Smartphone usage : devil or angel? : an examination of the influence of smartphone usage on employees' work outcomes

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Master thesis

MSc. in Business Administration-Leadership and Management track

Smartphone usage: devil or angel?

An examination of the influence of smartphone usage on

employees’ work outcomes.

XIA CAI 11373369

June 21

st

, 2017

Thesis supervisor

Dr. Wendelien van Eerde

Dr. Merlijn Venus

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Statement of originality

This document is written by Student Xia Cai who declares to take full

responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and

that no sources other than those mentioned in the text and its references have

been used in creating it.

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Abstract

Smartphones have become an indispensable part of people’s life. The connectivity and availability of smartphones have facilitated people’s life. However, there are also disadvantages associated with smartphone usage. This dairy study examined the influence of smartphone usage for different purposes on employees’ job satisfaction, engagement, productivity and stress. Moreover, self-control and exercise were investigated as moderators in the relationship between smartphone usage for different purposes and employees’ work-related outcomes. A total of 84 employees completed a personality survey for one time and a diary survey for 10 consecutive working days (N=550-570 data points). Multi-regression analyses showed that work-related smartphone use is positively related to employees’ engagement. In addition, positive relationship between work-related smartphone use and employees’ stress was only found in employees with high self-control. No significant results were found between personal smartphone use and employees’ work outcomes. These findings indicate that the purpose of smartphone use and individual differences in self-control would have different influences on employees’ work outcomes.

Keywords: Smartphone use, self-control, exercise, job satisfaction, engagement, productivity,

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1. Introduction

In the 21st century, the competition between companies depends largely on their talents. Improving employee job satisfaction is the key to attract and retain talents while increasing employee productivity would contribute to the organization’s sustainable competitive advantage. Employee job satisfaction is the overall evaluation of work experience while employee productivity is measured by employees’ contribution to the company per resource consumed (Bain, 1982; Howard & Kelsey, 2015). Harter, Schmidt and Keyes (2003) found that employee’s positive perceptions of work experience are effective in increasing organizational productivity and profitability, decreasing employee turnover as well as changing employees’ work attitudes and behaviors towards the objectives of the organization. Employee’s attitudes and behaviors can be strong predictors to individual performance, the accumulation of which contributes to organizational performance (Guest, 2002).

The way of improving employees’ behaviors and attitudes evolves over time, since the development of technology has changed how people work and communicate in today’s high-tech world. The introduction of visual communication tools at work made it possible for employees to connect with colleagues regardless of geographical dispersion (Townsend, DeMarie & Hendrickson, 1998). The prevalence of using electronic devices in every field of life has substantial impact on individuals. For instance, before the introduction of telecommunication tools, employees had to commute to the company to deal with work-related business. However, nowadays, more and more companies allow employees to work from home on a regular basis (Olson & Primps, 1984). Among all electronic devices, smartphones gained the greatest popularity due to their multiple functions and pocket-friendly size. It is reported that more than 25% of the world population are using smartphones (Kissonergis, 2015). Obviously, the use of smartphone improves the efficiency of interaction and communication among individuals. It also helps employees overcome geographical limitations and allows them to work anywhere possible. Instant messaging, quick responses and increased availability are considered to be key to improve company performance by means of improved efficiency and customer satisfaction. The increased flexibility associated with smartphone use is also considered to be useful in assisting individuals to fulfill different

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roles at the same time (Diaz, Chiaburu, Zimmerman, & Boswell, 2012; Allen & Shockley, 2009). Since the roles individuals play in society are context-specific (for instance, as a father at home and as an employee at work), the introduction of smartphones can help employees overcome the space or context limitations to simultaneously fulfill different roles. For example, an employee can check emails at home while at the same time watch his child playing.

Controversially, some scholars had opposite conclusions about the influence of smartphone usage. Derks and Bakker (2014) found that work-related smartphone use in the evening is positively related to work-home interference and those who use smartphone more frequently are more likely to experience burnout with work-home interference. Work-home interference refers to the inter-role conflict associated with fulfilling incompatible roles in work and home settings (Greenhaus & Beutell, 1985). The pressure of work-home interference results from incompatibility of the two roles, meaning that the fulfillment of a work role makes the fulfillment of a family role difficult. Moreover, Lanaj, Johnson and Barnes (2014) concluded that late smartphone use for work caused a higher level of depletion on the next day by lowering employees’ sleep quality. Furthermore, work-related smartphone use in the evening increased employees’ job overload and stress (Yun, Kettinger & Lee, 2012). The prevalence of social media also sparked companies’ concern that employees’ personal smartphone use of social activities would influence their productivity and health (Chou, Sinha, & Zhao, 2010).

Regarding the aforementioned conflicting results, Ohly and Latour (2014) concluded that smartphone use with different motivations would generate different outcomes. Smartphone use can therefore be categorized into two types. The first category is for personal use, including watching TV, playing video games, using social applications, and all other activities that are non-work related. The other one is work-related use such as checking work emails, receiving business calls and attending phone meetings. Previous studies usually focused on the relationship between smartphone use and its consequences, mostly negative, including permeable work-home boundaries (Collins & Cox, 2014; Derks, Duin, Tims & Bakker, 2015), exhaustion (Derks et al., 2014; Derks, van Mierlo & Schmitz, 2014; Elhai, Levine, Dvorak & Hall, 2016), impaired recovery (Derks, ten Brummelhuis, Zecic & Bakker,

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2014), etc. However, previous studies either focus only on work-related smartphone use or overall smartphone use. Therefore, the results cannot explain whether personal smartphone use is also a predictor for employee performance and if so, to what extent it can explain the influence compared to work-related smartphone use.

One mechanism that gives insight on how smartphones influence employees’ work-related outcomes is that they can influence employees’ daily recovery processes (van Hooff, Geurts, Beckers & Kompier, 2011). Daily recovery processes are essential in ensuring employees’ energy levels on the next day. Adequate recovery from job stress requires employees to be mentally detached from work. In other words, psychologically detachment is the key for recovery. For employees who still get access to job-related business in the evening, it is difficult to be totally detached from work psychologically. Danna and Griffin (1999) found that stressful work without psychological detachment in the evening would be detrimental to employees’ well-being. Since daily recovery plays a critical role in maintaining employees’ performance, health and well-being, adequate hours of detachment from work in the evening should be ensured (Sonnentag, 2001). Moreover, Sparks, Cooper, Fried and Shirom (1997) conducted a meta-analysis and concluded that there is a positive link between long working hours and ill-health. Personal smartphone use, on the other hand, differentiates itself from work smartphone use in the sense that, it might be a form of relaxation which serves as an accelerator of recovery processes.

Drawing upon the argument above, it can be seen that the influence of the smartphone depends largely on its usage purposes. When investigating the effects of smartphone use on employee outcome with multiple motivations, what would buffer or strengthen these effects is also worth studying. Two potential indicators, namely self-control and exercise, will be examined in the conceptual model. Self-control is an individual’s ability to regulate emotions and behaviors toward goals in a voluntarily way (Tangney, Baumeister & Boone, 2004). Individuals vary in the amount of self-control they possess. Whether employees with different self-control level will react differently to smartphone use with different purposes is underexplored. Physical exercise, on the other hand, is considered to be effective in reducing depression (Fox, 1999). It then can be assumed that the influence of exercise over employees’ emotions would have effects on employees’ work outcomes as well. Very few studies have

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examined the influence of self-control and exercise on the relationship between smartphone use and employees’ work-related outcomes. Therefore, the aim of this study is to first, shed light on the relationship between smartphone usage for different purpose and its corresponding influence on employees’ work outcomes, including job satisfaction, engagement, productivity and stress. Second, examine the impacts of self-control and exercise on the aforementioned relationship, providing both employers and employees an insight on how to use smartphones “smartly”.

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2. Literature review

2.1 Work smartphone use and employee work outcomes

2.1.1 Role boundary

Each individual plays several roles in different settings. Employees need to perform well to meet job requirements as well as take adequate responsibilities at home. The successful transition between different roles is the key to ensure a good work-life balance. However, easy accessibility to work-related emails and other files through smartphones have blurred the boundaries between work and non-work domain (Derks et al., 2015). Two elements in boundary theory are role flexibility and role permeability (Ashforth, Kreiner & Fugate, 2000). Roles with flexible boundary allow individuals to fulfill their duty at different settings and at various times (Ashforth et al., 2000). For instance, some IT developers can work from anywhere they want while others, like traders, can not fulfill their duty out of the standard setting. Role permeability is the extent to which one role allows an individual to fulfill the role physically while psychologically or behaviorally attached to another role (Ashforth et al., 2000). A working father who is answering a business call at home, while watching his kids playing around, enacts two roles at the same time. Boundary theory indicates that role boundaries are more permeable and flexible among employees who need to reply emails after work hours (Ashforth et al., 2000). Higher flexibility and permeability means that employees have better control over role transition while at the same time, it would cause confusion about which role is the priority at certain setting (Ashforth et al., 2000).

2.1.2 Recovery

Recovery, which is interpreted as the process of resource replenishing, is essential in helping employees restore energy after daily work (Sonnentag & Zijlstra, 2006). Meijman and Mulder (1998)’s effort-recovery model is based on the assumption that effort spent on work is related to various load reactions. Load refers to a threating interruption of physiological system balance caused by task performance. To ensure employees’ health, load reactions should go back to pre-work level with the help of off-work relaxation and psychological detachment. However, continuous consumption of efforts would impede the recovery mechanism which ultimately impairs individual’s health and happiness. Empirical researches

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have found proof that social activities and leisure activities that require low effort are effective in helping employees be mentally detached from work (Sonnentag, 2001, Fritz & Sonnentag, 2005). Studies also showed that off-work leisure time has positive effects on employees’ working experience the subsequent day (Westman & Etzion, 2001; Sonnentag, 2003).

The introduction of electronic devices for both work and personal usage has substantial impact on employees’ role boundary and recovery processes. The development of technology has facilitated human-beings’ communication. Among technological devices, smartphones are dominating the market, with an estimated user number of 2.6 billion by 2017 (Kissonergis, 2015). People use smartphones for recreation, communication as well as work. The introduction of smartphones not only enriched human being’s entertainment but also increased employees’ work flexibility by means of instant messaging and email checking (Rood, 2005). Pica and Kakihara (2003) found that smartphone use increased collaboration as well as interaction between colleagues. Allen and Shockley (2009) also pointed out that the introduction of smartphones made it possible for employees to fulfill responsibilities from different roles at the same time. Therefore, the entertainment and connectivity functions of smartphones have positive effects on employees’ recovery.

Nevertheless, other scholars reached completely different conclusions. Firstly, work-related smartphone use blurred the boundaries between work and private life (Schieman & Young, 2013). Derks and Baker (2014) found that compared to employees who did not use smartphones to deal with work-related business, those who did were more likely to experience burnout. Derks and Baker (2014)’s study implies that connecting to job during off-work hours had influence on employee’s recovery. It could be explained through role boundary theory (Meijman and Mulder,1998) and conservation of resources theory (Hobfall, 2001) that limited resources, in this case, energy and effort, fail to meet the demands of both work and family at the same time. Furthermore, the use of smartphones might also imply that people feel that they have unfinished work which would impede employees’ recovery mechanism, making employees feel depleted and exhausted.

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2.1.3 Job satisfaction

When examining the consequences of smartphone use on employee’s work-related performance, most researches focus on the relationship between work-related smartphone use and its negative consequences (Collins & Cox, 2014; Derks et al., 2015; Derks & Bakker, 2014; Derks et al., 2014; Elhai et al., 2016). However, very few studies have examined the influence of smartphone use for different purposes separately. Nowadays, companies are facing employee turnover problems worldwide since the labor market is highly competitive (Ramlall, 2004). Employee turnover is defined as an employee’s voluntary action of ending a work relationship with an employer (Hom & Griffeth, 1995). High employee turnover rate is harmful to organizational performance (Koys, 2001; Ton & Huckman, 2008). Therefore, it is of great importance to find ways to decrease employee turnover.

One importance factor that can influence employee turnover is employees’ job satisfaction (Mobley, 1977; Arnold & Feldman, 1982). Job satisfaction is best understood as an attitude in which an employee evaluates his or her work situations and experience (Howard & Kelsey, 2015). It began to gain research popularity and attention since 1930s (Locke, 1969). Psychologists found it useful to build concepts that illustrate how people think and feel about their work experience (Judge, Weiss, Kammeyer-Mueller & Hulin, 2017). Job satisfaction can be conceptualized in overall satisfaction and facet satisfaction (Judge et al., 2017). Overall satisfaction reveals one’s favorability over a job as a whole while facet satisfaction indicates one’s favorability over a certain aspect of the job, for instance salary, career development and relationship with colleagues (Judge et al., 2017). Overall satisfaction can be interpreted as the aggregation of facet satisfaction. Therefore, in this article, job satisfaction refers to the employees’ overall viewpoint about their job, measured on a continuum from negative to positive (Judge et al., 2017).

Judge, Thoresen, Bono and Patton (2001) reviewed 301 studies about the relationship between job satisfaction and employee performance. The result showed a positive correlation (r=.30). The correlation is even higher among jobs with high complexity. Lambert, Hogan and Barton (2001) found that job satisfaction is a strong predictor of employees’ turnover intent. Understanding the antecedents of job satisfaction is of salient value in order to increase employee performance and reduce turnover. Using smartphones to deal with work during

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off-work hours not only increased employees’ workload but also occupied their resources, such as time and energy, that could have been allocated to their personal lives. Schieman & Young (2013) discovered that work communication during non-work hours is accompanied with sleep trouble, high levels of distress and work-home conflict. Work-home conflict results from incompatible role pressure from work to the home domain (Kahn, Wolfe, Quinn, Snoek & Rosenthal, 1964). The work-home interference as well as dysfunction of recovery process caused by work-related smartphone use are considered to diminish employees’ positive feeling thus negatively influence their perception of job experience. Therefore, work-related smartphone use would have an overwhelming negative effect over positive ones in influencing employee job satisfaction. Therefore, it is proposed that:

H1 Work-related smartphone use is negatively related to employees’ job satisfaction.

2.1.4 Employee engagement

One definition of engagement comes from burnout literature. Burnout scholars proposed that employee engagement is the positive antipode of burnout. In Maslach, Jackson and Leiter (1997)’s definition, employee engagement is conceptualized as the employees’ involvement, energy and efficacy in their work, which is opposed to the three burnout dimension, exhaustion, cynicism and inefficacy. González-Romá, Schaufeli, Bakker & Lloret (2006) tested the proposal and found out that the dimensions of burnout and engagement can be seen as opposite, which provides support for Maslach et al. (1997)’s definition. However, rather than treating burnout and engagement as two opposite poles, it’s better to consider them as two independent yet negatively correlated states of mind since positive and negative affects are not opposite dimension but independent states (Schaufeli &Bakker, 2004; Russell & Carroll, 1999).

Developed from Maslch et al. (1997)’s definition, Roberts and Davenport (2002) conceptualized employee engagement as enthusiasm and involvement in the job. This involvement can be seen as an investment of one’s complete self into a job role (Rich, Lepine & Crawford ,2010). Their definition is focused on the motivation perspective, arguing that highly engaged employees are intrinsically motivated to work harder. The most cited definition which also emphasizes motivation is by Schaufeli, Salanova, González-Romá and Bakker (2002, p.74). They define engagement as “a positive, fulfilling, affective-motivational state of

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work-related well-being that is characterized by vigor, dedication, and absorption”. Like job satisfaction, the concept of employee engagement also has positive valence (Warr &Inceoglu, 2012). However, it incorporates two more components, that are, energy and enthusiasm. Kahn (1990) found that engaged employees are more likely to exert effort to overcome difficulties, even though the process is energy consuming. The combination of burnout literature and motivational perspective provides a comprehensive understanding of the essence of engagement.

The functions of employee engagement in organizational performance have triggered scholars’ interest over the years. Studies have found evidence that employee engagement is positively related to organizational performance, including task performance, organizational citizenship behavior (OCB), financial returns and positive work attitude (Roberts & Davenport, 2002; Bakker, 2011; Rich et al., 2010; Xanthopoulou, Bakker, Demerouti & Schaufeli, 2009; Saks, 2006). Although employee engagement is an individual level construct, it influences organizational outcomes by means of accumulated individual attitudes, motivations and behaviors (Saks, 2006). There are three ways in which engaged employees contribute to organizational performance. They promote the organization to internal and external stakeholders, stay longer in the organization and exert extra effort to achieve organizational goals (Markos & Sridevi, 2010; Baumruk, 2006). While the benefit of improving employee engagement is obvious, employee disengagement is detrimental to organization’s business outcomes. Employees who are not engaged in work have higher risk to exhibit inefficient and ineffective work behaviors, feel less loyal to the organization and are less likely to take initiative to drive organizational changes (Markos & Sridevi, 2010).

Considering the significance of employee engagement, scholars have made efforts to understand what drives employee engagement. One conceptual model that lays the theoretical foundation of engagement drivers is the job demand and resources model, namely, JD-R model (Demerouti, Bakker, Nachreiner& Schaufeli, 2001; Mauno, Kinnunen & Ruokolainen, 2007). Based on JD-R model, job demands are the characteristics of a job that consume employees’ physical as well as psychological efforts, while job resources are the job characteristics that enable employees to accomplish goals, decrease job demands and advance in their career development (Demerouti et al., 2001). While job demands increase strain

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reactions (e.g., stress), resource scarcity would impede accomplishment which eventually results in negative feeling (e.g., frustration, demotivation) (Mauno et al., 2007). In the burnout view of engagement, to meet job demands, employees need to invest efforts that can in turn increase the possibility of energy drain out (Crawford, LePine & Rich, 2010). Therefore, job demands have direct influence over depletion, which is the opposite of engagement. Secondly, in the motivational view, employees who possess more job resources have higher chance to achieve their goals which in turn will increase their willingness to be more involved and dedicated at work (Crawford et al., 2010). Another antecedent of employee engagement is the support that employees perceive in the workforce (Saks, 2006; Markos et al., 2010). According to social exchange theory (SET), where obligations and reciprocal relationship is generated from interactions between parties, employees who perceive higher support from the organization or work-related relationship (relationship with co-worker or leadership, for instance) would feel more obliged to work hard to repay the resources and the support obtained from the organization (Cropanzano & Mitchell, 2005; Saks, 2006).

The use of smartphones after work influences not only job demands but also job resources. On the one hand, the connectivity of smartphones enables employees to use more job resources, namely time here, to solve problems and cope with job requirements. Employees thus are more likely to feel relaxed and confident the other day at work since their information is up to date and they can base the information to arrange their job resources in an efficient way. However, the extra use of time and effort at work would also be at the cost of increased employee fatigue and tiredness. On the other hand, employees who need to work during non-work hours need to cope with more job demands. They would perceive higher job demands that would cause psychological as well as physical depletion. Furthermore, based on effort-recovery model, continuous consumption of efforts by means of working overtime would impede recovery processes (Meijman & Mulder, 1998). Employees who still use smartphones for work during off-work hours make more efforts and have less time for relaxation. Therefore, they have higher risk of experiencing work-life interference as well as impaired recovery processes. Therefore, it is proposed that:

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2.1.5 Productivity

Employee productivity is described as the contribution to corporate goals per resources consumed (Bain, 1982). It can be measured both by time and resources expenditure and by quality of work delivered (Sutermeister, 1963). Checking work-related emails and documents regularly in off-work hours will increase employees’ productivity by means of lowering stress and increasing the feeling of coping (Yun et al., 2012; Barley, Meyerson & Grodal, 2011). According to JD-R model, employees who have the opportunity to utilize their skills and make decisions with autonomy cope better with job demands. Therefore, they would experience less strain from work. Employees who deal with work-related business after office hours get more information about job demands and have the autonomy to allocate and schedule time to accomplish the job efficiently.

However, using smartphones after work hours may come at the expense of poor recovery (Binnewies, Sonnentag & Mojza, 2009). Park, Fritz and Jex (2011) found that segmentation between work and non-work roles is positively related to employees’ psychological detachment and recovery experience. Work-related smartphone use after work would engage employees in work roles thus that impede employees’ recovery processes from job demands. The ambiguous boundary between work and non-work roles for employees who still use smartphones for business caused imbalance and conflict between work and personal life. It has been tested that work life imbalance leads to higher levels of absenteeism, less work engagement and lower employee job satisfaction (Ernst Kossek & Ozeki, 1998). All these factors are indicators of low productivity. HR policies that promote off-work email checking and employee availability would be perceived as intents to over-control employees in their spare time and to deprive them from job autonomy. According to self-determination theory (SDT), autonomy, relatedness and competences are three basic psychological needs that motivate individuals to initiate behaviors that are beneficial for an individual’s health and happiness (Ryan & Deci, 2000). When employees perceive less job autonomy, demotivation would be inevitable. By engaging in work-related business during office off-hours, employees would experience higher job demands, which are sources of burnout (García‐Sierra, Fernández-‐Castro & Martínez-‐Zaragoza, 2016). Furthermore, when employees perceive the company to be immoral in the way it treats its employees, they are more likely to conduct

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counterproductive work behaviors such as cyber loafing and absenteeism. When employees choose to be absent or to use work time for non-job related tasks, productivity decrease would be unavoidable. Considering the arguments above, it is proposed that:

H3 Work-related smartphone use is negatively related to employees’ productivity.

2.1.6 Stress

The term “stress” used in different studies has inconsistent meanings. By arguing that the definitions of stress conceptualized by different scholars share some communalities, Cohen, Kessler and Gordon (1995) integrated different concepts and defined stress as the result of environmental demands exceeding a person’s capacity for adaptation. Their definition emphasizes on capacity, arguing that the exceeding of capacity would trigger employees’ stress reactions that might cause employees’ health problems. While Cohen et al. (1995)’s definition focuses more on the environmental contexts, Ganster and Rosen (2013) however, pointed out that stress concerns not only how the external environment acts on individuals, but also how individuals respond to certain external stimulations and how those two factors interact (Kahn & Byosiere, 1992). The definition emphasizes both the occurrence of stress, which is called “stressor”, and individual’s response to the stress, which is called “stress response” (Cohen, Janicki-Deverts & Miller, 2007). In this thesis, the focus is on how employees feel about stress rather than how they act on it.

Many studies have shown that work stress is associated with employees’ health problems, including heart disease, headaches, mental disease and cancer (DeLongis, Folkman & Lazarus, 1988; Chandola et al., 2008; Cohen et al., 2007; Keller et al., 2012). Cohen et al. (1995) pointed out that stress influences health by means of triggering individuals’ negative feelings (e.g., depression, anxiety, anger). Work-related smartphone use may increase employees’ workload. Thus, employees may have higher risk of experiencing role overload. Role overload occurs when employees feel overwhelmed by the time and ability limitations to fulfill responsibilities and activities expected of them (Rizzo, House & Lirtzman, 1970). Work-related smartphone use increases employees’ workload and occupies their personal time to finish work that is supposed to be finished within work hours. In the meanwhile, allocating the time to business means that employees would have less time to fulfill their roles in the family, which would cause a vicious cycle. Moreover, according to Kahn et al. (1964),

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stress is transferable across different domains of an individual’s life. The stress an employee experiences from work also has an influence on his or her personal life. Therefore, it is proposed that:

H4 Work-related smartphone use in the evening is positively related to employees’ stress.

2.2 Personal smartphone use and work outcomes

When it comes to personal smartphone use, the influence might be totally different. Personal smartphone use includes social networking with friends and family through social media, watching movies and news, playing games, etc. Today’s smartphone not only has the basic function of calling, but also works as TV, camera, videogame console, music player, etc. The multi-functional device has gain great popularity in recreational activities over the years. Recreational activities serve as a way of relaxation, which would help increase employees’ well-being level. As Sonnentag (2001) mentioned, low effort activities are beneficial in helping individuals recover form work. Playing games, watching TV programs are recreational activities that consume little effort. Good recovery would ensure employees’ energy and positive mood as well as replenish resources the subsequent day. Ilies and Judge (2002) found that mood influences employees’ job satisfaction greatly. Rothbard and Wilk (2011) found a positive relationship between employees’ mood in the beginning of the workday and employees’ performance. By keeping a good work-life balance and relaxing adequately, employees are more likely to report higher job satisfaction, engagement and productivity. Therefore, the following hypotheses are proposed:

H5 Personal smartphone use during off-work hours is positively related to employees’ job satisfaction.

H6 Personal smartphone use during off-work hours is positively related to employees’ engagement.

H7 Personal smartphone use during off-work hours is positively related to employees’ productivity.

At the same time, the availability and connectivity of smartphones may increase employees’ social capital by means of improving individuals’ interpersonal interactions (Yang, Kurnia & Smith, 2011). Here, social capital is conceptualized as the resources

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possessed by individuals to facilitate cooperation and communication (Kawachi, 2006). Brislin and Kim (2003) found that social support can be a buffer between stress and disease. Fujiwara and Kawachi (2008) found that social capital is significantly related to individual health. Employees’ concerns about their individual health may be a cause of life stress, which may influence their work experience. Furthermore, many employees nowadays live far away from their intimate social contacts. Using smartphones to connect with family members may help employees buffer the effects of stressors and gain social support. Social support is conceptualized as information that make individuals feel that they are loved and belong to a network with mutual obligations (Cobb, 1976). Viswesvaran, Sanchez and Fisher (1999) summarized that social support not only reduces the subject’s perceived stressor but also reduces the strain that they are experiencing. Social support is also effective in reducing role overload, which is a main cause of work stress, indicating that social report will release employees’ stress burden (Marcelissen, Winnubst, Buunk & de Wolff, 1988). Given the arguments above, it is proposed that:

H8: Personal smartphone use is negatively related to employees’ stress.

2.3 The influence of self-control and exercise

2.3.1 Self-control

According to McGonigal (2011), self-control refers to desire suppression, temptation resistance and emotion expression. MaGonigal (2011)’s definition emphasizes how individuals regulate their emotions and behaviors. Barkely (1997) argued that people exerting self-control for the purpose of increasing long term benefit. People with high self-control are deemed to have higher possibility to achieve their goals because they adhere to rules and regulations (Gailliot et al., 2007). Many studies show that self-control is associated with positive life outcomes (Galla & Duckworth, 2015), healthy diet habits (Kahan, Polivy & Herman, 2003), less crime (Gottfredson & Hirschi, 1990) and less risk taking behavior (Fischer, Kastenmüller & Asal, 2012).

Among the theories of self-control, ego depletion is the most popular one. Ego depletion theory has been introduced by Baumeister, Bratslavsky, Muraven and Tice (1998). By conducting 4 experiments, they concluded that resources that enable individuals to exert self-control is limited. Gailliot et al. (2007)’s study supported ego depletion by finding a clear

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linkage between self-control and glucose consumption. They found that exerting self-control will lead glucose, which is an energy source, to drop below optimal level, and influence individuals’ subsequent self-control actions. Subsequent studies also found that the depletion of self-control is related to impaired task performance, perceived fatigue and subjective difficulty (Muraven & Baumeister, 2000; Hagger, Wood, Stiff & Chatzisarantis, 2010).

While plenty of studies have shown that self-control is a limited source, some studies, however, pointed out that self-control is not a finite resource. Carter, Kofler, Forster, and McCullough (2015)’s meta-analysis found very little evidence on the existence of ego depletion theory. Inzlicht, Schmeichel and Macrae (2014) criticized the resources based model of self-control because most researches did not observe the resources depletion directly. Furthermore, Job, Dweck and Walton (2010) found that instead of depleting resources, finishing a demanding job only influences people’s perception about the availability of later self-control.

Employees who possess high self-control ability are considered to have superior social interactions and interpersonal skills (Tangney et al., 2004). Social interactions increase employees’ social capital, which is beneficial for their career success (Seibert, Kraimer & Liden, 2001). By making efficient and effective use of time, they will be able to cope with job demands better. For instance, they would stick to the schedule and finish everything in time to prevent spending too much time at work in the evening. Employees with high self-control can resist temptation for long term interests. They make more efforts and stay focused in achieving their goals (Kugelmann, 2013). With the capability to resist temptation, they can set clear boundaries between work and personal life. Even when there is need to check work-related business after work, they would probably follow a minimum principle to solve important and urgent tasks and leave the rest to work hours. Furthermore, employees who possess a higher level of self-control are less prone to smartphone addiction, which often leads to depression, problematic sleep and anxiety (Demirci, Akgönül & Akpinar, 2015). Therefore, it is proposed that:

H9 The relationship between work-related smartphone use and employees’ job satisfaction is moderated by employee’s self-control level, thus that the negative relationship would be weaker.

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H10: The relationship between work-related smartphone use and employees’ engagement is moderated by employees’ self-control level, thus that the negative relationship would be weaker.

H11 The relationship between work-related smartphone use and employees’ productivity is moderated by employees’ self-control level, thus that the negative relationship would be weaker.

H12 The relationship between work-related smartphone use and employees’ stress level is moderated by employees’ self-control level, thus that the positive relationship would be weaker;

H13 The relationship between personal smartphone use and employees’ job satisfaction is moderated by employees’ self-control level, thus that the positive relationship would be stronger.

H14 The relationship between personal smartphone use and employees’ engagement is moderated by employees’ self-control level, thus that the positive relationship would be stronger.

H15 The relationship between personal smartphone use and employee’s productivity is moderated by employee’s self-control level, thus that the positive relationship would be stronger.

H16 The relationship between personal smartphone use and employees’ stress level is moderated by employees’ self-control level, thus that the negative relationship would be stronger.

2.3.2 Exercise

While self-control possessed by an individual is more or less stable over a period of time, physical exercise, however, can fluctuate. Physical exercise is physical activities that serve to maintain or improve individuals’ physical fitness (Caspersen, Powell & Christenson, 1985). As a subset of physical activities, physical exercise consumes energy. Some research has shown that physical exercise not only has physiological influence, but also has psychological influence on individuals (Hassmen, Koivula & Uutela, 2000).

Studies about the exercise’s physiological outcomes are mostly focused on obesity, cancer, chronic disease and premature death (Penedo, & Dahn, 2005; Fox & Hillsdon, 2007;

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Lynch, Neilson & Friedenreich, 2010; Warburton, Nicol & Bredin, 2006). Pretty, Peacock, Sellens and Griffin (2005) found that exercise has positive influences on individuals’ health, especially when the exercise takes place in a natural environment. Evidences also show that exercise not only helps individuals prevent cancer, but also benefits patients both during and after treatment (Batty & Thune, 2000; Knols, Aaronson, Uebelhart, Fransen & Aufdemkampe, 2005). Grilo (1994)’s study supported the idea that exercise is effective in improving individuals’ health by means of weight control as well as in enhancing their psychological functioning. The results of aforementioned studies indicate that exercise is effective in increasing individuals’ health level.

Despite its positive influence on physiological health, regular and proper exercise have also been proven to be effective in improving individuals’ mental health (Weyerer & Kupfer, 1994; Landers & Arent, 2001). Physical exercise is effective in reducing depression (Fox, 1999). Ross and Hayes (1988) found out that exercise has anti-anxiety effects on individuals. Hassmen, Koivula and Uutela (2000) studied the relationship between exercise and individuals’ psychological well-being and discovered that those who exercised two or more times per week not only experienced less stress, anger and distrust but also reported better fitness self perception. Therefore, exercise’s influence on individuals’ health is positive both psychologically and physically.

While the effects of exercise are mostly being studied in the clinical field, few studies linked physical exercise in the evening with work-related outcomes. Firstly, exercise has anti-depressant effect (Byrne & Byrne, 1993). Employees who need to work during non-work hours may experience negative feelings such as anxiety, depress and sadness. By exercising, the negative effects might be mitigated. Therefore, employees’ evaluation about work experience would be less negative and they might be more tolerant to work during off-work hours. Secondly, physical activities can facilitate recovery process that would eventually ensure employees’ energy level (Sonnentag & Natter, 2004). Adequate energy will ensure that employees have enough resources to cope with job demands. Thus, there would be less strains resulting from employees’ work experience. The combination of relaxation and exercise might create synergies to ensure that employees have both the energy and the willingness to devote themselves at work the next day. Therefore, it is proposed that:

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H17 The relationship between work-related smartphone use and employees’ job satisfaction is moderated by daily physical exercise, thus that the negative relationship would be weaker.

H18 The relationship between work-related smartphone use and employees’ engagement is moderated by daily physical exercise, thus that the negative relationship would be weaker.

H19 The relationship between personal smartphone use and employees’ job satisfaction is moderated by daily physical exercise, thus that the positive relationship would be stronger.

H20 The relationship between personal smartphone use and employees’ engagement is moderated by daily physical exercise, thus that the positive relationship would be stronger.

At the same time, depression also has major effects on employee’s performance (Lerner & Henke, 2008). It is found that employees who experience higher level of depression have higher risk to encounter mental-interpersonal problems as well as time management problems (Adler et al., 2006). Furthermore, regular exercise help employees stay energetic and be confident both for their personal and business life. Therefore, the influence of exercise on the relationship between smartphone use and employees’ work outcomes is two-fold. Firstly, exercise’s anti-depression effects might mitigate the negative feelings resulting from work-related smartphone use after work. Secondly, the recovery quality improvement and energy increase may increase employees’ resources for achieving a good performance. Thus, the following hypotheses are proposed:

H21 The relationship between work-related smartphone use and employees’ productivity is moderated by daily physical exercise, thus that the negative relationship would be weaker.

H22 The relationship between personal smartphone use and employees’ productivity is moderated by daily physical exercise, thus that the positive relationship would be stronger.

Exercise can also be seen as a way to release pressure. Since regular exercise can increase muscle mass and resistance to stressors, employees who exercise adequately would

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have higher capability to handle stress as well as release them to a healthy level (Radak, Chung, Koltai, Taylor & Goto, 2008). Moreover, exercise helps employees to relax, reduce fatigue and recover, which are also useful in mitigating the stress associated with smartphone usage for work during off-work hours as well as strengthening the influence of personal smartphone use on employee work outcomes. Therefore, the following hypotheses are proposed:

H23 The relationship between work-related smartphone use and employees’ stress level is moderated by employees’ daily physical exercise, thus that the positive relationship would be weaker;

H24 The relationship between personal smartphone use and employee’s stress level is moderated by employee’s daily physical exercise, thus that the negative relationship would be stronger.

Figure 1 Conceptual model

As shown in Figure 1, this thesis builds a conceptual model to analyze the influence of work-related and personal smartphone use on employees’ job satisfaction, engagement, productivity and stress. The relationships in the model will be examined later on.

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3. Method

3.1 Participants and procedure

94 employees who work at least 4 days per week in the Netherlands were approached during the study. The large target population and access difficulty is the main driver for the use of convenience sampling technique to approach the potential participants. The main source of participants were friends, family members of the researchers, or people recommended by the researchers’ acquaintances. Considering the international labor market in the Netherlands, the languages of the questionnaires were English and Dutch. The translation was checked by three researchers and one supervisor to ensure the accuracy and quality. Coupons were used to motivate participants to fill in as many surveys as possible. The more surveys participants finished, the higher chance they would win a coupon. The sample consisted of 43 men (51%) and 41 women (49%). 72 participants (86%) finished the survey in Dutch and the remaining 12 participants (14%) finished in English. Participants were employed in different sectors, including manufacturing, banking, healthcare, etc.

Firstly, a one-time survey was conducted to measure demographic variables and self-control and 88 valid responses (response rate: 94%) were received. One week later, a diary study in which participants needed to fill in a morning and an afternoon survey over 10 consecutive working days was administered. In the diary survey, employees’ exercise time, smartphone use time, experienced stress level, job satisfaction, engagement and productivity were measured. The morning survey, which measured employees’ smartphone use time, exercise time, engagement, stress and productivity in the morning, was sent at 11 am. And the afternoon survey was sent at 4 pm to assess employees’ stress, engagement and productivity in the afternoon and employees’ job satisfaction. The average duration between morning and afternoon surveys was 5.25h with a standard deviation of 1.7h. 570 morning responses and 550 afternoon responses were received during the study; the final response rate was 60%.

3.2 Measurement

Smartphone use

Whether smartphone use with different purposes will influence employees’ work on the next day differently is the interest of this study. Therefore, to measure work-related

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smartphone use, the measurement from Lanaj et al. (2014) was adopted. The following item is used: “How many minutes did you use your smartphone for work after 9 PM last night?”. To distinguish personal smartphone use from the aforementioned work-related smartphone use, the following question was added: “How many minutes did you use your smartphone for private purposes after 9 PM last night?”

Exercise

Exercise was measured with one item adopted from Hassmen et al. (2000). Instead of asking about frequency, the study is more focused on the time spent on exercise after work. So the question was “Yesterday after work, how many minutes did you spend doing exercise?”

Self-control

Self-control was measured in the one-time survey with a four-item subscale (Cronbach’s α = .714) of the 13-item self-control scale developed by Tangney et al. (2004). Respondents indicated to which extent they agreed with the statements using a 5 Likert scale with 1 representing “completely disagree” and 5 representing “completely agree”. The statements were :1) I am good at resisting temptation; 2) I have a hard time breaking bad habits (reverse-scored); 3) I wish I had more self- discipline (reverse-scored); 4) People would say that I have iron self-discipline.

Job satisfaction

Employee’s job satisfaction was assessed by asking participants to indicate to what extent they agreed with the statement “I feel satisfied with my current job”. 5 scale Likert scale with 1 indicating “completely disagree” and 5 indicating “completely agree” was used to measure the responses.

Employee engagement

Adopted from Schaufeli, Bakker and Salanova (2006), 3 items (average α=.755 for the morning and α=.782 for the afternoon across the 10 days) were used to measure employee engagement. Participants indicated their agreement (5-point Likert scale with 1 representing “completely disagree” and 5 representing “completely agree”) with each statement related to their work engagement in the morning and in the afternoon separately, for the period of 10

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working days. The statements were: 1) Today, time flew when I was working; 2) Today while working, I forgot everything else around me; 3) Today, I was immersed in my work.

Employee productivity

Employee productivity was assessed by using Griffin, Neal, and Parker (2007)’s 3-item (average α=.8 for the morning and α=.736 for the afternoon across the 10 days) individual task proficiency scale. Participants indicated their agreement (5-point Likert scale with 1 representing “completely disagree” and 5 representing “completely agree”) with each statement related their productivity at work in the morning and in the afternoon separately, for the period of 10 working days. The questions were: 1) Carried out the core parts of your job well; 2) Completed your core tasks well using the standard procedures; 3) Ensured your tasks were completed properly.

Stress

A 3-item (average α=.716 for the morning and α=.777 for the afternoon across the 10 days) scale that was adapted from a 4 item scale developed by Motowidlo, Packard and Manning (1986) was used to measure employees’ stress level. Employees were asked to indicate to what extent they agreed with the statements using a 5 Likert scale with 1 representing “completely disagree” and 5 representing “completely agree”. The statements were: 1) My job was extremely stressful; 2) I experience a lot of stress because of work; 3) I felt hardly stressed because of work (reverse-scored).

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4. Results

Data collected through the two questionnaires were combined correspondingly and analyzed through IBM SPSS version 24. Counter-indicative items were recoded, followed by steps to test the scale reliability. Consistent with Field (2013)’s recommendation, no changes are needed for the current item composition since all scales’ Cronbach’s alpha coefficient are above 0.7 and reducing any of the items would not improve their reliability significantly.

To test if gender and survey language have any influence on employees’ use of smartphones, a one-way ANOVA test was conducted. For personal smartphone use, female employees (m=33.15) tend to spend more time on personal smartphone use than male employees (m=22.70) in the sample. The difference of personal smartphone use between employees who filled in the survey in Dutch and those who filled in English was not significant. Interestingly, men spent almost double of the time using smartphone for work(m=9.88) compared to women (m=5) while non-Dutch employees spent nearly as much as three times of minutes using smartphone for work (m=17.15) compared to Dutch employees (m=5.8). However, no significant differences in job satisfaction, engagement, productivity and stress were found between gender and survey language groups.

4.1 Descriptive statistics

Descriptive statistics and correlations for the variables are listed in Table 1. The highest within-variable is between morning engagement and afternoon engagement and the highest between-variable correlation is between engagement and productivity, which are still lower than 0.7, indicating that none of the variables need to be removed from the model. The data in Table 1 indicate that employees in the sample on average spend 7.27 minutes on work-related smartphone use, 28.31 minutes on personal smartphone use and 17.24 minutes on exercise. Employees in the sample also showed medium to high level of self-control (m=3.14), job satisfaction (m=3.78), engagement (m=3.20 for the morning and m=3.22 for the afternoon), productivity (m=3.69 for the morning and m=3.70 for the afternoon) and rather low level of stress (m=2.30 for the morning and m=2.38 for the afternoon) over the period of the study.

The data are treated as hierarchical data with two levels. The first level (n=570 for the morning and n=550 for the afternoon) is day level variable and the second level is employee

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(n=84) level. To examine whether the variance is from within-person (Level 1) or between-person (Level 2), a null model using mixed linear technique was tested in SPSS. The result is shown in Table 2. Recommended by Field (2013), group center mean technique was used to deal with level one variable including work-related smartphone use time, personal smartphone use time and exercise. For self-control (level 2 variable), grand center mean technique was used. Group centering removes the between-level influence and focuses on the difference in level 1 (Field, 2013). As can be seen in Table 2, 59%, 64% and 81% of the variance of work-related smartphone use, personal smartphone use and exercise respectively were attributable to within-person variations. For engagement, the level 1 variance is 75% for the morning and 55% for the afternoon. For productivity, the numbers are 80% and 70% for the morning and afternoon respectively. For stress, 72% of the variance in the morning is caused by with-person variation while in the afternoon, the number is 63%. For job satisfaction, however, there are more between-person variance (75%) than within-person variance (25%), indicating that employees’ job satisfaction is less fluctuating.

Table 1 Mean, SD and correlation

Note: N at level 1= 570 for the morning variables, N at level 1=550 for the afternoon variables, N at level 2=84. Statistical significance: *p <.05; **p <.01; ***p <.001.

Table 2 Null model

Note: N at level 1= 570 for the morning variables, N at level 1=550 for the afternoon variables, N at level 2=84. Statistical significance: *p <.05; **p <.01; ***p <.001.

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4.2 Hypotheses testing

In testing the hypothesis, no meaningful relationship was found between smartphone use and employees’ engagement, productivity and stress in the afternoon. Therefore, this part will only show the results of the dependent variables in the morning.

In Hypotheses 1 and 5, the proposals state that work-related smartphone use would be negatively related to job satisfaction while personal smartphone use would have the opposite effect. These two hypothesis were tested using mixed linear regression in SPSS. The result is shown in Table 3.

Table 3 Predictor and interaction model for job satisfaction

Job satisfaction

Predictor Interaction

B SE B SE

Intercept 3.8298** .0845 3.8285** 0.0782

Work-related smartphone use -.0010 .0012 .0001 .0015

Personal smartphone use .0006 .0009 .0010 .0010

Exercise -.0002 .0008

Self-control .3947** .1131

Work-related smartphone use * Exercise -.0001 .0001 Work-related smartphone use * Self-control -.0025 .0022

Personal smartphone use * Exercise .0001 .0000

Personal smartphone use * Self-control .0013 .0012

-2 Log Likelihood 741.3447 725.1717

No significant result is found in the model. Therefore, the null hypothesis cannot be rejected. In other words, the evidence is not adequate to support the assumption that there is relationship between work-related smartphone use and employee job satisfaction as well as that of personal smartphone use and job satisfaction cannot be confirmed. After the predictor only model, an interaction model was conducted to test the influence of exercise and self-control on the aforementioned relationships. The model showed a significant improvement over the predictor model, ∆-2x log=16.17, df=6. In the interaction model, only

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self-control has significant influence over job satisfaction (β=0.3947, SE=0.1131, p<0.01) and there was no significant interaction effect found in this model. Therefore, no evidence was found for hypotheses 1, 5, 9, 13, 17 and 19.

Then, the influence of smartphone use with difference purposes over employees’ engagement was examined. The results are shown in Table 4.

Table 4 Predictor and interaction model for employees’ engagement

Employees’ engagement

Predictor Interaction model

Estimate SE Estimate SE

Intercept 3.1948** .0519 3.1921** .0519

Work-related smartphone use 0.0029* .0014 0.0032* .0015

Personal smartphone use -.0004 .0011 -.0005 .0011

Exercise .0007 .0009 .0004 .0009

Self-control .0312 .0752 .0307 .0751

Work-related smartphone use * Exercise -.0001 .0000 Work-related smartphone use * Self-control -.0001 .0026

Personal smartphone use * Exercise -.0001 .0001

Personal smartphone use * Self-control -.0005 .0015

-2 Log Likelihood 1046.5414 1043.2177

Despite the fact that employees use smartphone for personal purpose (m=28.31) almost 4 times as much as for work (m=7.25), significant relationship was only found between work-related smartphone use and engagement (β =0.0029, p<0.05). Opposed to the hypothesis, the relationship between work-related smartphone use and employees’ engagement found in this model was positive. In the interaction model, there was no significant interaction effect found but the introduction of the two moderators improved the correlation (∆β=.0003) between work-related smartphone use and employee engagement. Besides, the interaction model did not significantly improve the predictor only model (∆-2x

log=3.32, df=6). Therefore, it can be concluded that hypotheses 2, 6, 10, 14, 18 and 20 were

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Multi-level analyses are shown in Table 5 in testing hypotheses that are related to employees’ productivity. Surprisingly, no significant result was found, indicating that no evidence was found at .05 level to support the existence of main effects. In the interaction model, it was found that the interactions were not significant. However, the introduction of self-control and exercise increased the regression weight (β from .0025 to .0028) for the relationship between work-related smartphone use and employee productivity to become significant. Self-control is also positively related to productivity (β=0.1182, SE=0.0487,

p=0.017). The results gave partial support to hypothesis 3, but rejected the hypotheses 7, 11,

15, 21 and 22.

Table 5 Predictor and interaction model for employees’ productivity

Productivity

B SE B SE

Intercept 3.6825** .0353 3.6805** 0.0339

Work-related smartphone use .0025 .0013 0.0028* .0014

Exercise -.0002 .0008

Self-control 0.1182* .0487

Work-related smartphone use * Exercise .0000 .0000 Work-related smartphone use * Self-control -.0012 .0024

Personal smartphone use * Exercise -.0001 .0000

Personal smartphone use * Self-control .0014 .0013

-2 Log Likelihood 883.2153 872.3241

The same procedure was followed to test the hypotheses that are related to employees’ stress level. Multi-level analyses are shown in Table 6. In the predictor model, there was no significant result. In the interaction model, it was found that the interaction effect of self-control on the relationship between work-related smartphone use and employee stress was significant (β=.0068, SE=.0028, p=.016). Furthermore, the interaction model significantly improved the model fit with ∆-2x log=11.02 and df=6.

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Table 6 Predictor and interaction model for employees’ stress

Stress

B SE B SE

Intercept 2.3234** .0486 2.3254** .0480

Work-related smartphone use .0024 .0016 .0010 .0017

Exercise -.0009 .0009

Self-control -.0885 .0692

Work-related smartphone use * Exercise .0000 .0001 Work-related smartphone use * Self-control 0.0068* .0028

Personal smartphone use * Exercise .0001 .0001

Personal smartphone use * Self-control -.0011 .0016

-2 Log Likelihood 1114.4346 1103.4101

Figure 2 Interaction plot

Figure 2 shows the interaction plot. It can be inferred that the effect of work-related smartphone use on stress is not significant (β=-.0058, p=.117) for employees who have low self-control. However, for employees with high self-control (β=.0078, p=.005), the stress level increases with the increase of smartphone usage time. Therefore, it can be concluded

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that only the work outcomes of employees with high self-control will be influenced by employees’ work-related smartphone use.

Figure 3 Conceptual model with results

To summarize, main effects were found between work-related smartphone use and employees’ engagement, self-control and job satisfaction as well as self-control and employees’ productivity. In the meanwhile, self-control was found to have moderation effect on the relationship between work-related smartphone use and employee stress. When spending more time on work-related smartphone use, high self-control employees would experience increased stress levels while low self-control employees’ stress levels would not be influenced.

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5. Discussion

In this study, multi-level analysis was used to examine how smartphone use with different purposes, namely work-related and personal related, and different daily duration influenced employees’ work-related outcomes the subsequent day. The study is aimed to shed light on the mechanism of how smartphone use influences employees’ outcomes in work settings. An interesting finding is that non-Dutch participants in the sample spent more time on work-related business in the evening. One plausible explanation is that compared to natives, immigrants possess less social network and resources for career development (Seibert et al., 2001). Therefore, they are less secure and are more willing to exert effort to be competitive. Another possible explanation might be cultural difference. In the Netherlands, the quality of life is highly valued (Hofstede, 1980). Therefore, employees cope well with work-life balance. The international samples are mostly from less developed counties, such as China, Bulgaria and Russia, where the emphasis on quality of life is not as strong as in the Netherlands.

Smartphone use for work was expected to influence employees’ job satisfaction, engagement, productivity and stress respectively in this study. However, significant main effect between independent variables and dependent variables was only found between smartphone use for work and employees’ engagement on the next day. The finding was in favor of job demands and resources theory (Demerouti et al, 2001), indicating that employees who use more job resources would experience higher engagement level the next day. Alternative implication is that the positive influence resulting from the increase of job resources is stronger than the negative influence resulting from the increase of job demands and the possible decreases in recovery quality. Another alternative explanation might be that the negative effects would only be salient when the job demands increase to the level that they are not able to handle with the resources in hand. The time (m=7.27) employees spent on work-related smartphone use was relatively short. Besides, inferred from the data, 71.7% of the days, employees did not use their smartphone for business after 9 pm at all. The relatively short usage time of smartphones for work might not be enough to deplete employee’s resources over job demand. However, the short usage did significantly improved employees’

Smartphone usage : devil or angel? : an examination of the influence of smartphone usage on employees' work outcomes

Master thesis

MSc. in Business Administration-Leadership and Management track