The Relationship between Data and Information Visibility, Organizational Citizenship Behaviour and Task Performance in Organizations:
The role of Explicit and Tacit Knowledge Sharing
Eliza van der Beek University of Amsterdam
Eliza van der Beek 10733841
Master’s Thesis
Graduate School of Communication
Master’s programme Communication Science Supervisor: mw. dr. C.L. ter Hoeven
Abstract
During the last two decades, organizations have adopted an increasing number of new information and communication technologies that ensure that data and information in
organizations become more visible. This study contributes to the existing literature about data and information visibility by providing insight in how visibility of organizational data and information relates to important organizational outcomes and the underlying mechanisms that play a role in explaining this relationship. An online survey among 220 employees has been conducted to examine the relationship between data and information visibility, organizational citizenship behavior (OCB) and task performance in organizations and the role of explicit and tacit knowledge sharing within this context. Results show that the relationship between visibility and both OCB and task performance can be explained by explicit knowledge sharing. In other words, the outcomes demonstrate that visibility of data and information can enhance OCB and task performance within organizations by increasing explicit knowledge sharing. However, inscription and storage of data and information did not contribute to predicting OCB through explicit knowledge sharing, and for task performance approval to disseminate data and
information did not play a role in predicting the outcome. Furthermore, contrary to expectations, no relationship was found between directory knowledge and task performance. Especially enhancing employees’ skills to acquire and interpret data and information and the ease to access the data and information appears to be important in explaining the relationship between visibility and OCB and task performance through explicit knowledge sharing. Theoretical and practical implications are discussed.
Keywords: Data and Information Visibility, Knowledge Sharing, Organizational
Introduction
During the last two decades, organizations have adopted an increasing number of new information and communication technologies (ICTs) (Leonardi, 2014; Leonardi & Meyer, 2015; Yuan, Rickard, Xia, & Scherer, 2011). These digital technologies provide an increasing amount of information sources and rapidly expand the dissemination of information. Furthermore, they offer increasing opportunities for the storage, retrieval and processing of that information and make the transfer of information faster, easier and cheaper (Stohl, Stohl & Leonardi, 2016). In this way digital technologies ensure that information about collective and individual behaviors, expertise, communications, and decisions in organizations becomes more visible (Couldry, 2012; Leonardi, 2014; Leonardi & Meyer, 2015; Stohl et al., 2016; Yuan et al., 2011). Recently,
researchers havefocused their attention on this technology-enabled information visibility (Flyverbom, 2015; Flyverbom, 2016; Flyverbom, Leonardi, Stohl, & Stohl, 2016; Hansen & Flyverbom, 2014; Hansen, Christensen, & Flyverbom, 2015; Leonardi, 2014; Stohl et al., 2016; Treem & Leonardi, 2012). For example, Flyverbom’s (2015) article on transparency and the management of visibilities raises the question how creating particular visibilities shapes organizations, practices, policy issues and relationships between different actors.
From previous literature on information and communication technology it is already known that the increased use of ICT’s, and the visibility of information that comes with this, can have important implications for organizational processes and work outcomes like deliberations, research and development and decision making among all types of organizations (Stohl et al., 2016, Yaziji & Doh, 2009). For example, digital technologies increase the ability to monitor corporate activities and production processes and enable access to data and information heretofore unavailable and therefore enable organizations to optimize the production process (Cannata, Karnouskos, & Taisch, 2009).
But despite the alleged opportunities of technology-enabled data and information visibility, its effect on organizational processes and work outcomes has further remained understudied (e.g. Flyverbom et al., 2016). Perhaps it may also have outcomes that are less desirable. For instance, as Flyverbom (2016) shows, visibility management is not always providing openness, insight, and clarity and can have more elaborate, complex, and sometimes even paradoxical effects. Likewise, Stohl et al. (2016) theorize that increasing the availability, approval and accessibility of information, which makes it more visible, can have the paradoxical effect of making information in organizations more opaque rather than more transparent. This is because it may produce a flood of unstructured data and information that overwhelms the
cognitive and interpretive capabilities of employees, and hence renders information meaningless, confusing and opaque. In other words, when there is an abundance of information available, it is often difficult to obtain useful, relevant information, because important pieces of information become hidden in the large amount of information (Stohl et al., 2016). Thus, it is important to notice that when data or information is visible, it is capable of being seen, but that it does not always mean that it can be easily seen through or detected (Stohl et al., 2016). The supposed paradoxical effects of data and information visibility raise the question what underlying
mechanisms play a role in explaining this effects. To fill the knowledge gap in the literature, this study will examine the relationship between visibility and important organizational outcomes and pay attention to underlying processes.
Work outcomes that gained a lot of scientific attention in the organizational literature because of their importance for long-term organizational success are task performance and Organizational Citizenship Behavior (OCB; e.g. Borman & Motowidlo, 1997; Lee & Allen, 2002; Moorman, 1991; Motowidlo & Van Scotter, 1994; Organ 1988; Podsakoff, Whiting, Podsakoff & Blume, 2009; Van Dyne, Graham, & Dienesch, 1994). Task performance consists of job-specific behaviors including core job responsibilities (Conway, 1999). Task performance
can be defined as “the effectiveness with which job incumbents perform activities that contribute to the organization's technical core either directly by implementing a part of its technological process, or indirectly by providing it with needed materials or services” (Borman & Motowidlo, 1997, p. 99). But in an increasingly competitive market, in addition to task performance, OCB also becomes important for overall organizational functioning (Lee & Allen, 2002). OCB is “individual behavior that is discretionary, not directly or explicitly recognized by the formal reward system, and that in the aggregate promotes the effective functioning of the organization” (Organ, 1988, p. 4). The main reasons for the interest in OCB’s is the potential positive
consequences of these behaviors for organizational-level outcomes (e.g. productivity, efficiency, reduced costs and customer satisfaction). Examples of OCB include voluntarily carrying out tasks that are not formally part of the job and helping and cooperating with others in the organization to get tasks accomplished (Borman & Motowidlo, 1997). The majority of the research on task performance and OCB in organizations focuses on how they relate to employee characteristics, organizational processes and social factors (e.g., Debusscher, Hofmans, & De Fruyt, 2016). However, as far as can be verified, it is still unknown how the increasing amount of data and information is related to task performance and OCB.
In an attempt to manage this increased amount of data and information and to utilize it to enhance organizational outcomes, companies have invested an enormous amount of resources in knowledge management technology, which according to Babcock (2004) often fail or show little results. He states that companies lose a lot of money every year by failing to share knowledge. De Long and Fahey (2000) in their study on barriers to knowledge management underline the importance of the sharing of knowledge for organizations by showing that the benefits of a new technology infrastructure were limited if they did not support knowledge sharing. Thus, both researchers suggest that knowledge sharing is an explaining factor of the effects of information management systems that try to make data and information visible and usable on organizational
outcomes. In particular, previous research has highlighted the importance of knowledge sharing for performance (e.g. Cummings, 2004; Hau, Kim, Lee & Kim, 2013; Liao et al., 2004; Wang & Noe, 2010) and OCB (e.g. Allen, Shore, & Griffeth, 2003; Bartol & Srivastava, 2002) in
organizations. This raises the question if knowledge sharing may act as an underlying
mechanism that explains the relationship between visibility of data and information and both task performance and OCB. Knowledge sharing is generally defined as “activities of transferring or disseminating knowledge from one person, group or organization to another” (Lee, 2001a, p. 324). An example of knowledge sharing within an organization is the sharing of work-related knowledge and expertise with other members within one’s organization (Yi, 2009). Two
different forms of knowledge sharing can be distinguished: explicit and tacit knowledge sharing. Explicit knowledge sharing can be defined as knowledge that can be easily expressed and communicated in formal, systematic language. This type of knowledge is objective and often consists of written documents such as reports or manuals (Nonaka & Takeuchi, 1995). Because explicit knowledge is easily expressed and communicated, it is easy to share this knowledge within an organization. Tacit knowledge is knowledge that is less easily visual or verbal
expressed and communicated because it is subjective, context-specific and difficult to describe. It is often embedded in the knowledge or experience of an individual. Tacit knowledge is therefore also shared less easily within an organization than explicit knowledge (Hau et al., 2013; Kim & Ju, 2008; Nonaka, 1994; Ramayah, Yeap & Ignatius, 2014).
In summary, this study contributes to the existing organizational literature in three ways. First of all, it broadens the understanding of the effects (positive or negative) of making data and information visible on important organizational outcomes. More specifically, it provides insight in the relationship between data and information visibility and both task performance and OCB.
Second, it contributes to the understanding of the underlying mechanisms that explain the relationship between visibility and organizational outcomes. By doing so, the study discusses the
different domains of visibility and the technological and mediated foundations of it.
Furthermore, it focusses on the dynamics of visibility practices resulting from efforts to make behavior, knowledge and processes visible. In other words, this study does not only pay attention to the provision of information but considers the wider social processes and dynamics at work in visibility. The role of knowledge sharing in the relationship between visibility and both OCB and task performance will be analyzed, which adds to the literature by emphasizing that the
relationship between visibility and work outcomes is more complex than previously established and that mediating factors must be considered. A distinction will be made between explicit knowledge sharing and tacit knowledge sharing.
Third, this study contributes to the organizational literature by translating the Visibility Scale developed by Ter Hoeven, Stohl, Banghart, Leonardi and Stohl (2017) into Dutch and validating the scale. The factor structure will be tested and the reliability of the scale will be estimated in terms of scale item homogeneity and internal consistency. In this way, it is possible to conduct research among both English-speaking and Dutch-speaking employees, which will benefit the external validity of visibility research.
Finally, in addition to the contributions to the scientific literature, the study also contributes to organizational practice by discussing the implications of the results of the study for the implementation, support, and effectiveness of visibility initiatives in organizations. The research question raised in this article is as follows:
What is the relationship between data and information visibility, Organizational
Citizenship Behavior and Task Performance in organizations, and what is the role of Explicit and Tacit Knowledge Sharing within this context?
Theoretical framework Visibility
Before the role of organizational data and information visibility in relation to other organizational variables can be considered, it is necessary to start with offering a
conceptualization of visibility. Stohl et al. (2016) describe visibility as the combination of three attributes: availability of information, approval to disseminate information, and accessibility of information to third parties.
Information becomes available through inscription of actions into recognizable data forms and the storage of those data. For instance the documentation of decisions in an organizational meeting or the description of work-related knowledge and skills. In addition, approval to disseminate the information is a prerequisite for its visibility. The approval of the dissemination of information usually has to do with legal obligations, industry or field norms or social consciousness of the organization (Stohl et al., 2016). The third major attribute of
visibility is the accessibility of data and information. This is the ease associated with retrieving and interpreting the information. For data and information to be visible, employees have to know what information and data exist within the organization and where to find it. Moreover, the data and information have to be classified or arranged in a certain way to make it easier for
employees to obtain them and employees need the skills to acquire and interpret the data and information. Finally, the amount of effort required to access the data and information is important; the greater the effort to obtain certain information, the less accessible it is. If the information is not accessible to the employees who want to consult it, it will not be visible (Stohl et al., 2016).
Earlier research (Stohl et al., 2016, Yaziji & Doh, 2009) presumes that visibility practices can have important implications for organizational processes, behaviors and outcomes within an organization. For instance, there are several reasons to assume that visibility of data and
information has an effect on work outcomes, which can influence the effective functioning of an organization (e.g. Borman & Motowidlo, 1997; Podsakoff, Whiting, Podsakoff & Blume, 2009).
Explaining the relationship between visibility, OCB and Task Performance
Earlier research has theorized that the increasing amount of data and information in organizations that is available, public and accessible can have important implications for work outcomes in organizations (Cannata et al., 2009; Yaziji & Doh, 2009). However, De Long and Fahey (2000) and Babcock (2004) indicate that visibility practices do not have a direct effect on work outcomes. Their research suggests that knowledge sharing acts as an underlying
mechanism that explains the outcomes of visibility of data and information on work outcomes within the organization.
More precisely, although there are a lot of other factors that promote or inhibit knowledge sharing in organizations (e.g. trust, perceived time pressure, organizational commitment,
organizational justice and organizational culture, see for example Bartol & Srivastava, 2002; Kim & Mauborgne, 1998; Lavanya, 2012; Staples & Webster, 2008), it is expected that visibility of data and information is a key enabler of knowledge sharing in organizations by providing data and information where knowledge is based on (Bock and Kim, 2001), which according to earlier research in turn can influence outcomes like OCB and task performance (e.g. Allen et al., 2003; Bartol & Srivastava, 2002; Cummings, 2004; De Cremer & Van Knippenberg, 2002; Hau et al., 2013; Liao et al., 2004; Wang & Noe, 2010)
To be able to understand the relationship of data and information visibility with OCB and task performance through knowledge sharing, it is important to have insight in what knowledge is and to make a distinction between knowledge and the related concepts data and information. According to Ackoff (1999, p. 170) data are “symbols that represent properties of objects and events”, and have no meaning by themselves. Information consists of processed data that have
been given meaning by way of relational connection. It consists of descriptions of objects and events and gives answers to questions that begin with words as who, what, when, where, and how many. Knowledge is the application of information. It combines information with
experience, contextualization and insight. Knowledge is the result of the synthesis of multiple sources of information over time and can be obtained either by transmission from other people who have the knowledge (i.e. vicarious learning or instructions) or by extracting it from own experience (Rowley, 2007).
Because, as Boddy, Boonstra and Kennedy (2005, p. 9) state “Knowledge builds on information that is extracted from data”, it is expected that practices that make data and information available, public and easy accessible, can enhance the creation of knowledge and therefore enable its dissemination. This is in line with Bandura’s theory of Vicarious Learning, that states that a critical ability of human is to adopt knowledge from information communicated through a wide array of mediums (Bandura, 2002). Subsequently, creating and collecting
knowledge influences knowledge sharing in a positive sense. Van den Hooff and De Riffer (2004) for instance show that the more knowledge a person collects, the more he or she is willing to share it with others. The link between visibility and knowledge sharing is also confirmed by Hendriks (1999) who shows in his research on motivations for knowledge sharing that access to information, for example with the help of information – and communication technology (ICT), can enhance knowledge sharing. Also Zander and Kogut (1995) and Renzl (2006) indicate that the documentation of information can facilitate both knowledge sharing within teams as knowledge sharing between teams.
In addition knowledge about where to find information is according to Renzl (2006, p. 210) important for fostering knowledge sharing. He points out “it is not just a matter of data mining, but also a matter of coordination, to identify where knowledge and expertise reside in the organization”. When employees know where to find knowledge, it can be shared more easily.
Other preconditions for using data and information for knowledge creation and as a basis for knowledge sharing are for instance that the owners of the data and information have to approve their dissemination to ensure that data and information are public, and that employees need the skills to acquire and interpret the data and information. Otherwise the data and information are not visible and cannot be used. Therefore it is expected that approving the dissemination of data and information and providing information aimed at increasing the skills of employees to acquire data and information and make sense out of the data and information will foster knowledge sharing in organizations.
Moreover, when the access to data and information is easier, because of the classification of data and information or other factors that ensure that the amount of effort required to access data and information is low, this can increase the visibility and therefore the accessibility of the data and information (Stohl et al., 2016), making the data and information better utilized for the creation and sharing of knowledge. This is confirmed by Lavanya (2012) who shows in her research on the antecedents of knowledge sharing that time and effort of practices involved in the knowledge sharing process had a strong negative relationship with knowledge sharing.
Altogether the following is expected.
H1: Visibility is positively related to Explicit Knowledge Sharing (a) and Tacit Knowledge Sharing (b).
Previous research indicates that knowledge sharing in turn can influence OCB within organizations in three ways. First, visibility of data and information can through its effect on knowledge sharing contribute to promoting OCB when knowledge is shared to help other employees in the organization. For instance, knowledge can be shared by employees to help others performing tasks that are not formally part of the own job (Borman & Motowidlo, 1997).
Second, the sharing of knowledge can increase OCB because employees who receive knowledge from others, are inclined to increase their personal contribution and efforts and exhibit higher levels of extra-role behaviors as a counter-gift to the employee (or employer) that provided the knowledge or to the organization as a whole. This can be explained on the basis of on the basis of reciprocity norms (Paré & Tremblay, 2007; Tsui, Pearce, Porter, & Tripoli, 1997).
Third, knowledge sharing can increase OCB by enhancing feelings of mutual trust and making individuals feel important to the company (e.g. Meyer & Allen, 1991; Rodwell, Kienzle, & Shadur, 1998). Moreover, through the sharing of knowledge employees can develop social relationships with other employees in the organization, which can increase feelings of
belongingness (e.g. Chiu, Hsu, & Wang, 2006), emotional attachment and commitment (e.g. Lawler, 1986) what leads to higher levels of OCB. Especially when knowledge sharing is a form of dialogue, this creates a shared context and atmosphere (Tenkasi, 1996; Van Den Brink, 2001) that is important for OCB (Allen et al., 2003; Bartol & Srivastava, 2002; De Cremer & Van Knippenberg, 2002; Deluga, 1994; Den Hartog, De Hoogh, & Keegan, 2007; Feather & Rauter, 2004; Konovsky & Organ, 1996; Konovsky & Pugh, 1994; Meyer & Allen, 1991; Meyer & Smith, 2000; Munene, 1995; Moorman, Niehoff & Organ, 1993; Paré et al., 2000; Saks, 2006; Wayne & Green 1993). This is also supported by Paré and Tremblay (2007) who state that due to the sharing of knowledge, originated from data and information within the organization,
employees develop a sense of community, which leads to more OCB within the organization. Because knowledge sharing can take place in both virtual communities and offline workgroups in organizations and OCB can be shown both online as through face-to-face contact as well, it is expected that the relationship between knowledge sharing and OCB holds for both explicit and tacit knowledge sharing. Therefore the following is expected:
H2: Explicit Knowledge Sharing (a) and Tacit Knowledge Sharing (b) are positively related to OCB.
In addition to its supposed effect on OCB, knowledge sharing - that is based on data and information in organizations – can according to previous research also affect task performance in organizations. Research shows that knowledge sharing contributes directly to task performance because of the provision of task information and know-how that helps employees to effectively perform their tasks (Wang & Noe, 2010). In addition, knowledge sharing includes the
contribution of ideas, facts, expertise, and judgments from other employees relevant for a specific task, allowing employees to adjust their approach to perform tasks better (e.g., Alavi & Leidner, 2001; Bartol & Srivastava, 2002). Employees can do so because they are not only affected by direct experiences, but also indirect events conveyed in messages and as a result construct possible solutions, and evaluate the anticipated outcomes (Bandura, 2002). In other words, they are able to learn from experiences of others, which can benefit their task
performance. Also Liebowitz (2001) in his research on knowledge management indicates that the sharing of experiences, opinions, and insights with one another due to their effect on
performance on brainpower and intellectual capital can enhance task performance.
Moreover, through the sharing of knowledge, employees can collaborate with others to solve problems and develop new ideas (Cummings, 2004; Pulakos, Dorsey, & Borman, 2003) which can contribute positively to task performance, because, as Law and Ngai (2008, p. 2343) state: “by sharing their knowledge, individuals can realize synergistic results greater than those achievable by any individual alone, and therefore increase their performance”. This is supported by other research that showed a positive relationship between knowledge sharing and reductions in production costs, faster completion of new product development projects, performance and innovation capabilities (e.g., Arthur & Huntley, 2005; Collins & Smith, 2006; Cummings, 2004; Lin, 2007; Mesmer-Magnus & DeChurch, 2009).
Finally, research shows that task performance behavior does not only have to do with the ability and the willingness to engage in such actions, but that employees must feel that they can
do so (Hsu, Ju, Yen, & Chang, 2007; Lee, 2001b; Locke, Frederick, Lee, & Bobko, 1984). The feeling that one is capable of successfully performing a particular task or activity is called self-efficacy (Bandura, 1982). It is “the extent to which individuals perceive that they have the necessary knowledge, skills and ability to do their job well and cope with any unexpected problems in their work” (Lee, 2001, p. 1035). Knowledge sharing can increase the self-efficacy of employees by providing expertise, skills and knowledge, and as a result improve task
performance (e.g. Locke et al., 1984). Keskin (2005) found that the relation between knowledge sharing and task performance applied to both explicit and tacit knowledge sharing. Therefore the following is expected:
H3: Explicit Knowledge Sharing (a) and Tacit Knowledge Sharing (b) are positively related to Task Performance.
In summary, it is expected that knowledge sharing acts as a mediator of the relationship between visibility of data and information in organizations and OCB, because visibility practices that ensure that data and information in organizations become available, public and accessible, facilitates knowledge creation and therefore its dissemination. The sharing of knowledge, as a result of the visible of data and information, can increase OCB in organizations because knowledge sharing can be a way to help others, develops an atmosphere that promotes OCB (Chiu, Hsu, & Wang, 2006; Lawler, 1986; Tenkasi, 1996; Van Den Brink, 2001) and can
stimulate OCB as a counter-gift (Paré & Tremblay, 2007; Tsui, Pearce, Porter, & Tripoli, 1997). Because it is expected that visibility is associated with explicit as well as tacit knowledge
sharing, and that both are, in turn, related to OCB, it is also expected that both forms of knowledge sharing mediate the relationship between visibility and OCB. This leads to the following hypothesis:
H4: Explicit Knowledge Sharing (a) and Tacit Knowledge Sharing (b) mediate the relationship between Visibility and OCB.
In addition, altogether it is also expected that knowledge sharing mediates the
relationship between visibility of data and information in organizations and task performance, because visibility practices that ensure that data and information in organizations become available, public and accessible can increase task performance if the data and information are made relevant and useful for a specific task. This will be achieved by combining the data and information with experiences of employees, a specific task context and insight (i.e. knowledge). A way to achieve this is through the transmission of knowledge from other people or by
collaborating with other people (De Long & Fahey, 2000). In other words, the sharing of knowledge links visibility of data and information to task performance, because knowledge sharing processes ensures that data and information can be utilized to improve the performance of a specific task (Alavi & Leidner, 2001; Bartol & Srivastava, 2002). Altogether the following is expected:
H5: Explicit Knowledge Sharing (a) and Tacit Knowledge Sharing (b) mediate the relationship between Visibility and Task Performance.
Figure 1. Conceptual diagram total mediation model
Method
In order to answer the research question ‘What is the relationship between data and information visibility, Organizational Citizenship Behavior and Task Performance in
organizations, and what is the role of Explicit and Tacit Knowledge Sharing within this context?’ an online survey has been conducted.
Sample and data collection
In the present study a convenience sample is used. The first part of the sample consists of employees from an international company in the construction industry and the second part consists of employees from different branches, gathered through online channels such as email, and different social media channels like LinkedIn, Facebook, and Whatsapp.
The company is a large company employing over 10,000 employees. In addition to delivering products and solutions for the construction industry, the company delivers advice on safe, efficient, and sustainable building. They focus on the entire construction process, from design and specification, to the planning of both new construction and renovations. Employees were invited to complete an online survey. Email invitations were sent to a group of 70
employees from the organization. A total of 39 employees completed the survey between December 7 and December 19, 2017. The response rate was 55.7%.
Next an invitation to partake in the study including a link to the questionnaire were send through email, LinkedIn, Facebook, and Whatsapp. An additional 188 employees from different organizations completed the survey between December 20 and December 31, 2017.
The survey contained two attention checks, such as ‘This question checks your
attentiveness, please select 'Always'’. Those people who filled in a wrong answer on one or two of the attention checks (n = 7), were excluded from the study.
The overall sample consists of 220 employees with an average age of 31.21 years old (SD = 10.65), 54.5% were female. Most of the employees had earned a bachelor’s degree (55,0%) and 30.0% had earned an advanced degree (master's or doctorate degree). Furthermore, the respondents indicated that they worked 32.45 hours per week on average (SD = 15.15, range = 1 - 99). This is representative of the Dutch workforce who on average works 31 hours per week (Centraal Bureau voor de Statistiek, 2017). A total of 25.0% of the participants held managerial positions and the employees had on average approximately eight years of work experience (M = 7.97, SD = 9.29).
Measures
Data were collected through a self-administered questionnaire featuring the Visibility scale, a Knowledge sharing scale, an Organizational Citizenship Behavior scale and a scale of
Task Performance, al gleaned from existing measures in the literature. Appendix A-D presents the measures used in the study along with all the factor loadings of the measures. To test for reliability of the constructs, a Cronbach’s alpha was computed for the different constructs (see Appendix E).
All of the constructs except for Explicit Knowledge Sharing had a Cronbach’s alpha higher than 0.7. This means the reliability of all these scales is acceptable or good (George & Mallery, 2003). Explicit Knowledge Sharing had a Cronbach’s alpha of 0.69, this means that the reliability of this scale is questionable. Nevertheless the scale was included in the analysis because Cronbach’s alpha values are not only based on the correlations between different items but are also dependent on the number of items in the scale. When there is a small number of items in the scale, as is the case with the Explicit Knowledge Sharing scale, Cronbach’s alpha values are smaller. Therefore, as recommended by Briggs and Cheek (1986), it may be good to calculate and report the mean inter-item correlation for the items. Optimal mean inter-item correlation values range from .20 to .40. The mean inter-item correlation value for Explicit Knowledge Sharing is .37.
Visibility. The measurement of data and information visibility was derived from the Visibility Scale constructed by (Ter Hoeven et al., 2017). Data and information visibility was conceptualized as the combination of three attributes: availability of information, approval to disseminate information, and accessibility of information to third parties. Each of these three
attributes consisted of two to four sub-dimensions. The nine sub-dimensions had three items each, totaling 27 items.
Availability of information is measured by the constructs Inscription of information and
Storage of information, using items such as “My organization documents the data used in
ranged from .82 to .90. Approval to disseminate information is measured by the constructs Legal Obligations, Norms and Social consciousness. Items include “My organization shares
information when others in our sector/industry share similar information.” The factor loadings ranged from .70 to .88. The last attribute, Accessibility of information, is measured by the constructs Directory knowledge, Classification, Skills and Effort. These constructs included items such as “My organization makes it clear where to find relevant information”, with factor loadings ranging from .77 to .94. The constructs were measured using five-point Likert scales, ranging from never (1) to always (5).
Knowledge sharing. The measurement of knowledge sharing was constructed by Ter Hoeven et al. (2017). The original scale measures both explicit and tacit knowledge sharing with four items each. However, one of the items of Tacit Knowledge Sharing had a factor loading lower than .45 and was therefore not included in the analysis. Example items of Tacit
Knowledge Sharing include: “Most people in my organization share their tricks of the trade with each other”. The new Tacit Knowledge Sharing scale consists of three items and factor loadings ranged from .86 to .90.
Example items of Explicit Knowledge Sharing include: “Most people in my organization share explicit standardized knowledge with each other” and factor loadings ranged from .70 to .79. For both knowledge sharing scales coworkers were asked to indicate how much they (dis)agreed with each of the statements, using a five-point Likert scales, ranging from Strongly disagree (1) to Strongly agree (5).
Organizational Citizenship Behavior. Items used in the present study to measure OCB
were derived from Lee and Allen (2002). The original scale consists of one component of eight
was therefore excluded from the scale. Example items of the 7-item scale include: “I willingly
give my time to help others who have work-related problems”. Coworkers were asked to indicate
how often they engaged in these behaviors, using a five-point Likert scale, ranging from never (1) to always (5). The factor loadings ranged from .55 to .79.
Task performance. Task performance is measured with four items adopted from Van Dyne and LePine (1998) that were drawn from the Williams and Anderson (1991) In-Role Behavior Scale. Items include: “I meet performance expectations.” All items were measured on a five-point Likert scale ranging from strongly disagree (1) to strongly agree (5). The factor
loadings ranged from .78 to .86.
Control variables. Based on other researchers’ (e.g., Hitt & Tyler, 1991)
recommendations, the respondents were also asked to indicated their gender (male, female, other), age, education, work experience, work hours per week and job type (managerial versus nonmanagerial). Those data were gathered because of their potential to affect the relationships among other organizational variables. In this way it was possible to control for this effect. Age, work experience and work hours were measured as a continuous variable and education was measured with seven categories.
Translation and back-translation
Because the questionnaire was conducted among Dutch and English speaking employees from both national and international organizations, it was necessary that the questionnaire was available in both Dutch and English. Brislin (1970) showed several methods to put the same measure in different languages while preserving the same ideas across the measures. One of these methods is back-translation, which was used in this research, by which the measurement
instrument is translated and back-translated in the original language by different researchers. Afterwards, the equivalency of the original version and back-translated version were evaluated
(Slavec & Drnovsek, 2012).
Analyses
The hypotheses are tested using a series of multi mediation analyzes (see Figure 2 and 3) as conducted by Hayes (2013).
Different models are tested to isolate the contribution of different terms. First, nine separate models are used to test the relationship between the nine constructs of Visibility, Knowledge Sharing and OCB. The control variables, gender, age, education, work experience, work hours per week and job type are also included in the model. Second, this step is repeated by testing nine similar models with Task Performance as a dependent variable.
In order to confirm the mediating role of knowledge sharing in the model (hypothesis 4 and 5), the mediation model also tests the relationship between Visibility and OCB and Task Performance; Visibility and Explicit (H1a) and Tacit (H1b) Knowledge Sharing; and the relationship between Explicit Knowledge Sharing (H2a), Tacit Knowledge sharing (H2b) with
OCB; as well as Explicit Knowledge Sharing (H3a) and Tacit Knowledge Sharing (H3b) and Task Performance. Thus, the mediation model is also used to examine hypothesis 1-3.
To test the significance of the mediation of the relationship between both Visibility and OCB, and Visibility and Task Performance through Knowledge Sharing, the unstandardized indirect effects were computed for each of 5,000 bootstrapped samples as well as the bias-corrected 95% confidence intervals, to examine if the indirect effects are statistically different from zero (Hayes, 2012).
Results
The correlations, means, standard deviations and Cronbach’s alpha’s are found in
Appendix E. All the constructs of Visibility are significantly correlated, except for Classification and Legal obligations (p = .09). The constructs of Visibility are also significantly correlated with Explicit Knowledge Sharing, and - except for Inscription and Storage - with Tacit Knowledge Sharing. The correlations between the constructs of Visibility and Knowledge Sharing are strongest for the items belonging to the attribute 'Accessibility of information'. This attribute contains Directory Knowledge, Classification, Skills and Effort, whereby Skills has the highest correlation with Tacit Knowledge Sharing (r = .40, p <.001) and Explicit Knowledge Sharing (r =.49, p <.001). Tacit Knowledge Sharing and Explicit Knowledge Sharing are significantly correlated with each other as well (r = .43, p <.001).
Furthermore, the results show that Legal Obligations, Norms, Social consciousness, Directory knowledge, Skills and Effort are significantly correlated with OCB, and that all constructs except for Directory Knowledge are significantly correlated with Task Performance. OCB is strongest related to providing information aimed at increasing the skills of employees to acquire data and information and make sense out of the data and information (‘Skills’, r = .28, p <.001) and Task Performance is strongest related to the storage of data and information
correlated with OCB and Task Performance, whereby the correlation with Explicit Knowledge Sharing (OCB: r = .37, Task Performance: r = .29) is stronger than with Tacit Knowledge Sharing (OCB: r = .20, Task Performance: r = .18).
Finally, the results show that women, in line with previous research (Centraal Bureau voor de Statistiek, 2017) work less hours per week (M = 29.60, SD = 13.80) than man do (M = 36.36, SD = 15.92) and have less work experience (M = 5.56, SD = 7.58) than man (M = 11.09, SD = 10.39). The correlation between the control variables and the constructs of Visibility,
Knowledge Sharing, OCB and Task Performance is non-significant or low. Therefore it is expected that these control variables will not have a significant or large influence on the proposed relations in the model, nevertheless they are included as control variables in the analyses.
Hypothesis testing
To test the hypotheses, a mediation analysis was conducted separately for every construct of Visibility with OCB and with Task performance through Explicit and Tacit Knowledge Sharing. The control variables gender, age, education, work experience, work hours and job type were added to the analysis as covariates.
Visibility, OCB and Task Performance. In order to confirm whether knowledge sharing significantly mediates the relationship between Visibility and OCB, as well as between Visibility and Task Performance, first the significance of the relationship between the constructs of
Visibility and both OCB and Task Performance have to be confirmed.
All the models that tested the relationship between Visibility and OCB are significant, what means that the models as a whole are significantly predicting OCB (see Table 1). Providing information aimed at increasing the skills to acquire and interpret data and information within
organizations (Skills) is the strongest predictor of OCB, explaining together with the control variables 21.2% of the variance in OCB.
All the constructs of Visibility, except for Inscription and Storage, are positively and significantly related to OCB, over and above the control variables. This means that if the owners of data and information in organizations approve their viewing by others more (because of Legal obligations, Norms or Social consciousness), and if the data and information are more accessible to others (because of Directory knowledge, Classification, Skills, or Effort), this leads to more OCB of employees (or vice versa).
Table 1. Visibility and OCB*
F (9, 210) R2 b t p 95% CI Inscription 3.63 .14 0.01 0.26 .794 -0.07 - 0.09 Storage 3.64 .14 0.02 0.38 .702 -0.07 - 0.10 Legal obligations 4.71 .17 0.13 2.92 .004 0.04 - 0.21 Norms 4.90 .17 0.15 3.16 .002 0.06 - 0.24 Social consciousness 4.61 .17 0.12 2.78 .006 0.04 - 0.21 Directory knowledge 4.56 .16 0.12 2.71 .007 0.03 - 0.20 Classification 4.34 .16 0.11 2.37 .019 0.02 - 0.20 Skills 6.27 .21 0.19 4.55 <.001 0.11 - 0.27 Effort 5.87 .20 0.17 4.19 <.001 0.09 - 0.25
Note: *All models as a whole are significant, p < .001. Controlled for gender, age, education, work experience, work hours and job type.
The models that tested the relationship between Visibility and Task Performance are significant for Inscription, Storage, Classification, Skills and Effort. This means that the models as a whole are significantly predicting Task Performance (see Table 2). The models with the other constructs of Visibility are not significant, so they are not significant predictors of Task Performance. The storage of data and information within organizations (Storage) is the strongest
predictor of Task Performance of employees, explaining together with the control variables 13.1% of the variance in Task Performance.
Inscription, Storage, Classification, Skills and Effort are positively and significantly related to Task Performance, over and above the control variables. This means that if the availability of data and information in organizations is higher (due to inscription and storage) and if the data and information are more accessible to others (because of the classification of data and information, the skills to acquire and interpret data and the effort that is required to access the data and information), this leads to higher task performance of employees (or vice versa). Only the constructs of Visibility that showed a significant relationship with OCB or Task Performance will be included in the mediation analysis.
Table 2. Visibility and Task Performance*
F (9, 210) R2 b t p 95% CI Inscription 2.80 .11 0.15 3.73 < .001 0.07 - 0.24 Storage 3.51 .13 0.20 4.47 < .001 0.11 - 0.28 Classification 2.54 .10 0.17 3.41 .001 0.07 - 0.27 Skills 1.94 .08 0.12 2.56 .011 0.03 - 0.21 Effort 2.16 .09 0.13 2.90 .004 0.04 - 0.22
Note: *Only the models that are significant as a whole, are depicted in this table, p < .05. Controlled for gender, age, education, work experience, work hours and job type.
Visibility and Knowledge Sharing. The models that tested hypothesis 1a, that predicted a positive relationship between Visibility and Explicit Knowledge Sharing are significant, what means that the models as a whole are significantly predicting Explicit Knowledge Sharing (see Table 3). Providing information aimed at increasing the skills to acquire and interpret data and information within organizations (Skills) is the strongest predictor of Explicit Knowledge
Sharing of employees, explaining together with the control variables 26.9% of the variance in Explicit Knowledge Sharing.
All the constructs of Visibility are significant predictors of Explicit Knowledge Sharing, over and above the control variables. If organizations score higher on data and information visibility, this leads to more explicit knowledge sharing within the organization (or vice versa). Therefore, hypothesis 1a is confirmed.
Table 3. Visibility and Explicit Knowledge Sharing*
F (9, 210) R2 b t p 95% CI Inscription 3.76 .14 0.25 4.66 < .001 0.14 - 0.35 Storage 3.17 .12 0.24 4.09 < .001 0.12 - 0.35 Legal obligations 3.67 .14 0.28 4.57 < .001 0.16 - 0.39 Norms 3.54 .13 0.30 4.45 < .001 0.17 - 0.43 Social consciousness 2.70 .10 0.22 3.55 < .001 0.10 - 0.34 Directory knowledge 4.78 .17 0.32 5.52 < .001 0.21 - 0.44 Classification 4.86 .17 0.35 5.58 < .001 0.23 - 0.48 Skills 8.57 .27 0.43 7.93 < .001 0.33 - 0.54 Effort 6.85 .23 0.38 6.94 < .001 0.27 - 0.49
Note: *All models as a whole are significant, p < .01. Controlled for gender, age, education, work experience, work hours and job type.
The models that tested hypothesis 1b, that predicted a positive relationship between Visibility and Tacit Knowledge Sharing are significant for Legal obligations, Norms, Social consciousness, Directory knowledge Classification, Skills and Effort, what means that this models as a whole are significantly predicting Tacit Knowledge Sharing. The models with Inscription and Storage are not significant, therefore the models as a whole are not significant predictors of Tacit Knowledge Sharing (see Table 4). Providing information aimed at increasing the skills to acquire and interpret data and information within organizations (Skills) is also the
strongest predictor of Tacit Knowledge Sharing of employees, explaining together with the other independent variables 20.1% of the variance in Tacit Knowledge Sharing.
After controlling for the control variables, Legal obligations, Norms, Social
consciousness, Directory knowledge, Classification, Skills and Effort are all significantly related to Tacit Knowledge Sharing. This means that if the owners of data and information in
organizations approve their viewing by others more (because of Legal obligations, Norms or Social consciousness), and if the data and information are more accessible to others (because of Directory knowledge, Classification, Skills or Effort), employees in the organization share more tacit knowledge with each other (or vice versa). Therefore, hypothesis 1b is only partially confirmed.
Table 4. Visibility and Tacit Knowledge Sharing*
F (10, 209) R2 b t p 95% CI Legal obligations 2.96 .11 0.24 3.52 .001 -0.01 - 0.25 Norms 3.10 .12 0.28 3.69 < .001 0.01 - 0.29 Social consciousness 2.26 .09 0.18 2.54 .012 -0.05 - 0.21 Directory knowledge 3.57 .13 0.28 4.19 < .001 0.01 - 0.28 Classification 3.81 .14 0.32 4.42 < .001 0.03 - 0.31 Skills 5.86 .20 0.39 6.08 < .001 0.10 - 0.37 Effort 4.28 .16 0.31 4.85 < .001 0.03 - 0.29
Note: *Only the models that are significant as a whole, are depicted in this table, p < .05. Controlled for gender, age, education, work experience, work hours and job type.
Knowledge Sharing, OCB and Task Performance. All the models that tested
hypothesis 2 and 3, that proposed a positive relationship between Knowledge Sharing and OCB as well as Knowledge Sharing and Task Performance are significant (p < .01), what means that the models as a whole are significantly predicting OCB and Task Performance.
After controlling for the constructs of Visibility (separately or all together) and Tacit Knowledge Sharing, Explicit Knowledge Sharing is significantly related to OCB (p < .001) and Task Performance (p < .01) (see Table 1 and 2 in Appendix G). This means that if Explicit Knowledge Sharing between employees is higher, this leads to more OCB and task performance (or vice versa).
The relationship of Tacit Knowledge Sharing with OCB and with Task Performance, after controlling for Explicit Knowledge Sharing and the constructs of Visibility (separately or all together), is not significant (see Table 3 and 4 in Appendix G). Therefore, hypothesis 2a and 3a are only confirmed. The results reject hypothesis 2b and 3b.
Figure 4. Conceptual model direct relations
Note: Continuous arrows are significant, p < 0.05; dashed arrows represent a non-significant relationship.
Mediation Visibility, OCB and Task Performance through Knowledge Sharing. As indicated above, only the constructs of Visibility that showed a significant relationship with OCB or Task Performance are included in the mediation analysis. The mediation models that tested hypothesis 4, that proposed that Explicit Knowledge Sharing (a) and Tacit Knowledge Sharing (b) mediate the relationship between Visibility and OCB, and also the mediation models that tested hypothesis 5, that proposed that Explicit Knowledge Sharing (a) and Tacit Knowledge Sharing (b) mediate the relationship between Visibility and Task Performance are significant (see Table 5 and 6). This means that the models as a whole predict OCB and Task Performance.
After controlling for Tacit and Explicit Knowledge Sharing, the previously found significant relationships between the constructs of Visibility and OCB are not significant anymore, as is the case with the relationship of Classification, Skills and Effort with Task Performance, indicating a full meditation between the variables. The significance of this mediation was tested using bootstrapping procedures. Unstandardized indirect effects were computed for each of 5,000 bootstrapped samples, and the 95% confidence interval was computed (see table 7).
The results show a statistically significant full mediation between the constructs Legal obligations -, Norms -, Social consciousness -, Directory knowledge -, Classification -, Skills -, and Effort, and OCB through Explicit Knowledge Sharing (see Figure 5), and a statistically significant full mediation between Classification -, Skills -, and Effort, and Task Performance through Explicit Knowledge Sharing (see Figure 6).
The relationships between Inscription and Task Performance - and between Storage and Task Performance, change as well after controlling for Tacit and Explicit Knowledge Sharing, but remain significant. This means that there is a partial mediation between Inscription and Task Performance, and Storage and Task Performance, through Explicit Knowledge Sharing. The mediations are also statistically significant (see Figure 7).
Table 5. Visibility and OCB controlling for Knowledge Sharing* F (11, 208) R2 b t p 95% CI Legal obligations 6.89 .27 0.05 1.24 .217 -0.03 - 0.14 Norms 6.99 .27 0.07 1.53 .129 -0.02 - 0.17 Social consciousness 6.98 .27 0.06 1.51 .133 -0.02 - 0.15 Directory knowledge 6.76 .26 0.03 0.66 .509 -0.06 - 0.11 Classification 6.71 .26 0.01 0.23 .815 -0.08 - 0.10 Skills 7.15 .27 0.09 1.90 .059 -0.00 - 0.18 Effort 7.12 .27 0.08 1.84 .068 -0.01 - 0.17
Note: * Only the constructs of Visibility that showed a significant relationship with OCB are depicted in this table. All models as a whole are significant, p < .001. Controlled for gender, age, education, work experience, work hours, job type and Knowledge Sharing.
Table 6. Visibility and Task Performance controlling for Knowledge Sharing*
F (11, 208) R2 b t p 95% CI Inscription 3.68 .16 0.11 2.64 .009 0.03 - 0.19 Storage 4.23 .18 0.16 3.49 .001 0.07 - 0.24 Classification 3.32 .15 0.10 0.33 .064 -0.01 - 0.20 Skills 2.96 .14 0.02 0.33 .744 -0.09 - 0.12 Effort 3.06 .14 0.05 1.00 .318 -0.05 - 0.15
Note: * Only the constructs of Visibility that showed a significant relationship with Task Performance are depicted in this table. All models as a whole are significant, p < .01. Controlled for gender, age, education, work experience, work hours, job type and Knowledge Sharing.
Table 7. Indirect effects between Visibility and Task Performance through Explicit Knowledge Sharing
OCB Task Performance
Indirect effect 95% CI Indirect effect 95% CI
Inscription - - .04* .01 - .09 Storage - - .04* .01 - .08 Legal obligations .06* .03 – .10 - - Norms .06* .03 – .12 - - Social consciousness .05* .02 – .09 - - Directory knowledge .07* .04 – .13 - - Classification .08* .04 – .14 .06* .02 - .12 Skills .08* .04 – .15 .08* .02 - .16 Effort .15* .07 - .27 .07* .02 - .13 Note: * p < .05.
Because after controlling for Explicit Knowledge Sharing a relation between Tacit Knowledge Sharing and both OCB and Task Performance was not found. Tacit Knowledge Sharing did not mediate the relationship between Visibility and OCB and Task Performance either. Neither a mediation path from the constructs of Visibility to Explicit Knowledge Sharing to Tacit Knowledge Sharing to the outcome variables OCB or Task Performance was found. Therefore, hypothesis 4a and 5a, that proposed that Explicit Knowledge Sharing mediates the relationship between Visibility and OCB (4) and between Visibility and Task Performance (5), are confirmed. Hypothesis 4b and 5b, that proposed that Tacit Knowledge Sharing mediates the relationship between Visibility and OCB (4) and between Visibility and Task Performance (5), are rejected.
Figure 5. Mediation model A
Note: Continuous arrows are significant, p < 0.05; dashed arrows represent a non-significant relationship after controlling for the mediator.
Figure 6. Mediation model (B1)
Note: Continuous arrows are significant, p < 0.05; dashed arrows represent a non-significant relationship after controlling for the mediator.
Figure 7. Mediation model (B2)
Note: Continuous arrows are significant, p < 0.05.
Conclusion and Discussion
To broaden the understanding of the outcomes associated with the visibility of data and information, the relationship between data and information visibility and both task performance and OCB have been examined in this study. By doing so, the study paid attention to knowledge sharing as possible underlying mechanism.
Results of the sample of in total 220 employees show that the relationship between visibility of data and information (due to Availability and Accessibility of the data and information and the Approval to disseminate it) and both OCB and task performance can be explained through explicit knowledge sharing. More precisely, explicit knowledge sharing acts as a mediator of the relationship between visibility and OCB as well as between visibility and task performance in the way that visibility enhances OCB, and task performance by fostering explicit knowledge sharing (or vice versa). The underlying idea is that visibility of data and information enables knowledge creation and sharing and through knowledge sharing increases OCB in organizations, because knowledge sharing can be a way to help other employees, develops an atmosphere that promotes OCB (Chiu, Hsu, & Wang, 2006; Lawler, 1986; Tenkasi, 1996; Van Den Brink, 2001) and can stimulate OCB as a counter-gift (Paré & Tremblay, 2007; Tsui, Pearce, Porter, & Tripoli, 1997).
However, the mediation between visibility and OCB through explicit knowledge sharing did not apply to all the domains of visibility. Although the relationship between availability of
data and information and explicit knowledge sharing was positive and significant, as well as the relationship between explicit knowledge sharing and OCB, inscription and storage of data and information have not been found to contribute significantly to OCB through explicit knowledge sharing. An explanation for this result is that the inscription and storage of data and information have paradoxical effects that both promote and inhibit OCB at the same time (Eatough, Chang, Miloslavic, & Johnson, 2011; McClure, 1978). For instance, an increase in the amount of available information has shown to promote knowledge sharing, but at the same time it can overwhelm the cognitive and interpretive capabilities of employees and thereby decreasing OCB. Namely, according to the Job Demand-Resources model (JD-R model) this high cognitive
demands can reduce the energy of employees what in turn has a negative impact on OCB, and in addition negatively affects the relationship between job resources (the provision of data and information) and the motivation to perform OCB (Bakker & Demerouti, 2007; Bakker, Demerouti, & Sanz-Vergel, 2014; Maslach, Schaufeli, & Leiter, 2001).
Contrary the expectation, a significant relationship of both approval to disseminate data and information and directory knowledge with task performance through explicit knowledge sharing was not found either. Especially the non-significant relationship between directory knowledge and task performance is surprising, because earlier research shows the importance of knowing about what data and information exist and where to find it. Abrams, Cross, Lesser and Levin (2003, p. 72) for instance state that “An often-overlooked but critical skill in business is the ability to accurately assess who knows what”. A possible explanation is that both the approval to disseminate data and information and information about what data and information exist, contribute to the information overload and thereby reinforce the negative effects of it and as a result decrease task performance (Eatough et al., 2011).
In addition, factors that ensure that data and information is accessible, contribute to both OCB and task performance through explicit knowledge sharing. Such factors are for instance
information about where to find data and information (only for OCB), classification of those data and information, information aimed at increasing the skills of employees to acquire and interpret data and information, and the ease of retrieving the data and information. The reason for this is that they ensure that employees are able to utilize the available and public data and information for the benefit of the organization (e.g. Alavi & Leidner, 2001; Bartol & Srivastava, 2002).
Besides the mediations through explicit knowledge sharing, also a direct relation between inscription and storage of data and information and task performance was found, indicating that available manuals for instance can affect task performance. Another explanation is that if information about collective and individual behaviors in organizations becomes more visible, employees will increase their efforts in performing task that are part of their job description (Bolino, Klotz, Turnley, & Harvey, 2013). Employees will do so to enhance or protect their image in the eyes of others (Bolino, Kacmar, Turnley, & Gilstrap, 2008) or to achieve their self-serving goals, because they expect that those behaviors are tied to recognition or rewards (Bolino et al., 2013). This is consistent with the tenets of social exchange, that views behavior as an social exchange that may result in both economic and social outcomes (Lambe, Wittmann, & Spekman, 2001).
Contrary to the expectations no relationship between tacit knowledge sharing and OCB and task performance was found after controlling for the effects of explicit knowledge sharing. This can be explained on the basis of research of Nonaka and Takeuchi (1995) who state that tacit knowledge needs to be converted into explicit knowledge to make it useful. Although not hypothesized in this study, but in line with the prediction of Nonaka and Takeuchi (1995), the current research shows a full mediation between tacit knowledge sharing and both OCB and task performance through explicit knowledge sharing (see Appendix H). This indicates that tacit knowledge sharing does not act as a mediator of the examined relationships between visibility, OCB and task performance, but instead as an antecedent of explicit knowledge sharing.
Theoretical implications
This study has contributed to the existing organizational literature by broadening the understanding of the effects of making data and information visible on important organizational outcomes (i.e. OCB and task performance) and showed that explicit knowledge sharing acts as an underlying mechanism that explains the relationship between visibility and the outcomes OCB and task performance. This adds to the literature by emphasizing that the relationship between visibility and work outcomes is more complex than previously established.
Furthermore, this study contributes to the organizational literature by offering a
translating of the Visibility Scale of Ter Hoeven et al. (2017) into Dutch and by validating the scale. The validated scale appears to be a reliable and useful instrument for examining data and information visibility in relation to other organizational variables. Because of the availability of the scale in multiple languages, it is possible to conduct research among both English-speaking and Dutch-speaking employees, which will benefit the external validity of visibility research.
Practical implications
Additionally, this study contributes to organizational practice in two ways. First of all, this study contributes by showing that making data and information visible in itself does not necessarily lead to OCB and task performance when organizations fail to tackle other organizational factors that impede knowledge sharing (such as lack of trust, lack of
organizational commitment, lack of support or a negative image of procedural justice; see for example Bartol & Srivastava, 2002; Kim & Mauborgne, 1998; Lavanya, 2012; Staples & Webster, 2008). In other words, if other factors in the organization hamper knowledge sharing, positive outcomes of visibility of data and information might not occur. Although a very small direct relationship was found between inscription and storage of data and information and task performance, it is expected that this does not outweigh the supposed disadvantages of an
abundance of unstructured data and information in organizations (Babcock, 2004; Flyverbom, 2016; Stohl et al., 2016). It is therefore important that organizations ensure that employees have the opportunity to share knowledge not only by managing visibility of data and information, but also by fostering other factors that stimulate knowledge sharing, and counteract the factors that inhibit knowledge sharing (De Long & Fahey, 2000).
If organizations want to increase knowledge sharing by managing data and information visibility, information and communication technologies (ICTs) can be promising (Babcock, 2004). However, if organizations adopt new (ICTs) to ensure that data and information in organizations become available and public and above all to promote the accessibility of
information, it is wise to use technologies that also promote interaction and knowledge sharing. The study of De Long and Fahey (2000) endorses the importance of this functionality by finding that the benefits of a new technology infrastructure were limited if they did not support
knowledge sharing.
Second, the study shows that inscription and storage of data and information by itself do not contribute to the promotion of tacit knowledge sharing, neither to the promotion of OCB through explicit knowledge sharing. In addition to explicit knowledge sharing, tacit knowledge sharing is important for competitive advantage of organizations because tacit knowledge is hard to copy, and also OCB has shown to be important for overall organizational functioning (Lee & Allen, 2002). Thus, if organizations want to promote tacit knowledge sharing and OCB,
developing a database with a large quantity of data and information is not the solution. The approval to disseminate data and information, but especially the accessibility of it, has shown to be very important for organizational outcomes. Daft and Lengel (1986) for instance indicate that when it comes to information and knowledge practices in organizations, a major problem is the lack of clarity, not lack of data. The results of the current study show that in particular when organizations provide information to promote skills to interpret data and information, this is
important for the promotion of both explicit and tacit knowledge sharing. Because when employees do not have the skills to consult and understand data and information, this prevents them from linking the information to previous knowledge and a specific context (e.g. creating knowledge) and sharing this knowledge with others. Likewise, when it takes too much effort to gather information to create knowledge, this impedes the sharing of knowledge.
Limitations and future research directions
Even though this research has drawn theoretically and practically meaningful implications, the study has a few limitations. First of all, despite the fact that on the basis of scientific theories and earlier research the direction of the relationships between variables can be substantiated, it is not possible to form conclusions about the causality of the reported
relationships among visibility, OCB, task performance and knowledge sharing because of the use of a cross sectional survey as a research method. For example, this studies hypothesized an effect of knowledge sharing on OCB, but it can be argued that it can also be the other way around. Because, in addition to the expected effect of knowledge sharing on OCB, the effect of OCB on knowledge sharing can also be substantiated with literature (e.g. Hsu & Lin, 2008; Ramasamy & Thamaraiselvan, 2011; The & Sun, 2012). However, it can be expected that knowledge sharing and OCB interact with each other, that is to say, knowledge sharing can promote OCB which in turn can promote knowledge sharing. To test this proposition in future research other pathways have to be measured to measure what direction best fits the data. Other types of research are required to substantiate the causal relationship between visibility OCB and task performance through knowledge sharing. An experiment in which the amount of data and information is manipulated would be the best way to examine the research question. But because this would be difficult in practice, longitudinal research can offer an alternative.
Second, in this study a convenience sample is used, which might limit the generalizability of the results. To improve the generalizability of the results, a random sample ha to be used to minimize sampling bias in future studies.
A third limitation is the use of self-report scales to measure the study variables, because this may lead to a common method bias for some of the results. More precisely, Harris and Schaubroeck's (1988) conclude that self-ratings are affected by egocentric biases. Their meta-analysis showed self-ratings to be higher on average than ratings from supervisors or peers. This bias can be prevented by developing more direct and objective measures for visibility of data and information, knowledge sharing, OCB and task performance. For example, supervisor-ratings or peer-ratings can be used.
Because social factors have proven to be important for promoting knowledge sharing, future studies could examine conditional factors in the visibility-knowledge sharing relationship. For example, the relationship between Visibility and outcomes may differ depending on the work environment (Bandura, 2002). Future research that builds on the current study, have to provide an integrated model that includes also social factors and other contextual factors in explaining the effects of visibility.
Furthermore, because skills to acquire and interpret knowledge and effort to acquire knowledge have shown to be the important factors for increasing knowledge sharing and
organizational outcomes, future research can contribute to scientific literature and organizational practice by measuring this domains in a more comprehensive manner, and pay attention to factors that positively influence those domains and in this way increase, knowledge sharing, OCB and task performance in organizations.
In addition, to explain opposite effects of data and information availability on OCB, future research should not only take knowledge sharing into account as mediator, but should also investigate other opposing mechanisms that underlie the relationship between availability of data
