How does private versus work smartphone use in the evening affect your workday the next day? : a study of whether smartphone use in the evening leads to both more procrastination at work and less work engagement the nex

(1)

How does private versus work smartphone

use in the evening affect your workday the

next day?

A study of whether smartphone use in the evening leads to both more

procrastination at work and less work engagement the next day, moderated by

self-control

Master of Science – Business Administration Track Leadership & Management

Master Thesis Author: Nadia van Baarsen Student number: 10009574 Supervisor: dr. W. van Eerde

Second reader: dr. M. Venus Due date: June 23rd, 2017

(2)

2

Statement of Originality

This document is written by student Nadia van Baarsen 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.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

(3)

3

Abstract

In this thesis, the relationship between the independent variables work-related and private smartphone use and the dependent variables work engagement and procrastination at work is examined. Furthermore, I studied what effect the personality trait self-control has as a moderator on these relationships. In total 84 participants filled in a one-shot survey and a diary survey which measured variables twice a day during two weeks. Results indicated that work-related

smartphone use in the evening influences work engagement the next day and that this relationship is even stronger positively related when a person is low on the personality trait self-control and that this relationship is even stronger negatively related when a person is high on the personality trait self-control. The implications of these findings are discussed.

Key words: procrastination at work, work engagement, self-control, work-related smartphone

(4)

4

List of Tables and Figures

Tables

Table 1. Respondent Characteristics Table 2. Cronbach’s Alpha

Table 3. Mean, Standard Deviations and Correlations Table 4. Results of the mixed models at T2

Table 5. Results of the mixed models at T1

Figures

Figure 1. Conceptual Model

Figure 2. Histogram work-related smartphone use Figure 3. Histogram private smartphone use

Figure 4. The relationship between work-related smartphone use and work engagement in the afternoon, moderated by self-control

(6)

6

1. Introduction

Recently, the use of smartphones in the evening for work-related matters has been an often-debated topic (Ohly & Latour, 2014). For instance, in France a new employment law has been passed which grants employees the right to ignore work e-mails outside of work hours (Wang, 2017). Furthermore, almost half of the smartphone users in the Netherlands admits to checking their phone in the first fifteen minutes after waking up (“Nederlanders zijn dol op 4G. En vergroeid met hun smartphone”, n.d.). For a lot of smartphone users, the first thing they look at when they wake up is their smartphone, and the last thing they look at before going to sleep, is their smartphone (Lee, Chang, Lin & Cheng, 2014).

Smartphones have become an everyday technology as they provide employees with direct access to work-related information and communication inside and outside the office (Lanaj, Johnson & Barnes, 2014). This technology enables employees to perform some or all their work outside of a conventional office setting, yet this technology also has made it possible for

employees in conventional work settings to stay in touch with the job during non-work hours (Boswell & Buchanan, 2007). But, as Boswell and Buchanan (2007) state, there is little comprehension of how the use of these technologies outside the normal working hours might relate to important facets of work and work-life balance. The use of technology may be an advantage, but using a smartphone in the evening may influence work the next day.

The benefits of smartphone use for work at home may be canceled out by the inability of employees to fully recover from work activities while being away from the office, especially at night (Lanaj et al., 2014). Employees feel more and more obligated to respond immediately to messages that are work-related, even in the evening hours (Derks, Van Mierlo & Schmitz, 2014).

(7)

7

Although smartphones make it possible for people to stay in touch with their job during non-work hours (Boswell & Buchanan, 2007), this may interfere with employees’ sleep and replenishment of regulative resources (Lanaj et al., 2014). Furthermore, when employees are not able to replenish the depleted resources, work engagement may suffer the following day.

Work engagement indicates the relationship the employee has with his or her work (Schaufeli & Bakker, 2010). Schaufeli, Bakker and Salanoya (2006) define work engagement as a positive and fulfilling work-related state. Previous research has shown that work engagement is related to positive outcomes for the employee and the organization. But to experience these positive outcomes, Sonnentag, Mojza, Binnewies and Scholl (2008) suggest that employees also need psychological detachment from work. As the more and more refined technology of

smartphones has made it more possible for employees to stay connected to work (Boswell & Buchanan, 2007), work engagement and smartphone use might be negatively related. In this study, it will be researched if work engagement and smartphone use in the evening are indeed negatively related. Also, it will be researched if the results change when smartphone use is work-related (for example, checking work e-mail) and when the smartphone is used for private matters (for example, playing games, talking to friends).

In a study of Malachowski (2005), the average employee confesses wasting 2,09 hours per 8-hour workday, excluding lunch and breaks. Salary.com claimed that organizations spend $759 billion a year on salaries for which work was expected, but not performed (Malachowski, 2005). The tendency to delay the initiation or finalization of work activities is known as

procrastination (Kühnel, Bledow & Feuerhahn, 2016). The work activity is important to the individual, but is seen as something unattractive (Van Eerde, 2003). The above-mentioned numbers suggest that procrastination at work has a negative effect on work. Furthermore, previous research has focused mostly on academic and general-life domains, but procrastination

(8)

8

at work has hardly been researched (Metin, Taris & Peeters, 2016). Therefore, in this thesis I will investigate whether using a smartphone in the evening will result in more procrastination than if an individual does not use a smartphone in the evening. As for work engagement, I will assess the difference between work-related smartphone use and smartphone use for private purposes. This difference will be investigated to be able to distinguish a difference in work-related time spent on the smartphone and private time spent on the smartphone, as these might have different

consequences. It will be difficult to restrain an individual from spending private time on their smartphone. It is possible to restrain an individual from spending time on the smartphone for work-related matters, as is the case in France already.

Furthermore, for employees to effectively function in the workplace, self-control

resources are essential (Lanaj et al., 2014). With effective self-control, people are able to inhibit impulses, block out diverting emotions and align their behaviour with social norms and task standards. The ability of people to exercise their self-control is not limitless (Lanaj et al., 2014). One of the possible reasons for this is ego depletion. Ego depletion theory suggests that

continuous acts of self-control come from a limited pool of resources that is sensitive to depletion. Once a person’s pool of resources is depleted, this person finds subsequent work activities more demanding (Lanaj et al., 2014). This person becomes vulnerable to non-task related distractions and impulses. In an organizational setting, ego depletion makes it more difficult to refrain from deviant and unethical acts (Lanaj et al., 2014).

The central aim of this study is to examine what effect work-related and private

smartphone use in the evening has on the variables procrastination and work engagement at work the next day. The research question will be: “What is the effect of private/work-related

smartphone use in the evening on procrastination at work/work engagement the next day and how does self-control moderate this relationship?”.

(9)

9

It will be hypothesized that using your smartphone in the evening will lead to both more procrastination at work and less work engagement the next day. Furthermore, I will research whether self-control moderates this relationship. In this study the focus will be on full-time employees in the Netherlands across different organizations.

In this thesis, I will first draw a theoretical background, in which I will define the variables and bring together existing literature and relevant studies related to the topic and include hypotheses and conceptual model. Data will be collected using a one-shot survey and diary study. This will be outlined in the method section. After this, the results will be presented. Furthermore, the research findings will be discussed in the discussion section.

(10)

10

2. Theoretical Background

2.1 Smartphone use

“Smartphones – handheld personal computers – represent the most recent step in the evolution of portable information and communication technology.” (Oulasvirta, Rattenbury, Ma, & Raita,

2012, p. 1) Smartphones have the possibility to create new habits associated with Internet use (Oulasvirta et al., 2012). Nowadays, smartphones make it possible for people to stay in touch with their job during non-work hours (Boswell & Buchanan, 2007). The smartphone has made it possible for employees to have a connection with their work tasks everywhere and at any time (Derks, Duin, Tims & Bakker, 2015). Consequently, there are gains and setbacks related to this always being connected.

Gains for using smartphones for work are for instance that the access to work e-mail and work files is faster and easier than before (Lanaj et al., 2014). Furthermore, smartphones provide a direct contact with clients, colleagues and supervisors. Another advantage of using a

smartphone is the flexibility it brings employees in being able to balance work- and family life (Derks, Bakker, Peters & Van Wingerden, 2016). Most employees acknowledge this flexibility, yet also acknowledge that it makes it more challenging to disconnect from work when being away from work (Derks, Van Mierlo & Schmitz, 2014).

Therefore, one of the setbacks of using a smartphone for work-related matters at home is for instance the employee’s inability to fully recover from work activities while being away from their workplace, particularly at night (Lanaj et al., 2014). As organizations expect employees to be more and more available, these same employees feel obliged to respond instantly to messages

(11)

11

from work, even during non-work hours (Derks, Van Mierlo & Schmitz, 2014). Furthermore, employees regularly use their smartphones for work within an hour from going to sleep and a majority of employees sleeps within reach of their smartphones. Thus, smartphone use in the evening may intervene with employees sleep and the renewal of regulatory resources (Lanaj et al., 2014). When employees fail to renew their depleted resources, because for instance their sleep quality suffers, work engagement may suffer the next day.

Current research on smartphone use has looked at smartphone use in general or at smartphone use for work. Smartphone use for private purposes has not yet gained attention in research. There has not been a distinction between smartphone use for private purposes (for example playing games, contact with friends) or work-related purposes (for example, checking work e-mail). Therefore, in this thesis, I will investigate if results differ when smartphones are used privately or for work-related matters. The reason for this distinction is that it is more

difficult to restrain individuals from using their smartphone for private purposes, but it is possible to restrain an individual from work-related smartphone use.

2.2 Work engagement

Work engagement indicates the relationship the employee has with his or her work (Schaufeli & Bakker, 2010). Schaufeli, Bakker and Salanoya (2006) define work engagement as a positive and fulfilling work-related state. Work engagement consists of vigor, dedication and absorption. Vigor is defined by high levels of energy and mental flexibility during work, the willingness to invest effort in one’s work and persistence even during times of difficulty. Dedication is defined as being highly involved in one’s work and experiencing a feeling of significance, enthusiasm, inspiration, pride and challenge. Absorption is defined as being entirely concentrated and happily

(12)

12

intended in one’s work, whereby time passed quickly and one has difficulties with detaching oneself from work (Schaufeli et al., 2006).

Work engagement should not be confused with workaholism. Being too engaged in your work can lead to workaholism. Workaholism is defined as working extremely hard and

compulsively (Sonnentag et al., 2008). Therefore, being vigorous, dedicated and absorbed in work does not mean that an individual works extremely hard or long hours. Engaged employees feel an energetic and effective connection with their work; engaged employees regard their work as challenging (Bakker, Schaufeli, Leiter & Taris, 2008).

When employees are engaged in their work, ego depletion may arise. The definition of ego depletion is “a state in which the self does not have all the resources it has normally”. (Baumeister & Vohs, 2007, p. 2) The theory of ego depletion states that when a self-regulatory resource is depleted, it is unavailable until it is refilled (Muraven & Baumeister, 2000).

Therefore, it prohibits the attention and effort that can be allocated to subsequent activities (Lanaj et al., 2014). This ego depletion is not consistent with the vigor, dedication and absorption that is necessary for work engagement. Employees who are engaged in their work bring and apply some form of energy to their work. This energy is a resource that can be depleted (Lanaj et al., 2014). Furthermore, sleep is recognized as necessary to renew replenished resources (Lanaj et al., 2014). It can therefore be expected that depletion is associated with less work engagement, as work engagement needs replenished resources in order for employees to be vigorous, dedicated and absorbed in their work.

Results from the research of Lanaj et al. (2014) are consistent with predictions made from the ego depletion theory. Results indicated that smartphone use for work at night disturbed sleep that night, which led to greater depletion the next morning and less work engagement the next day. Also, former research has shown that work engagement relates to positive outcomes, for

(13)

13

example positive experiences and effects for the individual. But to experience these positive outcomes Sonnentag et al. (2008) suggest that employees also need psychological detachment from work. Since smartphone use is able to extend hours spent on work (Derks et al., 2016), in this thesis it will be hypothesized that the relationship between smartphone use and work engagement is negatively related. There has been a lot of research on work engagement in literature, but not yet which effects smartphone use in the evening has on work engagement. Therefore, in this thesis it will be hypothesized:

H1. Work-related time spent on the smartphone in the evening is related to less work engagement

at work the next day.

H2. Private time spent on the smartphone in the evening is related to less work engagement at

work the next day.

Since in literature only smartphone use in general or work-related smartphone use is researched, I will investigate if the results differ when a distinction is made between work-related and private smartphone use, to make sure this gap in the literature is addressed. Furthermore, the dependent variables are tested at two points in time: in the morning and in the afternoon. Therefore, both points in time will be analyzed. The prediction is that the results will be methodologically stronger in the afternoon.

2.3 Procrastination at work

Procrastination is defined as “the avoidance of the implementation of an intention” (Van Eerde, 2000, p. 375). The essence in procrastination is “the ability to control one’s attention and thus

(14)

14

overcome the more pleasant distraction” (Van Eerde, 2003, p. 422). Procrastination at work, the

variable which will be used in this thesis, can be defined as “putting off work-related action by

engaging in nonwork-related actions during work hours”. (Metin, Taris & Peeters, 2016, p. 1)

Procrastination at work is related to high costs and refers to a “self-regulatory failure of work

tasks” (Metin et al., 2016, p. 254). Since in literature procrastination in the general life domain is

the variable mostly addressed, procrastination in the workplace has been chosen as a variable to make sure that this research will be an important addition to existing literature.

In a study of Kühnel et al. (2016), procrastination is examined from a self-regulation viewpoint. In their study, they argue that depleted self-regulatory resources are a crucial way to explain when and why employees procrastinate. Furthermore, procrastination can be frustrating for employees, because it suggests a disparity between the intention of an employee and the actions of an employee. Also, it hinders the positive experience of work task progression.

Consequences of procrastination include undergoing more stress, it is more likely that an individual will miss deadlines that are important, that self-set deadlines will be non-optimal and that an individual spends less time on the preparation of important tasks (Kühnel et al., 2016). This may all lead to poor quality of work. As with work engagement, the ego depletion theory is also central with procrastination at work, because the replenishing of self-regulatory resources is necessary for being able to begin working. As mentioned before, research has indicated that sleep is a possibility to replenish self-regulatory resources. Furthermore, theory has indicated that smartphone use for work at night disturbs sleep in that same night, which will lead to greater depletion the next morning. Kühnel et al., (2016) found that employees procrastinate more after nights where sleep quality was low and sleep duration was shorter than normal. In this thesis, therefore will be hypothesized:

(15)

15

H3. Work-related time spent on the smartphone in the evening is related to more procrastination

at work the next day.

H4. Private time spent on the smartphone in the evening is related to more procrastination at

work the next day.

As for the variable work engagement, this thesis investigates if the results differ when a distinction is made between work-related and private smartphone use, to make sure this gap in the literature is addressed. And again, since the dependent variables are tested at two points in time (in the morning and in the afternoon), both points in time will be analyzed. The prediction is that the results will be methodologically stronger in the afternoon.

2.4 Self-control

Self-control is defined as “the ability to override or change one’s inner responses, as well as to

interrupt undesired behavioral tendencies (such as impulses) and refrain from acting on them”.

(Tangney, Baumeister & Boone, 2004, p. 274) Self-control allows persons to resist temptations on the short-term (for example, surfing on the web) to achieve goals in the long term (for example, finish the deadline of today) (Gino, Schweitzer, Mead & Ariely, 2011).

Self-control resources are essential for effective functioning in the workplace (Lanaj, et al., 2014). With effective self-control, people are able to inhibit impulses, block out diverting emotions and align their behaviour with social norms and task standards. Rahimnia and Mazidi (2015) describe self-control as a personality trait which regulates types of impulses in activities that are work-related. The ability of people to exercise their self-control is not limitless (Lanaj et al., 2014), it is a finite resource (Gino et al., 2011). Ego depletion theory proposes that continuous

(16)

16

acts of self-control come from a limited pool of resources that is sensitive to depletion. Once an individual’s pool of resources is depleted, this individual finds subsequent work activities more demanding (Lanaj et al., 2014). This individual becomes vulnerable to non-task related

distractions and impulses. In an organizational setting, ego depletion makes it difficult to refrain from deviant and unethical acts (Lanaj et al., 2014).

To avoid certain behaviour, the self changes its own behavioral patterns by using its self-control, to avert the main response (Muraven & Baumeister, 2000), for instance, going on Facebook instead of working on the work task the individual needs to complete. The act of not going on Facebook may cost more self-control than going on Facebook. Furthermore, when the resource is depleted, individuals tend to be unsuccessful in using their self-control (Muraven & Baumeister, 2000). Therefore, people who are high in self-control, will be better able to restrain themselves than people who are low in self-control. From this, the following is hypothesized:

H5. The relationship between work-related smartphone use and work engagement will be

moderated by self-control, such that work-related smartphone use will be stronger related to work engagement when a person scores low on self-control.

H6. The relationship between private smartphone use and work engagement will be moderated

by self-control, such that private smartphone use will be stronger related to work engagement when a person scores low on self-control.

According to research, people who did not have to use their self-control recently are less likely to procrastinate, compared to people who did have to use their self-control recently (Gino et al., 2011). Therefore, the last hypotheses will be:

(17)

17

H7. The relationship between work-related smartphone use and procrastination will be moderated

by self-control, such that work-related smartphone use will be stronger related to procrastination when a person scores low on self-control.

H8. The relationship between private smartphone use and procrastination will be moderated by

self-control, such that private smartphone use will be stronger related to procrastination when a person scores low on self-control.

Again, for both hypotheses will be looked if the results differ when smartphone use is use for work-related matters or for private purposes. Furthermore, as mentioned before, the dependent variables are tested at two points in time: in the morning and in the afternoon. Both points in time will be analyzed. The prediction is that the results will be methodologically stronger in the

afternoon. After all the hypotheses have been formulated, these hypotheses lead to the following model:

(18)

18

Figure 1. Conceptual Model

Independent Variables

Work-related smartphone use Private smartphone use

Dependent Variables

Procrastination at work Work engagement

Moderator

(19)

19

3. Method

In this section, the research methods are described. Firstly, the research design is described. Secondly, the sample and procedures are explained. Thirdly, the measures of the study are discussed. And finally, the section ends with a description of the data analyses.

3.1 Research Design

This quantitative research was carried out using a one-shot survey prior to a diary study of ten working days. Together with the other students and supervisors in my project group a survey has been designed. First, a survey one week in advance of a diary study was administrated. This survey is referred to as the one-shot survey. In this survey, level 2 moderators were measured, which included personality traits. Subsequently, one week after this one-shot survey, the diary study started. The diary surveys included level 1 variables. The diary study took place for two weeks on workdays (Monday till Friday). The one-shot survey was sent out on Tuesday,

February 28, 2017. The first week of the diary study started on Monday, March 6, 2017. The last day of the diary study was Friday, March 17, 2017. Surveys were offered both in English and in Dutch.

3.2 Sample and procedures

The population of interest of this study was full-time employees working in the Netherlands. A convenience sample was used as the participants consisted of family and friends. The reason for

(20)

20

this was that it was important that most of the participants finished the diary study and did not stop participating after, for example, two days. Participants of the survey needed to be full-time (at least 32 hours a week) employed. If participants were not full-time employed, they could not take part in the surveys. As an incentive, ten Bol.com vouchers of 20 euros were raffled under all the persons who filled out all the surveys. Thirteen participants filled out all the surveys. In total 84 persons participated in the study. These 84 persons led to a total of 570 measurements. 270 measurements were missing. Furthermore, the surveys were sent out in the morning and in the afternoon. The morning surveys were available for the participants from 11.00 till 15.00 hours. The afternoon survey was available from 16.15 till 22.30 hours. After this, the surveys were not available anymore to avoid invalid responses.

3.3 Measures

There were several variables in the survey. Only the ones that are relevant for this study will be reported. The one-shot survey contained the measure for self-control. The diary study contained the measure for work-related and private smartphone use in the evening, procrastination and work engagement. Furthermore, the one-shot survey contained some general questions like gender (nominal variable), age and tenure (ratio variables), years of work experience, average working hours per week and in which field the participant is working. For the items, relevant to this study, a 5-point Likert scale was used (ranging from completely disagree to completely agree). The surveys were first set up in English and then translated into Dutch and transported into Qualtrics. All the above steps were checked and coordinated by a supervisor.

(21)

21

Smartphone Use in the Evening

We followed the article of Lanaj, Johnson and Barnes (2014) and used the same formulation of the questions for work-related smartphone use and private smartphone use.

Work-related smartphone use

‘‘How many minutes did you use your smartphone for work after 9 PM last night?’’

Private smartphone use

In order to distinguish between smartphone use for private purposes, the following question was added: ‘How many minutes did you use your smartphone for personal use after 9 PM last night?’

Work engagement

The questions for the construct work engagement were extracted from the article of Barnes, Lucianetti, Bhave and Christian (2015). The average Cronbach’s Alpha of the items for this measure is 0.86. Items included “Today, time flew when I was working”, “Today while working, I forgot everything else around me” and “Today, I was immersed in my work”.

Procrastination at work

Following Kühnel et al. (2016), the same formulation for procrastination was used. Items included: “Today, I was a time waster but I couldn’t seem to do anything about it”, “Today, I promised myself I’ll do something and then dragged my feet” and “Today, I was an incurable time waster”. The Cronbach’s Alpha of these items ranged from 0.85 till 0.88.

(22)

22

Self-control

The items used read “I am good at resisting temptation”, “I have a hard time breaking bad habits”, “I wish I had more self-discipline” and “People would say I have iron self-discipline”. The Cronbach’s Alpha of this four-item subscale is 0.75 (Smit & Barber, 2015).

3.4 Data Analyses

To test the proposed hypotheses, data was collected by sending out a one-shot survey prior to a diary study of ten working days. There was a survey one week in advance of a diary study. As mentioned before, this study measured level 2 moderators, which includes personality traits. A week after this one-shot survey, the diary study started. The diary study took place for two weeks on workdays (Monday till Friday).

After the diary study ended, the data collection was complete. First, a new variable was added: RespondentID. This new variable was necessary to distinguish the different respondents. Furthermore, the people who did not fill out the diary study, but did fill out the one-shot survey were deleted. People who filled out only the diary study and not the one-shot survey were also deleted. Only participants who filled out both the one-shot survey and the diary study surveys were considered valid.

As there were two items on self-control phrased in a way that an agreement with the item responded with a low level of the construct, these were recoded. In this way, all the items of the measure self-control were pointing in the same direction, such that the scale scores ranged from low (1) to high (5). Furthermore, since the variables work engagement and procrastination are measured on two different points in time (the morning and the afternoon), there are two variables for each variable: Work Engagement T1, Work Engagement T2, Procrastination T1 and

(23)

23

Procrastination T2. T1 refers to the variable measured in the morning, T2 refers to the variable measured in the afternoon.

After these steps, descriptive statistics were calculated. To this end, the dataset was restructured in the Statistical Package for Social Sciences (SPSS) to a wide dataset. See Table 1 for descriptive statistics.

In total 84 persons participated in the study. A little over half of the participants were male (51,2%). The average age of all the participants was 35 years old (SD = 12.45). These persons had an average work experience of 13.24 years (SD = 13.06) of which more than half that time was spent working at the current employer (M = 7.54, SD = 10.26). On average, the

participants spent 41.6 hours at work (SD = 6.90).

Table 1: Respondent Characteristics

Characteristics N (%) / M (SD) Gender - male - female 43 (51,2%) 41 (48,8%) Age (years) 35 (SD 12.45)

Work experience (years) 13.24 (SD 13.06)

Working at current employer (years) 7.54 (SD 10.26)

Weekly time spent on work (hours) 41.6 (SD 6.90)

The reliability of the items was checked using the Cronbach’s Alpha (see Table 2). In some cases, the Cronbach’s Alpha would be higher if an item was deleted. In the case of the variable Procrastination T2 the Cronbach’s Alpha would rise to 0.967. Since this is only a minor increase of 0.006, it is chosen to not delete this item. Furthermore, the construct would then only include two items, which would result in a decreasing validity. This was also the case for the

(24)

24

variable Work Engagement T1 (Cronbach’s Alpha if item deleted would then be 0.839) and Work Engagement T2 (Cronbach’s Alpha if item deleted would then be 0.923).

Table 2. Cronbach’s Alpha Variables Items in Questionnaire Based on Cronbach’s Alpha in literature Cronbach’s Alpha in study sample

Procrastination T1 3 Kühnel et al. (2016) 0.85 – 0.88 0.914

Procrastination T2 3 Kühnel et al. (2016) 0.85 – 0.88 0.961

Work Engagement

T1 3

Barnes, Lucianetti, Bhave & Christian (2015)

0.86 0.742

Work Engagement

T2 3

Barnes, Lucianetti, Bhave & Christian (2015)

0.86 0.795

Self-Control 4 Smit & Barber (2015) 0.75 0.714

Furthermore, to demonstrate how work-related and private smartphone use is divided amongst the participants, two histograms were created. See Figure 2 and Figure 3.

(25)

25

Figure 2. Histogram work-related smartphone use

(26)

26

4. Results

In this section, the results are presented. The aim of this study was to examine the relationship between work-related/private smartphone use and the variables work engagement/procrastination, moderated by self-control. Both work engagement and procrastination were measured at two different points in time: in the morning (T1) and in the afternoon (T2). Both points in time were analyzed.

4.1 Correlations

As a final step before the hypotheses could be tested, new variables as a function of the existing variables were created. The mean of all items that was used to describe a variable was calculated. This was first done per item (as it was a diary study) and then for the total variable.

To find the mean, standard deviation and correlations of the study variables, the dataset was restructured back to a wide dataset. Table 3 reports the mean, standard deviations and correlations of the study variables.

(27)

27

Table 3. Mean, Standard Deviations and Correlations

Variables Mean SD 1 2 3 4 5 6 7 1. Work-related smartphone use 7.689 16.289 1 2. Private smartphone use 28.703 22.295 .228* ₁ 3. Work Engagement T1 3.198 0.480 -.079 -.206 1 4. Work Engagement T2 3.234 0.561 .022 -.137 .766** ₁ 5. Procrastination T1 2.023 0.558 .234* _.261* _-.161 _-.152 ₁ 6. Procrastination T2 2.651 0.833 .114 .265* _-.194 _-.070 _.825** ₁ 7. Self-control 3.140 0.679 .007 .003 .023 .096 -.210 -.201 1 * Correlation is significant at the 0.05 level (2-tailed).

** Correlation is significant at the 0.01 level (2-tailed). N = 84.

(28)

28

4.2 Mixed model analysis

To examine the relationship between the independent variable, dependent variable and the moderator, the problem of aggregating data needed to be solved. Measurements are done at level 1 and individuals are at level 2. Each individual can have a different intercept. Therefore, the SPSS dataset is restructured back to a long data set. Hereby multiple measurements per individual are allowed by including random effects. Then, generalized linear mixed models in SPSS is used. Random effects were entered by including a random intercept first and an unstructured

covariance structure was used. The need for a random slope was also investigated, but this was not an extra addition to the model by comparing the -2*log likelihood fit between models, so it was decided not to use a random slope, because only the random intercept improved the model.

Table 4 presents the outcomes for the mixed model analysis when the variables were

tested in the morning. Table 5 presents the outcomes for the mixed model analysis in the afternoon.

(29)

T1 T1

B SE t B SE t

Constant 3.183 .053 59.641*** 1.996 .062 32.270***

Work-related smartphone use .002 .001 1.515 .000 .002 .006

Random intercept .175 .035 .237 .048

-2 log likelihood 1060.401 1223.945

Moderation

Constant 3.073 .250 12.287*** 2.393 .286 8.357***

Work-related smartphone use -.001 .008 -.152 .005 .009 .556

Self-control .036 .078 .458 -.127 .089 -1.430

Work-related smartphone use x self-control .001 .002 .410 -.002 .003 -.562

-2 log likelihood 1073.476 1234.197

Constant 3.234 .058 55.361*** 1.983 .068 29.084***

Private smartphone use -.001 .001 -1.247 .0000 .001 .415

-2 log likelihood 1061.828 1224.511

Moderation

Constant 3.075 .261 11.796*** 2.611 .300 8.705***

Private smartphone use -.000 .004 -.064 -.008 .005 -1.680

Self-control .051 .082 .625 -.205 .094 -2.176*

Private smartphone use x self-control -.000 .001 -.246 .003 .002 1.820

-2 log likelihood 1076.212 1233.295

* p < .05 ** p <.01 *** p < .001

(30)

30

Table 5. Results of the mixed models tested in the afternoon (T2)

Variable Work Engagement T2 Procrastination T2 B SE t B SE t Constant 3.241 0.067 48.155*** 2.640 .097 27.159***

Work-related smartphone use -.002 .001 -1.428 .002 .002 .830

-2 log likelihood 858.782 1268.886

Moderation

Constant 2.716 0.309 8.795*** 3.461 .438 7.896***

Work-related smartphone use 0.017 0.008 2.215* .016 .012 1.341

Self-control .165 .096 1.718 -.262 .136 -1.935

Work-related smartphone use x self-control -.006 .002 -2.521* -.004 .003 -1.209

-2 log likelihood 864.991 1274.645

Constant 3.229 .072 44.653*** 2.616 .105 24.821***

Private smartphone use -.000 .001 -.095 .001 .002 .859

-2 log likelihood 861.039 1269.441

Moderation

Constant 2.686 .321 8.363*** 3.709 .455 8.153***

Private smartphone use .007 .004 1.835 -.007 .006 -1.066

Self-control .178 .101 1.773 -.353 .142 -2.477*

Private smartphone use x self-control -.003 .001 -1.915 .003 .002 1.316

-2 log likelihood 870.399 1276.078

(31)

hypotheses are rejected. The relationship between work-related smartphone use and work

engagement, measured in the morning, was found not significant, with B = .002, SE = .001 and t = 1.515. The p-value was .130. The relationship between work-related smartphone use and procrastination at work, measured in the morning, was not significant either, with B = .000, SE = .002 and t = .006. The p-value was .996.

Furthermore, H3 and H4 are rejected. The relationship between work-related smartphone use and work engagement, measured in the morning, moderated by self-control was not

significant, with B = .001, SE = .002 and t = .410. The p-value was .682. The relationship between work-related smartphone use and procrastination at work measured in the morning, moderated by self-control, was not significant, with B = -.002, SE = .003 and t = -.562. The p-value was .574.

H5 and H6 were also rejected as the relationship between private smartphone use and

work engagement, measured in the morning, was not significant, with B = -.001, SE = .001 and t = -1.247. The p-value was .213. The relationship between private smartphone use and

procrastination at work, measured in the morning, is not significant either, with B = .000, SE = .001 and t = .415. The p-value was .678.

Lastly, when tested in the morning H7 and H8 are also rejected since the relationship between private smartphone use and work engagement measured in the morning, moderated by self-control is not significant, with B = -.000 SE = .001 and t = -.246. The p-value was .806. The relationship between private smartphone use and procrastination at work, measured in the morning, moderated by self-control, was not significant either, with B = .003, SE = .002 and t = 1.820. The p-value was .069.

(32)

32

Looking for differences between the different measurement times, the same mixed model analyses were done for the variables in the afternoon (T2). Although the results in the afternoon were methodologically strongest, I wanted to test whether relationships at one point in time were significant or not significant might differ from the other point in time.

First of all, when tested in the afternoon, H1 and H2 are rejected, since the relationship between work-related smartphone use and work engagement, measured in the afternoon, was not significant, with B = -.002, SE = .001 and t = -1.428. The p-value was .154. This means that the relationship between work-related smartphone use and work engagement does not necessarily lead to lower work engagement the next day. The relationship between work-related smartphone use and procrastination at work, measured in the afternoon, was not significant either, with B = .052, SE = .063 and t = .830 – and therefore, the relationship between work-related smartphone use and procrastination at work does not necessarily lead to lower procrastination at work the next day. The p-value was .407.

Secondly, H3 is accepted, since the relationship between work-related smartphone use and work engagement measured in the afternoon, moderated by self-control was found

significant, with B = -.006, SE = .002, t = -2.521. The p-value was .012. Since the p-value was below .05, H3 is accepted. The mixed model showed a statistically relevant relationship between the independent variable work-related smartphone use and the dependent variable work

engagement measured in the afternoon, moderated by self-control. Therefore, when an individual spends one more minute on his or her smartphone for work-related matters in the evening, this will lead to a decrease in work engagement at work the next day. This relationship is stronger positively related, when an individual is low on the personality trait self-control. When an individual is high in the personality trait self-control, this relationship is stronger negatively related. See Figure 4.

(33)

33

Figure 4. The relationship between work-related smartphone use and work engagement in the afternoon, moderated by self-control

H4 is rejected as the relationship between work-related smartphone use and

procrastination at work measured in the afternoon, moderated by self-control was not found significant, with B = -.004, SE = .003 and t = -1.209. The p-value was .227.

Thirdly, H5 and H6 are rejected. The relationship between private smartphone use and work engagement measured in the afternoon was not found significant, with B = -.000, SE = .001 and t = -.095. The p-value was .925. The relationship between private smartphone use and

procrastination at work measured in the afternoon, was also not found significant, with B = .001, SE = .002, t = .859 and a p-value of .391.

Fourthly, also H7 and H8 are rejected. The relationship between private smartphone use and work engagement measured in the afternoon, moderated by self-control was also not significant, with B = -.003, SE = .001 and t = -1.915. The p-value was .056. The relationship

1 1,5 2 2,5 3 3,5 4 4,5 5 Low work-related smartphone use High work-related smartphone use W o rk En gage m en t in th e afte rn o o n Low self-control High self-control

(34)

34

between private smartphone use and procrastination at work, measured in the afternoon,

moderated by self-control was not significant, with B = .003, SE = .002, t = 1.316 and a p-value of .189.

(35)

35

5. Discussion

In this section, the theoretical implications of the results are discussed. Limitations and suggestions for further research will also be explained.

5.1 Theoretical implications

The purpose of this study was to examine the relationship between work-related and private smartphone use in the evening and work engagement and procrastination at work the next day, moderated by self-control. This is done at different points in time, in the morning and in the afternoon. The research questions in this study was: “What is the effect of private/work-related

smartphone use in the evening on procrastination at work/work engagement the next day and how does self-control moderate this relationship?”. Following from the research question, eight

hypotheses were formulated. Only H3 (tested in the afternoon) was accepted. This means that the relationship between work-related smartphone use and work engagement, moderated by self-control was found significant. All other hypotheses have been rejected, both when tested in the morning and in the afternoon.

Based on the results, I found that the relationship between work-related smartphone use and work engagement (measured in the afternoon), moderated by self-control, was significant. This means that for every additional minute an individual spends on his or her smartphone, work engagement the next day suffers. When an individual is low on the personality trait self-control, this relationship is even stronger positive related. When an individual is high on the personality trait self-control, this relationship is negative related. This is in contrast with what was

(36)

36

hypothesized in the theoretical background. Initially, people who are high in self-control, were thought to be better able would be more able to restrain themselves, than individuals who are low in control. In this study, the opposite was found to be true. This result can mean that self-control is an important determinant in the relationship between the independent variable work-related smartphone use and the dependent variable work engagement in the afternoon, but since the result are not in line with the literature, it is also possible that the results in this study are coincidental.

The relationship between the variable work-related smartphone use and work engagement, measured in the afternoon, without the moderation effect, was not significant. This implies that a personality trait, such as self-control, might be an important determinant of the relationship between the two variables work-related smartphone use and work engagement. The relationships between all the other relationships examined in this study were found not to be significant. It is also a possibility that other factors are important determinants in indicating a relationship between work-related or private smartphone use and work engagement or procrastination. For instance, Lanaj et al. (2014) used sleep as an important determinator of why smartphone use in the evening has an effect on work the next day.

Since there is not a lot of research yet about smartphone use, this study seems a good addition to existing literature. Furthermore, in the existing literature only work-related

smartphone use has been tested. Private smartphone use has not been tested as a variable and thus this was the first study to include it.

(37)

37

5.2 Limitations and suggestions for further research

Although this study has theoretical implications, the study also suffered from some limitations. First of all, it is a diary study. Therefore, people are self-reporting. The risk of self-reporting is common method bias. Common method bias could have been minimized, if there had been access to the direct colleagues of the participants and these results had been compared to each other.

Also, the sample consists mostly of family and friends. A convenience sample was used, because family and friends were more likely to fill out all or most of the days of the diary study. A probability sample has therefore not been used, so there is a risk of low external validity. Since the data collection of this study was done amongst four students with different backgrounds and all the participants were all from different sectors, ages and backgrounds, the risk of low external validity is minimized.

Another limitation of this study is that it is questionable if there is a need to separately test private smartphone use. Private smartphone use has not been tested as a variable before. Since none of the hypotheses around the variable private smartphone use in this study was found to be significant, it is questionable if there is a need for testing this variable at all.

Further research on smartphone use, procrastination at work, work engagement and self-control can entail in many new directions. First, research in the domain of smartphone use should continue. Since smartphones will most likely not disappear from our everyday life anytime soon, research on how individuals should deal with smartphones in work or private settings is

important. Furthermore, other variables that smartphone use may affect at work the next day, could also be researched, as for instance Lanaj et al. (2014) did with sleep as a consequence.

(38)

38

Since the variables procrastination at work and work engagement are measured at two points in time, further research can also examine what the effect of a variable in the afternoon is on the variable in the morning the following day. Also, since only a relationship has been found between work-related smartphone use and work engagement, measured in the afternoon, when this relationship is moderated by self-control, further research could examine if this is indeed because of the personality trait self-control or that also other variables could cause this.

(39)

39

6. Conclusion

The research question of this study was: “What is the effect of private/work-related smartphone

use in the evening on procrastination at work/work engagement the next day and how does self-control moderate this relationship?”. The only relationship that was found to be significant was

the relationship between work-related smartphone use and work engagement, moderated by self-control. This means that for every minute more an individual spends time on his or her

smartphone in the evening, work engagement is lower the next day. Furthermore, this relationship is stronger positive related when an individual in low in self-control and this relationship is stronger negative related when an individual is high in self-control.

(40)

40

References

Bakker, A. B., Schaufeli, W. B., Leiter, M. P., & Taris, T. W. (2008). Work engagement: An emerging concept in occupational health psychology. Work & Stress, 22(3), 187-200.

Barnes, C. M., Lucianetti, L., Bhave, D. P., & Christian, M. S. (2015). “You wouldn’t like me when I’m sleepy”: Leaders’ sleep, daily abusive supervision, and work unit

engagement. Academy of Management Journal, 58(5), 1419-1437.

Baumeister, R. F., & Vohs, K. D. (2007). Self‐Regulation, ego depletion, and motivation. Social

and Personality Psychology Compass, 1(1), 115-128.

Boswell, W. R., & Olson-Buchanan, J. B. (2007). The use of communication technologies after hours: The role of work attitudes and work-life conflict. Journal of Management, 33(4), 592-610.

Derks, D., Bakker, A. B., Peters, P., & van Wingerden, P. (2016). Work-related smartphone use, work–family conflict and family role performance: The role of segmentation

preference. human relations, 69(5), 1045-1068.

Derks, D., Duin, D., Tims, M., & Bakker, A. B. (2015). Smartphone use and work–home interference: The moderating role of social norms and employee work

(41)

41

Derks, D., van Mierlo, H., & Schmitz, E. B. (2014). A diary study on work-related smartphone use, psychological detachment and exhaustion: examining the role of the perceived segmentation norm. Journal of occupational health psychology, 19(1), 74.

Eerde, W. V. (2000). Procrastination: Self‐regulation in initiating aversive goals. Applied

Psychology, 49(3), 372-389.

Eerde, W. V. (2003). Procrastination at work and time management training. The Journal of

psychology, 137(5), 421-434.

Gino, F., Schweitzer, M. E., Mead, N. L., & Ariely, D. (2011). Unable to resist temptation: How self-control depletion promotes unethical behavior. Organizational Behavior and Human

Decision Processes, 115(2), 191-203.

Kühnel, J., Bledow, R., & Feuerhahn, N. (2016). When do you procrastinate? Sleep quality and social sleep lag jointly predict self‐regulatory failure at work. Journal of Organizational

Behavior.

Lanaj, K., Johnson, R. E., & Barnes, C. M. (2014). Beginning the workday yet already depleted? Consequences of late-night smartphone use and sleep. Organizational Behavior and

(42)

42

Lee, Y. K., Chang, C. T., Lin, Y., & Cheng, Z. H. (2014). The dark side of smartphone usage: Psychological traits, compulsive behavior and technostress. Computers in Human

Behavior, 31, 373-383.

Malachowski, D. (2005). Wasted time at work costing companies billions. San Francisco

Chronicle, 11.

Metin, U. B., Taris, T. W., & Peeters, M. C. (2016). Measuring procrastination at work and its associated workplace aspects. Personality and Individual Differences, 101, 254-263.

Muraven, M., & Baumeister, R. F. (2000). Self-regulation and depletion of limited resources: Does self-control resemble a muscle?. Psychological bulletin, 126(2), 247.

Nederlanders zijn dol op 4G. En vergroeid met hun smartphone. (n.d.). Retrieved February 02, 2017, from https://www.nrc.nl/advertentie/deloitte/nederlanders-zijn-dol-op-4g-en- vergroeid-met-hun-smartphone-2

Ohly, S., & Latour, A. (2014). Work-Related Smartphone Use and Well-Being in the Evening. Journal of Personnel Psychology.

Oulasvirta, A., Rattenbury, T., Ma, L., & Raita, E. (2012). Habits make smartphone use more pervasive. Personal and Ubiquitous Computing, 16(1), 105-114.

(43)

43

Rahimnia, F., & Mazidi, A. R. K. (2015). Functions of control mechanisms in mitigating

workplace loafing; evidence from an Islamic society. Computers in Human Behavior, 48, 671-681.

Schaufeli, W. B., & Bakker, A. B. (2010). Defining and measuring work engagement: Bringing clarity to the concept. Work engagement: A handbook of essential theory and research, 10-24.

Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006). The measurement of work engagement with a short questionnaire: A cross-national study. Educational and psychological

measurement, 66(4), 701-716.

Smit, B. W., & Barber, L. K. (2015). Psychologically detaching despite high workloads: The role of attentional processes.

Sonnentag, S., Mojza, E. J., Binnewies, C., & Scholl, A. (2008). Being engaged at work and detached at home: A week-level study on work engagement, psychological detachment, and affect. Work & Stress, 22(3), 257-276.

Tangney, J. P., Baumeister, R. F., & Boone, A. L. (2004). High self‐control predicts good adjustment, less pathology, better grades, and interpersonal success. Journal of

(44)

44

Wang, A. (2017, January 01). French employees can legally ignore work emails outside of office hours. Retrieved January 19, 2017, from

https://www.washingtonpost.com/news/worldviews/wp/2017/01/01/french-employees- can-legally-ignore-work-emails-outside-of-

(45)

45

Appendix

Dutch Survey (diary study)

Smartphone use

Hoeveel minuten heeft u gisteren na 21.00 uur uw smartphone voor werk gebruikt? Hoeveel minuten heeft u gisteren na 21.00 uur uw smartphone voor privézaken gebruikt?

- Time spent on the smartphone for work-related or private use was only asked at T1.

Work engagement

Tot nu toe vandaag…

Vloog de tijd om toen ik aan het werk was Tijdens het werk, vergat ik alles om mij heen Was ik geabsorbeerd in mijn werk

- Work-engagement was tested in the morning and afternoon (Work Engagement T1 and Work Engagement T2).

Procrastination

Tot nu toe vandaag…

Verspilde ik mijn tijd, maar was ik niet in staat om hier iets aan te doen

Had ik mijzelf beloofd om iets te doen, maar uiteindelijk was ik niet vooruit te branden Was ik een enorme tijdverspiller

(46)

46

- Procrastination was tested in the morning and afternoon (Procrastination T1 and Procrastination T2).

Dutch Survey (one-shot)

Self-control

Ik ben goed in het weerstaan van verleidingen

Ik vind het nogal moeilijk om slechte gewoonten te doorbreken Ik zou willen dat ik meer zelfdiscipline had

Mensen zeggen over mij dat ik over een ijzeren discipline beschik

English Survey (diary study)

Smartphone use

How many minutes did you use your smartphone for work after 9 PM last night?

How many minutes did you use your smartphone for personal use after 9 PM last night? - Time spent on the smartphone for work-related or private use was only asked at T1.

Work engagement

Today, time flew when I was working

Today while working, I forgot everything else around me Today, I was immersed in my work

- Work-engagement was tested in the morning and afternoon (Work Engagement T1 and Work Engagement T2).

(47)

47

Procrastination

Today, I was a time waster but I couldn’t seem to do anything about it Today, I promised myself I’ll do something and then dragged my feet Today, I was an incurable time waster

- Procrastination was tested in the morning and afternoon (Procrastination T1 and Procrastination T2).

English Survey (one-shot)

Self-control

I am good at resisting temptation I have a hard time breaking bad habits I wish I had more self-discipline

How does private versus work smartphone use in the evening affect your workday the next day? : a study of whether smartphone use in the evening leads to both more procrastination at work and less work engagement the nex