Consumer Herding Behavior in Online Buying: A Literature Review

The purpose of this review paper is to present the application of herding behavior in online buying. The simplest description of herding behavior is the imitation of others in making decisions. Online buying platforms have facilitated observing others' buying behavior, thereby increasing possibilities of social influence on our information search, evaluation, and buying. The concept of herding is multi-disciplinary; however, the literature review on herding behavior is mainly grounded in economics and finance. There is little understanding of herding behavior in marketing literature. Therefore, this study covers herding behavior literature through high-quality research papers published from 2000 to 2020 in journals indexed in the social science citation index, science citation index expanded, and emerging source citation index. This paper discusses the conceptualization of herding in online buying, herding situations, information-processing view of herding, measuring herding effect, herding models and theories, and areas for future research to enrich herding literature in online buying. This paper proposes a herding model (HCMMD) based on the stimulus-organism-response (SOR) theory to study herding behavio

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

Do reviews from friends and the crowd affect online consumer posting behaviour differently?

User-generated reviews are valuable resources for consumers to gain information of products which has significant impact on their following decision-making. With the development of social network service, consumers are exposed to reviews coming from both friends and the crowds (non-friends). However, the impact of friends’ and crowds’ reviews on consumer posting behaviour has not been well differentiated. Using the online review information as well as the underlying social network from Yelp, this paper develops a multilevel mixed effect probit model to study the impact of consumer characteristics and reviews of different sources, i.e. friends or crowds, on the possibility of consumer further engaging in posting behaviour. Despite the common perception that the volume, valance and variance of reviews significantly impact the possibility of following posting behaviour, we show that such influence majorly comes from the friend reviews. The volume of friend reviews has much stronger impact on the target user’s posting behaviour than that of the crowds. The valance and variance of the crowd reviews show no significant influence when ignoring the friend reviews, but negative influence when considering it. The friend reviews and crowd reviews are further divided as positive and negative ones, and only the positive friend reviews and negative crowd review are found significantly enhancing the posting possibility

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

Predictive Analytics on Emotional Data Mined from Digital Social Networks with a Focus on Financial Markets

This dissertation is a cumulative dissertation and is comprised of five articles. User-Generated Content (UGC) comprises a substantial part of communication via social media. In this dissertation, UGC that carries and facilitates the exchange of emotions is referred to as “emotional data.” People “produce” emotional data, that is, they express their emotions via tweets, forum posts, blogs, and so on, or they “consume” it by being influenced by expressed sentiments, feelings, opinions, and the like. Decisions often depend on shared emotions and data – which again lead to new data because decisions may change behaviors or results. “Emotional Data Intelligence” ultimately seeks an answer to the question of how all the different emotions expressed in public online sources influence decision-making processes. The overarching research topic of this dissertation follows the question whether network structures and emotional sentiment data extracted from digital social networks contain predictive information or they are just noise. Underlying data was collected from different social media sources, such as Twitter, blogs, message boards, or online news and social networking sites, such as Xing. By means of methodologies of social network analysis (SNA), sentiment analysis, and predictive analysis the individual contributions of this dissertation study whether sentiment data from social media or online social networking structures can predict real-world behaviors. The focus lies on the analysis of emotional data and network structures and its predictive power for financial markets. With the formal construction of the data analyses methodologies introduced in the individual contributions this dissertation contributes to the theories of social network analysis, sentiment analysis, and predictive analytics

Management Response to Online WOM: Helpful or Detrimental?

Firms are shifting their social media emphasis from passive listening to active intervening. This paper investigates the performance implications of managerial response to online word of mouth (WOM). Using the longitudinal data of online reviews, managerial responses and financial performance for 730 hotels in a specific U.S. market from 2005 to 2011, we lay out an empirical strategy to examine the performance effects of managerial responses given different conditions of online WOM, while dealing with potential estimation challenges of endogenous managerial response and online WOM, serially correlated financial performance, and unobserved heterogeneity in hotels. Our findings suggest that managerial intervention should be strategic – whether and how to respond depends on the different levels of online WOM metrics

HOW DO CONSUMERS USE SOCIAL SHOPPING WEBSITES? THE IMPACT OF SOCIAL ENDORSEMENTS

Social endorsements are user-generated endorsements of products or services, such as “likes” and personal collections, in an online social platform. We examine the effect of prior social endorsements on subsequent users’ tendency to endorse or examine a product in a social shopping context, where a social platform connect consumers and enable a collaborative shopping experience. This research consists of two parts. In part I, we identify two ways prior social endorsements can affect subsequent user behavior: as a crowd endorsement, which is an aggregate number of endorsements a product receives for anyone who comes across the product, and as a friend endorsement, which is an endorsement with the endorser’s identity delivered only to the endorser’s friends or followers. Using a panel data of 1656 products on a leading social shopping platform, we quantify the relationship between crowd and friend endorsements and subsequent examination (“click”) and endorsement (“like”) of the products, noting that examination is a private behavior while endorsement is a public behavior. Our results are consistent with the identity signaling theory where identity-conscious consumers converge with the aspiration group (the followers) in their public behavior (e.g. endorsement) and diverge from the avoidance groups (the crowd). We also find differences between public and private behaviors. Moreover, the symbolic nature of social shopping platform trumps the traditional dichotomy of symbolic/functional product attributes. Part II of this study seeks to clarify the underlying mechanism through lab experiments. We hypothesize that consumers’ evaluative attitude, specifically the value-expressive type, moderates the relationship between crowd and friend endorsements and a focal user’s product choice. Our initial results of the second study show support for this idea in the cases when the product choice is not obvious

Which Factors Determine User’s First and Repeat Online Music Listening Respectively? Music Itself, User Itself, or Online Feedback

In the era of Web 2.0, does online feedback mainly dominant online users’ buying behavior, or are user’s own preference and product quality still important? Previous studies paid more attention to the influence of online feedback on users’ online buying behavior, however this paper focuses on how users’ own factors, product quality related factors and online feedback factors together influence a user’s buying behavior, and also how does this effect change as time goes by. Taking online music as our research industry and using the data from Last.fm website, this research shows that users’ preference and product quality are still the two most dominate factors influencing users’ online music listening, while online feedback plays an important role on users’ first listening. It is also found that the different influences of crowds and friends

How to Steer Users Away from Unsafe Content

Online social networks have brought along much convenience to our daily lives. On the other hand, they also provide platforms for the rapid propagation of unsafe content. Providing easy-to-use ways for ordinary users to avoid unsafe content online is an open issue. In this thesis, we mainly study two schemes that are based on social navigation to identify unsafe content. The first one is crowdsourcing, which has two main drawbacks: (a) a time lag before unsafe content is flagged as such, and (b) the difficulty of dealing with subjective perceptions of 'inappropriateness''. We propose a machine learning approach to address the time lag problem and get a promising result. This approach could be used to complement crowdsourcing. We also study the notion of 'groupsourcing'': taking advantage of information from people in a user's social circles about potentially unsafe content. Groupsourcing can both address the time lag problem and identify inappropriate content. To test its effectiveness, we have implemented FAR, which allows savvy Facebook users to warn their friends about potentially unsafe content, and conducted a controlled laboratory study. The results show that groupsourced signals can complement other types of signals and compensate for their weaknesses by countering viral spreading of unsafe content in a more timely fashion. The current version of FAR, consisting of a Facebook application and a Firefox browser extension is publicly available for use