Journal of Knowledge Management

Journal of Knowledge Management Sourcing knowledge for innovation: Knowledge reuse and creation in project teams Anis KHEDHAOURIA Arshad JAMAL Article information: To cite this document: Anis KHEDHAOURIA Arshad JAMAL , (2015),"Sourcing knowledge for innovation: Knowledge reuse and creation in project teams", Journal of Knowledge Management, Vol. 19 Iss 5 pp. Permanent link to this document: http://dx.doi.org/10.1108/JKM-01-2015-0039 Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) Downloaded on: 20 August 2015, At: 04:46 (PT) References: this document contains references to 0 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 31 times since 2015* Users who downloaded this article also downloaded: Andrea Conchado, José Miguel Carot, María Carmen Bas, (2015),"Competencies for knowledge management: development and validation of a scale", Journal of Knowledge Management, Vol. 19 Iss 4 pp. 836-855 http://dx.doi.org/10.1108/ JKM-10-2014-0447 Claude Paraponaris, (2015),"Guest Editorial: Knowing across boundaries", Journal of Knowledge Management, Vol. 19 Iss 5 pp. Claude PARAPONARIS, Martine Sigal, (2015),"From knowledge to knowing, from boundaries to boundary construction", Journal of Knowledge Management, Vol. 19 Iss 5 pp. - Access to this document was granted through an Emerald subscription provided by emerald-srm:487754 [] For Authors If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. 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The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. *Related content and download information correct at time of download. Sourcing knowledge for innovation: Knowledge reuse and creation in project teams Abstract: Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) Purpose- Present research investigates motivations of team members to source knowledge and how the sourced knowledge increases their reuse and creation outcomes. Methodology- A model based on knowledge sourcing perspective is proposed and tested to link knowledge sourcing methods in teams to their performance outcomes. The hypotheses are tested on data collected from a survey of 341 project teams. Findings- Findings show (1) critical role of team members’ learning orientation in increasing knowledge sourcing, reuse, and creation; (2) group knowledge sourcing and repositories are more appropriate to increase knowledge reuse; (3) Internet is more effective to increase knowledge creation; and (4) knowledge reuse increases knowledge creation among team members with a strong learning orientation. Research limitations/implications- Further studies can replicate our model and introduce group characteristics to improve its explanatory power. Also, use of self-reported measures in data collection may lead to biases, future research should collate different measures longitudinally or use separate primary and secondary observations. Practical implications- Team leaders should enhance team effectiveness by ensuring diversity of knowledge and skills. Current research emphasizes that team leaders can integrate a crowdsourcing or ‘users as co-creators’ approach to increase knowledge creation by team members. Team members’ learning orientation can be increased by promoting a climate that encourages open discussion of problems, mistakes, and errors. Originality/value- Our research highlights that knowledge sourcing methods produce different performance outcomes regarding knowledge reuse and creation. These insights can be useful to team leaders and researchers in order to better understand what motivations team members to source knowledge and how it increases their reuse and creation outcomes. Keywords: Knowledge sourcing, Knowledge reuse, Knowledge creation, creativity, Innovation. Article Classification: Research paper. 1 1. Introduction In a complex business environment, team projects allow companies to earn a major share of their profit (Aubry and Lièvre, 2010; Garel and Lièvre, 2010; Melkonian and Picq, 2010). Team projects allow new innovations to be conceived, developed, and implemented within an organization or on the market (Archibald, 2003). The improvement of innovations may require new knowledge and new routines. For instance, the development of information technology within an organization requires the creation of new technical solutions and the Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) reuse of existing ones (Keller, 1992). The ability to facilitate knowledge sourcing, reuse and creation is therefore critical to improve innovations (Gray and Meister, 2006; Majchrzak et al., 2004; Markus, 2001). Knowledge sourcing refers to the ability of team members to actively engage in the process of searching, accessing, transferring, and applying knowledge (Khedhaouria and Ribiere, 2013; Staats et al., 2014). Knowledge sourcing allows team members to reflect on the sourced knowledge and to adjust their understanding of a given problem. It reflects team members’ ability to perform together towards a common goal, which results in collective competence (Melkonian and Picq, 2010). The role of team leaders is critical for defining a common goal and organizing team members to accomplish collective competence (Aubry and Lièvre, 2010; Bass et al., 2003; Edmondson, 2003; Pitrola-Merlo et al., 2002; Strang, 2010; Turner and Müller, 2007; Zaccaro et al., 2001). Studies argued that knowledge reuse is more likely to enhance innovation because it is easily manageable (Markus, 2001). Although knowledge creation is critical for innovation, it is difficult to manage due to costs in terms of time, resources, and efforts required to producing new knowledge (Armbrecht et al. 2001; Majchrzak et al., 2004). Most studies investigated knowledge reuse and creation with an emphasis on knowledge management systems, driven by the goal to make knowledge available to team members in the required format when they need it (Alavie and Leidner, 2001; Majchrzak et al., 2004; Majchrzak et al., 2013; Markus, 2001; Watson and Hewett, 2006). However, little is known about what motivates team members to source knowledge and how the sourced knowledge increases their reuse and creation outcomes. Three main points can justify the present research: First, research on knowledge sourcing suggests that merely making knowledge available does not guarantee its use by team members but rather understanding what motivates them to source knowledge can encourage its use (Gray and Meister, 2004). Second, there is evidence that different knowledge sourcing 2 methods (e.g., group knowledge sourcing, technical support knowledge repositories and the Internet) have varied performance outcomes (Gray and Meister, 2006). Understanding knowledge sourcing methods that influences knowledge reuse and creation by team members will be useful for the team’s collective competence. Last, it was suggested that knowledge reuse enhances its creation by stimulating creative cognitive processes (Majchrzak et al., 2004). Therefore, understanding how knowledge is reused and created can help to identify Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) ways to facilitate its reuse and creation. The present research investigates what motivates project team members to source knowledge and how it increases their reuse and creation outcomes. To address this question, we adapted and tested a model based on knowledge sourcing perspective (Gray and Meister, 2004). We included learning orientation as a motivational input that inspires team members to initiate knowledge sourcing processes, which result in various performance outcomes (Gray and Meister, 2006; Khedhaouria and Ribiere, 2013). Our hypotheses are tested on data collected from a survey of 341 French project teams from 53 companies across France. Our findings show that group knowledge sourcing and repositories are more appropriate to increase knowledge reuse, while the Internet is effective to increase knowledge creation. Interestingly and somewhat contrary to prior research, group knowledge sourcing has no effect on knowledge creation. Results highlight the role of team members’ learning orientation in increasing knowledge sourcing, reuse and creation in teams. Knowledge reuse increases knowledge creation. Findings have many theoretical and practical implications on how knowledge should be managed to increase knowledge reuse and creation among team members. In the following section, we present the theoretical development of individual and team-level concepts, knowledge sourcing, reuse and creation concepts. In the method section, we present our sample and variables. Afterward, we elaborate on the results and conclude. 3 2. Theoretical development 2.1. Team members and collective competence Project-based working, widely used in firms of all sizes and in different sectors, is particularly useful for knowledge sourcing, reuse and creation in teams (Garel and Lièvre, 2010; Melkonian and Picq, 2010; Turner and Müller, 2007). Nevertheless, the integration of the sourced knowledge within a team is conditioned by the involvement and commitment of its Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) members to the common goal (Turner and Müller, 2007). The ability to build a team-level collective competence poses a challenge for team leaders (Zaccaro et al., 2001). To federate interdisciplinary team members towards a common goal, team leaders are often entrusted in a unique position to understand how different skills fit together in the project and to help members to share knowledge (Melkonian and Picq, 2010). For example, innovation to be developed may require new knowledge and new routines (Keller, 1992). It is therefore a learning process that involves collective discussion and experimentation, sharing both technical knowledge and social knowledge about who knows what (Edmondson, 2003). The challenge of team leaders lies in meshing together variety of individual skills and channeling them toward a collective team competence that leads to successful innovation (Melkonian and Picq, 2010; Pitrola-Merlo et al., 2002; Strang, 2011; Turner and Müller, 2007; Zaccaro et al., 2001). 2.2. Knowledge sourcing in teams Most studies, have investigated knowledge management (KM) on supply-side issues such as sharing, learning, and transferring knowledge (Alavie and Leidner, 2001; Cohen and Levinthal, 1990). These studies are motivated by the goal of making knowledge available and accessible to individuals who need it, when they need it, and in the format they need it. Nevertheless, it will not be correct to assume that knowledge availability guarantees its use by individuals. Gray and Meister (2004) adopted a knowledge sourcing perspective to address this theoretical gap in KM research by helping to articulate the missing segment in the causal chain connecting knowledge availability to its individual learning outcomes. In the present research we adapt the knowledge sourcing perspective to connect knowledge availability to its performance outcomes in project teams. Knowledge can be obtained from various sources (Huggins et al., 2010): team knowledge sourcing can mean using group members’ experience and expertise to facilitate innovation 4 (e.g., direct contact, conversations and exchanges amongst team members). It may involve learning about problems encountered inside the organization from technical support knowledge repositories (e.g., published documents posted on the company’s intranet and access to knowledge-based systems). Or, it may involve drawing on new knowledge using expert advice and technical or business development expertise that is not available within the organization but accessible through the Internet (e.g., access to community network sites and Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) virtual communities). Knowledge sourcing allows team members to reflect on the sourced knowledge and to reuse it to adjust their understanding of a given problem. They can then create new knowledge that integrates the sourced knowledge with their new understanding of the problem (Staats et al., 2014). Learning orientation, i.e., team members’ intrinsic motivation and dedication to learn in the project, is found to be the major driver influencing team members’ willingness to source the required knowledge for problem-solving (Shalley et al., 2009). Team members vary in the extent to which they seek to acquire new knowledge or skills and, thus, to improve a collective competence (Dweck and Elliott, 1989). Those with a strong learning orientation believe that their own skills can be improved through effort and experience, and have a desire to do so. As such, they “persist, escalate effort, engage in solution-oriented self-instruction, and report enjoying the challenge” (Brett and Van de Walle, 1999, p. 864). They are more likely to source knowledge to improve their skills (Gray and Durcikova, 2005). Team members’ disposition to directing increased attentional resources towards learning orientation is therefore an important predictor of their knowledge sourcing activities. In projects, team members face challenging problems regularly for which they have no systematic solution available to be recalled from their own memories (Sethi et al., 2001). In such situations, members may consult their groups (i.e., peers), repositories, and the Internet to explore and exploit potential solutions (Aubry and Lièvre, 2010). Consistent with knowledge sourcing perspective, when team members are not able to solve problems using their own skills and expertise, those with strong learning orientation are more likely to source knowledge from peers, repositories, and the Internet. We hypothesize the following: H1a: Team members with strong learning orientation will source knowledge from their peers. H1b: Team members with strong learning orientation will source knowledge from technical support knowledge repositories. H1c: Team members with strong learning orientation will source knowledge from the Internet. 5 2.3. Performance outcomes Knowledge sourcing perspective distinguishes two main activities that can be used by team members to solve problems: - Replication and adaptation: team members reuse existing knowledge to replicate and adapt it to new situations, e.g., use shared "best management practices" to find generic Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) solutions used in previous similar problems (Bogan and Enlish, 1994) ; - Innovation: team members take risks to explore and create entirely new knowledge, e.g., experiment new knowledge by trial and error to develop new outcomes (Albers and Henzinger (2000). 2.3.1. Replication and adaptation: Knowledge reuse Knowledge reuse is defined as the replication and adaptation activities used to solve common problems (Markus, 2001). This constitutes an exploitation of resources that generates value through the efficiencies that result from not recreating knowledge that already exists (Kostopoulos and Bozionelos, 2011; March, 1991). Capturing and documenting knowledge that can be reused for replication and adaption occur in two major ways. First, replication occurs within a structure providing explicit and published knowledge. Hansen et al. (1999) suggested that published knowledge is considered to be a superior mechanism for transferring best practices. Transferring knowledge through published documents is more likely to be superior to direct contact because it saves time and efforts when the required knowledge is used to be replicated (Gray and Meister, 2006). Transferring knowledge through published documents may also be superior because it is more objective and clear compared to knowledge provided through conversation, which is often irrelevant and hard to be interpreted (Daft and Lengel, 1986). Technical support knowledge repositories also provide useful knowledge that can be easily replicated as it is properly indexed, and easily searchable (Davenport and Klahr, 1998). Indeed, knowledge-based systems facilitate organizational memory by providing the required knowledge to be replicated (Haseman et al., 2005). In contrast, sourcing knowledge from the Internet is less likely to be replicated effectively given the fact that knowledge that has not been previously appraised by experts may be perceived as inaccurate, inappropriate, or incomplete, and cannot be trusted (Constant et al., 1996). Second, adaptation occurs within a structure providing support for communication and interactivity among team members. Daft and Weick (1984) suggested that understanding the relevance of the needed knowledge to be adapted often requires interactive cycles of 6 discussions and interpretations. Discussions are important for individuals to understand the applicability of the knowledge and then to adjust it to a given situation (March and Olsen, 1987). High level of interactivity is important to acquire feedback so that individuals can adjust what they perceive useful and correct any misunderstandings (Hinds and Kiesler, 1995). The dialogue helps groups to understand the implications of a particular knowledge (Gray and Meister, 2006). Group knowledge sourcing favors a richer dialogue between members. It enhances knowledge adaptation because it increases trust between team members Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) and favors collaboration. Therefore, knowledge reuse resulting from replication and adaption activities will occur within a structure providing support for group knowledge sourcing and repositories. We hypothesize the following: H2a: Sourcing knowledge from groups will increase knowledge reuse by team members. H2b: Sourcing knowledge from repositories will increase knowledge reuse by team members. H2c: Sourcing knowledge from the Internet will not increase knowledge reuse by team members. Finally, as explained above learning orientation has been found to influence knowledge reuse. When team members face common problems, those with a strong learning orientation are more likely to activate their cognitive structures for searching systematic solutions available in their own memories to adapt them to new situations (Farr et al., 2003). We hypothesize the following: H2d: Team members with strong learning orientation will show an increase in knowledge reuse. 2.3.2. Innovation: Knowledge creation Knowledge creation is defined as the extent to which team members adopt creative approach to solve problems (Von Krogh, 2000). This constitutes an exploration of new possibilities to find out new knowledge (Kostopoulos and Bozionelos, 2011). Team members can assimilate their understanding of the problem and invent new solution favoring a radical innovation with greater modification representing greater novelty (Farr et al., 2003). Radical innovation is differentiated from incremental innovation by involving discontinuous development where unprecedented improvements or performance features are achieved (Leifer et al. 2000). Team members are more likely to produce creative solutions when they perceive a situation from a new perspective (Amabile, 1996). Because knowledge sourcing in group setting suitably taps a wider range of perspectives than does repositories, it is most likely to enhance innovation. 7 In an appropriate team work context, team members’ diversity encourages divergent thinking and trigger creative cognitive processes (West, 2002). Cognitive diversity has been suggested as predictive of idea generation and creativity (Farr et al., 2003). Team members with potentially diverse backgrounds increase the number of minority viewpoints, which lead members to develop more novel solutions. Gray and Meister (2006) argued that group discussions provide valuable exposure to comparative experiences, which consequently leads to more creative outcomes. Group knowledge sourcing would be more effective to the extent Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) that members engage in learning behavior such as seeking feedback, enjoying challenges, sharing information, asking for help, talking about errors, and experimenting (Edmondson, 1999). Online communities also provide powerful tools for sharing, accessing useful knowledge, and enhancing innovation (Füller et al., 2009). Online community question answering sites provide a place that team members can ask their questions and they will be answered by other online participants and experts. The answers are validated by a “start-based” system where team members give feedback whether the answer was helpful or not. Some of the well-known community questions answering systems are “yahoo!” for answering general questions, “Stack Overflow” for answering questions on programming, “Server Fault” for server administrators and IT professionals, and many others answering systems. Further, virtual communities enable consumers to actively engage in co-creation activities and participate in innovation process (Dahan and Hauser, 2002). Consumers are invited to actively participate by generating and evaluating new ideas, discussing and improving optional solution details. In contrast, transferring knowledge through repositories is found to be inappropriate for triggering creative outcomes (Gray and Meister, 2006). Internal published documents and knowledge-based systems are more likely to encourage the syndrome of “cognitive inertia” by preventing individuals to view the problem from different perspectives (McFadzean, 2001). We hypothesize the following: H3a: Sourcing knowledge from groups will increase knowledge creation by team members. H3b: Sourcing knowledge from repositories will not increase knowledge creation by team members. H3c: Sourcing knowledge from the Internet will increase knowledge creation by team members. 8 Furthermore, learning orientation has been found to influence knowledge creation. When team members perceive a problem from a new perspective, those with strong learning orientation are more likely to develop new cognitive structures, which trigger their creative processes (Tierney and Farmer, 2002). We hypothesize the following: H3d: Team members with strong learning orientation will show an increase in knowledge creation. Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) Finally, knowledge reuse can enhance knowledge creation by stimulating creative processes mainly in two ways (Majchrzak et al., 2004). First, by replication, although replicated knowledge is just relatively novel, it can be used creatively in new contexts to resolve problems with notably better quality or more economically than the original solution (Sternberg et al., 2003). Last, by adaptation, sourced knowledge provides an alternative lens through which prior knowledge and existing problems can be viewed, so that team members can revisit and adapt the knowledge to generate entirely new solutions and solve existing problems (Majchrzak et al., 2004). Knowledge reuse is the exploitation of existing diverse ideas previously unknown to team members when creating new products or services (Armbrecht et al. 2001). If team members limit their search for solutions to their current personal knowledge base or existing network of sources, the extent to which radical solution is achieved will be limited (Leifer et al. 2000). When team members reuse peers’ knowledge, previously unknown to them, the creativity envelope will be expanded (Armbrecht et al. 2001). A knowledge management system that expands the creativity envelope, improves the research and development process through quicker access and movement of new knowledge. This leads to our final hypothesis. H3e: Knowledge reuse by team members will increase their knowledge creation. 9 2.4. Research model All our hypotheses are depicted in the figure 1. Figure 1 : Research model Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) H3d Knowledge sourcing/ group (KSG) H3a H1a Learning Orientation (LO) H1b H2a Knowledge sourcing/ repositories (KSR) Knowledge creation (KCREA) H3b H3e H2b H3c H1c Knowledge sourcing/ Internet (KSI) Knowledge Reuse (KREU) H2c H2d 3. Methodology 3.1. Data collection In the present research, we used a quantitative approach to test our hypotheses. Quantitative approach allows replication and adaptation of a model to understand a set of relationships in new contexts (Black, 2005). Indeed, a model based on knowledge sourcing perspective was adapted and used to explain how team members source knowledge and how it enhances their reuse and creation outcomes. Data was collected using key informants approach (Bharadwaj and Menon, 2000; Duggan, 2013; Egan, 2005). Key informants are highly knowledgeable about team events and practices (Sethi et al., 2001). They often are highly experienced and well informed team leaders who are knowledgeable about team members and projects that they coordinated (Egan, 2005). Consequently, their perceptions and experiences are instrumental in understanding how members of a team source knowledge in projects (Egan, 2005). 10 Using a French Business School’s database, an email invitation was sent to former graduates who were selected as key informants based on two main criteria: (a) they should have acted the role of team leaders in at least one innovation project in the last two years; and (b) they should be able to report the shared perceptions of their team members regarding team projects they coordinated. Team projects included problem solving activities and resulted in successful innovations (Aubry and Lièvre, 2010) such as projects to improve organizational processes; innovation projects to address specific management needs and innovation projects to solve Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) specific organizational problems. First, a pilot study was conducted with ten business graduates that we selected because of their frequent involvement in managing project teams in their organizations. They answered the initial questionnaire and provided pertinent comments during a 30 minute telephone conversation. According to their feedback, the questionnaire was restructured and reworded, in order to improve its clarity and the logical succession of questions. Last, the improved questionnaire was then posted on a website and the invitation to participate in the survey was sent to all business graduates. Those interested in participating as key informants were able to click on a link embedded in the email invitation to be automatically directed to the survey website. A total of 417 responses were received from 53 large and medium-size French companies working in various economic sectors (industry, commerce, and services). Only 341 responses were from team leaders out of which 207 occupied middle management positions and 134 occupied senior management positions in their organizations. Their key roles in projects consisted of defining team goals and organizing team members. They were between the ages of 24 and 66 (average 34.87) and 45.45% were men and 54.54% women. The majority hold a graduate degree (87.39%). Their work experience varies from less than 1 year to more than 25 years (average 10.02). The projects that they coordinated are classified in six major categories: - Organizational change projects: to improve management processes, organizational restructuring and legal proceedings. - Communication systems projects: to improve network communication systems and to switch to wireless communications. - Software implementation projects: to improve customer relations and to increase organizational integration. 11 - Facilities projects: to improve manufacturing processes for new products. - Product and service development projects: to develop new products and services. - Research and development projects: to improve consumer services. 3.2. Measures The model presented in Figure 1 includes five constructs measured by adapting valid and Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) reliable scales from the knowledge management literature (see Table 1). [Table 1: Items and PLS factor loadings] The respondents indicated their agreement with a set of statements using a 7 point Likert-type scale ranging from (1) “strongly disagree” to (7) “strongly agree”. Cronbach’s α is used to estimate the reliability of questions. As shown below, all measures display an acceptable level of reliability because α exceeds the threshold of 0.7 (Cronbach, 1951). - Knowledge reuse (KREU) explains the replication and adaptation of existing knowledge. In the present study knowledge reuse construct is measured using three items (α α = 0.759) covering replication and adaptation activities adapted from Gray and Meister (2006). - Knowledge creation construct (KCREA) is measured using three items (α α = 0.866) related to creative problem solving. Items are adapted from the original works of Denison et al. (1996), and Tiwana and McLean (2005). - Knowledge sourcing construct is measured using ten items related to groups, repositories and the Internet adapted from Gray and Meister’s studies (2004 and 2006) as follows: group sourcing construct (KSG) is measured using three items (α α = 0.811); repositories construct (KSR) is measured using six items (α α = 0.881); and the Internet construct (KSI) is measured using two items (α α = 0.733). - Learning orientation construct (LO) is measured using three items (α α = 0.827) adapted from Gray and Meister (2004). 12 3.3. Data analysis and results Data was analyzed using partial least squares path modeling (PLSPM Version 2013), following the general procedures suggested by Chin (1998). PLS is suggested to be an alternative to structural equation modeling (SEM) because it places minimum requirements on measurement levels and is more suitable for large samples as well as small samples (Chin, 1998). PLS also qualifies to be appropriate for models with complex relationships (Fornell Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) and Bookstein, 1982). 3.3.1. Testing the model We first assessed the psychometric properties of the measurement scales in terms of convergent validity, discriminant validity, and reliability, using confirmatory factor analysis (CFA). Measurement scales have good convergent validity if the factor loadings of the items exceed 0.60 on their corresponding constructs or the average variance extracted (AVE) of the construct exceeds 0.5 (Hair et al 2010). All items exceed the 0.60 threshold, indicating adequate convergent validity (see Table 1). Discriminant validity is ensured when the square root of the average variance extracted (AVE) for every construct is greater than the inter-correlation estimates (Chin 1998). The correlation matrix in Table 2 shows a good evidence of discriminant validity. [Table 2: Discriminant validity] The composite reliability scores for measurement scales range from 0.849 to 0.918 (see Table 2), exceeding the recommended 0.70 threshold, which indicates a good level of reliability (Hair et al 2010). Finally, to address the common method variance (CMV) problem, we used Harman’s (1976) one factor test in an attempt to isolate the covariance due to artifactual reasons (Podsakoff and Organ 1986). The rule of thumb is that a single un-rotated principal component should not explain more than the threshold level of 50 % of the variance for all the indicators measured with the same method. Our results show an explained variance of 29.59% indicating no concern with CMV. 13 3.3.2. Results As shown in Figure 2, the model accounts for 9.1% of the variance of knowledge reuse (KREU) and 17% of the variance of knowledge creation (KCREA). The goodness of fit value of the model (GoF) is 0.295, which exceeds the cut-off value of 0.250 for medium effect sizes of R² suggested by Tenenhaus et al. (2005), indicating satisfactory overall fit of our model. Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) Figure 2: PLS results H3d: β = 0.252*** (t = 4.422) Knowledge sourcing/ group (KSG) H1a: β = 0.408*** (t = 8.222) Learning orientation (LO) H1b: β = 0.352*** (t = 6.931) H1c: β = 0.246*** (t = 4.663) H3a: β = 0.112 (t = 1.959) Knowledge creation (KCREA) H2a: β = 0.132* (t = 2.223) Knowledge sourcing/ repositories (KSR) Knowledge sourcing/ Internet (KSI) R² = 17% H3b: β = -0.03 (t = -0.498) H2b: β = 0.131* (t = 2.121) H3e: β = 0.119* (t = 2.286) H3c: β = 0.149** (t = 2.709) H2c: β = -0.033 (t = -0.582) Knowledge Reuse (KREU) R² = 9.1% H2d: β = 0.143* (t = 2.425) Legend: β represents standardized path coefficients; ***p < 0.001; **p < 0.01; *p < 0.05 The satisfactory overall fit of our model provides confidence in our results. Figure 2 supports the importance of team members’ learning orientation in increasing their knowledge sourcing from groups (H1a), repositories (H1b), and Internet (H1c). Furthermore, sourcing knowledge from groups (H2a) and repositories (H2b) has increased knowledge reuse compared to the Internet (H2c). Conversely, sourcing knowledge from the Internet has increased knowledge creation (H3c) than sourcing knowledge from groups (H3a) and repositories (H3b). Learning orientation has a strong effect on knowledge creation (H3d) than on knowledge reuse (H2d). These results support the hypothesis that knowledge reuse has increased knowledge creation (H3e) among team members with a strong learning orientation. 14 4. Discussion and implications The present research examines the following question: what motivates project team members to source knowledge and how it increases their reuse and creation outcomes? To address this question, we adapted a model based on knowledge sourcing perspective by considering learning orientation as a motivational input that inspire team members to initiate knowledge sourcing process, which results in various performance outcomes (Khedhaouria Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) and Ribiere, 2013). The first finding highlights that knowledge reuse and creation outcomes result from various knowledge sourcing methods. Group knowledge sourcing and repositories are more appropriate to increase knowledge reuse, while the Internet is more effective to increase knowledge creation (Gray and Meister, 2006). Interestingly and somewhat contrary to prior research, group knowledge sourcing has no effect on knowledge creation (Farr et al., 2003; West, 2002). Farr et al. (2003) argued that an “optimal” level of knowledge diversity exists for a given task that will encourage creativity through enhanced task performance capabilities, varieties of perspectives and approaches to problems, and constructive conflict. Too little diversity among team members leads to conformity and common approaches to solve problems. However, too much diversity (or insufficient overlap in knowledge and skills) among team members may result in disparate mental models and poor levels of coordination and communication that, in turn, slow down creative outcomes. Thus, diversity is likely to have a curvilinear relationship with group processes that mediate its link with creative outcomes. Furthermore, it is suggested that the role of team leaders is critical to manage diversity and to facilitate creativity amongst team members (Aubry and Lièvre, 2010; Egan, 2005; Pitrola-Merlo et al., 2002; Strang, 2010; Turner and Müller, 2007). These arguments indicate that further research is needed to clarify the influence team diversity and leadership has on knowledge creation. The second finding reveals that learning orientation is the main driver for knowledge sourcing, reuse and creation within project teams (Edmondson, 1999). Learning orientation encouraged team members to access knowledge from various sources such as groups, repositories, and the Internet. Learning orientation has high effect on knowledge creation outcomes (β = 0.252, p < 0.001). This emphasizes the role of learning orientation in motivating team members to source knowledge from the Internet in order to learn about problems and to create new solutions. Team members with high motivation activated their cognitive structures in order to explore and create entirely new solutions (Amabile, 1993). 15 Nevertheless, learning orientation has less effect on knowledge reuse (β = 0.143, p < 0.05). This emphasizes that team members with low motivation activated their cognitive structures in order to replicate and adapt existing solutions to their problems (Farr et al., 2003). The third finding highlights that replication and adaption of existing knowledge can increase knowledge creation. This finding reinforces our argument about the importance of learning orientation. Team members with a strong learning orientation will activate their cognitive Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) structures in order to replicate and adapt existing knowledge in new contexts, which results in creative outcomes (Majchrzak et al., 2004). Furthermore, the accumulation of knowledge stored in team members’ memories expanded their creative cognitive structures (Moreland and Myaskovsky, 2000). Our findings have interesting implications for both theory and practice. 4.1. Theoretical Implications From a theoretical perspective, the articulation between knowledge sourcing, reuse and creation has received little attention in the KM literature. Most studies, have investigated KM on supply-side to make knowledge available to team members when they need it. However, making knowledge available does not guarantee its use but rather understanding what motivates team members to source knowledge can enhance its reuse and creation. First, our study clarifies the importance of knowledge sourcing in increasing knowledge reuse and creation outcomes. This is an important contribution because it highlights the fact that knowledge reuse and creation can be managed through different sources of knowledge: group knowledge sourcing and repositories increase knowledge reuse, while the Internet increases knowledge creation. This result can be helpful to answer the question on how knowledge can be reused and created (Majchrzak et al., 2004; Markus, 2001). Second, our study emphasizes on the critical role of learning orientation in increasing knowledge sourcing, reuse and creation outcomes. It has been observed that many team projects fail to develop successful innovations (Khedhaouria et al., 2014; Sarker and Lee, 1999), suggesting that this may be due to a lack of motivation among team members. Team members are more likely, and willing, to source the required knowledge when they are motivated to learn within the team project. Last, our study demonstrates the importance of knowledge reuse in stimulating creation outcomes. Knowledge creation has been often examined in KM literature as distinct from knowledge reuse and it needs exploration capabilities that are different from exploitation capabilities (Markus, 2001). Our study demonstrates that knowledge reuse can increase knowledge 16 creation when team members are motivated to learn within the project. This finding is particularly interesting for team projects because it emphasizes the importance of learning orientation in stimulating creative cognitive structures through either the exploration or the exploitation of knowledge (Aubry and Lièvre, 2010; Kostopoulos and Bozionelos, 2011). 4.2. Practical Implications Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) From a practical perspective, our findings can be helpful for team leaders to manage existing knowledge. Understanding how knowledge is sourced, reused and created might help identifying ways to enhance knowledge reuse and creation outcomes. First, our study highlights the importance of group knowledge sourcing and repositories in increasing knowledge reuse. Thus, team leaders should guarantee the work team effectiveness by ensuring diversity of knowledge and skills among team members, clarifying and ensuring commitment to team objectives, managing conflict effectively, developing intra-team safety, reflexivity, and developing team members’ integration skills (West et al., 1998). As technical support knowledge repositories provide useful knowledge that can be reused, team leaders should ensure that knowledge is properly indexed, and easily searchable (Davenport and Klahr, 1998). Second, our study emphasizes the critical role of Internet such as online communities in enhancing teams’ knowledge creation behaviors. For team leaders integrating a crowdsourcing approach into their innovation process may contribute to enhance creative contributions (Brabham, 2008). Another approach is to integrate users as co-creators in the innovation process (Füller et al., 2009). Inviting users to participate in the creation of new products is considered as a suitable mean in generating and evaluating new product ideas. Last, our study emphasizes the critical role of learning orientation in increasing knowledge sourcing, reuse, and creation. For team leaders supporting a climate that encourages open discussion of problems, mistakes, and errors is a necessary condition for ensuring learning to occur in teams (Edmondson, 1996). Team leaders should ensure that some team members possess a strong learning orientation (i.e., assessed through learning style tests, or by previous observations), it is likely that these members will explore new avenues of bringing new knowledge to the team (Farr et al., 2003). 4.3. Limitations and conclusion The present study has a number of limitations that need to be addressed in future research. 17 First, although a substantial amount of variance of knowledge reuse and creation can be explained in the model, the explanatory power could be improved. Many antecedents of knowledge sourcing are not included in the present study. For instance, group characteristics such as the intellectual demands and project complexity (Gray and Meister, 2004), and risk aversion (Gray and Durcikova, 2005) have been shown to influence knowledge sourcing, reuse and creation behaviors. Further studies are needed to replicate our model and introduce other group characteristics to improve the explanatory power of knowledge reuse and Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) creation. Second, although our measurement strategy is unlikely to suffer from common method biases, more research is warranted (Podsakoff et al., 2003). In particular, the data collection instrument makes use of self-reported measures. Self-reported measures based on perceptions may lead to biases, especially when data are collected at the same point in time. To overcome this issue, future research should collate different measures spread over time or use separate primary and secondary observations. Third, our conceptual model doesn’t take into account the specificity, the complexity and the characteristics of team projects (Garel and Lièvre, 2010; Aubry and Lièvre, 2010). To overcome this limitation, further research is required for in-depth explorations. Despite these limitations, our findings reveal some interesting patterns that merit replication to better understand what motivates teams to source the required knowledge and how it increases their reuse and creative outcomes. Understanding how knowledge is reused and created within teams may help identifying ways to facilitate knowledge reuse and creation, which contributes to enhance innovations. Our research has offered evidence that knowledge sourcing methods (i.e., group knowledge sourcing, repositories, and Internet) produce different performance outcomes (i.e., knowledge reuse and knowledge creation). Our research provides insights on how team leaders should manage existing knowledge to increase its reuse and creation. 18 Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) References Alavi, M., Leidner, D.E. (2001), “Review: knowledge management and knowledge management systems: conceptual foundations and research issues”, MIS Quarterly, 25(1), p. 107-136. Albers, S., Henzinger, M. (2000), “Exploring unknown environments”. SIAM Journal on Computing, 29(4), p. 1164-1188. Amabile, T.M. 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Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) Author biographies Anis Khedhaouria is an Associate Professor at Montpellier Business School, France. He holds PhD (Management and Technology of Information Systems) from Department of Information Systems at the University of Savoy, France. His research interests include ICT-enabled creativity and innovation, Knowledge Management, ICT-enabled social works, E-commerce and Mobile Internet Services. His research articles have been published in Journal of Knowledge Management, Small Business Economics, Système d’Information & Management, International Journal of Technology and Human Interaction, and The Learning Organization. Arshad Jamal is a Senior Lecturer and Course Director, BSc Computing Systems at QA Business School, University of Ulster London Campus. He holds PhD (Information Systems and Computing) from Department of Information Systems and Computing at Brunel University, UK. He received his MS in Interactive Systems Engineering from Royal Institute of Technology, Sweden, MS in Software Engineering from National University of Sciences and Technology, Pakistan, an MA Economics from University of the Punjab, Pakistan and Postgraduate Certificate in Professional Studies in Education from Kingston University, UK. His research interests include knowledge management, social media, information privacy and human computer interaction. He has served as a reviewer in journals, namely IJIM and JEIM and conferences including ICIS 2011, AMCIS 2009–2010, EMCIS 2012 and ECIS 2012. 23 Items Mean KREU KCREA KSG KSR KSI Knowledge reuse behaviors (KREU) My project group frequently experiments with proven solutions to resolve problems. 5.150 0.740 0.079 0.145 0.157 0.029 My project group efficiently exploits existing ideas to resolve new problems 5.625 0.912 0.264 0.278 0.225 0.086 My project group frequently adapts existing solutions for resolving new problems. 5.326 0.763 0.074 0.062 0.115 0.053 Knowledge creation behaviors (KCREA) My project group frequently experiments with new alternatives. 4.933 0.199 0.880 0.195 0.161 0.217 My project group is highly imaginative in thinking about new or better solutions to resolve problems. 5.317 0.200 0.905 0.262 0.195 0.196 My group project often invents new ideas to resolve non-routine situations. 4.900 0.159 0.879 0.222 0.145 0.191 Knowledge Sourcing from the group (KSG) In my group project we frequently discuss difficulties when we need to improve knowledge on issues related to the project. 5.757 0.277 0.303 0.886 0.322 0.145 We frequently consult with my project group to improve knowledge on a topic or issue 5.657 0.181 0.186 0.825 0.297 0.140 We rarely use conversations in my project group to acquire required knowledge [r]. 5.930 0.133 0.148 0.842 0.388 0.139 Knowledge Sourcing from repositories (KSR) In my group project we often refer to available documents to learn more about a problem. 4.619 0.147 0.109 0.240 0.725 0.297 In my project group we often consult documents posted on the company’s intranet. 4.282 0.166 0.200 0.292 0.779 0.353 In my project group we rarely consult available documents [r] 4.968 0.021 0.051 0.356 0.699 0.291 In my project group we often consult knowledge-based systems to improve our knowledge on a topic or issue. 4.716 0.263 0.217 0.340 0.881 0.317 In my project group we often consult knowledge-based systems to find solutions for similar encountered problems. 4.757 0.294 0.154 0.313 0.866 0.338 In my project group we rarely consult knowledge-based systems [r]. 4.915 0.083 0.130 0.341 0.790 0.324 Knowledge Sourcing from Internet (KSI) In my project group we often consult documents available on the Internet 4.692 0.074 0.201 0.190 0.426 0.916 In my project group we often consult community network sites on the Internet to find useful knowledge on a topic or issue. 3.845 0.061 0.202 0.094 0.272 0.857 Learning behaviors (LB) In my project group we prefer tasks that really challenge as so we can learn new things. 5.147 0.260 0.311 0.342 0.275 0.157 In my project group we often look for opportunities to develop new skills and knowledge. 5.097 0.139 0.250 0.328 0.319 0.238 In my project group we enjoy challenging work where we will learn new knowledge. 5.097 0.206 0.343 0.382 0.318 0.240 Legend: KREU = Knowledge reuse behaviors; KCREA = Knowledge creation behaviors; KSG = Knowledge sourcing from the group; KSR = Knowledge sourcing from repositories; KSI = Knowledge sourcing from Internet; LB = Learning behaviors; [r] = Reverse-coded item. Table 1: Items and PLS factor loadings Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) 0.842 0.841 0.902 0.210 0.229 0.188 0.275 0.241 0.336 0.344 0.258 0.364 0.304 0.371 0.270 0.328 0.336 0.146 0.247 0.131 LB Table 2: Discriminant validity Variables LB KSI KSR KSG KCREA KREU Composite reliability 0.897 0.881 0.910 0.888 0.918 0.849 LB 0.862 0,245 0,352 0,408 0,352 0,235 Correlation of constructs(a) KSI KSR KSG KCREA 0.887 0,403 0,166 0,226 0,077 0.793 0,393 0,189 0,220 0.851 0,256 0,237 0.888 0,210 KREU 0.808 Downloaded by Groupe Sup de Co Montpellier Business School At 04:46 20 August 2015 (PT) Legend: (a) Diagonal elements are the square root of the AVE; KREU = Knowledge reuse behaviors; KCREA = Knowledge creation behaviors; KSG = Knowledge sourcing from the group; KSR = Knowledge sourcing from repositories; KSI = Knowledge sourcing from Internet; LB = Learning behaviors. View publication stats