Information & Management 50 (2013) 304–313

Exploitative and exploratory learning in transactive memory systemsand project performance

Yong-Hui Li a,1, Jing-Wen Huang b,*a National Pingtung Institute of Commerce, No. 51, Minsheng E. Rd., Pingtung City, Pingtung County 90004, Taiwan, ROCb 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

Article history:

Received 5 March 2012

Received in revised form 10 May 2013

Accepted 15 May 2013

Available online 22 May 2013

Keywords:

Transactive memory system

Exploitative learning

Exploratory learning

Project performance

New product development

A B S T R A C T

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.

� 2013 Elsevier B.V. All rights reserved.

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Practitioner points

(1) The success of new product development projects requires thecultivation of transactive memory systems to enlarge teammembers’ motivation to engage in team learning activities.

(2) Managers should stimulate the work atmosphere to augmentexploitative and exploratory learning activities in their projectteams.

(3) Managers also need to maintain a balance between exploitativeand exploratory learning for new product developmentprojects to ensure current viability and future flexibility.

1. Introduction

New product development is considered to be a criticalmechanism to enhance the ability of firms to adapt to environ-mental turbulence and to maintain innovation [33]. Owing to theincreasing importance of new product development, previousresearch has focused on knowledge learning in new productdevelopment project teams [3,6]. Organizational learning theoryand the resource-based view depict firms as repositories ofknowledge and expertise that form the basis for sustainable

* Corresponding author. Tel.: +886 8 7226141.

E-mail addresses: yhli@npic.edu.tw (Y.-H. Li), jwhuang@mail.npue.edu.tw

(J.-W. Huang).1 Tel.: +886 8 7238700.

0378-7206/$ – see front matter � 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 organizationallearning theory, firms need to actively manage knowledge andexpertise to develop innovative products through organizationallearning [15,35,46]. In the case of the resource-based view,knowledge and expertise are viewed as distinctively uniqueresources because of tacitness, stickiness, and inimitability[8,46,18]. Tacit knowledge is not easy to spread across membersand transform into organizational memory [47,49]. In the contextof new product development, projects teams can engage in teamlearning to facilitate information-processing activities and recip-rocal exchanges between team members [10]. Team learning canbroaden and improve the knowledge base of project teams. Teammembers can translate tacit knowledge into embodied products.They can increase their ability to respond to the market, solveproblems, and enhance performance outcomes [3,6,10]. Thus, teamlearning plays an important role in the contribution of new productsuccess [33,6,45].

While team learning is critical in new product development, littleresearch has explored the potential antecedent of team learning orhas integrated the concept of ambidexterity in team learning. Basedon previous research, this study frames two predominant styles ofteam learning, including both exploitative learning and exploratorylearning [33,6]. The focus of this study is to identify a potentialantecedent and to explore the interactive effect of exploitative andexploratory learning on project performance.

Learning involves reciprocal exchange and joint effort betweenindividual members [10]. The effectiveness of team learningdepends on the extent to which team members get to know one

Y.-H. Li, J.-W. Huang / Information & Management 50 (2013) 304–313 305

another and establish routines for interaction and task accom-plishment [29]. Members need shared memory systems to assistthem in learning and exchanging knowledge. Transactive memorysystems are originally conceptualized by Wegner [52] to explainthe combination of the knowledge possessed by each individualand the mutual awareness of who knows what [29,22,7].Transactive memory systems can generate the conditions thatfacilitate members to encode, store, and retrieve group knowledgefrom different domains [29,52,12]. Teams that develop transactivememory systems are more likely to utilize embedded teamknowledge and enhance team-level learning [30,32,4]. Whenperforming project tasks, team members share collective transac-tive memory to access the knowledge and expertise of others.Through transactive memory systems, team members can learnand spread their learning effectively to facilitate new productdevelopment. Based on transactive memory theory, this studyidentifies a transactive memory system as a potential antecedentof team learning and examines the relationship betweentransactive memory systems and team learning.

New product development activities not only rely on existingcapabilities but also disrupt existing capabilities or require thebuilding of new ones [53,36]. Team members can engage in bothexploitative and exploratory learning to integrate and reconfigureexisting and new knowledge at dispersed locations [33,6].Exploitative learning captures refinement, efficiency, and im-provement that reduces variance and enables incrementalinnovation, while exploratory learning entails search, discovery,and experimentation that fosters the variation and novelty neededfor more radical innovation [33,9]. Although the attributes ofexploitation and exploration create inconsistent and paradoxicalchallenges [20,40,13], the integration and interaction betweenexploitation and exploration can enhance learning and theperformance outcome [40,13,25].

The concept of ambidexterity reflects a combination ofexploitative and exploratory learning within an organization[20,13,25]. According to the dynamic capability view, ambidex-terity represents the dynamic capability of enterprises to mobilize,coordinate, and transform knowledge into complex bundles[40,16,48]. New product development teams can develop andleverage complementary knowledge and resources betweenexploitative and exploratory learning. These teams can senseand seize new opportunities and further reconfigure dynamicprocesses of innovation to enhance value and prosperity[40,25,48]. Drawing on the above perspectives, this study is basedon organizational learning theory and the dynamic capability viewto discuss the relationships between two types of team learning,including exploitative and exploratory learning, and projectperformance in new product development teams. We furtherexamine the interactive effect between exploitative and explor-atory learning on project performance.

The rest of the paper is set out as follows: Section 2 considersthe related literature and sets out the hypotheses of this research.Section 3 discusses the research design to collect data. Section 4presents the results of the empirical study in achieving the goals asset out above. Section 5 provides theoretical and practicalimplications, limitations, and directions for future research.

2. Research background and hypotheses

2.1. Transactive memory system

The concept of transactive memory describes the beliefs aboutknowledge possessed by others and the accessibility of thatknowledge [29,52]. Individuals will frequently supplement theirown memory capacities by making use of knowledge storedby their partners [29,52,38]. A transactive memory system is a

group-level phenomenon that refers to the collective memorysystem with respect to the encoding, storage, retrieval, andcommunication of information from different knowledge domains[12,30,32]. People in close relationships develop transactivememory systems to assign responsibility for information basedon the recognition of one another’s expertise [52,12]. Within workgroups, transactive memory systems facilitate members to retrieveand allocate tacit knowledge related to their teammates’ areas ofexpertise [4,17,14]. Transactive memory systems reduce thecognitive load of each member and decrease the redundancy ofeffort in teams due to collective memory [4,43]. Members canapply a greater amount of task-critical knowledge and coordinatemembers’ interactions more effectively [32,17]. Research hasindicated that transactive memory systems help organizationalteams to fully utilize member expertise and also provide benefitsto improve team performance and project outcomes [30,14]. Forexample, Lewis [30] suggested the critical role of transactivememory systems in knowledge-worker teams as it relates to teamperformance. Choi et al. [14] conducted a field study and indicatedthat transactive memory systems enhance knowledge sharing andapplications that improve team performance.

Early studies on transactive memory systems have beendemonstrated in laboratory settings [52,38,34]. However, recentresearch has extended transactive memory systems to look forgroup dynamics related to the knowledge of workers in groups ororganizations [30,14,43,2]. These dynamics include the specializa-tion of tasks, task coordination activities, and task credibilityactions. This study is based on previous research and examinesthree 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 ateam specialize in remembering different aspects of a given task.Credibility reflects members’ beliefs about the accuracy andreliability of other members’ knowledge. Coordination indicatesthe ability of team members to work effectively together whileconducting a task. New product development is a knowledge-intensive activity and requires project teams to develop transac-tive memory systems to learn and utilize multiple facets ofknowledge [4]. Transactive memory systems provide a knowledgenetwork among individuals that leads to interchange, storage, andretrieval of information and the completion of work [17,37]. Asteams work together throughout the new product developmentphase, they develop shared transactive memory systems by whichthey leverage and coordinate diverse expertise and knowledge[14].

2.2. Team learning

Organizational learning theory suggests that learning bringsbehavioral change and organizational adaptation by which firmscan respond to dynamic challenges in their environments [28].According to organizational learning theory, organizations canenhance their capability to sustain competitive advantage in waysthat are difficult to imitate and replicate by their competitors[15,35,46]. In the case of the resource-based view, tacit knowledgeis embedded in different individuals’ minds [47,18]. Knowledgetacitness reflects the growing need for organizational learning.Organizational learning can transcend knowledge beyond theindividual mind to become a collective entity. The interactive andreciprocal nature of the learning process facilitates the knowledgeexchange between explicit and tacit knowledge [10]. Thus,organizational learning has a great potential for continuousimprovement, such as new product development [3,6,45] andperformance enhancement [10,54,50].

Learning is particularly important in the new productdevelopment context because innovation spans many functional

Y.-H. Li, J.-W. Huang / Information & Management 50 (2013) 304–313306

areas and is accompanied by significant changes in organizationalroutines [3,53]. New product development teams frequently arecomposed of individuals from different backgrounds and per-spectives, and they need team learning to establish a sharedunderstanding of proposed solutions and potential improvements[39]. Team learning reflects information-processing activities forteam members to share, transfer, and combine existing and newknowledge [15,35,10]. New product development entails a seriesof exploitative and exploratory learning events related to problemsolving and task implementation. This study follows previousresearch in new product development to explore two types ofteam learning, including exploitative and exploratory learning[33,6]. Exploitative learning focuses on the refinement andextension of existing knowledge, skills, and technologies.Exploratory learning emphasizes the experimentation with newalternatives and the acquisition of new knowledge, skills, andtechnologies [33,35].

2.3. Transactive memory system and team learning

The complex nature of a new product development projectrequires effective team learning necessary for understanding thedifferentiated expertise of other members [4,2]. Transactivememory theory highlights that team members can utilize eachother as an external memory aid to increase and improve their ownmemories [29,30,21]. The transactive memory system of a team isable to lead to the development of interpersonal congruence andprovides a focal frame to recognize functional similarities andunderlying principles common to tasks [32]. With the help of atransactive memory system, team members enhance their ownmemory stores and reinforce their understanding of others’expertise [17,14]. Team members are more likely to collaborateto encode, interpret, and recall knowledge embedded in a group’sstructures and processes [32,38].

Transactive memory systems help members retrieve andexploit prior knowledge related to new tasks and develop anabstract understanding of the principles relevant to a specific taskdomain [32,4]. The development of abstract knowledge is criticalto a group’s ability to leverage and transfer what they learned onprevious tasks. Transactive memory systems also entail theexploration of knowledge to new problem-oriented situationsduring projects. Lewis et al. [32] suggested that groups withtransactive memory systems produce not only relevant knowledgefor current tasks but also create transferable knowledge for othertasks in a similar domain. Such shared transactive memorypromotes group learning and learning transfer. Similarly, Akgunet al. [2] indicated that when teams establish an effectivetransactive memory system, they develop new products withfewer technical problems and solve product problems in areasrelated to customer dissatisfaction. Transactive memory systemshave a positive association with team learning and new productsuccess [4].

Group level studies have suggested that specialization,credibility, and coordination are recognized as cognitive manifes-tations of transactive memory systems [29,17,37]. Specializationrefers to the differentiation of member knowledge [30,17]. In newproduct development processes, project members cultivatespecialized expertise from different functional areas to assembleand apply project tasks [7,4]. Transactive memory is the set ofknowledge possessed by members of a team and combined withmembers’ social perceptions about each other’s expertise [52,38].To engage in knowledge learning, team members need to be awareof where the required knowledge is located and must be able toacquire it in a timely manner [29,30]. Individuals rely on otherteam members to serve as human repositories for informationoutside of their own domains [22,30]. Hollingshead’s (2000)

research on retrieval processes in transactive memory systems hasshown that specialization can reduce repetition of effort andenable better access to a wide range of expertise. Team memberstend to retrieve and exploit task-specific knowledge moreefficiently for exploitative learning. Furthermore, team membersupdate their directories of diverse knowledge to facilitateexploratory learning among members [32,4,14]. Accordingly, thefollowing hypotheses (hereafter H) are proposed:

H1a: In NPD teams, specialization is positively related to exploit-ative learning.

H1b: In NPD teams, specialization is positively related to explor-atory learning.

Credibility reflects the degree of trust and reliability of othermembers’ knowledge [30,17]. In team and group work contexts,different team members have different professions and back-grounds, and they tend to seek relevant knowledge from trustedand capable colleagues [43,41]. Group members with high-trustrelationships are more likely to perceive each other’s behaviorsand actions positively [43]. Research has revealed that trustencourages interdependency and interaction among organization-al members [49,11]. Trust can help in developing a learningenvironment through social exchange and knowledge disclosure[49,43,11]. Accordingly, perceptions of credibility enhance thewillingness of members to exchange and absorb each other’sknowledge [17,43,41], thereby leading to greater team learning.

H2a: In NPD teams, credibility is positively related to exploitativelearning.

H2b: In NPD teams, credibility is positively related to exploratorylearning.

Coordination indicates the degree of effective and orchestratedknowledge processing occurring in a specific environment[30,17]. Innovative activities are increasingly interactive, andproject teams need coordinative effort to take advantage ofmultiple viewpoints [49]. Smooth and efficient coordinationconstitutes information channels that reduce the time andinvestment required to seek necessary information from team-mates [30]. Through coordinated assignments of expertise, teammembers have a better understanding of who knows what andfrom whom to retrieve knowledge [52,12,30,17]. Team memberscan engage in exploitative learning to identify and exploitdistributed knowledge. Members also develop exploratorylearning to find gaps in expertise that can then be filled by eachteam member [30,37]. Accordingly, the coordination componentof transactive memory systems helps to stimulate the formationof common interests that support the team learning needed in theproject team [4,2].

H3a: In NPD teams, coordination is positively related to exploit-ative learning.

H3b: In NPD teams, coordination is positively related to explor-atory learning.

2.4. Team learning and project performance

Team learning reflects information-processing activities andreciprocal exchanges between individual members in a team or agroup [10]. The process of team learning involving differentmembers is complex and dynamic. Furthermore, knowledgetacitness inhibits knowledge flow and exchange between individ-ual actors [47,18]. In the context of new product development,team members need to learn collectively to establish sharedmental 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 providesopportunities for an organization to translate tacit knowledgeinto embodied products [45]. Organizational learning theorysuggests that learning can facilitate individual knowledge totransform into organizational capacity for innovation and growth[15,10]. The resource-based view indicates that valuable knowl-edge is tacit, unique, and inimitable [8,18]. Based on theseviewpoints, team learning can be accumulated as organizationalintelligence leading to competitive advantage because of thecharacteristics of flexibility, uniqueness, and imperfect imitationor substitution on the part of competitors [8,46,18].

Team learning enables a firm to gain favorable performanceoutcomes and synergistic benefits from the process of learning andexchanging knowledge and resources available among teammembers [3,10,54,50]. For example, Blazevic and Lievens [10]indicated that project learning increases the effectiveness ofknowledge usage throughout organizations and suggested thatproject learning has a leveraging effect on project performance.Akgun et al. [3] used the socio-cognitive theory of learning ingroups and organizations and found that team learning in newproduct development teams has a positive effect on new productproject success. Zellmer-Bruhn and Gibson [54] integratedliterature on international management and team effectivenessand indicated that team learning has a positive link with teamperformance. Likewise, Tucker et al. [50] examined learningactivities in project teams and suggested that learn-how activitiesare positively associated with the implementation success of newpractices in hospital intensive care units.

New product development and innovation require the applica-tion of existing knowledge combined with new knowledge.Projects teams can engage in exploitative and exploratory learningto share, combine, and utilize knowledge [6]. The form of learningdetermines the pattern by which a firm devotes effort andattention to new product development activities. Exploitativelearning involves information searches within a well-defined andlimited product/market solution context related to a firm’sprevious experience [6,35,28]. Exploitative learning allows teammembers to combine existing knowledge and apply lessonsderived from past experiences. Team members can reduce errorsin problem solving and avoid mistakes related to new productdevelopment [6,45]. Based on exploitation, project teams canbetter recognize customer needs and diminish repetitive dis-turbances in particular technologies and product-market areas.Moreover, increased familiarity with an existing knowledgedomain can strengthen the ability of project members to speedup new product introduction [33]. In this respect, exploitativelearning generates better project efficiency of new productdevelopment.

Exploratory learning involves experimentation with newalternatives and a search for technology and market informationthat is new to organizations [6,35,28]. New product developmentrelies on exploratory learning to increase creative thinking andidea sharing among team members. Exploratory learningenhances the breadth and depth of knowledge available toproject teams [10,39]. In addition, exploratory learning adds newelements to a project’s repertoire and provides new insights intoproduct design [33,6]. Project teams are able to create moreinnovative products and explore emerging customer needs byreconfiguring resources to capitalize on market opportunities[45,53]. Thus, exploratory learning allows for greater experimen-tation and flexibility as they apply to new product development[33,3,6].

The literature on exploitative and exploratory learning indi-cates that the outcomes of these two types of learning are quitedifferent, not only in terms of incremental and radical innovationsbut also in terms of the risk variance and concomitant benefits/

costs [24]. Exploitative learning does not capture some types ofnew product development outcomes such as radical new products,new technological trajectories, etc. This study expects thatexploitative learning has a stronger impact on project efficiencyas compared to exploratory learning. Thus, the followinghypotheses are proposed.

H4a: Exploitative learning is more positively related to projectefficiency than project effectiveness.

H4b: Exploratory learning is more positively related to projecteffectiveness than project efficiency.

2.5. Interactive effect

Facing fierce competition and challenge, firms not only have toexploit their existing capabilities but also have to explore businessopportunities and capabilities that they will need in the future[9,20]. Some research has indicated the concept of ambidexterityto reflect the integration and interaction between exploitation andexploration [20,13,25]. Ambidexterity can be seen as the dynamiccapability for adaptation and reconfiguration of resource employ-ment to deal with challenging environments [16,48]. An ambidex-trous organization is capable of exploitive and exploratoryactivities [20,40,13]. Exploitation involves refinement, efficiency,and improvement, while exploration involves experimentation,discovery, and flexibility [35,9,20]. The combination of exploita-tion and exploration helps organizations to overcome structuralinertia that results from focusing on exploitation but also preventsthem from accelerating exploration without gaining benefits[36,13,25]. Drawing on the capability perspective, Menguc andAuh [36] indicated that ambidexterity captures the tendency ofdeftness, agileness, and flexibility, and these authors alsosuggested that ambidextrous firms can balance both their short-and long-term gains.

As Katila and Ahuja [27] noted, exploitation of existingcapabilities is often needed to explore new capabilities, andexploration of new capabilities also enhances a firm’s existingknowledge base. According to the dynamic capability view, theinteraction between exploitation and exploration enables firms tomobilize, coordinate, and transform existing knowledge intocomplex bundles [40,16,48]. Furthermore, firms can sense andseize new opportunities and reconfigure resources to generate newapplications for the purpose of innovation [40,25,48]. In thecontext of new product development, an appropriate balancebetween exploitative and exploratory learning provides a basis forproject teams to reconfigure the dynamic processes inherent ininnovation to enhance value and prosperity. New product teamshave greater potential to develop and leverage complementaryknowledge and resources between exploitative and exploratoryefforts [3,6,13]. Team members can better recognize existingcustomer needs and reduce repetitive disturbances in productsand technologies [53]. They also can expand into new markets andenhance their capability for new product development [6,53].Conversely, the failure to achieve a balance between these twotypes of learning can leave a firm susceptible to the risk ofobsolescence or overinvestment in experimental activities [20,13].

As stated above, the interaction between exploitative andexploratory learning is expected to be favorable to new productsuccess and project performance. Thus, the following hypothesis isformulated:

H5a: The interaction between exploitative and exploratory learn-ing is positively related to project efficiency.

H5b: The interaction between exploitative and exploratory learn-ing is positively related to project effectiveness.

Y.-H. Li, J.-W. Huang / Information & Management 50 (2013) 304–313308

3. Research methodology

3.1. Study context and sample

The empirical study employed a questionnaire approachdesigned to collect data for testing the research hypotheses. Thesurvey instrument contained instructions for completion, theresearch variables, and demographic questions for the firm.Respondents rated each item on seven-point Likert-type scalesin which higher values were associated with higher levels of theconstruct. The population for the study was the top 5000Taiwanese firms listed in the yearbook published by the ChinaCredit Information Service, Ltd. This study divided the 5000 firmslisted in the yearbook into five levels with 1000 rankings each.Then, we used a stratified random sampling method to select 120firms in each level. The total sample included 600 companies. Atotal of 600 questionnaires were distributed, along with a coverpage that explained the nature of the study. The primary recipientswere R&D managers or product development managers who areknowledgeable about their firm’s innovation and new productdevelopment processes. Each recipient was informed by e-mailsand phone calls to assure anonymity and participation. Two weeksafter the first mailing, we send follow-up e-mails and made phonecalls to non-respondents to appeal for cooperation. A total of 233surveys were returned; of the returned surveys, 218 werecomplete in all independent and dependent variables, giving usa usable response rate of 36.33%. The possibility of nonresponsebias was examined by using a two-tailed t-test to compare thecharacteristics of respondent firms with nonrespondents. Respon-dent firms did not significantly differ from nonrespondents interms of firm size, firm age, and team size (p > 0.10). The resultsindicated that nonresponse bias was not a significant problem inthe current data.

The Harman one-factor test was conducted to examinecommon method bias. A principal factor analysis on themeasurement items yielded six factors that accounted for 76.5%of the total variance, and the first factor accounted for 15.2% of thevariance. Because no single factor emerged and one general factordid not account for most of the variance, common method bias wasdetermined to not be serious in the data [42].

3.2. Measures, validity, and reliability

A transactive memory system refers to a collective memorysystem with respect to the encoding, storage, retrieval, andcommunication of information from different knowledge domains[29,12]. The construction of the measures of transactive memorysystems is primarily based on the work of Lewis [29] and Lewis[30]. A fifteen-item scale is developed to assess three dimensions ofa transactive memory system including specialization, credibility,and coordination. Specialization refers to the differentiation ofmember knowledge. Credibility reflects the degree of trust andreliability of other members’ knowledge. Coordination indicatesthe degree of effective and orchestrated knowledge processing[30,17].

Exploitative learning in the project team consists of five itemsregarding the refinement of common methods and ideas, thesearch for generally proven methods and solutions, the acquisitionof information to ensure productivity and update the firm’s currentproject and market experiences, and the emphasis on the use ofknowledge related to existing project experience. Exploratorylearning includes five items focusing on learning activities thatinvolve experimentation and high market risks, the search forknowledge that leads the firm enter into new markets andtechnological areas, and the acquisition of novel information thatwent beyond current market and technological experiences [6].

Project performance concerns the outcome or perceivedsuccess of the project team in meeting project goals, budget,schedule, and operational efficiency considerations [26]. As Wanget al. [51] note, project performance is a combination of projectefficiency and effectiveness as perceived by the respondents. Wemeasure project efficiency with three items including theexpected amount of work completed, the quality of workcompleted, and the facility of task operations. Project effective-ness consists of three items that include meeting project goals,meeting schedule, meeting budget.

Four control variables are entered in the analysis, including firmsize, firm age, team size, and industry type. The number ofemployees is used to control for the possible firm-size effect, and itis calculated by taking the logarithm of each firm’s total number ofemployees. Firm age is measured as the number of years from thefounding date. Team size is measured by the logarithm of thenumber of members in the new product development team fromthe information that the respondents to our questionnaire offered.To assess the industry type, one dummy variable is included toindicate whether a firm belongs to a manufacturing industry or ahigh-tech industry (0 = manufacturing industry, 1 = high-techindustry).

Because the measurement scales are adapted, we estimateconvergent validity and discriminant validity using confirmatoryfactor analysis (CFA) in a structural equation model [5]. The CFAfit indexes for the proposed model range from adequate toexcellent (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 CFAresults suggest that the model of a transactive memory system,team learning, and project performance provides a reasonablygood fit for the data [19]. Moreover, all the measurement itemsload on their underlying construct, and none of the confidenceintervals for each pairwise correlation estimate contain a value ofone [5]. The average variance extracted estimates range from0.63 to 0.85. In addition, we constrain the correlation betweeneach pair of constructs, one at a time, to be equal to 1 [5,23]. Thechi-square test comparing this model to the model freeing thatcorrelation is significant (p < 0.001). These results indicate thatthe constructs demonstrate both convergent and discriminantvalidity [5,23].

The reliability of the multi-item scale for each dimension isassessed by calculating composite reliability coefficients for all ofthe scales. Table 1 summarizes all measurement items, factorloadings, composite reliabilities, average variance extractedestimates, and their scales for all of the items. Compositereliabilities of each scale range from 0.84 to 0.97, which are abovethe recommended minimum standard of 0.70 [19]. Thus, weconclude that the measures utilized in the study demonstrateinternal consistency.

4. Analysis and results

The hypotheses are tested with multiple regression analyses fortransactive memory system, team learning, and project perfor-mance. Fig. 1 depicts the proposed theoretical model. Table 2displays descriptive statistics and correlations of all variables. Wemean-center the relevant variables before creating the interactionterm [1]. None of the variance inflation factors in the regressionmodels 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 ofunequal variances, suggesting the presence of homoskedasticity inthe regression tests.

Models 1 and 3 in Table 3 test the effects of the control variableson exploitative and exploratory learning, respectively. Forexploitative learning, Model 2 in Table 3 adds the main effects

Table 1Measurement 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. 0.77 0.92 0.70

I have knowledge about an aspect of the project that no other team member has. 0.92

Different team members are responsible for expertise in different areas. 0.84

The specialized knowledge of several different team members is needed to complete the

project deliverables.

0.88

I know which team members have expertise in specific areas. 0.76

Credibility I am comfortable accepting procedural suggestions from other team members. 0.79 0.90 0.64

I trust that other members’ knowledge about the project is credible. 0.92

I am confident relying on the information that other team members bring to the

discussion.

0.91

When other members give information, I don’t need to double-check it for myself. 0.57

I have much faith in other members’ ‘‘expertise.’’ 0.76

Coordination Our team works together in a well-coordinated fashion. 0.86 0.93 0.73

Our team has very few misunderstandings about what to do. 0.81

Our team needs not to backtrack and start over a lot. 0.87

We accomplish the task smoothly and efficiently. 0.91

There is not confusion about how we would accomplish the task. 0.83

Exploitative learning Our aim is to search for information to refine common methods and ideas in solving

problems in the project.

0.71 0.90 0.63

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.

0.79

We search for the usual and generally proven methods and solutions to product

development problems.

0.86

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.

0.83

We emphasize the use of knowledge related to our existing project experience. 0.78

Exploratory learning In information search, we focus on acquiring knowledge of project strategies that involve

experimentation and high market risks.

0.90 0.97 0.85

We prefer to collect information with no identifiable strategic market needs to ensure

experimentation in the project.

0.93

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.

0.92

We collect novel information and ideas that go beyond our current market and

technological experiences.

0.92

Our aim is to collect new information that forces us to learn new things in the product

development project.

0.94

Project efficiency expected amount of work completed 0.73 0.86 0.67

quality of work completed 0.82

efficiency of task operations 0.89

Project effectiveness ability to meet project goals 0.91 0.84 0.65

adherence to schedule 0.87

adherence to budget 0.60

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

Y.-H. Li, J.-W. Huang / Information & Management 50 (2013) 304–313 309

of the transactive memory system, which contribute 29%(DF = 32.51, p < 0.001) above the variance explained by thecontrol variables in Model 1. For exploratory learning, Model 4adds the main effects of the transactive memory system, whichcontribute 39% (DF = 50.19, p < 0.001) above the varianceexplained by the control variables in Model 3. H1a and H1bpredict that there is a positive relationship between specializationand both exploitative and exploratory learning. H1a is supported(b = 0.19, p < 0.05); however, H1b is not supported. Both H2a and

Spec ializationExploitativ

Credibility

CoordinationExplorato r

Transac tive Memory System Tea m Le

Fig. 1. Research mod

H2b are supported; credibility has a positive relationship toexploitative learning (b = 0.37, p < 0.001) and exploratory learning(b = 0.45, p < 0.001). H3a and H3b predict that coordination ispositively related to exploitative and exploratory learning. H3a isnot supported, and H3b is supported (b = 0.25, p < 0.001).

Models 5 and 8 in Table 4 report the main effects of the controlvariables on project performance. Models 6 and 9 add the maineffects of exploitative and exploratory learning, which contribute26% (DF = 40.16, p < 0.001) and 23% (DF = 33.25, p < 0.001) over

e lea rning

Project Performa nce

y le arning

arning

Project efficiency

Project effec tiveness

el of this study.

Table 2Descriptive statistics and correlationsa

Variables Mean s.d. 1 2 3 4 5 6 7 8 9 10

1. Firm size 2.80 0.55

2. Firm age 28.33 12.08 �0.00

3. Team size 1.03 0.04 0.10 �0.34

4. Industry type 0.54 0.50 0.05 �0.61 0.43

5. Specialization 4.99 0.89 �0.10 �0.04 �0.06 0.11

6. Credibility 5.03 0.89 0.08 �0.08 0.03 0.11 0.64

7. Coordination 5.32 0.81 0.09 �0.12 �0.01 0.14 0.55 0.67

8. Exploitative learning 5.10 0.85 0.25 �0.12 �0.02 0.10 0.43 0.54 0.43

9. Exploratory learning 4.76 1.08 0.08 �0.06 0.00 0.17 0.43 0.62 0.56 0.33

10. Project efficiency 5.19 0.98 0.15 �0.13 �0.01 0.14 0.22 0.37 0.20 0.52 0.36

11. Project effectiveness 5.21 0.92 0.19 �0.06 �0.02 0.10 0.19 0.31 0.16 0.49 0.33 0.62

a 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 3Results of the regression analysis for team learninga

Variable Exploitative learning Exploratory learning

Model 1 Model 2 Model 3 Model 4

Firm size 0.26*** 0.25*** 0.08 0.02

Firm age �0.13 �0.12 0.06 0.09

Team size �0.12 �0.08 �0.09 �0.05

Industry typeb 0.07 �0.01 0.25** 0.17**

Specialization (H1) 0.19* �0.01

Credibility (H2) 0.37*** 0.45***

Coordination (H3) 0.05 0.25***

R2 0.09 0.38 0.05 0.44

DR2 0.09 0.29 0.05 0.39

F 5.40*** 18.39*** 2.57* 23.99***

DF 5.40*** 32.51*** 2.57* 50.19***

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

Y.-H. Li, J.-W. Huang / Information & Management 50 (2013) 304–313310

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

Table 4Results of the regression analysis for project performancea

Variables Project efficiency

Model 5 Model 6

Firm size 0.15* 0.02

Firm age �0.09 �0.05

Team size �0.10 �0.03

Industry typeb 0.12 0.04

Exploitative learning (H4a) 0.43***

Exploratory learning (H4b) 0.21***

Exploitative � exploratory learning (H5)

R2 0.05 0.31

DR2 0.05 0.26

F 2.85* 15.98***

DF 2.85* 40.16***

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

efficiency. The results show that exploitative learning has apositive impact on efficiency (b = 0.43, p < 0.001) and effective-ness (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 exploitativelearning focuses more on project efficiency for refinement andimplementation as compared to exploratory learning.

Models 7 and 10 in Table 4 add the interaction term forexploitative and exploratory learning. These variables increase theexplained variance by 5% (DF = 14.38, p < 0.001) and 7%(DF = 24.63, p < 0.001) over the explained variance obtained inModels 6 and 9. H5a and H5b predict that the interaction ofexploitative and exploratory learning is positively related toproject efficiency and effectiveness. The result shows that thecoefficients for the interaction term are positive and significant(b = 0.23, p < 0.001 and b = 0.30, p < 0.001, respectively). Fig. 2presents the interactive effect of exploitative learning andexploratory learning on project efficiency. Fig. 3 reveals theinteractive effect of exploitative learning and exploratory learningon project effectiveness. These findings are consistent with thetheoretical prediction. Thus, H5a and H5b are supported.

In addition to the above analysis, this study considers othertests to explain the possibility of the statistically insignificantresults. As Lewis and Herndon [31] indicated, too muchdifferentiated knowledge within the team may reduce the amountof shared knowledge within the team thereby creating difficulties inknowledge integration. We investigate whether the relationships

Project effectiveness

Model 7 Model 8 Model 9 Model 10

�0.01 0.19* 0.07 0.03

�0.06 �0.03 0.01 �0.01

�0.03 �0.10 �0.04 �0.04

0.05 0.12 0.04 0.05

0.38*** 0.41*** 0.35***

0.16** 0.18** 0.13**

0.23*** 0.30***

0.36 0.05 0.28 0.35

0.05 0.05 0.23 0.07

16.62*** 2.85* 13.56*** 16.44***

14.38*** 2.85* 33.25*** 24.63***

Exploitative Learning

Pro

ject E

ffic

iency

Low exploratory

learning

High exploratory

learning

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

efficiency.

Exploitative Learning

Pro

ject E

ffectiveness

Low exploratory

learning

High exploratory

learning

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

effectiveness.

Y.-H. Li, J.-W. Huang / Information & Management 50 (2013) 304–313 311

between the three dimensions of transactive memory systems andteam learning may be inverted U-shaped. The curvilinear test showsinsignificant results. We further investigate the presence ofcurvilinear effects related to team learning and NPD projectperformance. The results of the curvilinear test are insignificant.In contrast, it is possible that the impacts of transactive memorysystems on team learning and the impacts of team learning onproject performance are moderated by other salient variables, suchas team size [44]. We mean-center team size as an interactivevariable and examine the moderating effect. The regression resultsindicate an insignificant moderating effect of team size.

5. Discussion and conclusions

Previous research shows that team learning plays an importantrole in new product development; however, our understanding ofthe antecedents that lead to effective team learning is stillemerging. In addition, previous research does not fully consider theconcept of ambidexterity in team learning. Akgun et al. [4] haveexplored transactive memory systems in new product develop-ment teams and examined the mediating role of a collective mindand the moderating role of environmental turbulence in therelationship. This study applies transactive memory theory onproject teams and identifies a transactive memory system as anantecedent of team learning echoing the notion of Akgun et al. [4].The contribution to the gap in the scholarship is that this studyintegrates the concept of ambidexterity in team learning andexplores the interactive effect of exploitative and exploratorylearning on project performance. This study builds a shared view(or collective memory) among new product development teams.We integrate the organizational learning literature and transactivememory literature to examine how new product development

teams manage transactive memory systems to develop teamlearning. Organizational learning theory suggests that learning canenhance organizational capability and sustain competitive advan-tage [15,35,10]. Transactive memory theory suggests that trans-active memory systems help organizational teams to utilizemember expertise and provide benefits that improve teamperformance and project outcomes [30,17,14]. The empiricalresults provide considerable support for the proposed modelusing data from Taiwanese firms.

The first major avenue for a novel contribution would be toprovide more insight into the nature of the relationships betweenthe three dimensions of transactive memory systems and the twotypes of learning. The findings show that three dimensions oftransactive memory systems have differential effects on the levelof exploitative and exploratory learning. Particularly, specializa-tion enhances exploitative learning by promoting knowledgeexchange and recombination; however, specialization does nothave a strong enough effect on exploratory learning. A plausibleexplanation for this result is that specialization may decrease thewillingness of team members to gain diverse knowledge andperspectives from outside their professional domain, thus hinder-ing exploratory learning. If teams have too much differentiatedknowledge, they may reduce the amount of shared knowledge andinhibit knowledge integration [31]. Credibility is positively relatedto both exploitative and exploratory learning, indicating that moretrust among team members tends to steer the team to both exploitand explore in the area of new product development. Coordinationincreases exploratory but not exploitative learning. The resultimplies that coordination offers opportunities for experimentationand innovation needed for exploration activities, which has a moresignificant impact on exploratory learning.

The second avenue for a contribution related to the core themeof this study pertains to the relationship between the two types oflearning and project performance. Scholars have emphasizedexploitative and exploratory learning as imperatives for organiza-tions to gain competitive advantages [33,3,6]. Our study advancesthe organizational learning literature [15,35,10] by framingexploitation and exploration as learning mechanisms for knowl-edge sourcing. The results provide empirical evidence of thepositive link between team learning and project performance,which provides additional grounding for the value of exploitativeand exploratory learning in innovation and new product develop-ment. Nonetheless, there are fundamental differences betweenexploitative and exploratory learning in terms of the innovationgoals, knowledge search strategies, risk taking, technologicaltrajectories, etc. This study contributes to new product develop-ment literature to further indicate that exploitative learning has astronger impact on efficiency-oriented project performance ascompared to exploratory learning.

The third avenue for a contribution is in regard to theinteractive effect of exploitative and exploratory learning. Scholarsrecognize the challenge of exploitation and exploration and therole of ambidexterity [20,13,25]. In the case of new productdevelopment, ambidexterity becomes more relevant becauseproject teams confront the dual demands of exploiting existingknowledge and exploring new knowledge. The results show thatthe interaction between exploitative learning and exploratorylearning has a significant impact on project performance. Theresults support the integral concept of ambidexterity to simulta-neously pursue exploitation and exploration within an organiza-tion. Ambidexterity represents dynamic capability focusing on theadaptation and reconfiguration of resource employment to matchthe opportunities in the marketplace [40,16,48]. To be ambidex-trous, organizations need to be able to effectively reconcile andharness internal tensions that are inherent in exploitation andexploration [20,13,27]. The interaction between exploitative and

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exploratory learning enables team members to mobilize, coordi-nate, and integrate the dispersed knowledge and competencerequired for innovation and new product development.

Our results raise some practical implications. Project manage-ment requires the recognition of the importance of transactivememory systems and team learning. Managers need to cultivatetransactive memory systems to promote a collective mind for newproduct development. Managers can develop supportive andtrustful relationships between members to increase socialinteraction and knowledge exchange during the learning process.Shared credibility enlarges the motivation of team members toengage in exploitative and exploratory learning activities. Anotherstrategy may be to assign team members to perform a series ofspecialized tasks to facilitate exploitative learning. The coordina-tion of project tasks encourages an ongoing dialog among teammembers that may facilitate exploratory learning. Managers alsoneed to actively create a stimulating atmosphere to augmentexploitative and exploratory learning activities in their projectteams. Exploitative learning facilitates better product develop-ment efficiency by reducing errors in problem solving and avoidingmistakes related to new product development. Exploratorylearning enables team members to unlock their learning potential.Team members can generate greater experimentation andinnovation to develop new products and to further achieve a highlevel of project performance. Furthermore, exploitative andexploratory learning can be mutually reinforcing and profitable.Managers should make a conscious choice to maintain anappropriate balance between exploitative and exploratory learn-ing in new product development projects. An organization canengage in exploitative learning to ensure current viability and, atthe same time, can devote enough energy to exploratory learningto ensure future flexibility.

The findings of the empirical study have several limitations.First, the data employed in this study are from a cross-sectionalresearch design. Although our results are consistent withtheoretical reasoning, our cross-sectional design prevents us fromdrawing causality concerning the hypothesized relationships.Future research might address this issue by using a longitudinaldesign in drawing causal inferences. Second, this study treatstransactive memory systems as a static construct. Future studiesmight gain additional insights by exploring how transactivememory systems change over time as teams develop. Third, therelationships between transactive memory systems, team learn-ing, and project performance are not all necessarily linear. Forexample, the impacts of specialization on learning may attenuatebeyond a certain level [31]. Future research might investigate othereffects to gain additional insights. Finally, this study is done byempirically investigating Taiwanese firms. Potential culturallimitations should be noted, and future research is suggested tobe conducted in different cultural contexts to generalize or modifythe concepts.

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