Excessive social media use at work: Exploring the effects of social media overload on job performance

Lingling Yu (School of Management, University of Science and Technology of China, Hefei, China)
Xiongfei Cao (School of Management, Hefei University of Technology, Hefei, China)
Zhiying Liu (School of Management, University of Science and Technology of China, Hefei, China)
Junkai Wang (School of Management, University of Science and Technology of China, Hefei, China)

Information Technology & People

ISSN: 0959-3845

Publication date: 3 December 2018

Abstract

Purpose

The purpose of this paper is to explore the effects of excessive social media use on individual job performance and its exact mechanism. An extended stressor–strain–outcome research model is proposed to explain how excessive social media use at work influences individual job performance.

Design/methodology/approach

The research model was empirically tested with an online survey study of 230 working professionals who use social media in organizations.

Findings

The results revealed that excessive social media use was a determinant of three types of social media overload (i.e. information, communication and social overload). Information and communication overload were significant stressors that influence social media exhaustion, while social overload was not a significant predictor of exhaustion. Furthermore, social media exhaustion significantly reduces individual job performance.

Originality/value

Theory-driven investigation of the effects of excessive social media use on individual job performance is still relatively scarce, underscoring the need for theoretically-based research of excessive social media use at work. This paper enriches social media research by presenting an extended stressor–strain–outcome model to explore the exact mechanism of excessive use of social media at work, and identifying three components of social media-related overload, including information, communication and social overload. It is an initial attempt to systematically validate the casual relationships among excessive usage experience, overload, exhaustion and individual job performance based on the transactional theory of stress and coping.

Keywords

Citation

Yu, L., Cao, X., Liu, Z. and Wang, J. (2018), "Excessive social media use at work", Information Technology & People, Vol. 31 No. 6, pp. 1091-1112. https://doi.org/10.1108/ITP-10-2016-0237

Download as .RIS

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


1. Introduction

With the rapid development of mobile technology and smart devices, social media such as wikis, blogs, instant messaging (IM), and social networking sites (SNSs) have penetrated into people’s daily life. These tools can be used for socializing, entertainment, self-promotion, communication and information seeking (Park et al., 2009) by almost anyone, anywhere, at anytime. Attracted by the prevalence and convenience of social media, social media are now becoming an indispensable part of organizational life (Koch et al., 2012). A social media users’ behavior research report launched by China Internet Network Information Center (CNNIC) indicates that 31 percent of respondents are enterprise/company employees, which accounts for the highest proportion (China Internet Network Information Center (CNNIC), 2016). Employees use various social media applications for knowledge sharing, problem solving, collaboration and communication in organizations (Landers and Schmidt, 2016; Aral et al., 2013), which in turn enhance job performance (Landers and Schmidt, 2016).

However, pervasive access to social media is more likely to result in excessive usage, which can incur negative consequences. The CNNIC report indicates that 79.5 percent of China’s social media users spend two hours and above online per day; the number of heavy users is increasing (CNNIC, 2016). The increased social media usage at work may unintentionally create a more stressful working environment (Bucher et al., 2013). Social media provides a large amount of information for individuals to process which may generate information overload (Karr-Wisniewski and Lu, 2010). Individuals may bring personal activities into the workplace, resulting in life-work conflict and eventually leading to exhaustion (van Zoonen et al., 2016). Furthermore, the widespread use of social media in the workplace might lead to loss in employees’ productivity as a result of time wasting and distraction at work (Sherman, 2009; Zhang et al., 2015). For example, a study by Nucleus Research found that full access to Facebook at work results in a 1.5 percent drop in productivity (Nucleus, 2009). Individuals who depend on social media excessively are likely to suffer feelings of conflict, overload and lower well-being (Zheng and Lee, 2016; Bucher et al., 2013; Brooks, 2015). These feelings may ultimately increase technostress caused by the usage of social media, and thereby result in decreased job performance (Brooks, 2015). Therefore, the phenomenon of excessive use of social media in the workplace has become a great problem for organizations, and deserves more attention from scholars.

Despite the practical pertinence of this issue, the potential negative effects of excessive social media use on individual job performance have not been investigated sufficiently in information system (IS) research. Among the few studies that explored excessive social media use, Hou et al. (2014) and Ndasauka et al. (2016) developed and validated a psychometric scale for assessing excessive use of microblogs or Twitter among college students through measuring the magnitude of “health and withdrawal problems,” “time management and performance” and “social comfort.” Their studies did not develop a general theory on the consequences of excessive use of social media for individuals. Aladwani and Almarzouq (2016) investigated the antecedents (self-esteem and interaction anxiousness) and consequences (problematic learning outcomes) of compulsive social media use. However, they did not examine the underlying mechanism of the effect of compulsive social media use on the academic outcomes of students. These studies provided insights into the understanding of excessive social media use, but only a few theory-based empirical studies explored the effects of excessive social media use on individual job performance. Particularly, as an important part of social media, the use of IM at work has been widely studied in information and communication technology (ICT) research. However, prior research mainly focused on the positive influence of IM on employees (Cho et al., 2005; Ou et al., 2010; Ou and Davison, 2011). Although some studies investigated the impact of IM on the level of interruption in the workplace (Garrett and Danziger, 2007; Li et al., 2011; Mansi and Levy, 2013), the results indicated that interruption had no significantly negative impact at work to some degree. They failed to examine other negative consequences of IM use at work. Additionally, the extent usage of IM may impact individual job performance has not been thoroughly studied. Based on these arguments, this study attempts to explore the potential negative consequences of excessive social media use on individual job performance, asking the following research question:

RQ1.

How does excessive social media use at work influence individual job performance?

To answer the research question, our study explores the underlying mechanism of excessive use of social media in the workplace. In ICT research, increased use of technologies can lead to technostress, which is stress experienced by individuals due to their use of ICTs (Ragu-Nathan et al., 2008). Generally, technostress is caused by stressors, which are stimuli encountered by individuals, and strain is individuals’ psychological response to stressors (Ayyagari et al., 2011). Overload as a representative stressor and exhaustion as a form of strain have been widely used to capture technostress of an ICT workforce (Ahuja et al., 2007; Moore, 2000). In the context of this study, social media combine use-generated content with social networking features, which can be used for various purposes, including establishing and maintaining social ties, seeking information, communicating with others and relaxing. When using social media excessively, individuals might be confronted with new kinds of overload, including excessive information, frequent communication and constant social requests. When the demands of usage exceed the processing capabilities of employees, they may become stressed and experience three types of social media overload (information overload, communication overload and social overload). Stressful situations subject employees to emotional exhaustion, which in turn induces reduced job performance. Therefore, drawing upon the transactional theory of stress and coping, we develop an extended stressor–strain–outcome framework, which is a useful tool for understanding technostress process, to examine excessive use of social media at work and its negative consequences.

This study offers several contributions to social media research. First, this study extends the conventional stressor–strain model and employs the transactional theory of stress and coping to explore the potential negative consequences of excessive social media use in the workplace from the perspective of technostress. Usage experience, stressors, psychological strain and behavioral outcome are considered. Second, this study identifies the three components of social media-related overload, including information, communication and social overload as stressors to capture the key characteristics of the overburdened social media environment and to improve our understanding of this emerging phenomenon. Third, this study also advances our understanding of how irrational use of social media in the workplace can influence the cognitive and emotional states of employees, as well as their job performance. This study offers empirical evidence on the need of employees to regulate their social media usage behavior to avoid the harmful outcomes of overuse.

2. Theoretical background and related research

2.1 Transactional theory of stress and coping

The transactional theory of stress and coping (Lazarus, 1966; Lazarus and Folkman, 1987) is widely used as the basis for understanding technostress in ICT research in work environment (Srivastava et al., 2015; Lee et al., 2016). It explains the phenomenon of stress as a transaction between a person and the surrounding environment (Lazarus and Folkman, 1987). This transaction depends on the impact of stress creators, which are environmental demands that break the balance and thereby influence individual’s psychological reactions and behavioral responses, requiring action by the individual to restore the balance (Srivastava et al., 2015; Lazarus and Cohen, 1977). This theory views stressors and strain as the core components of stress process: stressors are stimuli encountered by individuals and strain is the psychological response of individuals to stressors. Extent research on technostress in ICT environment adopt overload as a representative stressor. Many scholars have applied this concept to different research areas to describe the perception of kinds of thing that exceed individuals’ capability to cope with (Zhang et al., 2016), such as technology overload (Karr-Wisniewski and Lu, 2010), connection overload (Larose et al., 2014) and social overload (Maier et al., 2012). Meanwhile, strain is a psychological outcome of stress. In SNS research, psychological strain variables such as fatigue (Lee et al., 2016), and exhaustion (Maier, Laumer, Eckhardt and Weitzel, 2015; Maier, Laumer, Weinert, and Weitzel, 2015) are most widely studied in the context of technostress. Furthermore, in the workplace, strain can lead to adverse organizational outcomes. For example, Ahuja et al. (2007) examined that work exhaustion can decrease organizational commitment and increase turnover intention.

In the social media context, extended use of social media would lead to technostress. Prior studies have investigated the antecedents of technostress in individuals including technology characteristics and SNS characteristics (Ayyagari et al., 2011; Lee et al., 2016). However, the role of extent of technology usage in inducing technostress in individuals has not been systematically addressed. In addition, the recent studies have demonstrated that technostress creators have directly negative effects on individuals’ psychological health and job outcomes, respectively (Tarafdar et al., 2015; Ayyagari et al., 2011; Maier, Laumer, Eckhardt and Weitzel, 2015; Maier, Laumer, Weinert, and Weitzel, 2015), but the relationship between psychological strain and job performance is inadequately examined in the context of excessive social media use at work, which can directly influence the performance of a whole organization and become key indicators that can determine the success of an organization. Thus, this study applies the transactional theory of stress and coping as grounding theory to develop an extended stressor–strain–outcome model by considering usage experience. Specifically, our model is composed of excessive social media use as usage experience, social media-related overload (i.e. information, communication and social overload) as stressors, social media exhaustion as strain and job performance as outcome variable. The framework can explain the exact mechanism of how excessive social media use at work influences individual job performance.

2.2 Usage experience: excessive social media use at work

In recent years, social media use in the workplace is becoming more commonplace. A great number of studies have focused on the informational and communicative aspects of social media usage at work (Moskaliuk and Kimmerle, 2009; Jackson et al., 2007; Ou and Davison, 2011; van Zoonen et al., 2017). For example, Schmidt et al. (2016) reported that social media might provide significant informational benefits; people use social media to connect with work colleagues can gain information relating to work. Sheer and Rice (2017) explored that employees increasingly use mobile IM to communicate with work contacts about work—or business—related matters. These studies indicate that social media is primarily used for work-related purposes in organizations. Meanwhile, originally characterized as personal interaction platform, the social affordances of social media has made it difficult for individuals to fully distance themselves from their social activities even if they are in a work setting (Koch et al., 2012). In addition to seeking information and communicating with others for task-related purposes, employees can also use social media to develop and maintain personal relationships with other people for socializing (Sun and Shang, 2014; Zhang et al., 2015; Cho et al., 2005). These inconclusive patterns of use would thereby promote individuals’ combination of professional and personal behavior on social media in the workplace (DiMicco et al., 2009), namely social media can be used for both work-related and social-related purposes at work.

Generally, the rational use of social media can enhance the relationships and performance of employees (Ali-Hassan et al., 2015; Kang et al., 2012; Cao et al., 2016). Cho et al. (2005) explored that IM use can help employees develop and improve their work relationship with coworkers within and across organizational boundaries. Moqbel et al. (2013) investigated that the boundary blurring impact of SNS use on work and life can result in enhanced job performance through job satisfaction and organizational commitment. However, as the use of social media continues to increase, individuals are likely to spend a considerable amount of time and energy on social media (Deryakulu and Ursavaş, 2014) that exceeds the optimal level; such excessive use might results in negative outcomes (Karr-Wisniewski and Lu, 2010). For instance, when individuals seek information through social media to complete work tasks, they might be exposed to a substantial amount of information to cope with which may result in information overload (Edmunds and Morris, 2000) or technology overload (Karr-Wisniewski and Lu, 2010), and eventually may lead to social media fatigue (Bright et al., 2015). Frequent communication via IM can lead to interruptions of employees while performing their tasks in the workplace (Mansi and Levy, 2013), resulting in productivity losses (Warnakula and Manickam, 2011). Furthermore, the increasing number of friends in individuals’ social network may result in feelings of social overload, which can be understood as their feelings of too high social demands as being responsible to give too much social support, such as clicking likes to show sympathy, taking care of friends, addressing their problems or amusing them (Maier et al., 2012). Therefore, excessive use of social media for both professional and personal activities has caused a range of problems in the organizational context.

Thus, far, scholars have proposed a number of terms to describe the phenomenon, such as excessive use (Hou et al., 2014), compulsive social media use (Aladwani and Almarzouq, 2016), social media dependence (Wang et al., 2015) and SNS addiction (Choi and Lim, 2016). However, theory-based empirical studies on the development of excessive use in the workplace are relatively rare. Previous research primarily viewed excessive use as a symptom of problematic use or addiction (Deryakulu and Ursavaş, 2014); studies on the related consequences of excessive social media use at work were still limited and the exact mechanism was not systematically investigated. Drawing on findings about different patterns of social media use, excessive social media use at work in this study is defined as the degree to which an individual feels that she or he spends too much time and energy seeking information, communicating and socializing on social media in the workplace (Zhang et al., 2015; Deryakulu and Ursavaş, 2014). Compared with the general use of social media, excessive use is more likely to result in stress and leave a negative effect on individual job performance. Therefore, this study attempts to describe how the excessive usage of social media influences individual job performance in the workplace.

2.3 Stressor: social media overload

According to prior research on excessive technology use, overload is a key stressor that leads to adverse consequences from the use of technologies (Barley et al., 2011; Ahuja et al., 2007; Bucher et al., 2013). From a person-environment fit perspective, overload emerges from the misfit between environmental demands and the person’s coping abilities (Edwards and Cooper, 1990). The meaning and components of overload differ based on research content. In a study of SNS fatigue, Lee et al. (2016) proposed three dimensions of overload: information overload, communication overload and system feature overload. Information overload occurs when people are exposed to information beyond their processing capabilities. Communication overload occurs when communication demands from ICT channels exceed users’ communication capacities. System feature overload captures individuals perception of technological characteristics and reflects a situation when technology is extremely complicated for a given task. However, their research did not embody the social interaction nature of social media, which may create social overload, wherein users feel that they give too much social support to their virtual friends (Maier, Laumer, Eckhardt and Weitzel, 2015; Maier, Laumer, Weinert and Weitzel, 2015). Additionally, unlike professional ICTs in the work environment, social media, particularly mobile social networking applications, have simpler system features that are relatively easier to use and master. The few dominant systems in the market are also strikingly similar in terms of their operation, which is beneficial to the flattening of the learning curve for new adopters. As the user-friendly interface of social media platforms continues to improve, it will not take too long time for users to master social media. On the other hand, ease of use is becoming less important in the post-adoption behavior than initial adoption phase as users are getting more skilled. Consequently, system feature overload due to high system complexity has become less of a problem for users (Hung et al., 2015). Instead of using system feature overload, we use social overload to suit the context of our study. In this study, social media overload refers to an overburdened usage environment and the state induced by a level of incoming stimuli in the social media environment that surpasses the processing ability of an individual, and includes three kinds of overload, namely information overload, communication overload and social overload.

Information overload is defined as a situation when individuals are presented with a large amount of information generated on social media, which exceeds the capacity they can process (Farhoomand and Drury, 2002; Eppler and Mengis, 2004). Communication overload describes a situation when the communication demands from social media platforms exceed the communication capacities of individuals, thereby causing excessive interruptions in their jobs to the point individuals become less productive (Karr-Wisniewski and Lu, 2010; Cho et al., 2011). The difference between information overload and communication overload is that information overload stresses more on processing a large amount of information that individuals seek, while communication overload is related to the interruption and frequent unplanned communication initiated by a third party. In the context of social media use at work, the two kinds of overload are mainly associated with work activities. However, the pervasiveness of social media has incorporated aspects of employees’ social life into the work setting, which can be used to build and maintain interpersonal relationships in the virtual social network. Individuals may be confronted with an increasing number of social requests and have to provide social support. Thus, social overload is related to personal socialization and social support.

In addition to neutral information (e.g. I enjoy this restaurant), individuals sometimes encounter social requests that require reaction and assistance (e.g. help me to fill in this online questionnaire; I have a cold). Due to the law of reciprocity in social relationships, individuals feel obliged to respond to others’ social requests and provide some form of support (Yang and Lin, 2017), such as caring about others’ existence and issues, and offering emotional support and material assistance (Maier et al., 2012). As the number of social relationships embedded in social media increases, users might encounter more social support requests than they can supply; they are then drawn into exhausting social states, thereby resulting in their perceptions that they are giving excessive social support to their online contacts (Maier, Laumer, Eckhardt and Weitzel, 2015; Maier, Laumer, Weinert and Weitzel, 2015). This emerging phenomenon is called “social overload.” When social requests exceed the level that an individual is comfortable of providing, the feeling of losing control over the social situation emerges over a negative environmental stimuli (Evans et al., 2000). Maier, Laumer, Eckhardt and Weitzel (2015) and Maier, Laumer, Weinert, and Weitzel (2015) applied social support theory and empirically verified that social overload, which is a part of individuals’ social media lives, is a dark side of technology use. Therefore, we view social overload as the third dimension of social media overload. In this study, social overload is described as a situation when individuals perceive they are giving too much social support to people embedded in their social media network out of a sense of duty to respond to social support requests.

2.4 Strain: social media exhaustion

Exposure to social media-related overload can result in corresponding psychological strain. To capture an individual’s strain, prior studies in IS research used the term “exhaustion” to refer to an individual’s psychological response to stressful situations. Exhaustion represents the depletion of mental resources associated with long-term involvement in demanding situations (Schaufeli et al., 1995). Moore (2000) focused on work exhaustion among technology professionals and found that perceived work overload is the strongest predictor of work exhaustion. Maier, Laumer, Eckhardt and Weitzel (2015) and Maier, Laumer, Weinert, and Weitzel (2015) examined SNS exhaustion as a consequence of social overload in the context of SNS. We use these studies on exhaustion as basis in employing the construct of social media exhaustion to reflect an individual’s aversive and unconscious psychological response to stressful conditions in social media environment, and an example of these conditions is social media-related overload caused by excessive social media use at work. It describes an individual’s tired feeling from the use of social media (Maier, Laumer, Eckhardt and Weitzel, 2015; Maier, Laumer, Weinert and Weitzel, 2015). When individuals are exposed to social media-related overload, they are likely to feel exhausted because of excessive usage of social media.

2.5 Outcome: job performance

A number of studies have focused on organizational outcomes of technostress due to the use of ICTs in work environment, such as decreased productivity, lower job engagement, reduced organizational commitment and increased turnover intention (Hung et al., 2011; Srivastava et al., 2015; Ahuja et al., 2007). In particular, reduced job performance is a critical work behavior to the stress-creating conditions from the use of ICTs in the work environment (Cooper et al., 2001). Prior research has examined the influence of psychological strain on job performance. For example, Kim et al. (2012) indicated that frontline service employees’ service recovery performance can be affected by customer-related social stressors through emotional exhaustion. Halbesleben and Wheeler (2011) investigated how exhaustion predicts performance outcomes, including in-role performance and organizational citizenship behaviors. Job performance is an important variable of study because organizations need to justify the value of their employees resource investments such as time and energy on social media (Ali-Hassan et al., 2015). Thus, the present study adopts job performance as organizational outcome, and investigates the effect of technostress due to excessive use of social media.

3. Research model and hypotheses

We establish an extended stressor–strain–outcome research model based on the transactional theory of stress and coping. Figure 1 shows that excessive social media use at work is a determinant of social media overload, and three dimensions of overload affect social media exhaustion, which further exerts a negative impact on individual job performance. Social media exhaustion and job performance, as psychological strain and behavioral outcome, are the consequences of technostress. The definitions of key constructs in the model are illustrated in Appendix 1.

3.1 Excessive social media use at work and overload in social media

According to previous IS research on individual usage behavior and overload (Maier, Laumer, Eckhardt and Weitzel, 2015; Maier, Laumer, Weinert and Weitzel, 2015; Turel et al., 2011; Karr-Wisniewski and Lu, 2010), we contend that the excessive use of social media at work that exceeds the optimum level can cause overload from social media.

Social media encompasses a wide variety of information and communication tools, and participation in these various tools exposes individuals to different types of information. In a networked work environment, when searching information through social media to solve problems and make decisions, individuals can be provided with information deeper and broader than necessary (Landers and Schmidt, 2016); such excessive information requires individuals to spend considerable time and effort extracting which information is effective to them. When the social media usage of individuals at work increases, they are confronted with volumes of information that exceeds their ability to efficiently process. This situation results in information overload. Exposure to information is a significant precondition of whether or not an individual perceives information overload. The extent of an individual’s use of social media is crucial in predicting the likelihood of information overload. We then hypothesize that:

H1a.

Excessive social media use at work is positively related to information overload.

According to Karr-Wisniewski and Lu (2010), communication overload occurs when a third-party solicits the attention of the employee through social media that creates excessive interruptions in their jobs to the point individuals become less productive. Social media can facilitate ubiquitous and continual connectivity, and enable individuals to be open to contact at anytime no matter what they are doing. Regardless of the work at hand, individuals often handle continuous streams of communication from different sources. Unscheduled communication that is not initiated by the focal employee can reduce the employee’s attention on the work at hand thereby resulting in interruption (Ou and Davison, 2011; Karr-Wisniewski and Lu, 2010). A certain level of interruption can increase an individual’s focus on the primary task and allow the individual to multitask, but excessive interruptions can cause disruptions in continual tasks, which may ultimately lead to decreased job performance through different interruption characteristics such as frequency, duration and content (Karr-Wisniewski and Lu, 2010; Tarafdar et al., 2010; Speier et al., 1999). Furthermore, when individuals encounter equivocal information from multiple channels of electronic communication with social media members, they will make effort to resolve the equivocality requiring more communication (Lee et al., 2016). When the communication demands from social media platforms exceed the communication capacities of the individual, communication overload may occur in an always-connected communication environment. Therefore, we propose the following hypothesis:

H1b.

Excessive social media use at work is positively related to communication overload.

In addition to seeking information and communicating with others, social media can also be used for socialization and developing relationships with other people. In work setting, individuals will have larger social networks including personal socializing/networking, and networking within company (Skeels and Grudin, 2009). Social media can be used to build and maintain social relations not only with family members, friends, acquaintances, but also with colleagues. As a result, they may be presented with social requests from their virtual friends on social media. Individuals feel obliged to respond to these requests and provide some form of support due to the law of reciprocity in social relationships. As the increased social media usage exposes individuals to frequent demands to supply social support to others embedded in their large social network, they feel that additional and excessive enacted social support is required to provide the amount of social support needed (Maier, Laumer, Eckhardt and Weitzel, 2015; Maier, Laumer, Weinert and Weitzel, 2015). Social overload is, thus, a negative consequence of excessive social media use. Thus, we propose the following hypothesis:

H1c.

Excessive social media use at work is positively related to social overload.

3.2 Overload and social media exhaustion

As a core stressor in stress research, overload can result in strain from the use of ICT (Ayyagari et al., 2011; Ahuja et al., 2007). In the present study, three types of overload that resulted from excessive use of social media at work are associated with feelings of exhaustion and burnout (Moore, 2000). When individuals are provided with information at a level that exceeds their ability to handle and use, they may generate a sense of losing control (Edmunds and Morris, 2000). Such situation can negatively affect their psychological state and thereby cause anxiety and fatigue (Lee et al., 2016; Ragu-Nathan et al., 2008). As the volume of information increases, negative emotion becomes stronger and may eventually lead to burnout when an individual is overburdened (Ahuja et al., 2007). In other words, individuals are likely to experience exhaustion when they perceive information overload as a result of the interaction of high information load and limits of one’s cognitive process (Grisé and Gallupe, 1999). Therefore, we propose the following hypothesis in the context of social media use at work:

H2.

Information overload is positively related to social media exhaustion.

Social media can offer a variety of tools such as IM that can facilitate communication in organizations (Ou and Davison, 2011; Garrett and Danziger, 2007). When individuals receive communication requirements from social media, they tend to discontinue their activities and immediately process communication demands (Karr-Wisniewski and Lu, 2010). After processing the communication interruption initiated by another party, individuals will take several minutes to resume interrupted work activities (Karr-Wisniewski and Lu, 2010; Ou and Davison, 2011). Considering the cognitive limitation of human beings, the communication initiated by an unexpected object can disturb individuals because of the distracted attention and the cognitive burden. Therefore, individuals who encounter frequent interruptions induced by communication overload may experience difficulties in concentrating on their work tasks; thereby they may feel overwhelmed and experience exhaustion by using social media. Hence, we hypothesize that:

H3.

Communication overload is positively related to social media exhaustion.

When individuals experience social overload, they bear the burden of caring for the existence and issues of virtual friends, which are created by excessive relationships in social media that provide them with social support (Maier et al., 2012). In this situation, individuals are forced to expand their social contacts and interactions as a duty to respond to such social demands and give them support. When social demands exceed one’s social communicative ability (Choi and Lim, 2016), they can elicit negative psychological reactions from individuals, which can be captured by emotional exhaustion (Maier et al., 2012). In SNS context, Maier, Laumer, Eckhardt and Weitzel (2015) and Maier, Laumer, Weinert, and Weitzel (2015) find that social overload is important in explaining stress-induced outcomes in terms of SNS exhaustion. In this study, excessive social demands from virtual friends similarly result in feelings of social media exhaustion among individuals. Therefore, we assume that:

H4.

Social overload is positively related to social media exhaustion.

3.3 Social media exhaustion and job performance

According to the conservation of resource model (Hobfoll, 2001), when employees encounter misfit from demands of excessive social media use that exceed their abilities to process, they feel threatened by reduced resources or receive insufficient return of supplementary resources on investments of resources, and experience stress thereby leading to emotional exhaustion. Exhausted individuals would take action toward conserving their resources in a selective manner. They may become protecting whatever scarce resources remain and may not have enough resources to complete the tasks, duties, and functions required by their jobs (Halbesleben and Wheeler, 2011). As a result, exhausted individuals may have a lower job performance. Previous research has provided evidence to support this point of view (Cropanzano et al., 2003; Halbesleben and Bowler, 2007; Chang et al., 2014). Thereby, we hypothesize the following:

H5.

Social media exhaustion is negatively related to job performance.

4. Research methodology

4.1 Measurement

The tested scales used in our study were adapted from prior literature. The wording of the questionnaire was modified to fit the study context. In terms of measurement of specific constructs, the items of excessive social media use at work were adapted from Caplan (2002) and Caplan and High (2006). The scales of information overload and communication overload were adapted from Karr-Wisniewski and Lu (2010), whereas the scales of social overload were adapted from Maier, Laumer, Eckhardt and Weitzel (2015) and Maier, Laumer, Weinert and Weitzel (2015). The measurements for social media exhaustion were adapted from Moore (2000) and Ayyagari et al. (2011). The items of job performance were derived from Janssen and Van Yperen (2004). All items were measured using a five-point Likert scale. Appendix 2 lists the measurement items and their corresponding sources.

In addition to the constructs in the proposed research model, several demographical variables, including gender, age, education, industry type and social media usage duration and frequency, were included as control variables.

4.2 Sample and data collection

We used online survey method to collect the data. The required sample was obtained using the services of a market research company, which is a leader in market research firms in China. The company provides required samples from their panel to participate in a variety of research projects, and the company’s panel is composed of over 500,000 active members. These members comprise a high-quality, representative sample chosen through strict recruiting methods.

This study examined the potential adverse effects of excessive use of social media on individual job performance in the workplace. Thus, the target participants should be employees with certain experience in social media usage in organizations. In the beginning of the survey, we provided a brief description of the research objective emphasizing that all questions are about situations in work context. Then, our surveys were sent to working professionals obtained from the sample provided by the research company in China. They were told that social media includes IM (e.g. WeChat and QQ), SNSs (e.g. Qzone and Renren), microblogs (e.g. Sina Weibo and Tencent Weibo), wikis (e.g. Wikipedia and Baidu Encyclopedia), online communities (e.g. Zhihu and Douban) and others. Only those who self-reported as using social media in organizations were valid for this study. To encourage participation, monetary rewards were provided at the end of the survey through a lucky draw. To ensure that each respondent submitted only one response, the IP address and demographic information of the participants were tracked and examined. After eliminating 80 responses of employees who did not use social media in the workplace, who answered the questionnaire less than 3 min based on the number of questions, and who chose one choice for all items, 230 responses were selected for the final sample. Table I shows the demographic characteristics of respondents.

Specifically, as listed in Table I, more than 80 percent of participants in the sample choose IM as the social media tool most frequently used at work, which accords with the actual situation in China. According to the CNNIC report, the most frequently used social media applications in China is IM, and its penetration rate has reached 91.1 percent in December 2016 (CNNIC, 2017). Particularly, WeChat, the most popular IM application in China, has also become the most used mobile social media application among users; 79.6 percent of internet users adopt WeChat in the mobile terminal, whereas the usage rate of mobile QQ is 60 percent (CNNIC, 2017). Given the feature of Chinese sample, IM, thus, can be regarded as a representative social media in the context of this study.

5. Data analysis

5.1 Measurement model

We used SPSS and SmartPLS to conduct confirmatory factor analysis and assess construct reliability, convergent validity, and discriminant validity. Reliability can be assessed by checking if Cronbach’s α and composite reliability (CR) (Fornell and Larcker, 1981) are above 0.70. Convergent validity can be assessed by determining whether the item loadings of the questionnaire on the respective constructs are high enough. Two criteria of convergent validity were proposed, namely, the average variance extracted (AVE) should be higher than 0.50 and item loadings should exceed 0.60 (Fornell and Larcker, 1981; Bagozzi and Yi, 1988). Table II shows that Cronbach’s α and CR for all constructs are larger than 0.70, the AVE of each construct ranges from 0.55 to 0.75, and all item loadings are greater than 0.60. The reliability and the convergent validity of our measurement instrument are established because all measures meet the recommended levels.

Discriminant validity is demonstrated when the square root of AVE for each construct is greater than the correlations between the construct and all other constructs (Fornell and Larcker, 1981). Table III shows that the square root of AVE exceeds correlations with other constructs. This finding indicates that the discriminant validity of the measurements is supported.

To examine possible multicollinearity, we calculated the variance inflation factor (VIF) for all independent variables used in the subsequent analysis. The maximum value of VIFs was 2.49, which is lower than the threshold of 5 (Hair et al., 2011). This indicates that the risk of multicollinearity can be ruled out.

We examined self-reported data from a single respondent using subjective measures and tested common method bias to ensure that any potential common method bias is not a serious threat to the study. First, using the method proposed by Liang et al. (2007), we included in the PLS model a common method factor whose indicators included all observed items. The results demonstrated that the substantive factors explained 67.3 percent of the overall variance, while the method factors explained only 2.3 percent of the variance, suggesting that common method bias was not a threat. Second, we examined the correlation matrix based on the procedure recommended by Pavlou et al. (2007). As listed in Table III, the highest inter-construct correlation was 0.72 and no correlation exceeded the threshold of 0.90 (Bagozzi et al., 1991), indicating no common method bias. Hence, these tests indicated that common method bias was not a significant problem in this study.

5.2 Structural model

The structural model was tested using the PLS graph. Figure 2 shows that the research model is largely supported by the data, except for H4. Excessive social media use at work had a significantly positive influence on information overload (β=0.44, t=6.73), communication overload (β=0.46, t=7.21) and social overload (β=0.56, t=11.10), supporting H1aH1c. Information overload (β=0.39, t=4.45) and communication overload (β=0.35, t=4.06) showed significantly positive effects on social media exhaustion, thereby validating H2 and H3. The influence of social overload on social media exhaustion was not significant (β=−0.04, t=0.59), thereby rejecting H4. The results also demonstrated strong support for H5, which posited that social media exhaustion had a significantly negative effect on job performance (β=−0.32, t=4.70). Moreover, the control variables indicated no significant effect on job performance. The variances explained by information overload, communication overload, social overload, social media exhaustion and job performance were 20, 22, 31, 45 and 12 percent, respectively.

6. Discussion and conclusion

6.1 Key findings

This study examines the effects of excessive social media use on individual job performance from the perspective of technostress. The proposed research model supports the argument that excessive usage of social media results in reduced job performance because it triggers negative perceptions and psychological strain in individuals. The data collected from the sample largely support the theoretical process. Results reveal several key findings.

First, the results show that excessive social media use at work has a significant effect on the negative cognition and emotions of individuals. This finding suggests that the extent of social media usage plays a critical role in the development of social media-related overload. The finding is consistent with the evidence offered in previous research on ICT, which indicated that high usage of ICT results in high perception of overload (Karr-Wisniewski and Lu, 2010; Maier, Laumer, Eckhardt and Weitzel, 2015; Maier, Laumer, Weinert and Weitzel, 2015).

Second, in terms of the stressor–strain paradigm, information overload and communication overload emerge as significant predictors of social media exhaustion, but the contention that social overload is a predictor for social media exhaustion is not supported. Unlike information overload and communication overload that required to be handled immediately at work due to the association with work tasks, social overload mainly focuses on private activities which can be ignored temporarily during work hours and processed at a later time in a desired sequence. In particular, when the social requests are difficult to deal with at work, individuals will be more likely to process them outside of the workplace. This helps alleviate the problem of social overload. Moreover, the existing studies have shown that social-related usage can facilitate the development of social capital and promote job performance (Sun and Shang, 2014; Ali-Hassan et al., 2015). Thus, individuals might feel less exhaustion when processing social requests in the workplace.

Third, this study investigates the effect of social media exhaustion on individual job performance. The results show that social media exhaustion has a strong negative impact on job performance. When employees use social media excessively, their time and energy on work tasks will be occupied, as well as their emotional resources. Exhausted employees do not have sufficient resources to accomplish work through social media, which in turn leads to lowering job performance. This finding is line with the extant research (Brooks and Califf, 2017), which suggested that social media-induced technostress associated with using social media at work had a negative effect on job performance.

6.2 Theoretical implications

This study contributes to theoretical understanding in several ways. First, unlike previous studies that mainly investigated the positive effects of social media in organizational context (Ou and Davison, 2011; Ali-Hassan et al., 2015), the present study focuses on the dark side of social media usage in the workplace, thus enriching social media research. Social media can create problems as the usage increases. The negative consequences of social media use at work should not be ignored, especially when the usage exceeds the optimum level.

Second, this study employs the transactional theory of stress and coping in the context of excessive social media use in an organizational environment. This study proposes an extended stressor–strain–outcome model through the technostress perspective by considering usage experience, stressors, psychological strain, and job outcome. Prior technostress research developed transactional theory to examine causal relationships between stressors and outcomes in the context of professional technology tools (Srivastava et al., 2015; Tarafdar et al., 2015). This theory did not investigate the role of strain in the negative consequences of excessive technology use at work. Moreover, Ayyagari et al. (2011) and Lee et al. (2016) suggested that certain technology characteristics are related to stressors, but they ignored the critical role of the extent of usage. Given the widespread use of social media tools in organizations, individual usage behavior has become a significant source of social media-related perceptions. Thus, the present study integrates excessive usage of social media as a predictor of social media-related overload, which enriches technostress research.

Third, this study advances our understanding of types of social media overload. It identifies three types of overload (i.e. information, communication and social overload) as social media-related stressors and investigates their effects on the psychological response of individuals, which also contributes to the research on technostress. Information and communication overload are the most common stressors in organizational ICT research. These two types of overload are usually work-related. Social overload pertains to personal relationships building and maintenance. This type is rarely investigated in the work environment. Our results indicate that all three types of overload are important sources of social media-related overload, which highlights the contingency of the meaning and components of technology overload in the specific research context.

Finally, the present study advances existing research on individual job performance by exploring the exact mechanism of how excessive social media use at work influence individual job performance. It also complements prior research on technostress that stops at the strain stage (Lee et al., 2016; Ayyagari et al., 2011) and contributes to a clearer and more comprehensive understanding of the negative outcomes of excessive social media usage. This study offers empirical evidence on the need of employees to regulate their social media usage behavior to avoid the adverse outcomes of irrational use.

6.3 Practical implications

Excessive social media use may be insufficient to addiction syndromes, but the results of this study strongly suggest that excessive use should not be ignored. Excessive use of social media can bring adverse consequences for employees at work. This study improves our understanding of the seemingly common abnormal use of social media and provides several important practical implications. First, employees who spend excessive time using social media should be aware of its negative consequences. They should actively manage their own behavior to avoid the negative outcomes. For example, they can control use time and frequency, and develop other approaches (e.g. face-to-face communication) as alternative to alleviate their reliance on social media.

Second, organizations should be aware that social media usage of employees during work hours can lead to unexpected outcomes. They should introduce management strategies to regulate employees’ use of social media and relieve their pressure. For instance, firms can publish policies that fit within the organizational culture and state how, when and which social media should be used and should be avoided. Particularly, firms can set a specific time period for employees to use social media for processing personal activities. They should also encourage offline relations to limit intensity of usage in the workplace.

Third, once excessive social media use triggers adverse consequences that cannot be handled by employees, organizations can forbid the use of social media at work and employees themselves should reduce or discontinue their usage. To attract and maintain users of social media, service providers should implement measures to mitigate and prevent harm on users and seek healthy and sustainable usage behavior. For example, providers could offer filter mechanisms that allow users to find interesting and most relevant content. They can also offer optional functions that can be used to set limits to communication requests at certain times.

6.4 Limitations and future research

This study has a number of limitations. First, this study used excessive social media use at work as an omnibus measure of the primary antecedent (Moqbel et al., 2013). Future studies can separate this variable into fine graded components, such as cognitive use, hedonic use and social use (Ali-Hassan et al., 2015), and distinguish between work and private purposes clearly, in order to enhance our understanding of the relationship between dimensions of excessive social media use and job performance. Second, we did not discuss other stressors from excessive usage such as conflict, invasion and uncertainty (Bucher et al., 2013; Ayyagari et al., 2011), which can influence social media exhaustion. Future research can investigate the effects of these different kinds of stressors on outcome variables. Moreover, we did not examine situational factors such as technostress inhibitors that can reduce exhaustion. Future research can expand the scope of research to include these factors as positive coping strategies to avoid overload and reduce stress. Third, this study advanced our understanding of the relationship between excessive social media use and its effect on job performance. However, responses to stress associated with excessive technology use differ because of personality traits, such as extraversion or neuroticism. Future research may focus on the important role of individual differences on the excessive usage behavior of individuals and their job performance. Fourth, our data were collected from Chinese participants. Perception of excessive social media may differ in other countries and cultures. Additionally, we collect cross-sectional data to examine this study. Future longitudinal research may include other countries with different cultural orientations to increase the generalizability of results.

Figures

Research model

Figure 1

Research model

PLS results of the structural model

Figure 2

PLS results of the structural model

Respondents’ demographics

Demographics Items Percent (%)
Gender Male 43.5
Female 56.5
Age 18–25 37.0
26–35 52.6
36–45 8.3
46 and above 2.2
Education High school degree or below 4.8
Junior College 16.1
Bachelor’s degree 50.0
Master’s degree 26.5
Doctorate degree 2.6
Industry type Computers 19.6
Education 14.3
Manufacturing 8.7
Service 13.4
Banking/finance 13.5
Construction 5.7
Health care 9.1
Others 15.7
Social media tools most frequently used at work IM 86.9
SNS 9.1
Microblogs 3.0
Others 1.0
Average daily duration of social media usage at work Rarely 2.6
Less than 1 h 13.0
1–2 h 20.9
2–3 h 20.4
More than 3 h 43.0
Average daily frequency of social media usage at work Rarely 3.5
1–3 times a day 15.2
4–6 times a day 19.6
7–9 times a day 9.6
More than 10 times a day 52.2

Mean, standard deviation, standard loadings, cronbach α values, CR and AVE

Construct Item Mean SD Standard loading Cronbach α CR AVE
Excessive social media use at work (ESMU) ESMU1 3.06 1.09 0.87 0.83 0.90 0.75
ESMU2 2.87 1.13 0.91
ESMU3 2.82 1.10 0.82
Information overload (IO) IO1 3.00 1.05 0.83 0.76 0.86 0.67
IO2 2.75 1.00 0.86
IO3 3.04 0.93 0.77
Communication overload (CO) CO1 2.73 0.99 0.80 0.73 0.83 0.55
CO2 3.40 1.06 0.69
CO3 2.80 0.96 0.78
CO4 3.24 0.92 0.71
Social overload (SO) SO1 2.77 1.02 0.80 0.85 0.89 0.62
SO2 2.98 1.05 0.76
SO3 2.63 1.03 0.79
SO4 2.71 1.05 0.80
SO5 3.03 1.04 0.79
Social media exhaustion (SEM) SME1 2.91 1.02 0.76 0.84 0.89 0.68
SME2 2.70 0.98 0.84
SME3 2.80 1.08 0.84
SME4 2.63 1.00 0.86
Job performance (JP) JP1 3.72 0.79 0.82 0.84 0.90 0.68
JP2 3.78 0.77 0.83
JP3 3.73 0.81 0.83
JP4 3.83 0.94 0.82

Construct correlation matrix and the square root of AVE in the diagonal

Construct Mean(SD) ESMU IO CO SO SME JP
ESMU 2.91 (0.96) 0.87
IO 2.93 (0.81) 0.44 0.82
CO 3.05 (0.73) 0.46 0.72 0.74
SO 2.83 (0.82) 0.56 0.44 0.44 0.79
SME 2.76 (0.84) 0.31 0.62 0.62 0.29 0.82
JP 3.77 (0.68) −0.03 −0.15 −0.08 −0.12 −0.31 0.82

Definitions of key constructs

Construct Definition Reference
Excessive social media use at work The degree to which an individual feels that she or he spends too much time and energy on social media for information seeking, communicating, and socializing in the workplace Deryakulu and Ursavaş (2014) and Zhang et al. (2015)
Information overload A situation when individuals are presented with a large amount of information generated on social media, which exceeds the capacity they can process Farhoomand and Drury (2002) and Eppler and Mengis (2004)
Communication overload A situation when the communication demands from social media platforms exceed the communication capacities of individuals thereby causing excessive interruptions in their jobs to the point individuals become less productive Karr-Wisniewski and Lu (2010) and Cho et al. (2011)
Social overload A situation when individuals perceive they are giving too much social support to people embedded in their social media network out of a sense of duty to respond to social support requests Maier, Laumer, Eckhardt and Weitzel (2015) and Maier, Laumer, Weinert and Weitzel (2015)
Social media exhaustion An individual’s aversive and unconscious psychological response to stressful conditions in social media environment Maier, Laumer, Eckhardt and Weitzel (2015), Maier, Laumer, Weinert and Weitzel (2015)
Job performance The performance of mandatory job-related tasks, duties, and responsibilities that are coordinated and rewarded by the organization Sparrowe et al. (2001) and Janssen and Van Yperen (2004)

Construct measures

Variables Items Measurements Sources
Excessive social media use at work (ESMU) ESMU1 I think the amount of time I spend using social media at work is excessive Caplan (2002) and Caplan and High (2006)
ESMU2 I spend an unusually large amount of time using social media at work
ESMU3 I spend more time using social media at work than most other people
Information overload (IO) In my organization Karr-Wisniewski and Lu (2010)
IO1 I am often distracted by the excessive amount of information in social media
IO2 I find that I am overwhelmed by the amount of information that I process on a daily basis from social media
IO3 I feel some problems with too much information in social media to synthesize instead of not having enough information
Communication overload (CO) In my organization Karr-Wisniewski and Lu (2010)
CO1 I feel that in a less connected environment, my attention would be less divided allowing me to be more productive
CO2 I often find myself overwhelmed because social media has allowed too many other people to have access to my time
CO3 I waste a lot of my time responding to messages, voice messages, and voice calls from social media that are business-related but not directly related to what I need to get done
CO4 The availability of social media has created more of an interruption than it has improved communications
Social overload (SO) In my organization Maier, Laumer, Eckhardt and Weitzel (2015) and Maier, Laumer, Weinert, and Weitzel (2015)
SO1 I take too much care of my friends’ well-being on social media
SO2 I deal too much with my friends’ problems on social media
SO3 My sense of being responsible for how much fun my friends have on social media is too strong
SO4 I am too often caring for my friends on social media
SO5 I pay too much attention to posts of my friends on social media
Social media exhaustion (SME) SME1 I feel drained from activities that require me to use social media Moore (2000) and Ayyagari et al. (2011)
SME2 I feel tired from my social media activities
SME3 Working all day with social media is a strain for me
SME4 I feel burned out from my social media activities
Job performance (JP) JP1 I always complete the duties specified in my job description Janssen and Van Yperen (2004)
JP2 I always meet all the formal performance requirements of my job
JP3 I always fulfill all responsibilities required by my job
JP4 I often fail to perform essential duties (R)

Appendix 1

Table AI

Appendix 2

Table AII

References

Ahuja, M.K., Chudoba, K.M., Kacmar, C.J., McKnight, D.H. and George, J.F. (2007), “IT road warriors: balancing work-family conflict, job autonomy, and work overload to mitigate turnover intentions”, MIS Quarterly, Vol. 31 No. 1, pp. 1-17.

Aladwani, A.M. and Almarzouq, M. (2016), “Understanding compulsive social media use: the premise of complementing self-conceptions mismatch with technology”, Computers in Human Behavior, Vol. 60, pp. 575-581.

Ali-Hassan, H., Nevo, D. and Wade, M. (2015), “Linking dimensions of social media use to job performance: the role of social capital”, The Journal of Strategic Information Systems, Vol. 24 No. 2, pp. 65-89.

Aral, S., Dellarocas, C. and Godes, D. (2013), “Introduction to the special issue-social media and business transformation: a framework for research”, Information Systems Research, Vol. 24 No. 1, pp. 3-13.

Ayyagari, R., Grover, V. and Purvis, R. (2011), “Technostress: technological antecedents and implications”, MIS Quarterly, Vol. 35 No. 4, pp. 831-858.

Bagozzi, R.P. and Yi, Y. (1988), “On the evaluation of structural equation models”, Journal of the Academy of Marketing Science, Vol. 16 No. 1, pp. 74-94.

Bagozzi, R.P., Yi, Y. and Phillips, L.W. (1991), “Assessing construct validity in organizational research”, Administrative Science Quarterly, Vol. 36 No. 3, pp. 421-458.

Barley, S.R., Meyerson, D.E. and Grodal, S. (2011), “E-mail as a source and symbol of stress”, Organization Science, Vol. 22 No. 4, pp. 887-906.

Bright, L.F., Kleiser, S.B. and Grau, S.L. (2015), “Too much Facebook? An exploratory examination of social media fatigue”, Computers in Human Behavior, Vol. 44, pp. 148-155.

Brooks, S. (2015), “Does personal social media usage affect efficiency and well-being?”, Computers in Human Behavior, Vol. 46, pp. 26-37.

Brooks, S. and Califf, C. (2017), “Social media-induced technostress: its impact on the job performance of it professionals and the moderating role of job characteristics”, Computer Networks, Vol. 114, pp. 143-153.

Bucher, E., Fieseler, C. and Suphan, A. (2013), “The stress potential of social media in the workplace”, Information, Communication & Society, Vol. 16 No. 10, pp. 1639-1667.

Cao, X., Guo, X., Vogel, D. and Zhang, X. (2016), “Exploring the influence of social media on employee work performance”, Internet Research, Vol. 26 No. 2, pp. 529-545.

Caplan, S.E. (2002), “Problematic internet use and psychosocial well-being: development of a theory-based cognitive–behavioral measurement instrument”, Computers in Human Behavior, Vol. 18 No. 5, pp. 553-575.

Caplan, S.E. and High, A.C. (2006), “Beyond excessive use: the interaction between cognitive and behavioral symptoms of problematic Internet use”, Communication Research Reports, Vol. 23 No. 4, pp. 265-271.

Chang, H.C., Kim, T., Lee, G. and Lee, S.K. (2014), “Testing the stressor–strain–outcome model of customer-related social stressors in predicting emotional exhaustion, customer orientation and service recovery performance”, International Journal of Hospitality Management, Vol. 36 No. 6, pp. 272-285.

China Internet Network Information Center (CNNIC) (2016), “2015 China social media users’ behavior research report”, CNNIC, available at:www.cnnic.net.cn/hlwfzyj/hlwxzbg/sqbg/201604/P020160722551429454480.pdf

China Internet Network Information Center (CNNIC) (2017), “The 39th statistical report on internet development in China”, CNNIC, available at: www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201701/P020170123364672657408.pdf

Cho, H.K., Trier, M. and Kim, E. (2005), “The use of instant messaging in working relationship development: a case study”, Journal of Computer-Mediated Communication, Vol. 10 No. 4, available at: https://doi.org/10.1111/j.1083-6101.2005.tb00280.x

Cho, J., Ramgolam, D.I., Schaefer, K.M. and Sandlin, A.N. (2011), “The rate and delay in overload: an investigation of communication overload and channel synchronicity on identification and job satisfaction”, Journal of Applied Communication Research, Vol. 39 No. 1, pp. 38-54.

Choi, S.B. and Lim, M.S. (2016), “Effects of social and technology overload on psychological well-being in young South Korean adults: the mediatory role of social network service addiction”, Computers in Human Behavior, Vol. 61, pp. 245-254.

Cooper, C.L., Dewe, P.J. and O’Driscoll, M.P. (2001), Organizational Stress: A Review and Critique of Theory, Research, and Applications, Sage.

Cropanzano, R., Rupp, D.E. and Byrne, Z.S. (2003), “The relationship of emotional exhaustion to work attitudes, job performance, and organizational citizenship behaviors”, Journal of Applied Psychology, Vol. 88 No. 1, pp. 160-169.

Deryakulu, D. and Ursavaş, Ö.F. (2014), “Genetic and environmental influences on problematic internet use: a twin study”, Computers in Human Behavior, Vol. 39, pp. 331-338.

DiMicco, J.M., Geyer, W., Millen, D.R., Dugan, C. and Brownholtz, B. (2009), “People sensemaking and relationship building on an enterprise social network site”, 42nd Hawaii International Conference on System Sciences, pp. 1-10.

Edmunds, A. and Morris, A. (2000), “The problem of information overload in business organisations: a review of the literature”, International Journal of Information Management, Vol. 20 No. 1, pp. 17-28.

Edwards, J.R. and Cooper, C.L. (1990), “The person-environment fit approach to stress: recurring problems and some suggested solutions”, Journal of Organizational Behavior, Vol. 11 No. 4, pp. 293-307.

Eppler, M.J. and Mengis, J. (2004), “The concept of information overload: a review of literature from organization science, accounting, marketing, MIS, and related disciplines”, The Information Society, Vol. 20 No. 5, pp. 325-344.

Evans, G.W., Rhee, E., Forbes, C., Allen, K.M. and Lepore, S.J. (2000), “The meaning and efficacy of social withdrawal as a strategy for coping with chronic residential crowding”, Journal of Environmental Psychology, Vol. 20 No. 4, pp. 335-342.

Farhoomand, A.F. and Drury, D.H. (2002), “Managerial information overload”, Communications of the ACM, Vol. 45 No. 10, pp. 127-131.

Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.

Garrett, R.K. and Danziger, J.N. (2007), “IM=Interruption management? Instant messaging and disruption in the workplace”, Journal of Computer-Mediated Communication, Vol. 13 No. 1, pp. 23-42.

Grisé, M.L. and Gallupe, R.B. (1999), “Information overload: addressing the productivity paradox in face-to-face electronic meetings”, Journal of Management Information Systems, Vol. 16 No. 3, pp. 157-185.

Hair, J.F., Ringle, C.M. and Sarstedt, M. (2011), “PLS-SEM: Indeed a silver bullet”, Journal of Marketing Theory and Practice, Vol. 19 No. 2, pp. 139-152.

Halbesleben, J.R. and Bowler, W.M. (2007), “Emotional exhaustion and job performance: the mediating role of motivation”, Journal of Applied Psychology, Vol. 92 No. 1, pp. 93-106.

Halbesleben, J.R. and Wheeler, A.R. (2011), “I owe you one: coworker reciprocity as a moderator of the day-level exhaustion-performance relationship”, Journal of Organizational Behavior, Vol. 32 No. 4, pp. 608-626.

Hobfoll, S.E. (2001), “The influence of culture, community, and the nested-self in the stress process: advancing conservation of resources theory”, Applied Psychology, Vol. 50 No. 3, pp. 337-421.

Hou, J., Huang, Z., Li, H., Liu, M., Zhang, W., Ma, N., Yang, L., Gu, F., Liu, Y. and Jin, S. (2014), “Is the excessive use of microblogs an internet addiction? Developing a scale for assessing the excessive use of microblogs in Chinese college students”, PloS One, Vol. 9 No. 11, e110960.

Hung, W.H., Chang, L.M. and Lin, C.H. (2011), “Managing the risk of overusing mobile phones in the working environment: a study of ubiquitous technostress”, Pacific Asia Conference on Information Systems, Citeseer, p. 81.

Hung, W.H., Chen, K. and Lin, C.P. (2015), “Does the proactive personality mitigate the adverse effect of technostress on productivity in the mobile environment?”, Telematics & Informatics, Vol. 32 No. 1, pp. 143-157.

Jackson, A., Yates, J. and Orlikowski, W. (2007), “Corporate blogging: building community through persistent digital talk”, 40th Annual Hawaii International Conference on System Sciences, p. 80.

Janssen, O. and Van Yperen, N.W. (2004), “Employees’ goal orientations, the quality of leader-member exchange, and the outcomes of job performance and job satisfaction”, Academy of Management Journal, Vol. 47 No. 3, pp. 368-384.

Kang, S., Lim, K.H., Kim, M.S. and Yang, H.D. (2012), “Research note-a multilevel analysis of the effect of group appropriation on collaborative technologies use and performance”, Information Systems Research, Vol. 23 No. 1, pp. 214-230.

Karr-Wisniewski, P. and Lu, Y. (2010), “When more is too much: operationalizing technology overload and exploring its impact on knowledge worker productivity”, Computers in Human Behavior, Vol. 26 No. 5, pp. 1061-1072.

Kim, T.T., Paek, S., Choi, C.H. and Lee, G. (2012), “Frontline service employees’ customer-related social stressors, emotional exhaustion, and service recovery performance: customer orientation as a moderator”, Service Business, Vol. 6 No. 4, pp. 503-526.

Koch, H., Gonzalez, E. and Leidner, D.E. (2012), “Bridging the work/social divide: the emotional response to organizational social networking sites”, European Journal of Information Systems, Vol. 21 No. 6, pp. 699-717.

Landers, R.N. and Schmidt, G.B. (2016), Social Media in Employee Selection and Recruitment: Theory, Practice, and Current Challenges, Springer.

Larose, R., Connolly, R., Lee, H., Li, K. and Hales, K. (2014), “Connection overload? A cross cultural study of the consequences of social media connection”, Information Systems Management, Vol. 31 No. 1, pp. 59-73.

Lazarus, R.S. (1966), “Psychological stress and the coping process”, McGraw-Hill, New York, NY.

Lazarus, R.S. and Cohen, J.B. (1977), Human Behavior and Environment, Springer, pp. 89-127.

Lazarus, R.S. and Folkman, S. (1987), “Transactional theory and research on emotions and coping”, European Journal of Personality, Vol. 1 No. 3, pp. 141-169.

Lee, A.R., Son, S.M. and Kim, K.K. (2016), “Information and communication technology overload and social networking service fatigue: a stress perspective”, Computers in Human Behavior, Vol. 55, pp. 51-61.

Li, H., Gupta, A., Luo, X. and Warkentin, M. (2011), “Exploring the impact of instant messaging on subjective task complexity and user satisfaction”, European Journal of Information Systems, Vol. 20 No. 2, pp. 139-155.

Liang, H., Saraf, N., Hu, Q. and Xue, Y. (2007), “Assimilation of enterprise systems: the effect of institutional pressures and the mediating role of top management”, MIS Quarterly, Vol. 31 No. 1, pp. 59-87.

Maier, C., Laumer, S., Eckhardt, A. and Weitzel, T. (2012), “When social networking turns to social overload: explaining the stress, emotional exhaustion, and quitting behavior from social network sites’ users”, European Conference on Information Systems Proceedings, p. 71.

Maier, C., Laumer, S., Eckhardt, A. and Weitzel, T. (2015), “Giving too much social support: social overload on social networking sites”, European Journal of Information Systems, Vol. 24 No. 5, pp. 447-464.

Maier, C., Laumer, S., Weinert, C. and Weitzel, T. (2015), “The effects of technostress and switching stress on discontinued use of social networking services: a study of Facebook use”, Information Systems Journal, Vol. 25 No. 3, pp. 275-308.

Mansi, G. and Levy, Y. (2013), “Do instant messaging interruptions help or hinder knowledge workers’ task performance?”, International Journal of Information Management, Vol. 33 No. 3, pp. 591-596.

Moore, J.E. (2000), “One road to turnover: an examination of work exhaustion in technology professionals”, MIS Quarterly, Vol. 24 No. 1, pp. 141-168.

Moqbel, M., Nevo, S. and Kock, N. (2013), “Organizational members’ use of social networking sites and job performance: an exploratory study”, Information Technology & People, Vol. 26 No. 3, pp. 240-264.

Moskaliuk, J. and Kimmerle, J. (2009), “Using wikis for organizational learning: functional and psycho-social principles”, Development and Learning in Organizations: An International Journal, Vol. 23 No. 4, pp. 21-24.

Ndasauka, Y., Hou, J., Wang, Y., Yang, L., Yang, Z., Ye, Z., Hao, Y., Fallgatter, A.J., Kong, Y. and Zhang, X. (2016), “Excessive use of Twitter among college students in the UK: validation of the microblog excessive use scale and relationship to social interaction and loneliness”, Computers in Human Behavior, Vol. 55, pp. 963-971.

Nucleus (2009), Facebook: Measuring the Cost to Business of Social Networking, Nucleus Research Inc., Boston, MA.

Ou, C.X., Davison, R.M., Zhong, X. and Liang, Y. (2010), “Empowering employees through instant messaging”, Information Technology & People, Vol. 23 No. 2, pp. 193-211.

Ou, C.X.J. and Davison, R.M. (2011), “Interactive or interruptive? Instant messaging at work”, Decision Support Systems, Vol. 52 No. 1, pp. 61-72.

Park, N., Kee, K.F. and Valenzuela, S. (2009), “Being immersed in social networking environment: facebook groups, uses and gratifications, and social outcomes”, Cyberpsychology, Behavior, and Social Networking, Vol. 12 No. 6, pp. 729-733.

Pavlou, P.A., Liang, H. and Xue, Y. (2007), “Understanding and mitigating uncertainty in online exchange relationships: a principal-agent perspective”, MIS Quarterly, Vol. 31 No. 1, pp. 105-136.

Ragu-Nathan, T.S., Tarafdar, M., Ragu-Nathan, B.S. and Tu, Q. (2008), “The consequences of technostress for end users in organizations: conceptual development and empirical validation”, Information Systems Research, Vol. 19 No. 4, pp. 417-433.

Schaufeli, W., Leiter, M. and Kalimo, R. (1995), “The general burnout inventory: a self-report questionnaire to assess burnout at the workplace”, Work, Stress and Health, Vol. 95, pp. 14-16.

Schmidt, G.B., Lelchook, A.M. and Martin, J.E. (2016), “The relationship between social media co-worker connections and work-related attitudes”, Computers in Human Behavior, Vol. 55, pp. 439-445.

Sheer, V.C. and Rice, R.E. (2017), “Mobile instant messaging use and social capital: direct and indirect associations with employee outcomes”, Information & Management, Vol. 54 No. 1, pp. 90-102.

Sherman, B. (2009), “When the bird tweets, does anyone learn”, Chief Learning Officer, Vol. 8 No. 8, pp. 36-39.

Skeels, M.M. and Grudin, J. (2009), “When social networks cross boundaries: a case study of workplace use of Facebook and Linkedin”, International Conference on Supporting Group Work, pp. 95-104.

Sparrowe, R.T., Liden, R.C., Wayne, S.J. and Kraimer, M.L. (2001), “Social networks and the performance of individuals and groups”, Academy of Management Journal, Vol. 44 No. 2, pp. 316-325.

Speier, C., Valacich, J.S. and Vessey, I. (1999), “The influence of task interruption on individual decision making: an information overload perspective”, Decision Sciences, Vol. 30 No. 2, pp. 337-360.

Srivastava, S.C., Chandra, S. and Shirish, A. (2015), “Technostress creators and job outcomes: theorising the moderating influence of personality traits”, Information Systems Journal, Vol. 25 No. 4, pp. 355-401.

Sun, Y. and Shang, R.A. (2014), “The interplay between users’ intraorganizational social media use and social capital”, Computers in Human Behavior, Vol. 37, pp. 334-341.

Tarafdar, M., Pullins, E.B. and Ragu-Nathan, T.S. (2015), “Technostress: negative effect on performance and possible mitigations”, Information Systems Journal, Vol. 25 No. 2, pp. 103-132.

Tarafdar, M., Tu, Q. and Ragu-Nathan, T.S. (2010), “Impact of technostress on end-user satisfaction and performance”, Journal of Management Information Systems, Vol. 27 No. 3, pp. 303-334.

Turel, O., Serenko, A. and Bontis, N. (2011), “Family and work-related consequences of addiction to organizational pervasive technologies”, Information & Management, Vol. 48 No. 2, pp. 88-95.

van Zoonen, W., Verhoeven, J.W. and Vliegenthart, R. (2017), “Understanding the consequences of public social media use for work”, European Management Journal, Vol. 35 No. 5, pp. 595-605.

van Zoonen, W., van Zoonen, W., Verhoeven, J.W., Verhoeven, J.W., Vliegenthart, R. and Vliegenthart, R. (2016), “Social media’s dark side: inducing boundary conflicts”, Journal of Managerial Psychology, Vol. 31 No. 8, pp. 1297-1311.

Wang, C., Lee, M.K. and Hua, Z. (2015), “A theory of social media dependence: evidence from microblog users”, Decision Support Systems, Vol. 69, pp. 40-49.

Warnakula, W. and Manickam, B. (2011), “Employees’ behaviour in online social networking websites (SNSs)”, Tropical Agricultural Research, Vol. 22 No. 1, pp. 94-106.

Yang, H.L. and Lin, R.X. (2017), “Determinants of the intention to continue use of SoLoMo services: consumption values and the moderating effects of overloads”, Computers in Human Behavior, Vol. 73, pp. 583-595.

Zhang, S., Zhao, L., Lu, Y. and Yang, J. (2016), “Do you get tired of socializing? An empirical explanation of discontinuous usage behaviour in social network services”, Information & Management, Vol. 53 No. 7, pp. 904-914.

Zhang, X.Y., Gao, Y., Chen, H., Sun, Y. and De Pablos, P.O. (2015), “Enhancing creativity or wasting time? The mediating role of adaptability on social media-job performance relationship”, Pacific Asia Conference on Information Systems.

Zheng, X. and Lee, M.K.O. (2016), “Excessive use of mobile social networking sites: negative consequences on individuals”, Computers in Human Behavior, Vol. 65, pp. 65-76.

Corresponding author

Xiongfei Cao be contacted at: caoxf@ustc.edu.cn