error. Even when the effects on the two outcomes are in the same direction, ignoring the selection effects due to review voting would increase the risk of committing type II error that cannot be mitigated with a larger sample. We discuss the implications of the findings on research and practice