The influence from flexible working arrangements on
tacit- and explicit knowledge sharing:
A social capital perspective
Floortje Bek 10759492
Master thesis
Graduate school of communication
Master program: Corporate communication science
Thesis supervisor Ward van Zoonen Word count: 6467 Date: 27-06-2019
Abstract
Recently, a few large organizations retracted their flexible working arrangements (FWA’s) for employees, arguing that it has negative consequences for the speed and quality of communication between them. However, the evidence that FWA’s may harm knowledge sharing processes is lacking, or at least inconclusive. Besides, several studies found positives outcomes of FWA’s, which might be compromised if those policies were to be abolished. An online survey was conducted under 161 employees, in order to find out if FWA’s have a negative influence on knowledge sharing processes (tacit- and explicit knowledge sharing) and if this relationship is mediated by social capital and moderated by communication technology use (CTU). The results of this study show that social capital did not mediate the relationship between FWA’s and knowledge sharing processes, nor did CTU moderate the relationship between FWA’s and social capital. Some significant, direct relationships did occur and shows that FWA’s have a different impact on tacit- and explicit knowledge sharing. FWA’s have a negative impact on tacit knowledge sharing, while the opposite was found for explicit knowledge sharing. It was also found that social capital has a positive influence on tacit knowledge sharing between employees, but it did not have an influence explicit knowledge sharing. The results are discussed and implications for future research and knowledge management are made.
Introduction
Workplace flexibility seems to be a critical concept in contemporary organizational life to deal with global expansion (Putnam, Myers, & Gaillard, 2014). Many research have shown that FWA’s are beneficial on an individual level in terms of employee health, well-being, job performance and organizational commitment (Joyce, Pabayo, Critchley, & Bambra, 2010; Ter Hoeven, van Zoonen & Fonner, 2016; Chen & Fulmer, 2018). As well as on an organizational level, in terms of: organizational commitment, job satisfaction, decreasing
turnover and increasing productivity (Kelliher & Anderson, 2008; McNall, Masuda, &
Nicklin, 2009; Ollo-Lopez, Bayo-Moriones & Larraza-Kintana, 2010; Gajendran & Harrison, 2007). Despite these positive outcomes of FWA’s, recent developments show that larger companies are reducing or even eliminating their flexible working policies.
Yahoo, a large internet company, abolished the possibility to work from home in 2013, stating that some of the best decisions and insights come from hallway and cafeteria discussions, meeting new people and spontaneous team meetings. The employees of Yahoo were told that speed and quality are often sacrificed when working from home and that working side-by-side is necessary for good collaboration and communication (Smith, 2013). After Yahoo, other companies such as Bank of America, Atnea and recently IBM followed in reducing their flexible working policies (Spector, 2017).
The tendency to move away from FWA’s seems to contradict recent developments concerning the increasing quality of communication technologies, which enables employees to communicate with each other on a frequent base, while working in different locations or times (Groen, Triest, Coers & Wtenweerde, 2018; Ter Hoeven, van Zoonen & Fonner, 2016). Yahoo seems to imply that policies like working from home damages social interaction between employees and that this endangers knowledge sharing among employees.
Social interaction is an important antecedent for the existence of social capital in organizations (Hau, Kim, Lee & Kim, 2014). Social capital can be defined as: “the sum of the actual and potential resources embedded within, available through, and derived from the network of relationships possessed by an individual or social unit” (Nahapiet & Ghoshal, 1998, p. 243). Social capital is based on three dimensions: social ties, social trust and shared goals that employees have with each other (Hau, Kim, Lee, & Kim, 2014). A lack of social interaction and socialization between employees might restrict the level of social capital, which is essential for an organization to work effectively (Drury, 2016). In addition, social
capital is an important antecedent for knowledge sharing intentions between employees in an organization (Hau, Kim, Lee & Kim, 2014; Wei, Zheng & Zhang; 2011; Chow & Chan, 2008). Social capital facilitates knowledge sharing intention and creation through the social ties, social trust and shared goals that employees have with each other (Wei, Zheng & Zhang, 2011).
While much research has been done on the relationship between social capital and knowledge sharing intentions (Hau, Kim, Lee & Kim, 2014; Wei, Zheng & Zhang, 2011; Taskin & Bridoux, 2010; Chow & Chan, 2008), the empirical evidence that FWA’s might have a negative influence on social capital, is lacking or inconclusive. The decision of Yahoo to restrict flexible working in order to create better collaboration and communication between employees, is mostly based on assumptions. It is important to empirically study this
relationship in order to add empirical knowledge for future research, and use this grounded knowledge to substantiate future decision making processes. Especially when improved communication technologies might have the capability to preserve social capital by
overcoming a certain ‘distance’ between employees, who work on different locations or with different time schedules (Van Yperen, Rietzschel & De Jonge, 2014).
When organizations like Yahoo restrict the possibilities of flexible working for employees, the positive outcomes on an individual level, as well as for the organization as a whole, might be compromised. Looking from a socio/psychological view it may compromise employees’ well-being, job satisfaction and health when eliminating certain flexible
arrangements (Joyce, Pabayo, Critchley, & Bambra, 2010; Ter Hoeven, van Zoonen & Fonner, 2016; Chen & Fulmer, 2018). In light of a more economical perspective, it might compromise the organizational performance and productivity (Chen & Fulmer, 2018;
Gajendran & Harrison, 2007). Besides, eliminating FWA’s may also backfire in terms of the organizations’ competitive advantage. Todays’ economy requires employees to work
non-traditional hours, to keep up with other organizations (Van Dyne, Kossek & Lobel, 2007; Coenen & Kok, 2014). To test whether FWA’s have an impact on social capital, when communication technologies are used, and in turn, if this influences knowledge sharing intentions between employees, the following research question is stated:
RQ: What is the influence of FWA’s on social capital in organizations, when accounting for communication technology use, and how does this affect employees’ knowledge sharing intentions?
Theoretical Framework FWA’s
In some countries, greater availability of FWA’s are encouraged by governments (Menezes & Kelliher, 2017). More organizations provide FWA’s, and more employees make use of these FWA’s (ten Brummelhuis, Haar & van der Lippe, 2010). Flexible work schedules (flexitime) and working from home (telecommuting) are the two most common flexible arrangements that organizations provide and employees use (Shockley & Allen, 2007). Flexitime describes the freedom employees have, in deciding which time they start working and when they finish their workday. Telecommuting is a form of flexible working which gives employees the freedom to decide from which location they can work.
Many organizations offer FWA’s in their policies, but it is possible that employees have the feeling that they cannot make use of these arrangements (Kirby & Krone, 2002). It seems, there is a discrepancy between the existence of FWA’s in organizations and the actual use of FWA’s, due to cultural norms. Since the existence of FWA’s does not necessarily mean that employees make use of FWA’s, it seems more appropriate to measure the actual use of these arrangements, in order to study the impact it has on social capital and in turn knowledge sharing.
CTU
The use of different communication technologies gives employees a better opportunity to work together, more place and time -independent (Van Yperen, Rietzschel & De Jonge, 2014). As mentioned earlier, the increasing quality of communication technologies, have made it possible to work on different locations and at different times (Groen, Triest, Coers & Wtenweerde, 2018). Drawing on the affordances theory, media richness and interactivity are two important quality aspects of communication technologies (Shao & Pan, 2019 ). Media richness can be described as the capability of a technology to convey more social and visual cues between the users (Daft & Lenglel, 1984). Interactivity of a communication technology refers to the degree to which the communication users can act on each other and to what extend these actions can occur synchronously (Liu & Shrum, 2002).
Next to the quality of communication technologies, it is also important that employees are using these technologies on a frequent base, to maintain their relationships with other employees (Shao & Pan, 2019 ). Due to the technological advantages, a media rich work environment is created which in turn, leads to more frequent use of technologies (van Zoonen, Verhoeven & Vliegenthart, 2017; Leonardi, Treem & Jackson, 2010). Given the enhanced quality, in terms of their richness and interactivity, and the high frequency in which
communication technology is often used, it seems plausible that CTU might have an impact on the relationship that FWA’s might have on social capital in organizations. Therefore, CTU will be accounted for, when further exploring the relationship between FWA’s and social capital.
Social Capital and FWA’s
Close social interaction between employees is a basic need for the maintenance of social capital in organizations (Shao & Pan, 2019 ). Through the structural, relational and cognitive dimension, a network of relationships between employees can be formed (Hau,
Kim, Lee & Kim, 2014). The structural dimension of social capital explains how social interaction forms structural links between employees in a network (Wei, Zheng & Zhang, 2011). The relational dimension explains the nature of the connections between employees, where trust, norms and identification between employees form these connections. The cognitive dimension of social capital explains to what extent employees share a common perspective or language and to what extent they have shared goals (Wei, Zheng & Zhang, 2011).
In order to maintain social capital of organizations, while working under flexible conditions, CTU needs to facilitate social interaction between employees. A large amount of research has focused on how social capital can be affected in the context of virtual
communities (Wu & Chang, 2005; Zhao et al., 2012; Chiu et al., 2006; Chang & Chuang, 2011), as well as how social capital can be influenced through the use of one specific communication technology (Shao & Pan, 2019 ; Lin, Luo, Cheng & Li, 2018). However, there is little research that includes how social capital might be influenced by FWA’s, in terms of flexitime and telework, when accounting for different types of CTU. There are some studies suggesting that the ‘distance’ FWA’s create between employees, restrict the social interaction between employees (ten Brummelhuis, Haar & van der Lippe, 2010; Taskin & Bridoux, 2010). It seems that virtual communication cannot fully replace face-to-face contact (Coenen & Kok, 2014). However, given the increasing quality of communication technology and the frequency in which communication technology have been used, it seems plausible that CTU might overcome this ‘distance’ between employees. It is possible that CTU can facilitate social interaction on a sufficient level, so that the social capital of an organization is not threatened. Therefore, the following hypotheses is posed:
H1: FWA’s have a negative influence on social capital, but CTU moderates this relationship.
Social capital and knowledge sharing
Social capital is also one of the key facilitators for knowledge sharing practices, in a way that the social ties, social trust and shared goals can influence employees’ intentions to share knowledge (Hau, Kim, Lee & Kim, 2014; Wei, Zheng & Zhang; 2011; Chow & Chan, 2008). A lack of social capital signals distrust and higher costs and risks associated with sharing knowledge (Shao & Pan, 2019 ). There are two types of knowledge that are likely to be shared in different ways between employees (Hau, Kim, Lee, Kim, 2014). First, explicit knowledge, which can be defined as knowledge that has been explained, recorded or
documented and therefore can be easily explained and transferred to other employees (McInerney, 2002). Most of this knowledge is codified and stored in databases, so it is easy for employees to get access to it (McInerney, 2002). Tacit knowledge on the other hand, is more difficult to share because it is knowledge that is based on the expertise and assumptions that individuals develop (McInerney, 2002). This type of knowledge is dependent on the employees’ ‘know how’. It is knowledge that is based on the employees’ experience and is often transferred through conversations between employees (McInerney, 2002), which is not documented or difficult to document.
Previous research has shown that social capital seems to be more important for sharing tacit knowledge, than for sharing explicit knowledge (Al-Qdah & Salim, 2013; Noorderhaven & Harzing, 2009). Especially social trust seems to be an important antecedent for tacit
knowledge sharing (Levin & Cross, 2014; Huang, Davidson & Gu, 2011). ‘Tacit knowledge is shared through socialization, which requires extensive social interaction between
individual experience and cannot be documented, it takes more effort to transfer it to others. When trust exists, employees are more willing to share their knowledge with others, as well as receiving knowledge from others (Levin & Cross, 2014). Additionally, tacit knowledge sharing is more difficult to share than explicit knowledge, because the information is much more complex than explicit, codified knowledge (Al-Qdah & Salim, 2013).
Sharing tacit knowledge is more dependent on social and visual cues, which communication technology needs to facilitate in order to transfer this type of knowledge (Shao & Pan, 2019). Explicit knowledge on the other hand, is easier to transfer because this knowledge is mostly documented and takes less effort to share this with other employees (Al-Qdah & Salim, 2013). Although the impact of social capital might be bigger for tacit
knowledge sharing, previous research have shown that social capital is also an important antecedent explicit knowledge sharing (Hau, Kim, Lee & Kim, 2014). If FWA’s might have a negative influence on social capital, it is plausible that both explicit- and tacit knowledge sharing are affected. Therefore, we assume that both explicit and tacit knowledge sharing are affected and when comparing them, tacit knowledge sharing is more negatively impacted than explicit knowledge sharing. This is stated in the following hypotheses:
H2a: FWA’s have a negative influence on social capital in organizations, which in turn, has a negative influence on explicit knowledge sharing between employees.
H2b: FWA’s have a negative influence on social capital in organizations, which in turn, has a negative influence on tacit knowledge sharing between employees. H3: FWA’s have a negative influence on social capital, which in turn, has a more negative impact on tacit knowledge sharing, than on explicit knowledge sharing between employees.
Figure 1. Conceptual model
Method
Procedure
The online survey has been spread across employees during a time period of two and a half weeks. A non-probability convenient sample was used to gather all the data. The link of the survey was spread through personal connections via e-mail or WhatsApp. Most of these personal connections have spread the survey in their organizations to other colleagues, which caused a snowballing effect. Before starting the questionnaire the respondents got information about the subject of the questionnaire. Respondents were asked to sign an informed consent. In the informed consent the respondents were told that their anonymity was safeguarded and that there information will not be passed on to third parties. They also had the possibility to quit the survey at any time. The respondents were asked to make the survey in an individual setting, because of constructs such as social trust, which can be sensitive subjects.
questionnaire a timeline was shown to let the respondents see their progress. The average time of completing the survey was 8 minutes.
Sample
The respondents had to be employees older than 18, who work more than 24 hours a week, in an a knowledge -based organization and where they have the possibility to work under flexible conditions. The organization had to have a certain amount of employees in order to be able to share knowledge with each other. Therefore, organizations from one to 30 employees were excluded. Because employees were are asked to give feedback about social capital they have with other employees, they must have worked at least one year for the organization, because social capital might be difficult to have when you do not know the other employees that well. The questionnaire was in English, so all respondents needed to have sufficient understanding of the English language. To make sure that this target group was reached, the survey was spread through personal connections that work in large knowledge-based organizations and have the possibility to work under flexible conditions. These criteria were also included in the questionnaire to do a check afterwards and were excluded when they did not meet these requirements.
184 respondents filled in the survey from which 23 responses have not been taken into analyses. These respondents did not complete the survey or fell outside of the target group because they: a) have worked for the organization less than one year b) worked less than 24 hours per week c) works in an organization with less than 30 employees. In total, 161
responses were recorded and used for the analyses (N=161), from which 64 are male (39.8%) and 97 are female (60.2%). The age varied from a minimum age of 19 years old and a
maximum age was 67 (M= 32.2, SD= 7.2). The biggest part of respondents work in organizations with 501 to 1000 employees (19.9%). 13.7% of the respondents works in an
organization with 5001 or more employees and 13% of the respondents worked for an organization with 30 to 100 other employees. The respondents’ average working week was 41.5 hours (SD= 6,9). The minimum hours in a working week was 24 hours and the maximum was 65 hours per week. The average amount of years that respondents have worked for the organizations was 3.1 years (SD= 2.3), with a minimum of one year and a maximum of 14 years. From all the respondents, 21.1% (N= 34) had a management position in the
organization and 78.9% did not (N= 127).
Measurements
FWA’s. This variable was measured with one construct. The existing scale from ten Brummelhuis, ter Hoeven, Bakker and Peper (2011) has proven to be reliable (Cronbachs’ alpha .76) and will be used to measure perceived FWA’s. This scale contains six items and contains statements like: ‘I can choose which location I work at’ and ‘I can decide myself when I begin the workday’. These statements could be answered on a five-point Likert scale ranging from ‘Totally disagree’ (0) to ‘Totally agree (4). Since this construct has proved to be valid in previous research, this study will use the scale the way theory suggests in previous research (Brummelhuis, ter Hoeven, Bakker & Peper, 2011). To see if the scale in this research is also reliable in this study a reliability check is done. The scale turned out to be reliable, with a Cronbach’s alpha of .75 (M=2.20, SD=.79). All items where included, since deleting one item could not make the scale more reliable.
Social Capital. This variable was measured with three constructs: social ties, social trust and shared goals. Social ties is a construct consisting of three items, that is retrieved from an existing scale (Chow & Chan, 2008) and has proven to be reliable (Cronbachs’ alpha = .72). This scale contains items like: ‘In general, I have a very good relationship with my organizational members’ and ‘In general, I am very close to my organizational members’.
These statements could be answered on a five-point scale ranging from ‘Totally disagree’ (0) to ‘Totally agree (4). Social trust consists of three items from the same existing scale from Chow and Chan (2008) and has proven to be reliable (Cronbachs’ alpha = .79). This scale contains statements like: ‘I can always trust my organizational members to lend me a hand if I need it’ and ‘I know my organizational members will always try and help me out if I get into difficulties’. These statements can be answered on a five-point scale ranging from ‘Totally disagree’ (0) to ‘Totally agree (4). Shared goals is a construct consisting of three items, also retrieved from (Chow & Chan, 2008) and has proven to be reliable (Cronbachs’ alpha = .77). This scale contains items like: ‘My organizational members and I always agree on what is important at work’ and ‘My organizational members and I are always enthusiastic about pursing the collective goals and missions of the whole organization’. These statements could be answered on a five-point scale ranging from ‘Totally disagree’ (0) to ‘Totally agree (4).
Since the construct of social capital has proved to be valid in previous research, this study will use the scale the way theory suggests (Chow & Chan, 2008) The scale turned out to be reliable, with a Cronbach’s alpha of .85 (M=3.02, SD=.57). All items where included, since deleting one item could not make the scale more reliable.
Explicit knowledge sharing. The sharing processes are divided in two constructs: tacit knowledge sharing and explicit knowledge sharing. Both constructs are retrieved from the existing scale from (Bock, Zmud, Kim & Lee, 2005). The scale for explicit knowledge sharing has proven to be reliable (Cronbachs’ alpha = .92) and has four items in total with statements like: ‘Most people in my organization share formal reports and documents with other colleagues’ and ‘Most people in my organization share explicit standardized knowledge with colleagues’. These statements could be answered on a five-point Likert scale ranging from ‘Totally disagree’ (0) to ‘Totally agree (4).
A factor analysis with varimax rotation was conducted to confirm that the items measure the latent constructs. All the items, measuring explicit knowledge sharing had a factor loading above .45 and had an eigenvalue of 2.44. The construct accounted for 61.0% of the total variance. The scale has proven to be reliable with a Cronbachs’ alpha of .78 (M= 2.62, SD=.69). All items where included, since deleting one item could not make the scale more reliable.
Tacit knowledge sharing. Tacit knowledge sharing has also proven to be reliable (Cronbachs’ alpha = .93). Tacit knowledge sharing was measured with four items, which contains statements like: ‘Most people in my organization share their tricks of the trade with other colleagues’ and ‘In my organization most people share their rules of thumb and other insights about work with other colleagues’. These statements could be answered on a five-point Likert scale ranging from ‘Totally disagree’ (0) to ‘Totally agree (4). A factor analysis with varimax rotation was conducted to confirm that the items measure the latent constructs. All the items, measuring tacit knowledge sharing had a factor loading above .45 and an eigenvalue of 2.96. The construct accounted for 73.9% of the total variance. The scale has proven to be reliable with a Cronbachs’ alpha of .88 (M= 2.50, SD=.77). All items where included, since deleting one item could not make the scale more reliable.
Communication Technology Use. To measure how frequent employees use different communication technologies for work, an index scale of nine items was used (Van Yperen, Wörtler, & De Jonge, 2016). The scale contains items like: ‘I use e-mail to communicate’, ‘I use video conferencing to communicate’ and ‘I use enterprise social media (such as Yammer, Communicator or Lync) to communicate’. These statements could be answered on a six-point Likert scale ranging from ‘Never’ (0) to ‘Very regularly’ (5).
These nine items measure the use of different types of communication technologies. This means that the frequency of use of one technology does not necessarily mean that it must be consistent with the frequency of use of other technologies. Therefore, these items will be taken together to form an index variable that measures the frequency of use of different types of communication technologies.
Data-analyses
To test if FWA’s have an influence on social capital in organizations, when accounting for CTU, and if this in turn influences knowledge sharing, two
moderation/mediation analyses where done using Procces V3.3, model 7. The multiple regression analyses were done for tacit knowledge sharing and for explicit knowledge sharing. The outcomes are explained per hypotheses.
Results
To test whether the control variables correlate with the variables used for this study, a correlation analyses with Pearsons’ r was conducted. FWA’s, CTU and tacit - and explicit knowledge sharing were the variables that had a significant relationship with one of the control variables. Social capital had no significant correlation with any of the control
variables. The control variables ‘age’ and ‘management position’ correlated the highest with FWA’s and therefore will be taken into the analyses to test if they might interact with FWA’s. In table one, all the correlations between the variables are shown.
Table 1. Correlation table (N = 161)
Gender Age Size
organization Working hours Tenure Management position FWA's -.08 .29** .13 .09 .19* .27** CTU .10 -.03 .24** .22** .06 .09 Social capital -.03 -.14 .02 .02 .012 -.07 Tacit knowledge .03 -.19* -.06 .00 -.07 -.15 Explicit knowledge .26** -.07 .10 .13 -.02 -.02
* Significant at the .05 level (2-tailed).
** Significant at the .01 level (2-tailed).
A multiple regression analyses, with social capital as the dependent variable, shows that the model is not significant (F(5, 155) = .24, p = .265). FWA’s do not have a direct significant impact on social capital (b = -.16, b* = .23, t = -.68, p = .501, CI[-.62 – .30]), neither is this relationship moderated by CTU (b = .04, b* = .08, t = .49, p = .625, CI[-.11-.19]). Therefore, the first hypothesis is rejected. FWA’s do not have a negative influence on social capital when accounting for CTU.
The multiple regression analyses with explicit knowledge sharing as the dependent variable was not significant (F(4, 156) = 1.76, p = .141). FWA’s do not have a an indirect negative impact on explicit knowledge sharing. Neither are the age (b = .01, b* = .01, t = -1.18, p = .239, CI[-.03-.01]) and management position (b = -.02, b* = .15, t = -.11, p = .913, CI[-.32-.28])of a significant influence. Therefore, hypotheses 2a is rejected. FWA’s do not have a negative influence on social capital, when accounting for CTU (b = .04, b* = .08, t =
.49, p = .625, CI[-.11-.19]) and this has no significant negative impact on explicit knowledge sharing between employees.
However, the direct impact of FWA’s on explicit knowledge sharing was found to be significant (b = .16, b* = .07, t = 2.14, p = .034, CI[.01-.30]).This relationship turned out to be positive. FWA’s explain 4.3% of the variance in explicit knowledge sharing. When FWA’s are high, more explicit knowledge is shared among employees.
The multiple regression analyses with tacit knowledge sharing as the dependent variable shows that the model is not significant (F(5, 155) = .83, p = .528). FWA’s do not have a significant negative impact on social capital, when accounting for CTU (b = .04, b* = .08, t = .49, p = .625, CI[-.11-.19]) and this has no significant negative impact on tacit knowledge sharing. Neither did the control variables age (b = -.00, b* = .01, t = -.35, p = .727, CI[-.03-.01]) and management position (b = -.04, b* = .14, t = -.30, p = .763, CI[-.31-.23]). Therefore, hypotheses 2b is rejected as well.
However, the model shows that the direct effect of FWA’s on tacit knowledge sharing is significant, as well as the direct effect from social capital on tacit knowledge sharing. The direct influence of FWA’s and the direct influence of social capital, together, account for 35% of the total variance in tacit knowledge sharing (F(4, 156) = 21.04, p < .000). FWA’s have a negative impact on tacit knowledge sharing (b = .32, b* = .07, t = 4.78, p < .005, CI[.45 -.19]). When FWA’s are high, less tacit knowledge is shared among employees. Social capital has a significant positive impact on tacit knowledge sharing (b = .61, b* = .09, t = 6.92, p < .005, CI[.44 – .78]). When social capital in an organization is higher, more tacit knowledge is shared among employees. Although the third hypotheses is rejected, parts of the model do explain some direct, significant impact on tacit knowledge sharing.
The third hypotheses suggests that FWA’s has negative impact on social capital and in turn, this will have a greater negative influence on tacit knowledge sharing than on explicit
knowledge sharing. Both the multiple regression models for explicit- and tacit knowledge sharing were not significant. FWA’s do not have an impact on social capital and this is not moderated by CTU (F(5, 155) = .83, p = .528) and neither does social capital mediate the relationship between FWA’s and explicit knowledge sharing (F(4, 156) = 1.76, p = .141).For tacit knowledge one part of the model turned out to be significant (F(4, 156) = 21.04, p < .000), but only direct significant relationships were found. This means that these two models cannot be compared. The third hypotheses cannot be confirmed and therefore, will be
rejected. FWA’s do not have a significant, negative influence on social capital and this has no greater negative impact on tacit knowledge sharing than on explicit knowledge sharing.
Discussion
The present research intended to show that FWA’s might have a negative influence on social capital in organizations, but that the negative impact would be mitigated by CTU, and in turn, social capital would have an impact on both tacit- and explicit knowledge sharing. However, the findings of this study did not prove that CTU moderated the relationship between FWA’s and social capital, nor did social capital mediated the relationship between FWA’s and the two types of knowledge sharing. Although, no indirect relationship was found from either social capital or CTU on these two knowledge sharing processes, some direct effects did occur. These direct relationships might be explained by other underlying mechanisms, that shows employees’ motivational aspects of sharing these two types of knowledge.
Theoretical implications
Similar to previous research, the findings of this study show that when the level of FWA’s is high, the amount of tacit knowledge that is shared between employees decreases
have a different impact on tacit – and explicit knowledge sharing (Hau, Kim, Lee & Kim, 2014; Coenen & Kok, 2014). Where FWA’s have a negative influence on tacit knowledge sharing, it seems to have a positive relationship with explicit knowledge sharing between employees. There are two implications that might explain employees’ motivation to share explicit knowledge under FWA’s and why they might be less motivated to share tacit knowledge under FWA’s.
The first explanation might be that explicit knowledge has relatively high levels of availability and accessibility, which costs employees less effort to share it among employees (Stohl, Stohl & Leonardi, 2016). Most explicit knowledge is available to other employees, because it is often recorded somewhere and stored in databases. Because this type of
knowledge is mostly documented it makes it easier for other employees to get access to this type of knowledge and takes little effort to share this knowledge with others (Stohl, Stohl & Leonardi, 2016). On the other hand, tacit knowledge will be less available and accessible for other employees. This knowledge is difficult to document and store, since it is highly reliant on the individuals’ explanation, perception and ‘know how’. It will take more effort for employees to share this type of knowledge, which is why employees might be less motivated to share tacit knowledge with other employees. It is important to take in account that sharing high volumes of explicit knowledge, might not always be beneficial for organizations, as it may lead to opacity. Opacity can occur when employees share loads of information with each other, which makes it harder to find the relevant pieces of knowledge in this information overload (Stohl, Stohl & Leonardi, 2016).
What the increase in explicit knowledge sharing under FWA’s might imply as well, is that it creates a feeling of being ‘in touch’ with other employees, without restricting their feeling of autonomy (Mazmanian & Yates, 2013). Explicit knowledge allows communication between employees to be asynchronous, which means that employees do not have to provide
and receive immediate feedback to other employees (Mazmanian & Yates, 2013; Peltokorpi, 2015). Since explicit knowledge is mostly documented and can be looked upon later,
employees have more control over when, where and how they can share this knowledge with others. This feeling of control and flexibility increases the employees’ feeling of autonomy when working under flexible conditions (Mazmanian & Yates, 2013). For tacit knowledge sharing between employees, communication technology has to be synchronously in order to be able to transfer it to other employees (Noorderhaven & Harzing, 2009). Tacit knowledge is difficult to store and retrieve later, since it is highly reliant on the employees’ perception. In order to let communication run synchronously, employees are more dependent on one another and have less control over when, where and how to share this knowledge (Mazmanian & Yates, 2013). Because tacit knowledge sharing will restrict the feeling of autonomy, this might explain why employees will be less motivated to share tacit knowledge with each other.
Although, social capital did not mediate the relationship between FWA’s and
knowledge sharing in this study, it did have a direct relationship with tacit knowledge sharing, in a way that higher levels of social capital leads to more tacit knowledge sharing between employees. As mentioned earlier, for the existence of social capital it seems important that there is sufficient social interaction between employees (Noorderhaven, 2009; Hau, Kim, Lee & Kim, 2014.) and at the same time, social capital is a key antecedent for tacit knowledge sharing between employees. Since tacit knowledge sharing takes more effort for employees to share with each other (Leonardi, 2016), the motivation to still share this knowledge has to come from strong social ties, trust and shared goals between employees.
According to previous research, communication technologies with a high level of ‘richness’ and interactivity are able to transfer social and visual cues to other employees. The use of these technologies can maintain strong connections between employees, which in turn,
However, this study did measure if ‘rich’ and interactive media (e.g. video conferencing and social media) where used, but this did not have any significant relationship on social capital or tacit knowledge sharing. A possible explanation might be that these type of technologies are less often used, because this restraints there freedom of when and how to share their
knowledge with others (Mazmanian & Yates, 2013).
Practical implications
The findings of this study show that so tacit knowledge sharing is negatively affected by FWA’s, but that social capital in organizations may enhance tacit knowledge sharing between employees. Thus, to further develop tacit knowledge sharing between employees, it seems that organizations need to focus their attention on how to create and maintain social capital in their organization. At the same time, FWA’s do have a positive relationship with explicit knowledge sharing between employees.
When we go back to the case of Yahoo, who abolished the possibility to work from home, the results of this study can partially substantiate Yahoos’ decision, in that FWA’s do have a negative impact on tacit knowledge sharing between employees. It seems that for tacit knowledge sharing, it is important to still work closely with other employees in terms of place and time. Therefore, it would be wise for organizations to stimulate face-to-face contact between employees. Stimulating face-to-face contact between employees, may also lead to positive outcomes for the social capital in organizations, which in turn leads to more tacit knowledge sharing.
However, the decision of Yahoo to retract all forms of FWA’s might be premature. Explicit knowledge sharing is not harmed when employees have the possibility to work from home or work in different time schedules. This implies that Yahoo and other companies should reconsider if it is really necessary to fully retract FWA’s, since FWA’s has proven to
be beneficial for the well-being and performance of employees and organizations, as well. Therefore, it seems important that managers make employees aware of how different
knowledge can be affected while working under FWA’s, so that employees are stimulated to find a balance in how frequently and when they can make use of FWA’s.
Limitations of the study and suggestions for future research
This study might have few limitations that are important to mention. First, the method of a cross sectional survey has its limitations in that we cannot prove the causality of the relations that where found. Meaning that it cannot be told if FWA’s lead to a decrease in tacit knowledge sharing, or that lower levels of tacit knowledge sharing leads to higher levels of FWA’s. The same goes for all the other significant relationships that were found in this study.
Secondly, the data was gathered via personal connections of the researcher and these personal connections reached out to their personal connections. This convenient sampling method, in combination with using a snowballing method, has most likely led to gathering data from a very specific group. The sample will more or less, share a same set of
characteristics, which is why it cannot represent the variety of the whole working population. This causes the results of this study to be ‘colored’ and therefore less generalizable.
Third, social capital is a construct that intended to measure a network of relationships between employees. Social capital can exist between employees who work in the same teams or departments of organizations, and thus are likely to have interdependent relationships with each other. Because this sample measured social capital between employees who work in different organizations, these interdependent relationships cannot be measured with this sample. Thus, for future research it seems appropriate to do a case study on one organization, to measure the existence of social capital in a correct manner.
Fourth, no analyses extra were done on the different dimensions of CTU and Social capital. This study cannot tell if the use of specific technologies might have a different impact on social capital and the two types of knowledge sharing. Since previous research proved that there are certain characteristics from technologies (e.g. synchronicity, richness and
interactivity) that may cause a different impact on tacit- and explicit knowledge sharing, it seems appropriate for future research to study how different technologies can influence the relationship between FWA’s and knowledge sharing processes. The same goes for social capital, where a more fine-grained analyses could be done on the structural, relational and cognitive component to see if these dimensions have a different impact on the two knowledge sharing processes.
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