Observable behaviors and their impact on others is far from a new research topic. It has been extensively studied in offline contexts, from both theoretical and empirical perspectives and by various academic disciplines. For example, economists have argued that individuals tend to disregard their own private signals, both prior knowledge and/or intuition, when exposed to opposing choices made by as little as two other people (Bikhchandani et al., 1998). Elaborate experiments have been performed by psychologists, such as the ‘sky-watching experiment’ by Milgram, Bickman, and Berkowitz (1969), showing that people are greatly influenced by observing the mere behaviors of others. Driven by the challenges and possibilities originating from digitization, in recent years scholars have shown a renewed interest in making user behaviors easily observable within products and services and uncovering the impact hereof.
The following sections will review this literature and provide an overview of the type of empirical studies performed, distill their findings, and finally demonstrate the current conceptualization of behavior-based information.
18 2.2.1 Overview of findings in extant literature
The articles reviewed in the following sections are not positioned neatly in a well-defined body of literature. Rather, they share the common theme of behavior-based information being incorporated into products and services, while the research itself represents different approaches and are based in varied fields of literature. This review brings together these scattered literatures, identifying the common thread, the current state of knowledge as well as its gaps.
The nature of this dissertation’s primary aim – to theorize an emerging and ill-defined
phenomenon - made it impossible, as well as less relevant, to perform one systematic literature review that narrowly identifies the current state of research in the field. Rather, the review that follows in this section is the result of several rounds of literature reviews over the course of producing this PhD. The main elements in this process were:
1) Quasi-systematic review of consumption experiences shared in active as well as passive manners in social media contexts (early stage of project). The search was rather broad and spanned across several disciplines and levels of academic outlets.
2) Systematic review of eWOM literature in leading IS, marketing, and e-commerce journals for the years 2014-17. This was done for Paper 3 with the aim of uncovering current conceptual understandings of eWOM and, where possible, how eWOM and eWOB compare. In the course of this search, a few papers were found which directly seek to compare eWOM with behavior-based information (eWOB). Those articles have been included in the following synthesis of literature.
3) Use of seminal articles as basis for back- and forward tracking.
The different phases of the literature search process reflect how the conceptual understanding of the phenomenon of interest matured over time. In the early phases, for example, the main interest was behaviors (actively and passively) shared in social media contexts. As the concept developed, the requirement of social media context was no longer relevant. In that sense, the original pool of literature was broadened up. However, the relevant literature was narrowed down in other areas, e.g. from including both the active and passive sharing of “consumption experiences” to in the end being focused on the more passive types of behavior disclosure
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(digital traces of behaviors being disclosed). The result is a pool of literature that I do not claim is an exhaustive collection of literature about the use of behavior-based information in products and services, but which can be viewed as a foundation for further exploration of this theme.
Table 2 provides an overview of the prominent research identified throughout this project and the associated findings.
Table 2. Overview of current literature about behavior-based information
PAPER OUTLET TYPE OF
STUDY EMPIRICAL
CONTEXT TYPE OF BEHAVIOR FINDINGS
Salganik &
Watts (2008)
Social Psychology Quarterly
Online experime nt
Experiment music website
Aggregated no. of previous downloads for a song - disclosed on the website
Significant positive impact of behaviors on music downloads; even for songs whose download count had been manipulated high.
Duan, Gu
&
Whinston (2009)
MIS Quarterly Panel data analysis
CNET (software)
Aggregated no. of prior downloads for a piece of software -disclosed on CNET.
Significant positive impact of behaviors on software downloads, whereas ratings (opinions) only has impact on less popular products.
Aral &
Walker (2011)
Management Science
Online experime
nt Online gaming
Facebook friends’
achievements in online game automatically posted to Facebook.
Modest, but significant, positive impact of behavior disclosure on game adoption.
Behaviors are overall more impactful than WOM-style messages because of the high volume and minimal manual effort required Chen,
Wang &
Xie (2011)
Journal of Marketing Research
Online experime nt
Digital cameras on Amazon
Sales rank (based on aggregated no. of previous purchases – disclosed on Amazon
Significant positive impact of behaviors on sales. However, if the number of prior purchases is low, the disclosure hereof has neither positive nor negative impact.
Tucker &
Zhang (2011)
Management Science
Online experime nt
Yellow Pages-style website for wedding services
Aggregated no. of link clicks per vendor – disclosed on the website.
Significant positive impact of behavior disclosure on website traffic. Narrow-appeal vendors gain more website traffic from disclosure of behaviors than do broad-appeal.
Bond et al.
(2012) Nature Online
experime
nt Voting
(Self-reported) voting-behavior in US election posted to voters’ Facebook pages
Modest but significant positive impact of behaviors on friends’ and friends of friends’
actual voting behavior and information seeking. Impact largest among close ties.
Cheung, Xiao & Lui (2014)
Decision Support Systems
Panel data analysis
Asian beauty forum
Prior purchases of beauty products - disclosed on users’
profile pages.
Significant positive impact of behaviors in terms of influencing purchase decisions.
Behaviors are found more impactful than eWOM (opinions)
Bapna &
Umyarov (2015)
Management Science
Online experime nt
Last.fm (music streaming)
Users’ status as premium subscriber is disclosed on users’
profile pages
Significant positive impact of behaviors on purchases of premium subscriptions. Impact largest on users with small number of friends.
Thies, Wessel &
Benlian (2016)
Journal of Management Information Systems
Panel data analysis
Indiegogo (crowdfunding )
Aggregated no. of previous backers for a campaign – disclosed on Indigogo
Significant positive impact of behaviors on funding decisions. However, the impact decays faster than that of eWOM.
As shown in Table 2 the current literature is mainly based on large-scale experiments or panel datasets with data points in the millions. Here, the most prominent perspective taken is that of
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the business, where the impact of behavior-based information on user choices is studied as a variable which can potentially affect the company’s bottom line. Generally, a positive impact of behavior-based information has been identified across a number of diverse product categories, such as digital cameras (Y. Chen et al., 2011), online gaming (Aral & Walker, 2011), premium subscriptions in freemium-based music streaming (Bapna & Umyarov, 2015), and wedding services (Tucker & Zhang, 2011) to name a few.
The majority of studies have investigated the impact of behavior-based information when presented on an aggregate level, for example displaying the total previous number of backers of a crowdfunding project (Thies et al., 2016) or the aggregate number of link clicks on a website (Tucker & Zhang, 2011). However, exceptions do exist where behaviors are displayed at the individual-specific level (e.g. Aral & Walker, 2011; Bapna & Umyarov, 2015; Bond et al., 2012). Both aggregate and individual behavior-based information is found to have a positive impact on consumer choices, albeit moderated by a number of variables, including total amount of social ties in a user’s network (Bapna & Umyarov, 2015), tie strength (Bond et al., 2012), impact over time (Thies et al., 2016), size of potential market for the product (Tucker &
Zhang, 2011), and user expertise (Cheung et al., 2014).
2.2.2 Thematic & theoretical approaches of extant literature
Thematically, scholars dealing with the integration of behavior-based information in products and services have taken slightly different approaches. One stream explicitly recognizes the design-driven nature of disclosing behavior-based information, and can be characterized as
‘social design’. Godes et al. (2005) posited that “at least some of the social interaction effects are partially within the firm’s control” (p. 415). Building on this observation, a focus on how to design ‘viral’ or ‘social’ products has emerged (Aral, Dellarocas, & Godes, 2013; Aral & Walker, 2011; Bapna & Umyarov, 2015; Dou, Niculescu, & Wu, 2013). Here, researchers focus on how social elements, including but not restricted to behavior-based information, can be
incorporated into the product design to stimulate adoption, and customer engagement and retention (Bapna & Umyarov, 2015). As such, this stream of literature acknowledges how product design in a digital age often merges with marketing communications. Because this stream is not constrained to investigating behaviors, the focus is put on social information and social features. This includes both the behaviors and opinions of users, as well as social features such as the use of referral options built into the product, and how collectively these can help diffuse a product as well as retain users.
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Another stream takes point of departure in the interrelated literatures of eWOM and social interactions. Likely the most widely used definition of eWOM, with 4,194 citations according to Google Scholar, is “any positive or negative statement made by potential, actual, or former customers about a product or company, which is made available to a multitude of people and institutions via the Internet” (Hennig-Thurau, Gwinner, Walsh, & Gremler, 2004, p. 39). Here, eWOM is defined as “positive or negative statements”, implying a consumer-driven activity which involves the expression of opinions. Scholars departing from the eWOM/social interactions perspective have been especially interested in studying the impact of opinions (eWOM) in comparison to behaviors when integrated into digital products and services. For example, Thies et al. (2016) applied the perspective of social interactions in a crowdfunding context, where eWOM was contrasted against what they refer to as ‘popularity information’, a behavior-based social interaction, operationalized as the number of previous backers of a given project. They found that although popularity information had an overall larger impact than eWOM, the effect diminished relatively quickly compared to that of eWOM. Another study by Cheung et al. (2014) directly compared the effect on consumer decision making of “opinion-based social information” in the form of peer consumer reviews, which is a common type of eWOM, to that of “action-based social information” described as “publicly observable online social information about other consumers’ actions” (p. 51) and operationalized as users’ self-reported past purchases. Based on the analysis of a large dataset from an online beauty community, they find that behavior-based is more influential than the opinion-based information. Relatedly, Chen et al. (2011) investigated the impact of consumer opinions (eWOM) against what they referred to as “observational learning information” operationalized as the purchases of users, which is behavior-based information. Although this study did not directly compare the effectiveness of the two, the findings document a positive impact of both positive eWOM and the presence of behavior-based information. Further, in contrast to negative eWOM, the absence of behavior-based information (viewed in this study as negative behavior-based information) does not harm sales.
Across the two thematic approaches described above, social design and eWOM/social
interactions, scholars mainly apply the theoretical perspective of observational learning (Chen et al., 2011) and the related concept of informational cascades (Duan et al., 2009; Thies et al.
(2016). Informational cascades encapsulate the phenomenon when individuals follow the past behavior of others and disregard their own information (Huang & Chen, 2006). Based on these dominant theoretical perspectives it is reasonable to conclude that the current literature treats
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the presence of behavior-based information as a design element that aims to reduce consumers’
efforts in decision-making processes.
2.2.3 Current conceptualizations of behavior-based information in products & services In the above, I have shown how the disclosure of users’ behavior-based information within digital products and services can be viewed as a behavioral social interaction and how in extant literature this is often contrasted against the opinion-based social action, unanimously referred to as eWOM. However, while there is rich literature about the concept of eWOM, (cf. King et al.
(2014) and Cheung and Thadani (2012) for reviews), behavior-based social interaction is much less explored conceptually. Table 3 provides an overview of some of the various
conceptualizations of behavior-based information in studies that contrast opinion-based and behavior-based social interactions.
Table 3. Conceptualizations of behavior-based information in extant literature
Paper Type of Social Interaction Conceptualization
Chen et al. (2011) Opinion “WOM”
Behavior “Observational learning information”
Cheung et al. (2014) Opinion “eWOM”
Behavior “Action-based information”
Thies et al. (2016) Opinion “eWOM”
Behavior “Popularity information”
Libai et al. (2010) Opinion “WOM”
Behavior “Observational learning”
Firstly, it is evident that there is a lack of a common concept to capture behavior-based social interaction. Whereas the opinion-based is unanimously referred to as eWOM, multiple
different concepts are used to describe the behavior-based. In other cases the behavior-based is not even explicitly recognized as being based on behaviors, but rather regarded as an
instantiation of peer-to-peer influence (Aral & Walker, 2011; Bapna & Umyarov, 2015). I argue that such lack of a common theoretical ground hinders the effective accumulation of
knowledge in a given field, as posited by Gregor (2006) and King et al. (2014).
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Secondly, the absence of a common concept and nuanced insights into the characteristics and building blocks of behavior-based information poses challenges in terms of comparison across empirical findings. Specifically, although extant literature does provide empirical insights into the positive impact of behavior-based information, these studies are based on very different types of behavior-disclosure, and the nuances of these are not explicated. Rather, the current application of behavior-based information seems to suffer from a taken for granted-ness.
Thirdly, the use of the term “observational learning information” employed by Chen et al.
(2011) and Libai et al. (2010) presupposes that behavior-based information always has an impact, and a specific kind that relates to learning from the observation of others. This is not the case, just as a review (a piece of eWOM) need not always lead to an impact on those exposed to it – it depends on various factors such as the expertise and the trustworthiness of the person crafting the review (Cheung & Thadani, 2012). Such more nuanced mechanisms of behavior-based information are still to be uncovered.
Finally, the use of the term ‘popularity information’ signals that behavior-based information carries a specific meaning among those exposed to it. This might be the actual meaning ascribed to behavior-based information, however, to the best of my knowledge, there is currently no evidence that this is the case, and researchers have recently been warned to not jump to conclusions about how such subtle traces of behavior are interpreted by users (Freelon, 2014).
2.2.4 Synthesizing the status quo of current literature
Summarizing on the above review of literature on the use of behavior-based information in products and services, it is evident that the disclosure of user behavior has been found to significantly affect other users’ choices and behaviors, in some cases more so than the
disclosure of users’ opinions (Cheung et al., 2014; Wenjing Duan et al., 2009; Thies et al., 2016).
Further, the impact is moderated by factors such as tie strength (Bond et al., 2012), total amount of ties of a user (Bapna & Umyarov, 2015), impact over time (Thies et al., 2016), size of potential market for the product (Tucker & Zhang, 2011), and user expertise (Cheung et al., 2014).
Finally, such use of behavior-based information has been recognized to be part of designing a product with mechanisms that support marketing goals (Aral & Walker, 2011; Bapna &
Umyarov, 2015; Dou et al., 2013).
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However, what is also evident from the reviewed literature is that it suffers from a lack of conceptual clarification which a) hinders the effective accumulation of knowledge b) neglects important nuances, which make it hard to compare findings as well as set guidance for future research. Further, extant empirical findings at the individual-specific level lack insight into the impact when incorporated into a product (rather than disclosed on an external platform such as Facebook). Finally, extant research is dominated by large-scale experiments all of which apply a business-perspective. Building on Freelon's (2014) recent call for more research that applies a user-perspective on such digital traces of behavior, I argue that this lack of user-perspective risks faulty design and untapped opportunities.