How does social network diversity affect users’ lurking intention toward social network services? A role perspective

Journal Pre-proof How does social network diversity affect users’ lurking intention toward social network services? A role perspective Xiaodan Liu (Conceptualization) (Methodology) (Investigation) (Formal analysis) (Writing - original draft) (Writing - review and editing), Qingfei Min (Conceptualization) (Methodology) (Investigation) (Writing - review and editing) (Funding acquisition) (Supervision) (Project administration), Dezhi Wu (Investigation) (Writing - review and editing) (Funding acquisition), Zilong Liu (Conceptualization) (Methodology) (Investigation) (Writing - review and editing) (Funding acquisition)

PII:

S0378-7206(18)30727-4

DOI:

https://doi.org/10.1016/j.im.2019.103258

Reference:

INFMAN 103258

To appear in:

Information & Management

Received Date:

4 September 2018

Revised Date:

13 December 2019

Accepted Date:

16 December 2019

Please cite this article as: Liu X, Min Q, Wu D, Liu Z, How does social network diversity affect users’ lurking intention toward social network services? A role perspective, Information and amp; Management (2019), doi: https://doi.org/10.1016/j.im.2019.103258

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How does social network diversity affect users’ lurking intention toward social network services? A role perspective How does social network diversity affect users’ lurking intention toward social network services? A role perspective Xiaodan Liu School of Economics and Management, Dalian University of Technology, 2 LingGong Road, 116024 Dalian, China Email: [email protected]

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Zilong Liu (Corresponding Author) Associate Professor School of Management Science and Engineering, Dongbei University of Finance and Economics, 217 Jianshan Street, 116025 Dalian, China Email: [email protected]

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Dezhi Wu Associate Professor Department of Integrated Information Technology University of South Carolina, 550 Assembly Street, Columbia, SC 29208, USA Email: [email protected]

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Qingfei Min Professor School of Economics and Management, Dalian University of Technology, 2 LingGong Road, 116024 Dalian, China Email: [email protected]

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Abstract With the increase of users’ social connections on social network services, their social circles become more diverse, which make it difficult to maintain their ideal images and thus lead to their lurking behaviors. Based on the self-discrepancy theory, an integrated model was proposed to link role-related constructs to lurking intention. Results based on data from 641 WeChat users show that social interaction anxiety and disappointment positively influence lurking intention. Role conflict and role overload positively influence social interaction anxiety and disappointment. Moreover, role overload positively moderates the relationship between role conflict and disappointment. Lastly, study implications and conclusions are discussed. Keywords: Lurking; Role Conflict; Role Overload; Self-discrepancy Theory; Social Network Services

Keywords: Lurking; Role Conflict; Role Overload; Self-discrepancy Theory; Social Network Services

1

Introduction

As an emerging communication paradigm, today’s social network services (SNSs) have become ubiquitous for users to create, consume, and distribute digital contents [1]. Popular SNSs, including Facebook, Twitter, Snapchat, WeChat, etc. enable users to effectively broadcast and share information about their activities, opinions, and statuses in their diverse social networks. According 1

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to a recent report [2], as the most popular SNS in China, WeChat has approximately 1.098 billion monthly active users. The number of Facebook monthly active users in the globe is around 2.32 billion [3]. With the extensive use of SNSs in people’s daily lives, their social networks have been widely extended. The average number of friends per WeChat user is about 128 [4], and the average number of friends per Facebook user is roughly 338 [5]. As such, their social networks become more diverse and complicated, so one user can have multiple, and often conflicting, roles in their different social networks on an SNS. For example, one’s roles can be both a coworker and a boss in a job-related social network, while being a parent and a child in a family social connection all at once. The variety and complexity of such social relationships on SNSs have also been termed as “Social Network Diversity.” [6]. When participating in social communications, users intend to behave and evaluate themselves based on the same social network criteria in evaluating its members [7]. Thus, a series of relevant norms and expectations are implicitly formed to guide user behaviors in their respective social networks on SNSs, in which they play different roles and fulfill their different and conflicting role expectations. Traditional offline social environments are constrained by temporal and spatial separations [7], which limit users to regularly play their sole roles that fit in their specific situations, e.g., coworkers only play a colleague role at a workplace. By contrast, these constraints diminished on SNSs [8], which lead to more explicit role conflict and overload for their users. SNSs allow users to have more control over their self-presentational behaviors by providing “asynchronous” and “controllable” ways in comparison to traditional face-to-face communications [9]. By creating online self-presentations, SNS users have options to optimize their online images, such as sharing their best photos; thus they can manage their self-images more strategically than in face-to-face situations [10]. Therefore, impression management has been considered as a significant motive for users to participate actively in SNSs [10-13]. However, the stresses caused by role issues make it challenging for users to adjust their behaviors effectively, and even destroy their impression management plans, in particular, for those whose social networks are more diverse [8]. Because of time and cognitive constraints, an increase in role conflict or role overload results in users not being able to effectively cope with them. Lurking is thus chosen as a much safer and easier social strategy for users to cope with such difficult situations. For example, according to an interview asking ten active WeChat users: “When you use WeChat, have you ever been troubled by role conflict or role overload?” A participant who is a Ph.D. student stated: “I would never post on WeChat Moments since my supervisor was on my contact list. It’s terrible to post something that has nothing to do with my PhD project.” Another participant who is a company employee expressed: “I have experienced a lot of trouble from role conflict and role overload. That’s why I have two mobile phones with two WeChat accounts respectively, one just for work and the other for life.” Considering that SNSs do not create content by themselves, the survival and prosperity of SNSs business entirely rely on usergenerated content. Because user lurking phenomenon is common for today’s SNSs [14-16], the sustainability of SNSs turns out to be a challenge. As such, it is crucial for businesses to understand the underlying mechanisms of how to increase their user engagement, especially for lurkers, and to maintain the well-being of their users. User participation in SNSs often follows Pareto’s 80/20 law, meaning that 80% of the content on SNSs are created by 20% of the active users [17]. For SNS providers, users’ active participations significantly contribute to the success of their products; lurking is not desirable to their business. For SNS users, lurking, as a maladaptive coping strategy, can reduce their short-term stress at the expense of increased long-term stress [18]. Therefore, this study aims to explore how social network diversity leads to SNS users’ lurking intention from a role perspective, which has been largely ignored and unknown in the current lurking literature. Specifically, this study used WeChat Moments (WM) as the target SNS. WeChat is a super app in China [19], which had around 1.098 billion monthly active users in December 2018 [2]. It has similar features as WhatsApp to generate both text and voice messages. Users can communicate and interact with friends and others through text messaging, hold-to-talk voice messaging, one-to-many messaging, many-to-many messaging, and photo/video sharing. WM (see its snapshots in Appendix A) is the most dominant SNS in China. The social networks established by WM users differ from other traditional online communities that are usually composed of strangers. WM social networks are mainly established by close and strong connections and supplemented by weak links, and more than 80% users on WM are relatives, friends, schoolmates, acquaintances, and colleagues who are familiar with each other in real life [20]. The composition of characteristics indicates that role issues are more salient on WM than most online communities; thus, it is appropriate for this research context. Previous studies have reported that social networks that are high in diversity are associated with a range of positive outcomes including access to job information [21]; better physical and mental health [22]; less anxiety, depression, and psychological distress [23]; health benefits; [6, 24] and economic development of communities [25]. Building on the social capital perspective, these studies generally persist that the diversity of social networks is a direct measure of the resources that people can access through their networks. The resources that are embedded in networks are termed as social capital [26]. Those with higher social capital can access more support and are exposed to more diverse information, which is associated with those positive outcomes. SNSs allow people to more easily access social support from outside the neighborhood setting, reducing reliance on local ties, but increasing opportunities for the formation and maintenance of more diverse ties overall [27]. SNSs provide users an online social environment, where diverse social networks are collocated; and thus, the boundaries of their various roles have been blurred. The focus of this study is on the influence of social network diversity from a role perspective, from which we argue that role issues are more salient on SNSs and will lead to adverse outcomes, such as vulnerable emotions, which are likely to result in users’ lurking behaviors. To address this gap, we developed an integrated model combining two impression-related theories (i.e., role theory and self-discrepancy theory) to explore the effects of social network diversity on users’ lurking intention toward SNSs. Specifically, we used role stressors as the agencies of social network diversity and used the Self-Discrepancy theory (SDT) to explain the logic between role stressors and lurking intention. The traditional Role theory [28] is appropriate to reflect the degree of perceived social network diversity, but cannot adequately explain users’ lurking behaviors because of its weakness in elaborating on users’ motivational behavior [29]. Whereas, SDT [30] provides a sound paradigm to further understand the users’ vulnerable emotions aroused by failing expected duties or expectations, which can lead to behavioral outcomes. By integrating these theoretical lenses, we focus on the role of social 2

network diversity by examining how role conflict and role overload influence users’ lurking intention through their vulnerable emotions on SNSs. This study contributes to the literature in several significant ways: first, it provides new insights into social network diversity by considering its adverse outcomes on SNSs from a role perspective, which are contrary to the predictions in previous research and are largely ignored. Second, introducing the SDT, which classified three types of online self-state representations (i.e., online actual self, online ought self and online ideal self), is a novel notion of understanding the users’ roles on SNSs. The use of SDT as an overarching framework allows us to examine individual lurking intention as the results of vulnerable emotions (i.e., social interaction anxiety and disappointment), which soundly enriches the current personal traits and motivation-focused lurking literature. Third, by integrating both the Role theory and the SDT, we were able to identify the underlying mechanism between role-related constructs and lurking intention mediated by the users’ vulnerable emotions. The integration between the Role theory and the SDT also theoretically extends these two theories. Therefore, our study findings theoretically enrich our understanding of the lurking phenomenon on SNSs with empirical evidence. The remainder of the paper is organized as follows. In Section 2, the related theoretical background is introduced, followed by the proposed research model and hypotheses in Section 3, and then the research method and data analyses are presented in Section 4 and Section 5, respectively. Afterwards, in Section 6, the study findings are reported. Lastly, study conclusions and implications are discussed in Section 7.

2

Theoretical Background

2.1 Lurking

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Lurking is associated with non-posting behaviors [31], and is defined as a type of inactive online user behaviors with very seldom posting [32], being silent [33], nonparticipation [31] or without contributing inputs [34]. The lurking phenomenon has been extensively examined in the context of online communities, in which several theoretical lenses are explored. First, personal trait theory regards lurking as a personal trait. Under this theory, lurkers are a type of person (namely loafer, freeloader) [35], that is, lurkers are “born” that way, and thus, their behaviors can be predicted as a personal trait [36]. Second, engagement theory predicts that both lurking and contributing are a form of engagement in the online community, where some users engage to a greater degree (contributors), and some users engage to a lesser degree (lurkers) [37-39]. Third, social learning theory postulates that a new member of a community will begin with relatively few contributions, perhaps initially with no contributions, as a lurker. Over time, the new member gains knowledge and confidence and begins to contribute [40, 41]. Fourth, Uses and Gratification theory states that the biggest reason for lurkers was to satisfy their information needs without posting [42-44]. Specifically, Preece et al. [42] proposed the top five reasons for lurking: not needing to post, needing to find out more about the group before participating, thinking that they were being helpful by not posting, not being able to make the software work (i.e., poor usability), and not liking the group dynamics, or the community was a poor fit for them. Through a literature review, Sun et al. [16] identified four types of lurking reasons in online communities: environmental influence, personal preference, individual-group relationship, and security consideration. SNSs differ from traditional online communities in several aspects (see more details in Table 1). Fundamentally, SNSs are designed to help people build social networks and establish their online presence, while the majority of traditional online communities are established to improve one’s understanding of the topic [45]. Thus, users on SNSs make connections mainly because they are interested in the user behind the profile and want to maintain their reciprocal relationships, which are likely to lead to more personal social interactions and strong social ties. Furthermore, users’ social networks on SNSs are high in diversity, and thus, have as many identities as distinct networks of relationships in which they take positions and play roles. SNSs technically collocate all users’ social roles together, which are likely to hinder their online presence and relationship maintenance, and ultimately influence their online participations. Consequently, the user motivations for visiting and postings on SNSs are different from those in traditional online communities.

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Table 1. Social Network Services vs. Online Communities

Definition

Motivations posting

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Social Network Services (SNSs)

Online Communities

Social network services (SNSs) allow individuals to (1) construct a public or semipublic profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system [46].

Online community is a collective group of entities, individuals, or organizations that come together either temporarily or permanently through an electronic medium to interact in a common problem or interest space [47].

Self-presentation, relationship development.

Knowledge sharing, reputation establishment.

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Users make connections mainly because they are interested in the user behind the profile and want to maintain their reciprocal relationships [46]. Thus, the connections come before contents on SNSs [45].

Users make connections because they are interested in specific contents that a user provides and want to share their similar interests and expertise [47]. Thus, the contents come before connections in an online community [45].

Nature information

SNSs provide users with the important means for them to make new friends, connect/maintain with current friends, reconnect with old friends, share information, and establish/develop a sense of belonging [48]. A user on SNSs is engaged in social activities with other users, so topics on SNSs are typically related to the individual person.

Users in an online community come together as they have a vested interest in the discussion themes, and ultimately community members’ interactions transform into shared practices of the community [49]. Discussions and comments are based upon contents posted, which focus on a specific theme that interests the community.

Tie strength

Users on SNSs are primarily communicating with people who are already a part of their extended social networks [46]. Social interactions on SNSs are more interpersonal, multiplex and frequent, thus, social ties on SNSs are strong.

Social exchanges in an online community mainly occur between strangers rather than acquaintances in real life [50]. Thus, social ties in an online community are weak.

Identity

SNSs provide users with an online presence that contains shareable personal information, such as a birthday, hobbies, preferences, photographs, writings, etc. [51]. Each posting (i.e., creation of user-generated contents (UGC)) is an exhibition or creation of the poster’s self-identity, and each view (i.e., consumption of UGC) is an acknowledgement or consumption of a user’s self-identity. [52]. The social networks on SNSs are believed to reflect the real-life social relationships of people more accurately than any other online networks [51]. Most users have their entire social networks on SNSs, which include their relatives, friends, acquaintances, coworkers, and beyond [53]. These different social circles often have both different background and different behavioral norms. Therefore, social network diversity on SNSs is high.

Users in an online community are likely to have similar goals, rules, and interests, and therefore share a common group-identity, which refers to the common cognitive state of users, as well as moral and emotional connections with the online community [16].

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Due to the wide distribution of users in an online community, the user population often cross cultural and geographical boundaries. While community members often have no idea of the demographics of other members and have even less idea of what they are like, what they know is solely upon the similar interests and needs to follow the same group norm in one online community. Social connections in an online community serve as a subnetwork of the user, which relates to a specific social network. Therefore, social network diversity in an online community is low.

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Social network diversity

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Main purposes to make connections

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Moreover, the factors influencing the user’s lurking behaviors on SNSs should also differ. The current limited SNSs lurking studies mainly followed the traditional online communities by comparing the differences between lurkers and contributors [50, 5456]. The identified influencing factors including personal traits [55], demographics [54], shyness [56], computer anxiety [57], privacy risk [58], etc., were not significantly different from previous lurking studies on online communities. As a result, the most essential difference between SNSs and online communities was still unexplored, because of lack in the integration of unique characteristics of the complexity of users’ roles and their dynamics on SNSs. Up to now, to our best knowledge, no related lurking research has empirically examined this perspective on SNSs. Moreover, previous studies on lurking phenomenon have presented conflicting opinions without identifying its underlying mechanism. On the basis of whether users’ behavior is autonomous, some previous studies have considered lurking to be positive and should be encouraged, while other studies have found lurking to be negative [59]. However, sometimes users prefer to lurk because of their fear of the negative outcomes [58], or being uncomfortable in online social environments [7]. One of the key reasons for such user behaviors is likely to be users’ diverse roles on SNSs. Thus, we embarked on exploring the critical drivers for lurking on SNSs through a role perspective to enrich the current SNSs literature. Technically, the concept of lurking has some overlaps with passive SNS use, namely non-posting behaviors on SNSs. As one of the three types of specific SNS activities based on whether and where the users create contents (i.e., passive SNS use, active private SNS use, and active public SNS use), lurking is defined at the general level to the extent that a user does not create contents for a relatively long period [31]. Whereas, passive SNS use is referred to as monitoring other people’s lives by viewing the contents of others’ profiles based upon the frequency of a user visits others’ profiles or posts on SNSs [60]. An SNS user can have all three specific types of activities with varying degrees, but only one general degree of lurking. Moreover, studies on passive SNS use mainly emphasize on its outcomes, whereas research on lurking primarily focuses on its antecedents. The difference was caused by their different starting points, given that the passive SNS use starts from users’ well-being and mental health [60-64], and lurking starts from the non-posting phenomenon as a whole.

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2.2 Social Network Diversity from a Role Perspective

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Contrary to traditional offline social environments, SNSs free up users from the geographical boundaries and alter their structure of social interactions [7]. Various social networks (e.g., families, coworkers, and friends) of a user can be collocated on SNSs, and thus result in social network diversity on SNSs. Due to various roles that users taking in different social environments, their diverse roles need to be justified when the environment changes. A role is generally defined as a person’s perception about himself/herself when he/she relates to the surrounding environment and people whom he/she interacts with [65]. As an example, an individual may serve as a father in a family, as a subordinate at work, or as a friend with some familiar people. When people intend to conduct cross-boundary activities, they need to adjust their roles properly. The role transitions in such social dynamics become more challenging, when a person moves among more segmented roles [66]. Role ambiguity, role conflict, and role overload are identified as three typical role stressors [67] that make role transition more challenging [68]. Role ambiguity describes users’ uncertainty about their assigned role as a result of confusion about the conveyance of rolerelated information in unfamiliar work or personal social environments [69]. In the workplace, role ambiguity can occur when an individual has insufficient information to carry out job duties, when management expectations are vague, or when uncertainty exists concerning job requirements [70]. In the context of information communication technologies, role ambiguity can take place when the individual is faced with learning new technologies [71]. Users’ social networks on SNSs are believed to reflect the real-life social relationships [51], and most of their social networks are related to their relatives, friends, acquaintances, coworkers, etc. [53]. Thus, the role norms and expectations from different groups turn out to be explicit for users, due to their relatively stable roles on SNSs. Therefore, role ambiguity would not to be a significant issue on SNSs induced by social network diversity, given that we examine the users’ roles from a self-standpoint of view based on SDT in this study. As a result, role ambiguity was excluded from the analysis in this study. Role conflict is defined as incompatibility and incongruity in the expectations associated with a particular role [72]. When users participate in conversations on SNSs, social activities can be conducted across multiple social networks, expectations are usually inconsistent and conflicting from each other, so to the likelihood to result in role conflicts is high. For example, users from a job-related group tend to evaluate an individual by how hard he/she is working, while people from a game group may evaluate an individual through his/her game skills. Role overload is referred to as various role expectations regarding the focal person exceeding the amount of resources available to manage them appropriately [73], because of limited cognitive resources that a user can process within a given period of time [74]; the increases in role overload results in individuals being overwhelmed to process information [75]. For instance, a user can be engaged in multiple conversations with diverse groups of people on SNSs simultaneously, and therefore, the active engagement in all conversations on SNSs, without making errors and adjustments, can be a real challenge to an SNS user. Accordingly, role conflict and role overload are two consequences of role transitions [7, 76], which can be induced by multiple and possibly conflicting social networks under an SNS social context. Furthermore, role conflict and role overload can originate in social networks, which cause role incumbents to handle their behaviors inadequately [77]. When social conversations involve conflicting roles, a user has to face the challenges to satisfy someone’s expectations while fulfilling another’s. Furthermore, when the number of intertwining social networks becomes excessive, the social conversations can overwhelm the user. As such, role conflict and role overload can make it difficult for users to adjust their behaviors appropriately on SNSs, and thus, cause them to fail to live up to the expectations from others or themselves. As a consequence, the users may get negative evaluations from others and fail to maintain their online images. The discrepancies between how they behave and how others or they expect themselves to behave are likely to make them reflect and change their behaviors in the future. In this context, the SDT provides a sound paradigm to understand how these discrepancies affect people’s emotions and produce behavioral outcomes [30]. Therefore, SDT serves as the theoretical basis for this study described as follows.

2.3 Self-discrepancy Theory (SDT)

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As part of impression management-related theories, SDT provides a paradigm to understand vulnerable emotions aroused by failing to fulfill assigned duties, obligations, and expectations during social conversations. In particular, it predicts how different types of discrepancies between self-state representations are related to various emotional vulnerabilities [30]. This theory postulates two cognitive dimensions underlying the self-state representations: domains of the self (actual, ideal, and ought) and standpoints on the self (own, other). Self-discrepancy is the gap between two of these self-representations, representing different types of negative psychological situations associated with different kinds of discomfort. SDT provides a framework of how selves are related to effects [78]. People attempt to obtain responses from others that confirm their self-concepts. Conflicts or inconsistencies between an individual’s self and external behavioral feedback can occur from the individual’s responses or the responses from others. When people behave in a manner that is inconsistent with their self-concepts, they experience discomforts[79]. Therefore, SDT is critical to systematically define different self-states by distinguishing the different domains of the self in terms of their different standpoints, which, in turn, clearly distinguish the causes of two different emotional vulnerabilities (i.e., social interaction anxiety and disappointment). The actual self is your representation of the attributes that someone (yourself or another) believes you actually possess [30]. Self-understanding is a major motivation for individuals to use SNSs, as they can obtain insights into themselves by interacting with others [80]. By talking, discussing, and responding to opinions with others or posting on SNSs, individuals can further comprehend their own beliefs, behaviors, and self-concepts. This highlights the actual/own self-state representation. The ought self is your representation of the attributes that someone (yourself or another) believes you should or ought to possess (i.e., a representation of someone’s sense of your duty, obligations, or responsibilities) [30]. When people participate in a social network on SNSs, they intend to behave and evaluate themselves along with the same values used by the social network in evaluating 5

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its members [7]. The norms from the various social networks provide a frame of reference for other peers to evaluate the focal person. These norms consist of a set of distinctive expectations, which remind the focal person what he/she can do or not. This highlights the ought/other self-state representation. The ideal self is your representation of the attributes that someone (yourself or another) would like you, ideally, to possess (i.e., a representation of someone’s hopes, aspirations, or wishes for you) [30]. When an individual enters the presence of others, he/she will consciously or subconsciously try to influence the perception of his/her image by regulating and controlling information in social interactions [81]. Impression management is also considered to be a major motive for users to actively participate on SNSs [11]. Thus, their desired online images would be the ideal selves for the users. For example, people tend to portray an idealized version of themselves on dating websites by describing their weight as significantly less than the true amount [82]. Users on SNSs might describe themselves the way they want to be (e.g., beautiful, humorous, professional, etc.). This highlights the ideal/own selfstate representation. Given the above mentioned three salient self-state representations on SNSs, two types of self-discrepancies can be concluded, that is, actual/own: ought/other discrepancy and actual/own: ideal/own discrepancy. A discrepancy between one’s actual behavior and the behavior prescribed by significant others (an actual/own: ought/other discrepancy) signifies the presence of adverse outcomes, which has often been said to create social anxiety because of apprehension over anticipated sanctions or negative responses by others [30, 83]. Whereas, a discrepancy between what one actually is and what one wants or hopes to be (an actual/own: ideal/own discrepancy) signifies the absence of positive outcomes, which leads to disappointment for not achieving one’s pretensions or aspirations [30]. Social anxiety has been defined as the discomfort felt when in the presence of others [84], which can be conceptualized as the negative cognitive and affective responses to social situations (imagined or real), in which personal evaluation is present or expected [85]. The conflicting and overloaded role expectations on SNSs precisely form such a difficult situation to make users more vulnerable to negative evaluations, which, in turn, leads to their anxious emotion. Social anxiety is a broad construct, which includes social phobia and social interaction anxiety [86]. Social phobia refers to as anxiety and fear at the prospect of being observed or watched by other people, which is an extreme manifestation of “normal” social anxiety [87]. The individual expresses distress when undertaking certain activities in the presence of others. These activities may include eating, drinking, writing, signing one’s name, using public toilets, working, traveling on public transport in the view of others, walking in front of others, or simply by being looked at. Whereas, social interaction anxiety is an excessive fear of social situations or interactions with others, and of being evaluated or scrutinized by other people, particularly when encountering strangers in public settings [88]. The central concerns include fears of being inarticulate, boring, sounding stupid, not knowing what to say or how to respond within social interactions, and of being ignored. In this study, we employed social interaction anxiety as the agency of social anxiety, not only because of the suitable concept but also the valid measurement currently used to study an SNS context [88-90]. Disappointment is a psychological reaction to an outcome that does not match up to users’ own expectations [91]. It arises from comparing an obtained outcome with a better outcome that might have resulted from the same choice that users made [92]. These alternative outcomes may be real, or construed by the process of thinking counterfactually [93]. The disappointment emotion may cause people feeling that they are not always able to control their own destiny, so they perceive a lack of control [94]. Thus, the experience of disappointment involves feeling powerless, wanting to do nothing, and getting away from the situation, and turning away from the event. Moreover, disappointment occurs in situations in which the person does not feel responsible for the outcome, but in this case the disappointing event can be attributed to one or more other persons who are responsible [92]. Hence, users’ disappointment resulted in their complaining to the service provider, and in talking to others about the bad experience[95]. SNSs provide users a difficult external social environment where role conflict and role overload are severe, which is also beyond users’ control. Instead, it is easy for the users to attribute their disconfirmed expectations to the services. Therefore, disappointment is appropriate to reflect users’ actual emotion in such a situation. As a summary, based on SDT, we proposed that role conflict and role overload would be the main obstacles for users to fulfill others’ expectations and maintain their online images on SNSs, which will lead to the two discrepancies, namely the actual/own: ought/other discrepancy and the actual/own: ideal/own discrepancy. As the outcomes of the two discrepancies on SNSs, social interaction anxiety and disappointment would serve as two critical mediating variables to explain the influences between role stresses (i.e., role conflict and role overload) and lurking intention.

Research Model and Hypotheses

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This study assumed that social network diversity will influence users’ negative participation (i.e., lurking) on SNSs; thus, the users’ lurking intention was chosen as the dependent variable. According to a review study of lurkers [31], lurking intention is defined as the intention to decrease or discontinue posting content on SNSs in this study. Our proposed research model is illustrated in Figure 1. In general, the model suggests that role conflict and role overload lead to social interaction anxiety and disappointment, which, in turn, influence users’ lurking intentions. In addition, we expect that role overload is likely to moderate the effects of role conflict on social interaction anxiety and disappointment.

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Notes: “—” lines in the figure represent the hypotheses; “- -” lines in the figure represent the control effects. Figure 1. The Research Model.

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3.1 The Impacts of Social Interaction Anxiety and Disappointment on Lurking Intention

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Social interaction anxiety arises when people are motivated to make a preferred impression on real or imagined audiences but doubt they will do so, and thus perceive or imagine unsatisfactory evaluative reactions from subjectively important audiences [85]. In the SNSs context, the central concerns of social interaction anxiety include fears of being ignored, boring, sounding stupid and not knowing what to say or how to respond to others within social interactions. Users that perceived social interaction anxiety are likely to feel uncomfortable to post on SNSs or communicate with others [90]. Anxious individuals often avoid social interactions when associated with stress [96] and engage in safety behavior to alleviate anxiety [97]. Hence, for the fear of misusing words in social interactions, anxious users appear to minimize self-disclosure to avoid a negative social outcome, as part of their safety behaviors [89, 98]. As a result, lurking would be a safer option for the anxious users on SNSs without being actively involved in conversations [57]. Therefore, we proposed the following hypothesis: H1: Social interaction anxiety positively affects lurking intention of users on SNSs. Disappointment is one of the most frequently experienced negative emotions after users fail to live up to their expectations, which are closely associated with hope, desire, and promise [99]. SNSs provide users with an online platform to exhibit or create their own self-identities [51]. Individuals shape their identities through their online profiles, which give them the flexibility to control how they represent themselves so that users can manage their online images more strategically [52]. If their actual image management process fails to live up to their initial expectations, they will feel disappointed [91, 100]. The users’ experience of disappointment involves feeling powerless and exhibiting a tendency to do nothing and to get away from the situation. Feelings of powerlessness might lead people to think that making any decision at all will not make a difference, and could, therefore, lead to inertia[101], resulting in lurking being a future risk-avoiding strategy. Thus, we proposed the following hypothesis: H2: Disappointment positively affects lurking intention of users on SNSs.

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3.2 The Impacts of Role Conflict and Role Overload on Social Interaction Anxiety and Disappointment

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When users participate in communications on SNSs, the expectations from their various social networks provide a frame of reference for them to behave. However, expectations are usually incongruent or conflicting from different social networks. Under the situation of role conflict, users’ activities are usually accepted by some people but rejected by others [7, 102]. Thus, it is difficult or impossible for them to fulfill others’ beliefs about their duties, responsibilities, or obligations simultaneously. Clearly, there will be a discrepancy between what their actual presences are and the expectations from others. This will make users feel anxious because of apprehension over anticipated sanctions or negative responses by others [103, 104]. This leads to the following hypothesis: H3: Role conflict positively affects social interaction anxiety of users on SNSs. Impression management is regarded as one of the most important motivations for users to participate in social conversations on SNSs [11]. However, role conflict becomes one of the biggest obstacles to users’ impression management on SNSs. Under the situation of role conflict on SNSs, users often have to balance two or more conflicting activities, which make it challenging for them to regulate and control information during social conversations [7]. Sometimes, they need to break a rule or norm to complete the thing they would like to do, or they have to hide their true feelings and thoughts to appease the conflicting activities [102]. Thus, their actual presences would not be up to their own expectations to maintain their ideal online images. Therefore, there will be a discrepancy between their actual presences and their ideal expectations. This is likely to make users feel disappointed because they do not accomplish their own expectations, wishes, or aspirations [30, 91, 94]. This leads to the following hypothesis: H4: Role conflict positively affects the disappointment of users on SNSs. SNSs provide users a social environment where all their social networks are collocated; thus, they need to change their roles from conversation to conversation to behave appropriately. When the number of roles from multiple networks becomes excessive, the number of social roles that a user takes on SNSs could be overloaded [7]. Role overload leads to users’ lack of personal resources needed to fulfill others’ expectations, and thus cannot handle them comfortably [77]. Because of cognitive constraints, users are limited in the amount of information they can process within a given period of time [74]. An increase in role overload is likely to 7

result in overwhelming users to effectively deal with multiple role demands [7], which cause the discrepancy between what their actual presences are and the expectations from others. This discrepancy can probably make users feel anxious because of apprehension over anticipated sanctions or negative responses by others [30]. This leads to the following hypothesis: H5: Role overload positively affects social interaction anxiety of users on SNSs. The more the number of roles that an individual needs to take on SNSs, the more role expectations the individual needs to handle and satisfy. An individual with a high degree of role overload will feel that the available resources are inadequate to deal with multiple role demand [105]. The overloaded role expectations on SNSs will make users too tired to participate in or enjoy the social activities, and even interfere with the things that they would like to do [7]. As such, they cannot create their ideal online images in the way they expected. Moreover, an increase in role overload will also make users feel powerless or lose control of maintaining their ideal online image because they have limited time and energy to cope with their numerous roles. This will disappoint users largely for not accomplishing or feeling powerless to obtain their own expectations, wishes, or aspirations [30, 91, 94]. This leads to the following hypothesis: H6: Role overload positively affects the disappointment of users on SNSs.

3.3 Moderating Effects of Role Overload

re

-p

ro of

SNSs free users from the geographical boundary and make all of their social networks be collocated, which result in a social environment where both role conflict and role overload are salient [7]. When users participate in social conversations on SNSs, they need to cope with both conflicting and overburdened role expectations. Thus, the effects of role conflict on social interaction anxiety and disappointment could be completely exaggerated by unfavorable situational appraisals, if users perceive a high level of role overload during their social conversations on SNSs. Unfavorable situational signals about the excessive roles will directly inform users to feel pressed and overwhelmed, as they need to allocate more resources to cope with the additional duties and responsibilities [75]. A strong sense of perceived role conflict on SNSs makes users incapable of balancing conflicting expectations. What’s more, a high level of role overload results in competing users’ limited available time and cognitive resources. Users who experience role overload are likely not to be able to effectively gain the necessary resources to cope with new role expectations caused by their role conflicts. In such a situation, the effects of the users’ natural apprehension about the potential self-discrepancies by their conflicting roles will become salient, when they have relatively unfavorable appraisals about the number of roles that they are taking. Therefore, with a high level of role overload, users who perceive more role conflicts are likely to feel much more challenged to decrease their self-discrepancies. Moreover, they will also perceive more self-discrepancies if they cannot cope with so many social roles during social conversations on SNSs. All these factors are likely to lead to high levels of social interaction, anxiety, and disappointment. Hence, we present the following hypotheses: H7a: The perceived role overload on SNSs positively moderates the relationship between role conflict and social interaction anxiety. H7b: The perceived role overload on SNSs positively moderates the relationship between role conflict and disappointment.

3.4 Control variables

Methodology

4.1 Instruments

ur

4

na

lP

Individuals’ demographic factors (i.e., gender, age, education, career, and income) and SNSs use of experiences (i.e., number of friends, tenure, frequency, and average time spent) were usually suggested to influence SNSs usage [106, 107]. Previous studies also confirmed the effects of privacy concern [107, 108], SNS self-efficacy [109], social interaction need [110], and self-expression need [110] on SNSs posting behaviors. Privacy concern was also considered as a key influence factor of lurking [14, 16]. Therefore, all of them have been incorporated to the research model as control variables to the proposed lurking intention. Moreover, privacy concern is known to exacerbate cynical perceptions and induce worries about others’ opportunism [111]. Consequently, privacy concern was supposed to influence users’ online social interaction anxiety, for fear of someone accessing and misusing their personal information [112]. Furthermore, users often feel betrayed when their personal information is misused, thereby inducing a sense of unfairness, inequality, and emotional disappointment [113]. Thus, privacy concern was used as the control variable for social interaction anxiety and disappointment (see Figure 1).

Jo

The measurement instruments for this study have been theoretically validated by previous studies, so we directly adapted them for this study. As the original items were in English, the instruments were first translated into Chinese and then translated back to English (i.e., from Chinese to English) [106]. This process was performed by three professors who were proficient in both Chinese and English. Any disagreements or inconsistencies in the translation process were resolved to ensure that the final instruments were of high quality. Before the formal survey was conducted, a focus group consisting of three researchers from information systems research and five graduate students who are experienced SNS users was formed to verify these measurement items further. A pretest was also administered to 20 students who were SNS users. The respondents were also contacted for face-to-face interviews so that their opinions on the questionnaire were collected. After analyzing the respondents’ feedback, several minor revisions were made to refine the questionnaire before the official field survey study was conducted. The final scales are presented in Appendix B. All items were measured using seven-point Likert scales ranging from “strongly disagree” (1) to “strongly agree” (7).

4.2 Data Collection We developed a web-based survey to measure research variables and basic demographic information related to individual use of SNSs; in this study, we referred WM, the most popular SNS in Asia. To collect data, we closely worked with a marketing research firm that maintains a nationwide online SNSs panel of Chinese adults who are 18 years old or older to reach out to our survey respondents. To increase the response rate, we provided an incentive of RMB 10 (approximately US$ 1.46) for every respondent. For this study, we sent invitations through emails to randomly select 1,000 WM users in the marketing firm’s panel pool. From the 1,000 invitations, a total of 641 valid responses were received (64.1% response rate) over a two-week period. The nonresponse bias 8

was examined by comparing the means of all variables and demographics for early and late respondents. No significant differences were found from the t-test results. Table 2 shows the sample demographics, which were consistent with the actual user group characteristics of WM. Table 2. Demographic Characteristics of the Respondents Percent (%)

344 297

53.66 46.34

61 216 224 101 33 6

9.51 33.7 34.94 15.76 5.15 0.94

20 200 387 31 3

3.12 31.2 60.37 4.84 0.47

ro of

Frequency

na

lP

48 240 172 70 46 65

re

123 188 130 129 71

14.35 21.37 5.15 29.02 19.97 10.14

-p

92 137 33 186 128 65

2 153 278 143 65

Jo

5

19.19 29.33 20.28 20.12 11.08 7.49 37.44 26.83 10.92 7.18 10.14 0.31 23.87 43.37 22.31 10.14

ur

Measure Gender Male Female Age <=19 <=29 <=39 <=49 <=59 >60 Education High school or below Associate degree Bachelor’s degree Master’s degree Doctoral degree or higher Career Student Civil servant Institution staff Enterprise staff Self-employed Others Income (Yuan/monthly) <=2000 <=5000 <=8000 <=15000 >15000 Number of Friends <=100 <=200 <=300 <=400 <=500 >500 Tenure (Years since registered) <=1 <=3 <=5 <=7 >7 Frequency Hourly Several times a day Several times a week Several times a month less or none Average Time Spent <=15 min <=30 min <=1 h <=2 h <=3 h >3 h Total

88 405 124 14 10

13.73 63.18 19.35 2.18 1.56

23 100 201 178 71 68 641

3.59 15.6 31.36 27.77 11.08 10.6 100

Data Analysis

The proposed model and hypotheses testing were examined by using SmartPLS 3.0. PLS-SEM modeling has become popular in modern research, particularly because it has specific advantages, such as minimal demands on measurement scales, sample distribution, and sample size. The main objective of PLS-SEM analyses is to maximize the explained variance of a model’s 9

endogenous constructs [114]. It excels at causal-predictive analysis, in that hypothesized relationships are complex, and few bases have been established [20]. We chose PLS-SEM because it is intended to aid the discovery-oriented or theory development process for research seeking to identify key drivers of a construct, and its ability to model complex latent constructs with a large number of items [115, 116]. In a research model with only reflective constructs, the sample size required by PLS-SEM is at least 10 times that of the larger number of paths leading to an endogenous construct [117]. In our research model, all latent constructs were implemented as reflective, and the maximum number of paths entering an endogenous construct was 15. Therefore, the sample size of 641 was adequate to analyze our research model using the PLS technique.

5.1 Common Method Bias

Role Conflict -> Disappointment Role Conflict -> Social Interaction Anxiety Role Conflict -> Disappointment Role Overload -> Social Interaction Anxiety Role Overload -> Disappointment Social Interaction Anxiety -> Lurking Intention Notes: ∗∗ p < 0.01; ∗∗∗ p < 0.001.

5.2 Assessment of the Measurement Model

p-Value 0 0 0.085 0 0 0.108

-p

Without Marker Path 0.13*** 0.181*** 0.069 0.355*** 0.466*** 0.064

Relationships

ro of

As with all self-reported data, there is a potential for common method biases resulting from multiple sources such as consistency motif and social desirability [118, 119]. We performed statistical analyses to assess the severity of common method bias. First, following Podsakoff and Organ [118], a Harmon one-factor test was conducted on the six conceptually crucial variables in our theoretical model including role conflict, role overload, social interaction anxiety, disappointment, lurking intention, and privacy concern. Results from this test indicate that the most covariance explained by one factor is 24.284%. Therefore, no single factor could explain most of the variance, indicating that common method biases are not a likely contaminant of our results. Second, considering that the measured latent marker variable approach can help detect and control common method bias [120]. To apply the marker-variable technique, we carefully identified variables that would not relate to the phenomena under investigation. Therefore, fantasizing (Fant), which is defined as the extent to which one has a vivid imagination, was selected as a marker variable [121]. The results show that the path loadings and model fit values are consistent with the original estimates (shown in Table 3). Therefore, we conclude that common method bias is not a severe concern overall. Table 3. Measured latent marker variable approach for common method bias With Marker Path 0.129*** 0.143** 0.075 0.332*** 0.467*** 0.043

p-Value 0.001 0.005 0.068 0 0 0.3

Jo

ur

na

lP

re

The measurement model was assessed to ensure item reliability, convergent validity, and discriminant validity. A general method for checking individual item reliability involves seeing whether individual item loadings are above 0.60 or, ideally, 0.70 [122]. The measurement items in the model used in this study generally load heavily on their respective constructs (see Table 4), with loadings above 0.6, thus demonstrating adequate reliability. As shown in Table 5, the Cronbach’s α and composite reliability of each construct were higher than the recommended value of 0.8; the average variance extracted (AVE) of all the constructs were above 0.5, and the values of rho_A were more than 0.7 [123]. Hence, the internal consistency criteria were met. The third step in assessing the measurement model involves examining its discriminant validity. As shown in Table 6, the Fornell and Larcker criterion indicates that the square roots of the AVEs (the bold numbers in the diagonal) were higher than the correlations between the latent variable and other latent variables [124]. As shown in Table 7, the values of the heterotrait–monotrait ratio of correlations were below the threshold of 0.9 [123]. These results provided sufficient evidence of discriminant validity for these constructs. Discriminant validity is further confirmed when the loadings for the items on their targeted constructs are higher than loadings on other constructs in the model. Table 4 contains the loadings and cross-loadings for items used in this study; all items load more highly on their constructs than they load on any other constructs.

Table 4. Matrix of Cross-Loadings RC1

RC 0.722

RO 0.361

SIA 0.258

Dis 0.242

LI -0.019 10

PC 0.272

SE 0.156

SIN 0.205

SEN 0.2

0.052 0.081 0.031 0.049 0.131 -0.046 -0.054 -0.087 -0.077 -0.06 0.055 0.058 -0.014 0.118 -0.13 -0.072 -0.103 -0.054 -0.124 -0.065 -0.063 -0.127 -0.037 -0.139 -0.143 -0.153 -0.209 -0.153 -0.181 -0.138 -0.128 -0.278 0.091 0.124 0.171 0.079 0.83 0.89 0.75 0.359 0.406 0.375 0.367 0.36 0.388 0.361 0.348 0.4

0.105 0.15 0.08 0.107 0.199 0.03 0.004 -0.023 -0.006 0.111 0.178 0.126 0.083 0.252 -0.127 -0.024 -0.126 -0.102 -0.145 -0.119 -0.101 -0.193 -0.1 -0.173 -0.154 -0.211 -0.194 -0.138 -0.203 -0.169 -0.111 -0.276 0.114 0.176 0.245 0.162 0.385 0.451 0.346 0.691 0.805 0.784 0.812 0.769 0.527 0.536 0.561 0.537

0.101 0.137 0.072 0.056 0.184 -0.006 -0.019 -0.057 -0.033 0.072 0.152 0.081 0.071 0.203 -0.132 -0.039 -0.066 -0.061 -0.107 -0.113 -0.112 -0.159 -0.079 -0.196 -0.207 -0.19 -0.203 -0.145 -0.205 -0.174 -0.143 -0.246 0.096 0.138 0.2 0.123 0.402 0.448 0.322 0.433 0.54 0.528 0.58 0.558 0.77 0.827 0.71 0.79

ro of

0.209 0.158 0.197 0.294 0.289 0.284 0.27 0.281 0.333 0.246 0.394 0.402 0.321 0.252 0.226 0.239 0.153 0.195 0.148 0.049 0.026 0.077 0.091 0.108 0.063 0.094 0.107 0.111 0.129 0.081 0.092 0.066 0.791 0.867 0.887 0.845 0.092 0.15 0.092 0.217 0.154 0.152 0.13 0.163 0.174 0.087 0.146 0.122

-p

0.056 -0.029 0.147 0.209 0.027 0.227 0.309 0.337 0.319 0.12 0.116 0.133 0.187 0.064 0.309 0.249 0.249 0.18 0.268 0.276 0.26 0.274 0.275 0.742 0.751 0.822 0.838 0.848 0.863 0.841 0.836 0.728 0.133 0.13 0.043 0.084 -0.181 -0.204 -0.124 -0.128 -0.184 -0.193 -0.174 -0.185 -0.184 -0.219 -0.115 -0.188

re

0.188 0.173 0.266 0.424 0.223 0.393 0.442 0.474 0.374 0.298 0.237 0.235 0.238 0.125 0.755 0.742 0.754 0.696 0.774 0.613 0.601 0.699 0.681 0.241 0.252 0.281 0.266 0.284 0.297 0.334 0.323 0.35 0.167 0.243 0.136 0.152 -0.117 -0.105 -0.085 -0.111 -0.17 -0.161 -0.065 -0.096 -0.12 -0.109 -0.018 -0.126

lP

0.262 0.232 0.281 0.366 0.3 0.391 0.383 0.365 0.299 0.708 0.821 0.815 0.719 0.641 0.315 0.327 0.295 0.321 0.223 0.026 0.047 0.06 0.106 0.146 0.117 0.161 0.154 0.143 0.16 0.158 0.145 0.059 0.339 0.427 0.384 0.336 -0.018 0.057 0.067 0.204 0.123 0.129 0.143 0.17 0.141 0.096 0.18 0.08

na

0.359 0.271 0.452 0.805 0.671 0.852 0.853 0.844 0.7 0.329 0.382 0.324 0.359 0.244 0.452 0.461 0.459 0.447 0.367 0.165 0.144 0.226 0.232 0.227 0.195 0.253 0.247 0.264 0.24 0.323 0.293 0.241 0.326 0.332 0.295 0.276 -0.034 -0.03 0.003 0.016 0.038 0.008 0.051 0.091 -0.001 -0.014 0.076 0.01

ur

0.727 0.669 0.761 0.496 0.476 0.425 0.377 0.4 0.267 0.258 0.291 0.256 0.299 0.229 0.259 0.292 0.271 0.321 0.211 0.101 0.081 0.126 0.133 0.042 0.023 0.023 0.029 0.059 0.066 0.093 0.079 0.032 0.222 0.254 0.279 0.233 0.086 0.114 0.063 0.119 0.146 0.117 0.155 0.175 0.128 0.119 0.187 0.134

Jo

RC2 RC3 RC4 RO1 RO2 RO3 RO4 RO5 RO6 SIA1 SIA2 SIA3 SIA4 SIA5 Dis1 Dis2 Dis3 Dis4 Dis5 Dis6 Dis7 Dis8 Dis9 LI1 LI2 LI3 LI4 LI5 LI6 LI7 LI8 LI9 PC1 PC2 PC3 PC4 SE1 SE2 SE3 SIN1 SIN2 SIN3 SIN4 SIN5 SEN1 SEN2 SEN3 SEN4

Notes: Role Conflict (RC), Role Overload (RO), Social Interaction Anxiety (SIA), Disappointment (Dis), Lurking Intention (LI), Privacy Concern (PC), SNS Self-efficacy (SE), Social Interaction Need (SIN), and Self-expression Need (SEN).

Table 5. AVE, CR, CA, and rho_A Constructs

AVE

Composite Reliability (CR)

Cronbach’s Alpha (CA)

rho_A

Role Conflict

0.519

0.812

0.812

0.7

11

Role Overload Social Interaction Anxiety Disappointment Lurking Intention Privacy Concern SNS Self-efficacy Social Interaction Need Self-expression Need

0.626 0.553 0.501 0.655 0.72 0.681 0.598 0.601

0.909 0.86 0.898 0.945 0.911 0.864 0.881 0.858

0.909 0.86 0.898 0.945 0.911 0.864 0.881 0.858

0.892 0.811 0.888 0.935 0.882 0.807 0.841 0.804

Table 6. Discriminant Validity: Fornell-Larcker Criterion RC

RO

SIA

Dis

LI

G

A

E

C

I

NoF

T

F

ATS

PC

SE

SIN

0.72

RO

0.509

0.791

SIA

0.36

0.445

0.744

Dis

0.306

0.502

0.308

0.708

LI

0.061

0.314

0.171

0.362

0.809

G

-0.049

-0.129

0.025

-0.067

-0.041

sf

A

0.189

0.208

0.004

0.171

-0.14

-0.095

sf

E

-0.032

-0.001

0.013

-0.018

-0.042

0.015

0.221

sf

C

0.147

0.067

-0.05

0.118

-0.049

-0.106

0.3

-0.179

sf

I

0.143

0.139

-0.043

0.118

-0.124

-0.181

0.673

0.331

0.325

sf

NoF

0.068

0.025

-0.018

0.08

-0.102

-0.004

0.132

0.079

0.162

0.245

sf

T

0.093

0.05

-0.071

-0.005

-0.026

-0.03

0.333

0.207

0.249

0.351

0.201

sf

F

0.019

0.086

0.021

0.074

0.131

-0.05

-0.041

-0.172

-0.072

-0.041

-0.095

-0.112

sf

ATS

0.156

0.07

0.04

0.027

-0.126

0.039

0.172

0.121

0.085

0.165

0.192

0.292

-0.457

sf

PC

0.292

0.364

0.443

0.211

0.117

-0.044

0.131

0.105

0.038

0.125

-0.031

0.065

0.035

0.087

0.849

SE

0.11

-0.028

0.039

-0.125

-0.212

0.016

0.071

0.083

0.003

0.077

0.155

0.129

-0.11

0.121

0.138

0.825

SIN

0.185

0.054

0.193

-0.157

-0.227

0.068

0.065

-0.011

0.053

0.041

0.092

0.146

-0.069

0.158

0.206

0.482

0.773

SEN

0.174

0.014

0.15

-0.13

-0.236

-0.015

0.072

-0.038

0.031

0.041

0.057

0.088

-0.063

0.109

0.164

0.481

0.686

SEN

-p

ro of

RC

0.775

lP

re

Notes: (a) The bold fonts in the leading diagonals are the square root of AVEs. (b) Off-diagonal elements are correlations among latent constructs. (c) Role Conflict (RC), Role Overload (RO), Social Interaction Anxiety (SIA), Disappointment (Dis), Lurking Intention (LI), Gender (G), Age (A), Education (E), Career (C), Income (I), Number of Friends (NoF), Tenure (T), Frequency (F), Average Time Spent (ATS), Privacy Concern (PC), SNS Self-efficacy (SE), Social Interaction Need (SIN), and Self-expression Need (SEN). (d) sf: single factor.

Table 7. Discriminant Validity: Heterotrait-Monotrait Ratio of Correlations SIA

Dis

LI

PC

SE

SIN

0.652 0.481 0.356 0.112 0.372 0.147 0.246 0.25

0.527 0.522 0.338 0.421 0.114 0.107 0.107

0.328 0.196 0.517 0.119 0.255 0.21

0.404 0.211 0.146 0.196 0.159

0.128 0.24 0.251 0.264

0.163 0.247 0.206

0.597 0.609

0.856

na

RO

Jo

ur

Role Overload (RO) Social Interaction Anxiety (SIA) Disappointment (Dis) Lurking Intention (LI) Privacy Concern (PC) SNS Self-efficacy (SE) Social Interaction Need (SIN) Self-expression Need (SEN)

Role Conflict

5.3 Assessment of the Structural Model Figure 2 presents the results of the structural model. The model explains 30.3% of the variances in social interaction anxiety, 26.8% of the variances in disappointment, and 25.1% of the variances in lurking intention. We performed bootstrapping to determine the t-value and the significance level of each path coefficient. As shown in figure 2, H1 and H4 were marginally significant (p < 0.05). H3 was significant with a p value of <0.01. Hypothesized paths (H2, H5, and H6) were significant with p values of <0.001. H7b, which stated that role overload positively moderates the relationship between role conflict and disappointment, was supported (β = 0.104, t = 2.62, p < 0.01). However, H7a, which stated that role overload positively moderates the relationship between role conflict and social interaction anxiety, was not supported. Among the control variables, age, tenure, privacy concern, and selfexpression need were statistically significant. Age and self-expression need decreased the users’ lurking intention on SNSs, whereas privacy concern and tenure increased the users’ intention to lurk on SNSs. Moreover, privacy concern had a significantly positive effect on social interaction anxiety; however, the effect between privacy concern and disappointment was not significant. This could 12

ro of

be because privacy concern reflects the worry of anticipated privacy risks, which is more aligned with the core fear in social interaction anxiety. Whereas disappointment is usually caused by a negative outcome that has already happened and disconfirms one’s expectations.

Figure 2. Results of Hypotheses Testing

5.4 Overall Model Fit Evaluation

-p

We evaluated the goodness of fit of the research model by examining the standardized root mean square residual (SRMR), unweighted least squares discrepancy (dULS), and geodesic discrepancy dG values [123]. The lower the SRMR, dULS, and dG, the better the fit of the theoretical model [123]. Table 8 shows the results of the model fit. The SRMR value was below the recommended threshold of 0.08, and all discrepancies were below 95% of bootstrap quantile (HI95) [123], which suggests a good fit between the research model and the data.

Value

SRMR dULS dG

0.059 5.904 1.306

lP

Discrepancy

re

Table 8. Model Fit Evaluation

HI95

Conclusion

0.068 7.837 1.358

Support Support Support

5.5 Alternative Empirical Model Specifications

Jo

ur

na

To further assure the robustness of our findings, based on our research design, we also examined a few alternative models. Specifically, we considered (1) using a base model to account for the effects of control variables, (2) adding independent variables to the base model to account for the direct effects of role-related constructs toward lurking intention, and (3) a complete model in which direct, indirect, and interactive effects are present. The estimated results are shown in Table 9. Model 1 served as a baseline to compare the proposed model’s prediction of control variables, and it explained 14.9% of the variance of lurking intention. Model 2 included independent variables to predict lurking intention, and it explained 25.1% of the variance. Model 3 included social interaction anxiety and disappointment to predict lurking intention, and it explained 28% of the variance. Model 4 shows the results of the proposed research model in this study, which explained 25.1% of the variance. As shown in Model 3, and the results in Table 9, role overload and disappointment were found to have significant effects on lurking intention; however, role conflict was not found to have a direct effect on lurking intention. Compared with the Model 4, there is only a 2.9% increase in R2 when compared to the Model 3. Moreover, in the Model 3, the effect between social interaction anxiety and the lurking intention was not found to have a significant effect, and neither was the effect between role conflict and disappointment. Overall, Model 4 has a better explanation than Model 3.

13

Table 9. Summary of Alternative Models Testing Results with Coefficients and Standard Errors

Age –> Lurking Intention Education –> Lurking Intention Career –> Lurking Intention Income –> Lurking Intention Tenure –> Lurking Intention Number of Friends –> Lurking Intention Average Time Spent –> Lurking Intention Frequency –> Lurking Intention SNS Self-efficacy –> Lurking Intention Self-expression Need –> Lurking Intention Social Interaction Need –> Lurking Intention Privacy Concern –> Lurking Intention

Model 2

Model 3

Model 4

-0.037 (0.038) -0.109* (0.052) -0.011 (0.044) -0.005 (0.044) -0.075 (0.061) 0.099* (0.047) -0.027 (0.041) -0.057 (0.042) 0.067 (0.044) -0.102* (0.043) -0.125* (0.056) -0.118* (0.057) 0.203*** (0.035)

0.001 (0.036) -0.175*** (0.05) 0.011* (0.041) -0.005 (0.041) -0.063 (0.053) 0.101* (0.042) -0.041 (0.038) -0.08* (0.041) 0.033 (0.043) -0.066 (0.044) -0.102* (0.05) -0.13** (0.05) 0.071* (0.034) 0.059 (0.078) 0.334*** (0.041)

-0.011 (0.036) -0.188*** (0.049) 0.007 (0.041) -0.014 (0.039) -0.056 (0.052) 0.122** (0.042) -0.065 (0.039) -0.079 (0.041) 0.033 (0.041) -0.059 (0.041) -0.098 (0.051) -0.088 (0.051) 0.054 (0.039) -0.071 (0.043) 0.236*** (0.05) 0.029 (0.039) 0.256*** (0.051) 0.131** (0.049) 0.07 (0.042) 0.272*** (0.043) 0.39*** (0.044) 0.306*** (0.038) -0.014 (0.037) -0.018 (0.028) 0.106** (0.039) 0.516*** (0.03) 0.109* (0.044) 0.28 0.26

-0.033 (0.036) -0.165*** (0.048) 0.002 (0.042) -0.025 (0.04) -0.057 (0.053) 0.126** (0.043) -0.067 (0.038) -0.076 (0.04) 0.043 (0.04) -0.069 (0.041) -0.115* (0.051) -0.075 (0.052) 0.081* (0.039)

Role Conflict –> Lurking Intention

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Role Overload –> Lurking Intention

Role Conflict –> Social Interaction Anxiety

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Role Conflict –> Disappointment

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Social Interaction Anxiety –> Lurking Intention Disappointment –> Lurking Intention

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Gender –> Lurking Intention

Model 1

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Relationship

Role Overload –> Social Interaction Anxiety

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Role Overload –> Disappointment

Privacy Concern -> Social Interaction Anxiety Privacy Concern -> Disappointment

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Role Conflict * Role Overload –> Social Interaction Anxiety Role Conflict * Role Overload –> Disappointment Role Overload –> Role Conflict Social Interaction Anxiety –> Disappointment R2 for Lurking Intention Adjusted R2 for Lurking Intention

0.149 0.132

0.251 0.233

0.076* (0.038) 0.327*** (0.046) 0.132** (0.047) 0.084* (0.041) 0.273*** (0.043) 0.42*** (0.042) 0.308*** (0.037) 0.018 (0.035) -0.022 (0.028) 0.104** (0.04)

0.251 0.233

Notes: (a) Model 1 presents results with only control variables for lurking intention. Model 2 adds focal independent variables to Model 1. Model 3 adds the mediators to Model 2, which also includes the moderating effects and direct effects between independent variables and dependent variables. Model 4 is the proposed research model in Figure 1. (b) ∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001. 14

6

Discussion

6.1 Key Findings

6.2 Theoretical Contributions

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The objective of this study was to investigate the influence of social network diversity from a role perspective on users’ lurking intention on SNSs. The results indicate that both social interaction anxiety and disappointment have considerably positive effects on users’ lurking intention. This study also confirms that both role conflict and role overload have significant positive effects on users’ social interaction anxiety and disappointment, which substantially leads to users’ lurking intention. Moreover, role overload has a significant moderating effect on the relationship between role conflict and disappointment. However, our study results suggest that role overload does not reinforce the influence of role conflict and social interaction anxiety. This can be explained by the differences between social interaction anxiety and disappointment. In the context of SNSs, social interaction anxiety can be driven by a user’s cognitive appraisal of a specific activity (e.g., a single post). In a short and urgent time period, users’ cognition will be occupied by the most difficult situation, that is, they will first determine the conflicting primary roles and the most important social groups. Following this logic, the extent of social interaction anxiety will be mostly dependent on conflicting primary roles. Thus, even an increase in role overload, will do little on the perceived social interaction anxiety without increasing the conflict substantially. Whereas, disappointment is an emotion after users fail to live up to their expectations, which involve users’ feeling powerless, the tendency to do nothing, and to get away from the situation [94]. An increase in role overload will lead to a worse situation, which is mainly beyond users’ control and cause additional competing demands to their limited time and cognitive resources. The uncontrollable circumstances can further increase users’ disappointment of being powerless and the likelihood to do nothing. As a result, users’ role overload significantly moderates the relationship between role conflict and disappointment. Contrary to previous studies, which mostly reported that users who are connected to large numbers of social connections are more likely to post [125-127], this study provides a paradoxical and intriguing evidence that the increased numbers of social connections may decrease users’ intention to post on SNSs from a role perspective. Given that users’ social networks become more diverse and complicated, users’ self-presentations become more challenging, as they need to allocate more time and cognitive resources to cope with increasingly diverse and conflicting role expectations. In consequence, SNSs that are designed to enable selective and asynchronous self-image environment have been weakened. Because the majority of previous studies considered users’ social networks as one large community, our study findings uniquely identified the essential social dynamics by dividing a large SNS community into diverse sub-social networks caused by social network diversity, which can arouse users’ social interaction anxiety and disappointment based on users’ role conflict and role overload from a self-state representation point of view. Our study provides refreshing insights by empirically demonstrating the unknown lurking mechanism through a role perspective. Our findings also provide additional evidence that users’ vulnerable emotions, caused by others and themselves (i.e., social interaction anxiety and disappointment), are the important antecedents of their lurking intention on SNSs. Affect dominates social interactions, and it is the dominant currency in which social intercourse is transacted [128]. The intersection between users’ effect and technology has been widely studied in a context where technology serves as the stimulus of users’ feelings to understand users’ emotions in technology use behaviors. For example, organizational technology and IT implementation are often studied as the stimulus of user affect, because of the emotional reactions to IT in the workplace that influence task and tool adaptation behaviors [129, 130], attitude toward use [131], habit formation [132], and coping behaviors [133]. In this current study, we found that affective components can also be the significant drivers for users’ lurking intentions on SNSs. Our results verified that both social interaction anxiety and disappointment positively influence users’ lurking intention on SNSs.

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This study contributes to the literature in three significant ways. First, it provides new insights into social network diversity by filling an important research gap that users’ vulnerable emotions are largely overlooked and unexplored in the current SNS lurking literature. We successfully identified users’ two vulnerable emotions, including social interaction anxiety and disappointment, caused by their role conflict and role overload on SNSs, significantly result in users’ lurking intentions on SNSs. The study results are paradoxical to previous studies. Specifically, we explored the effects of social network diversity on users’ SNSs usage from two important aspects, that is; first, the increasing numbers of social networks cause users’ role overload and the incongruence of different social networks (i.e., role conflict). Our results suggest that users’ vulnerable emotions are dependent on their perceptions of role conflict and role overload. This finding highlights the importance of social network diversity on users’ SNSs usage. We contend that users’ social networks on SNSs should not be limited to examine as a holistic community but require a more in-depth analysis of users’ complexity and diversity of their social network structures, in that there are many distinguishable sub-communities among their online social networks, which may cause users’ lurking behaviors to differ from each other. Second, by introducing the SDT, we effectively identified the three types of online self-state representations (i.e., online actual self, online ought self, and online ideal self). The use of SDT as an overarching framework allowed us to examine individual lurking intention as the result of vulnerable emotions (i.e., social interaction anxiety and disappointment), which complement the existing lurking literature in an innovative role perspective that has not been elaborated in the information systems field. Third, by integrating role theory and selfdiscrepancy theory, we explained an underlying mechanism between role-related constructs and lurking intention, which was mediated by users’ vulnerable emotions. The integration between the role theory and SDT also theoretically extends these two theories. The findings enrich our understanding of lurking phenomenon on SNSs and provide new insights into social network diversity analysis on SNSs. Distinctive to the existing lurking literature, which emphasizes the effects of users’ traits, such as engagement, social learning process, or personal needs; we found that users’ vulnerable emotions are important influencing factors on their lurking intention. Moreover, distinguishing from previous studies which divided users into lurkers or contributors, we report that lurking serves as a social interaction strategy for SNSs users. Even contributors who post frequently can choose to lurk under some circumstances. Therefore, lurking should be differentiated in various contexts. Accordingly, lurking behaviors should be further empirically examined to understand their underlying mechanisms of social dynamics that may occur in SNSs in the future. 15

6.3 Managerial Implications Users’ active participation is the key to keeping the prosperity of SNSs, which generates new knowledge and values for businesses [134], economy [135], and public politics [136]. However, in most online communities, the reality shows that the majority of the users are lurkers who never contribute [137]. Our findings provide important evidence that users’ role conflict and role overload perceptions on their SNSs elicited their vulnerable emotions, i.e., social interaction anxiety and disappointment, which led to their lurking behaviors on SNSs. Given the business sustainability concerns, social media providers rely on users’ active posting behaviors to grow. First, the increasing social connections are likely to lead to more diverse social networks, which result in role conflict and role overload, so it becomes more challenging and effort-consuming for users to perform well during their social interactions. As a result, users’ propensity to participate in SNSs conversations could be reduced. Therefore, SNSs providers should consider providing their users with an intelligent context-aware relationship management tool to help them manage their various and increasingly complicated social connections. Second, our results show that role conflict and role overload are critical factors leading to users’ lurking behaviors. Therefore, SNSs providers should design more useful functionalities to effectively separate their roles during their social interactions in a non-intrusive way. Although the existing SNSs platforms (e.g., WM and Facebook, etc.) allow users to separate their friends into their preferred individual groups to share their personal information, the current mechanism is not fully effective in that it could be a tiring process for the users if they always manually choose, which people can see their posts. Thus, more effective and user-friendly mechanisms, like AI-based applications, should be developed to learn users’ current SNS posting behavior and suggest more customized controls over users’ role stresses, while recommending their postings to a particular group that they intend to see.

6.4 Limitations and Future Research Directions

Conclusions

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Several limitations of this study should be noted. First, in this study, we explored users’ lurking intention by considering the effects of their role conflict and role overload. More studies are needed to explore the antecedents of lurking intention in more aspects of their social contexts, which would yield a more holistic understanding of users’ lurking behaviors on SNSs. Second, we used only the role conflict and role overload to measure the effects of social network structure diversity on users’ lurking intention. We did not consider the priority, hierarchical structure, cultural norms, and importance among different roles in different social network contexts because the levels of stress from different roles can be different. Moreover, it is valuable to further explore how dynamic role transitions among different SNSs impact users’ lurking behaviors by looking into more comprehensive social network contexts rooted in different cultures, organizational norms, and role hierarchies. Third, our current cross-sectional study limits us to more accurately address actual users’ lurking behavior or bidirectional relationships. Future research may consider a field experiment to observe the objective behavioral and procedural outcomes through capturing users’ longitudinal social interactions on SNSs. Moreover, our data were collected from members of WM in China. Further research can consider collecting data from multiple types of SNSs, such as Facebook or Twitter.

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Given that SNSs enable users to reach out to people, they have greatly changed the ways by which we express and communicate among families, friends, colleagues, and many others anytime anywhere. Despite being the primary place for today’s users to express their emotions and share their daily lives with others, different role expectations on SNSs can be a barrier for users to enjoy social activities on SNSs. In this study, the SDT serves as the overarching framework to explore the effects that the role stresses on users’ lurking intention. Our results suggest that both role conflict and role overload have significant positive effects on social interaction anxiety and disappointment, which were the principal determinants of lurking intention. The influence of role conflict on SNSs disappointment was strong when users perceived a high level of role overload during social conversations. This study provides an innovative perspective to understand the complicated lurking behaviors on SNSs, offers practical implications to SNS providers, and also opens up future opportunities to further explore the lurking behaviors on SNSs.

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Funding This work is supported by the National Natural Science Foundation of China (Nos. 71772022, 71771040, 71431002, 71831003).

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Author Contribution Statement Xiaodan Liu: Conceptualization, Methodology, Investigation, Formal analysis, Writing - Original Draft, Writing - Review & Editing. Qingfei Min: Conceptualization, Methodology, Investigation, Writing - Review & Editing, Funding acquisition, Supervision, Project administration. Dezhi Wu: Investigation, Writing - Review & Editing, Funding acquisition.

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Zilong Liu: Conceptualization, Methodology, Investigation, Writing - Review & Editing, Funding acquisition.

Appendices

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Appendix A. Snapshots of WeChat Moments: (a) The navigation menu to enter WeChat Moments; (b) A post; (c) The post button for images or videos; and (d) The menu button to give a comment or like.

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ro of -p re lP na ur Jo Appendix B. Measurement constructs, items, and sources. Constructs

Items

Sources

Role Conflict

[RC1] I frequently receive incompatible expectations from two or more parties on WeChat Moment. 18

[7, 102]

[RC2] I do things on WeChat Moment that are apt to be accepted by one person and not accepted by others. [RC3] I often have to try to balance two or more conflicting activities on WeChat Moment. [RC4] I sometimes have to break a rule or norm to complete the thing I would like to do on WeChat Moment.

[RO1] I feel overburdened by different roles I have taken on WeChat Moment. [RO2] I have been given too much role expectation from other peers on WeChat Moment. [RO3] I feel I have too many roles to comfortably handle on WeChat Moment. [RO4] Different expectations from others on WeChat Moment make me too tired or irritable to participate in or enjoy the activities on WeChat Moment. [RO5] The amounts of roles I have to play interfere with the actual things I would like to do on WeChat Moment. [RO6] I think there is a need to reduce some roles I have taken on WeChat Moment.

[7, 75, 77]

Social Interaction Anxiety

[SIA1] I get nervous if I have to interact with someone in authority (teacher, boss, etc.) on WeChat Moment. [SIA2] I worry about expressing myself in case I appear awkward on WeChat Moment. [SIA3] I worry about saying something embarrassing on WeChat Moment. [SIA4] I worry about my posts will be ignored on WeChat Moment. [SIA5] I find it difficult to disagree with another’s point of view on WeChat Moment.

[86, 90]

Disappointment

[Dis1] I feel powerless to establish an ideal online image on WeChat Moments. [Dis2] I feel lost control to establish an ideal online image on WeChat Moments. [Dis3] I feel disappointed with my online image on WeChat Moments. [Dis4] I feel frustrated with my online image on WeChat Moments. [Dis5] I feel it is impossible to establish an ideal online image on WeChat Moments.

[94]

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Role Overload

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[Dis6] I want to do nothing to improve my online image on WeChat Moments. [Dis7] I become inactive to establish an ideal online image on WeChat Moments. [Dis8] I no longer desire to establish an ideal online image on WeChat Moments.

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[Dis9] I want to do nothing to establish an ideal online image on WeChat Moments.

[LI1] I will decrease the number of my posts on WeChat Moment. [LI2] I will post on WeChat Moment far less than today. [LI3] I will decrease the number of comments on WeChat Moment. [LI4] I will comment on WeChat Moment far less than today. [LI5] I will decrease the number of likes that I give others on WeChat Moment. [LI6] I will give others likes on WeChat Moment far less than today. [LI7] I will decrease the number of my responses to others on WeChat Moment. [LI8] I will response others on WeChat Moment far less than today.

[31, 106]

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Lurking Intention

[LI9] If I could, I would not create anything on WeChat Moment anymore.

[PC1] I am concerned that the information I submit on WeChat Moment could be misused.

[138]

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Privacy Concern

[PC2] I am concerned that a person can find private information about me on WeChat Moment. [PC3] I am concerned about submitting information on WeChat Moment, because of what others might do with it. [PC4] I am concerned about submitting information on WeChat Moment, because it could be used in a way I did not foresee.

SNS efficacy

Self-

[SE1] I feel confident using WeChat Moments for posting information.

[109]

[SE2] I am confident in utilizing WeChat Moments in general. [SE3] I feel confident understanding terms/words relating to WeChat Moments. [SIN1] I can get information about my friends on WeChat Moments. 19

[110]

Social Interaction Need

[SIN2] I can communicate and interact with my friends on WeChat Moments.

Self-expression Need

[SEN1] I can express my personal interests or preferences on WeChat Moments. [SEN2] I can express my feelings on WeChat Moments. [SEN3] I can post information about myself to let others know about me on WeChat Moments. [SEN4] I can express my ideas and opinions on WeChat Moments.

[110]

Fantasizing

[Fant1] I daydream a lot. [Fant2] When I go to the movies, I find it easy to lose myself in the film. [Fant3] I often think of what might have been.

[120]

[SIN3] I can show concern and support to my friends on WeChat Moments. [SIN4] I can get opinion and advice from my friends on WeChat Moments. [SIN5] I can express my ideas and advice to friends on WeChat Moments.

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Author Biography:

Xiaodan Liu is a Ph.D. candidate of Management Science and Engineering at School of Economics and Management, Dalian University of Technology, China. His research focuses on information system adoption and social media lurking. He has published in the Behaviour & Information Technology. Qingfei Min is professor of Information Systems at School of Economics and Management, Dalian University of Technology, China. His research interests include: IT/IS implementation and adoption, e-commerce/m-commerce behavior and strategies, global virtual team, and social media. He received his Ph.D. in Information Systems from Dalian University of Technology. He has published several studies in Information & Management, International Journal of Mobile Commerce, Computers in Human Behavior, Behaviour & Information Technology as well as in some Chinese academic journals and international conferences. 25

Dezhi Wu is an associate professor in the Department of Integrated Information Technology, University of South Carolina, Columbia, SC, USA. Her current research interests include: human-computer interaction, UX design, health IT, artificial intelligence, behavioral information security and cyberlearning. She explores how users interact with computers, the Internet, robotics and smart devices, as well as other emerging technologies, to accomplish their goals. Her passion also extends to creating innovative and cutting-edge interfaces and designing transformative experiences that fill the gaps between users and today's evolving technologies. Her research has been widely published in the Computers in Human Behavior, Information & Management, Communications of the Association for Information Systems, Journal of Information Systems Security, Computers & Education, IEEE Internet Computing, and others in addition to ICIS, HICSS, AMCIS, PACIS and HCII conference proceedings. She served as the Chair for AIS SIGHCI (http://sighci.org/) and is currently serving as an advisory board member for the SIGHCI. She regularly chairs the HCI tracks and workshops for several leading conferences including ICIS, AMCIS, PACIS and HCII. She is currently serving as an associate editor for AIS Transactions on Human-Computer Interaction, and an associate editor for a gamification special issue for European Journal of Information Systems.

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Zilong Liu is a professor in the Department of E-Commerce, School of Management Science and Engineering, Dongbei University of Finance and Economics, China. His research interests include human-computer interaction, information privacy and social media. He has published several studies in academic journals （e.g. Information Systems Journal, Information & Management, Computers in Human Behavior, Information Technology & People）.

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