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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 21st, 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,
stress.
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Table of Contents Table of Contents .............................................................................................................. 4
1. Introduction .................................................................................................................. 5
2. Literature review ........................................................................................................... 9
2.1 Work smartphone use and employee work outcomes ...................................................... 9
2.1.1 Role boundary ........................................................................................................... 9
2.1.2 Recovery ................................................................................................................... 9
2.1.3 Job satisfaction ........................................................................................................ 11
2.1.4 Employee engagement ............................................................................................ 12
2.1.5 Productivity ............................................................................................................. 15
2.1.6 Stress ....................................................................................................................... 16
2.2 Personal smartphone use and work outcomes ............................................................... 17
2.3 The influence of self-control and exercise ..................................................................... 18
2.3.1 Self-control ............................................................................................................. 18
2.3.2 Exercise ................................................................................................................... 20
3. Method ........................................................................................................................ 24
3.1 Participants and procedure ............................................................................................. 24
3.2 Measurement .................................................................................................................. 24
4. Results ......................................................................................................................... 27
4.1 Descriptive statistics ...................................................................................................... 27
4.2 Hypotheses testing ......................................................................................................... 29
5. Discussion .................................................................................................................... 34
6. Contribution and limitations ....................................................................................... 38
6.1 Theoretical contribution ................................................................................................. 38
6.2 Limitations and future direction ..................................................................................... 38
7. Conclusion ................................................................................................................... 41
8. Reference ..................................................................................................................... 42
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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:
H2 Work-related smartphone use is negatively related to employees’ engagement.
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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),
17
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
18
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.
20
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;
21
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:
22
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
23
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
25
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
28
(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.
29
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
30
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
rejected.
31
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
Predictor Interaction
B SE B SE
Intercept 3.6825** .0353 3.6805** 0.0339
Work-related smartphone use .0025 .0013 0.0028* .0014
Personal smartphone use -.0009 .0010 -.0006 .0010
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.
32
Table 6 Predictor and interaction model for employees’ stress
Stress
Predictor Interaction
B SE B SE
Intercept 2.3234** .0486 2.3254** .0480
Work-related smartphone use .0024 .0016 .0010 .0017
Personal smartphone use -.0008 .0012 -.0013 .0012
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
33
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’
35
engagement level, indicating the importance of job resources in employees’ engagement.
Thirdly, employees can also access emails and work-related issues during the time interval
that was not covered by this survey. The assumption is that smartphone use after 9 pm would
have significant influence over employee’s subsequent work outcomes. However, if
employees constantly engage themselves in work during the time interval from the moment
that employees finish work in the office to 9 pm, it’s reasonable to assume that it also has
influence on employees’ outcomes.
Surprisingly, no significant main effect was found between work-related smartphone use
and the other three variables, namely job satisfaction, productivity and stress. One reason
might be the low use time of smartphone for work in the sample. Another potential
explanation is that Dutch society emphasizes a lot on the quality of life (Hofstede, 1980).
Therefore, employees might reduce their use of smartphone for work to the minimum to keep
a good work-life balance. The assumption behind the relationship between work-related
smartphone use and job satisfaction is that work-related smartphone use might increase
work-home interference and impede employees’ recovery process. However, the relatively
small amount of time spending on work-related smartphone use had a relatively small
influence on the employees’ role conflict since they could still spend enough time with their
family and enjoy their personal life. For productivity, although the hypothesis was rejected at
p=.05 level, the β weight was positive with a p value of .06. Similar to engagement, it can be
inferred that the effect of increased feeling of coping was greater than that of increased
pressure, resulting in overall positive influence of smartphone use for work over employee
productivity.
Another interesting finding is that personal smartphone use seem to have no influence on
work outcomes for employees. In the sample, participants on average spend 28.3 minutes on
personal smartphone after 9 pm. The data was 4 times higher than that of work-related
smartphone use. The non significant results indicate that personal smartphone use is rather
remote and irrelevant in predicting employees’ outcomes on the next day. Another
explanation would be that Dutch society focuses on work-life balance, whereas employees
know how to separate personal life from work, such that personal life and work life are two
parallel lines with minimum intercepts. Last but not least, employees’ personal use of
36
smartphones can be divided into different categories, such as social activities, games, music,
etc. Different categories might have different influences, for instance, listening to relaxing
music can prevent stress while playing violent video games can increase employees’
aggressive behaviors (Ferguson, 2007; Knight & Rickard, 2001). The combination of all
categories might turn out to have no influence on work-related outcomes. Further segment
investigations are needed to provide more profound understanding of the relationship between
personal smartphone use and work-related outcomes, both behavioral (engagement and
productivity) and attitudinal (job satisfaction and stress).
In line with the literature, it was found that self-control has a positive influence on
employees’ work outcomes. In the study, positive relationships were found between
self-control and employees’ job satisfaction and productivity. The results were consistent with
previous studies, indicating that employees with high self-control are valuable to
organizations. It was also found that high self-control employees and low self-control
employees respond differently to work-related smartphone use. Consistent with ego depletion
theory (Baumeister et al., 1998), for high self-controller, their stress level would increase with
the increase in work-related smartphone usage since they exert more effort to deal with work.
When there is a need to work during non-work hours, high self-controllers will concentrate on
the job and devote themselves in the job thus that the time spent was efficient and necessary.
The increase in workload would then increase their fatigue and stress level. However, the
increase in work-related smartphone use time seem to have no influence on low
self-controllers. One plausible explanation might be that for the same workload, low
self-controllers might spend more time. Their process of finishing the job might be more
relaxing and less concentrating, such that their stress level seems not to be influenced by
work-related smartphone use.
When it comes to exercise, there was no support for its buffering effect in the conceptual
model. The average time employees spent on exercise was 17 minutes with a standard
deviation of 32 minutes. In this study, the focus is on exercise during the evening. However,
there might be employees who have the habit of doing exercise in the morning or during
lunch break. The morning and lunch break exercise effects were not captured in this study,
resulting in possible result deviation. In the dataset, 68.1% of time, employees do not exercise
37
during evenings at all. While the assumed positive effect of exercise increases over time, the
lack of data variation would probably contribute to the result deviation as well. Another
explanation is that the effect of exercise on the relationship between smartphone use and
work-related outcomes is indirect and remote. The findings suggest that exercise is not central
to the antecedents of work-related outcome.
38
6. Contribution and limitations
6.1 Theoretical contribution
The study contributes to the extant literature in several ways. First of all, the study
confirmed that the influence of smartphone use on employee attitudinal and behavioral
outcomes depends largely on the specific context (smartphone usage types, individual
characteristics, etc.) Previous studies on similar topic mostly did not distinguish the purpose
of smartphone use, which would lead to result bias. By distinguishing personal smartphone
use from work-related smartphone use, the study supported the idea that smartphone use with
different purposes do not have the same effects on employees’ work outcomes. The study
also supported the job demands and resources theory, indicating that possessing more job
resources to cope with job demands significantly improves employees’ engagement and
performance.
Secondly, the influence of two potential moderators in the relationship between
smartphone use and work-related outcomes were examined. By focusing on self-control and
daily fluctuation of exercise, it was found that self-control is a strong predictor of employees’
job satisfaction and productivity while the effect of exercise is rather insignificant. The
findings were contrary to the previous finding that self-control consumption is related to
impaired task performance (Muraven et al., 2000). The result sheds light on the importance
and benefits of self-control over employees’ performance and job satisfaction, giving
organizational practical and empirical implications in candidate’s selection. The inconsistent
results about the influence of work-related smartphone use on different types of employees
(low self-controller and high self-controller), on the other hand, provide organizations with
empirical evidences that HR policy makers can base on to make better decisions.
6.2 Limitations and future direction
Like any other study, this study is not exempted from limitations. Firstly, since
employees were the only source of the data, common source bias is inevitable. For example,
employees’ subjective perception of productivity might be influenced by other variables like
mood, relationships with others, etc. Another important factor that influences the result is the
fluctuation of workload. Employees’ workload from Monday to Friday would probably
39
follow certain patterns. For instance, employees would probably perceive themselves as
highly productive during the days that they can finish all the job on time with a low workload
but less productive when the workload is too high to finish. Secondly, one problem
associated with convenience sampling technique is that the sample is not as valid as
probability sampling technique, thus its generalization ability is limited. Thirdly, socially
desirable answer may also be a concern even though the data collected would remain
anonymous and only for the use of the study. Employees tend to over-report good outcomes
(e.g., satisfaction) and under-report negative outcomes (e.g., stress) since most of them are
acquaintances of the researchers. They might have the pressure to answer questions that
might influence their careers objectively. Last but not least, the time that employees spent on
work-related activities during off-work hours using other tools (e.g., computer, iPads, etc.)
was not taken into consideration in this study. Nowadays, more and more employees are able
to access to business activities through laptops. Not taking this part into account has potential
influence on the conclusions of this thesis.
The study was conducted in the Netherlands, a society that values a lot on the quality of
life (Hofstede,1980). It would be interesting to conduct the study in cross cultural settings.
By conducting the study in different societies, scholars can examine whether employees who
holds different culture values, norms and work beliefs would spend different time and have
different experience using smartphones for work during off-work hours. For instance, in
Confucius countries, persistence is a key cultural value (Hofstede & Bond, 1988). Employees
from Confucius countries are likely to work more hours during their personal time. The
differences in the sample would be interesting to study. Furthermore, future studies can also
separate voluntary work-related smartphone use from involuntary use. According to
self-determination theory, autonomy is critical psychological need, the impairment of which
would cause negative effects (Ryan et al., 2000). Employees would have more job autonomy
and control when they voluntarily work overtime. Beckers et al. (2008) found that voluntary
overtime workers reported higher satisfaction and less fatigue. Involuntary work, however,
would probably result in employee’s unwillingness to work, which eventually influences
their work attitude and behavior. Bechkers et al. (2008) found that involuntary overtime
workers are at a higher risk of depletion. Therefore, the study of motivations behind
40
employees’ work-related smartphone use after work would be meaningful to give HR
practitioners and mangers some management guidance. These two areas are of great potential
to be examined in the future to help organizations as well as scholars have a comprehensive
understanding about the influence of smartphone use.
41
7. Conclusion
Extant literature fails to answer the question whether the purpose of smartphone use
matters in predicting work-related outcomes. The conclusions of this study indicate that the
purpose of smartphone use have different effects on employees’ work outcomes. The
influence also depends on individual characteristics. The self-control an individual possesses
has significant influence over work-related outcomes both directly and in the interaction with
other variables. While smartphones allow employees to use more resources to cope with job
demands, its side effect on stress, which is an antecedent of mental health, should also be
considered comprehensively. The conclusions provide empirical evidence for companies to
make policies and regulations that would best improve employees’ work-related outcomes.
42
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