Master’s Thesis
Master’s programme Communication Science
Stay connected: A study about the relationship between
work-related smartphone use and job satisfaction.
Author:
Joannes Marten Wouda
11638435
Supervisor:
Claartje ter Hoeven
Abstract
In the current study, we examined the extent to which there was a relationship between work-related smartphone use and the employee’s perceived level of job satisfaction, through employee engagement and organizational identification. In addition, we took into account the possible impact of cultural orientation on this relationship too. The study was of great relevance because of the enormous amount of smartphone users in the contemporary workplace, the relatedness of job satisfaction to several important organizational factors such as turn-over rates and financial performances and lastly because of the increasing role that cultural differences seem to play within the workplace due to the
globalization. First, it was expected that the relationship between work-related smartphone use and the employee’s perceived level of job satisfaction would be mediated by both employee engagement and organizational identification. Second, we expected this mediated relationship to be stronger among employees who held strong collectivistic values and norms, compared to employees who held weak collectivistic values and norms. In total, 152 respondents who used their smartphones for work-related purposes completed the entire questionnaire. The results of our bootstrapped mediation analyses supported our expectation of work-related smartphone use being positively related to job satisfaction, through employee engagement and organizational identification. However, this mediated relationship between work-related smartphone use and job satisfaction did not turn out to be significantly stronger among employees who held strong collectivistic values and norms, compared to employees who held weak collectivistic values and norms.
Stay connected
Due to contemporary communication technologies, employees nowadays are able to work from any place at any moment of the day. Smartphones are an example of these technologies, and with their option to connect to the internet, the online connectedness has only become bigger (Karlson, Meyers, Jacobs, Johns, & Kane, 2009). It should not come as a surprise then, that the amount of studies focusing on the possible consequences of this constant connectedness has grown tremendously (Boswell & Olson-Buchanan, 2007; Derks & Bakker, 2014; Derks, van Mierlo, & Schmitz, 2014; Lanaj, Johnson, & Barnes, 2014). Some of these consequences were negative, such as an increased work-to-life conflict (Boswell & Olson-Buchanan, 2007), or a decreased detachment from work (Derks, van Mierlo, & Schmitz, 2014). It has even been linked to experienced burn-out (Derks & Bakker, 2014).
However, some studies found positive consequences as well (Dery, Kolb, & MacCormick, 2014; Sardeshmukh, Sharma, & Golden, 2012). Teleworking for example, which is described as the increasingly common practice which involves working away from the office while staying connected by using communication technology, led to reduced work pressure and role conflict and increased feelings of autonomy (Sardeshmukh, Sharma, & Golden, 2012). Further, research by Dery, Kolb and MacCormick (2014) showed that most of the interviewed employees saw modern communication technologies as liberating them physically from the office while still maintaining connectivity. As a result, employees felt more productive throughout the entire day (Dery, Kolb, & MacCormick, 2014).
Another possible consequence of the constant connectedness provided by contemporary communication technologies is its impact on an employee’s perceived level of job satisfaction. The importance of a study into this possible impact becomes clear when taking into account job
satisfaction’s relatedness to other organizational factors such as financial performances (Wood, van Veldhoven, Croon, & de Menezes, 2012), absenteeism (Scott & Taylor, 1985) and turnover rates (Lambert, Hogan, & Barton, 2001). However, despite this, results of studies about the use of contemporary communication technologies and its impact on an employee’s perceived level of job satisfaction do not seem to be congruent with each other yet.
For instance, Fonner and Roloff (2010) found that teleworkers were more satisfied with their jobs than office-based employees, which was partly due to the possibility to work from anywhere at any moment of the day. In line with this, the study conducted by Diaz, Chiaburu, Zimmerman and Boswell (2012) found a positive relationship between the use of communication technologies and job satisfaction as well. However, contrary to these findings, Morganson and colleagues (2010) found no significant effect of teleworking on job satisfaction. Although the work-related use of contemporary communication technologies led to increased feelings of autonomy and flexibility, it was due to isolation from the workplace that, in their study, there was no significant difference in impact on job satisfaction between those who were able to work from anywhere at any time and those who were not. This finding was supported by the study conducted by Baily and Kurland (2002), who found little
evidence of telework, and thus the use of mobile communication technologies, leading to an increase in job satisfaction. Concluding, there is still no generally accepted idea about the relationship between the use of modern communication technologies and an employee’s perceived level of job satisfaction, which is worrisome for several reasons, including the enormous amount of smartphone users we have in the world (Dery et al, 2014) and the impact that job satisfaction turned out to have on several significant organizational factors (Lambert et al, 2001; Scott & Taylor, 1985; Wood et al, 2012). Therefore, the main goal of the current study is to provide new insights into this relationship. More specifically, this study will focus on the following research question:
“To what extent is there a relationship between an employee’s work-related smartphone use and his or her personal perceived level of job satisfaction ?”.
To answer this question, the research first takes a closer look at work-related smartphone use and its possible effect on job satisfaction via employee engagement. Employee engagement is defined as the positive relationship a person has with his or her job, characterized by a sense of meaning, competence and impact (Macey & Schneider, 2008), leading to an employee that is mentally, emotionally and physically involved in one’s work (MacCormick, Dery, & Kolb, 2012). In the same study by MacCormick, Dery and Kolb (2012), there was suggested a possible relationship between the use of communication technology on one hand and engagement on the other, stating that high
engagement should be seen as a consequence of having a technological device (e.g. smartphone) within reach most of the time. This increased engagement, in turn, proved to be positively related to the employee’s perceived level of job satisfaction (Alarcon & Lyons, 2011), and the current study therefore will first investigate employee engagement as an underlying mechanism in the relationship between work-related smartphone use and the employee’s perceived level of job satisfaction.
Second, the possible role of organizational identification in the relationship between work-related smartphone use and job satisfaction will be examined. Organizational identification can be defined as the ‘oneness’ of employees with their organizations and the extent to which one shares beliefs about the organization’s central, enduring and distinctive characteristics (Ashforth & Mael, 1989). Several studies showed communication to be positively related to organizational identification (Bartels, Peters, de Jong, Pruyn, & van der Molen, 2010; Postmes, Tanis, & de Wit, 2001). For
example, Bartels and colleagues (2010) found that especially the vertical dimension of communication was of great importance for organizational identification. This finding was in line with the results of Postmes, Tanis and de Wit (2001), who found that it was due to communication that comprehension about what the organization stands for increased, with organizational identification as a result. However, organizational identification should not only be seen as a consequence of communication, as it also turned out to serve as an important antecedent of other organizational factors, including job satisfaction (van Dick et al, 2004). Therefore, with organizational identification
functioning as both a consequence of communication (Bartels et al, 2010) and a antecedent of job satisfaction (van Dick et al, 2004), organizational identification is expected to be an underlying mechanism in the relationship between work-related smartphone use and job satisfaction as well. Lastly, cultural orientation among employees will be taken into account, which is of great value because of the increased role that cultural diversity seems to play in the workplace, mostly due to worldwide globalization (Martin, 2014). Cultural orientation already turned out to have
consequences for numerous organizational factors, including work-related communication (Sinha & Kanungo, 1997). For example, Sinha and Kanungo (1997) found that collectivists attached more value to being connected to their coworkers than individualists. Hence, one would expect cultural orientation to have an effect on the work-related smartphone use of the respondent as well, since work-related smartphone use facilitates this connection between coworkers.
In sum, this study aims to gain insight into the possible relationship between work-related smartphone use and job satisfaction and the mediating roles that employee engagement and
organizational identification play within this relationship. Moreover, the effect of cultural orientation on these relationships will be taken into account as well. With that, the study will contribute to existing literature for two reasons. First, there is still no generally accepted idea about the relationship between work-related smartphone use and employees’ perceived level of job satisfaction (Baily & Kurland, 2002; Fonner & Roloff, 2010), or the role that employee engagement and organizational identification could play in this relationship. Second, it will contribute to existing literature on the impact of cultural orientation on organizational factors such as job satisfaction (Hui, Yee, & Eastman, 1995). Figure 1 displays the conceptual model for this study.
Figure 1. Conceptual model Work-related smartphone use Employee engagement Employee’s job satisfaction Organizational identification Cultural orientation
Work-related smartphone use, employee engagement and job satisfaction
Nowadays, people use their smartphones everywhere, for any purpose and at any moment of the day. In the Netherlands alone, eighty-six percent of Dutch citizens own a smartphone (Wijkman – van Aalst, 2016). Worldwide, more than half of all handheld devices are smartphones (Dery et al, 2014). Consequently, online connectedness is stronger than ever before.
Numerous studies discussed the possible impact of this connectedness on, for example, employees’ family life (Boswell & Olson-Buchanan, 2007; Derks & Bakker, 2014). However, the impact goes further than the employees’ family life alone, as there seem to be consequences for the organization as a whole and the professional life of the individual employee as well (Collins, Cox, & Wootton, 2015; Dery, Kolb, & McCormick, 2014). Regarding the consequences for the organization as a whole, work-related smartphone use led to an increased productivity during non-work days (Collins, Cox, & Wootton, 2015), and a greater customer satisfaction which, in turn, increased organizational performance (Dery et al, 2014). On the level of the individual employee, one of the consequences turned out to be the (sometimes worrying) increase in employee engagement (Mazmanian, Orlikowski, & Yates, 2013).
Employee engagement is defined as the positive relationship that a person has with his or her job, characterized by a sense of meaning, competence and impact (Macey & Schneider, 2008), leading to an employee that is mentally, emotionally and physically invested in one’s work (MacCormick et al, 2012). Schaufeli, Salanova, Gonzalez-Roma and Bakker (2002) further stated that engagement consisted out of three separate dimensions: Vigor, dedication and absorption. Vigor could be best described as high levels of energy and mental resilience while working, leading to persistence in case of challenging situations (Salanova, Agut, & Peiró, 2005). Dedication could be best described as a sense of significance, enthusiasm, inspiration and pride (Hakanen, Bakker, & Schaufeli, 2006). Lastly, absorption refers to the situation in which a person is engrossed in his or her tasks so that it feels like time is passing quickly (Basikin, 2007).
Several studies came with antecedents of employee engagement, with communication being one of them (Welch, 2011). According to Welch, communication in the workplace led to several responses which were highly comparable to the three dimensions of engagement as explained by Schaufeli and colleagues (2002). Likewise, Bindl and Parker (2010) found that communication turned out to have the potential to share the organizational values and goals that, in turn, resulted in increased employee engagement. Hence, it could be concluded that communication may be an important
predictor of employee engagement and with work-related smartphone use facilitating this
communication, one would expect work-related smartphone use to be related to employee engagement as well.
The social exchange theory by Cropanzano and Mitchell (2005) explains the possible relationship between work-related smartphone use and employee engagement. According to this theory, there could develop a relationship that is considered as favorable when any kind of beneficial
resource is offered to an individual. As a consequence, the recipient of the resource will reciprocate with positive and beneficial cognitions, emotions and behaviors which form engagement (Karanges, Johnston, Beatson, & Lings, 2015). Applied to the current study, it could be stated that it is work-related smartphone use that facilitates the interactions via which beneficial resources could be exchanged. So it is also through work-related smartphone use that favorable relationships in the workplace could develop, which lead to employee engagement.
However, besides communication as an important antecedent of employee engagement, some studies derived consequences of employee engagement as well (Bakker, Schaufeli, Leiter, & Taris, 2008; Saks, 2006). Engagement, for example, predicted job performances and client satisfaction (Bakker et al, 2008), just as it predicted organizational commitment, intention to quit, organizational citizenship behavior and, lastly, job satisfaction (Saks, 2006).
Locke (1976, p. 1300) described job satisfaction as “the positive emotional state resulting from the appraisal of one’s job or job experiences”. With job satisfaction being linked to other
organizational factors such as financial performances (Wood et al, 2012), absenteeism (Scott & Taylor, 1985) and turnover (Lambert et al, 2001), it is especially job satisfaction as a consequence of employee engagement that will be discussed more in-depth in the current study.
An explanation for job satisfaction as a consequence of employee engagement was provided by Karanika-Murray, Duncan, Pontes and Griffiths (2015). They found that the three dimensions of employee engagement (vigor, dedication and absorption) were good predictors of job satisfaction, with dedication showing the strongest relationship with job satisfaction. With dedication consisting out of feelings of significance, enthusiasm, inspiration and pride (Schaufeli et al, 2002), one could state that there are good feelings which are a part of engagement that lead to overall job satisfaction. This was supported by the research conducted by Herzberg and Mausner (1959), who found two factors that turned out to be good predictors of job satisfaction: Motivators and hygiene factors. One of them, motivators, was described as the good feelings that related directly to the job itself. Hygiene factors were described as the conditions that surrounded doing the job, such as salary, working conditions and job security. So it indeed seem to be the good feelings that directly follow from doing the job that explain the relationship between employee engagement and job satisfaction.
In sum, it could be expected that it is through employee engagement that work-related
smartphone use is related to job satisfaction. First of all, with work-related smartphone use facilitating the exchange of beneficial resources, favorable relationships within the workplace could develop, which employees are expected to reciprocate with positive and beneficial cognitions, emotions and behaviors, that together form engagement (Karanges et al, 2015). At the same time, we can conclude that it is through this increased engagement that job satisfaction is expected to increase (Herzberg & Mausner, 1959; Karinka-Murray et al, 2015). This is why employee engagement is expected to be an underlying mechanism of the relation between work-related smartphone use and employees’ job satisfaction, which leads us to our first hypothesis:
Hypothesis 1: Work-related smartphone use and job satisfaction are positively related, through enhanced employee engagement.
Work-related smartphone use, organizational identification and job satisfaction According to the social identity theory, social identity is the perception of being one with a group (Ashforth & Mael, 1989). This perception leads to behaviors that traditionally are associated with group thinking and reinforcement of the antecedents of identification, such as behaviors that are in line with the identity of the group, support for groups that embody this identity and stereotypical perceptions of the self. A specific type of social identity is one’s organizational identity, which is the result of organizational identification.
Organizational identification is defined as “the perceived oneness with an organization and the experience of the organization’s successes and failures as one’s own” (Mael & Ashforth, 1992, p. 103). Organizational identification turned out to have several antecedents (Mael & Ashforth, 1992), with organizational communication being one of them (Smidts, Pruyn, & van Riel 2001).
In their study, Smidts, Pruyn and van Riel (2001) explored the relationships between organizational identification, perceived external prestige and communication. The study led to the conclusion that internal communication predicted organizational identification. Two explanations for this relationship were given by research conducted earlier, in which communication first of all turned out to be related to the creation of a shared meaning which, through an enhanced sense of
organizational identity, consequently increased the organizational identification (Wiesenfeld,
Raghuram, & Garud, 1999). Second, communication turned out to lead to organizational identification by providing employees with a sense of ownership (Wiesenfeld, Raghuram, & Garud, 1999). Another study that focused on the relationship between communication and organizational identification found that communication helped individuals identify with the organization by sending them messages that included the goals, values and achievements of the organization and by supplying the employees with information regarding the guidelines for individual and collective action (Cheney, 1983). So, as Dutton, Dukerich and Harquail (1994) already mentioned, it is through its ability to expose individuals to the identity of the organization that communication leads to organizational identification.
However, besides communication as an important antecedent of organizational identification, there were found several consequences of organizational identification as well (van Dick, van
Knippenberg, Kerschreiter, Hertel, & Wieseke, 2008; Riketta, 2005). Job satisfaction, for example, turned out to be a consequence of communication, as explained in the paper by Riketta (2005). The paper was written with the aim to present a comprehensive meta-analysis of the research regarding organizational identification, and its results indicated that organizational identification correlated with a wide range of work-related attitudes, with job satisfaction being one of them. Likewise, van Dick et al (2007) found several consequences of organizational identification, including job satisfaction.
Therefore, as organizational identification increases, supportive attitude towards the organization and the work they are doing are expected to increase as well (Karanges et al, 2015).
An explanation for the relationship between organizational identification and job satisfaction could be given based on the social identity theory by Tajfel and Turner (1985), which states that the parts of individuals’ identities that come from their membership of a particular group have a big impact on the image they have of themselves, also known as their self-concept. This self-concept, in turn, influences their affect, cognitions and behavior towards the group they are a member of. Ashforth and Mael (1989) applied the social identity theory to the organizational context and found out that the organization one works for could be seen as a distinct part of individuals’ identities as well. As one therefore could expect, this organizational identity turned out to have an impact on the individual’s self-concept and thus could be used to explain attitudes and behaviors towards the job one had. This is why organizational identification is expected to be positively related to an individual’s job satisfaction. This was supported by the research conducted by Karanika-Murray and colleagues (2015), who found organizational identification to be a good predictor of job satisfaction indeed.
In sum, it could be expected that it is also through organizational identification that related smartphone use is related to the employee’s level of job satisfaction. First of all, with work-related smartphone use facilitating the creation of a shared meaning and a sense of ownership (Wiesenfeld et al, 1999), work-related smartphone use is expected to increase organizational
identification. However, it is through this increased organizational identification that job satisfaction is expected to increase as well (Ashforth & Mael, 1989; Karanika-Murray et al, 2015). This is due to the fact that the organization one works for is expected to lead to a particular self-concept which, in turn, influences attitudes and behaviors. Therefore, organizational identification is expected to be an underlying mechanism of the relation between work-related smartphone use and job satisfaction as well, which leads us to our second hypothesis:
Hypothesis 2: Work-related smartphone use and job satisfaction are positively related, through increased organizational identification.
Cultural orientation
Cultural orientation refers to the attitude one has towards different cultures and the levels of engagement therein (Wong, Newton & Newton, 2014). It often is measured by using the framework developed by Triandis (1989), who used the division of cultural orientation into the individualism-collectivism dimensions as proposed by Hofstede (1980), supplemented with a horizontal and vertical dimension. Consequently, the following four dimensions were born: horizontal individualism, vertical individualism, horizontal collectivism and vertical collectivism. Each dimension is characterized by specific behaviors, with horizontal individualism being characterized by individuals who view themselves as equal to others and therefore strive for more individual uniqueness (Wong, Newton, &
Newton, 2013). Individuals with a vertical individualistic cultural orientation value personal status, which is earned via direct competition with others (Triandis, 1989). Horizontal collectivism is characterized by a strong dependency on other individuals, which leads to the aversion towards authoritarian aggression (Triandis, 1989). Lastly, individuals with a vertical collectivistic cultural orientation are characterized by their willingness to sacrifice their own dreams in order to achieve the goals of the in-group (Triandis, 1989).
With the four dimensions of cultural orientation and the specific characteristics that come with them, cultural orientation is also expected to impact the relationship between work-related smartphone use and job satisfaction and the effect that employee engagement and organizational identification are expected to have on this relationship.
First and foremost, cultural orientation is expected to influence the work-related smartphone use of the respondents, which is based on findings that suggest that as employees held stronger collectivistic norms and values, they turned out to attach more value to being connected with their coworkers (Sinha & Kanungo, 1997). With smartphone use providing this connection between coworkers, work-related smartphone use is also expected to be different among individuals with different cultural orientations. The idea of smartphones facilitating the connection was supported by the research of Park and Lee (2012), who explored the relationship between motives of smartphone use, social relation, and psychological well-being. Their results showed that smartphones helped users to maintain human relations and develop feelings of belongingness.
Second, cultural orientation is expected to impact employee engagement as well. As mentioned by Rurkkhum and Bartlett (2012), employee engagement refers to “the positive psychological conditions that lead employees to invest themselves actively in their role and
organization” (p. 159). Another term that relates to the employees’ willingness to invest themselves actively in the organization is Organizational Citizenship Behavior (OCB). OCB could be described as the work-related behaviors that are not officially rewarded, but still increase the organization’s
effectiveness (Moorman & Blakely, 1995). Numerous studies found that as people held stronger collectivistic values and norms, they were more likely to perform organizational citizenship behaviors and thus were more likely to invest in the organization (Dávila de León & Finkelstein, 2011;
Moorman & Blakely, 1995). With OCB being significantly and positively related to employee engagement (Babcock-Roberson & Strickland, 2010) it also could be expected that employee engagement will get stronger as collectivistic values and norms among employees get stronger too.
Lastly, cultural orientation is expected to have an impact on organizational identification. This is because of the fact that organizational identification can be seen as an aspect of the self (Dutton et al, 1994) and the impact of cultural orientation has already been proven for aspects of the self. For example, one study showed that as the collective aspect of the self played a bigger role, people were more likely to be influenced by the norms, role definitions and values of a particular collective of which they were part of, such as an organization one worked for (Triandis, 1989). With one’s
organizational identity being seen as a collective aspect of the self (Dutton et al, 1994), the impact of organizational identification on the relationship between work-related smartphone use and job satisfaction is therefore expected to be stronger among employees who hold stronger collectivistic values and norms.
In conclusion, based on the results of these former studies, it could be expected that as the collectivistic values and norms among employees get stronger, the mediated relationship between work-related smartphone use and job satisfaction gets stronger as well. (Babcock-Roberson &
Strickland, 2010; Dávilla de León & Finkelstein, 2011; Dutton et al, 1994; Moorman & Blakely, 1995; Triandis, 1989). Therefore, the third and last hypothesis is as follows:
Hypothesis 3: The relationship between work-related smartphone use and job satisfaction, through employee engagement and organizational identification, will be stronger among employees who hold strong collectivistic values and norms, compared to employees who hold weak collectivistic values and norms.
Method
Here, the components of the method section will be discussed, consisting of the sample and procedure, the demographics of the participants and the central variables and measurement thereof.
Sample and procedure
A non-probability convenience sampling method was used to recruit the participants for our online survey by sending them an e-mail, a direct message or wall-post via social media in which they were asked to take part in a quantitative survey research about work-related smartphone use and its effect on job satisfaction. To qualify for the research, participants had to own a smartphone and use it for work-related purposes. Those who indicated they did not own a smartphone, or did not use it for work-related purposes were kindly requested to not take part in the survey. If the participant met the criteria, he or she could proceed to the actual questionnaire by simply clicking on a link. Once arrived at the website, the respondents were instructed to fill out the entire questionnaire. After giving
informed consent, answering the filter questions and answering the questions referring to daily work-related smartphone use, employee engagement, organizational identification, job satisfaction and cultural orientation, the respondent eventually proceeded to several demographic questions. In total, 175 individuals participated (N = 175), of which 152 respondents (87%) completed the entire
questionnaire. The age of the respondents ranged from 21 to 62 (Mage = 32, SD = 9.49). Further, n = 52
(30 %) respondents identified as male, n = 77 (44 %) identified as female and n = 46 (26 %) respondents did not report their gender. There were no missing data cases for the main variables tested. Anonymity was guaranteed at all time, and all respondents participated on a voluntary basis and did not receive any incentives.
Demographics
The current study included numerous demographical variables (see Appendix A, Table 1), which were selected based on their expected link to the other study variables. Gender was included because of its possible relatedness to the smartphone use of the participant (Chen et al, 2017), just as age, organizational tenure and managerial position were included for the same reason (Al-Showarah, Naseer, & Sellehewa, 2014; Brett & Stroh, 2003; Zenger & Lawrence, 1989). The amount of working hours was included as a demographical variable as well, due to its possible relatedness to job
satisfaction (Scandura & Lankau, 1997). In order to respect respondent privacy, the demographics section of the survey did not utilize forced response, resulting in missing demographic data for some respondents.
Measures
In order to test the hypotheses and answer the research question regarding the relationship between work-related smartphone use and the participant’s perceived level of job satisfaction, the variables were operationalized into numerous survey questions. Most of the questions originate from existing scales which already have been validated in previous studies.
Daily work-related smartphone use was measured with the 4-item intensive smartphone-use scale, developed by Derks and Bakker (2014) to measure work-related smartphone use after working hours, supplemented with 4 additional questions in order to measure work-related smartphone use during working hours. The Derks and Bakker (2014) questionnaire was used because of its proven validity in other studies (Derks, Duin, Tims, & Bakker, 2015; Derks, Mierlo, & Schmitz, 2014) and adapted to the organizational context of the current study. Each question had to be rated on a 7-point Likert scale ranging from 1= strongly disagree to 7 = strongly agree. Example items from Derks and Bakker (2012) were: “I use my smartphone for work-related purposes until I go to sleep” and “I feel obligated to answer work-related messages during non-work hours”. Example items from the four questions that were added were: “I use my smartphone intensely for work-related purposes” and “I need a
smartphone to do my job properly”. A principal component analysis (PCA) showed that the eight items together formed two separate scales: There were two components with an eigenvalue above 1. Component 1 had an eigenvalue of 4.234, with an explained variance of 52.92 % and component 2 had an eigenvalue of 1.207 with an explained variance of 15.09 %. There were no double loaders with a difference below 0.20. Therefore, component 1 was computed into a new scale, called ‘smartphone use work time’ (α = .88). Component 2 was computed in a new scale as well, called ‘smartphone use non-work time’ (α = .76). To be able to measure work-related smartphone use in its widest form, the two scales were merged, so that both work-related smartphone use during and after worktime were measured. This scale, called ‘smartphone use total’ (M = 4.45, SD = 1.32 ), led to a Cronbach’s alpha of .87.
Employee engagement was measured using the Utrecht Work Engagement Scale (Schaufeli & Bakker, 2003). The reason behind the use of this questionnaire was its thorough development and the extensive reporting of this development (Schaufeli & Bakker, 2003). The items referred to each of the three dimensions of engagement: vigor (“At my job I feel strong and vigorous”), dedication (“I am
enthusiastic about my job”) and absorption (“I am immersed in my work”). For every item an answer was given by the use of a 5-point Likert scale (from 1 = never to 5= always). PCA showed that the sixteen items together formed four separate scales: Four components had an eigenvalue above 1 (see Appendix A, Table 2). There also were four double loaders with a difference below 0.20. After excluding these four items (“When I am working, I forget everything else around me”, “Time flies when I am working”, “At my work, I feel bursting with energy” and “When I get up in the morning, I feel like going to work”) a new factor analysis led to three components with an eigenvalue above 1, as one would expect based on the fact that the variable itself consisted out of three dimensions. After further examination, it turned out that all items measured loaded on three components (vigor, absorption, dedication) mirroring the original scale. The three components were computed into new scales. Component 1 was computed into a new scale, called ‘employee engagement dedication’ (α = .91). Component 2 was computed into a new scale, called ‘employee engagement absorption’ (α = .69). Lastly, component 3 was computed into a new scale, called ‘employee engagement vigor’ (α = .79). However, to be able to measure employee engagement in its widest form, the three scales were merged into a final scale (α = .88), called ‘employee engagement total’ (M = 3.51, SD = 0.62).
Organizational identification was measured with Mael and Ashforth’s (1992) 6-item scale. This scale turned out to be very useful in several other studies (Bhattacharya, Rao, & Glynn, 1995; Knippenberg & Schie, 2000). Respondents were asked to indicate their level of agreement on each question, ranging from 1= strongly disagree to 7 = strongly agree. Example items were: “When someone criticizes the organization, it feels like a personal insult” and “The organization’s successes are my successes”. PCA led to the conclusion that the six items formed a single scale: There was only one component with an eigenvalue above 1 (eigenvalue 3.394), with an explained variance of 56.56 %. All items correlated positively and high with the first component (all factor loadings were above 0.65) and therefore, the six items formed a single dimensional scale (α = .84) that measured organizational identification. As a result, a new variable was created, called ‘organizational identification total’ (M = 4.90, SD = 1.11).
Job satisfaction was measured with three items adapted from the Job Diagnostic Survey (Hackman & Oldham, 1980) and five items adopted from the Job satisfaction Subscale of Michigan Organizational Assessment Questionnaire (Cammann, Fichman, Jenkins, & Klesh, 1979). This combination of questionnaires was chosen because, in this way, both general job satisfaction and more specific job satisfaction aspects were measured. Respondents were requested to indicate how true the statements were for them. Answers could be given on a 7-point Likert scale (from 1= strongly disagree to 7 =
strongly agree). An example item was “In general, I like my job”. PCA led to the conclusion that the eight items formed a single scale: There was only one component with an eigenvalue above 1 (eigenvalue 4.928), with an explained variance of 61.61 %. All items correlated positively with the first component (all factor loadings were above 0.53) and therefore, the eight items formed a single dimensional scale (α = .89) that measured job satisfaction. As a result, a new variable was created, called ‘job satisfaction total’ (M = 5.32, SD = 0.94).
Lastly, cultural background was measured with the sixteen items from the Individualism and Collectivism Scale (Triandis & Gelfland, 1998). The scale already consisted out of four separate dimensions, which were horizontal individualism, vertical individualism, horizontal collectivism, and vertical collectivism. Respondents had to indicate their level of agreement on a 7-point Likert scale, with answer options ranging from 1 = strongly disagree to 7 = strongly agree. Example items were “I would rather depend on myself than on others” and “The well-being of my coworkers is important to me”. PCA showed that the sixteen items together formed five separate scales: there were five
components with an eigenvalue above 1 (see Appendix A, Table 3). Moreover, there were three double loaders with a difference below 0.20. After excluding these three items (“My personal identity, independent of other, is very important to me”, “It is important to me that I respect the decisions made by my groups” and “When another person does better than I do, I feel ashamed of myself”) a new factor analysis showed only four components with an eigenvalue above 1, as was expected from the original scale. All items loaded on the four components indicted by the original scale, and were computed as follows: Component 1 was computed into a new scale, called ‘horizontal collectivism’ (α = .61). Component 2 was computed in a new scale, called ‘horizontal individualism’ (α = .71).
Component 3 was computed into a scale called ‘vertical collectivism’ (α = .75), and lastly, component 4 was computed into a scale, called ‘vertical individualism’ (α = .67). Since H3 only focused on employees with weaker or stronger collectivistic values and norms, the two collectivistic scales (‘horizontal collectivism’, ‘vertical collectivism’) were merged into one new scale (α = .72), called ‘collectivistic total’ (M = 5.42, SD = 0.76).
Analysis
Multiple analyses have been conducted in order to test the hypotheses. First, a Chi-square test of independence was conducted in order to test if the association between work-related smartphone use and the control variables gender and managerial position was significant. Further, to test whether heavy work-related smartphone users differed significantly from light work-related smartphone users regarding the control variables age, organizational tenure and working hours, an one-way analysis of variance (ANOVA) was conducted. For hypothesis 1 and 2, the PROCESS model 4 tool (Hayes, 2013) was used. For both hypotheses, job satisfaction was used as the dependent variable and work-related smartphone use as independent variable. However, employee engagement served as mediation variable
in hypothesis 1, whereas organizational identification served as the mediation variable in hypothesis 2. In order to compare respondents with strong collectivistic values and norms to those with weak collectivistic values and norms (H3), PROCESS model 7 (Hayes, 2013) was used, with the collectivistic scale as moderating variable.
Results
Before the hypotheses testing results are presented, the results regarding our control variables will be displayed. Then the results of the PROCESS analysis regarding the mediating effects of employee engagement and organizational identification on the relationship between work-related smartphone use and job satisfaction will be presented. Lastly, a comparison between the two cultural orientations will be made. Table 4 (see Appendix A) presents the means (M), standard deviations (SD) and zero-order Pearson correlations (r) for the study variables and reliability coefficients (Cronbach’s α) for all study variables.
Control variables
In order to test whether work-related smartphone use was independent of gender and
managerial position, a Chi-square test for independence was conducted. The results revealed that there was no significant association between work-related smartphone use and both gender (X2 (1) = 29.02, p = .823) and managerial position (X2 (1) = 30.51, p = .770). Further, to test whether work-related smartphone use differed significantly regarding the remaining control variables (age, organizational tenure and working hours), an one-way analysis of variance (ANOVA) was conducted. Again, there were no significant associations found between work-related smartphone use and age (F(37, 128) = 1.30, p = .16), organizational tenure (F(37, 128) = 1.00, p = .50), or working hours ( F(36, 124) = .78, p = .80). Based on these results it could be concluded that the control variables were independent of the main variables, and therefore will not be taken into further consideration.
Work-related smartphone use, job satisfaction and employee engagement
Hypothesis 1 proposed that work-related smartphone use was positively related to job satisfaction, through enhanced employee engagement. PROCESS model 4 (Hayes, 2013) was used to test this hypothesis, including 5000 bootstrap samples, with work-related smartphone use as the independent variable, job satisfaction as the dependent variable and employee engagement as the mediator. Mediation analysis showed a significant and positive relation between work-related smartphone use and job satisfaction, through employee engagement (see Appendix A, Figure 2). The confidence interval for the indirect effect (b* = .17, SE = .05) was entirely above zero (.08 to .26). Therefore it could be concluded that H1 was supported: Work-related smartphone use is positively related to job satisfaction, through enhanced employee engagement.
Work-related smartphone use, job satisfaction and organizational identification
Hypothesis 2 proposed that work-related smartphone use and job satisfaction would be
positively related through increased organizational identification. Again, PROCESS model 4 by Hayes (2013) was used to test this hypothesis, including 5000 bootstrap samples, with work-related
smartphone use as the independent variable, job satisfaction as the dependent variable and
organizational identification as the mediator. Mediation analysis showed a significant indirect effect of work-related smartphone use on the employee’s level of job satisfaction (see Appendix A, Figure 3). Employees who used their smartphone to a higher degree reported increased organizational
identification and, in turn, exhibited a higher perceived level of job satisfaction. The confidence interval for the indirect effect (b* = .09, SE = .03) was entirely above zero (.03 to .16) and therefore it could be concluded that H2 was supported as well: Work-related smartphone use and job satisfaction are positively related, through increased organizational identification.
Cultural orientation
Hypothesis 3 proposed that the relationship between work-related smartphone use and job satisfaction, through employee engagement and organizational identification, would get stronger as the collectivistic norms and values within the respondent got stronger as well. This hypothesis was based on the finding that as employees held stronger collectivistic norms and values, they attached more value to being connected with their coworkers (Sinha & Kanungo, 1997). A PROCESS model 7 by Hayes (2013) was used to test this hypothesis, including 5000 bootstrap samples. First, the analysis was run with work-related smartphone use as the independent variable, job satisfaction as the
dependent variable, employee engagement as mediator and collectivism as moderator. Results showed an insignificant indirect effect of work-related smartphone use on the employee’s level of job
satisfaction (b* = .05, SE = .05, t = 1.00, p = .32). Therefore, with employee engagement as mediating variable, H3 had to be rejected. Then, the PROCESS model 7 by Hayes (2013) was run again, but this time with work-related smartphone use as the independent variable, job satisfaction as the dependent variable, organizational identification as mediator and collectivism as moderator. Again, results showed an insignificant indirect effect of work-related smartphone use on employees’ level of job satisfaction (b* = -.07, SE = .09, t = -.75, p = .46). Therefore, with organizational identification as mediating variable, H3 had to be rejected as well.
In sum, the results led to the conclusion that the third hypothesis had to be rejected: The relationship between work-related smartphone use and job satisfaction, through employee engagement and organizational identification, is not stronger among employees who hold strong collectivistic values and norms, compared to employees who hold weak collectivistic values and norms.
Discussion
The main goal of the current study was to investigate the relationship between work-related smartphone use and the perceived level of job satisfaction of employees, through both employee engagement and organizational identification. Furthermore, the current research tried to gain insight into the possible effect of cultural orientation on this relationship.
Regarding our first hypothesis, expectations were confirmed: Work-related smartphone use indeed led to job satisfaction through employee engagement. This was in line with Karanges and colleagues ( 2015), who found that the possibility to exchange beneficial resources, such as knowledge or organizational goals and values, led to favorable relationships within the workplace which, in turn, led to an increased engagement. Our finding was also in line with a study conducted by Karanika-Murray et al (2014), in which employee engagement turned out to be a significant predictor of job satisfaction too. Regarding our second hypothesis, the results confirmed the assumption that the relationship between work-related smartphone use and job satisfaction would be mediated by organizational identification. This supported the findings of Bartels et al (2010), who found that communication was an important predictor of organizational identification indeed. It further supported the research conducted by van Knippenberg and van Schie (2000), in which it was found that
organizational identification led to an increase in job satisfaction.
In sum, regarding the main research question of the current study, it is through an increase in both employee engagement and organizational identification that work-related smartphone use is positively related to job satisfaction. With this finding, the current research contributes to existing literature by offering new insights into the work-related use of contemporary communication technologies and the effects thereof.
In regards to the effect of cultural orientation, it turned out that as the employees’ collectivistic values and norms got stronger, the relationship between work-related smartphone use and job
satisfaction through employee engagement and organizational identification did not significantly increase in strength. H3 was rejected. A possible explanation for this finding could be found in Marcoccia’s (2012) study, which states that, in cyberspace, users more or less share the same cultural codes and attitudes, regardless of their individual cultural background. So, although employees’ might have different values and norms in their private lives, once part of a collective, the impact of these personal values and norms could vanish, and be replaced by values and norms standardized by the collective. Marcoccia (2012) even warned that this cultural standardization would lead to a set of cultural codes and attitudes which reflects a North American ethos. Thus, if work-related smartphone use could lead to a same sort of cultural standardization, it could be expected that the differences in impact of cultural orientation on the mediated relationship between work-related smartphone use and job satisfaction will diminish. Regardless of the strength of employees’ collectivistic values and norms. However, to our knowledge, work-related smartphone use has not yet been linked to cultural standardization, and therefore, future research is advised to take into account this possible relationship.
Limitations and future research
There were several limitations regarding the current study. Firstly, due to the fact that data was collected at one point in time, potential common method variance might be present. Therefore, future research could choose for a temporal, proximal, psychological or methodological separation of measurement (Posakoff, MacKenzie, Lee, & Podsakoff, 2003). Further, it is due to this cross sectional data that nothing can be said about causality. In order to solve this, future research should conduct a longitudinal study. Another limitation is formed by the answer options of the scale that was used to measure cultural orientation. Since people who hold collectivistic values and norms tend to be more strongly influenced by the norms, role definitions and values of a particular collective in which they partake (Triandis, 1989), they are also expected to be less explicit about their own opinion than people who hold individualistic values and norms. Future research therefore should investigate the possible interaction between cultural orientation and the way questionnaires are answered, and the possible effect this could have on scales such as the one we used to measure cultural orientation. Future research should also take into account the possible relationship between work-related smartphone use and cultural standardization. This could lead to a clarification for the findings of our third hypothesis. Lastly, since the current study used a non-probability convenience sampling method, future research is advised to use a random probability sampling method. This should increase the representativeness of the entire population.
Conclusion and implications
It could be concluded that, work-related smartphone use is positively related to job satisfaction through both employee engagement and organizational identification. In practice, these findings provide us with new knowledge regarding the effect that contemporary communication technologies could have on the way employees perceive their job. Based on the results, organizations should choose more often for the use of a smartphone in order to share the company’s goals, values, performances and identity in order to enhance employee engagement and organizational identification what, in turn, will lead to an increase in job satisfaction among their employees. However, despite the positive impact that work-related smartphone use turned out to have on job satisfaction, the possible negative impact on, for example the family-life (Boswell & Olson-Buchanan, 2007; Derks & Bakker, 2014), should be taken into account as well. Therefore, organizations should make their employees aware of the possible negative effects of work-related smartphone use as well, and encourage them to strive for healthy boundaries between work and family life. Moreover, since the current study is among the first to simultaneously take into account work-related smartphone use, job satisfaction, employee
engagement, organizational identification and cultural orientation, future research should supplement the findings of the current study with bigger samples in longitudinal studies and include other
mediating variables, such as organizational culture or commitment, in order to increase the insight into how contemporary communication technologies such as smartphones could enhance job satisfaction.
References
Alarcon, G. M., & Lyons, J. B. (2011). The relationship of engagement and job satisfaction in working samples. The Journal of Psychology, 145(5), 463-480.
Al-Showarah, S., Naseer, A. J., & Sellahewa, H. (2014). Effects of user age on smartphone and tablet use, measured with an eye-tracker via fixation duration, scan-path duration, and saccades proportion. In International Conference on Universal Access in Human-Computer
Interaction(8514), 3-14.
Ashforth, B. E., & Mael, F. (1989). Social identity theory and the organization. Academy of Management Review, 14(1), 20-39.
Babcock-Roberson, M. E., & Strickland, O. J. (2010). The relationship between charismatic leadership, work engagement, and organizational citizenship behaviors. The Journal of
Psychology, 144(3), 313-326.
Bailey, D. E., & Kurland, N. B. (2002). A review of telework research: Findings, new directions, and lessons for the study of modern work. Journal of Organizational Behavior, 23(4), 383-400. 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.
Bartels, J., Peters, O., de Jong, M., Pruyn, A., & van der Molen, M. (2010). Horizontal and vertical communication as determinants of professional and organizational identification. Personnel Review, 39(2), 210-226.
Basikin, B. (2007). Vigor, Dedication and Absorption: Work engagement among secondary school English teachers in Indonesia. In: 2007 AARE International Conference, 27-28 November
2007, University of Notre Dame, Fremantle, Perth, Australia. (Unpublished)
Bhattacharya, C. B., Rao, H., & Glynn, M. A. (1995). Understanding the bond of identification: An investigation of its correlates among art museum members. The Journal of Marketing, 59(4), 46-57.
Bindl, U. K. and Parker, S. K. (2010). “Feeling good and performing well? Psychological engagement and positive behaviors at work”, in Albrecht, S.L. (Ed.), Handbook of Employee Engagement: Perspectives, Issues, Research and Practice, Edward Elgar, Cheltenham.
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.
Brett, J. M., & Stroh, L. K. (2003). Working 61 plus hours a week: why do managers do it?. Journal of Applied Psychology, 88(1), 67.
Chen, B., Liu, F., Ding, S., Ying, X., Wang, L., & Wen, Y. (2017). Gender differences in factors associated with smartphone addiction: a cross-sectional study among medical college students. BMC Psychiatry, 17(1), 341.
Cheney, G. (1983). The rhetoric of identification and the study of organizational communication. Quarterly Journal of Speech, 69(2), 143-158.
Collins, E. I., Cox, A. L., & Wootton, R. (2015). Out of work, out of mind? Smartphone use and work-life boundaries. International Journal of Mobile Human Computer Interaction, 7(3), 67-77.
Cropanzano, R., & Mitchell, M. S. (2005). Social exchange theory: An interdisciplinary review. Journal of Management, 31(6), 874-900.
Dávila de León, M. C., & Finkelstein, M. A. (2011). Individualism/collectivism and organizational citizenship behavior. Psicothema, 23(3), 401-406.
Derks, D., & Bakker, A. B. (2014). Smartphone use, work–home interference, and burnout: A diary study on the role of recovery. Applied Psychology, 63(3), 411-440.
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 engagement. Journal of Occupational and Organizational Psychology, 88(1), 155-177.
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.
Dery, K., Kolb, D., & MacCormick, J. (2014). Working with connective flow: how smartphone use is evolving in practice. European Journal of Information Systems, 23(5), 558-570.
Diaz, I., Chiaburu, D. S., Zimmerman, R. D., & Boswell, W. R. (2012). Communication technology: Pros and cons of constant connection to work. Journal of Vocational Behavior, 80(2), 500-508.
Dutton, J. E., Dukerich, J. M., & Harquail, C. V. (1994). Organizational images and member identification. Administrative Science Quarterly, 39(2), 239-263.
Fonner, K. L., & Roloff, M. E. (2010). Why teleworkers are more satisfied with their jobs than are office-based workers: When less contact is beneficial. Journal of Applied Communication Research, 38(4), 336-361.
Hackman, J. R., & Oldham, G. R. (1974). The job diagnostic survey: An instrument for the
diagnosis of jobs and the evaluation of job redesign projects. New Haven, CT: Yale University. Hakanen, J. J., Bakker, A. B., & Schaufeli, W. B. (2006). Burnout and work engagement among teachers. Journal of School Psychology, 43(6), 495-513.
Hayes, A. F. (2013), Introduction to Mediation, Moderation and Conditional Process Analysis: A regression-based approach. New York, NY: The Guildford Press.
Herzberg, F. M., & Mausner, B. (1959). B. & Snyderman, B.(1959). The motivation to work, New York, NY.
Hofstede, G. (1980), Culture’s Consequences: International Differences in Work-Related Values, Sage, Beverly Hills, CA.
Hui, C. H., Yee, C., & Eastman, K. L. (1995). The relationship between individualism— collectivism and job satisfaction. Applied Psychology, 44(3), 276-282.
Karanges, E., Johnston, K., Beatson, A., & Lings, I. (2015). The influence of internal
communication on employee engagement: A pilot study. Public Relations Review, 41(1), 129-131.
Karanika-Murray, M., Duncan, N., Pontes, H. M., & Griffiths, M. D. (2015). Organizational identification, work engagement, and job satisfaction. Journal of Managerial Psychology, 30(8), 1019-1033.
Karlson, A. K., Meyers, B. R., Jacobs, A., Johns, P., & Kane, S. K. (2009, May). Working overtime: Patterns of smartphone and PC usage in the day of an information worker. In
International Conference on Pervasive Computing (pp. 398-405). Springer, Berlin, Heidelberg. Knippenberg, D., & Schie, E. (2000). Foci and correlates of organizational identification. Journal
of Occupational and Organizational Psychology, 73(2), 137-147.
Lambert, E. G., Hogan, N. L., & Barton, S. M. (2001). The impact of job satisfaction on turnover intent: a test of a structural measurement model using a national sample of workers. The Social Science Journal, 38(2), 233-250.
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 Human Decision Processes, 124(1), 11-23.
Locke, E. A. (1976). The nature and causes of job satisfaction. In: Dunette, M. D., Handbook of Industrial and Organizational Psychology, Vol. 1, 1297-1343.
MacCormick, J. S., Dery, K., & Kolb, D. G. (2012). Engaged or just connected? Smartphones and employee engagement. Organizational Dynamics, 41(3), 194-201.
Macey, W. H., & Schneider, B. (2008). The meaning of employee engagement. Industrial Organizational Psychology, 1, 1–83. doi: 10.1111/j.1754-9434.2007.0002.x
Mael, F., & Ashforth, B. E. (1992). Alumni and their alma mater: A partial test of the reformulated model of organizational identification. Journal of Organizational Behavior, 13(2), 103-123. Marcoccia, M. (2012). The internet, intercultural communication and cultural variation. Language
and Intercultural Communication, 12(4), 353-368.
Martin, G. C. (2014). The effects of cultural diversity in the workplace. Journal of Diversity Management, 9(2), 89.
Mazmanian, M., Orlikowski, W. J., & Yates, J. (2013). The autonomy paradox: The implications of mobile email devices for knowledge professionals. Organization Science, 24(5), 1337-1357. Moorman, R. H., & Blakely, G. L. (1995). Individualism‐collectivism as an individual difference predictor of organizational citizenship behavior. Journal of Organizational Behavior, 16(2),
Morganson, V. J., Major, D. A., Oborn, K. L., Verive, J. M., & Heelan, M. P. (2010). Comparing telework locations and traditional work arrangements: Differences in work-life balance support,
job satisfaction, and inclusion. Journal of Managerial Psychology, 25(6), 578-595. Park, N., & Lee, H. (2012). Social implications of smartphone use: Korean college students'
smartphone use and psychological well-being. Cyberpsychology, Behavior, and Social Networking, 15(9), 491-497.
Postmes, T., Tanis, M. and De Wit, B. (2001). Communication and commitment in organizations: a social identity approach. Group Processes and Intergroup Relations, 4(3), 227-46.
Riketta, M. (2005). Organizational identification: A meta-analysis. Journal of Vocational Behavior, 66(2), 358-384.
Rurkkhum, S., & Bartlett, K. R. (2012). The relationship between employee engagement and organizational citizenship behavior in Thailand. Human Resource Development International, 15(2), 157-174.
Saks, A. M. (2006). Antecedents and consequences of employee engagement. Journal of Managerial Psychology, 21(7), 600-619.
Salanova, M., Agut, S., & Peiró, J. M. (2005). Linking organizational resources and work engagement to employee performance and customer loyalty: the mediation of service climate. Journal of Applied Psychology, 90(6), 1217.
Sardeshmukh, S. R., Sharma, D., & Golden, T. D. (2012). Impact of telework on exhaustion and job engagement: A job demands and job resources model. New Technology, Work and Employment, 27(3), 193-207.
Scandura, T. A., & Lankau, M. J. (1997). Relationships of gender, family responsibility and flexible work hours to organizational commitment and job satisfaction. Journal of Organizational Behavior, 18(4), 377-391.
Schaufeli, W., & Bakker, A. B. (2003). Work Engagement Scale: Preliminary Manual. Retrieved 27 October, Retrieved from http://www, schaufeli, corn/downloads/tests/Test% 20manual% 20UWES% 20% 28version% 201.1% 292010. pdf.
Schaufeli,W. B., Salanova, M., Gonzalez-Roma, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two-sample confirmatory factor analytic approach. Journal of Happiness Studies, 3(1), 71–92. doi: 10.1023/A:1015630930326
Scott, K. D., & Taylor, G. S. (1985). An examination of conflicting findings on the relationship between job satisfaction and absenteeism: A meta-analysis. Academy of Management Journal, 28(3), 599-612.
Sinha, J. B. P., & Kanungo, R. N. (1997). Context sensitivity and balancing in Indian organizational behavior. International Journal of Psychology, 32(2), 93–105.
Spector, P. E. (1985). Measurement of human service staff satisfaction: Development of the Job Satisfaction Survey. American Journal of Community Psychology, 13(6), 693-713.
Tajfel, H., & Turner, J. C. (1985). The social identity theory of intergroup behavior. In S. Worchel & W. G. Austin, Psychology of Intergroup Relations, 2(1), 7-24. Chicago: Nelson-Hall. Triandis, H. C. (1989). The self and social behavior in differing cultural contexts. Psychological Review, 96(3), 506.
Triandis, H. C. & Gelfland, M. J. (1998). Converging measurement of horizontal and vertical individualism and collectivism. Journal of Personality and Social Psychology, 74(1), 118-128. Van Dick, R., Christ, O., Stellmacher, J., Wagner, U., Ahlswede, O., Grubba, C., Hauptmeier, M., Höhfeld, C., Moltzen, K., & Tissington, P. A. (2004). Should I stay or should I go? Explaining turnover intentions with organizational identification and job satisfaction. British Journal of Management, 15(4), 351-360.
Van Dick, R., van Knippenberg, D., Kerschreiter, R., Hertel, G., & Wieseke, J. (2008). Interactive effects of work group and organizational identification on job satisfaction and extra-role
behavior. Journal of Vocational Behavior, 72(3), 388-399.
Wiesenfeld, B. M., Raghuram, S., & Garud, R. (1999). Communication patterns as determinants of organizational identification in a virtual organization. Journal of Computer‐Mediated
Communication, 3(4), 777-790.
Wijkman – van Aalst, T. (2016, June 26). 86 procent Nederlands bezit een smartphone. Retrieved from http://www.gsmhelpdesk.nl/nieuws/12857/86-procent-nederlanders-bezit-een-smartphone Wong, J., Newton, J. D., & Newton, F. J. (2014). Effects of power and individual-level cultural
orientation on preferences for volunteer tourism. Tourism Management, 42(1), 132-140.
Wood, S., Van Veldhoven, M., Croon, M., & de Menezes, L. M. (2012). Enriched job design, high involvement management and organizational performance: The mediating roles of job
satisfaction and well-being. Human Relations, 65(4), 419-445.
Zenger, T. R., & Lawrence, B. S. (1989). Organizational demography: The differential effects of age and tenure distributions on technical communication. Academy of Management Journal, 32(2), 353-376.
Appendices
Appendix A:
Table 1 Demographical Information and Descriptive Statistics
M SD Range
Age (years) 32.00 9.49 21.00 – 62.00
Organizational tenure (years) 3.50 1.34 0.50 – 7.00
Amount of working hours 39.23 10.95 9.00 – 70.00
Frequency % Gender Male 52 30.0 Female 77 44.0 Missing 46 26.0 Managerial position Yes 46 26.3 No 83 47.4 Missing 46 26.3
Table 2 Results initial principal factor analysis ‘Employee engagement’
Component Eigenvalue Explained variance (%)
1 7.757 45.63
2 1.532 9.01
3 1.286 7.56
4 1.012 5.95
Table 3 Results initial principal factor analysis ‘Cultural orientation’
Component Eigenvalue Explained variance (%)
1 3.298 20.61
2 2.952 18.45
3 1.541 9.63
4 1.207 7.54
Table 4 Means, standard deviations, correlations and reliability M SD 1 2 3 4 5 6 7 8 9 10 Age (1) 32.17 9.49 Gender (2) 1.60 0.49 -0.31* Working hours (3) 39.23 10.95 0.33* -0.43* Org. tenure (4) 3.50 1.34 0.67* -0.20* 0.26* Managerial position (5) 1.36 0.48 .37* -0.28* 0.39* 0.36* Smartphone use (6) 4.45 1.32 0.20* -0.07 0.20* 0.09 0.22*(.87) Employee engagement (7) 3.52 0.62 0.10 -0.06 0.13 0.02 0.27* 0.30*(.88) Organizational identification (8) 4.90 1.11 0.08 0.03 0.06 0.04 0.17* 0.30* 0.50*(.84) Job satisfaction (9) 5.32 .94 -0.00 -0.04 0.11 -0.44 0.14 0.12 0.69* 0.36*(.89) Cultural orientation (10) 4.61 0.60 -0.04 -0.11 0.04 -0.02 -0.6 -0.04 0.19* 0.09 0.18*(.72)
Notes: p < 0.05, one tailed
Figure 2. The mediation model for H1 (n = 129)
Notes: Mediation performed using PROCESS model 4 (Hayes, 2013), bootstrapped 95% confidence intervals, based on 5.000 samples.
Figure 3. The mediation model for H2 (n = 129)
Notes: Mediation performed using PROCESS model 4 (Hayes, 2013), bootstrapped 95% confidence intervals, based on 5.000 samples. Employee engagement Work-related smartphone use Job satisfaction b = 0.09, p = 0.19 Organizational identification Work-related smartphone use Job satisfaction b = 0.09, p = 0.19 R2 = 0.10 R2 = 0.10 R2 = 0.48 R2 = 0.15
Appendix B
Stay connected
Start of Block: Welcome
Q1 Welcome to the questionnaire.
You are about to participate in a research study to be conducted under the auspices of the Graduate School of Communication, a part of the University of Amsterdam.
In this study, you will be asked to answer several questions regarding your daily work-related smartphone use and its effect on your personal job satisfaction. Work-related smartphone use can range from texting a colleague for lunch to answering emails from
management. Due to the international orientation of this study, the entire study will be in English, all questions included. This study should take no longer than 15 minutes.
You are guaranteed that your anonymity will be safeguarded, and that your personal information will not be passed on to third parties under any condition. You can refuse to participate in the research or cut short your participation without having to give a reason for doing so. Furthermore you have up to 24 hours after participating to withdraw your permission to allow your answers or data to be used in the research. Lastly, no later than five months after the conclusion of the research, we will be able to provide you with a research report with the general results of the research.
For more information about the research and the invitation to participate, you are welcome to contact the project leader Marten Wouda at any time via marten.wouda@student.uva.nl.
Should you have any complaints or comments about the course of the research and the procedures it involves as a consequence of your participation in this research, you can contact the designated member of the Ethics Committee representing ASCoR, at the following address: ASCoR Secretariat, Ethics Committee, University of Amsterdam, Postbus 15793, 1001 NG Amsterdam; 020-525 3680; ascor-secr-fmg@uva.nl.
We would like to take this opportunity to thank you in advance for your assistance with this research, which we greatly appreciate.
o
I hereby declare that I have been informed in a clear manner about the nature and method of the research and I agree, fully and voluntarily, to participate in this research study. (1)End of Block: Welcome Start of Block: Filter question 1
Q2 Do you own a smartphone?
o
Yes (1)o
No (2)End of Block: Filter question 1 Start of Block: No smartphone
Q3 Since this study is about daily work-related smartphone use and you indicated that you do not own a smartphone, no further input from your side is needed. Therefore, the study ends here.
Thank you for your time.
