Abstract

The present study examines the assumption that online users would be influenced by widely reported privacy threats; namely, that public servants might monitor personal online communications between instant messenger users, and compel the Korean domestic social network service (SNS) providers to cooperate with their surveillance efforts. Utilizing uses and gratifications (U&G) theory, we integrate previous research on government surveillance, privacy concern, and motivation variables regarding SNS use. A survey of South Korean users reveals that privacy concern is mediating the relationship between governmental online surveillance and SNS switching intention. Structural equation modeling results suggest that SNS switching intention is predicted positively by interaction motivation and negatively by convenience motivation. Privacy concern mediates the relationship between governmental online surveillance and SNS switching intention. These findings illustrate the measures that users take in response to telecommunication policy actions, particularly those that might logically pose a threat to online privacy. Study findings thus help provide support for a novel theoretical framework that illustrates the utility of media U/G variables in the context of online privacy conceptions stemming from perceived threats of online government surveillance. We conclude by discussing implications for policymakers stemming from user remedies to circumvent state surveillance initiatives.

How Government Surveillance Modifies Social Network Service Use in South Korea

Online government surveillance systems have been expanded with advances in new media.1 The ongoing specter of government online surveillance could pose a serious threat to the providers of web services such as instant messengers and social network services (SNSs). Furthermore, the lack of regulation on surveillance system developments, which was propelled by the private data management companies, has victimized the less competent technology users of the new media services.2 When the story of the Korean government's monitoring of an SNS messenger service was broadcast through major television news shows in 2014, Koreans were moving away from the dominant KakaoTalk messenger service—monitored by Korean prosecutors—to the encrypted German messenger service called Telegram.3 A special task force investigated the impact of online messages on rumors about the government and then-President, after she complained about the unidentified stories.4,5

The alternative service, Telegram, was recorded as the most downloaded application in the Fall of 2014. This growth was spurred by reports that KakaoTalk—serving 35 million users—cooperated with the prosecutors monitoring personal messages exchanged through the messenger service.6 Meanwhile, Daum—the service provider of KakaoTalk—lost users of their SNS, KakaoStory.7 In 2014, 39.9 percent of South Koreans used SNSs. The ratio of KakaoStory service users among Korean SNS users dropped from 55.4 percent in 2013 to 46.4 percent in 2014, while the ratio of Facebook users in South Korea increased from 23.4 to 28.4 percent in 2014.8 A case study involving South Korea, which boasts the world's most advanced telematic infrastructure, can thus help inform our understanding of how government surveillance modifies users' behaviors regarding SNS.9

Based on press accounts of the Korean government's online surveillance, those who use online SNSs to gratify their online needs may switch to alternative services, particularly when they regard government surveillance as a potentially negative influence on privacy. The present study integrates past work—addressing government surveillance, privacy concern, and SNS use motivations—to understand the factors influencing the use of online SNSs. While previous research10 has used motivation variables to explain causes of online service use, we examine the negative attributes of online service use, which include exposure to surveillance news and privacy concerns. We also test a model outlining how news stories concerning heightened government surveillance could prompt privacy concerns surrounding the use of SNSs.

Background

Government Online Surveillance

According to Fernback, “surveillance is the practice of rigorous monitoring, sometimes openly and sometimes illicitly, of human data for the purposes of control.”11 The information society, whose name connotes utopian notions of information technology development, has been transformed into a surveillance society.12 Surveillance capabilities have been increased by the aggregation of information and technology development. These emerging capabilities are enabled by emerging technologies that enhance the monitoring of Internet users' personal information and their online behaviors.13

Dystopian scholars anticipated that the development of information and communication technology would enhance government control over their citizenry and facilitate ongoing surveillance.14,15 Online government surveillance systems have been enhanced by the development of myriad new media technologies, such as data analysis of online communication records, remote system controlling software, and spyware installation techniques.16 These surveillance technologies enable government agencies to target civilians through the algorithms of online surveillance technologies. While law enforcement agencies maintain that their online surveillance programs are used to identify suspicious persons as probable terrorists since the 9/11 Al Qaeda terror attacks in 2001, surveillance technologies gather data from the entire universe of Internet users.17 Intelligence agencies as well as government investigators have been expanding their possible surveillance targets by generating propaganda regarding the need to prevent possible threats to public security.18

Online information regarding personal identities, which is targeted by government surveillance agencies, has drastically increased since online SNSs became popular tools to share users' personal texts and files.19 These emerging capabilities enable bureaucratized government powers to monitor the users' data accumulated in the servers of SNSs, including personal video clips, website IDs, and social network use histories.20,21 In addition, government agencies can take advantage of social advertising systems managed by SNS providers, who are tempted to make profits by commodifying the aggregated data collected through the user activities on their services.22 Government investigators have the ability to aggregate personal information collected from SNSs into their databases, which are reanalyzed to screen SNS users for the agencies' convenience without user consent on the information use.23

Exposure to news stories on government surveillance increases the netizens' concerns about their online privacy.24 Unrestricted governmental surveillance of the citizenry—without their consent—represents one of the gravest privacy threats, because users do not have any sense that personal online messages may be monitored by governmental authorities.25 Government surveillance of online service provider servers prompts user insecurity via privacy threats.26 Whitley explains that privacy protection actions and surveillance perceptions can thus be strengthened by increases in mass media coverage of these issues.27 These emerging digital surveillance capabilities thus necessitate consideration of public opinion dynamics underpinning privacy concerns surrounding state surveillance.

Theorizing Privacy Concern and SNS Switching Behavior

Privacy concern

In the user-created content environment, privacy threats are expected to increase dramatically.28 Privacy, as a legal concept was first conceptualized by Warren and Brandeis in 1890.29 According to Westin, privacy can be defined as “the claim of individuals, groups, or institutions to determine for themselves when, how, and to what extent information about them is communicated to others.”30 Sanfilippo et al. found strong empirical support for an integrative framework, one that conceptualizes privacy in terms of information flow rules-in-use.31 Lee recounts the evolution of privacy as a legal right, including its application in digital media contexts.32 Since the Internet's earliest days, in which privacy protections were deemed the most perilous obstacle to its development,33 privacy concerns and remedies gained top priority.34 SNSs, in particular, have been confronted with privacy complaints on numerous fronts.35 Heavier social network users, who feel privacy concern more acutely, exhibit negative attitudes toward the adoption of newly developed functions on social network pages.36 Past work found that privacy concern is a negative predictor of SNS use frequency.37 Privacy concern is positively related to refraining from disclosure of personal information online.38 SNS users tend to not reveal their names or other personal information on websites when they have privacy concerns. Considering that SNS users reveal their personal information to their friends, privacy concerns can dampen their intention to use SNSs. Facebook users tend to modify the privacy settings of their Facebook accounts, particularly when they experience privacy invasions.39

Taken together, these studies suggest that in the social media realm, where one's identity is highly prominent, privacy is a crucial determinant of how and with whom users interact.40 As the mobile environment expands and smartphone use increases, reducing privacy concerns about SNSs becomes ever more critical.41 Privacy concern negatively influences self-disclosure and increases misrepresentation, which entails providing false information.42 In addition, people are more likely to take actions to reduce privacy concerns when they have more privacy protection knowledge.43 When people do not have trust in the media environment in the political realm, they tend to devote greater effort to maintaining privacy management actions.44

Conceptualizing Switching Intention

Switching from less secure services' newer platforms—with better privacy protection functions—seems to be an effective privacy protection action.45 The surveillance agents take advantage of algorithmic digital surveillance systems to produce information originated from netizens, and the development of new information hardware such as Google Glass is vulnerable to the digital surveillance algorithm systems.46,47 In the media environment driven by media developers, online service users can realize effective privacy management actions, particularly when media developers devise alternative online services equipped with more secured privacy protection functions.48 As a power struggle is unfolding between surveillance enhancement groups and countersurveillance groups, the progress of online services with more advanced privacy protection methods—developed by countersurveillance groups—is expected to enhance consumer sovereignty to adopt alternative services requiring less effort in privacy management.49 Service switching has been identified as an effective strategy for new media adopters of mobile phone and web blogs to avoid privacy threats, particularly when they lose faith in their incumbent Internet service.50

Previous research on media switching intention has benefitted from the migration metaphor, termed the Push-Pull-Mooring Migration Framework; this theoretical framework has considered switching intention as a dependent variable.51 The migration framework has effectively explained positive and negative determinants of media switching in the fields of blog use,52 SNS use,53 and instant messenger service. Schreiner and Hess explored privacy as a critical factor determining switching intention in their structural equation model.54 Their European case study found that people switched their instant messaging system from a less privacy-sensitive application, WhatsApp, to a real-time end-to-end encryption service, Threema. Several factors influenced the decision to switch, including switching cost, peer influence, dissatisfaction with previous application, and privacy protection function of new messenger service.

Based on the literature and theory reviewed earlier, it's useful to examine key mechanisms underpinning the influence of privacy concerns on SNS switching intentions. A better understanding of user reactions to government surveillance of social media can help inform policymaking in that domain. In particular, we assume that exposure to news of government surveillance would logically increase user privacy concerns regarding SNS use. Assuming that users would take action to reduce any resulting stresses, these concerns are also likely to increase one's intention to switch SNSs. Exposure to actual government surveillance would also be likely to encourage SNS switching intention. More formally, we posit that:

  • Hypothesis 1: Exposure to government online surveillance news increases privacy concern regarding SNS use.

  • Hypothesis 2: Exposure to government online surveillance news increases SNS switching intention.

  • Hypothesis 3: Privacy concern regarding SNS increases SNS switching intention.

In order to enhance our understanding of these social media use dynamics, it's useful to consider the influence of user motivations on SNS switching intention.

Uses and Gratifications Theory

Uses and gratifications (U&G) theory can help inform our understanding of switching intentions in the realm of the migration framework. In U&G theory, media switching behavior can be understood as a type of service innovation, which may satisfy users more than a previous option.55 While the migration framework describes users as immigrants moving to alternative services, U&G theory understands media users as adopters of newly introduced media featuring technology advancements.56 The active user perspective of U&G provides the research framework for exploring users' positive motivations and negative motivations to switch to alternative media. Also, privacy concern is considered as a negative motivation to make users actively avoid engaging with new media.57

U&G theory provides a research framework for considering individuals' internal motivational dynamics when choosing a media channel and consuming contents of mass media.58 The theory explains that audiences are active in choosing media channels and content.59 Online media options can help satisfy a wide range of gratifications, the selection of which is determined by the strength of user motivations.60 Individuals exhibit differences in media consumption behaviors, as audiences tend to use media in variegated contexts.61 The dependency of audiences on media increases when audiences clearly recognize their purposes for media use, which increases user gratification.62 Furthermore, the Internet environment increases users' power to choose media channels and contents.63 While traditional mass media satisfy information and entertainment needs, online media systems are more likely to fulfill interpersonal interaction needs among users.64

Recent U&G research has identified several motivations for using SNSs. First, SNS use—operationalized as uses of interactive features—tends to be determined by entertainment motivation.65 While there seems to be no direct relation between interpersonal interaction motivation and SNS use, SNSs enable their users to increase social network coverage by enhancing interpersonal interactions. In addition, Ha and associates found that entertainment needs and mobile convenience are the great predictors of SNS use. In South Korea, Facebook users score higher in hedonic gratification, while uses of online instant messenger tend to be determined by convenience motivations.66 Interactivity is a critical predictor of entertainment gratification, to gather information, and to be connected with other users.67

Based on the literature and theory reviewed earlier, privacy factors can be more broadly cast in the context of user motivations and their influence on SNS switching intentions. U&G theory assumes that media selection and use is determined by the strength of user motivations, and past work suggests that those various motivations are often interrelated.68 Drawing from the assumptions of U&G theory, we anticipate that switching intention will be driven by the strength of one's use motivations. In this study, the actions of users concerning their privacy were tested with the notion of switching intention, which could lead to one of the most assertive media selection actions, media switching.

We assume that users will satisfy usage gratifications via online social interaction. In sum, we maintain that media use is predicted by the strength of audience motivations. The strength of these motivations should, in turn, determine SNS switching intention. Based on that logic, we expect to find positive relations between SNS switching intention and the motivation variables—namely, social interaction motivation, convenience motivation, and entertainment motivation. To the extent that users believe that an alternative modality could better serve these needs—as governed by the factors reviewed earlier (e.g., privacy)—we expect that more powerfully motivated users would express a greater intention to switch their SNS provider. Drawing from these assumptions, the following hypotheses can be posited to explore the influence of these various motivations on people's switching intentions with SNSs. More formally:

  • Hypothesis 4: Entertainment motivation increases social interaction motivation.

  • Hypothesis 5: Social interaction motivation increases SNS switching intention.

  • Hypothesis 6: Convenience motivation increases SNS switching intention.

  • Hypothesis 7: Entertainment motivation increases SNS switching intention.

A more panoramic representation of the proposed research model can be displayed, as seen in Figure 1.

FIGURE 1

Research Model: Influence of Government Surveillance on SNS Use

FIGURE 1

Research Model: Influence of Government Surveillance on SNS Use

Close modal

To summarize the proposed process model, exposure to surveillance is predicted to increase privacy concern, which should also increase intention to switch SNS. Exposure to surveillance should also directly influence intention to switch SNS. Based on the assumption that media use is determined by strength of user motivations, entertainment motivation is predicted to positively influence social interaction motivation. These motivations, along with convenience motivation, should also increase user intentions to switch SNS.

Methods

Study data were collected through the SurveyMonkey data collection system. From March 1, 2016 to March 3, 2016, 525 people—recruited from the respondent pool of the Banana Lab in Seoul, Korea—participated in the survey. Banana Lab is the Korean affiliate of SurveyMonkey. The participants were Korean social media users who were at least 18-years-old and live in South Korea. Two filter questions were included in the survey questionnaire to screen out unqualified respondents: “The capital of Korea is Busan”; “I have a resident registration card issued by the Korean government.” After screening out unqualified respondents, the institutional review board (IRB)-approved survey rendered responses from 402 people, which provided study data.

The timing of data collection was designed to coincide with prominent public discussions concerning the Korean government's surveillance practices. In particular, 38 lawmakers from the opposition parties held a parliamentary filibuster over 192 hours to stop legislation addressing the “Anti-terrorism Act.”69 The law is known to allow the National Intelligence Service to investigate individuals' social media use records extensively.70 Since the filibuster started on February 23, 2016, people's interest in the risk of government surveillance on communicating via smartphones, including SNS use, was keen during the period of data collection.71 The parliamentary filibuster increased Korean people's attention on issue of government online surveillance.72

All told, 207 participants (51.5 percent) of the sample were males, and 195 (48.5 percent) were females. The participants ranged from 19 to 59 years old (M = 40.16, SD = 11.45). Some 219 (54.5 percent) of the participants answered that they were married, 176 (43.8 percent) single, and 7 (1.7 percent) of them marked “Other” for marital status. The average respondent household size is 3.51 persons (SD = 1.12). The average reported SNS log-in time per day was 7.19 times (SD = 9.88), and participants have 98.56 (SD = 163.45) SNS friends, on average. When asked about their favorite SNS, respondents answered as follows; 132 (32.8 percent) KakaoStory, 181 (45.0 percent) Facebook, 20 (5.0 percent) Twitter, 33 (8.2 percent) Naver BAND, 3 (0.07 percent) Cyworld, and 33 (8.2 percent) KakaoGroup.

Measurement

Exposure to government online surveillance news

Exposure to government surveillance news was gauged by five questions, measured with a five-point scale, ranging from “never” to “very often.”73 The factor loading values of study items are as follow (Cronbach's alpha = .87); 1. “I hear news stories on government online surveillance from television (.83; M = 3.13, SD = 1.27)”; 2. “I hear news stories on government online surveillance from radio” (.81; M = 2.34, SD = 1.36); 3. “I hear news stories on government online surveillance from newspapers” (.86; M = 2.59, SD = 2.40); 4. “I hear news stories on government online surveillance from magazines” (.82; M = 2.13, SD = 1.33); 5. “I hear news stories on government online surveillance from the Internet” (.73; M = 3.50, SD = 1.26).

Privacy concerns regarding social network use

The questions regarding privacy concerns on SNS use probe how secure respondents feel when logged on SNSs.74,75 The seven questions—labeled as the “Privacy Concern on SNS Use scale”—were tested to confirm the factor structure. The factor consists of questions on respondent privacy concerns regarding SNS use, such as personal information posting, SNS messenger use, and personal information inputs. They were grouped in one single factor (Cronbach's alpha = .90) and the factor included such items as “I feel insecure when posting personal information on my SNS pages” (.72; M = 4.05, SD = .83). Confirmatory factor analysis (CFA) tables are available from the authors.

SNS switching intention

SNS switching intention was measured with a five-point scale, ranging from “strongly disagree” to “strongly agree.”76 The six-item scale (Cronbach's alpha = .93) included such items as “I am considering about switching to another social network service” (.83; M = 2.85, SD = .87).

Entertainment motivation

Entertainment motivation is measured with four five-point questions, ranging from “strongly disagree” to “strongly agree.”77,78 The factor loading values of each items are seen as follows (Cronbach's alpha = .74); 1. “I would like to have some enjoyable and relaxing time” (.50; M = 4.40, SD = .67; dropped from the scale); 2. “I would like having fun” (.85; M = 3.97, SD = .71); 3. “I would like feeling pleased” (.83; M = 4.22, SD = .65); 4. “I would like to have something to entertain my mind” (.77; M = 3.83, SD = .81).

Social interaction motivation

Social interaction motivation has four questions arrayed along a five-point scale, ranging from “strongly disagree” to “strongly agree.”79 The factor loading values of each item in the scale (Cronbach's alpha = .76) are: 1. “I would like to see what other people said” (.83; M = 3.55, SD = .82); 2. “I would like to see what is going on” (.77; M = 3.82, SD = .77); 3. “I would like to express myself freely” (.72; M = 3.73, SD = .87); 4. “I would like to meet someone with the same interests as me” (.74; M = 3.93, SD = .79).

Convenience motivation

Convenience motivation is measured with four five-point scale questions, using response categories ranging from “strongly disagree” to “strongly agree.”80,81 The resulting scale (Cronbach's alpha = .76) included such items as “I would like to gather information with less effort” (.79; M = 3.96, SD = .75).

Data Reduction

Confirmatory factor analysis was conducted to assess the validity of measures. The analysis verified good convergent and discriminant validity for survey items. The factor loadings exceeded the .50 threshold recommended for significant convergent validity. The values for composite reliability above .70 as well as t-values above 1.97 revealed good convergent validity. Moreover, the confidence intervals around the correlation of any two constructs did not include one. In short, the measurement showed sufficiently robust reliability and validity to proceed with further analysis.

Study variables were analyzed via structural equation modeling, using the Amos 18 statistical analysis package, in accordance with the research model detailed in the following.

Results

With regard to data analysis, components of each variable were transformed into scales by calculating mean values of items. The scale governing Exposure to Government Online Surveillance News has a mean value falling between “disagree” and “moderate” (M = 2.27, SD = 1.07). The mean value of Privacy Concern on SNS Use scale was located between “agree” and “moderate” (M = 3.39, SD = .68). The mean value of the SNS Switching Intention scale is located between “disagree” and “moderate” (M = 2.59, SD = .83). The mean values of motivation scales show that the participants score somewhat highly in SNS use motivation, including Entertainment Motivation (M = 4.01, SD = .61), Social Interaction Motivation (M = 3.76, SD = .62), and Convenience Motivation (M = 4.02, SD = .57).

Pearson correlation coefficients between main research variables show that each correlation between the research variables—Exposure to Government Online Surveillance News, Privacy Concern on SNS Use, SNS Switching Intention, Entertainment Motivation, Social Interaction Motivation, and Convenience Motivation—is sufficiently robust to stand for further structural equation modelling (SEM) analysis. These variables are positively interrelated—with r values ranging in magnitude from .10 to .55. The one exception involves the correlation between SNS switching intention and convenience motivation, which approaches significance (r = .09; N.S.). Further information on unique individual relationships can be found in Table 1.

TABLE 1

Correlation coefficients between variables

Variable (M, SD)123456
Exposure to Government Online Surveillance News (2.27, 1.07)      
Privacy Concern on SNS Use (3.39, .68) .20**     
SNS Switching Intention (2.59, .83) .27** .26**    
Entertainment Motivation (4.01, .61) .15** .13** .15**   
Social Interaction Motivation (3.76, .62) .27** .22** .30** .48**  
Convenience Motivation (4.02, .57) .11* .22** .09 .41** .55** 
Variable (M, SD)123456
Exposure to Government Online Surveillance News (2.27, 1.07)      
Privacy Concern on SNS Use (3.39, .68) .20**     
SNS Switching Intention (2.59, .83) .27** .26**    
Entertainment Motivation (4.01, .61) .15** .13** .15**   
Social Interaction Motivation (3.76, .62) .27** .22** .30** .48**  
Convenience Motivation (4.02, .57) .11* .22** .09 .41** .55** 

Notes: 1 = Exposure to Government Online Surveillance News; 2 = Privacy Concern on SNS Use; 3 = SNS Switching Intention; 4 = Entertainment Motivation; 5 = Social Interaction Motivation; 6 = Convenience Motivation.

**

p < .01;

*

< .05

Focusing on the Pearson correlation coefficients between variables in this study, multicollinearity does not seem to be a concern. Although the Pearson correlation coefficient between Social Interaction Motivation and Convenience Motivation is over .50, the variance inflation factor (VIF) suggests that there is no critical multicollinearity issue between these two variables (VIF = 1.000).

Based on the research hypotheses, the research variables are organized in the order of the suggested research model (Figure 2) for hypothesis testing.

FIGURE 2

Hypotheses Tests in SEM: Influence of Government Surveillance on SNS Use

Notes: x2 = 621.854, p = .000, CMIN/DF = 1.818, CFI = .957, NFI = .909, RMSEA = .045.

*p < .05; **p < .01; ***p < .001; N.S. = Not Significant.

FIGURE 2

Hypotheses Tests in SEM: Influence of Government Surveillance on SNS Use

Notes: x2 = 621.854, p = .000, CMIN/DF = 1.818, CFI = .957, NFI = .909, RMSEA = .045.

*p < .05; **p < .01; ***p < .001; N.S. = Not Significant.

Close modal

The model fit of the structural equation model derived from the suggested model is acceptable (x2 = 621.86, p = .000, CMIN/DF82 = 1.89, CFI = .96, NFI = .91, RMSEA = .05). Focusing on unique individual predictive relationships, exposure to government online surveillance news increases privacy concern regarding SNS use (ß = .27, p < .001); this provides support for Hypothesis 1. Similarly, exposure to government online surveillance news increases SNS switching intention (ß = .17, p < .05), providing support for Hypothesis 2. In addition, privacy concern regarding SNS use increases SNS switching intention (ß = .21, p < .001). Hypothesis 3 is thus supported.

Moving on to the influence of motivation factors in the model, Hypothesis 4 posited entertainment motivation as a predictor of SNS interaction motivation. Results suggest that entertainment is, in fact, a powerful predictor (ß = .84, p < .001); this provides support for the Hypothesis. Similarly, Hypothesis 5 casts social interaction motivation as a predictor of SNS switching intention. Results indicate a moderately strong relationship between the variables (ß = .55, p < .05). Hypothesis 5 is thus supported.

Finally, the last two hypotheses are not supported. In particular, Hypothesis 6 posited that convenience motivation would increase SNS switching intention. But, the direction of influence involving convenience motivation is in the opposite direction, suggesting that this factor actually decreases SNS switching intention (ß = −.18, p < .05). Although entertainment motivation is positively related to SNS switching intention, per Hypothesis 7, the relationship (ß = −.21) is not significant. This leaves Hypothesis 7 without support.

To summarize the process model, results suggest that (a) privacy concern mediates the relationship between governmental online surveillance and SNS switching intention, and (b) SNS switching intention was predicted positively by social interaction motivation and negatively by convenience motivation.

Discussion

The rather extraordinary sequence of events following the South Korean ferry tragedy—and subsequent government moves to monitor online communications—provided an ideal backdrop to study the influence of individual privacy concerns on SNS switching intentions. The present study set out to examine the assumption that online users would be influenced by widely reported privacy threats; namely, that public servants might monitor personal online communications between instant messenger users, and compel Korean domestic SNS providers to cooperate with their surveillance efforts.83 The finding that privacy concern mediates the relationship between governmental online surveillance and SNS switching intention underscores the measures that users take in response telecommunication policy actions, particularly those that might pose a threat to their online privacy.

This seems consistent with the aforementioned migration of users from the government-monitored incumbent SNS to the German messenger service, Telegram. Taken together, these trends provide an interesting case study in the internationalization of Internet communication. By way of explanation, a growing number of Korean SNS users seem keen on switching to the alternative SNS, which remains free from surveillance by Korean public servants. The alternative SNS was more likely not only useful in fulfilling audience media use motivations, but also an effective vehicle for avoiding government surveillance.

The Korean online social networks, many of which felt victimized by government online surveillance, continued to lose users through 2015. On the question ascertaining participants' favorite SNS, 41 percent (n = 165) of the participants responded that their favorite SNSs are KakaoStory or KakaoGroup. Both services belonged to the SNS provider, Daum Communications, which cooperated with the National Intelligence Service to surveil people's communication on the SNS. South Korean SNS users are thus willing to use alternative SNSs in order to reduce privacy concerns caused by government online surveillance. These users are willing to switch to another SNS, moreover, to fulfill their media use motivations.

SNS switching intention items in this study tapped participants' general ideas on how much they are “considering,” “likely,” or “determined” to switch to another social networking service.84 By asking these general questions, the influence of government online surveillance's on SNS switching intention can be compared with the influence of other motivation variables' influence on SNS switching intention. The results show that privacy concerns about SNS use and government online surveillance are significant predictors of SNS switching intention, and their influence is as great as other motivation variables to switch SNSs.

In particular, study results suggest that South Korean SNS users express an intention to switch to alternative SNSs when they feel privacy concerns about their SNS use. Study findings thus confirm those of previous research, which uncovers a strong negative influence of privacy concern on SNS use.85 These results further suggest that privacy concern functions as one of the factors that modify SNS use,78 and the negative effective effect of privacy concern is prevalent over diverse SNSs, including the Korean domestic SNSs.86,87

Of particular interest to policymakers, study findings suggest that privacy concern regarding SNS use is significantly influenced by exposure to government online surveillance news in South Korea. Government online surveillance was assumed to threaten Internet users' privacy.88 This finding is consistent with previous studies on relations between media exposure on government online surveillance and feeling secure about privacy protection.89 The influence of privacy concerns on switching intentions uncovered here can thus help explain the large-scale migration in SNS platforms noted earlier,90 representing a logical audience activity to circumvent government surveillance.

In addition, the present study establishes that SNS switching intention was predicted positively by social interaction motivation and negatively by convenience motivation. These findings thus help provide support for a novel theoretical framework that illustrates the utility of media U/G variables in the context of online privacy conceptions stemming from perceived threats of online government surveillance. Two motivational variables emerged as strong predictors of SNS switching intention. While convenience motivation is a negative predictor of switching intention, social interaction motivation emerges as a positive predictor of SNS switching intention. First, SNS users can enhance the interactivity in their social lives and public sphere, even by switching to a newly developed SNS.91 Those who are willing to interact with others seem to expand their media channels. Here, social interaction motivation was found to be a great predictor of SNS switching intention in the structural equation model. The selection of newly popular SNSs can be taken as tools to increase the users' ability to interact others.

Furthermore, the convenience motivation apparently causes ambivalence in SNS use. Previous research explains that individuals use SNSs to conveniently reach other users.92 However, the inability of convenience to predict switching intention can be explained by inertia; that is, Korean SNS users seem to regard SNS switching as a cumbersome process. Parallel work on resistance to change suggests that the adaptation to newly developed media can be an inconvenient process.93 Taken together, these research results suggest that users are active not only in media selection, but also in media avoidance.94

According to the U&G theoretical framework, SNS users tend to actively choose alternative SNSs to reduce their privacy concern or to feel secure in SNS use. In order to have their privacy guaranteed, they can abandon their present SNS. Users in South Korea choose alternative SNSs to fulfill social interaction needs and privacy enhancement needs; they do not choose alternative SNSs to preserve their convenience fulfillment. Although we did not posit links between all of the various U&G motives—since some linkages were nonobvious and the model was already ambitious—later work should examine links between gratifications. Drawing from this work, U&G theory can expand its research scope—from exploring the positive motivation variables of media use—to the negative motivations that restrain users from switching to alternative media in response to changing regulatory and market forces.

The present findings also point to other implications for policymakers, namely that surveillance efforts by governments can be avoided with the help of new media developers, who provide more secured SNSs. By switching to more secured services, the Korean SNS users could avoid surrendering their online sovereignty and bypass the consumption of mass media messages to shape a consensus of social agendas.95 When SNS users can have access to new media technology of privacy protection—facilitated by media developer efforts—they can abandon the giant SNS providers who cooperate with government surveillance efforts. Media developers, who ushered in the new media age, still have the power to advance media freedom. The alternative media—which can include surveillance circumvention tools—will nevertheless help Internet users to manage privacy threats under government surveillance.96

Online surveillance by the Korean government presents a great burden for their domestic online network service providers. Unfortunately, the recently impeached Korean president—the first daughter of the military dictator, Park Chung-hee—was directing her government to control civil society and media. The old governing paradigm of interventionism was thus threatening media freedom in emerging South Korean media industries, which in turn presents a threat to privacy and hence democracy in the region.97 Although these concerns have abated since President Park was impeached, the growing specter of state surveillance in neighboring North Korea and China point to the fragility of privacy rights in the digital era.

Since the democratic government took political power in 2017, Koreans have been enjoying more freedom of expression in social media use. This research was conducted during the period of the oppressive president, who is the daughter of a notorious dictator in 1970s. Ironically, her inauguration provided us with a compelling opportunity to investigate the influence of surveillance in a dynamic context where media freedom is valued. The present model can help aid our understanding of how citizens in a democratically advanced society consider media switching when they are informed about surveillance agents' actions to collect their personal information online.

Limitations and Future Research Implications

There are limitations in research design and the generalizability of research findings. While data derived from Korean participants can extend to other liberal East Asian democracies (e.g., Japan and Taiwan), they have little bearing on larger countries with heavier media censorship. Second, the survey window took advantage of an extraordinary period, when the South Korean Congress held filibuster sessions for the legislators from the opposition parties to pass the “Anti-terrorism Act.” The British Broadcasting Corporation observed that the filibuster increased Koreans' interest in the risk of government surveillance of online communication.98 Our study did not specify the SNSs to which participants are migrating, which could determine distinctive use activities and gratifications associated with each. Finally, this survey study cannot establish causality, which can be confirmed with the experimental research instrument, between government online surveillance and SNS switching intention.

Some research frameworks need to be considered in future research on SNS user behaviors in surveillance environments. First, legislative efforts to enhance users' right of media freedom imply that new media surveillance research can profile new media users by multiple points such as real-time data feeding, data collection, and data appropriation.99 In addition, users' behaviors to avoid surveillance on SNS cannot be emulated in the other telematic monitoring contexts, such as Google wearables. Surveillance research in an emerging media environment needs to consider other related factors, which could entail data intelligence mechanisms and user behavior types.100

Moving forward, the present study framework could be repeated in other, more culturally varied social contexts. As the previous research in China found, people in media-controlled societies might use more aggressive resistance strategies to expand their freedom of media access.101 Despite the increase in surveillance under Park's administration, the South Korean context is of course much more open than China's. In such media-controlled societies, fear of government can be a more critical variable bridging government online and resistance to using social media.102 In addition, service switching intentions need to be further explored to explain the relationship between switching intention and switching behaviors. Although the Korean government surveillance cases have been revealed to the public, the majority of the incumbent service users in South Korea continued using the government-surveilled SNS. If future research can target a critical period, in which most SNS users switch to an alternative service, it could help scholars uncover the relationships between switching intention and switching behavior.

Footnotes

1.

Smith and Lyon, “Comparison of Survey,”190.

2.

Park and Skoric, 77.

3.

Brandon.

4.

Waring.

5.

The President's complaints were linked to rumors on her veiled schedule in April of 2014, when about 300 high school students died on a sunken ferry. See Choi.

6.

Brandon.

7.

Kim.

8.

Ibid.

9.

Ha et al., 425–26.

10.

Kim et al., 7.

11.

Fernback, 12.

12.

Andrejevic and Burdon, 19–20.

13.

Millham and Atkin, 50–51.

14.

Foucault, 22.

15.

Lyon and Höller.

16.

Brown and Korff, 119–20.

17.

Millham and Atkin, 50–51.

18.

Brown and Korff, 119–20.

19.

Mou et al., 359–60.

20.

Fernback, 11.

21.

Zittrain, 36–37.

22.

Eldon.

23.

Fuchs, 14–15.

24.

Whitley, 154–55.

25.

Nissenbaum, 32–-33.

26.

Child, Haridakis, and Pertronio, 1859–60.

27.

Whitley, 154–55.

28.

Zittrain, 36–37.

29.

Warren and Brandeis.

30.

Westin, 6.

31.

Sanfilipo, Frischmann, and Standburg.

32.

Lee.

33.

Rice and Katz, 455–56.

34.

Litt and Hargittai, 520–21.

35.

Fernback, 13.

36.

Lin and Kim, 715.

37.

Cha.

38.

Mesch, 1475.

39.

Debatin, 102.

40.

Stutzman, Capra, and Thompson.

41.

Larsson et al., 260–61.

42.

Jiang, Heng, and Choi, 579–80.

43.

Park, Campbell, and Kwak, 1019–20.

44.

Mou, Wu, and Atkin, 837.

45.

Zhang, Yang, and Chen, 1374–75.

46.

Park, Chung, and Shin, 1319.

47.

Park and Skoric, 71.

48.

Fernback, 13–14.

49.

Ibid., 18.

50.

Hu and Hwang, 81.

51.

Boyle and Keith, 33.

52.

Zhang, Yang, and Chen, 1374–75.

53.

Yao, Phang, and Ling, 324–25.

54.

Schreiner and Hess.

55.

Papacharissi and Rubin, 175–76.

56.

Hunt, Lin, and Atkin, 234–35.

57.

Millham and Atkin, 64.

58.

Katz, 19–20.

59.

Raacke and Bonds-Raacke, 169–70.

60.

Papacharissi and Rubin, “Predictors,” 17.

61.

Sunder and Limperos, 504–05.

62.

Ball-Rokeach, 485–86.

63.

Abrahamson, 14–15.

64.

Ruggiero, 3–5.

65.

Hunt, Atkin, and Krishnan, 187–88.

66.

Ha et al., 426–28.

67.

Ha and James, 457–58.

68.

Atkin, Hunt, and Lin, 623–24.

69.

BBC.

70.

Kim.

71.

BBC.

72.

Kim.

73.

Nagler and Hornik, 75.

74.

Angst and Agarwal, 370.

75.

Wolfinbarger and Gilly, 198.

76.

Kim, Shin, and Lee, 893.

77.

Ha et al., 438.

78.

Nambisan and Baron, 62.

79.

Ha et al., 438.

80.

Ibid.

81.

Leung and Wei, 320.

82.

CMIN/DF is a term for the chi-square value in the SPSS® Amos statistical program, represented as the minimum discrepancy divided by the degrees of freedom. CFI refers to the comparative fit index, which compares the model of interest with some alternative, such as the null or independence model. NFI represents the normed fit index, which equals the difference between the chi-square of the null model and the chi square of target model, divided by the chi-square of the null model. RMSEA, or root-mean square residual, represents the square root of the average or mean of the covariance residuals—the differences between corresponding elements of the observed and predicted covariance matrix.

83.

Brandon.

84.

Kim, Shin, and Lee, 891.

85.

Cha.

86.

Fernback, 19.

87.

Dwyer, Hiltz, and Passerrini.

88.

Nissenbaum, 46.

89.

Child, Haridakis, and Petronio, 1870.

90.

Whitley, 175–176.

91.

Boyd and Ellison, 210–11.

92.

Ha et al., 436.

93.

Rogers, 114.

94.

Sundar and Limperos, 523.

95.

Harlow, 241–42.

96.

Mou et al., 367.

97.

Mou, Atkin, and Fu, 87–88.

98.

BBC.

99.

Park and Skoric, 81.

100.

Park, Chung, and Shin, 1330.

101.

Mou, Atkin, and Fu, 87–88.

102.

Clavel et al., 501–02.

Bibliography

Abrahamson, David. “The Visible Hand: Money, Markets, and Media Evolution.” Journalism & Mass Communication Quarterly 75, no. 1 (1998): 14–18.
Andrejevic, Mark, and Mark Burdon. “Defining the Sensor Society.” Television & New Media 16, no. 1 (2015): 19–36.
Angst, Corey M., and Ritu Agarwal. “Adoption of Electronic Health Records in the Presence of Privacy Concerns: The Elaboration Likelihood Model and Individual Persuasion.” MIS Quarterly 33, no. 2 (2009): 339–70.
Atkin, David J., Daniel S. Hunt, and Carolyn A. Lin. “Diffusion Theory in the New Media Environment: Toward an Integrated Technology Adoption Model.” Mass Communication and Society 18, no. 5 (2015): 623–50.
Ball-Rokeach, Sandra J. “The Origins of Individual Media-System Dependency: A Sociological Framework.” Communication Research 12, no. 4 (1985): 485–510.
BBC. “South Korea Record Parliamentary Filibuster Enters New Week.” BBC NEWS. February 29, 2016. Accessed April 2, 2016. http://www.bbc.com/news/world-asia-35686049.
Boyd, Danah M., and Nicole B. Ellison. “Social Network Sites: Definition, History, and Scholarship.” Journal of Computer-Mediated Communication 13, no. 1 (2007): 210–30.
Boyle, Paul, and Halfacree Keith. Exploring Contemporary Migration. New York: Routledge, 2014.
Brandon, Russell. “Surveillance Drives South Koreans to Encrypted Messaging Apps.” The Verge. October 6, 2014. Accessed April 2, 2016. http://www.theverge.com/2014/10/6/6926205/surveillance-drives-south-koreans-to-encrypted-messaging-apps.
Brown, Ian, and Douwe Korff. “Terrorism and the Proportionality of Internet Surveillance.” European Journal of Criminology 6, no. 2 (2009): 119–34.
Cha, Jiyoung. “Factors Affecting the Frequency and Amount of Social Networking Site Use: Motivations, Perceptions, and Privacy Concerns.” First Monday 15, no. 12 (2010). https://firstmonday.org/ojs/index.php/fm/article/view/2889/2685.
Child, Jeffrey T., Paul M. Haridakis, and Sandra Petronio. “Blogging Privacy Rule Orientations, Privacy Management, and Content Deletion Practices: The Variability of Online Privacy Management Activity at Different Stages of Social Media Use.” Computers in Human Behavior 28, no. 5 (2012): 1859–72.
Choi, Jieun. “Were Park Geun-hye's Seven ‘Missing Hours’ Actually More?” Korea Expose. October 16, 2017. Accessed February 2, 2019. https://www.koreaexpose.com/park-geun-hyes-seven-missing-hours-actually/.
Clavel, Chloé, Ioana Vasilescu, Laurence Devillers, Gaël Richard, and Thibaut Ehrette. “Fear-Type Emotion Recognition for Future Audio-Based Surveillance Systems.” Speech Communication 50, no. 6 (2008): 487–503.
Debatin, Bernhard, Jennette P. Lovejoy, Ann-Kathrin Horn, and Brittany N. Hughes. “Facebook and Online Privacy: Attitudes, Behaviors, and Unintended Consequences.” Journal of Computer-Mediated Communication 15, no. 1 (2009): 83–108.
Dwyer, Catherine, Starr Hiltz, and Katia Passerini. “Trust and Privacy Concern within Social Networking Sites: A Comparison of Facebook and MySpace.” AMCIS 2007 Proceedings (2007): 339.
Eldon, Eric. “Analysis: Some Facebook Privacy Issues Are Real, Some Are Not.” ADWEEK. May 11, 2010. Accessed April 2, 2016. http://www.adweek.com/socialtimes/analysis-some-facebook-privacy-issues-are-real-some-are-not/239787.
Fernback, Jan. “Sousveillance: Communities of Resistance to the Surveillance Environment.” Telematics and Informatics 30, no. 1 (2013): 11–21.
Foucault, Michel. Discipline and Punish: The Birth of the Prison. New York: Vintage, 2012.
Fuchs, Christian. “How Can Surveillance Be Defined? Remarks on Theoretical Foundations of Surveillance Studies.” The Internet and Surveillance Paper Series. Number 1, 2010.
Ha, Louisa, and E. Lincoln James. “Interactivity Reexamined: A Baseline Analysis of Early Business Web Sites.” Journal of Broadcasting & Electronic Media 42, no. 4 (1998): 457–74.
Ha, Young Wook, Jimin Kim, Christian Fernando Libaque-Saenz, Younghoon Chang, and Myeong-Cheol Park. “Use and Gratifications of Mobile SNSs: Facebook and KakaoTalk in Korea.” Telematics and Informatics 32, no. 3 (2015): 425–38.
Harlow, Summer. “Social Media and Social Movements: Facebook and an Online Guatemalan Justice Movement that Moved Offline.” New Media & Society 14, no. 2 (2012): 225–43.
Hu, A. W., and Ing-san Hwang. “Measuring the Effects of Consumer Switching Costs on Switching Intention in Taiwan Mobile Telecommunication Services.” Journal of American Academy of Business 9, no. 1 (2006): 75–85.
Hunt, Daniel S., Carolyn A. Lin, and David J. Atkin. “Communicating Social Relationships via the Use of Photo-Messaging.” Journal of Broadcasting & Electronic Media 58, no. 2 (2014): 234–52.
Hunt, Daniel, David Atkin, and Archana Krishnan. “The Influence of Computer-Mediated Communication Apprehension on Motives for Facebook Use.” Journal of Broadcasting & Electronic Media 56, no. 2 (2012): 187–202.
Jiang, Zhenhui, Cheng Suang Heng, and Ben C. F. Choi. “Research Note—Privacy Concerns and Privacy-Protective Behavior in Synchronous Online Social Interactions.” Information Systems Research 24, no. 3 (2013): 579–95.
Katz, Elihu. “Utilization of Mass Communication by the Individual.” In The Uses of Mass Communications: Current Perspectives on Gratifications Research. Edited by Elihu Katz, Jay G. Blumer, and Michael Gurevitch, 19–32. Beverly Hills, CA: Sage Publications, 1974.
Kim, Bo-eun. “Will Social Media Landscape Change?” The Korea Times. November 2, 2015. Accessed April 2, 2016. http://www.koreatimes.co.kr/www/news/biz/2015/11/123_189796.html.
Kim, Gimun, Bongsik Shin, and Ho Geun Lee. “A Study of Factors that Affect User Intentions Toward Email Service Switching.” Information & Management 43, no. 7 (2006): 884–93.
Kim, Yong-Chan, Euikyung Shin, Ahra Cho, Eunjean Jung, Kyungeun Shon, and Hongjin Shim. “SNS Dependency and Community Engagement in Urban Neighborhoods: The Moderating Role of Integrated Connectedness to a Community Storytelling Network.” Communication Research 46, no. 1 (July 2015): 7–32.
Larsson, Stefan, Måns Svensson, Marcin De Kaminski, Kari Rönkkö, and Johanna Alkan Olsson. “Law, Norms, Piracy and Online Anonymity: Practices of De-identification in the Global File Sharing Community.” Journal of Research in Interactive Marketing 6, no. 4 (2012): 260–80.
Lee, Laurie T. “Digital Media Technology and Individual Privacy.” In Communication Technology and Social Change. Edited by Carolyn Lin and David Atkin, 257–79. Mahwah, NJ: Lawrence Erlbaum Associates, 2007.
Leung, Louis, and Ran Wei. “More Than Just Talk on the Move: Uses and Gratifications of the Cellular phone.” Journalism & Mass Communication Quarterly 77, no. 2 (2000): 308–20.
Lin, Carolyn A., and Tonghoon Kim. “Predicting User Response to Sponsored Advertising on Social Media via the Technology Acceptance Model.” Computers in Human Behavior 64 (2016): 710–18.
Litt, Eden, and Eszter Hargittai. “A Bumpy Ride on the Information Superhighway: Exploring Turbulence Online.” Computers in Human Behavior 36 (2014): 520–29.
Lyon, David, and Christian Höller. “Surveillance Systems Towards an Electronic Panoptical Society.” (online) (visited 5.1. 2001). 1997. http://www.heise.de/bin/tp/issue/dlartikel.cgi.
Mesch, Gustavo S. “Is Online Trust and Trust in Social Institutions Associated with Online Disclosure of Identifiable Information Online?” Computers in Human Behavior 28, no. 4 (2012): 1471–77.
Millham, Mary Helen, and David Atkin. “Managing the Virtual Boundaries: Online Social Networks, Disclosure, and Privacy Behaviors.” New Media & Society 20, no. 1 (2018): 50–67.
Mou, Yi, David Atkin, and Hanlong Fu. “Predicting Political Discussion in a Censored Virtual Environment.” In Political Communication in China. Edited by Wenfang Tang and Shanto Iyengar, 87–102. London: Routledge, 2013.
Mou, Yi, David Atkin, Hanlong Fu, Carolyn A. Lin, and T. Y. Lau. “The Influence of Online Forum and SNS Use on Online Political Discussion in China: Assessing ‘Spirals of Trust’.” Telematics and Informatics 30, no. 4 (2013): 359–69.
Mou, Yi, Kevin Wu, and David Atkin. “Understanding the Use of Circumvention Tools to Bypass Online Censorship.” New Media & Society 18, no. 5 (2016): 837–56.
Nagler, Rebekah H., and Robert C. Hornik. “Measuring Media Exposure to Contradictory Health Information: A Comparative Analysis of Four Potential Measures.” Communication Methods and Measures 6, no. 1 (2012): 56–75.
Nambisan, Satish, and Robert A. Baron. “Interactions in Virtual Customer Environments: Implications for Product Support and Customer Relationship Management.” Journal of Interactive Marketing 21, no. 2 (2007): 42–62.
Nissenbaum, Helen. “A Contextual Approach to Privacy Online.” Daedalus 140, no. 4 (2011): 32–48.
Papacharissi, Zizi, and Alan M. Rubin. “Predictors of Internet Use.” Journal of Broadcasting & Electronic Media 44, no. 2 (2000): 175–96.
Park, Yong Jin, Scott W. Campbell, and Nojin Kwak. “Affect, Cognition and Reward: Predictors of Privacy Protection Online.” Computers in Human Behavior 28, no. 3 (2012): 1019–27.
Park, Yong Jin, Jae Eun Chung, and Dong Hee Shin. “The Structuration of Digital Ecosystem, Privacy, and Big Data Intelligence.” American Behavioral Scientist 62, no. 10 (2018): 1319–37.
Park, Yong Jin, and Marco Skoric. “Personalized Ad in Your Google Glass? Wearable Technology, Hands-Off Data Collection, and New Policy Imperative.” Journal of Business Ethics 142, no. 1 (2017), 71–82.
Raacke, John, and Jennifer Bonds-Raacke. “MySpace and Facebook: Applying the Uses and Gratifications Theory to Exploring Friend-Networking Sites.” Cyberpsychology & Behavior 11, no. 2 (2008): 169–74.
Rice, Ronald E., and James E. Katz. “Assessing New Cell Phone Text and Video Services.” Telecommunications Policy 32, no. 7 (2008): 455–67.
Rogers, E. M. Diffusion of Innovations. 5th ed. New York: Free Press, 2003.
Ruggiero, Thomas E. “Uses and Gratifications Theory in the 21st Century.” Mass Communication & Society 3, no. 1 (2000): 3–37.
Sanfilippo, Madelyn, Brett Frischmann, and Katherine Standburg. “Privacy as Commons: Case Evaluation Through the Governing Knowledge Commons Framework.” Journal of Information Policy 8 (2018): 116–66.
Schreiner, Michel, and Thomas Hess. “Examining the Role of Privacy in Virtual Migration: the Case of WhatsApp and Threema.” Paper presented at the 2015 Americas Conference on Information Systems, Fajardo, Puerto Rico, August 13–15, 2015.
Smith, Emily, and David Lyon. “Comparison of Survey Findings from Canada and the USA on Surveillance and Privacy from 2006 and 2012.” Surveillance & Society 11, no. 1 (2013): 190–203.
Stutzman, Frederick, Robert G. Capra, and Jamelia Thompson. “Factors Mediating Disclosure in Social Network Sites.” Computers in Human Behavior 27, no. 1 (2011): 590–98.
Sundar, S. Shyam, and Anthony M. Limperos. “Uses and Grats 2.0: New Gratifications for New Media.” Journal of Broadcasting & Electronic Media 57, no. 4 (2013): 504–25.
Waring, Joseph. “Koreans Drop KakaoTalk Over Privacy Concerns.” Mobile World Live. October 20, 2014. Accessed April 2, 2019. https://www.mobileworldlive.com/apps/news-apps/koreans-drop-kakaotalk-privacy-concerns/.
Warren, Samuel D., and Louis D. Brandeis. “The Right to Privacy.” The Harvard Law Review 4, no. 5 (1890): 193–220.
Westin, Alan F. Privacy and Freedom. Vol. 7. New York: Atheneum, 1967.
Whitley, Edgar A. “Perceptions of Government Technology, Surveillance and Privacy: The UK Identity Cards Scheme.” In New Directions in Surveillance and Privacy. Edited by Benjamin J. Goold and Daniel Neyland, 154–77. Devon, UK: Willan, 2013.
Wolfinbarger, Mary, and Mary C. Gilly. “eTailQ: Dimensionalizing, Measuring and Predicting Etail Quality.” Journal of Retailing 79, no. 3 (2003): 183–98.
Yao, Xinlin, Chee Wei Phang, and Hong Ling. “Understanding the Influences of Trend and Fatigue in Individuals' SNS Switching Intention.” 48th Hawaii International Conference on System Sciences (HICSS), IEEE, Kauai, Hawaii, January 5–8, 2015, 324–34.
Zhang, Gaofeng, Yun Yang, and Jinjun Chen. “A Historical Probability Based Noise Generation Strategy for Privacy Protection in Cloud Computing.” Journal of Computer and System Sciences 78, no. 5 (2012): 1374–81.
Zittrain, Jonathan. The Future of the Internet—and How to Stop It. New Haven, CT: Yale University Press, 2008.