The New Ways of Working of knowledge-intensive
organizations
The effect on employee job satisfaction and the mediating role of knowledge sharing.
Saartje Bakker 10384677
28th June 2017
Master’s Thesis
Graduate School of Communication Master’s programme Communication Science
University of Amsterdam
Supervisor: Suzanne de Bakker
Nowadays, more and more knowledge-intensive organizations opt for an
organizational structure according to the ‘New Ways of Working (NWW)’, which empowers employees to independently determine their own working hours (when they work) and work locations (where they work), supported by technological communication capabilities. This master thesis focuses on the influence of these flexible work arrangements on employee job satisfaction and knowledge sharing. An online survey (N = 186) showed that the various forms of NWW, meaning flexible work hours, flexible work locations and flexible communication technologies, are positively correlated with both employee job satisfaction and knowledge sharing. This implies that employees who can determine their own work hours, locations and
communication technologies are more satisfied with their job and also share more knowledge. This research also concludes that when employees share more knowledge with other employees, they are more satisfied with their job. In order to find an explanation for the possible relationship between NWW and employee job satisfaction, this study looked at the mediating role of knowledge sharing of employees. The results have confirmed that the relationship between NWW and employee job satisfaction is partly explained by the sharing of knowledge, implying that because employees who can determine their own work hours, locations and technologies share more knowledge, they are more satisfied with their job. From the findings of this research it can be concluded that flexible working conditions can be beneficial to employee job satisfaction and knowledge sharing and hence for the operating result of knowledge-intensive organizations.
Abstract 2
1. Introduction 4
2. Theoretical framework 6
2.1 New Ways of Working 6
2.2 Employee job satisfaction 8
2.3 NWW makes employees more satisfied with their job 9
2.4 Knowledge sharing 12
2.5 New Ways of Working leads to sharing more knowledge 13 2.6 The sharing of more knowledge leads to more satisfied employees 15
3. Method 17
3.1 Design & sample 17
3.2 Procedure 17
3.3 Measures 18
3.3.1 New Ways of Working 18
3.3.2 Knowledge sharing 19
3.3.3. Employee job satisfaction 19
3.4 Analysis 20
4. Results 21
4.1 Simple regression analyses 21
4.2 Multiple regression analyses 23
4.3 Mediation analyses 24
5. Discussion 26
References 29
Appendix 33
The familiar routine: every morning the alarm clock goes off much too early; the gruelling journey through the cold weather to your office; the delayed public transport or enormous traffic jams; the same route to your workplace where you spend an average of 8 hours and when the clock strikes 6 pm the journey back home starts. In a world where almost everyone is in possession of a (smart) phone, computer or tablet and is accessible 24/7, a knowledge intensive organization where employees are mandatory to be present at the office from 9 to 18 o'clock seems to become obsolete. New technological possibilities and social developments provide an opportunity to escape from the above routine. Flexibility and autonomy seem to be the magic words for a modern organization. More and more knowledge intensive organizations therefore opt for an organizational structure according to the ‘New Ways of Working’.
The New Ways of Working (NWW) offer employees the opportunity to choose where and when they work, supported by technological communication capabilities (Ten Brummelhuis, Bakker, Hetland & Keulemans, 2012). Due to technological communications, it is no longer necessary to be with colleagues in the same room to work as a team (Cascio & Shurygailo, 2003). Virtual communication, working from a 'cloud' and countless technological possibilities are blurring
traditional working structures. Within the NWW, employees enjoy more freedom and control over their working structure (Kelliher & Anderson, 2008).
However, there is a lack of consensus when it comes to measuring the
effectiveness of NWW. On the one hand, research shows many advantages of NWW. According to Rennecker and Godwin (2005) for example, NWW can lead to efficient work processes and reducing organizational costs. On the other hand, there are also disadvantages of NWW. Some examples are increasing interruptions through the advent of communication technologies and an accumulation of sudden and
unpredictable tasks (Edley, 2001). Because of this lack of consensus, future research in the direction of NWW is necessary.
According to Ortega (2009), the possibility to determine when, where and how you work is offered by European companies specifically to improve the satisfaction of the employee. This thesis will therefore focus on the effects of NWW on employee job satisfaction. However, De Menezes and Kelliher (2011) compared previous studies based on the findings of existing literature on this effect of NWW on the satisfaction of the employee and came to the conclusion that there also was a lack of
consensus when it comes to measuring the effect of NWW on employee job
satisfaction. Research has therefore not been able to demonstrate a well-founded and generalizable relationship between NWW and employee job satisfaction. This is partly due to the difference in the operationalization of NWW (De Menezes &
Kelliher, 2011) and partly because the right mediating and/or moderating factors have not been tested yet.
Not every work is independent of location and time, as the work in factories or on construction sites is less suitable for time and location independent working. In this study the focus is therefore mainly on knowledge workers: the proportion of the working population who mainly use intelligence and information for their work (Bijl, 2009). Knowledge workers often work in knowledge-intensive organizations: an organization where the production factor ‘knowledge’ has a dominant role
(Weggeman, 1997). Moreover, there is also little evidence of the effect of NWW on an important business objective of these knowledge-intensive organizations; namely the sharing of knowledge. Bartol and Srivastava (2002) define knowledge sharing as sharing information, ideas and expertise relevant to the tasks performed by
individuals, teams, work units and organizations.
As mentioned above, there have been several studies on the impact of NWW on the satisfaction of employees, but with a lack of agreement on the outcomes (De Menezes and Kelliher, 2011; Rennecker & Godwin, 2005; Edley, 2001). However, there has not previously been investigated to what extent the sharing of knowledge among employees affects the relationship between NWW and employee job
satisfaction. The sharing of knowledge is therefore measured as a mediating variable in this study. This states that the sharing of knowledge affects the relationship between the NWW and employee job satisfaction. Due to the fact that this model is not yet examined, future research may use this as a base or framework. An attempt has been made to find a well-founded and generalizable relationship between the NWW, the sharing of knowledge and the satisfaction of the employee. Therefore, the results of this research are scientifically relevant and contribute to the literature on NWW.
This research is also relevant to society. Because of the interest in the increased job satisfaction of employees, this study is relevant for managers at corporate levels. Its results can serve as a basis for the justification of future work
situations. If NWW do indeed have a favourable effect on the employee’s satisfaction, it eventually could lead to a better performing organization. In result of this research, future leadership can instruct to adopt a more active role in making the sharing of knowledge priority, which in turn will stimulate an increase in positive employee job satisfaction.
It is important for organizations to know the advantages and disadvantages of NWW. The purpose of this study is therefore to investigate to what extent the NWW affect employee job satisfaction and what role knowledge sharing among employees plays a role in this. This leads to the following research question:
‘To what extent do the New Ways of Working have an effect on employee job satisfaction and how does the sharing of knowledge among employees play a role in
this effect?’
2. Theoretical framework 2.1 New Ways of Working
In 2010, Baane, Houtkamp and Knotter (2010) combined different aspects of flexible work designs and introduced the term New Ways of Working (NWW). NWW
originally arose as a compensation of managers to employees in the form of flexible working. Parents were given the right to request flexible working arrangements from their employer so that they could perform their work at home and could better combine family related issues (De Menezes & Kelliher, 2011).
The NWW consist of three interrelated elements (Ten Brummelhuis et al., 2012; Baane et al., 2010). Firstly, the timing of the work has become more flexible. Employees have more autonomy over the decision when they work, which means that there are no fixed working hours and no "nine to five jobs". Secondly, the NWW provide options for the employee’s work location, including office locations, at home and on the road (as in the train); employees no longer have fixed workstations
(Kelliher & Anderson, 2008). Instead, office spaces are offered that are suitable and accessible to every employee who enters the organization and employees are able to work wherever they want. Thirdly, the NWW are facilitated by new media
technologies such as e-mail, smart phones and video conferencing. NWW offer options for employees to communicate with colleagues, managers and customers through the use of ICT and phone calls, online messaging and digital meetings (Baane et al., 2010). The first two elements enable employees to work wherever and
whenever they want; the third element makes the other two possible. Due to new media technologies employees can connect with others or gain access to digital resources wherever and whenever they want.
Studies on this topic show different outcomes and paradoxes (De Menezes & Kelliher, 2011). Besides various positive effects of NWW, Ten Brummelhuis et al. (2012) find a negative effect of NWW. With the advent of smartphones and other new means of communication, the employee suffers from more interruptions in the form of emails and phone calls; these interruptions lead to exhaustion. Ter Hoeven, van
Zoonen & Fonner (2016) add to this that the use of mediated communication at the same time raises and lowers the well-being of employees, since it is on the one hand more accessible and employees can operate more efficiently, but on the other hand, it is also disturbing by interruptions and unpredictability.
Another drawback is that the use of mediated communication leads to the continuous presence of work. Thus, employees are given little opportunity to distance themselves from the workplace (Edley, 2001). Employees might find it harder to let go of work in private situations because they are constantly connected to their work via communication technology. This makes it impossible for employees to enjoy relaxation in their private life. Research from Chesley (2014) shows that employees can be stressed by this constant connection with work through communication technology.
Added to the promotion of family life mentioned before, there are a lot more positive effects of NWW. In fact, research shows that NWW lead to efficient and effective communication between employees (Ten Brummelhuis et al., 2012). Efficient and effective communication ensures that employees keep their work flow, while the pleasure in performing tasks increases and stress reduces (Rennecker & Godwin, 2005). This makes that feelings of exhaustion by employees are minimized (Ten Brummelhuis et al., 2012).
Another positive effect of NWW is that confidence in the staff from the management increases, since there is a high degree of flexibility and therefore the
staff arranges their work to their own wishes (Kelliher & Anderson, 2008). Unlike the situation before the implementation of NWW, the leadership in the new situation no longer focuses on the presence and the way employees work. Instead, the supervisor attaches more importance to the final result and trusts that the employee can
determine when, where and how this result is achieved (Baane et al., 2010).
For employees, the benefits of NWW are easily recognizable. Additionally, from the organizational perspective there is also much interest in NWW. Baane et al. (2010) speak of both an increase in revenues as well as a reduction in costs.
Increasing revenues include increased productivity, more attractive employment image, strengthening the cooperation between colleagues and a higher degree of innovation. Reduction in costs can be scaled in savings in the ICT, absenteeism, travel expenses and facilities.
2.2 Employee job satisfaction
Provided by Locke (1976, p. 1300), a classic definition of job satisfaction is “a
pleasurable or positive emotional state resulting from the appraisal of one’s job or job experiences”. Employee job satisfaction is also defined in the literature as the extent to which the work environment corresponds to the wishes and preferences of
employees (Van der Voordt, 2004). This may be related to (1) the work itself (content, complexity, skills and knowledge and the degree of autonomy), (2) social environment (colleagues, management style and conditions of work, such as salary and career prospects), or (3) the physical environment (workplace, daylight and views).
While there is no consensus on the number of dimensions of the construct, some evaluated dimensions of job satisfaction are: pay, promotion, supervision, conditional benefits, working conditions, colleagues, nature of work and
communication (Spector, 1985). A widely used instrument for measuring job satisfaction is the Job Description Index (JDI) which highlights five aspects of job satisfaction: work, supervision, pay, promotion and co-workers (Smith, Kendall & Hullin, 1969; Stanton, Sinar, Balzer, Julian, Thoresen, Aziz, & Smith, 2002). The JDI is directed towards specific areas of satisfaction rather than mere global satisfaction and allows for different areas of the job to be independently measured (Spector, 1997). It requires respondents to describe their work as opposed to directly asking respondents how satisfied they are, thereby ensuring that respondents have a job
referent rather than a self-referent. In this thesis the abridged JDI developed by Stanton et al. (2002) will therefore be used to measure the extent of job satisfaction among employees. The facets included are: work, supervision, pay, promotion and co-workers. These measured facets can also be seen as to what extent the work
environment, in this case the work itself, supervision, pay, promotion and co-workers, corresponds to the wishes and preferences of employees (Van der Voordt, 2004).
2.3 New Ways of Working makes employees more satisfied with their job Satisfied employees are of great value to a company and can eventually lead to a better performing organization, as they perform the daily work and largely ensure the company's success (Rennecker & Godwin, 2005). It appears that New Ways of Working can have a positive effect on the satisfaction of employees; the relationship between NWW and job satisfaction has therefore been much researched (e.g.,
Gajendran & Harrison, 2007, Kossek & Ozeki, 1998; Baltes, Briggs, Huff, Wright & Neuman, 1999; Kelliher & Anderson, 2008; Raghuram & Wiesenfeld, 2004;
Fairbrother & Warn, 2003).
According to Gajendran and Harrison (2007), there are two main factors that help increase employee’s job satisfaction in a NWW environment. Firstly, as
mentioned before, NWW may result in a better work-life balance (De Menezes & Kelliher, 2011). Employees in a flexible work environment, who are able to diminish work-life conflict, report to be more satisfied with their job (Gajendran and Harrison, 2007). These results are in line with Kossek and Ozeki (1998) who suggest that employees who succeed in decreasing work interferences in personal lives are more satisfied with their job. The second factor is the perceived autonomy as a result of NWW (Baltes et al., 1999; Kelliher & Anderson, 2008). Because employees are in control of when and where they work, they perceive a higher feeling of autonomy (Gajendran & Harrison, 2007). Hackman and Oldham (1976) define autonomy at work as “the degree to which a job provides substantial freedom, independence, and discretion to the individual in scheduling the work and in determining the procedure to be used in carrying it out” (p. 258). According to Baltes et al. (1999), perceived autonomy fosters job satisfaction because the flexible work arrangements meet the employee’s need for independence and increase in responsibility. They found a significant positive association of the NWW on job satisfaction because flexible and compressed workweek schedules were related to productivity and performance, job
satisfaction, reduced absenteeism and satisfaction with work schedules. These results are in line with Kelliher and Anderson (2008) who also assume that there should be perceived autonomy for the employee. They found that employees who worked remotely one day a week and employees who had reduced their required weekly office hours tended to report higher job satisfaction, lower stress and higher loyalty to their company than employees who did not have flexible hours. The researchers posit that the reason for this phenomenon is a kind of payment to the employer from the employee in exchange for the freedom to choose where and when to work.In addition to these two factors, Raghuram and Wiesenfeld (2004) argue that employees who work independent of time and place experience less job related stress. According to Fairbrother and Warn (2003), less job related stress is positively related to job satisfaction because employees experience less uncertainty and they are more in control.
As previously stated, several conclusions can be drawn on the effect of NWW on job satisfaction of the employee and most researchers conclude that NWW have a positive effect on job satisfaction (Gajendran & Harrison, 2007; Baltes et al., 1999; Kelliher & Anderson, 2008). This study will therefore predominantly focus on the positive effects; in this thesis it is therefore expected that NWW have a positive effect on employee job satisfaction.
Earlier research focuses on the various forms of NWW, such as time
flexibility, location flexibility and communication flexibility. When employees can determine the time of their work schedules, it will lead to a positive effect on job satisfaction of the employee (Baltes et al., 1999; Harvey & Luthans, 1979). Possenriede and Plantenga (2014) analysed whether this flexibility in hours of the work schedule improves the work/leisure balance and increases employee’s overall job satisfaction. They used panel data on Dutch households with self-reported measures of job satisfaction; the analysis found that a flexible work schedule is
positively associated to job satisfaction. This can be explained by the Social Exchange Theory of Emerson (1976), which states that if employees are treated in a pleasant way by the organization, employees will have positive attitudes and behaviours. This in turn positively affects job satisfaction. In the case of NWW, the perception that it is possible to work flexibly regarding time schedules can influence an employee
can determine the hours of their work schedules, the more satisfied they are. As a result, the following hypothesis can be drawn:
Hypothesis 1. The more employees can determine the hours of their work schedules, the more satisfied they are about their job.
The meta-analysis of Gajendaran and Harrison (2007) found a significant positive correlation between flexible work locations and increased job satisfaction. They concluded that employees who spend more than two and a half days per week at an alternative work location are more satisfied with their work. The Expectancy Theory by Vroom (1964) can explain this positive relationship and states that employee motivation is high when there is belief that many efforts lead to good performance. Good performance will in turn lead to desired outcomes for employees, such as a bonus or compliment. As an employer relinquishes control over the work by allowing employees to work at flexible locations (and hours), the employee gets the feeling that the organization appreciates him or her; dedication to the organization and satisfaction of the employee will increase. It is therefore expected that the more employees can determine the location of their work schedule, the more satisfied they are. The following hypothesis can therefore be compiled:
Hypothesis 2. The more employees can determine the location of their work schedules, the more satisfied they are about their job.
Besides working time and location independent, technological innovations are also an important aspect of the NWW. Organizations enable their employees to perform their work using new information and communication technologies. These technologies support virtual collaboration between organizational members because it is possible to access information from any location. Technological innovations also support accessing company information from multiple locations (e.g. on the road, at home and at customers). These innovations are key drivers of NWW. Research by Batenburg and Voordt (2008) shows that technical facilities such as ICT affect work productivity. In a meta-analysis concluded by Petty, McGee and Cavender (1984) productivity is positively correlated to job satisfaction: higher productivity is associated with a higher level of job satisfaction. From the above, it is therefore
expected that the facility provision in the field of flexible working technologies positively relate to job satisfaction. Based on this, the following hypothesis derived:
Hypothesis 3. The more employees make use of flexible work technologies, the more satisfied they are about their job.
2.4 Knowledge sharing
Knowledge is information processed by individuals, which can be anything ranging from facts to judgements (Wang & Noe, 2010) and is seen as intellectual capital of organizations and as a major source of competitive advantage (Van den Hooff & De Leeuw van Weenen, 2004). If relevant knowledge is shared, it offers advantages for both the recipients of the information as for the organization itself. The development and use of knowledge should therefore be a core competency of organizations to keep ahead of changing opportunities and competition. Previous literature argues that knowledge should be available to all employees: knowledge must be shared (Van den Hooff & Huysman, 2009). Sharing knowledge is a dynamic and complex subject that has been much researched (Van den Hooff & Huysman, 2009; Van den Hooff & De Ridder, 2004; Van den Hooff & De Leeuw van Weenen, 2004) and involves the process in which individuals share their explicit and implicit knowledge and jointly create new knowledge (Van den Hooff & De Ridder, 2004). The extent of knowledge sharing is characterized by the donation and collection of knowledge. Communicating own intellectual capital of an employee to his or her colleagues is referred to as knowledge donation. Consulting intellectual capital from other colleagues is referred to as knowledge collection. Donating knowledge will increase as more knowledge is collected (Van den Hooff & De Leeuw van Weenen, 2004).
A distinction in the literature of knowledge sharing is between explicit and implicit knowledge. The sharing of explicit knowledge is easier than sharing implicit knowledge. Explicit knowledge can be seen as an object that can be stored and shared in the form of data, reports and manuals using information technology (IT) (Van den Hooff & Huysman, 2009); it captures documents, numbers, words and information, making this form of knowledge easily transferable. Implicit knowledge however requires a different way of transmission. This type of knowledge is related to context and is affected by skills, attitudes and experiences. It is difficult to formalize and therefore not easy to communicate and disseminate (Nonaka & Konno, 1998). It is not
structured by tools, but by rich social interaction, and is an intangible resource, embedded in the social context where it gains form and meaning (Van den Hooff & Huysman, 2009).
2.5 New Ways of Working leads to sharing more knowledge
A goal of NWW is to promote the communication between employees (Katz & Aarhus, 2002). Within NWW, employees therefore share information faster and they are constantly in contact with each other: by mobile activity, communication becomes an intense everyday activity and more knowledge is shared. It is therefore expected that NWW applications positively relate to the degree of knowledge sharing.
Working independent of time is made possible by providing information and communication possibilities to be available 24/7 for employees, but also by allowing them to work at any time. Time independence could result in more contact between employees, because they are able to work in evenings and weekends, which result in more opportunities to share knowledge. It is therefore expected that the more
employees are able to determine their own work hours, the more knowledge is shared among these employees. Based on this the following hypothesis can be drawn:
Hypothesis 4. The more employees can determine the hours of their work schedules, the higher the opportunity that more knowledge is shared.
Greater mobility, regarding to the work location, will also result in a higher extent of knowledge sharing, because it can be expected that work-related facilities to work flexibility support the formation of informal relations, as employees are given more freedom in determining where they work, allowing opportunities for new, informal contacts and ad hoc meetings (Tsai, 2002). These informal relations have a significant positive effect on knowledge sharing between departments (Van der Kleij, Blok, Aarts, Vos & Weyers, 2013). Another positive aspect about location
independence is the fact that employees can perform their tasks at office spaces that are suitable and accessible to every employee; where they are able to brainstorm with other employees; whereby spontaneous and informal meetings with people inside and outside the scope of the organization increase (Kiesler & Cummings, 2002). The opportunity for an employee to choose his or her work location will therefore lead to
more knowledge sharing among colleagues. Based on the above expectations, the following hypotheses can be drawn:
Hypothesis 5. The more employees can determine the location of their work schedules, the higher the opportunity that more knowledge is shared.
Technological communication technologies also contribute to an increased level of knowledge sharing within organizations, because it supports improved access to information and colleagues who possess knowledge. For example, the use of smartphones enables employees on the move or at home to not only perform their work duties, but also to exchange ideas with colleagues via SMS, email or social media apps. Alavi and Leidner (2001) note that ITC increases knowledge transfer by extending an individuals’ reach beyond formal lines of communication. Computer networks and discussion groups for instance facilitate contact between those seeking knowledge (collecting knowledge) and those who control access to knowledge (donate knowledge).
Moreover, ICT enables organizations to reduce temporal and spatial barriers between knowledge workers and therefore offers opportunities for working more easily time and location independent (Hendriks, 1999; Rico & Cohen, 2005). According to this reasoning, there is a correlation between the extent to which employees share knowledge and the effort it takes them to find that knowledge. The use of flexible working technologies, which are part of NWW, will ensure both an increase in the extent to which knowledge is collected among employees and an increase in the extent to which knowledge is donated. The overall sharing of knowledge is therefore expected to increase due to the use of flexible working technologies. Based on these expectations the following hypothesis can be compiled:
Hypothesis 6. The more employees make use of flexible work technologies, the higher the opportunity that more knowledge is shared.
2.6 The sharing of more knowledge leads to more satisfied employees
The sharing of knowledge is essential for every successful business organization (Quinn, Anderson & Finkelstein, 1996) and can also lead to higher levels of employee job satisfaction (De Vries, Van den Hooff, & De Ridder, 2006). Sharing knowledge
helps individual employees in doing their work better and developing their individual skills and learning ability: by learning from others, employees are able to do their work better and faster and work can be realized with fewer errors. Therefore, it is important for organizations that employees share knowledge. Optimal performance can be achieved by knowledge sharing and an advantage can be obtained over competing organizations resulting in more satisfied employees (Argote & Ingram, 2000).
Hall (2001) distinguishes two types of rewards to employees as a result of the sharing of knowledge, namely explicit and implicit rewards. Explicit rewards are easily measurable rewards for contributing to the knowledge processes in the
organization. Implicit rewards involve issues such as recognition from colleagues and superiors, personal satisfaction and a better reputation. This is similar to the
distinction described by Aronson, Wilson and Akert (2005), who maintain that motivation for employees can be divided into two different types: extrinsic and intrinsic motivation. Extrinsic motivation refers to external factors, which can be measured in tangible financial matters such as salaries and benefits. The employee is motivated due to the external rewards, not because the employee has pleasure or interest in the job. Intrinsic motivation involves internal factors such as satisfaction with the current job and with the working conditions. The employee enjoys or has an interest in his or her job; this is not because of the explicit rewards. In this thesis it is expected that the implicit and explicit rewards and extrinsic and intrinsic motivation of employees, and therefore the overall job satisfaction, will increase due to the sharing of more qualitativeknowledge.
Furthermore, the extent to which knowledge is shared between colleagues appears to have a positive impact on the affective commitment among employees (De Vries, Van den Hooff, & De Ridder, 2006). In addition, employees in an environment with a lot of knowledge sharing appear to experience less work-family conflict, deliver improved performance and help colleagues more often (Lautsch, Kossek & Eaton, 2009). When knowledge is shared largely between colleagues, employees will experience less exhaustion. As the sharing of knowledge leads to more affective commitment and less exhaustion, and affective commitment and less exhaustion lead to a higher extent of employee job satisfaction, the following hypotheses can be drawn:
Hypothesis 7. A higher extent of knowledge sharing leads to a higher level of employee job satisfaction.
It can also be concluded that because employees that can determine the hour and locations of their work schedules share more knowledge, they are more satisfied with their job. In addition, because employees that make use of flexible work technologies share more knowledge, they are more satisfied with their job. Following from all the above seven hypotheses, the following hypotheses can therefore be added to this research:
Hypothesis 8. Because employees that can determine the hours of their work schedules share more knowledge, they are more satisfied with their job.
Hypothesis 9. Because employees that can determine the location of their work schedules share more knowledge, they are more satisfied with their job.
Hypothesis 10. Because employees that make use of flexible work technologies share more knowledge, they are more satisfied with their job.
Figure 1. Conceptual model
3. Method 3.1 Design & sample
This research has used a cross-sectional online questionnaire design to answer the research question and hypotheses. The sampled population are Dutch employees who all make use of the NWW to a different extent. The respondents had to master the Dutch language and be older than eighteen years. A total of 250 respondents started the survey and 186 respondents completed the survey. The dropout respondents were not included in the study. The average age of the respondents is 33.37 years old (SD = 13,33); 53.8% was male (N = 100); 46,2% female (N = 86).
3.2 Procedure
In order to recruit respondents for the study, a convenience sample and a snowball sample were used. The respondents were recruited using social media and the researcher's network. Different acquaintances within several companies were approached via an email. The email consisted of a short explanation with an
anonymous link to the online questionnaire and the question whether they would like to email the questionnaire to their colleagues and friends. The online questionnaire was also distributed by social media via Facebook and WhatsApp. The current research was conducted on the basis of the Qualtrics computer program. Prior to the research, a pre-test was conducted with seven respondents to test whether the questions were clear and the questionnaire was not too long. The questionnaire was distributed in the first two weeks of May 2017 and the completion took no longer than six minutes. At the start of the questionnaire, the respondents were asked whether they agreed to participate in a consent statement.
3.3 Measures
3.3.1 New Ways of Working
The measurement of the construct ‘ New Ways of Working’ consisted of three aspects: (1) flexible work hours, (2) flexible work locations and (3) flexible work technologies. Together, these variables show the extent to which work is done as the
‘New ways of working’. The NWW are measured on the basis of nine statements according to the research of Ten Brummelhuis, Halbesleben & Prabhu (2011). All the three separate aspects consisted of a total of three statements, answered by
respondents using a 7-point Likert scale ranging from (1) completely disagree to (7) completely agree.
An example statement of flexibility in time is ‘I can choose when I work’. The reliability analysis showed that the three items measuring flexible work hours formed a very reliable scale (α = 0.93). On average, respondents scored 4.15 on flexible work hours (SD = 2.12).
An example statement of flexibility of work location is ‘I choose where I work’. Another reliability analysis showed that the three items measuring flexible work locations also formed a very reliable scale (α = 0.94). On average, respondents scored 4.08 on flexible work locations (SD = 2.15).
‘I decide when I send emails and answer questions’ is an example statement of the use of flexible work technology. A reliability analysis of the three items
measuring flexible work technology showed that it also formed a very reliable scale (α = 0.91). On average, respondents scored 5.17 on flexible work technology (SD = 1.81).
3.3.2 Knowledge sharing
According to the literature, the variable knowledge sharing consists of two theoretical dimensions: donating knowledge and collecting knowledge (Van den Hooff & De Ridder, 2004). As soon as a person ‘asks’ for knowledge, he or she is collecting knowledge; if knowledge is shared without being asked for, knowledge donating is taking place. To make donating knowledge measurable, six propositions are acquired from the study by Van den Hooff and De Leeuw van Weenen (2004) using a 5-point Likert scale ranging from (1) never to (5) always. One of the statements used is as following: ‘When I learn something new, I tell it to my colleagues in my department.’ Within this study, the six positions form a veryreliable scale (α = 0.91). To make collecting knowledge measurable, eight propositions from the study by Van den Hooff and De Leeuw van Weenen (2004) are taken using a 5-point Likert scale ranging from (1) never to (5) always. The statement ‘I share information I have with colleagues in my department when they ask me’ is one of the eight statements. In this research, the eight positions have a high degree of reliability (α = 0.96). The fourteen
statements that are used in total in order to measure the latent variable knowledge sharing have a high degree of reliability (α = 0.95). On average, respondents scored 3.70 on knowledge sharing (SD = 0.84).
3.3.3. Employee job satisfaction
The Job Descriptive Index (JDI) measures an employees overall job satisfaction (Smith, 1997) and is the most widely used instrument measuring employees’ job satisfaction within organizations. The JDI measures perceptions of satisfaction for five job facets, namely: pay, promotions, supervision, co-workers and the work itself (Spector, 1997). Employees are requested to indicate whether each statement does or does not describe their job. Stanton et al. (2002) have developed an abridged version of the JDI facet that preserved the desirable characteristics of this well-known and widely used measure. They selected a set of five items for each of the scales. Within each facet scale, they focused their attention on the items that, according to their quality metrics, contributed most to internal consistency, external correlations, subjective quality, and dispersion. They selected three positively worded phrases and two negatively worded phrases for each facet subscale. In this study, the abridged version of the JDI is used to measure the degree of employee job satisfaction using a 7-point Likert scale ranging from (1) completely disagree to (7) completely agree. A principal component analysis (PCA) showed that the five items measuring employee job satisfaction (pay, promotions, supervision, co-workers and the work itself) loaded on one component, with factor-loadings all above 0.81; explaining 72.53% of
variance. The 25 statements that are used in total in order to measure the latent variable employee job satisfaction also have a very high degree of reliability (α = 0.96) and were therefore computed into the variable ‘employee job satisfaction’. On average, respondents scored reasonably high on job satisfaction (M = 4.87, SD = 1.26).
3.4 Analysis
After performing the descriptive analyzes, the hypotheses of this study have been tested using inferential statistics. For the analysis of the results of this study, the statistical program SPSS is used. In this program various statistical analyzes were performed to test the previously described hypotheses. The first seven hypotheses are measured using a simple regression analysis, with flexible work hours, flexible work
locations and flexible work technologies as independent variable and employee job satisfaction and knowledge sharing as dependent variables. All variables are at interval level and are numeric measured on a scale. There will be measured to what extent the change in the independent variable leads to a change in the dependent variable, which allows the dependent variable to be declared or predicted based on the independent variable. Besides the simple regression analyses, a multiple regression analysis is performed to indicate which form of NWW (flexible hours, locations or technologies) has the most influence on both employee job satisfaction and
knowledge sharing. Finally, three separate mediation analyses are performed to test the hypotheses that knowledge sharing mediates the effect of use of the three forms of NWW on employee job satisfaction.
4. Results 4.1 Simple regression analyses
The first hypothesis, whether the more employees can determine the hours of their work schedules, the more satisfied they are about their job (H1) is tested with a simple regression analysis. The regression model with employee job satisfaction as dependent variable and flexible work hours as independent variable appears to be significant, F (1, 184) = 80.734, p = 0.000. The regression model is therefore useful to predict employee job satisfaction: 30.5 percent of the differences in employee job satisfaction can be predicted based on the use of flexible work hours (R2 = 0.31). The use of flexible work hours, b = 0.33, b * = 0.55, t = 8.985, p = 0.000, has a significant, positive relationship with employee job satisfaction. The first hypothesis is therefore accepted.
The second hypothesis, whether the more employees can determine the location of their work schedules, the more satisfied they are about their job (H2) is also tested with a simple regression analysis. The regression model with employee job satisfaction as dependent variable and flexible work locations as independent variable appears to be significant, F (1, 184) = 75.545, p = 0.000. The regression model is therefore useful to predict employee job satisfaction: 29.1 percent of the differences in employee job satisfaction can be predicted based on the use of flexible work locations
(R2 = 0.29). The use of flexible work locations, b = 0.32, b * = 0.54, t = 8.692, p = 0.000, has a significant, positive relationship with employee job satisfaction. Therefore, the second hypothesis is accepted.
The third hypothesis, whether the more employees make use of flexible work technologies, the more satisfied they are about their job (H3) is also tested with a simple regression analysis. The regression model with employee job satisfaction as dependent variable and flexible work technologies as independent variable appears to be significant, F (1, 184) = 120.524, p = 0.000. The regression model is therefore useful to predict employee job satisfaction: 39.6 percent of the differences in employee job satisfaction can be predicted based on the use of flexible work
technologies (R2 = 0.40). The use of flexible work technologies, b = 0.44, b * = 0.63, t = 10.978, p = 0.000, has a significant, positive relationship with employee job
satisfaction. The third hypothesis is therefore accepted.
The fourth hypothesis, whether the more employees can determine the hours of their work schedules, the higher the opportunity that more knowledge is shared (H4) is also tested with a simple regression analysis. The regression model with knowledge sharing as dependent variable and flexible work hours as independent variable also appears to be significant, F (1, 184) = 33.875, p = 0.000. The regression model is therefore useful to predict knowledge sharing: 15.5 percent of the differences in knowledge sharing can be predicted based on the use of flexible work hours (R2 = 0.16). The use of flexible work hours, b = 0.16, b * = 0.39, t = 5.820, p = 0.000, has a significant, positive relationship with knowledge sharing). Therefore, the fourth hypothesis is accepted.
The fifth hypothesis, whether the more employees can determine the location of their work schedules, the higher the opportunity that more knowledge is shared (H5) is also tested with a simple regression analysis. The regression model with knowledge sharing as dependent variable and flexible work locations as independent variable appears to be significant, F (1, 184) = 33.162, p = 0.000. The regression model is therefore useful to predict knowledge sharing: 15.3 percent of the differences in knowledge sharing can be predicted based on the use of flexible work locations (R2 = 0.15). The use of flexible work locations, b = 0.15, b * = 0.39, t = 5.759, p = 0.000, has a significant, positive relationship with knowledge sharing. The fifth hypothesis is therefore accepted.
The sixth hypothesis, whether the more employees make use of flexible work technologies, the higher the opportunity that more knowledge is shared (H6) is also tested with a simple regression analysis. The regression model with knowledge sharing as dependent variable and flexible work technologies as independent variable appears to be significant, F (1, 184) = 57.699, p = 0.000. The regression model is therefore useful to predict knowledge sharing: 23.9 percent of the differences in knowledge sharing can be predicted based on the use of flexible work technologies (R2 = 0.24). The use of flexible work technologies, b = 0.23, b * = 0.49, t = 7.596, p = 0.000, has a significant, positive relationship with knowledge sharing. Therefore, the sixth hypothesis is accepted.
The seventh hypothesis, whether a higher extent of knowledge sharing leads to a higher level of employee job satisfaction (H7) is also tested with a simple regression analysis. The regression model with employee job satisfaction as dependent variable and knowledge sharing as independent variable appears to be significant, F (1, 184) = 163.039, p = 0.000. The regression model is therefore useful to predict employee job satisfaction: 29.1 percent of the differences in employee job satisfaction can be predicted based on the extent of knowledge sharing (R2 = 0.29). The extent of knowledge sharing, b = 1.03, b * = 0.69, t = 12.771, p = 0.000, has a significant, positive relationship with employee job satisfaction. Therefore, the seventh hypothesis is accepted. Figure 2 shows the standardized beta-coefficient values between all variables.
4.2 Multiple regression analyses
A multiple analysis has been performed to identify which form of the NWW (flexible work hours, locations or technologies) has the most influence on employee job satisfaction. The regression model appears to be significant, F (3, 182) = 51.580, p = 0.000 and is therefore useful to predict employee job satisfaction: 45.1 percent of the differences in employee job satisfaction can be predicted based on the use of flexible work hours, locations and technologies (R2 = 0.45). Flexible work hours, b* = 0.18, t = 2.17, p = 0.031, flexible work locations, b* = 0.17, t = 2.09, p = 0.039, and flexible work technologies, b* = 0.43, t = 6.15, p = 0.000 all have a significant positive association with employee job satisfaction. However, it can be concluded that the variable flexible work technologies has the most influence on the variable employee job satisfaction due to the highest value of the standardized beta-coefficient and the lowest amount of signification (see table 1).
b * t sig.
Flexible work hours 0.18 2.169 0.031
Flexible work locations 0.17 2.084 0.039
Flexible work technologies 0.43 6.153 0.000
Table 1. Coefficients, t-values and significance NWW on employee job satisfaction
In addition, another multiple analysis has been performed to identify which form of NWW (flexible work hours, locations or technologies) has the most influence on knowledge sharing. The regression model appears to be significant, F (3, 182)
=21.550, p = 0.000 and is therefore useful to predict knowledge sharing: 25 percent of the differences in employee job satisfaction can be predicted based on the use of flexible work hours, locations and technologies (R2 = 0.25). In the multiple analysis, flexible work hours, b* = 0.10, t = 1.00, p = 0.319, and flexible work locations, b* = 0.11, t = 1.20, p = 0.231, do not have a significant association with knowledge sharing (see table 2). Because flexible work technologies, b* = 0.37, t = 4.52, p = 0,000 does still have a significant positive association with knowledge sharing and has the highest standardized beta-coefficient value, it can be concluded that the variable
flexible work technologies also has the most influence on the variable knowledge sharing.
b * t sig.
Flexible work hours 0.10 1.00 0.319
Flexible work locations 0.11 1.203 0.231
Flexible work technologies 0.37 4.518 0.000
Table 2. Coefficients, t-values and significance NWW on knowledge sharing
4.3 Mediation analyses
The last hypotheses of this research concern the mediating role of knowledge sharing. The PROCESS 2.17 macro for SPSS (Hayes, 2013) was used to investigate the hypothesis that knowledge sharing mediates the effect of use of NWW on employee job satisfaction. As previously indicated, the use of flexible work hours is a
significant predictor of employee job satisfaction, b = 0.33, SE = 0.04, p = 0.000, and that use of flexible work hours is a significant predictor of knowledge sharing, b = 0.16, SE = 0.03, p = 0.000. Use of flexible work hours is also a significant predictor of employee job satisfaction after controlling for the mediator knowledge sharing, b = 0.20, SE = 0.03, p = 0.000. 56.4% of the variance in employee job satisfaction is accounted for by the predictors (R2 = 0.56). The indirect effect is tested using a bootstrap estimation approach with 5000 samples. These results indicated the indirect coefficient is significant, b = 0.13, SE = 0.04, 95% CI = 0.064, 0.205. The eighth hypothesis is therefore accepted.
As also previously indicated, the use of flexible work locations is a significant predictor of employee job satisfaction, b = 0.32, SE = 0.04, p = 0.000, and that use of flexible work locations is a significant predictor of knowledge sharing, b = 0.15, SE = 0.03, p = 0.000. Use of flexible work locations is also a significant predictor of employee job satisfaction after controlling for the mediator knowledge sharing, b = 0.19, SE = 0.03, p = 0.000. 55.7% of the variance in employee job satisfaction was accounted for by the predictors (R2 = 0.56). The indirect effect is also tested using a bootstrap estimation approach with 5000 samples. These results indicated the indirect coefficient is significant, b = 0.13, SE = 0.04, 95% CI = 0.066, 0.204. The ninth hypothesis is therefore accepted.
Results had also already indicated that the use of flexible work technologies is a significant predictor of employee job satisfaction, b = 0.44, SE = 0.04, p = 0.000, and that use of flexible work technologies is a significant predictor of knowledge sharing, b = 0.23, SE = 0.03, p = 0.000. Use of flexible work hours is also a
significant predictor of employee job satisfaction after controlling for the mediator knowledge sharing, b = 0.19, SE = 0.03, p = 0.000. 58.4% of the variance in employee job satisfaction was accounted for by the predictors (R2 = 0.58). The indirect effect was tested using a bootstrap estimation approach with 5000 samples. These results indicated the indirect coefficient was significant, b = 0.17, SE = 0.04, 95% CI = 0.089, 0.256. Therefore, the tenth hypothesis is accepted.
5. Conclusion
The aim of this research was to answer the following research question: ‘To what extent do the New Ways of Working have an effect on employee job satisfaction and how does the sharing of knowledge among employees play a role in this effect?’ This question was answered through a total of ten hypotheses and a cross-sectional survey (N = 186). In this research NWW is defined as a way of working of knowledge-intensive organizations, offering employees the opportunity to choose where and when they work, supported by technological communication capabilities
independently of time and location.As expected, based on the hypotheses tested, it can be concluded that the various forms of NWW, meaning flexible work hours, flexible work locations and flexible communication technologies, all have a positive effect on both employee job satisfaction and knowledge sharing. This implies that employees who can determine their own work hours, locations and communication technologies, are more satisfied with their job and also share more knowledge. This research also concludes that when employees share more knowledge with other employees, they are more satisfied with their job. In order to find an explanation for the possible relationship between NWW and employee job satisfaction, this study looked at the mediating role of knowledge sharing of employees. The results have confirmed that the relationship between NWW and employee job satisfaction is partly explained by the sharing of knowledge, implying that because employees who can
determine their own work hours, locations and technologies share more knowledge, they are more satisfied with their job.
Despite the many negative effects associated with NWW, such as a disturbed work-private balance and an increased level of stress, the results of this study
predominantly show positive effects. Because it is concluded that knowledge-intensive organizations can benefit from the introduction of NWW, the trend to further integrate NWW into the existing structure of organizations can therefore be recommended. Firstly, the results are informative for companies and an incentive to enter the NWW in order to keep employees satisfied with their job. This is of importance because job satisfaction can lead to higher involvement in the
organization, lower absence, less intent to resign and more profit and productivity for the organization (Baltes et al., 1999). Secondly, the results also show that flexible work hours, locations and technologies can lead to an increase in knowledge sharing. This is an important finding because better utilization of knowledge can help achieve organizational goals and can lead to an advantage over other companies now and in the future (Argote & Ingram, 2000). Managers at corporate levels therefore must instruct to adopt a more active role in making the sharing of knowledge priority, which will also stimulate an increase in positive employee job satisfaction.
Concluding, this research indicates that NWW are positive for employees in a number of ways. It is therefore advisable to create more flexible working environments within organizations, where employees have control over where and when they work
supported by technological communication capabilities.
This research has a few small-scale limitations. Firstly, self-reporting was used in this study. Self-reporting always gives a more confounding image, because people are often unaware of their behavior. For example, the perceived quality of knowledge sharing, as measured in this study, does not reflect the actual quality and quantity of knowledge sharing within organizations. The question rises whether employees can make a good estimate of the extent to which they share knowledge. Also, the perceived employee job satisfaction as measured in this study might not reflect the actual job satisfaction. However, because this research focuses on activities while working and feelings about working, self-reporting is the best possible method to measure these variables. There are no objective measures to measure these feelings; the only way to get insight into this is to ask people.
Secondly, despite the many benefits of the digital questionnaire, such as the wide range, the lack of interviewer bias and the ability to ask conditional questions, there are also disadvantages of taking a digital questionnaire. For example, it is not clear who completes the questionnaire (Topp & Pawloski, 2002), what respondents do while completing and in what mental state they are. A slow Internet connection for example, may affect the respondent's state of mind in a negative way; this may have an impact on the results. There are also practical disadvantages as the risk that a computer crashes or the Internet connection fails and the respondent is forced to stop filling out the questionnaire.
Thirdly, future research on the various factors of NWW can provide
interesting insights. From the multiple regression analyses it was concluded that the variable flexible communication technologies is the most significant predictor of both employee job satisfaction and knowledge sharing. This can be explained by the fact that the technology is a prerequisite for hours and locations: the available
technologies help keep employees in constant communication with each other, which makes it possible for employees to decide whether or not to work with flexible working hours and locations. However, Van de Haterd (2010) states that only the availability of flexible communication technologies will not result in NWW. Communication technology must be seen as an enabler, not as the engine (Van de Haterd, 2010, p. 58). It can thus be said that technology is a prerequisite for
implementing the NWW and that flexible work hours and location are also important in measuring NWW. Future research on the various factors of NWW might provide further insights in the multidimensional character of this variable.
Finally, to measure the variable employee job satisfaction, the survey contained some statements about attitudes towards the supervisor. However, the survey has also been completed by a number of freelancers that have also answered this statement while this question would not always applicable to them, as they mostly are their own supervisor. Though this might have potentially affected the internal validity and reliability of this research, this was the best possible method to measure the satisfaction of the employee because the JDI requires respondents to describe their work as opposed to directly asking respondents how satisfied they are, thereby
ensuring that respondents have a job referent rather than a self-referent (Spector, 1997).
A direction for future research is the role of individual differences. Individual differences may play a role in interpreting the effects of NWW and predict the effectiveness in practice. Baruch (2000) for example shows that self-discipline and intrinsic motivation are of great importance for effective location- and
time-independent work. Individual dispositions and skills also seem to determine the acceptance of NWW. People with a high need for autonomy appear to respond positively to NWW and appreciate the benefits of NWW more than people with low autonomy. It is therefore interesting to investigate to what extent these attributes contribute to the success of NWW. This can help to develop training and identify which employees most benefit from the implementation of NWW.
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Appendix
1. Informed consent & Questionnaire Beste heer, mevrouw,
Hierbij wil ik u uitnodigen om deel te nemen aan een onderzoek naar 'het nieuwe werken' in organisaties. Het onderzoek wordt uitgevoerd onder toezicht van de Graduate School of Communication, onderdeel van de Universiteit van Amsterdam. Ik wil u vragen om een online vragenlijst in te vullen; het invullen van de vragenlijst duurt ongeveer 5 minuten. Uw deelname is hierbij van groot belang. In dit onderzoek zullen er stellingen getoond worden waarbij uw antwoord of mening gevraagd wordt. Per onderdeel staan de antwoordmogelijkheden aangegeven.
Het onderzoek wordt uitgevoerd onder de verantwoordelijkheid van The Amsterdam School of Communication Research (ASCoR). Omdat huidig onderzoek voldoet aan de richtlijnen van de ASCoR, kan ik u garanderen dat:
1. Uw anonimiteit in alle fasen van het onderzoek volledig wordt beschermd. Uw persoonlijke informatie wordt onder geen enkele conditie verspreid naar derde partijen, tenzij u hier vooraf toestemming voor geeft.
2. U ten allen tijden kunt weigeren om te participeren of uw deelname vroegtijdig kunt afbreken zonder hier een reden voor op te geven. Bovendien heeft u, na het completeren van de vragenlijst, 24 uur om uw deelname terug te trekken. 3. Deelname aan het onderzoek er niet toe leidt dat u wordt onderworpen aan enig merkbaar risico of discomfort. U zult niet opzettelijk misleid worden. Ook zult u niet blootgesteld worden aan expliciet aanstootgevend materiaal.
Wanneer u meer informatie over het onderzoek wilt, kunt u contact opnemen met scsbakker@gmail.com Bij klachten of opmerkingen over het onderzoek en de procedures, kunt u contact opnemen met het toegeschreven lid van de Ethische Commissie die ASCoR representeert. U kunt deze persoon bereiken via het volgende adres: ASCoR Secretatiaat, Ethische Commissie, Universiteit van Amsterdam, Postbus 15793, 1001 NG Amsterdam; telefonisch via 020-5253680 of via
e-mail: ascor-secr-fmg@uva.nl. Alle klachten of opmerken zullen in strikt vertrouwen worden behandeld.
Ik hoop u hiermee voldoende te hebben geïnformeerd. Hartelijk dank voor uw deelname in dit onderzoek. Uw deelname wordt enorm gewaardeerd en is van groot belang voor het onderzoek.
Met vriendelijke groet, Saartje Bakker
Q2. Hierbij verklaar ik dat ik op een duidelijke manier ben geïnformeerd over de aard en methode van het onderzoek. Ik ga volledig en vrijwillig akkoord om te participeren in dit onderzoek. Ik ben ervan op de hoogte dat ik mijn participatie in het onderzoek op elk moment mag stoppen. Als mijn onderzoeksresultaten worden gebruikt in wetenschappelijke publicaties of op een andere manier publiek worden gemaakt, zal dit worden gedaan op een manier waarin mijn anonimiteit compleet gewaarborgd wordt. Mijn persoonlijke data zal niet worden doorgegeven aan derde partijen zonder mijn expliciete toestemming.
Ik begrijp bovenstaande tekst en ik ga akkoord met deelname in het onderzoek. (1)
Q3 De volgende uitspraken hebben betrekking op uw activiteiten tijdens het werken. Lees de uitspraken zorgvuldig en evalueer hoe de uitspraken op u van toepassing zijn.
Gehe el mee oneen s (1) Grotendee ls mee oneens (2) Enigszi ns mee oneens (3) Neutra al (4) Enigszi ns mee eens (5) Grotendee ls mee eens (6) Gehe el mee eens (7) Ik bepaal zelf wanneer mijn werkdag start (1) Ik plan zelf mijn werktijden (2) Ik kies zelf op welke tijdstippen ik werk (3) Ik bepaal zelf waar ik werk (4) Ik kies zelf op welke locatie ik werk (5) Ik heb de vrijheid om de plaats van mijn werk te bepalen (6) In mijn
