Exploitative and exploratory learning in transactive memory systems and project performance

Information & Management 50 (2013) 304–313

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Exploitative and exploratory learning in transactive memory systems and project performance Yong-Hui Li a,1, Jing-Wen Huang b,* a b

National Pingtung Institute of Commerce, No. 51, Minsheng E. Rd., Pingtung City, Pingtung County 90004, Taiwan, ROC National Pingtung University of Education, No. 4-18, Minsheng Rd., Pingtung City, Pingtung County 90003, Taiwan, ROC

A R T I C L E I N F O

A B S T R A C T

Article history: Received 5 March 2012 Received in revised form 10 May 2013 Accepted 15 May 2013 Available online 22 May 2013

Based on organizational learning theory and the dynamic capability view, this study examines the relationships between transactive memory systems, team learning, and project performance in new product teams. Regression analysis is used to test the hypotheses in a sample of 218 Taiwanese firms. The findings indicate differential effects of three dimensions of a transactive memory system on exploitative and exploratory learning. Exploitative and exploratory learning are positively associated with project performance. The results also support that the interaction between exploitative and exploratory learning has a positive effect on project performance. Managerial implications and future research directions are discussed. ß 2013 Elsevier B.V. All rights reserved.

Keywords: Transactive memory system Exploitative learning Exploratory learning Project performance New product development

Practitioner points (1) The success of new product development projects requires the cultivation of transactive memory systems to enlarge team members’ motivation to engage in team learning activities. (2) Managers should stimulate the work atmosphere to augment exploitative and exploratory learning activities in their project teams. (3) Managers also need to maintain a balance between exploitative and exploratory learning for new product development projects to ensure current viability and future flexibility. 1. Introduction New product development is considered to be a critical mechanism to enhance the ability of firms to adapt to environmental turbulence and to maintain innovation [33]. Owing to the increasing importance of new product development, previous research has focused on knowledge learning in new product development project teams [3,6]. Organizational learning theory and the resource-based view depict firms as repositories of knowledge and expertise that form the basis for sustainable

* Corresponding author. Tel.: +886 8 7226141. E-mail addresses: [email protected] (Y.-H. Li), [email protected] (J.-W. Huang). 1 Tel.: +886 8 7238700. 0378-7206/$ – see front matter ß 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.im.2013.05.003

competitive advantage [8,15,47]. According to organizational learning theory, firms need to actively manage knowledge and expertise to develop innovative products through organizational learning [15,35,46]. In the case of the resource-based view, knowledge and expertise are viewed as distinctively unique resources because of tacitness, stickiness, and inimitability [8,46,18]. Tacit knowledge is not easy to spread across members and transform into organizational memory [47,49]. In the context of new product development, projects teams can engage in team learning to facilitate information-processing activities and reciprocal exchanges between team members [10]. Team learning can broaden and improve the knowledge base of project teams. Team members can translate tacit knowledge into embodied products. They can increase their ability to respond to the market, solve problems, and enhance performance outcomes [3,6,10]. Thus, team learning plays an important role in the contribution of new product success [33,6,45]. While team learning is critical in new product development, little research has explored the potential antecedent of team learning or has integrated the concept of ambidexterity in team learning. Based on previous research, this study frames two predominant styles of team learning, including both exploitative learning and exploratory learning [33,6]. The focus of this study is to identify a potential antecedent and to explore the interactive effect of exploitative and exploratory learning on project performance. Learning involves reciprocal exchange and joint effort between individual members [10]. The effectiveness of team learning depends on the extent to which team members get to know one

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another and establish routines for interaction and task accomplishment [29]. Members need shared memory systems to assist them in learning and exchanging knowledge. Transactive memory systems are originally conceptualized by Wegner [52] to explain the combination of the knowledge possessed by each individual and the mutual awareness of who knows what [29,22,7]. Transactive memory systems can generate the conditions that facilitate members to encode, store, and retrieve group knowledge from different domains [29,52,12]. Teams that develop transactive memory systems are more likely to utilize embedded team knowledge and enhance team-level learning [30,32,4]. When performing project tasks, team members share collective transactive memory to access the knowledge and expertise of others. Through transactive memory systems, team members can learn and spread their learning effectively to facilitate new product development. Based on transactive memory theory, this study identifies a transactive memory system as a potential antecedent of team learning and examines the relationship between transactive memory systems and team learning. New product development activities not only rely on existing capabilities but also disrupt existing capabilities or require the building of new ones [53,36]. Team members can engage in both exploitative and exploratory learning to integrate and reconfigure existing and new knowledge at dispersed locations [33,6]. Exploitative learning captures refinement, efficiency, and improvement that reduces variance and enables incremental innovation, while exploratory learning entails search, discovery, and experimentation that fosters the variation and novelty needed for more radical innovation [33,9]. Although the attributes of exploitation and exploration create inconsistent and paradoxical challenges [20,40,13], the integration and interaction between exploitation and exploration can enhance learning and the performance outcome [40,13,25]. The concept of ambidexterity reflects a combination of exploitative and exploratory learning within an organization [20,13,25]. According to the dynamic capability view, ambidexterity represents the dynamic capability of enterprises to mobilize, coordinate, and transform knowledge into complex bundles [40,16,48]. New product development teams can develop and leverage complementary knowledge and resources between exploitative and exploratory learning. These teams can sense and seize new opportunities and further reconfigure dynamic processes of innovation to enhance value and prosperity [40,25,48]. Drawing on the above perspectives, this study is based on organizational learning theory and the dynamic capability view to discuss the relationships between two types of team learning, including exploitative and exploratory learning, and project performance in new product development teams. We further examine the interactive effect between exploitative and exploratory learning on project performance. The rest of the paper is set out as follows: Section 2 considers the related literature and sets out the hypotheses of this research. Section 3 discusses the research design to collect data. Section 4 presents the results of the empirical study in achieving the goals as set out above. Section 5 provides theoretical and practical implications, limitations, and directions for future research. 2. Research background and hypotheses 2.1. Transactive memory system The concept of transactive memory describes the beliefs about knowledge possessed by others and the accessibility of that knowledge [29,52]. Individuals will frequently supplement their own memory capacities by making use of knowledge stored by their partners [29,52,38]. A transactive memory system is a

305

group-level phenomenon that refers to the collective memory system with respect to the encoding, storage, retrieval, and communication of information from different knowledge domains [12,30,32]. People in close relationships develop transactive memory systems to assign responsibility for information based on the recognition of one another’s expertise [52,12]. Within work groups, transactive memory systems facilitate members to retrieve and allocate tacit knowledge related to their teammates’ areas of expertise [4,17,14]. Transactive memory systems reduce the cognitive load of each member and decrease the redundancy of effort in teams due to collective memory [4,43]. Members can apply a greater amount of task-critical knowledge and coordinate members’ interactions more effectively [32,17]. Research has indicated that transactive memory systems help organizational teams to fully utilize member expertise and also provide benefits to improve team performance and project outcomes [30,14]. For example, Lewis [30] suggested the critical role of transactive memory systems in knowledge-worker teams as it relates to team performance. Choi et al. [14] conducted a field study and indicated that transactive memory systems enhance knowledge sharing and applications that improve team performance. Early studies on transactive memory systems have been demonstrated in laboratory settings [52,38,34]. However, recent research has extended transactive memory systems to look for group dynamics related to the knowledge of workers in groups or organizations [30,14,43,2]. These dynamics include the specialization of tasks, task coordination activities, and task credibility actions. This study is based on previous research and examines three measures for the existence of transactive memory systems, including specialization, credibility, and coordination [29,17,2,37]. Specialization addresses the idea that individual members of a team specialize in remembering different aspects of a given task. Credibility reflects members’ beliefs about the accuracy and reliability of other members’ knowledge. Coordination indicates the ability of team members to work effectively together while conducting a task. New product development is a knowledgeintensive activity and requires project teams to develop transactive memory systems to learn and utilize multiple facets of knowledge [4]. Transactive memory systems provide a knowledge network among individuals that leads to interchange, storage, and retrieval of information and the completion of work [17,37]. As teams work together throughout the new product development phase, they develop shared transactive memory systems by which they leverage and coordinate diverse expertise and knowledge [14]. 2.2. Team learning Organizational learning theory suggests that learning brings behavioral change and organizational adaptation by which firms can respond to dynamic challenges in their environments [28]. According to organizational learning theory, organizations can enhance their capability to sustain competitive advantage in ways that are difficult to imitate and replicate by their competitors [15,35,46]. In the case of the resource-based view, tacit knowledge is embedded in different individuals’ minds [47,18]. Knowledge tacitness reflects the growing need for organizational learning. Organizational learning can transcend knowledge beyond the individual mind to become a collective entity. The interactive and reciprocal nature of the learning process facilitates the knowledge exchange between explicit and tacit knowledge [10]. Thus, organizational learning has a great potential for continuous improvement, such as new product development [3,6,45] and performance enhancement [10,54,50]. Learning is particularly important in the new product development context because innovation spans many functional

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areas and is accompanied by significant changes in organizational routines [3,53]. New product development teams frequently are composed of individuals from different backgrounds and perspectives, and they need team learning to establish a shared understanding of proposed solutions and potential improvements [39]. Team learning reflects information-processing activities for team members to share, transfer, and combine existing and new knowledge [15,35,10]. New product development entails a series of exploitative and exploratory learning events related to problem solving and task implementation. This study follows previous research in new product development to explore two types of team learning, including exploitative and exploratory learning [33,6]. Exploitative learning focuses on the refinement and extension of existing knowledge, skills, and technologies. Exploratory learning emphasizes the experimentation with new alternatives and the acquisition of new knowledge, skills, and technologies [33,35]. 2.3. Transactive memory system and team learning The complex nature of a new product development project requires effective team learning necessary for understanding the differentiated expertise of other members [4,2]. Transactive memory theory highlights that team members can utilize each other as an external memory aid to increase and improve their own memories [29,30,21]. The transactive memory system of a team is able to lead to the development of interpersonal congruence and provides a focal frame to recognize functional similarities and underlying principles common to tasks [32]. With the help of a transactive memory system, team members enhance their own memory stores and reinforce their understanding of others’ expertise [17,14]. Team members are more likely to collaborate to encode, interpret, and recall knowledge embedded in a group’s structures and processes [32,38]. Transactive memory systems help members retrieve and exploit prior knowledge related to new tasks and develop an abstract understanding of the principles relevant to a specific task domain [32,4]. The development of abstract knowledge is critical to a group’s ability to leverage and transfer what they learned on previous tasks. Transactive memory systems also entail the exploration of knowledge to new problem-oriented situations during projects. Lewis et al. [32] suggested that groups with transactive memory systems produce not only relevant knowledge for current tasks but also create transferable knowledge for other tasks in a similar domain. Such shared transactive memory promotes group learning and learning transfer. Similarly, Akgu¨n et al. [2] indicated that when teams establish an effective transactive memory system, they develop new products with fewer technical problems and solve product problems in areas related to customer dissatisfaction. Transactive memory systems have a positive association with team learning and new product success [4]. Group level studies have suggested that specialization, credibility, and coordination are recognized as cognitive manifestations of transactive memory systems [29,17,37]. Specialization refers to the differentiation of member knowledge [30,17]. In new product development processes, project members cultivate specialized expertise from different functional areas to assemble and apply project tasks [7,4]. Transactive memory is the set of knowledge possessed by members of a team and combined with members’ social perceptions about each other’s expertise [52,38]. To engage in knowledge learning, team members need to be aware of where the required knowledge is located and must be able to acquire it in a timely manner [29,30]. Individuals rely on other team members to serve as human repositories for information outside of their own domains [22,30]. Hollingshead’s (2000)

research on retrieval processes in transactive memory systems has shown that specialization can reduce repetition of effort and enable better access to a wide range of expertise. Team members tend to retrieve and exploit task-specific knowledge more efficiently for exploitative learning. Furthermore, team members update their directories of diverse knowledge to facilitate exploratory learning among members [32,4,14]. Accordingly, the following hypotheses (hereafter H) are proposed: H1a: In NPD teams, specialization is positively related to exploitative learning. H1b: In NPD teams, specialization is positively related to exploratory learning. Credibility reflects the degree of trust and reliability of other members’ knowledge [30,17]. In team and group work contexts, different team members have different professions and backgrounds, and they tend to seek relevant knowledge from trusted and capable colleagues [43,41]. Group members with high-trust relationships are more likely to perceive each other’s behaviors and actions positively [43]. Research has revealed that trust encourages interdependency and interaction among organizational members [49,11]. Trust can help in developing a learning environment through social exchange and knowledge disclosure [49,43,11]. Accordingly, perceptions of credibility enhance the willingness of members to exchange and absorb each other’s knowledge [17,43,41], thereby leading to greater team learning. H2a: In NPD teams, credibility is positively related to exploitative learning. H2b: In NPD teams, credibility is positively related to exploratory learning. Coordination indicates the degree of effective and orchestrated knowledge processing occurring in a specific environment [30,17]. Innovative activities are increasingly interactive, and project teams need coordinative effort to take advantage of multiple viewpoints [49]. Smooth and efficient coordination constitutes information channels that reduce the time and investment required to seek necessary information from teammates [30]. Through coordinated assignments of expertise, team members have a better understanding of who knows what and from whom to retrieve knowledge [52,12,30,17]. Team members can engage in exploitative learning to identify and exploit distributed knowledge. Members also develop exploratory learning to find gaps in expertise that can then be filled by each team member [30,37]. Accordingly, the coordination component of transactive memory systems helps to stimulate the formation of common interests that support the team learning needed in the project team [4,2]. H3a: In NPD teams, coordination is positively related to exploitative learning. H3b: In NPD teams, coordination is positively related to exploratory learning. 2.4. Team learning and project performance Team learning reflects information-processing activities and reciprocal exchanges between individual members in a team or a group [10]. The process of team learning involving different members is complex and dynamic. Furthermore, knowledge tacitness inhibits knowledge flow and exchange between individual actors [47,18]. In the context of new product development, team members need to learn collectively to establish shared mental models and understanding of how to deal with their tasks [3,39]. Team learning brings a change in the organizational

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behavior and action patterns [28]. Team learning provides opportunities for an organization to translate tacit knowledge into embodied products [45]. Organizational learning theory suggests that learning can facilitate individual knowledge to transform into organizational capacity for innovation and growth [15,10]. The resource-based view indicates that valuable knowledge is tacit, unique, and inimitable [8,18]. Based on these viewpoints, team learning can be accumulated as organizational intelligence leading to competitive advantage because of the characteristics of flexibility, uniqueness, and imperfect imitation or substitution on the part of competitors [8,46,18]. Team learning enables a firm to gain favorable performance outcomes and synergistic benefits from the process of learning and exchanging knowledge and resources available among team members [3,10,54,50]. For example, Blazevic and Lievens [10] indicated that project learning increases the effectiveness of knowledge usage throughout organizations and suggested that project learning has a leveraging effect on project performance. Akgu¨n et al. [3] used the socio-cognitive theory of learning in groups and organizations and found that team learning in new product development teams has a positive effect on new product project success. Zellmer-Bruhn and Gibson [54] integrated literature on international management and team effectiveness and indicated that team learning has a positive link with team performance. Likewise, Tucker et al. [50] examined learning activities in project teams and suggested that learn-how activities are positively associated with the implementation success of new practices in hospital intensive care units. New product development and innovation require the application of existing knowledge combined with new knowledge. Projects teams can engage in exploitative and exploratory learning to share, combine, and utilize knowledge [6]. The form of learning determines the pattern by which a firm devotes effort and attention to new product development activities. Exploitative learning involves information searches within a well-defined and limited product/market solution context related to a firm’s previous experience [6,35,28]. Exploitative learning allows team members to combine existing knowledge and apply lessons derived from past experiences. Team members can reduce errors in problem solving and avoid mistakes related to new product development [6,45]. Based on exploitation, project teams can better recognize customer needs and diminish repetitive disturbances in particular technologies and product-market areas. Moreover, increased familiarity with an existing knowledge domain can strengthen the ability of project members to speed up new product introduction [33]. In this respect, exploitative learning generates better project efficiency of new product development. Exploratory learning involves experimentation with new alternatives and a search for technology and market information that is new to organizations [6,35,28]. New product development relies on exploratory learning to increase creative thinking and idea sharing among team members. Exploratory learning enhances the breadth and depth of knowledge available to project teams [10,39]. In addition, exploratory learning adds new elements to a project’s repertoire and provides new insights into product design [33,6]. Project teams are able to create more innovative products and explore emerging customer needs by reconfiguring resources to capitalize on market opportunities [45,53]. Thus, exploratory learning allows for greater experimentation and flexibility as they apply to new product development [33,3,6]. The literature on exploitative and exploratory learning indicates that the outcomes of these two types of learning are quite different, not only in terms of incremental and radical innovations but also in terms of the risk variance and concomitant benefits/

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costs [24]. Exploitative learning does not capture some types of new product development outcomes such as radical new products, new technological trajectories, etc. This study expects that exploitative learning has a stronger impact on project efficiency as compared to exploratory learning. Thus, the following hypotheses are proposed. H4a: Exploitative learning is more positively related to project efficiency than project effectiveness. H4b: Exploratory learning is more positively related to project effectiveness than project efficiency. 2.5. Interactive effect Facing fierce competition and challenge, firms not only have to exploit their existing capabilities but also have to explore business opportunities and capabilities that they will need in the future [9,20]. Some research has indicated the concept of ambidexterity to reflect the integration and interaction between exploitation and exploration [20,13,25]. Ambidexterity can be seen as the dynamic capability for adaptation and reconfiguration of resource employment to deal with challenging environments [16,48]. An ambidextrous organization is capable of exploitive and exploratory activities [20,40,13]. Exploitation involves refinement, efficiency, and improvement, while exploration involves experimentation, discovery, and flexibility [35,9,20]. The combination of exploitation and exploration helps organizations to overcome structural inertia that results from focusing on exploitation but also prevents them from accelerating exploration without gaining benefits [36,13,25]. Drawing on the capability perspective, Menguc and Auh [36] indicated that ambidexterity captures the tendency of deftness, agileness, and flexibility, and these authors also suggested that ambidextrous firms can balance both their shortand long-term gains. As Katila and Ahuja [27] noted, exploitation of existing capabilities is often needed to explore new capabilities, and exploration of new capabilities also enhances a firm’s existing knowledge base. According to the dynamic capability view, the interaction between exploitation and exploration enables firms to mobilize, coordinate, and transform existing knowledge into complex bundles [40,16,48]. Furthermore, firms can sense and seize new opportunities and reconfigure resources to generate new applications for the purpose of innovation [40,25,48]. In the context of new product development, an appropriate balance between exploitative and exploratory learning provides a basis for project teams to reconfigure the dynamic processes inherent in innovation to enhance value and prosperity. New product teams have greater potential to develop and leverage complementary knowledge and resources between exploitative and exploratory efforts [3,6,13]. Team members can better recognize existing customer needs and reduce repetitive disturbances in products and technologies [53]. They also can expand into new markets and enhance their capability for new product development [6,53]. Conversely, the failure to achieve a balance between these two types of learning can leave a firm susceptible to the risk of obsolescence or overinvestment in experimental activities [20,13]. As stated above, the interaction between exploitative and exploratory learning is expected to be favorable to new product success and project performance. Thus, the following hypothesis is formulated: H5a: The interaction between exploitative and exploratory learning is positively related to project efficiency. H5b: The interaction between exploitative and exploratory learning is positively related to project effectiveness.

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3. Research methodology 3.1. Study context and sample The empirical study employed a questionnaire approach designed to collect data for testing the research hypotheses. The survey instrument contained instructions for completion, the research variables, and demographic questions for the firm. Respondents rated each item on seven-point Likert-type scales in which higher values were associated with higher levels of the construct. The population for the study was the top 5000 Taiwanese firms listed in the yearbook published by the China Credit Information Service, Ltd. This study divided the 5000 firms listed in the yearbook into five levels with 1000 rankings each. Then, we used a stratified random sampling method to select 120 firms in each level. The total sample included 600 companies. A total of 600 questionnaires were distributed, along with a cover page that explained the nature of the study. The primary recipients were R&D managers or product development managers who are knowledgeable about their firm’s innovation and new product development processes. Each recipient was informed by e-mails and phone calls to assure anonymity and participation. Two weeks after the first mailing, we send follow-up e-mails and made phone calls to non-respondents to appeal for cooperation. A total of 233 surveys were returned; of the returned surveys, 218 were complete in all independent and dependent variables, giving us a usable response rate of 36.33%. The possibility of nonresponse bias was examined by using a two-tailed t-test to compare the characteristics of respondent firms with nonrespondents. Respondent firms did not significantly differ from nonrespondents in terms of firm size, firm age, and team size (p > 0.10). The results indicated that nonresponse bias was not a significant problem in the current data. The Harman one-factor test was conducted to examine common method bias. A principal factor analysis on the measurement items yielded six factors that accounted for 76.5% of the total variance, and the first factor accounted for 15.2% of the variance. Because no single factor emerged and one general factor did not account for most of the variance, common method bias was determined to not be serious in the data [42]. 3.2. Measures, validity, and reliability A transactive memory system refers to a collective memory system with respect to the encoding, storage, retrieval, and communication of information from different knowledge domains [29,12]. The construction of the measures of transactive memory systems is primarily based on the work of Lewis [29] and Lewis [30]. A fifteen-item scale is developed to assess three dimensions of a transactive memory system including specialization, credibility, and coordination. Specialization refers to the differentiation of member knowledge. Credibility reflects the degree of trust and reliability of other members’ knowledge. Coordination indicates the degree of effective and orchestrated knowledge processing [30,17]. Exploitative learning in the project team consists of five items regarding the refinement of common methods and ideas, the search for generally proven methods and solutions, the acquisition of information to ensure productivity and update the firm’s current project and market experiences, and the emphasis on the use of knowledge related to existing project experience. Exploratory learning includes five items focusing on learning activities that involve experimentation and high market risks, the search for knowledge that leads the firm enter into new markets and technological areas, and the acquisition of novel information that went beyond current market and technological experiences [6].

Project performance concerns the outcome or perceived success of the project team in meeting project goals, budget, schedule, and operational efficiency considerations [26]. As Wang et al. [51] note, project performance is a combination of project efficiency and effectiveness as perceived by the respondents. We measure project efficiency with three items including the expected amount of work completed, the quality of work completed, and the facility of task operations. Project effectiveness consists of three items that include meeting project goals, meeting schedule, meeting budget. Four control variables are entered in the analysis, including firm size, firm age, team size, and industry type. The number of employees is used to control for the possible firm-size effect, and it is calculated by taking the logarithm of each firm’s total number of employees. Firm age is measured as the number of years from the founding date. Team size is measured by the logarithm of the number of members in the new product development team from the information that the respondents to our questionnaire offered. To assess the industry type, one dummy variable is included to indicate whether a firm belongs to a manufacturing industry or a high-tech industry (0 = manufacturing industry, 1 = high-tech industry). Because the measurement scales are adapted, we estimate convergent validity and discriminant validity using confirmatory factor analysis (CFA) in a structural equation model [5]. The CFA fit indexes for the proposed model range from adequate to excellent (Chi-square = 21.89, df = 11, p-value = 0.02, IFI = 0.98, CFI = 0.98, GFI = 0.97, AGFI = 0.93, RMSR = 0.03). Overall, the CFA results suggest that the model of a transactive memory system, team learning, and project performance provides a reasonably good fit for the data [19]. Moreover, all the measurement items load on their underlying construct, and none of the confidence intervals for each pairwise correlation estimate contain a value of one [5]. The average variance extracted estimates range from 0.63 to 0.85. In addition, we constrain the correlation between each pair of constructs, one at a time, to be equal to 1 [5,23]. The chi-square test comparing this model to the model freeing that correlation is significant (p < 0.001). These results indicate that the constructs demonstrate both convergent and discriminant validity [5,23]. The reliability of the multi-item scale for each dimension is assessed by calculating composite reliability coefficients for all of the scales. Table 1 summarizes all measurement items, factor loadings, composite reliabilities, average variance extracted estimates, and their scales for all of the items. Composite reliabilities of each scale range from 0.84 to 0.97, which are above the recommended minimum standard of 0.70 [19]. Thus, we conclude that the measures utilized in the study demonstrate internal consistency. 4. Analysis and results The hypotheses are tested with multiple regression analyses for transactive memory system, team learning, and project performance. Fig. 1 depicts the proposed theoretical model. Table 2 displays descriptive statistics and correlations of all variables. We mean-center the relevant variables before creating the interaction term [1]. None of the variance inflation factors in the regression models are above 2.3, and all are well below the threshold of 10, which indicates that multicollinearity is not a serious problem [19]. The results of the Levene test (p > .10) indicate no threat of unequal variances, suggesting the presence of homoskedasticity in the regression tests. Models 1 and 3 in Table 3 test the effects of the control variables on exploitative and exploratory learning, respectively. For exploitative learning, Model 2 in Table 3 adds the main effects

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Table 1 Measurement items and reliabilitiesa Variables

Items

Factor loading

Composite reliability

Average variance extracted

Specialization

Each team member has specialized knowledge of some aspect of our project. I have knowledge about an aspect of the project that no other team member has. Different team members are responsible for expertise in different areas. The specialized knowledge of several different team members is needed to complete the project deliverables. I know which team members have expertise in specific areas. I am comfortable accepting procedural suggestions from other team members. I trust that other members’ knowledge about the project is credible. I am confident relying on the information that other team members bring to the discussion. When other members give information, I don’t need to double-check it for myself. I have much faith in other members’ ‘‘expertise.’’ Our team works together in a well-coordinated fashion. Our team has very few misunderstandings about what to do. Our team needs not to backtrack and start over a lot. We accomplish the task smoothly and efficiently. There is not confusion about how we would accomplish the task. Our aim is to search for information to refine common methods and ideas in solving problems in the project. Our aim is to search for ideas and information that we can implement well to ensure productivity rather than those ideas that could lead to implementation mistakes in the project and in the marketplace. We search for the usual and generally proven methods and solutions to product development problems. We use information acquisition methods (e.g., survey of current customers and competitors) that help us understand and update the firm’s current project and market experiences. We emphasize the use of knowledge related to our existing project experience. In information search, we focus on acquiring knowledge of project strategies that involve experimentation and high market risks. We prefer to collect information with no identifiable strategic market needs to ensure experimentation in the project. Our aim is to acquire knowledge to develop a project that leads us into new areas of learning such as new markets and technological areas. We collect novel information and ideas that go beyond our current market and technological experiences. Our aim is to collect new information that forces us to learn new things in the product development project. expected amount of work completed quality of work completed efficiency of task operations ability to meet project goals adherence to schedule adherence to budget

0.77 0.92 0.84 0.88

0.92

0.70

0.90

0.64

0.93

0.73

0.90

0.63

0.97

0.85

0.86

0.67

0.84

0.65

Credibility

Coordination

Exploitative learning

Exploratory learning

Project efficiency

Project effectiveness

a

0.76 0.79 0.92 0.91 0.57 0.76 0.86 0.81 0.87 0.91 0.83 0.71 0.79

0.86 0.83

0.78 0.90 0.93 0.92 0.92 0.94 0.73 0.82 0.89 0.91 0.87 0.60

This study measures all items with a seven-point Likert scale.

of the transactive memory system, which contribute 29% (DF = 32.51, p < 0.001) above the variance explained by the control variables in Model 1. For exploratory learning, Model 4 adds the main effects of the transactive memory system, which contribute 39% (DF = 50.19, p < 0.001) above the variance explained by the control variables in Model 3. H1a and H1b predict that there is a positive relationship between specialization and both exploitative and exploratory learning. H1a is supported (b = 0.19, p < 0.05); however, H1b is not supported. Both H2a and

Transactive Memory System Specialization

H2b are supported; credibility has a positive relationship to exploitative learning (b = 0.37, p < 0.001) and exploratory learning (b = 0.45, p < 0.001). H3a and H3b predict that coordination is positively related to exploitative and exploratory learning. H3a is not supported, and H3b is supported (b = 0.25, p < 0.001). Models 5 and 8 in Table 4 report the main effects of the control variables on project performance. Models 6 and 9 add the main effects of exploitative and exploratory learning, which contribute 26% (DF = 40.16, p < 0.001) and 23% (DF = 33.25, p < 0.001) over

Team Learning

Project Performance

Exploitative learning

Project efficiency

Exploratory learning

Project effectiveness

Credibility Coordination

Fig. 1. Research model of this study.

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310 Table 2 Descriptive statistics and correlationsa Variables

Mean

s.d.

1. Firm size 2. Firm age 3. Team size 4. Industry type 5. Specialization 6. Credibility 7. Coordination 8. Exploitative learning 9. Exploratory learning 10. Project efficiency 11. Project effectiveness

2.80 28.33 1.03 0.54 4.99 5.03 5.32 5.10 4.76 5.19 5.21

0.55 12.08 0.04 0.50 0.89 0.89 0.81 0.85 1.08 0.98 0.92

a

1

2 0.00 0.10 0.05 0.10 0.08 0.09 0.25 0.08 0.15 0.19

3

0.34 0.61 0.04 0.08 0.12 0.12 0.06 0.13 0.06

0.43 0.06 0.03 0.01 0.02 0.00 0.01 0.02

4

5

6

7

8

9

10

0.11 0.11 0.14 0.10 0.17 0.14 0.10

0.64 0.55 0.43 0.43 0.22 0.19

0.67 0.54 0.62 0.37 0.31

0.43 0.56 0.20 0.16

0.33 0.52 0.49

0.36 0.33

0.62

n = 218 (two-tailed test). Correlations with an absolute value greater than 0.13 are significant at p < 0.05, and those correlations greater than 0.17 are significant at p < 0.01

Table 3 Results of the regression analysis for team learninga Variable

Exploitative learning

Exploratory learning

Model 1

Model 2

Model 3

Firm size Firm age Team size Industry typeb Specialization (H1) Credibility (H2) Coordination (H3)

0.26*** 0.13 0.12 0.07

0.25*** 0.12 0.08 0.01 0.19* 0.37*** 0.05

R2 DR2 F DF

0.09 0.09 5.40*** 5.40***

0.38 0.29 18.39*** 32.51***

0.08 0.06 0.09 0.25**

0.05 0.05 2.57* 2.57*

Model 4 0.02 0.09 0.05 0.17** 0.01 0.45*** 0.25*** 0.44 0.39 23.99*** 50.19***

a

n = 218 (two-tailed test). Standardized coefficients are reported. Dummy variable coded as manufacturing industry, 0; high-tech industry, 1. * p < 0.05. ** p < 0.01. *** p < 0.001. b

the variance explained by the control variables. This study divides the performance data into project efficiency and project effectiveness and then tests the effects of both forms of learning on project efficiency and effectiveness. H4a predicts that exploitative learning has a more positive relationship to project efficiency than project effectiveness. H4b predicts that exploratory learning has a more positive relationship to project effectiveness than project

efficiency. The results show that exploitative learning has a positive impact on efficiency (b = 0.43, p < 0.001) and effectiveness (b = 0.41, p < 0.001). Thus, H4a is supported. Similarly, exploratory learning has a positive relationship with efficiency (b = 0.21, p < 0.001) and effectiveness (b = 0.18, p < 0.01). Thus, H4b is not supported. The findings indicate that exploitative learning focuses more on project efficiency for refinement and implementation as compared to exploratory learning. Models 7 and 10 in Table 4 add the interaction term for exploitative and exploratory learning. These variables increase the explained variance by 5% (DF = 14.38, p < 0.001) and 7% (DF = 24.63, p < 0.001) over the explained variance obtained in Models 6 and 9. H5a and H5b predict that the interaction of exploitative and exploratory learning is positively related to project efficiency and effectiveness. The result shows that the coefficients for the interaction term are positive and significant (b = 0.23, p < 0.001 and b = 0.30, p < 0.001, respectively). Fig. 2 presents the interactive effect of exploitative learning and exploratory learning on project efficiency. Fig. 3 reveals the interactive effect of exploitative learning and exploratory learning on project effectiveness. These findings are consistent with the theoretical prediction. Thus, H5a and H5b are supported. In addition to the above analysis, this study considers other tests to explain the possibility of the statistically insignificant results. As Lewis and Herndon [31] indicated, too much differentiated knowledge within the team may reduce the amount of shared knowledge within the team thereby creating difficulties in knowledge integration. We investigate whether the relationships

Table 4 Results of the regression analysis for project performancea Variables

Project efficiency Model 5

Project effectiveness Model 6

Model 7

Model 8

Model 9

Model 10

Firm size Firm age Team size Industry typeb Exploitative learning (H4a) Exploratory learning (H4b) Exploitative  exploratory learning (H5)

0.15* 0.09 0.10 0.12

0.02 0.05 0.03 0.04 0.43*** 0.21***

0.01 0.06 0.03 0.05 0.38*** 0.16** 0.23***

0.19* 0.03 0.10 0.12

0.07 0.01 0.04 0.04 0.41*** 0.18**

0.03 0.01 0.04 0.05 0.35*** 0.13** 0.30***

R2 DR2 F DF

0.05 0.05 2.85* 2.85*

0.31 0.26 15.98*** 40.16***

0.36 0.05 16.62*** 14.38***

0.05 0.05 2.85* 2.85*

0.28 0.23 13.56*** 33.25***

0.35 0.07 16.44*** 24.63***

a

n = 218 (two-tailed test). Standardized coefficients are reported. Dummy variable coded as manufacturing industry, 0; high-tech industry, 1. * p < 0.05. ** p < 0.01. *** p < 0.001. b

Project Efficiency

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Low exploratory learning High exploratory learning

Exploitative Learning

Project Effectiveness

Fig. 2. Interactive effects of exploitative and exploratory learning on project efficiency.

Low exploratory learning High exploratory learning

Exploitative Learning Fig. 3. Interactive effects of exploitative and exploratory learning on project effectiveness.

between the three dimensions of transactive memory systems and team learning may be inverted U-shaped. The curvilinear test shows insignificant results. We further investigate the presence of curvilinear effects related to team learning and NPD project performance. The results of the curvilinear test are insignificant. In contrast, it is possible that the impacts of transactive memory systems on team learning and the impacts of team learning on project performance are moderated by other salient variables, such as team size [44]. We mean-center team size as an interactive variable and examine the moderating effect. The regression results indicate an insignificant moderating effect of team size. 5. Discussion and conclusions Previous research shows that team learning plays an important role in new product development; however, our understanding of the antecedents that lead to effective team learning is still emerging. In addition, previous research does not fully consider the concept of ambidexterity in team learning. Akgu¨n et al. [4] have explored transactive memory systems in new product development teams and examined the mediating role of a collective mind and the moderating role of environmental turbulence in the relationship. This study applies transactive memory theory on project teams and identifies a transactive memory system as an antecedent of team learning echoing the notion of Akgu¨n et al. [4]. The contribution to the gap in the scholarship is that this study integrates the concept of ambidexterity in team learning and explores the interactive effect of exploitative and exploratory learning on project performance. This study builds a shared view (or collective memory) among new product development teams. We integrate the organizational learning literature and transactive memory literature to examine how new product development

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teams manage transactive memory systems to develop team learning. Organizational learning theory suggests that learning can enhance organizational capability and sustain competitive advantage [15,35,10]. Transactive memory theory suggests that transactive memory systems help organizational teams to utilize member expertise and provide benefits that improve team performance and project outcomes [30,17,14]. The empirical results provide considerable support for the proposed model using data from Taiwanese firms. The first major avenue for a novel contribution would be to provide more insight into the nature of the relationships between the three dimensions of transactive memory systems and the two types of learning. The findings show that three dimensions of transactive memory systems have differential effects on the level of exploitative and exploratory learning. Particularly, specialization enhances exploitative learning by promoting knowledge exchange and recombination; however, specialization does not have a strong enough effect on exploratory learning. A plausible explanation for this result is that specialization may decrease the willingness of team members to gain diverse knowledge and perspectives from outside their professional domain, thus hindering exploratory learning. If teams have too much differentiated knowledge, they may reduce the amount of shared knowledge and inhibit knowledge integration [31]. Credibility is positively related to both exploitative and exploratory learning, indicating that more trust among team members tends to steer the team to both exploit and explore in the area of new product development. Coordination increases exploratory but not exploitative learning. The result implies that coordination offers opportunities for experimentation and innovation needed for exploration activities, which has a more significant impact on exploratory learning. The second avenue for a contribution related to the core theme of this study pertains to the relationship between the two types of learning and project performance. Scholars have emphasized exploitative and exploratory learning as imperatives for organizations to gain competitive advantages [33,3,6]. Our study advances the organizational learning literature [15,35,10] by framing exploitation and exploration as learning mechanisms for knowledge sourcing. The results provide empirical evidence of the positive link between team learning and project performance, which provides additional grounding for the value of exploitative and exploratory learning in innovation and new product development. Nonetheless, there are fundamental differences between exploitative and exploratory learning in terms of the innovation goals, knowledge search strategies, risk taking, technological trajectories, etc. This study contributes to new product development literature to further indicate that exploitative learning has a stronger impact on efficiency-oriented project performance as compared to exploratory learning. The third avenue for a contribution is in regard to the interactive effect of exploitative and exploratory learning. Scholars recognize the challenge of exploitation and exploration and the role of ambidexterity [20,13,25]. In the case of new product development, ambidexterity becomes more relevant because project teams confront the dual demands of exploiting existing knowledge and exploring new knowledge. The results show that the interaction between exploitative learning and exploratory learning has a significant impact on project performance. The results support the integral concept of ambidexterity to simultaneously pursue exploitation and exploration within an organization. Ambidexterity represents dynamic capability focusing on the adaptation and reconfiguration of resource employment to match the opportunities in the marketplace [40,16,48]. To be ambidextrous, organizations need to be able to effectively reconcile and harness internal tensions that are inherent in exploitation and exploration [20,13,27]. The interaction between exploitative and

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exploratory learning enables team members to mobilize, coordinate, and integrate the dispersed knowledge and competence required for innovation and new product development. Our results raise some practical implications. Project management requires the recognition of the importance of transactive memory systems and team learning. Managers need to cultivate transactive memory systems to promote a collective mind for new product development. Managers can develop supportive and trustful relationships between members to increase social interaction and knowledge exchange during the learning process. Shared credibility enlarges the motivation of team members to engage in exploitative and exploratory learning activities. Another strategy may be to assign team members to perform a series of specialized tasks to facilitate exploitative learning. The coordination of project tasks encourages an ongoing dialog among team members that may facilitate exploratory learning. Managers also need to actively create a stimulating atmosphere to augment exploitative and exploratory learning activities in their project teams. Exploitative learning facilitates better product development efficiency by reducing errors in problem solving and avoiding mistakes related to new product development. Exploratory learning enables team members to unlock their learning potential. Team members can generate greater experimentation and innovation to develop new products and to further achieve a high level of project performance. Furthermore, exploitative and exploratory learning can be mutually reinforcing and profitable. Managers should make a conscious choice to maintain an appropriate balance between exploitative and exploratory learning in new product development projects. An organization can engage in exploitative learning to ensure current viability and, at the same time, can devote enough energy to exploratory learning to ensure future flexibility. The findings of the empirical study have several limitations. First, the data employed in this study are from a cross-sectional research design. Although our results are consistent with theoretical reasoning, our cross-sectional design prevents us from drawing causality concerning the hypothesized relationships. Future research might address this issue by using a longitudinal design in drawing causal inferences. Second, this study treats transactive memory systems as a static construct. Future studies might gain additional insights by exploring how transactive memory systems change over time as teams develop. Third, the relationships between transactive memory systems, team learning, and project performance are not all necessarily linear. For example, the impacts of specialization on learning may attenuate beyond a certain level [31]. Future research might investigate other effects to gain additional insights. Finally, this study is done by empirically investigating Taiwanese firms. Potential cultural limitations should be noted, and future research is suggested to be conducted in different cultural contexts to generalize or modify the concepts.

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