Abstract
Purpose
The adoption of social media has been extensively discussed. However, to explain the adoption of traditional social media, considering the benefits and risks accumulated from the experiences of social media use, the extent literature is limited. Thus, this paper investigated the act of traditional social media users’ switching behavior from a dynamic perspective and the level of information privacy concerns and social media privacy to measure the risks and benefit accumulated from this dynamic process.
Design/methodology/approach
This study of Facebook and Twitter users, who are regarded as representative of traditional social media, are selected as research targets surveyed and were required to answer a specially designed questionnaire in order to determine their general feeling on social media platforms they currently use. As a part of this process, quota sampling was used to collect different samples based on gender and age. In this paper, t-test, one-way ANOVA and multiple comparisons were used for the statistical analysis, conducted through SPSS.
Findings
Information privacy concerns and social media dependency affect the adoption of social media. Secondly, social media dependency is a more salient determinant for social media adoption. Therefore, social media firms should pay more attention to enhancing user dependency of social media by increasing user involvement of social media.
Originality/value
This study intends to conduct a research design that provides an overall and holistic understanding of user usage experience. To do this, it investigates the intensity of switching behavior through the level of dependency and the level of information privacy concern that users inevitably exhibit through the use of social media over long time.
Keywords
Citation
Kang, I. (2022), "A study on switching behavior of social media: from a dynamic perspective", International Trade, Politics and Development, Vol. 6 No. 3, pp. 107-120. https://doi.org/10.1108/ITPD-08-2022-0015
Publisher
:Emerald Publishing Limited
Copyright © 2022, Inwon Kang
License
Published in International Trade, Politics and Development. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (forboth commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/ legalcode
1. Introduction
Only as far back as 2019, there was a 53% increase in the number of online users concerned with Internet privacy issues, when compared with 2018 (Statista, 2022). In this environment of increasing privacy violation risks, the recently new social media facility MeWe, has emerged with the aim to protect user information from violations and combat personal data collection infringements for targeted advertisements. At the same time, there is a vastly growing movement from traditional social media sites, such as Facebook and Twitter, to that of MeWe (USA Today, 2021).
Despite this current understanding of growing data violation of users, many still keep to the more established and traditional social media sites, such as Facebook and Twitter. Previous studies suggest that individuals can use social media to build or maintain social relationships (Ancu and Cozma, 2009; Chu and Kim, 2011), and obtain information to help knowledge acquisition (Ha et al., 2015; Humphreys, 2007); providing the most basic function of social media. Through its development, social media firms increasingly design more functionality on their facilities, helping their users to achieve almost all critical everyday goals in their life (Lee and Choi, 2018). Hence, by achieving almost all they need through social media, a strong connection is built between users and social media. Consequently, users become more greatly dependent of social media, with increasing active engagement (Kim et al., 2020).
As such, users are increasingly dependency on the various benefits that social media offers; but at the same time, their information privacy concerns are also increasing, making social media a double-edged sword.
This study examines the so-called switching behavior of why users abandon the current service and switch to another service. Switching behavior can be defined as the consumer act of replacing or exchanging a current service provider with another service provider (Liu et al., 2016). Research on consumer switching behavior has largely focused on how various marketing factors drive switching, by increasing the expected utility of the new option, and decreasing the expected utility of the old option, or both (Su et al., 2017).
Despite the fact that numerous studies on switching behavior have varied views with regards to its cause and effects, most studies have identified the phenomenon of switching behavior at a specific point in time. However, since the switching behavior of social media is likely to appear as a result of a user’s experience accumulated over a long period of time, this study intends to conduct a research design that provides an overall and holistic understanding of user usage experience. To do this, it investigates the intensity of switching behavior through the level of dependency and the level of information privacy concern that users inevitably exhibit through the use of social media over long time. In other words, in order to properly reflect what consumers have experienced for more than a few years, the measurement variable itself must be a variable with such characteristics. For example, dependency is a variable formed through continuous use of social media, and since personal information and privacy issues are also perceived after numerous usage experiences, these variables can be used to measure social media consumption behavior in terms of so-called dynamism.
Also, as mentioned above, social media is a service with so many positive benefits having said that, there are also negative issues at the same time. Therefore, in order to elaborate social media evaluation, it is important to look concurrently at both coexisting positive and negative stimuli.
2. Theoretical backgrounds
2.1 Switching behavior
Switching behavior refers to a voluntary decision to move from an existing incumbent option to a new option (Jiang et al., 2014; Liu et al., 2016). Bansal and Taylor (1999) defined that switching behavior is a consumer replacing or exchanging current service provider to others. Marketing scholars have paid substantial attention to consumer switching behaviors from a relationship marketing perspective. Developing a long-term relationship with loyal customers grants the service firm direct value, in the form of lower customer price sensitivity and increased purchases, as well as indirect value through positive word of mouth (Hsieh et al., 2012).
Online services have prospered since the 1990s, and switching behavior from offline to online has attracted the attention of academia. With the development of second-generation Internet technology and the formation of user-oriented content (UGC) Internet product patterns, researchers gradually delved into switching behavior in social media (Yin et al., 2021).
Keaveney and Parthasarathy (2001) offer an early examination of customer switching behavior across online service providers. Chen and Hitt (2002) focus on customer characteristics and firm attributes to investigate switching behavior in the online brokerage industry. Kumar and Charles (2011) specifically define social media switching as a transition that is characterized by the changes from one platform to another or from one platform to another in the same category.
In line with the growing prevalence of the social media platform in recent years, the number of studies on switching behavior of social media has thus increased accordingly. And with the emergence of diversified online services, more recent studies have provided evidence on why users switch from one social media platform to another (Choi et al., 2013; Haj-Salem and Chebat, 2014; Hsieh et al., 2012; Zhang et al., 2012); <directly relating the switching likelihood to dissatisfaction?/, related to satisfaction and switching barrier.?> In today’s digital environment, switching can actually involve rapid trialing (Salo and Makkonen, 2018) and oscillations between social media platforms, rather than a long-term migration to a single system.
In summary, it is considered that many researchers studied the factor which can affect the users’ switching intention and switching behavior. However, most researchers have mainly focused on the switching behavior at a specific time. However the role of users’ experiences of social media use on switching behavior has not been examined and this highlights a need to further investigate this subject. Because users social media for more than a few years.
2.2 Information privacy concerns
Information privacy concerns refer to individual concerns about the possible loss of privacy as a result of information disclosure to a specific external agent or institution (Xu et al., 2011). Information privacy concerns have been identified as a major and central construct in a number of studies that have attempted to conceptualize “information privacy” in different contexts; including social media, commerce, governance and health care contexts (Rath and Kumar, 2021). Such concerns may vary from the intrusion of an individual’s privacy to potential breaches that can lead to identity theft (Bandyopadhyay, 2009). Information privacy concerns reflect an individual’s perception of their concerns and worries for how their personal information is handled by a specific institution; and this is different from their expectations, general perceptions or awareness of how the institution should handle their personal information (Hong and Thong, 2013).
Social media privacy concern has been found to have significant effects on user behavior and attitudes in a variety of contexts; such as in electronic health records (Angst and Agarwal, 2009), instant messaging (Lowry et al., 2011) and location-based services (Zhou, 2015). Social media can accumulate large-scale data that people have never seen on previous types of media. Through data mining technology and application development, it is possible to track the user’s daily lives and other personal information disclosed on social media (Lohr, 2012). For example, to better connect all individuals, social media usually encourages or even requires users to reveal real names, emails, locations and other identities, when they register or post. Thus, a large scale of data, including personal status and other private information is accumulated; and if users are unable to control or restrict access to this, their privacy is compromised.
As concerns about personal information privacy continue to grow, an increasing number of studies have empirically investigated the phenomenon. Bélanger et al. (2013) defined information privacy concerns as the desire of individuals to control data about themselves. In response to these growing concerns, researchers have investigated various issues pertaining to information privacy (Preibusch, 2013). Researchers have criticized previous studies in lacking contextual detail (Bélanger and Crossler, 2011). For example, Smith et al. (2011) criticize studies that have not investigated adequately how various contexts influence information privacy. As research on information privacy concerns continues to increase in quantity, it is important and timely to review prior information privacy concern research, to enhance our understanding of how the research field has evolved with time. Such understanding would thus help to assess the current state of literature, identify knowledge gaps and design research to address important issues that are understudied or warrant further investigation.
2.3 Social media dependency
Social media dependency refers to social media a user tendency to depend on social media to gain social interactive gratification through interpersonal communication and acquire knowledge (Ha et al., 2015). Stemming from the research field of mass media, such as TV and radio, studies of computer-mediated communication technologies and platforms have been quite diffused; particularly in the domain of social media (Carillo et al., 2017; Reychav et al., 2019; Wang et al., 2015). At the individual level, the term dependency often connotes a trait or psychological state of a person who depends on a media system. As the pace of ubiquitous Internet access increases, people use social media more and more often in their everyday lives. People’s heavy reliance on such gives rise to a sweeping phenomenon of social media dependency (Wang et al., 2015; Yang et al., 2015), drawing continuous research attention to examining the consequences; including negative outcomes, such as problematic usage or addiction (Wang et al., 2015).
Many different terms have been used to describe dependency on media, such as media dependency (Thadani and Cheung, 2011), media addiction (Ferris et al., 2021) and technology addiction (Charlton, 2002). Despite inconsistencies that exist between different dependence research studies, a growing scholar consensus views: (1) dependence as a psychological state and (2) addiction, as a related behavior (Thadani and Cheung, 2011). Psychological dependency is a form of dependence that involves emotional–motivational withdrawal symptoms upon exposure to a stimulus. Behavioral addiction is a form of addiction that involves a compulsion to engage in a rewarding non-drug-related behavior, despite any negative consequences to the person’s physical, mental, social or financial well-being (Robison and Nestler, 2011). Unlike other types of addiction, social media addiction can often be considered as some kind of soft addiction and is more likely to be perceived as a normal and socially accepted activity.
While numerous post-event studies have been conducted to analyze people’s activities on social media during emergencies (Houston et al., 2015; Reuter and Kaufhold, 2018), research rarely pays attention to the role of social media dependency in people’s behavioral intentions toward these activities. So, to further consider the remarkable phenomenon of social media dependency or even addiction among people (Ha et al., 2015), this paper incorporates social media dependency into the research model to address this gap.
3. Research hypotheses
3.1 Information privacy concerns driving switching behavior
For social media users, privacy concern refers to the extent to which users are concerned about the flow of their private information, including the transfer and exchange of that information (Shin, 2010). In other words, users worry about their submitted information or posted contents on social media, because it can be used in an unforeseen way or presented to undesirable third parties. Users with a high level of concern may frequently pay attention to protect their privacy and make sure that their personal information is not improperly collected, stored and used by other users or social media providers (Zhou, 2015).
Specifically, there are the following aspects: first, a high level of privacy concern makes social interactions on social media a more worrisome process. When users intend to post or share something, they may worry that the contents disclose too much private information. Secondly, now almost all social media platforms provide privacy settings, which can limit user personal information, posts, or status to be accessed, without the added permission or authorization. Users with higher privacy concerns hence spend time and effort to review a privacy policy and learn how to employ more restrictive privacy settings to protect their contents posted on social media (Lin and Liu, 2012). All such anxieties and effort investments can add to user discomfort and wasted time and consequently cause them to have a stronger intention to switch to another social media platform (Maier et al., 2015). Thus, it is hypothesize that:
Information privacy concerns positively influences switching behavior.
3.2 Social media dependency driving switching behavior
According to media dependency theory, individual-level media dependency can be assessed from either a psychological (Reychav et al., 2019) or a goal-oriented perspective (Carillo et al., 2017). In the framework of goal-oriented media dependency, the level of dependency is evaluated by goal achievements in respect of understanding, orientation and play. Such a framework involves a number of measurement items, whereas the measurement in terms of psychological dependency is much simpler, yet efficacious. Specifically, in this work, an individual’s dependency on mobile social media is defined by the level at which they express feelings of desire for social media (Reychav et al., 2019). With the penetration of mobile devices into daily life, people become increasingly dependent on them so therefore, that they become an integral part of people’s self-perception.
Previous research shows that heavy dependence on both the mobile phone and social media is characteristic of Gen Y (Eastman et al., 2014). In this respect, their dependency on mobile social media can be induced either by social media dependency (Wang et al., 2015; Yang et al., 2015) or by mobile phone dependency (Haug et al., 2015; Kim and Hahn, 2015) or be reinforced by the coupling of these two effects (Burnell and Kuther, 2016). People’s social media use frequency is positively related to their social media dependency (Wang et al., 2015; Yang et al., 2015). Likewise, in time-critical situations, as in emergencies for example, those who express stronger feelings of dependency toward social media are more likely to use them to seek various gratifications. Users thus become more dependent and actively engaged in social media (Kim et al., 2020). Consequently, the benefits from social media increases social media involvement and user connections to social media thus become stronger. It follows that as social media users become more dependent on the function, switching becomes more difficult and therefore less likely. The following hypothesis has been proposed based on these empirical studies:
Social media dependency negatively influences switching behavior.
3.3 Effect of information privacy concerns and social media dependency on switching behavior
Mindful of previous research mentioned above, with increasing development of information technology, the risks of privacy violations have also increased; also increasing user concerns over information privacy. Users with high concerns over information privacy are more likely to switch or discontinue a particular site. On the other hand, social media dependency is always associated with the level of involvement of social media (Wang et al., 2015; Yang et al., 2015). According to the definition of social media dependency (Ha et al., 2015), users with high dependency means that users almost always achieve everyday goals through social media; including not only social activities but also information acquisition. As for these users, the benefits from social media create a high involvement with it, and this in turn results in a strong dependency with social media. These users cannot easily leave the social media they use. Considering the risks and benefits, the following hypotheses, based on these empirical studies, are proposed:
Groups with different levels of information privacy concerns and social media dependency lead to different switching behavior.
The group with higher level of information privacy concerns and lower level of social media dependency leads to the strongest switching behavior.
The group with lower level of information privacy concerns and higher level of social media dependency leads to the weakest switching behavior.
The group with lower level of information privacy concerns and social media dependency leads to stronger switching behavior than the group with higher level of information privacy concerns and social media dependency.
4. Methodology
4.1 Sampling
This paper investigates factors that influence user adoption of traditional social media from a dynamic perspective, by focusing on users of a traditional social media platform (i.e. Facebook and Twitter). Here, switching behavior of a traditional social media user is examined. Thus, of Facebook and Twitter users, whom are regarded as representative of traditional social media, are selected as research targets surveyed. They were chosen based on their user experience of traditional social media, and were required to answer a specially designed questionnaire in order to determine their general feeling on social media platforms they currently use. As a part of this process, quota sampling was used to collect different samples based on gender and age. Although quota sampling is a non-probability sampling method, the samples accurately reflect the characteristics of the population when units are selected correctly.
Accordingly, any problems or difficult expressions were resolved. The questionnaires were distributed through an online survey in Korea. In total, 320 questionnaires were distributed, of which 293 were returned and 277 were valid and used for analysis.
4.2 Constructs and measurement items
For the measurement of this study, operational definitions were specified on each construct to meet the purpose of the study, and then measurement items were modified based on previous studies to increase the validity of the research. For social media dependency, items were generated and modified with borrowed concepts from studies by Lee and Choi (2018). Information privacy concerns were borrowed from studies conducted by Kang et al. (2020). Furthermore, switching behavior was borrowed from research conducted by Liu et al. (2016). Questionnaires were constructed on a five-point Likert scale with anchors ranging from “strongly disagree” to “strongly agree”. Except for two demographic variables, 10 items were designed for three constructs. The measurement items were collected from different previous research and modified to match the current research context appropriately. The respondents were required to answer the questionnaire based on their real feelings when using social media. The specific contents of the measurement items are shown in Table 1 below.
5. Empirical results
5.1 Sampling characteristics
This study divides the characteristics of 277 respondents who answered the questionnaire, as shown in Table 2. Of the respondents, male numbers constituted 47.6% (132) and female numbers were 52.4% (145). Respondents under 30s were the highest representatives being 185 persons (66.8%), followed by those in their 20s (102, 36.8%), 30s (83, 30.0%) and over 40s (51, 18.4%). This is an adequate representation of the users of Facebook and Twitter, because the majority of social media users are young adults (Statista, 2022). The demographic characteristics are detailed in Table 2 below.
5.2 Data analysis
In this paper, t-test, one-way ANOVA and multiple comparisons were used for the statistical analysis, conducted through SPSS. The main analysis of this research was a one-way ANOVA analysis using two variables (information privacy concerns and social media dependency) and multiple comparisons. To establish more detail on how the level of social media dependency and the level of information privacy concerns affect social media users’ switching behavior, data were collected and information privacy concerns were divided into two levels (i.e. high and low information privacy concerns) at the median level. Social media dependency was also divided into two levels (i.e. high and low social media dependency) at the median level. Using SPSS software, t-test, one-way ANOVA and multiple comparisons were calculated.
5.3 Empirical results
Firstly, an independent t-test was conducted, through SPSS, to examine separately, the relationship between switching behavior and information privacy concerns, and switching behavior and social media dependency. The results show each group’s difference, and the details are shown in Table 3.
The above analysis shows the mean, mean difference and t-value. Regarding information privacy concerns, the mean difference between high and low information privacy concerns shows 0.366 with a 99.9% confidence level (t-value: −4.142). Thus, it can be concluded that users with high information concerns are more likely to switch, which is consistent with the previous studies and individuals with high concerns over information privacy more easily perceive risks and respond through switching behavior (Son and Kim, 2008). On the other hand, the mean difference between high and low social media dependency shows −0.992 with a 99.9% confidence level (t-value: 14.992). The results implies that users with high dependency of social media cannot discontinue easily, which is in line with prior literature researched. High dependency of social media use weakens users’ switching behavior (Kim et al., 2020). Therefore, the hypotheses 1 and 2 are supported.
In order to achieve the paper’s objective, this paper further conducted a one-way ANOVA through SPSS software. As Table 4, the one-way ANOVA, with the low level of social media dependency, between low level of information privacy concerns and high level of information concerns, showed no differences over switching behavior of social media users. As for high level of social media dependency, the mean difference between high level and low level of information privacy concerns is 0.40 with a 99.9% confidence level. With the high level of social media dependency, the switching behavior of traditional social media with high level of information privacy concerns is stronger than low level of information privacy. The F value of one-way ANOVA is 74.295, which is much bigger than 1, and the significant level is 99.9%; concluding that there are differences between the four groups, as for old social media users, (1) low social media dependency and low information privacy concerns,(ii) high social media dependency and low information privacy concerns, (3) low social media dependency and high information privacy concerns and (4) high social media dependency and high information privacy concerns.
In order to find out where the differences lie, between the four groups, a post hoc test (LSD) was conducted. The results of LSD post hoc test are showed in Table 5. According to the results of the one-way ANOVA table and post hoc test table, due to different levels of social media dependency and information privacy concerns, the switching behavior of social media users is different, which is exactly uniform with previous studies. However, considering interrelationship of the two determinants, the conclusion is a little bit different to past studies.
Firstly, the current studies have proposed that information privacy concerns negatively affect user intent to engage in social media, and though social media dependency is positively associated with the engagement of social media. Thus, as expected, the combination of high information privacy concerns and low social media dependency leads to the highest results. Also, the combination of low information privacy concerns and high social media dependency lead to the lowest result. Thus, hypothesis 3a and hypothesis 3b are supported. In addition, according to the results of multiple comparisons, the difference between H-H and the L-L combination is found, with the found, with the L-L combination higher than the L-L combination. This is because users with high dependency of social media almost do everything through social media, meaning a higher involvement of social media. Moreover, they cannot discontinue easily, despite high information privacy concerns. On the other hand, the L-L combination means users have low social media involvement and are indifferent to information privacy, meaning they can discontinue easily. Therefore, hypothesis 3c is supported.
6. Discussion and conclusion
The adoption of social media has been extensively discussed. However, to explain the adoption of traditional social media, considering the benefits and risks accumulated from the experiences of social media use, the extent literature is limited. Thus, this paper investigated the act of traditional social media users’ switching behavior from a dynamic perspective and d the level of information privacy concerns and social media privacy to measure the risks and benefit accumulated from this dynamic process. By doing so, the level of information privacy concerns and social media dependency affecting switching behavior of traditional social media users were explored. Therefore, a better understanding of user’s behavior on social media platforms is presented to enrich relevant research of the past and provide suggestions for practitioners for the future.
Specifically, this paper at first separately examined the influence of information privacy concerns and social media dependency on switching behavior. According to the empirical test results, information privacy concerns were positively associated with switching behavior. When traditional social media users are highly concerned over information privacy, they are more likely to perceive the risks of using traditional social media; which strengthens the intention of switching behavior. Thus, the result is in line with previous studies. Users with high concerns over information privacy are more likely to respond to switching from the social media they currently use (Son and Kim, 2008).
On the other hand, the empirical results imply that social media dependency negatively affects user switching behavior. Users with high dependency of social media are more likely to retain existing usage of social media. That is because users with high dependency of social media usage achieve almost all of their everyday goals, including social intercourse and knowledge acquisition through social media. This in turn weakens the intention of switching behavior (Kim et al., 2020).
Furthermore, the empirical results also demonstrate that there are differences between users in terms of different levels of information privacy concerns and social media dependency. As expected, the combination of high information privacy concerns and low social dependency leads to the highest switching behavior. Conversely, the combination of low information privacy concerns and high social media dependency leads to the lowest switching behavior. Due to the high information privacy concerns and low social dependency, when using traditional social media, users are more concerned about information privacy compared with the dependency of social media and perceive more risks than benefits from social media.
On the other hand, users with low privacy concerns and high social media dependency are more dependent of social media and perceive more benefits than risks from traditional social media; which leads to the lowest results. In addition, the results of low information privacy concerns and low social media dependency are higher than the results of high information privacy concerns and high social media dependency. Traditional social media users with high–high, are highly concerned over their privacy though, due to the high frequency of social media usage; including keeping in touch with friends and obtaining knowledge or information. These users complete most of their tasks through social media in their daily life.
Hence, switching away from traditional social media is difficult. Whereas users with low–low are less dependent of traditional social media and the traditional social media involvement is also low. This implies that they use traditional social media or new social media indifferently and can thus switch away more easily. The differences between low–low and high–high also indicates that social media dependency is a more salient factor affecting switching behavior, and this point provides further direction to promote the development of social media.
Lastly, our results have the following managerial implications. Firstly, information privacy concerns and social media dependency affect the adoption of social media. Secondly, social media dependency is a more salient determinant for social media adoption. Therefore, social media firms should pay more attention to enhancing user dependency of social media by increasing user involvement of social media.
As such, social media firms can further enrich the function of social media, so that users can not only communicate with friends through the facility, but also perform other functions such as shopping and banking. For instance, there are plenty of functions in WeChat to facilitate innumerable activities including social intercourse, shopping, banking, calling a taxi and ordering a takeout, for example. Abundant functions markedly enhance user involvement and loyalty, as they do not switch readily. Bearing in mind the above, it is also important to conclude that social media firms should also promote privacy bills of rights and give information control back to the user, in order to reduce switching behavior.
7. Limitations and future research
In respect to the above conclusions, there are several limitations to mention. Firstly, regarding sample bias, the sampled data were collected from people resident in Korea, of which 66% were aged in their 20s or 30s, and only 34% in their 40s or 50s. Although social media users are mainly young adults, excessive numbers representing those in their 20s and 30s meant a certain limitation existed in terms of the greater age groups coverage of the more elderly. Secondly, for measurement items, the questionnaires could not fully cover constructs. Moreover, this study focuses on social media dependency and information privacy concern as psychological traits, to investigate the relations to individual behavior. Social media dependency and information privacy concern, however, are not the only psychological traits to explain switching behavior. In future research, it is necessary to identify other psychological traits not in this study’s terms of reference, in order to investigate and evaluate the models. Despite the above limitations, this research contributes to the understanding of how social media dependency and information privacy concern can influence the behavior of users. To marketers and practitioners, it is a valuable insight into understanding more fully a user’s behavior on social media.
Measurement items
| Constructs items researcher | ||
|---|---|---|
| Information privacy concerns | The social media I chose tends to provide too much information to many users | Kang et al. (2020) |
| The social media that I chose is difficult to find the information I want | ||
| The social media that I chose takes a long time to find the information I want | ||
| The social media I chose is annoying due to the amount of information too much | ||
| Social media dependency | I will continue to maintain relationships with my friends and classmates through the social media of my choice | Lee and Choi (2018) |
| I will continue to use the social media of my choice for a long time in the future | ||
| I do not think I’ll be in a good mood if the service of the social media I’ve chosen is interrupted | ||
| Switching behavior | I will explore information to move to social media that offer better benefits | Liu et al. (2016) |
| I am willing to move to a new social media in the future | ||
| I have tried to move from the current social media to another social media | ||
Demographic characteristics (n = 277)
| Item | Characteristics | Frequency | Ratio |
|---|---|---|---|
| Gender | Male | 132 | 47.6% |
| Female | 145 | 52.4% | |
| Age | 20s | 102 | 36.8% |
| 30s | 83 | 30.0% | |
| 40s | 51 | 18.4% | |
| 50s | 41 | 14.8% |
Switching behavior depending on the level of information privacy concerns and social media dependency: Independent t-test
| Mean (SD) | Difference | t-value | |
|---|---|---|---|
| Information privacy concerns level | |||
| High (N = 122) | 3.11 (0.65) | 0.366 | −4.142* |
| Low (N = 155) | 2.75 (0.82) | ||
| Social Media Dependency Level | |||
| High (N = 92) | 2.48 (0.72) | −0.992 | 14.992* |
| Low (N = 122) | 3.47 (0.35) |
Note(s): *p < 0.05
Switching behavior depending on level of social dependency and information privacy concerns (one-way ANOVA)
| Dependent variable: switching behavior | |||||
|---|---|---|---|---|---|
| Social media dependency level | Information privacy concerns | ||||
| High | Low | Mean difference | Std. error difference | Sig | |
| Low | 3.52 (0.35) | 3.40 (0.36) | 0.12 | −0.004 | 0.251 |
| High | 2.73 (0.62) | 2.33 (073) | 0.40 | 0.002 | 0.000 |
| Sum of squares | df | Mean square | F | Sig | |
| Between groups | 72.794 | 3 | 24.265 | 74.295 | 0.000 |
| Within groups | 92.428 | 283 | 0.327 | ||
| Total | 165.222 | 286 | |||
Post hoc test (LSD)
| Multiple comparisons | ||||
|---|---|---|---|---|
| Dependent variables: Switching behavior | ||||
| Information privacy concerns and social media dependency | Mean difference | Sig | Post hoc test (LSD) | |
| ① H - L | ② L - L | 0.119 | 0.251 | ①-③,④ |
| ③ H - H | 0.796 | 0.000 | ||
| ④ L - H | 1.192 | 0.000 | ||
| ② L - L | ③ H - H | 0.677 | 0.000 | ②-③,④ |
| ④ L - H | 1.073 | 0.000 | ||
| ③ H - H | ④ L - H | 0.396 | 0.000 | ③-④ |
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Further reading
Zhou, W. and Piramuthu, S. (2015), “Information relevance model of customized privacy for IoT”, Journal of Business Ethics, Vol. 131 No. 1, pp. 19-30.
Acknowledgements
Funding: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2021S1A5A2A01069343).