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Understanding the Determinants and Dynamic Process of User Exodus in Social Networking Sites: Evidence From Kaixin001

Yuxiang (Chris) Zhao School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China Xiaojuan Xu School of Management, Anhui University, Hefei, China. E-mail: [email protected] Xixian Peng Department of Information Systems and Analytics, National University of Singapore, Singapore Shijie Song School of Information Management, Nanjing University, Nanjing, China

Understanding the factors and mechanism that affect the users’ abandon behaviors is necessary for the sustained use of Social Networking Sites (SNSs). This study focuses on a special SNS discontinuance phenomenon— user exodus. Compared to individual discontinuance behavior studied in prior research, the SNS user exodus possesses its own characteristics: a huge magnitude of users abandon a given SNS product in a very short time. Given the fatal blow of exodus to SNS providers, this exploratory research seeks to unravel why and how the SNS user exodus occurs. Specifically, we conducted a mixed-method research combining laddering interview and network analysis. Drawing on the means-end chain theory, laddering interviews were first used to collect data from discontinuers of a Chinese SNS, Kaixin001, which has experienced a severe user dropout. Then we identified various factors triggering user exodus by content analysis. Last, network analysis was employed to establish the connections between various attributes, consequences, and values. The results showed that user exodus in SNS is strongly related to three types of value: functional, affective, and social value. More important, we also emphasized the dynamics process of exodus by highlighting the role of lurking. Possible contributions and implications of our findings are discussed.

Received November 27, 2016; revised September 5, 2017; accepted September 27, 2017 C 2017 ASIS&T Published online November 20, 2017 in Wiley Online V

Library (wileyonlinelibrary.com). DOI: 10.1002/asi.23974

Introduction Social networking site (SNS) has been flourishing rapidly since 2007. With billions of users, some SNSs such as Facebook, Twitter, and Weibo are very influential. However, SNSs have experienced severe tests in recent years, and some of them were inevitably shut down because a large number of users abandoned them in a very short time. For example, the comScore reported that Myspace experienced a horrific user loss in early 2011, losing 10 million unique users in only 1 month (Whittaker, 2011). Such a steep decline continued, causing New Corp. to sell Myspace for $35 million in June 2011, which was far less than the original purchase price of $580 million. Around the same period, Kaixin001, a once popular SNS in China, lost 65% of its users within 1 year, taking a path to demise. In this research, we use the term user exodus to represent this special discontinuance behavior. Such a large-scale defection in a short time is obviously the most fatal blow to a given SNS, as its sustainability heavily depends on active users and their frequent usage (O’Brien & Toms, 2008; Ravindran, Kuan, & Lian, 2014; Xu, Cheng, Cheng, & Lim, 2014). Thus, it is of great importance for researchers and practitioners to understand why and how the user exodus phenomenon occurs in the SNS context. Recently, several studies have been conducted to investigate the possible factors driving the discontinuance of SNS (Eckhardt, Laumer, & Weitzel, 2009; Maier, Laumer, Weinert, & Weitzel, 2015; Turel, 2016). Much of this research has focused on the individual perspective, examining the

JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY, 69(4):553–565, 2018

TABLE 1. Conceptualizing SNS user exodus vs. discontinuance. User exodus

Discontinuance

Volume

A large number of users; Collective behavior

Velocity

Simultaneously; At a high speed Extremely damaging and hard to recovery Myspace; Kaixin001

An acceptable number of users; Individual behavior Discretely; Slowly

Consequence Example

tolerable and restorable Facebook; Qzone

which can contribute to a more comprehensive understanding of the underlying mechanism of why and how the SNS user exodus happens (Xu et al., 2014). Given the increasingly intense competition in the SNS market, our findings, especially the hierarchical conceptualization of A-C-V based on the MEC perspective, may help designers and managers better understand the perceived affordances of SNS they should convey to users, and set up the related design principles and managerial rules to prevent user exodus. Literature Review

influence of individual factors on discontinuance intention of SNS. Meanwhile, the research contexts in the prior literature have mainly involved properly functioning SNS platforms such as Facebook and Qzone, on which the discontinuance behavior often occurs individually and may result from the routine fluidity of users. However, the unsettling phenomenon here, SNS user exodus, is a much more severe condition where users abandon collectively and almost simultaneously rather than individual discontinuance decisions (Kazmer, 2010). Therefore, prior literature on SNS discontinuance can offer limited insight to explain SNS user exodus. Meanwhile, some prior research has paid attention to the concept of users churn in SNSs (e.g., Adaji, Vassileva, & IEEE, 2015; Long et al., 2012). However, the overarching objectives of this research stream mainly concentrate on predicting and early warning rather than untangling the complex process of the user exodus phenomenon. Thus, these works also prove inadequate in understanding the SNS user exodus issue. This study aims to address the research gap by offering a theoretical framework to explain why and how the SNS user exodus occurs. Given the particular characteristics of the exodus phenomenon (i.e., collective and simultaneous), we believe that the SNS user exodus is triggered not only by intrinsic individual psychological factors, but also by the extrinsic social influence and specific characteristics of the concrete SNS. To unravel the various factors driving SNS user exodus and the dynamic and interactive relationships between them, we conducted a mixed-method research in the context of Kaixin001, a Chinese SNS that experienced a user exodus. Specifically, in Phase I, guided by the meansend chain (MEC) theory, we conducted in-depth laddering interviews with true Kaixin001 discontinuers, identified by their actual usage behaviors, to examine the various factors triggering user exodus; in Phase II, network analysis was used to establish the connections between various attributes, consequences, and values (A-C-V) identified in Phase I. This study offers important contributions both theoretically and practically. Our focus on the special SNS discontinuance phenomenon, user exodus, complements the prior understanding of reasons for SNS discontinuance. Differentiated from prior research mainly employing a single method, this study adopts a mixed-method approach combining laddering interview and a network analysis technique, 554

SNS User Exodus Exodus means that a large number of people depart from one place (physically or virtually) almost at the same time (Buttner & Moore, 1997). Here we adopt this concept in the context of SNS, representing a recent special discontinuance phenomenon that a large number of once active users discontinue to use a given SNS. Compared to the SNS discontinuance behavior examined in prior research,1 our focused phenomenon has distinguishing characteristics: (a) in terms of volume, user exodus highlights a mass user departure or leave in quantity, which dramatically shows a shocking decline of usage; (b) in terms of velocity, user exodus occurs unexpectedly and is complete in a short period as users abandon a given SNS almost simultaneously (see Table 1). These two characteristics indicate that user exodus should be a social or collective phenomenon rather than an individual behavior. More important, the consequence of user exodus is even more hellish. For example, once popular and influential websites (e.g., Myspace and Kaixin001) have totally lost their positions in the competitive market of SNS after user exodus. Thus, for researchers and practitioners, the SNS user exodus should be an interesting topic worthy of deeper exploration and understanding. SNS Discontinuance Turel (2016) noted that discontinuance use of ICT merits its own theoretical foundations, usually separating from the continuance use (Aggarwal, Kryscynski, Midha, & Singh, 2015; Birnholtz, 2010). In the context of SNS, a number of studies, as shown in Table 2, have been conducted to examine the possible factors driving the discontinuance of SNSs (e.g., Cho, 2015; Luqman, Cao, Ali, Masood, & Yu, 2017; Zhang, Zhao, Lu, & Yang, 2016). Much of this research has focused on the individual characteristic perspective, examining the influence of users’ psychological states (e.g., guilt, stress, dissatisfaction) or behavioral states (e.g., excessive use, addiction) on the discontinuance of an SNS (e.g., Maier et al., 2015; Turel, 2015), and on the social environment

1 In prior literature, the SNS discontinuance has been defined by using some related items, such as abandonment, disengagement, user loss, and user churn. These terms were also used as keywords when we searched the related works in this study.

JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY—April 2018 DOI: 10.1002/asi

TABLE 2. A review of SNS discontinuance behavior. Research perspective

Research context

Methodology (number of subjects)

Individual characteristics

Interview (45 discontinuers) Online Survey (360)

冑

冑

Cho (2015)

Facebook

Luqman et al. (2017)

Facebook

Maier et al. (2015)

Facebook

Stieger, Burger, Bohn, and Voracek, (2013)

Facebook

Longitudinal survey (82) Survey (310)

Turel (2015)

Facebook

Online survey (567)

冑

Turel (2016)

Not Specified

Online Survey (487)

冑

Vaghefi and Qahri-Saremi (2017) Zhang et al. (2016)

Not Specified

Survey (226)

冑

Qzone

Online Survey (525)

冑

Renren & Qzone Not specified Kaixin001, a SNS having experienced heavy dropout

Case study (10) Case study (11) Laddering interviews and network analysis (55)

Zhang et al. (2015) Zhou et al. (2016) This study

perspective, looking at the roles of social elements such as social capital and friendship (e.g., Zhou, Peng, Zhou, & Heng, 2016). Although prior research on SNS discontinuance have offered some insights, there are still several important limitations preventing us from understanding the SNS user exodus comprehensively. First, most of them have concentrated on individual intention or decision to discontinue using a given SNS, and their research contexts are SNSs that are still working successfully, such as Facebook and Qzone. However, user exodus is a collective phenomenon and its consequence is lethal to SNSs. Thus, the findings from those well-functioned SNS products are hardly applicable to understand SNS user exodus. Second, none of these studies investigated the role of SNSs’ concrete attributes. During the SNS user exodus, a great number of users abandon a certain SNS in a short time. We believe that, besides individual and social factors, there must be some characteristics of the concrete SNS or its provider inducing negative feelings to different users simultaneously, which thereby trigger user exodus. Therefore, our first objective is to comprehensively explore the various elements that may lead to SNS user exodus, which aims at answering why substantial users decided to leave a given SNS in a short period. The following is our first research question: RQ1: What are the influence factors and related dimensions that trigger user exodus in the SNS context?

Social environment

Attributes of SNS 冑

冑

冑

冑

冑

冑

冑冑冑

冑

Main factors Disturbance; Theoretical deliberation Technostress; SNS-exhaustion; Excessive use Stress creator; SNS-exhaustion Privacy concerns; Internet addition scores; Personalities Guilt; Self-efficacy; Habit; Satisfaction Attitude; Subjective norm; Perceived behavior control; Guilt Self-efficacy; Guilt Perceived overload; Social network fatigue; Dissatisfaction Social capital Friendship impairment Possible factors, their relationships as well as the dynamic process of user exodus

Means-End Chain Theory To better understand the underlying mechanism of the SNS user exodus, we not only need to identify the possible factors driving exodus, but also should investigate the interactive relationships among these factors. In this study, we adopted the means-end chain (MEC) theory as the theoretical approach to guide our investigation. The MEC theory was proposed by Gutman (1982) to understand how product/service attributes facilitate consumers’ decisions and values. It links product/service attributes (A) to consequences of product/service use (C), and to individuals’ personal values (V) sequentially. Attribute refers to the observable or perceived characteristics of a product or service. Consequence represents the perceived benefits or reflections associated with specific attributes. Personal values are achieved by the satisfaction of consequences (Olson & Reynolds, 2001). A major assumption of the MEC theory is that consumer’s knowledge and decision of a product can be organized as a hierarchy ladder from concrete thoughts to abstract perceptions (Botschen, Thelen, & Pieters, 1999). Stemming from consumer behavior and marketing research (Reynolds & Olson 2001), the MEC approach also has been successfully adopted in the areas of Information Science and HCI (Pai & Arnott, 2013). For example, Chiu (2005) adopted the MEC approach to elicit user requirements for a web-based document management system design, and


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proposed a model that fuses the A-C-V model and the Technology Acceptance Model (TAM). Jung and Kang (2010) applied the MEC approach to investigate user goals in social virtual worlds and cyberspaces, and they indicated that the MEC theory provides richer findings through explaining the linkages between different goals. In the SNS user exodus, the user’s behavioral pattern usually involves a dynamic process from the initial adoption to the final abandonment, during which various factors (i.e., individual, social, and technological) interact with each other. The most interesting part of this phenomenon is what happens during the adoption-abandonment procedure (Aggarwal et al., 2015; Birnholtz, 2010). As indicated in Table 2, prior research has identified some antecedents to the SNS discontinuance. However, these discrete findings are still insufficient to provide a consistent theoretical framework to understand the SNS user exodus phenomenon from an evolving and ensemble view. Therefore, guided by MEC theory, we seek to examine the A-C-V process of this phenomenon by treating it as a dynamic journey rather than a fixed event. By doing this, we can offer an integrated framework of the inner relationships among the various factors triggering user exodus. As such, we pose our second research question: RQ2: How to better understand the dynamic process of user exodus phenomenon through identifying the associations among attributes, consequences, and values in the SNS context? Research Design Research Method To address the two research questions, the present study conducted a mixed-method research combing a qualitative approach (i.e., laddering interview) and a quantitative approach (i.e., network analysis). As indicated in the Literature Review section, we find that most of the extant studies were conducted with quantitative methods. However, in terms of the user exodus phenomenon, there are still many novel questions of how and why that are not well addressed. Thus, in view of the nascent nature and methodological fit of this study, a qualitatively dominated approach is more appropriate to cope with the open-ended inquiry about a phenomenon of interest (Edmondson & Mcmanus, 2007). The laddering interview, developed by Olson and Reynolds (1983), is a semistructured in-depth, one-to-one interviewing technique used to develop an understanding of how people translate the attributes of objects (i.e., SNSs) into meaningful associations with respect to themselves (Reynolds & Gutman, 1988). The interview approach is useful to investigate the dynamics of an interesting phenomenon and its particular processes (Yin, 2013). Being interested in understanding why and how the SNS user exodus occurs, we believe that the laddering interview enables us to gather richer information of how users’ perceptions of an SNS’s features and their corresponding behavioral changes during the 556

interaction process with the SNS. Therefore, the laddering interview was used as the dominant method to explore the dynamic process of the SNS user exodus. We are more interested in the issues that may emerge from the interview data instead of proposing specific hypotheses between default constructs. Particularly, we conducted the laddering interviews following the A-C-V process based on the MEC theory. The laddering procedure mainly involved two steps: eliciting relevant attributes and building the ladders. The goal of the first step was to ask the interviewees to mention the main attributes of the focal SNS and distinguish it from other SNSs. To motivate interviewees to elicit as many product attributes as possible, some facilitating skills were employed, including evoking a situational SNS use context, postulating the absence of the SNS, etc. (Botschen et al., 1999; Reynolds & Gutman, 1988). In the second step, to identify the linkages among attributes, consequences, and value, we used iteratively probing “why” questions until we could not obtain any meaningful answers from the interviewees. Usually, the explicit and interpretable A-C-V processes are developed directly according to the results of content analysis from the interviews. However, prior research has indicated that this traditional classification of A-C-V may result in some classification errors during the coding process, which will weaken the reliability and validity of the AC-V ladders (Jung & Kang, 2010). In this study, we employed an alternative method proposed by Bagozzi and Dabholkar (1994) and Pieters, Baumgartner, and Allen (1995). Instead of coding the original contents into three categories, this method adopts the idea of network analysis (Scott, 1991) to build the hierarchical structure of laddering by counting and comparing the occurrence number of each element mentioned as the means versus the end (Jung & Kang, 2010). More important, integrating this quantitative method to analyze interview data can also facilitate the interpretation of the findings by demonstrating the relative importance among different paths. Thus, based on a compensation view suggested by Venkatesh, Brown, and Bala (2013), we designed a mixed-method research to improve the analytical process. Research Context We selected Kaixin001, a famous SNS in China, as our research context for the case investigation. In 2009, the registered users of Kaixin001 reached 100 million, with nearly 50 million active users. In 2010, Alexa.com estimated Kainxin001 as the 18th most influential website in China. However, the number of active users in Kaixin001 declined precipitously from 120 million (in mid-2010) to 42 million (in mid-2011; Alexa, 2011). According to the website health check index from Baidu Index (2015), Kaixin001’s popularity rating also experienced a sharp recession, from 800,000 in 2012 to 50,000 in 2013. These shocking figures show that Kaixin001 suffered a severe user exodus around 2011. As a result, Kaixin001 has been out of the market since 2015 according to the survival index from Baidu.


Therefore, Kaixin001 is a good case for investigating SNS user exodus. More important, a special feature of Kaixin001, named “Ticker,” enables us to identify true abandoners as our interviewees. Ticker is an updated feature launched by Kaixin001 in May 2012, which can trace the visible activities of a registered user, such as the number of logins. Previous research has defined abandonment as nonuse of a given SNS (Zhang, Peng, & Wang, 2015). Given the login information, Ticker thus serves as a good tool for finding potential interviewees. Data Collection In this research, we selected those who were once active users but who hadn’t logged in to Kaixin001 for more than 6 months as our potential interviewees. Then the theoretical sampling method, recommended by Myers and Newman (2007), was used to identify the final interviewees. As a result, 66 informants were interviewed, and 55 valid interviews out of 66 were considered for further analysis. Our data collection began with a focus group to gather rich information and narratives from six participants and we encouraged our participants to express their thoughts and experiences towards user exodus. Then the study continued to collect the rest of the 49 individual interviewees to identify more means-end elements. The interviews were conducted from April, 2015 to October, 2015. All the interviews were recorded on tape and transcribed to verbal cues for later analysis. Because our respondents were users of Kaixin001 in mainland China, we conducted all the interviews in Chinese. Before the interviews, interviewees were ensured that their responses and information would remain confidential and they could discontinue the interview at any time. The timing and form (i.e., telephone, video chatting, and face-to-face) of each interview were set at the interviewee’s convenience. Each interviewee was compensated with a ¥100 Taobao gift card to encourage them to provide as much information as possible. The length of each interview lasted from 40 to 90 minutes. In total, we obtained 3,846 minutes of recorded data and 472 pages of memos. Combining the observable data from interviewees’ Kaixin001 account, we finally obtained diverse and rich data for the subsequent analysis. An interview protocol was designed and used to help improve the relative consistency of interviewees’ responses (Myers & Newman, 2007). In the laddering process, interviewees were encouraged by means of interactive questions, and a set of 12 related questions was developed to elicit interviewees’ divergent and deep thoughts. As shown in Figure 1, in the first part the interviewees were required to answer questions relevant to their sociodemographic information, and some transitional questions such as P2, P5, P8, and P11 were used to drive the discussion in a fluid way. The second part was an application of the MEC theory. Among them, some questions (i.e., P3, P6, P7, P9, P10, and P12) were set up to elicit the decision criteria that

participants used in their decision process. Following each of them, we also raised a “why” question to elicit respondents’ detailed reasons for their decisions. Data Analysis First, content analysis served to reduce the raw data from the interviews to facilitate interpretation. Open coding was used to qualitatively analyze the feedback from the respondents. A coding worksheet in Excel was used to record data extracted from the interviews to provide uniformity, consistency, and completeness of the research. The 55 respondents were labeled with serial numbers ranging from 01 to 55. Open coding was conducted by two coders (labeled A and B) independently and a subset of interviews (from 01 to 45) was first examined. Coder A obtained 28 detailed codes and coder B summarized 27 detailed codes. A third researcher compared and collated the codes summarized by A and B. First, the codes with the same connotation were merged as a unified code. Second, the differential connotations of codes were determined by the third researcher, and then 28 detailed codes were obtained. The coding schema was developed throughout the coding process. After several iterations, we finalized the coding schema and independently coded the rest of the interviews (from 46 to 55). Overall, we achieved 91% agreement of coding that suggested an acceptable level of interreliability (Subramony, 2002). The disagreements were resolved by consulting the third researcher and final agreement was 100%. Given that there are no generally accepted guidelines or norms to discuss validity issues in qualitative research (Venkatesh et al., 2013), we proved the rigor of our study by providing high-quality data collection efforts and a step-by-step interview protocol. Second, we developed an implication matrix that counted the numbers of occurrence times each element was mentioned as the means versus the end (Jung & Kang, 2010). This matrix was asymmetric in that the cell values described the times of the codes in the row leading to the codes in the column. In addition, we also calculated the abstractness and centrality of each element. Last, we converted the implication matrix to a hierarchical value map (HVM) by using network analysis (Scott, 1991). HVM facilitated the interpretation of the relationships between different elements. To highlight the crucial linkages, it is necessary to select a cutoff level for linkages between goals (Reynolds & Gutman, 1988). Prior study indicates several applicable criteria for selecting the cutoff level (Pieters, Baumgartner, & Allen, 1995). We adopted the cutoff method recommended by Bagozzi and Dabholkar (1994) in this research. Results Participants’ Demographics Of the 55 participants, 31 (56.4%) were females and 24 (43.6%) were males. Their ages ranged from 24 to 47 years old (mean, 31.2; standard deviation [SD], 7.56). In terms of


557

FIG. 1.

Interview protocol on user exodus in Kaixin001.

education, 54.5% of them had bachelor’s degree, and 32.7% had postgraduate or above degrees, and the remaining seven had a high school degree. Their occupations also varied, including graduate students, software engineers, designers, accountant, project managers, purchasing manager, pharmacist, and so on. The participants came from Shanghai, Beijing, Guangzhou, and other large and medium-sized cities. They also reported that they had 3 to 7 years’ experience in using SNS (mean, 4.3; SD, 7.5), and in terms of Kaixin001, their use experience ranged from 2 to 6 years (mean, 3.9; SD, 7.2). Influence Factors and Related Dimensions for User Exodus in Kaixin001 In this research, an inductive approach was employed to identify influence factors and related dimensions from the 558

interview data. Regarding the means part of the MEC approach, eight dimensions and their responding codes were derived from the participants’ responses. Each code included a variety of indicators. Among these dimensions, some (i.e., content-related, design-related, and user experience-related) were derived from the perspective of Kaixin001’s features, indicating the importance of various details of SNS attributes. Furthermore, some dimensions (i.e., Guanxi [Chinese social network]-related and community-related) were derived from the socializing perspective of Kaixin001, representing that group and social influence may play a vital role in the sustained use of SNS. In addition, some dimensions were derived from the environment perspective, such as competitor-related, cost-related, and risk-related dimension. The results are shown in Table 3. Regarding the ends part of the MEC approach, we included three value-related codes (i.e., functional, social,


TABLE 3. Categories of factors and dimensions that trigger user exodus in Kaixin001. Dimension

Codes

Indicators

Examples (quotations)

Ambiguous labeling, Confusing navigation, Unreasonable/tedious layout, invalid search box, intricate metaphor, malfunction of mobile client, poor incentive design Absence of original content, Information overload, Lack of necessary censorship, plethoric advertisements

“I have some concern about the local navigation in Kaixin001.” “I am crazy that when I need to seek some relevant information on the homepage of Kaixin001, I could not find the search box.” “I am overwhelmed by the flooding information every day in Kaixin001. . .I hate so many useless advertisements.” “It is the service provider’s responsibility to scrutinize the source and credibility of content. . .there are so many false news here.” “The current managing tools for friend list are too fixed. . .I need some more dynamic tools to manage my friend list.” “Today, there are so many attractive social media (substitutes) for choice, and many of the functions are quite similar.” “I always received advertising promotions related to my personal information, which made me believe that my personal privacy was divulged.” “Actually, I do not quite trust the content, even the platform of Kaixin001. . .I have to protect my personal privacy on the Internet.” “After using Kaixin001 for several years, I gradually lost my interests. . .many of my friends are also lurkers now.” “The update I post on the circle of friends in Kaixin001 has little attention (social presence).” “The frequent updating of Kaixin001 drives me crazy since I need to take some time to learn and adapt to the new features.” “I prefer WeChat since it can provide me with a rich experience of autonomy and control. . ..really fascinating.” “Crash problem in the mobile client always makes me at a loss (poor usability).”

Design-related

C1: Poor interaction design

Content-related

C2: Low content quality

Guanxi-related

C3: Weak interpersonal relationship

Competitor-related

C4: Attractive alternatives

Risk-related

C5: High perceived privacy risk

Community-related

C6: Weak sense of virtual community

Lack of social presence, Weak self-identification, Prevalence lurking behavior, Lack of group norm

Cost-related

C7: High perceived effort cost

User experience-related

C8: Low perceived usefulness

Unnecessary economic cost, High maintenance cost, High learning cost Poor usability, Loss of control, lack of personalization

Fading away of friendship, Lack of effective friend list, Difficult to gain relatedness Appealing substitute, Intense competition from homogeneous products, Alternation of the web trend and fashion Distrust to the platform, Weak credibility for the content; Personal information disclosure, Unguaranteed privacy concern

and affective) with a variety of indicators, as shown in Table 4. Previous literature has noted that user stickiness highly depends on the perceived social, hedonic, and epistemic values of using a given SNS (Yang & Lin, 2014). Consistent with this existing finding, our codes of values thus stand up to scrutiny. Due to low levels of these values, interviewees reported that they chose different modes of abandonment. In all, 38 of 55 interviewees (69.1%) reported that they used several SNSs simultaneously. After abandoning Kaixin001, they turned to focus on another SNS such as Qzone, Renren, WeChat, and so on. There were three interviewees indicating that they didn’t use any SNSs at the interview time, which mainly resulted from severe social network fatigue. These results may indicate that user exodus from Kaixin001 was predominantly driven by the negative perceptions toward the SNS brand contingently rather than by the intrinsic sense of fatigue with general SNS products. More important, all the interviewees stated that before the final

abandonment of Kaixin001, they experienced a period of lurking, during which they reduced their login frequency and usage time gradually. This lurking period was around 5 months on average. Unfortunately, Kaixin001 did not come up with any appropriate coping strategies to prevent the transformation from lurking to abandonment. The Implication Matrix The codes and indicators derived from the content analysis can be employed to construct an implication matrix. The strengths of linkages between codes (C1 to C11) were calculated by the sum of the linkages between their corresponding indicators. The calculation of abstractness was based on the in-degrees and out-degrees of the element. The in-degrees refer to the number of times the element is located in the end of linkages with others (the column sum of the elements of Table 5), whereas the out-degrees indicate the number of times the element plays a role as the source (means) of


559

TABLE 4. Value-related factors that lead to user exodus in Kaixin001. Dimension Value-related

Codes

Indicators

Examples (quotations)

C9: Low functional value

Poor utilitarian value, Weak practicability Feeling of being overwhelmed, Lack of self-fulfillment, SNS fatigue

“Compared with other similar SNSs, I do not think that Kaixin001 provided me with exciting functionality.” “There is nothing but boring posts and uploads. . .I lost my interest.” “After several years’ use of Kaixin001, I recognized that my social network remains the same and my social capital doesn’t change.” “Emotionally, it is not interesting and fascinating anymore. Kaixin001 cannot arouse my attention.” “Overall, I am not satisfied with the services provided by Kaixin001. . .they did a bad job in user relationship management.”

C10: Low social value

C11: Low affective value

Poor perceived affective quality, Lack of fun and enjoyment, Poor level of satisfaction

TABLE 5. The implication matrix. Abstractness 0.00 0.00 0.30 0.00 0.44 0.65 0.58 0.59 0.90 0.96 0.96

Centrality 0.22 0.09 0.26 0.13 0.07 0.29 0.33 0.20 0.22 0.10 0.11

C1

C2

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 In-degrees

C3

C4

2 1

C5

C6

C7

C8

C9

6

2 1 23 15 1

34

4 15 2 1

3 1 2

1 12 3

4 1 1

1 2

18

7

1 1 44

2 1

1 3

2

44

27

C10

10

12 30 10

46

C11

Out-degrees

1 2 1 1 5 9

52 20 43 30 9 24 32 19 5 1 1 233

6

22

25

TABLE 6. Statistics for determining a cutoff level.

Cutoff level 1 2 3 4 5 6

Number of active cells

Percentage of active cells at or above the cutoff level

Number of active linkages

Percentage of active linkages at or above the cutoff level

42 26 18 15 13 12

100.00% 61.90% 42.85% 35.71% 30.95% 28.57%

233 217 201 192 184 179

100.00% 93.13% 86.27% 82.40% 78.97% 76.82%

linkages with others (the row sum of the elements of Table 5) (Bagozzi & Dabholkar, 1994). Abstractness of an element is the ratio of in-degrees over in-degrees plus out-degrees of the element, and ranges from 0 to l (Pieters et al., 1995). Normally, elements with high abstractness scores should be regarded as ends, whereas ones with low abstractness scores should be classified as means (Jung & Kang, 2010). Besides the abstractness of elements, the study also calculated the centrality of each element, which shows the degree to which the element has a central role in the whole structure (Freeman, 1979). Centrality was calculated by dividing the ratio of in-degree plus out-degree of a particular element by the sum of all active cells in the implication matrix (in this 560

study the total sum is 233). The active cell refers to the relation that should be mentioned at least one time. Hierarchical Value Map As discussed in the Data Analysis section, before building the HVM we need to select a cutoff level for linkages between goals (Reynolds & Gutman, 1988). We summarize our cutoff level selection process in Table 6. With a cutoff value of 3, 18 active cells contain linkages mentioned three or more times by respondents, which accounts for a high percentage of active linkages at or above the cutoff level (86.27%). Because inclusion of all linkages could decrease a


FIG. 2.

Hierarchical value map of user exodus in Kaixin001.

map’s usefulness and informativeness (Reynolds & Gutman, 1988), we set up a cutoff of 4 relations to construct the HVM, indicating that the included relations are counted at least four times by respondents. This cutoff level represented 35.71% of the active cells and 82.40% of the active linkages. Using the cutoff level of 4, the HVM was built by a network analysis approach (as show in Figure 2). In total, 11 elements with 15 relations provide a graphical summary of the means-end structure pertinent to the user exodus in Kaixin001. In the map, the locations of elements are placed according to the abstractness scores. The greater the score of abstractness, the higher position it gets. In addition, centrality is represented by the number of *. * indicates 0.100 or less, ** indicates the range from 0.100 to 0.200, *** indicates the range from 0.200 to 0.300, and **** indicates 0.300 or more. The frequency of the linkage is demonstrated by the degree of thickness of the arrows. The thicker the line, the more the frequency of the relation was mentioned by the respondents. The lowest degree of thickness in this study illustrates 4 to 10 relations mentioned, the middle degree of thickness represents 11 to 19 relations mentioned, and the highest degree of thickness indicates 20 or more relations mentioned. Discussion Major Findings To answer our first research question (RQ1), this study identified nine key dimensions of SNS attributes and three

dimensions of perceived value that triggered user’s abandonment of Kaixin001. Considering the centralities of the abstract topics, poor interaction design (0.22) is the most significant attribute influencing user exodus, followed by attractive alternatives (0.13) and low content quality (0.09). The findings work in concert with previous studies. Users socialize with others on SNSs through various activities such as searching, posting, commenting, and discussing interesting content. Therefore, it is reasonable that the SNS exodus behavior depends highly on how the interaction design can facilitate these activities as well as website content quality (Liu, Chu, Huang, & Chen, 2016). In addition to internal factors, attractive alternatives from a competing environment should also play a critical role (Kim, Shin, & Lee, 2006; Zhang et al., 2009). Meanwhile, perceived effort cost (0.33) has the strongest centrality in the HVM. Perceived effort cost emphasizes the cost paid to maintain the continuous use of a product/service, including time input, intellectual resources input, and marginal return on investment. To motivate sustained use, SNS providers often update various new functions (Zhang, 2016). But at the same time, an excess of functionality can increase too much effort cost, which may induce exodus behavior instead. In addition, a weak sense of virtual community (0.29) and weak interpersonal relationship (0.26) are also very critical. During our interview, many respondents expressed that they felt an unsatisfactory sense of belonging to the online community, or if most of their friends are leaving the SNS, it is unnecessary for them to stay in the SNS.


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FIG. 3.

A schematic model of user exodus in SNS context.

To answer our second research question (RQ2), we built the HVM and identified 15 dominant paths from the base to the top that can be viewed as a dynamic process of perceptual orientation. As indicated in the HVM, the decreasing of social value, affective value, and functional value are the main ends when users decide to leave SNSs. The most predominant relation is the link of Poor interaction design ! High perceived effort cost ! Low functional value. We infer that the poor interaction design may cause a high level of learning cost and maintenance cost, which will result in cognitive overload for use and thus weaken the user’s perceived functionality. The relatively strong linkage is Weak interpersonal relationship ! Weak sense of virtual community. Adding to previous findings that the weak interpersonal relationship contributes to SNS discontinuance (Wu, Tao, Li, Wang, & Chiu, 2014), this finding shows the social influence elements from the individual level to the group level, indicating a possible mediated hypothesis that the weak interpersonal relationship may result in the weak sense of virtual community, which further leads to the breakdown of social and affective value. It is also very interesting to find that the availability of attractive alternatives strongly connects to a weak interpersonal relationship and weak sense of virtual community. This may relate to the comparison effect. Some interviewees stated that they always keep their eyes on other social media products, and once there are better alternatives to build social ties, they may feel less connected to the present SNS and their friends on the SNS. Such an effect is rarely discussed in prior research, and thus we encourage further empirical investigations. Our research not only develops a hierarchical structure of various dimensions and their influence factors that result in 562

SNS user exodus, but also a dynamic process of how the exodus occurs based on the MEC theory. Figure 3 shows a schematic model of relationships between means, ends, and behaviors. The proposed model suggests an evolving view towards user’s decision making of using SNSs. The behavior path describes how an active stage changes to an exodus stage (Dutot & Mosconi, 2016; Massimi, Bender, Witteman, & Ahmed, 2014). Specifically, we propose that means (i.e., SNS products’ attributes and their consequences) stimulate a disengagement process from an active stage to a lurking stage, and sequentially ends (i.e., values induced by means) modulate an abandon process from a lurking stage to a final exodus stage. Here, lurking refers to a state during which users are inactively using a given SNS. According to our interviews, we suggest that the lurking stage is a critical period for determining the arrival of the final exodus. Almost all the interviewees stated that they became lurkers before the abandonment. More interestingly, we found that users’ changes from lurking to abandonment might be more like “passive acceptance” rather than “initiative process.” Some interviewees indicated that although they became inactive in socializing in Kaixin001 because of different poor attributes and their consequences, they didn’t choose to leave immediately and still expected the Kaixin001 provider to improve user experience. Unfortunately, as reported by many interviewees, Kaixin001 did not seize the opportunity to retain them. In most cases, Kaixin001 took little effective action. Sometimes even worse, Kaixin001 took unwise coping strategies to push away their inactive users. Therefore, the deficit of various perceived values became worse and worse. As an adaptation of continually decreasing functional, social, and affective values, lurkers decided to abandon Kaixin001 completely.


The following are some quotations of interviewees’ struggles during their transformations from lurking to exodus.

Since WeChat is more adaptive to the mobile environment, I gradually reduce the use of Kaixin001. Yet I still expect that Kaixin001 will make some improvements on its mobile client. In the later couple of months, I was disappointment to find that they made very limited efforts on the updating of its mobile design. . .Yes I quit. (Lurking period and low functional value.) My friends seem to leave Kaixin001 in the last couple of weeks, and I feel very lonely and gradually lose the sense of belonging.. . . After a few months, I found that most of my friends did not post anything, and the social games cannot play without the participation of in-group network. . .really terrible. (Lurking period and low social value.) I haven’t login Kaixin001 for some time. One day I got an email from Kaixin001 informing me that some of my friends commented on my weblogs. I was happy to get this notice and login again, but to find that the comment was posted a year ago.. . . I don’t know why Kaixin001 took such a cheating action to call me back, which made me very angry. (Lurking period and low affective value.)

Contributions This research contributes to information studies and practice in several ways. First, our focus on the special SNS discontinuance phenomenon—user exodus—contributes to the IT discontinuance literature. We take an ensemble view to conceptualize the SNS user exodus in terms of volume and velocity. Different from prior research that mainly investigated the individual discontinuance behavior in wellfunctioning SNSs (e.g., Facebook), our empirical research examined a dead SNS (i.e., Kaixin001), which experienced user exodus. By highlighting the fatal consequences induced by the SNS user exodus, we believe that this phenomenon should be of great importance for information-related areas and encourage further investigations. Second, our research offers a much more comprehensive view of users’ abandonment of SNS. Much previous research focused on the individual psychological or emotional perspective, examining the influences of individual factors on abandonment behavior (Keaveney & Parthasarathy, 2001). Through the MEC framework, our findings complement this perspective by emphasizing the important impact of the various attributes of a given SNS as well as its social elements. More important, we highlight the dynamics process of user exodus. Specifically, we found that there is a period of lurking between active use and final abandonment. The dissatisfaction on attributes and consequences of the interaction with SNSs may trigger the transition from active use to lurking. The final exodus occurs due to the SNS provider’s ignorance or inappropriate solutions in the lurking stage, which can be reflected by the decreased perceived values (i.e., functional, social, and affective).

Third, from a methodological perspective, this research adopted a mixed-method approach combining laddering interview and network analysis. Although the laddering interview enables us to collect rich information and understand the questions of why and how the SNS user exodus happens, it still suffers some limitations, such as the difficulty of classification and interpretation. Our research integrates network analysis to quantify the strength of the paths identified in the laddering interview. This facilitates our discussion on the relative significance of various paths. Moreover, taking advantage of the special “Ticker” feature in Kaixin001, we recruited real discontinuers as our interviewees. Our findings based on their responses have a high degree of credibility and reliability. The study has several implications for practice. First, SNS providers and managers should provide users with fresh content and functional and social affordances that are appropriate to facilitate engagement. Due to the high product homogeneity and fickleness of users’ needs, it is important for SNS providers to design the affordances in the postadoption stage, such as frequent renewal of the interesting content, seeking novel aspects of the new technology in SNS design, being aware of users’ needs and subculture, and being aware of the alternatives. Second, our results also suggest that managers and designers ought to pay close attention to how to improve users’ interpersonal relationship and sense of virtual community. For example, service providers and managers may launch some offline activities to reinforce the membership and commitment of online users toward the SNS. Third, managers should take into account the issues of privacy concerns and perceived effort cost. People will reduce their use of SNS when they perceive some privacy risks or high effort cost. Last but not least, practitioners should pay great attention to lurking behavior. Lurking is unavoidable from active usage to final abandonment. During the lurking stage, users may gradually lose interest and patience. SNSs providers should effectively monitor lurking behaviors and formulate quick responses. For example, when users become inactive, some incentive mechanisms should be designed to improve their enthusiasm and involvement. Conclusions and Future Work There are several limitations that should be noted when interpreting the results of our study. First, because the research object is a Chinese SNS named Kaixin001, our interview sample only contains Chinese users. Thus, applying our findings in examining SNSs in other cultures should be treated with caution due to cultural differences. An interesting future topic may be investigating the cultural effect in the user exodus phenomenon. Second, we adopted the network analysis method to quantify the linkages between different elements that are derived from content analysis. However, it may be still insufficient to verify causality. We encourage further investigations to empirically test our findings by quantitative methods such as surveys. Third, from


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