The balancing mechanism of social networking overuse and rational usage

Accepted Manuscript The Balancing Mechanism of Social Networking Overuse and Rational Usage

Jingjing Yao, Xiongfei Cao PII:

S0747-5632(17)30298-4

DOI:

10.1016/j.chb.2017.04.055

Reference:

CHB 4954

To appear in:

Computers in Human Behavior

Received Date:

09 December 2016

Revised Date:

26 April 2017

Accepted Date:

27 April 2017

Please cite this article as: Jingjing Yao, Xiongfei Cao, The Balancing Mechanism of Social Networking Overuse and Rational Usage, Computers in Human Behavior (2017), doi: 10.1016/j.chb. 2017.04.055

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ACCEPTED MANUSCRIPT Highlights ●

Filling the gap between first adoption and final termination stage of social

networking addiction. ●

Examing the underlying mechanism for rational use after excessive use of SNSs.

●

Pointing out technostress is an impetus for rational usage.

ACCEPTED MANUSCRIPT

The Balancing Mechanism of Social Networking Overuse and Rational Usage Jingjing Yao1, Xiongfei Cao1

1 School

of Management, University of Science and Technology of China, Hefei, P.R. China

Author Note Jingjing Yao Graduate student, School of Management, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui Province, 230026,P.R. China. E-mail: [email protected]

Xiongfei Cao Ph.D., Associate Professor, School of Management, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui Province, 230026,P.R. China. E-mail: [email protected]

ACCEPTED MANUSCRIPT

The Balancing Mechanism of Social Networking Overuse and Rational Usage Abstract Previous studies on excessive use of social networking services (SNSs) have relied on behavioral addiction theory to explain how users react when they face stress from overusing SNSs. Scholars have typically thought that users would stop using an SNS when they became addicted to the SNS and experienced stress from it. However, there seems to be a research gap between the initial adoption and the final intrusion stage of SNS usage. To fill that gap, our study uses a stimulus–organism–response paradigm to examine users’ balancing mechanism for social network overuse. Based on a survey of users of social networking services in China, we found that (1) social interaction overload, invasion of work, and invasion of privacy had significant positive impact on technostress; (2) perceived usefulness of SNSs, perceived enjoyment of SNSs, and technostress had significant positive impact on rational usage; (3) social interaction overload had a negative impact on perceived performance, and invasion of privacy had a negative impact on performance and happiness. This paper contributes to the social networking overuse literature by highlighting the mechanism by which technostress elicits the rational use of SNSs. Keywords: social networking services, technostress, rational use, stimulus–organism– response

paradigm

ACCEPTED MANUSCRIPT 1. Introduction With the rapid development of information network technology, social network services (SNSs) such as Facebook, Twitter, and Instagram have emerged as an alternative to the traditional face-to-face communication style, greatly enriching and expanding people’s social activities. As SNSs have expanded from websites on computers to smartphone applications in recent years, they have exerted an increasingly far-reaching influence on people’s social lives and economic activities.But there appears to be more and more users claim to feel unpleasant overusing social network. It should have been coined “online social network-induced stress ”(Maier et al., 2012) and make users visit social netwoking sites less. Nevertheless,in pratice social networking services still grow. Recent a report from November 2016 showed that by the end of 2016, there will be approximately 2.2 billion social network users. It represents about 30% of the global population and an 8.3% increase over 2015(eMarket, 2016). That is to say that despite the negative feeling, the chasing for social media tends to be still strong. It may develop addictions to SNSs. There are considerable researchers have focused on SNSs can provide a number of conveniences for users’ work and life and help meet their social psychological demands. However, excessive use of SNSs can also trigger a series of negative consequences, often causing users to become unhealthily obsessed. Bevan Gomes and Sparks (2014) found that more time people spent on SNSs, the lower their life quality. Especially for adolescents with poor self-control, excessive use of Facebook, Twitter, and other social networks is associated with addiction to these tools and may even result in psychological and behavioral problems. Individuals who are addicted to SNSs may suffer feeling of stress. Prior studies had also suggested that people stop using SNS when they were stressed by this technology (Gartner, 2011; Maier et al., 2012). Recent information system (IS) discontinuance research on the variables responsible for addiction to SNSs has been extended to the post-acceptance behavioral period. For example, Turel (2014) found that many users would stop using

ACCEPTED MANUSCRIPT SNSs to prevent the aggravation of addiction and the associated stress. Cooper and Zmud (1990) argued that the information system life cycle includes an initial adoption phase, a generalization stage, and a final intrusion stage. In the life cycle of an IS (Furneaux & Wade, 2010; Furneaux & Wade, 2011), discontinuation of usage is relevant to the final termination of the IS, but in practice, social networking sites may never reach that phase. In 2012, the Global Web Index-world’s leading research firmbegan to track the Internet usage of 31 markets and found that the amount of time spent using social networks increased in 28 countries. Social network traffic is especially high in economically developing countries. Furthermore, there are also many users consciously managing their SNS behavior and experience through selfcontrol for not “being missed out” (Przybyski et al. 2013). While there has been extensive research on IS continuance (Lankton & McKnight, 2012; Bhattacherjee & Lin, 2015) as well as some research on the final phase (Turel, 2014; Maier et al., 2015), there seems to be a research gap between the initial adoption stage and the final intrusion stage. To fill that gap, our study employs a stimulus–organism– response paradigm within a technostress research framework to examine the underlying mechanism for rational use after excessive use of SNSs. This study contributes to propose a full IS life cycle and give academia and practitioners deep insight about social networking overuse and technostress. The rest of the paper is organized as follows. First, we provide an overview of the related research literature, present a proposed theoretical model, and derive hypotheses from that model. We then describe the research methodology used in the study, followed by the results obtained by a third-party consulting company. We conclude the paper by discussing the implications of our findings for both theory and practice.

2. Theoretical basis 2.1. The SOR model The stimulus–organism–response (SOR) model is commonly used to explain how individual behaviors follow from stimulation (Mehrabian & Russell, 1974). This

ACCEPTED MANUSCRIPT model emphasizes that the stimulus comes from the external environment. Under such stimulation, individuals develop certain attitudes through their psychological judgment and, guided by these attitudes, behave in a certain way. This model has its foundation in Woodworth’s learning theory, developed in the early 20th century, and has mainly been used to explain the behaviors of consumers in the market. The SOR model was designed for general environment psychology at first, but has been proved to fit in a retail situation by several studies. Ward, Bitner, and Barnes (1992) developed a model of consumer behavior in the service environment based on a psychological framework, believing that consumers react to external stimuli with their perception, emotions, and body and that these reactions further influence their actual behaviors. Based on this model, Mehrabian & Russell (1974) proposed two dimensions of any emotional state in an individual, namely pleasure and arousal. For retailers, it’s important to generate custormers’ pleasure in order to increase income. Also, pleasure becomes an important dimension of individual’s emotional state in the SOR model. Later, the model was widely used to examine the relationships among external stimulation, consumer perception, and behavior intention in the shopping environment (Baker et al., 2009). The behaviors of SNS users also adhere to this model. Users accept the stimulation of a stress creator (Maier et al., 2012) when they first begin to use SNSs. They then get a pressing feeling after overusing social networking services as psychological state. Influenced by the corresponding stress, they either transform or terminate the current used SNSs or decrease usage times and return to a rational ratio. 2.1.2. SNS technostress creators: Environmental stimuli (S) Social networking services have brought about potentially excessive technology use and dependency (Hafner, 2009; Webley, 2011). An addiction to SNSs is a type of Internet addiction, which is considered as a compulsive behavioral disorder (Griffiths, 2000). When compulsive behaviors are perceived to be inescapable, adverse consequences such as depression and stress are more likely to be induced as well (Matusik & Mickel, 2011). The stress in the IS situation is called technostress refers to the stress felt by users while using information technology (Ragu-Nathan et al.,

ACCEPTED MANUSCRIPT 2008). Technostress is related to the stimuli, events, and demands induced by technology (Ragu-Nathan et al., 2008). And the stimuli,events and demands in the stress-creating conditions related to the usage of technology are called technostress creators (Tarafdar et al., 2007; Ayyagari et al., 2011). Once individuals begin to use SNSs, they expose themselves to technostress creators, which can lead to a variety of negative consequences. Ayyagari et al. (2011) attributed technostress creators in the IT field to work–home conflict, invasion of privacy, work overload, role ambiguity, and job insecurity. Taradar et al. (2007) believe that there are five stress creators for technostress: techno overload, techno invasion, techno complexity, techno insecurity, and techno uncertainty. The present research focuses on studying SNS users’ feelings, experiences, and behaviors. In our study, technostress creators are environmental stimuli. We focus on three crucial and specific SNS features: social interaction overload (SIO), invasion of work (IOW), and invasion of privacy (IOP). Boyd and Ellison (2008) defined social networking services as “web-based services that allow individuals to (1) construct a public or semi-public profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system.” SNSs help people connect with their offline networks online. Hence, social reasons are the most important motives for using SNSs, as existing studies have confirmed (e.g., Subrahmanyam et al., 2008). Users may feel compelled to maintain their social networks, which may lead to overusing SNSs. Due to key specific design features, social interaction may in turn relate to addiction experience (Ghassemzedeh, et al., 2008). In the present study, we focus on social interaction overload, defining it as the amount of interactions one person has to engage with that exceeds one’s communicative and cooperative capacity. Tarafer et al. (2007) found that communication technologies were most often used in the work environment. They found that technostress had a negative impact on productivity, and when employees felt higher technostress, they would experience a productivity decrease. Social networking service usage can also have a negative

ACCEPTED MANUSCRIPT impact in the workplace (Brooks, 2015). Cognitive symptoms are one of two prominent classes of indicators of problematic SNS use (Caplan, 2010; Haagsma et al., 2013), which impact social, personal, and professional life. LaRose et al. (2010) empirically demonstrated the influence of social networking addiction on work, social, and school activities (Turel and Serenko, 2012). Given these findings, we define invasion of work (IOW) as the exacerbation of users’ internal conflicts by constant engagement in SNSs due to reduced time to spend on work. With the development of social networks, new problems related to individuals’ privacy have arisen (Bright et al., 2015). Users disclose a great deal of personal information from personal profile to location information to the social networking platforms. Debatin et al. (2009) examined Facebook users' awareness of privacy issues and the perceived benefits and risks of utilizing Facebook. Users claimed to understand the importance of their privacy, but they still uploaded a lot of personal information. Privacy invasion risks were attributed more to others than to the self. In other words, users’ perceived privacy risks were not equal to the actual risks. This means privacy risks have taking place in the information disclosure process. Bélanger and Crossler (2011) gave privacy a widely accepted description as “one’s ability to control information about oneself.” Likewise, the invasion of privacy has been described as “people might experience being too accessible or receiving too much information from too many people” (Bright et al., 2015). The potential for invasion of privacy is an important technological feature of the SNS environment. 2.1.3. SNS stress, perception of pleasure, and perception of performance: Users’ internal states (O) In 1984, technostress was described by clinical psychologist Craig Brod as a modern disease caused by one’s inability to cope with new technology. An individual might react psychologically to technostress by feeling under strain, which are caused either directly or indirectly by technology (Weil & Rosen, 1997; Ayyagari et al., 2011). In recent years, technostress has become a topic of Internet system research (e.g., Tarafdar et al., 2007; Ragu-Nathan et al., 2008; Ayyagar et al., 2011). All most existing studies in technostress were conducted in organization. Maier et al. (2012)

ACCEPTED MANUSCRIPT confirmed that the findings of this research can also apply to SNS usage. When people use SNSs, they encounter a series of stress creators, which lead to a series of psychological responses that create SNS stress. SNS stress creators are the deciding factors of SNS stress (Maier, 2015). There are three critical characteristics of SNS stress: (1) It refers to users’ responses to excessive use of SNSs, rather than excessive use of SNSs itself; (2) the stress varies among individuals; 3) it indicates excessive psychological or physical need. Thus, SNS stress can be defined as the adaptive response to the external environment caused by the behavior of excessively using SNSs (Gartner, 2011). Overuse of SNSs can lead to psychological, physical, or behavioral changes in users (Kuss & Griffiths, 2011). When users experience SNS stress, they will generate corresponding behavioral responses to the stress itself. SNS stress is a key to understanding SNS user experiences and behaviors, and it is also a bridge to learning about the various result variables caused by excessive use of SNS and users’ ultimate behavioral choices. The technology acceptance model (TAM) proposed by Davis (1989) is one of the prominent models used to explain why users intend to adopt an information system. One of the key constructs in this model is perceived usefulness, defined as the degree to which a person believes that using a particular system will enhance his or her job performance. Given the importance of the TAM model in IS research, it is appropriate to use this construct to investigate users’ perception of actual SNS usage. The construct of perceived usefulness was also used in the Social Network Adoption Model introduced by Sledgianowski & Kulviwat (2009). In the context of SNSs, perceived usefulness refers to people’s feelings of SNS-triggered change, brought about by SNSs’s effects on individual productivity and performance. SNSs are used not only for utilitarian purposes (Khan & Jarvenpaa, 2010), but also for enjoyment (Sledgianowski & Kulviwat, 2009). Pleasure seeking is regarded as a basic human objective (Fordyce, 1988). Past research showed that SNSs can enrich users’ social life and make users enjoy the present (Pempek et al., 2009; Turel & Serenko, 2012). According to the TAM model, perceived usefulness alone is not enough to accept a technology; other perceptual experiences are also important

ACCEPTED MANUSCRIPT (Venkatesh & Brown, 2001). We therefore included perceived enjoyment, which represents the degree of fun and pleasure SNSs bring to users, in the present study. 2.1.4. Rational use: Behavioral response (R) Generally speaking, after users experience SNS stress, they may adopt the following three strategies to respond to the stress: 1) Temporary rest: When SNS use makes users feel uncomfortable, they temporarily stop using SNSs to recover. They then resume using the SNSs. 2) Use control: Users control their use of SNS. 3) Use termination: Users eliminate their SNS apps and permanently stop using SNSs. (Ravindram et al., 2013).

3. Research model and hypotheses Based on the above analysis, we propose a series of hypotheses derived from the S–O–R framework and its likely consequences. 3.1. Environmental stimuli and users’ internal states 3.1.1. Technostress creators Technostress has two unique and interconnected manifestations: The first is getting addicted to the new technology; the second is resisting the new technology. Ragu-Nathan et al. (2008) proposed two secondary structures of technostress, namely, factors generating technostress and factors restraining technostress. Generating factors create stress while users use IT, while restraining factors restrain stress through gradual use reduction. Based on the research of Ragu-Nathan, Tarafar, Tu(2010), and other scholars on technostress, technostress creators include techno overload, techno invasion, techno complexity, techno insecurity, and techno uncertainty. These technostress creators are also applicable to SNS stress. Based on the varying characteristics of SNSs and other IT, we divided the technostress creators of SNS into three factors: social interaction overload, invasion of work, and invasion of privacy. Social purposes have been found by many studies to be the most important motives for using SNS (e.g., Barker, 2009; Surbrahmanyam, 2008). Moreover, research suggests that SNSs are used for established offline networks (Ellison et al., 2007). In line with this, people may feel compelled to maintain their offline social networks

ACCEPTED MANUSCRIPT online, which make them feel strain. Invasion of work has likewise been found by various studies to be a technostress creator (Ragu-Nathan et al., 2008; Tarafdar et al., 2010; Gartner, 2011). Finally, SNSs have potential for invasion of privacy, wherein the privacy of users may be violated and users may get a feeling of being monitored. Especilly as SNSs have integrated location-based services (LBS), a new type of SNS has rapidly developed (Zhao et al., 2012) where other users can know a user’s specific location, which could make users feel pressured. Therefore, we put forward the following hypotheses: H1a: Social interaction overload has a positive influence on technostress. H1b: Invasion of work has a positive influence on technostress. H1c: Invasion of privacy has a positive influence on technostress. 3.1.2. Perceived usefulness Many scholars studying continual use of SNSs think that functionality is an important factor for users’ continuous use of SNSs (e.g., Sledgianowski & Kulviwat, 2009; Lin & Lu, 2011). SNSs are used for social interactions with friends and acquaintances to maintain friendships or to make new friends. Through SNSs, users disclose details about themselves and hence provide plenty of information to their friends (Krasnova et al., 2010).As matter of fact, information that is available to users through their social network can be described as “collective intelligence”.Individuals can use the collective intelligence provided through SNSs to improve their learning and work performance. However, excessive use of SNSs may take up too much time that would otherwise be used for offline social contact, learning, and work. Too much devotion to the virtual space might increase the interaction stress from online friends, offline friends, and relatives, creating both social interaction overload and privacy invasion. These factors can negatively impact users’ work and life. The resulting unpleasant feelings could lead users to form the irrational value judgment that the SNSs they used were of no use. Therefore, we hypothesize: H2a: Social interaction overload has a negative influence on perceived SNS usefulness. H2b: Privacy invasion has a negative influence on perceived SNS usefulness.

ACCEPTED MANUSCRIPT 3.1.3. Perceived enjoyment Individuals use SNSs to stay in touch not only with family and friends, but also with distant acquaintances (Donath & Boyd, 2004). The resulting excessive social interaction can manifest as maladaptive cognition. Kross et al. (2013) found that higher levels of Facebook use were associated with a distinct decrease in happiness. As mentioned previously, privacy invasion has also become an increasingly relevant negative factor of SNS usage. We postulate the more people worry about privacy invasion, the lower their perceived enjoyment will be. Therefore, we propose the following hypotheses: H3a: Social interaction overload has a negative influence on perceived enjoyment. H3b: Privacy invasion has a negative influence on perceived enjoyment. 3.2. Technostress Prior research has examined technostress in the business context and found that technostress was negatively correlated with productivity (Tarafder et al., 2007) and lowered individuals’ overall job satisfaction (Ragu-Nathan et al., 2008). It is reasonable to assume that users will perceive lower usefulness and enjoyment of using SNSs when they experience technostress. Thus, we propose the following hypotheses: H4a: Technostress has a negative influence on perceived usefulness. H4b: Technostress has a negative influence on perceived enjoyment. 3.3. Users’ internal state and behavioral response After experiencing the stress caused by excessive use of SNS, users will receive the stimuli in the enviroment, which may lead to a series of unfavorable psychological feelings. In order to reduce the negative influence of these feelings, users may decide to consciously control their use of SNS, that is, to return to the rational use of SNS. All humans have the capacity for rationality and are able to weigh the advantages and disadvantages of a given thing for themselves. If SNS use can be controlled properly, users can give full play to the advantages of SNS. Therefore, from the perspective of “rational man,” users can consciously manage their behaviors, control themselves, and return to the rational use of SNS. In this way, users can overcome the stress caused by excessive use of SNS by making a rational response. Therefore, the rational

ACCEPTED MANUSCRIPT use of SNS is the last step in the process of users’ excessive use of SNS. As long as users are reminded of their excessive use, they can move to this last step. Therefore, we propose the following hypotheses: H4: Perception of performance, technostress, and perception of pleasure have a positive influence on rational SNS use. 3.4. Control variables SNSs enable users to build their own social networks and reach large numbers of friends. Network size, which refers to how many friends users have on their SNS accounts, has not received much research attention (Salehan & Negahban, 2013). The theory of social support holds that population density affects individual cognition and important behavioral factors (Nasar & Julian, 1995). As the number of users in a social network increases, number of social support requests also increases (Baum et al., 1982; Evans & Lepore, 1993). That means a user needs to give more social support. So the higher the degree of activity is demanded by SNS users’ friends and social support requirements (Maier et al., 2014), the higher the SNS pressure would be exerted on the individual. However, this pressure can lead the user to make appropriate behavioral responses. Therefore, we put forward the following hypothesis: H5: Network size has a controlling effect on rational usage.

4. Research Method To test the proposed hypotheses, we conducted an online survey among social networking service users in China. We did not designate specific SNS apps in the survey, but asked the subjects to choose one SNS app which they often used. This method helped ensure that the variables in the model had sufficient variance. SNS on either a mobile or computer platform was acceptable. 4.1. Measures We adapted measurement scales from previous studies, in which they were found to be reliable and valid. Specifically, we adapted the Social Interaction Overload Scale from Sven, Christian and Christoph’s work in proceedings of the 21st European

ACCEPTED MANUSCRIPT Conference on Information Systems. Invasion of work and privacy were measured with items adapted from Ayyagari, Grover, and Purvis (2011). Perceived performance and technostress were measured with items adapted from Tarafdar (2007). Perceived happiness was measured with items adapted from Tarafdar, Tu, and Ragu-Nathan (2007). Items for all the constructs were rated on five-point Likert scales ranging from “strongly disagree” to “strongly agree.” The constructs and measures are shown in the Appendix. 4.2. Sample and Data Collection Procedure In order to obtain a high response rate and good response quality, we employed a professional survey and data collection company. Since the focus of this study was to examine how excessive use of SNS influences individuals' lives, we first had to distinguish the excessive users. Thus, the research process was divided into two steps. First, we use Young’s Internet Addiction Scale to pretest users. The scale consists of eight yes-or-no questions; respondents who gave more than five “yes” answers were selected as excessive users and invited to participate in the online survey, which generated the data used to test our model. Finally, 224 valid questionnaires were obtained and used in the data analysis. After one month, we e-mailed the 224 respondents and asked about their SNS usage intention again. Table 1 shows the profiles of the respondents in the samples. Around 60% of the respondents were female. Over 40% of respondents were between the ages of 26 and 30. Around 70% of respondents had more than 50 friends on their SNS accounts, and over 50% of respondents reported using their accounts every day. Table 1 Demographic Characteristics of Respondents Variables

Levels

Frequency

Percentage (%)

gender

male female 18-25 25-30 31-40 41-50 <10

88 136 21 97 80 13 6

39.29 60.71 9.38 43.3 35.71 5.8 2.68

age

Network size(friend

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intensity

10-50 51-100 101-150 >150 Once a week Twice or three times a week Four or six times a week More than once a day

34 78 79 27 11 19

15.18 34.82 35.27 12.05 4.91 8.48

63

28.13

128

57.14

5. Data analysis Partial least squares (PLS) regression was used to validate our model, as it is regarded to be appropriate for dealing with the data of a small sample (Hair et al., 2011). 5.1. Measurement Model We first examined our measurement model and assessed the reliability and validity of the measurement items. We evaluated reliability by examining the values of composite reliability (CR) and average variance extracted (AVE). As shown in Table 2, the CRs and AVEs for all the constructs were above the suggested threshold values of 0.7 and 0.5 (Fornell & Larcker, 1981), suggesting that all of the constructs had good reliabilities. In practice, we employed convergent validity and discriminant validity to assess the level to which the measurement scale reflected the constructs (Chen & Tung, 2014). The convergent validity and discriminant validity can be examined by testing the loadings and cross-loadings shown in Table 2. All of the items had loadings above 0.7, which is higher than the cutoff point of 0.4 (Hulland, 1999). PLS loadings are shown in Table 2. Table 2 PLS Loadings Construct

Items

Loadings

Composite reliability

AVE

Social interaction overload (SIO)

SIO1 SIO2 SIO3

0.838 0.765 0.819

0.893

0.625

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Invasion of work(IOW) Invasion of privacy(IOP) Perceived usefulness(PU)

Technostress(TS)

Perceived enjoyment(PE) Rational usage intention(RU)

SIO4 SIO5 IOW1 IOW2 IOW3 IOP1 IOP2 IOP3 POP1 POP2 POP3 POP4 TS1 TS2 TS3 TS4 POH1 POH2 POH3 RU1 RU2 RU3

0.741 0.785 0.849 0.902 0.855 0.919 0.902 0.855 0.813 0.843 0.717 0.773 0.813 0.843 0.717 0.773 0.785 0.855 0.850 0.685 0.896 0.883

0.902

0.755

0.942

0.845

0.867

0.621

0.867

0.796

0.870

0.690

0.865

0.684

5.2. Structural model Figure 1 shows the results of the structural model. Social interaction overload (β = .191, t = 3.016), invasion of work (β = .503, t = 6.631), and invasion of privacy (β = .206, t = 3.103) had significant positive impacts on technostress; thus, H1a, H1b, and H1c were supported. Social interaction overload (β = −.272, t = 3.061), and invasion of privacy (β = −.262, t = 2.902) had significant negative impacts on perceived usefulness; thus, H2a and H2b were supported. Social interaction overload (β = −.184, t = 1.988) had significant negative impact on perceived enjoyment, and invasion of privacy (β = −.265, t = 2.973) had significant negative impact on perceived enjoyment; thus, H3a and H3b were supported. Perceived usefulness (β = −.243, t = 3.41), technostress (β = .554, t = 10.669), and perceived happiness (β = −0.16, t = 2.018) had significant negative impact on rational usage; thus, H5 was supported. Rational usage was significantly influenced by network sizes (β = .093, t = 2.069); thus, H6 was supported. However, the effects of technostress on perceived

ACCEPTED MANUSCRIPT usefulness (β = −.038, t = 0.36) and perceived enjoyment (β = −.132, t = 1.119) were nonsignificant; H4a and H4b were therefore not supported.

Fig 1. The research model based on the S-O-R model

6. Discussion Building on the basis of the stimulus–organism–response paradigm, in the present study, we investigated the effect of technostress on users’ rational usage of SNSs. 6.1. General discussion First, the results showed that social interaction overload, invasion of work, and invasion of privacy had significant positive impact on technostress. These relationships indicate that users are faced with stress when they over-interact with others or when SNS usage becomes invasive to their work or privacy. These factors are specific stress creators for technostress. Social interaction overload in particular is a unique creator of technostress when it comes to SNSs. Second, the data analysis revealed that perceived usefulness of SNSs, perceived

ACCEPTED MANUSCRIPT enjoyment of SNSs, and technostress significantly negatively impacted rational usage. This finding shows that when users feel low levels of performance or happiness, or a high level of technostress, they will try to change their usage patterns to more rational usage. One possible reason is that SNSs in China are still comparatively new, and users are not bored with them. Theoretically, although technostress, perceived usefulness, and enjoyment showed a strong influence on rational usage, the relationships between technostress and perceived usefulness and enjoyment were not significant. Wang et al. (2015) found that people were likely to seek gratification through maximizing perceived enjoyment, and the seeking behavior made them feel that SNSs were irreplaceable. Hence, it can be concluded that users will still purse enjoyment even when they face technostress from overuse of SNSs. This finding is consistent with the findings of Anderson et al. (2016), which pointed out the neurochemical basis of individual susceptibility to value-driven attentional capture, which is known to involve in addiction. Compared to the value, negative outcomes of addiction become insignificant. That may also explain why people cannot give up addictions to SNSs. Third, social interaction overload negatively impacted perceived performance, and invasion of privacy negatively impacted perceived performance and enjoyment. Since people are busy communicating with others using SNSs, their time and energy for other things are reduced, and their feelings that SNSs improve their work and life are thereby also reduced. Likewise, since privacy is important to people, invasions of privacy will make them less happy and productive. 6.2. Implications and future research Our findings contribute to theory in three ways. First, this study applied the stimulus–organism–response paradigm to SNS research. Scholars have rarely adopted the SOR framework for SNS studies, even though the paradigm is highly suitable for this context. Social networks are a network of services as well as a kind of information technology. In studies of behavior relating to information technology, the most commonly used model is the TAM model proposed by Davis (1989).

In

addition, on the basis of this theory, Ajzen (1991) put forward the theory of planned

ACCEPTED MANUSCRIPT behavior (TPB). Venkatesh et al. (2003) later built an integrated science and technology acceptance model (unified theory of acceptance and use of technology, UTAUT). In these models, researchers only cared about users’ feeling from technological features and ignore individuals’ internal emotion. The main difference between SNS and an information system is that people using SNSs communicate with each other, not with technology. In other words, users respond to stimuli and organisms for biological reasons. So a model combine psychology with biology is more suitable. We build the gap between first adoption and final intrusion stage of social networking sites usage addiction based on SOR model.Second, we investigated stress creators which are specific to SNS circumstances. Previous studies confirmed that the findings of Ayyagari et al. (2011) and Tarafdar et al. (2010; 2011) also apply in SNS situations and revealed SNS stress creators. However, all of these creators were simply migrated from information system research and did not have SNS-unique characteristics. In this study, we introduced social interaction overload as an SNSrelated technostress creator, since SNSs tend to be used excessively to interact with others. Future research could examine other consequences of excessive SNS usage. Finally, our study showed that a large number of friends and intensity would influence users to revert to more rational SNS usage. In previous research, age and gender were always chosen as control variables. While these variables can affect people’s inner experiences, they can be chosen in any situation and are not specific to the SNS usage scenario. Future research can examine other factors of SNS itself to provide a better understanding of people’s behavior after overusing SNSs. This study also has some practical implications. First, the findings showed that after perceived stress from excessive use of SNSs, most users can return to rational usage spontaneously. Since SNSs in developing countries are only in their initial stages and do not appear to be going away anytime soon, we should try to lead addicted people back to reasonable usage and provide some guidelines to help them notice the stress they experience. For many companies in China, SNSs can serve as customer relationship management (CRM) systems, which employees use to communicate with clients and bosses. It is also a cheap way to aid the operations of

ACCEPTED MANUSCRIPT companies. SNS use for business functions is common in oriental culture. Therefore, in practice, we should teach people to use SNSs properly, rather than simply forbidding the usage of SNSs during working hours. In fact, the proper usage of SNSs should be an essential component of employee training. Second, our investigation demonstrated that social interaction, invasion of work, and invasion of privacy create stress for SNS users. Therefore, the relevant organizational department should help people interact with each other through SNSs appropriately. Applications developers should also pay more attention to protecting users’ privacy and to developing apps that will help keep company operations safe and sound. Finally, this study revealed that a large number of friends on SNSs is an important driver of rational usage. This is different from our common-sense assumption that the more friends a person has, the more irrational usage they are likely to engage in. In practice, a large number of friends on SNSs means less effective interaction, and, when they reach this stage, people try to avoid pointless usage. Considering the significant effect of friend number on spontaneous rational usage, it would be helpful for these apps to provide users with easy ways to reach a large number of friends. 6.3. Limitations This research has several limitations. First, our study had two steps, and only people with a tendency of excessive usage were chosen to answer our questionnaires. This limits the number and scope of our respondents. Second, we tried to invite people of all ages to engage in the study, but young people still constituted a major part of our sample. Previous studies also mainly focused on younger people. In reality, older people may have more time and money to spend on SNSs, and we hope to focus on older users in future investigations. Finally, the survey was conducted in China, which is a new destination for SNSs, so people may be less ready to give up SNS usage than people in countries where SNSs have been in place for a longer time. That may be why the users in our sample tried to go back to rational usage patterns rather than just abandoning SNS altogether. Cross-country studies may shed light on whether or not users with short and long SNS histories act similarly.

ACCEPTED MANUSCRIPT 7. Conclusion In this research, we examined how social interaction overload, invasion of work, and invasion of privacy are associated with technostress. The results showed that social interaction overload, invasion of work, and invasion of privacy are all significant predictors of technostress. Additionally, perceived usefulness, technostress, and perceived enjoyment significantly impacted rational usage. Perceived usefulness and perceived enjoyment were also negatively affected by social interaction overload and invasion of privacy. Overall, our findings confirm that technostress is an impetus for rational usage and most people will self-adjust when they experience such stress.

Appendix-Measures

Variables Social

Item

Measurement

SIO1

I receive more messages (chat, private messages),

interaction

notifications and announcements (pinboard, news-

overload

feed) on SNSs than I can respond to. SIO2

I am overextended from the messages (chat, private messages),

notifications

and

announcements

(pinboard, news-feed) I receive on SNSs. SIO3

The amount of trivial communication on SNSs is too high.

SIO4

I receive too many messages (chat, private messages),

notifications

and

announcements

(pinboard, news-feed) on SNSs. Invasion of

IOW1

work

SNSs create many more requests, problems, or complaints in my job than I would otherwise experience.

IOW2

I feel busy or rushed due to SNSs.

ACCEPTED MANUSCRIPT

Invasion of

IOW3

I feel pressured due to SNSs.

IOP1

I feel uncomfortable that my use of SNSs can be

privacy

easily monitored. IOP2

I feel my privacy can be compromised because my activities using SNSs can be traced.

IOP3

I feel my employer could violate my privacy by tracking my activities using SNSs.

Perceived

PU1

usefulness

Perceived

Use of SNSs enables me to accomplish tasks more quickly.

PU2

Use of SNSs improves the quality of my work.

PU3

Use of SNSs makes it easier to do my job.

PU4

Use of SNSs enhances my effectiveness on the job.

PE1

Using SNSs is enjoyable.

PE2

Using SNSs is pleasurable.

PE3

Using SNSs is fun.

TS1

I feel drained from activities that require me to use

enjoyment

Technostress

SNSs.

Rational usage

TS2

I feel tired from my SNSs activities.

TS3

Working all day with SNSs is a strain for me.

TS4

I feel burned out from my SNSs activities.

RU1

I intend to continue using Facebook rather than

intention

discontinue its use. RU2

I will spend less time on SNSs.

Ru3

I will minimize times of log in SNSs.

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