Transactive Memory System in New Product Development Teams

IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. 53, NO. 1, FEBRUARY 2006

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Transactive Memory System in New Product Development Teams Ali E. Akgün, John C. Byrne, Halit Keskin, and Gary S. Lynn

Abstract—With the increasing popularity of collective memory in the group behavior literature, the transactive memory system (TMS) attracts many researchers and practitioners from different fields, in particular, small group research. Nevertheless, the application of the theory of TMS on new product development teams is surprisingly scant. We argue that the TMS leverages the notion of project-team memory, which is mostly equated with mechanistic memory or electronic documents and databases, by facilitating an interpersonal awareness of who knows what and who has appropriate and adequate skills and expertise, and then receiving information from that person. We then empirically test the effects of TMS on new product development outcomes including mediating and moderating factors, i.e., the collective mind and environmental turbulence, respectively. By investigating 79 Turkish new product development project teams, we found that: 1) the TMS has a positive impact on team learning and speed-to-market; 2) the collective mind (i.e., team members’ attention to interrelating actions) mediates relations between the TMS, team learning, and speed-to-market; and 3) team learning and speed-to-market mediates relations between the TMS and new product success. Further, the moderating effect of environmental turbulence is investigated between the TMS, and team learning and speed-to-market. We found that the impact of the TMS on: 1) speed-to-market is negative when market and technology turbulence associated with the environment is high and 2) team learning changes quadratically with respect to the market and technology turbulence. Theoretical and managerial implications of the study findings are discussed. Index Terms—Collective memory, collective mind, new product development, team learning, transactive memory system (TMS).

I. INTRODUCTION

A

S A FASCINATING concept and intriguing research area, “memory” finds strong appeal in many disciplines outside of individual and cognitive psychology [102], [110]. One of the disciplines that provoked increased interest in the importance of memory is the group behavior literature [101], [103], [105]. The recent theoretical and empirical studies of group or collective memory (terms that are used interchangeably in this paper) on the transactive memory system (TMS), especially contributed to the group behavior and management and social cognition literature [53], [105]. A TMS indicates who will learn what and Manuscript received November 1, 2004; revised March 1, 2005 and May 1, 2005. Review of this manuscript was arranged by Department Editor R. T. Keller. A. E. Akgün and H. Keskin are with the School of Business Administration, Department of Science and Technology Studies, Gebze Institute of Technology, Gebze-Kocaeli 41400, Turkey (e-mail: [email protected]). J. C. Byrne is with the Lubin School of Business, Pace University, New York, NY 10038 USA. G. S. Lynn is with the Wesley J. Howe School of Management, Stevens Institute of Technology, Hoboken, NJ 07030 USA. Digital Object Identifier 10.1109/TEM.2005.857570

from whom. Specifically, a TMS depicts the “awareness of who knows what” in a group [11]. Here, the notion is that knowledge is distributed among people in the group, and to make effective use of it, individuals need to know who knows what. Nevertheless, most TMS researchers used lab experiments [53], [69], [83], relationships among intimate couples [54], [104], small social groups [93], and organizational work groups [13], [37], [68], while omitting multifunctional innovation teams. Specifically, the research on collective team memory, in general, and the TMS, in particular, is limited in the new product development (NPD) team context [4]. New product development (NPD) teams are considered an important functioning unit for organizational learning, change, and innovation processes, as well as for organizational knowledge depository and memory [25]. Since NPD teams often involve people with different views, perspectives, functional backgrounds and knowledge, and concurrently, team members’ reciprocal social interactions with other units in the organization, NPD projects require an effective collective memory. However, most of the NPD studies focus on the hard-data memory (e.g., “mechanistic memory” Lynn and Reilly [73]), which constitutes the records, databases, and files in projects [73], [107], as the NPD team’s collective memory. Although mechanistic memory, which just conveys past to present, is important in order for team members to perform the NPD activities effectively, it is inadequate, because it does not: 1) cover everything that is going on at a particular moment during the project; 2) record and retrieve the tacit information and knowledge, which is imperative for nonroutine and complex projects; and 3) facilitate the informal human connections due to its formal-structure nature. In this respect, a more socially constructed collective team memory (e.g., a TMS) is also needed to assist team members in performing the project’s activities effectively and thereby improve the team’s performance. For instance, Cross et al. [27, p. 100] state that: Usually when we think of where people turn for information or knowledge we think of databases, the Web, intranets and portals or other, more traditional, repositories such as file cabinets or policy and procedure manuals. However, a significant component of a person’s information environment consists of the relationships he or she can tap for various informational needs. Further, Cross et al. [27] give an example of how engineers and scientists are generally five times more likely to turn to a person for information than to an impersonal source such as a database or a file cabinet. In this vein, the TMS theory broadens the notion of collective memory in the context of the NPD team, and helps project managers and team members envisage the im-

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portance of social relations in gathering information and knowledge for a successful NPD project. This study, which is descriptive in nature, contributes to the NPD literature by investigating the social aspect of the collective team memory in projects, which has been long ignored. In particular, to our knowledge, we are among the first to empirically examine the relationship between the TMS and process and project outcomes in NPD project teams. This paper also examines some mediating and moderating processes through which TMS affects process and project performance, helping researchers to better understand how TMS works in project teams. Further, this paper adds to the previous work on the theory of TMS in group behavior literature by providing a field test using cross-functional project teams, because most of the TMS researches have been conducted in controlled settings, where tasks are well-understood and do not differ across comparison groups. However, NPD activities are complex and each project team has its own merits. The aim of this paper is, therefore, to investigate the TMS theory to enhance the literature on NPD team memory and cognition. Specifically, this paper investigates: 1) the impact of the TMS on the effectiveness of the NPD process, i.e., team learning, speed-to-market, and new product success; 2) the mediator role of a collective mind—team members’ attention to interrelating their actions between the TMS and the effectiveness of the NPD process; and 3) the moderator role of environmental turbulence between the TMS and the effectiveness of the NPD process. II. COLLECTIVE MEMORY IN NPD TEAMS Collective memory is an example of social memory as articulated by Durkheim [33] and Halbwachs [50] in the school of sociology [88]. Zelizer [110], for example, noted that, as a sociological rubric, collective memory refers to recollections that are generated beyond the individual by and for the collective or group at-large. Zelizer [110] further mentioned that the collective memory comprises recollections that are determined and shaped by the group, and thereby presume activities of sharing, discussion, negotiation, and contestation. Baker et al. [17] indicated that collective memory is a theoretical framework that explains how members of particular social groups retain, alter or expropriate the knowledge of history. Accordingly, as pointed out by scholars (e.g., [88], [90], and [108]), collective memory refers to a social process of articulating, exchanging, communicating and understanding information stored in the group’s procedures, norms, and physical artifacts. This view of collective memory emphasizes the importance of stored history in the records, files and documents. However, other scholars suggest that collective memory, which occurs at the inter-individual and intergroup levels, also integrates the individuals’ memories, their social interactions and the results of the social and nonsocial knowledge of those interactions [8], [24], [50]. For instance, Ackerman and McDonald [1, p. 334] wrote that: Typically, collective memories include information repositories (e.g., information databases, filing cabinets and documents). They can also include people (e.g., other organizational personnel). The collective memory to which a user has access includes at least the documentation, the

system programmers and his colleagues. However, the user may have great trouble finding the right piece of the collective memory that has the answer he needs. In other words, his access to the collective memory should be augmented. Some of that access will be formalized information (such as to documentation or other knowledge source); however, some of that access must also be to informal information (such as appropriate expertise or organizational work-arounds). In this respect, collective memory not only involves: 1) the mechanistic memory, a vessel for carrying the past into the present via records and files, but also involves 2) TMS, putting forward the organic experiential relations among the people [1], [21], [24], [110]. For instance, Wegner [103] noted that TMS is an example of collective memory. Nevo and Wand [85] also mentioned that TMS theory is in the theory of collective memory of small groups has been developed to explain how individuals in small workgroup form a collective memory. In a similar view, and as a special type of group, collective memory in NPD teams also offers a basis for how teams collect, store, retrieve, coordinate, and interrelate their knowledge. Collective memory in a NPD team is vital to coordinate the team members’ efforts throughout the project [63]. Specifically, since: 1) project team members require significant amounts of information inputs as well as should provide information outputs in a timely manner to fulfill the project task responsibilities [63] and 2) project team members’ limited cognitive capacities for memory, attention and perception inhibit them from perceiving most of the information that is available to them [51], NPD team members need to share knowledge and respond quickly to each other in order to achieve project related task works. Additionally, a NPD team is required to access a larger pool of information across domains to optimize the value of task-specific expertise and knowledge. Consequently, collective memory provides a broad range of knowledge and information storage that transcends each individual and the surrounding environment [68]. However, collective memory constructs have been investigated separately in an isolated manner in the NPD literature. Specifically, mechanistic memory, including formal documentations, project reports, meeting minutes, drawings, specifications, test results, correspondence and databases, has long been perceived as the project teams’ collective memories in the literature [35], [70]. Satzinger et al. [97, p. 148], for instance, state that, Group memory is the electronic capture of the group’s work, which is available for review by the group. Group memory records all remarks typed into the computer so that members can refer to them later in the discussion. It may be kept for future reference after the meeting(s) is over. All members of a group, thus, have a common, shared memory that can be used during or after the meeting. Additionally, the NPD literature indicates the possible benefits of mechanistic memory to NPD teams in the following ways (see [63] and [90]): 1) to access the common organizational and team information; 2) to capture, store, and integrate information generated during the team meetings; 3) to provide a means to educate the new members; 4) to supply multilocal information,

AKGÜN et al.: TRANSACTIVE MEMORY SYSTEM IN NEW PRODUCT DEVELOPMENT TEAMS

especially when team members are dispersed and need instantaneous access to shared information and explicit knowledge; 5) to help team members to modify the information promptly; and 6) to offer unlimited capacity for storing information and the durability of that information (for example, some assigned members of NPD teams may leave during the project due to illness, promotions, new jobs, etc.). Even though mechanistic memory is needed to perform project activities effectively, it is also important to include the dynamics of human connections, which fosters a socially shared memory in general and the TMS in particular. For instance, Weiser and Morrison [107, p. 152] argue that a project memory should attempt to capture, retain, and integrate “hard” project data, such as database records and documents, with “soft” items, such as memories of individuals, stories, and recollections. In this respect, the TMS helps team members utilize each other as an external memory aid to supplement their potentially equally limited and unreliable memory in areas that are less likely to overlap with others in the area of interest [53], [77]. Indeed, since team members have different expertise and knowledge that they bring to the project team, one individual does not know all of the information and knowledge that the team as a whole possesses [56]. For example, a marketing person is not expected to be knowledgeable on R&D. Knowing this, the marketing person does not need to learn the intricacies or details of R&D, because she/he is able to retrieve the information needed from the R&D personnel. Understandably, what marketing person remembers and learns is influenced what she/he understands about the R&D person’s memory. Conversely, R&D personnel rely on marketing personnel to get or learn market knowledge. When marketing and R&D personnel work together on a NPD project, both can access the information necessary to complete the task, even though the information is distributed between their individual memories. Essentially, marketing and R&D personnel use their knowledge of each other’s memories to allow them to retrieve the necessary information to complete the project successfully (indicating an example of a TMS in a NPD team modified from Lewis [68]). Accordingly, we recognize that the TMS is needed by team members to develop their individual expertise in a variety of knowledge domains, so that they can then become responsible for the provisioning of particular types of information to the rest of the team [53], [82]. In a NPD team context, the possible contributions or benefits of the TMS theory are as follows. •

The TMS theory enhances the studies on cross-functional team integration (CFI). Studies indicate that CFI is an umbrella or generic term describing a variety of cross-functional linkages (e.g., [45], [58], [60], [89], and [99]). Kahn [60], for instance, suggested that the definition of CFI focused on two attributes: interaction and collaboration. Interaction refers to the use and exchange of communication between functional units/departments; including activities like meetings, committees, teleconferencing, phone conversation, phone, mail, and e-mail. Interaction processes and measures denote the structural dimension of CFI. Collaboration refers to the collective work between departments that relies on a shared vision,

•

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common goals and mutual understanding. Especially, collaboration emphasizes the activities that build an esprit de corps across departments that unite the departments’ goal. The collaborative processes and measures show the behavioral dimension of CFI. However, the cognitive dimension of cross-functional linkage should also be evaluated and managed according to the support it provides to the CFI. According to Cicourel [23], the cognitive dimension refers to those resources, providing shared representations, interpretations, and systems of meaning among parties. The TMS, which takes the form of cognitive interdependence, leverages the CFI by considering the cognitive dimension of cross-functional linkage. Specifically, the TMS bridges the cognitions of team members (i.e., individual beliefs and opinions) and links disparate minds of a variety of people, such as marketing, manufacturing, operations, design, and R&D. In essence, the TMS helps team members to understand each other’s beliefs and understandings. Further CFI studies have emphasized the medium of communication (how the interaction was operationalized) or the mode of coordination (how the collaboration was operationalized). However, as Faraj and Sproull [37] noted, a TMS may occur through many media and modes. The TMS emphasizes the what rather than the how of coordination. We see that a TMS demonstrates the knowledge of coordinating, whereas interaction and collaboration denote how team members coordinate their activities during the project [37]. The TMS improves the knowledge integration in NPD teams. For instance, contemporary writings on the innovation process and group dynamics indicate that teams are a distributed knowledge system and a team’s knowledge and cognition is socially shared among individuals who constitute the team [109]. This is seen particularly in individuals who know only part of what the team as a whole knows [109]. In this regard, effective knowledge integration in teams requires knowing who has the required knowledge and expertise, where the knowledge and expertise are located and where and when they are needed [5]. Lewis [68, p. 587], for example, stated that, “The TMS construct specifically focuses on utilizing and integrating distributed expertise, making it an especially appropriate concept for understanding how knowledge-worker teams can optimize the value of members’ knowledge.” Accordingly, when people integrate the lower order, detailed, disparate information, they discover higher order or meta themes, generalizations, and ideas that are far beyond an individual’s singular knowledge [5], [68]. The TMS facilitates sharing and disseminating tacit information/knowledge of different knowledge domains [52], whereas mechanistic memory assists in the sharing of explicit knowledge. For instance, a marketing person is a locus of information and knowledge about customer information, marketing plans, market tests, etc., for other team members in a NPD project. Whenever market-related information is needed, the marketing person would be the

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marketing memory of the team, providing the needed information and knowledge. However, tacit knowledge is with the individual and not in the stored documents. In fact, the utilization and sharing of tacit knowledge in a project is tied to knowledge that is not written in documents but is realized through the expertise and understanding of the project personnel [37], [38]. • The TMS promotes more effective use of human resources in NPD teams. Since a TMS reduces the cognitive load of each individual and decreases the redundancy of effort in teams due to its shared memory, individual knowledge and efforts are channeled in more productive ways, because “when group members know who is good at what, they can plan their work more sensibly, assigning tasks to the people who will perform them best” [83]. As we have seen, the nature of the TMS is encompassing and the arguments to follow are developed to demonstrate the critical importance of TMS for effective NPD team projects. In this paper, we focused on the performance measures by selecting routinely studied items, namely team learning, speed-to-market and new product success. Consistent with the study of Moorman and Miner [80], we classify team learning (i.e., incorporating the lessons learned and correcting the product/project related problems), and speed-to-market (i.e., how fast new product is developed) as process effectiveness; and new product success (i.e., market performance of a new product) as product effectiveness variables. We also investigate the mediator role of a collective mind (i.e., team members’ attention to interrelating actions) between TMS and performance measures, a theory that has long been neglected in NPD literature [31]. The relationship between the TMS and a collective mind was established by Yoo and Kanawattanachai [109]. However, their research used groups assembled to study TMS and collective mind rather than on “real-world” and actively performing teams that deal with complex and unique problems in organizations, such as NPD teams. Finally, we investigate the moderator role of environmental turbulence (i.e., market and technology turbulence) between TMS and performance measures, because the NPD literature indicates that team related activities are contingent upon the environmental factors. III. TRANSACTIVE MEMORY SYSTEM AND PROCESS EFFECTIVENESS The impact of the TMS on group process effectiveness, such as group learning and functioning is well established in the group behavior literature [53], [69], [82], [83]. However, empirical research examining the impact of TMS on team learning at the level of NPD projects remains woefully inadequate. Although some initial attempts have been made in this direction (see [4]), significant work remains to be done. Team learning has been defined as a process in which a team takes action, obtains and reflects upon feedback, and makes changes to adapt or improve [34], [96]. For example, Lynn et al. [71] noted that NPD team learning can be seen as a process or an outcome (or behavior). The process approach highlights defining real activities or experiences rather than the consequences that unspecified activities might have or processes can

have [95]. The process perspective denotes how team learning takes place. An outcome or behaviorist perspective of NPD team learning refers the outcomes that processes can have. From a behaviorist perspective, team learning is considered to be incorporating lessons learned during the project and as correcting problems encountered, operationalized as information/knowledge implementation or usage by Moorman [79] and Lynn et al. [72]. Sarin and McDermott [96] also pointed out that team learning involves making use of information, which leads to the detection and correction of errors and improves the likelihood of effective new product development. Since this aspect of team learning, knowledge implementation, has been used widely in NPD literature and affords us a measurable outcome, we adapted this definition for this study (see [72], [79]). The impact of the TMS on team learning is implicitly mentioned in the NPD context. For instance, it has been asserted that team learning occurs when team members share and combine their knowledge [34], facilitate formal and informal communication [76], and seek and/or disseminate information to other individuals or units among others. Of particular importance are the human interactions and team knowledge of who has and who needs particular pieces of information in the NPD process, which play a large role in team learning—improving the NPD process and solving the product/project related problems. In particular, considering that NPD teams may encounter a lack of synergy and cohesiveness [38], and often include deeprooted biases and stereotypes, team members’ knowledge of who knows what; a TMS: 1) helps project teams to create new knowledge in order to solve product and process related issues and 2) entails the application of knowledge to new problem-oriented situations during the projects [38]. A TMS, in essence, impacts team learning by facilitating an integrative process of information/knowledge processing (i.e., information gathering, sharing, and disseminating) among team members, and aids in the intertwining and connecting the knowledge and expertise of team members. Sarin and McDermott [96] also argued that considering the knowledge embedded in the mind of the individual, the interaction among team members will have a direct impact on the successful application of their knowledge, such as uncovering and correcting problems or team learning. In addition to team learning, another process effectiveness factor is speed-to-market. Speed-to-market is not new in the NPD team context and there are numerous studies on the antecedents and consequences of it in the NPD literature [4], [46], [61]. However, how TMS impacts speed-to-market is barely mentioned in the literature. Specifically, since the TMS fosters: 1) a knowledge network for knowing who knows what; 2) task assignment to the qualified people for that task; and 3) a knowledge depository that transcends all of the individual team members [53], [77], [82]; therefore, in a TMS environment, team members: 1) synthesize, analyze and disseminate new knowledge and information in a shorter period [47]; 2) embrace a mutual understanding of the NPD process more quickly; 3) make decisions about product and process plans and alternatives faster [6]; and 4) solve product and process related problems more in a timely manner [37]. For instance, Fraidin [40, p. 104] noted that TMS can help groups to learn


more information in a shorter amount of time and process it more thoroughly to make decisions better. Cross et al. [28] pointed out that knowing who knows what allows information seekers to solve a given problem, and lead them to consider different alternatives about their problems quickly. By investigating 69 software development teams, Faraj and Sproull [37] found that team members’ knowledge of who knows what has a positive association with team efficiency, measured by time-to-completion (speed) and cost of the projects. Therefore, it is hypothesized that: : The TMS is positively associated with: 1) team learning and 2) speed-to-market. Even though the TMS system has a direct effect on process effectiveness, group level studies also indicate the importance of a collective mind as a mediating factor between the TMS and group performance [109]. For instance, Yoo and Kanawattanachai [109, p. 204] state that: Although the transactive memory system allows members to recognize the available expertise and knowledge in the team, it is the collective mind that enables team members to connect and relate the distributed expertise and knowledge to perform the task as a coherent unit. A. Collective Mind as Mediator Between Transactive Memory System and Process Effectiveness In their seminal study, Weick and Roberts [106], by using the individual mind as a metaphor, point out that the “collective mind” describes how individual members of a group act in a way that produces an overall operational reliability in complex and volatile task environments. Using the notion of “mind” from Ryle [94], they note that mind is not an entity, but rather an activity, and that mind is located in activities like playing football, driving a motor vehicle, and playing chess. In this sense, Weick and Roberts [106] note that a collective mind is manifest when organizational members heedfully interrelate their actions. From this perspective, individuals construct their actions while envisaging the system’s joint actions, and then interrelate those constructed actions with the system that is envisaged [106], [109]. Accordingly, a continuous interrelation of activities constructs an understanding of a collective action that no one person could possess in his or her individual mind [106]. In an NPD team context, a collective mind is particularly important for meeting the NPD process and product objectives. The collective mind in a NPD team describes a system in which members act with an understanding that the project relies on connected actions. A collective mind allows team members to act as one unit by meshing self-consciousness and mental models of team members [20], [106]. Here is an example of collective mind in NPD teams: the marketing personnel contributes to the project by interpreting users’ requests, in turn, the designers aids the project by drawing technical prototype details, and the engineering department assists the project by testing prototypes. When they work together to develop a new product, they also work to develop one another’s understanding of who is doing what and how the pieces fit together. During the project, the work by marketing is interpreted and synthesized by design and engineering. In this reflexive and mutual reciprocal

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knowledge flow among marketing, design and engineering, team members build their own internal model of the team, and create a metascript for project actions to visualize how they fit in, how others will act and how their actions will affect others [29]. In a sense, team members create an integrated big picture of the project. For example, Dougherty and Takacs [31], by investigating innovative firms, demonstrated that effective multifunctional NPD teams have a strong collective mind that their members interact with each other carefully, critically, willfully, and purposefully rather than habitually. The authors indicated that, during the NPD process, team members: 1) figured out what to do on their own, but appropriately; 2) developed their own actions rather than wait around to be told what to do; 3) did the right things, so that their actions not only fit with but also supplement the actions of other team members; 4) did not talk about other people’s duties, but about their own responsibility to make the project meaningful to others; and 5) had a common view of product concepts and shared understanding of project’s goals. Accordingly, with a collective mind: 1) team members build and maintain viable understanding of the activity system to which they belong and understand how their individual know-how and skill become linked together within the whole project, and their place in it, and 2) the TMS, that is knowledge of who knows what in the team, is appropriated for team learning and launching a product faster when team members’ mindful attention interrelate their actions.1 For instance, examining the development of a TMS, the collective mind and their influence on performance in 38 virtual teams of graduate management students, Yoo and Kanawattanachai [109] found that the collective mind acts as a mediator between the team performance and TMS. In a NPD team context, a TMS is a way to offer, share, or exchange information among team members to solve the product/project related problems. However, a TMS by itself does not mean that a team member will be able to benefit from it. Uncovering and correcting product and project related problems requires team learning, which is achieved when people learn who has the appropriate expertise and knowledge to solve problems, and each does his or her work in a way that fits into the parallel flow of activities of others in a joint action manner. Especially, a collective mind allows project teams to mitigate or resolve product/process related problems by developing a norm of reciprocity, or behavioral expectations 1It is also worthwhile to mention the difference between collective memory and collective mind. While a collective memory represents a shared knowledge base for the team members, collective mind describes cooperative activities or joint activities/actions of team members [7]. In essence, collective memory is a knowledge repository, whereas collective mind denotes a set of heedful interrelationships, which describe the relation of one’s actions to others’ actions carefully, critically and consistently [75]. The difference between TMS and collective mind is that a TMS indicates the knowledge of who knows what, that is the interconnection of different team members’ knowledge, whereas collective mind implies the interconnection of the activities or actions of each team members. Based on the social exchange theory [67], a TMS operates on the assumption of “univocal reciprocity,” a team member does benefit from another team member for which she/he does not expect immediate or direct reciprocation. For instance, marketing provides some benefits to engineering by giving the required information/knowledge to them, but marketing does not necessarily need information/knowledge from engineering at that moment. However, collective mind is based on the underlying principle of “mutual reciprocity,” a bidirectional movement of activity between persons. Within such a context of mutual reciprocity, the role of the team as a whole supersedes the role of the individual team members.

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about how knowledge is to be cooperatively exchanged, which is fostered by a TMS [22]. For example, during the formal and informal team meetings that accompany repeated joint actions during the projects, team members’ respective knowledge is blended and leveraged to uncover and correct product related issues. In addition to the team learning, the benefit of a TMS on the speed-to-market can also be obtained by collective mind. Specifically, joint actions facilitate the concurrent development of the various phases of the project that leads to quick product development, because the expertise and skills from each person are simultaneously involved in the development efforts and the concurrent consideration of specification, design, and product related issues are achieved. However, when team members engage in joint actions during the projects, an awareness of who knows what, a TMS, is needed to leverage the understanding of each team member’s requirements and to cross fertilize expertise and skills of team members for developing the new product in a timely fashion. Therefore: : The collective mind mediates the relationships between the TMS and a) team learning, and b) speed-to-market. B. Moderator Role of Environmental Turbulence Group level studies and theories indicate that collective memory processes occur differently based on the differing environmental conditions [82]. Also, memory studies in the management and marketing literature, and particularly in NPD teams, found that environmental turbulence moderates team processes and project outcomes [81]. However, since project outcomes are affected by the process effectiveness variables as well as external events, which are not within the control of team members and managers, the effect of the TMS on process effectiveness with respect to environmental turbulence is discussed. Teams developing new products in turbulent environments encounter quick depreciation of technology and market knowledge due to the rapidly changing customer needs, wants, desires, and technological “know-hows.” In this sense, when technology and market are turbulent, the team members must be able to share and synthesize information more quickly than when technology and market are more predictable. What we see is a TMS becoming an important source of memory, as it provides for more spontaneous actions and behavior and less comprehensive deliberations about any turbulent event in particular. For instance, quickly changing customer preferences urge design engineers to cooperate with marketing functions informally rather than waiting for and using memos and reports to adjust product designs. This is of particular note when a team’s rigid productdevelopment procedures and methods, and team members’ beliefs preserve predetermined routines and mindsets throughout a project (that is, being imprisoned by routines and habits), inhibit the reception and evaluation of new market and technology information, and reduce the value of perceived new information. A TMS helps teams to facilitate constructive confrontation, deviate from routine behaviors and methods and invite team members to develop nonconventional problem-solving during the

NPD project. In particular, a TMS allows for early and quick exchange of customer, market and technical information in order to solve project-related problems effectively—team learning. Thus, it is expected that the relationship between the TMS and team learning will be stronger when a high level of environmental turbulence is present. However, another school of thought indicates that knowing who knows what and from whom (i.e., a TMS), in essence, worsen team learning—gathering and implementing new knowledge and solving product related problems. Team members’ knowledge becomes obsolete, old-fashioned and misleading due to quickly changing market and technology knowledge [80]. Therefore, because a turbulent environment: 1) provides a reduction in information searching and processing and the range of alternatives that are considered; 2) triggers defensive reactions, such as neglecting important information that may be contrary to existing knowledge or might precipitate additional work; 3) fosters wrong judgment and evaluation; and 4) advances a sense or perception that the first or early signals/information from the environment as correct, while it might be false [12], it is expected that the relationship between the TMS and team learning will be weaker. Based on the above contradictory arguments, it is possible that the effect of the TMS on team learning will change nonlinearly with respect to the environmental turbulence. If we consider the punctuated equilibrium theory [43], and the problematic search process of the theory of bounded rationality [74], when an environmental turbulence increases, the effect of a TMS on team learning increases, however, at one point, extensive amounts of turbulence start to thwart team learning. Team learning via a TMS progresses through an alternation of status (i.e., a series of starts and stops with interposing periods of quiescence) in a turbulent environment. It is worth noting that, with an increasing rate of turbulence, team learning declines because team members find that old perspectives are no longer viable. Therefore, it is hypothesized that: : The effect of the TMS on the team learning changes quadratically (an “ ” shape) with respect to the environmental turbulence. The impact of a TMS on speed-to-market is also tentative to the turbulent conditions [4]. Since, turbulent environments generate new and quickly changing technology and market related information/knowledge, the internalization of new knowledge, turning explicit knowledge into tacit by team members, takes time and practice [12]. From this perspective, using other people as a source of memory, especially seeking market and technology related facts from other team members, slows down the operations of project teams due to the lack of an established and internalized knowledge structure, and an in-depth analysis of issues prior to decision-making. Take, for example, person X’s synthesis of a new technical know-how that emerged from the environmental turbulence. The internalization of that knowledge initially slows down person Y, who turned to person X seeking the technical knowledge. However, unlike what is seen in team learning, the effect of the TMS on the speed-to-market changes linearly with respect to the environmental turbulence, because the more turbulence that exists in the environment, the


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Transactive memory system in NPD teams.

more efforts are performed for the sake of accuracy of project actions, at the expense of speed. As a result, a TMS reduces the pace of the product development process as the rate of turbulence increases. Therefore: : The greater the environmental turbulence associated with the environment, the greater the negative relationship between the TMS and speed-to-market. IV. TRANSACTIVE MEMORY SYSTEM AND PRODUCT EFFECTIVENESS The positive influence of a TMS on group performance is well established in the group behavior literature [53], [82]. In TMS studies, for instance, Yoo and Kanawattanachai [109] found that a TMS has a positive impact on team performance as assessed by profit, ROA, ROE, stock price, and market share. However, the effect of TMS on new product success (NPS) was not empirically tested in a NPD team context. New product success is an important criterion for project managers as well as top management. Especially, the ultimate goal of forming a NPD project team and developing a new product is the success of that product in the market place. The scholars indicated that if companies can improve their effectiveness at launching new products, they can double their bottom line [71], [91]. In this case, project managers and top management desire to reduce the uncertainty and risks associated with the NPD project process. However, while some factors are out of the control of top management and project managers during the NPD projects, they do have control of other factors. When project managers effectively run the factors within their control, such as aiding the development of a TMS, the probability of success of the new product will be increased. Investigating the direct impact of the TMS on new product success is a logical jump however, because the NPD literature demonstrates that factors related to team processes, such as teamwork, empowerment, and TMS in the context mentioned here,2 influence the NPD process effectiveness, and the outcomes of that process impacts the NPS [72]. For instance, team-learning studies indicate that the collective knowledge generation, dissemination, and implementation have a positive influence on new product success [72], [79]. In addition to team learning, speed-to-market was also asserted as a critical new 2Studies of Liang et. al. [69] and Moreland et. al. [83], for example, conceive and measure TMS as an emergent property of team processes (cf., [37]).

product success factor [46], [78]. Thus, we posit that process effectiveness mediates the relationship between the TMS and product effectiveness. In particular, since NPS refers to the extent to which a project met its commercial objectives, it is reasonable to envisage that NPS depends on the how team members proficiently solved the customer and product related issues and how much faster they develop the new product than their competitors. However, knowing who knows what, a TMS, in the project team does not guarantee the commercial success of the new product. The benefits of a TMS can be obtained to the extent that team members tackle the product and process related issues in a synergistic mode through combination of knowledge and activities. Especially, when team members use and implement what they learned from others by help of a TMS, project teams will be able to solve the product/process related problems to launch the new product successfully and quickly. For example, with a lack of a TMS, an awareness of who knows what, the project team may take too long to crystallize a new product concept, incorporate new information/knowledge into the product prototype, and solve customer/technical issues on the new product. Ultimately, the project team may fail to successfully introduce the product into the market in a timely fashion and thus fail to reap the benefits of team members’ expertise and skills. Therefore, it is hypothesized that: : Process effectiveness mediates the relationships between the TMS and product effectiveness (i.e., new product success). Fig. 1 summarizes the hypotheses. V. RESEARCH METHODOLOGY A. Sampling To test the above hypotheses, multi-item scales adopted from prior studies for the measurement of constructs were used. All constructs were measured using five-point Likert scales ranging from “strongly disagree” (0) to “strongly agree” (5). By using the parallel-translation method, items were first translated into Turkish by one person and then retranslated into English by a second person. The two translators then jointly reconciled all differences. The suitability of the Turkish version of the questionnaires was then pretested by ten part-time graduate students working in industry and involved in at least one NPD project. After refining the questionnaire, based on interviews with the

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pretest subjects, the questionnaires were distributed and collected by one of the authors, applying the “personally administrated questionnaire” method. The sampling population consisted of 50 firms located in Istanbul. The firms were selected because they develop new products and export them to other countries, such as the U.K., Germany, Arabic countries, Central Asia, and Russia, as identified by the Istanbul Chamber of Industry. These firms were selected as the top 100 companies in the country: they are large firms with more than 1000 employees and have sales greater than 20 million USD per year. Also, these firms are organized and managed based on the Western management style, e.g., they operate in accordance with ISO quality standards. First, we contacted the firms’ General Managers by telephone and explained the aim of the study to them. Of the 50 firms contacted, 35 agreed to participate in the survey study. From these firms, we were able to gather usable data about 79 new product development projects in 18 firms (36% response rate). Data were not available from some of the project teams due to their small size (less than three persons in a project team), and the lack of the cooperation of some of the project managers. Also, to assess the product performance more accurately, the products used in this study must have been commercialized and launched into the marketplace for at least six months [72]. To reduce the possible problems associated with single sourcing, we selected the product or project managers to complete the survey. Kumar et al. [64], for instance, note that, “Response error is likely to be higher for informants whose roles are not closely related to the concepts under study.” These managers were chosen because: 1) product/project managers are likely to have a “bigger-picture view” of NPD projects than other team members; 2) project managers have a broader view of each member’s behaviors than other team members; and 3) product/project managers were expected to provide more reliable and objective data. It is possible that some team members in the study had preconceived opinions and expectations regarding differences in work behaviors for other team members, such as stereotypes. For instance, since team members’ belief structures vary and are likely to process stimuli differently, they are expected to assess the collective mind construct differently. In this regard, and since this study focused on the NPD team as a unit of analysis, product/project managers are likely to assess the TMS, collective mind, and team learning more accurately due to their “bird’s-eye view” of the project in general and operations, behaviors, and actions of the NPD team members in particular. Also, the sample of respondents in this study is similar to samples used in prior studies on innovation (e.g., [36], [65], [92], and [100]). After the respondents were selected, each was informed that his/her responses would remain anonymous and would not be linked to them individually, their companies or products. This was done to assure anonymity, increasing the motivation of informants to cooperate without fear of potential reprisals. Several industries were represented including telecommunications, computer and electronics, communication, software, manufacturing and machinery, chemical, service technologies, food, and material. The products were primary consumer

durables (30.4%), consumer service (12.7%), industrial materials or parts (46.8%), and industrial service (10.1%). B. Measures For NPS, items developed by Cooper and Kleinschmidt [26] were used. Six questions were asked to assess new product success with a view to assessing whether teams were meeting or exceeding profit and sales expectations. This operationalization was similar to Hackman’s [49, p. 323] view of performance operationalization as “meeting or exceeding the performance standards of the people who receive and/or review the team’s output.” To measure speed-to-market—the ability of a team to develop and launch a new product rapidly—four questions were asked, similar to those of Kessler and Chakrabarti [61]. Since a multicompany and multi-industry sample was used, control for speed-to-market differences was achieved by using relative speed measures, using an approach and item content similar to that of Kessler and Chakrabarti [61]. Speed-to-market was assessed relative to preset schedules, company standards, and similar competitive projects. To assess team learning, one of the team learning constructs operationalized by Lynn et al. [72] called “information/knowledge implementation,” and similar to the study by Akgün and Lynn [4], was adapted. Four questions were asked to assess team learning as incorporating the lessons learned during the project and correcting problems encountered. The TMS and collective mind questions were adapted from Yoo and Kanawattanachai [109]. The TMS was assessed by asking three questions to ascertain team members’ knowledge of who knows what, and who on the team has specialized skills and knowledge that is relevant to their work. The collective mind was assessed by asking four questions to capture three main behavioral aspects of a collective mind, contributions (acting), representation (understanding), and interrelation (interrelating) indicated by Weick and Roberts [106]. The environmental turbulence questions were adapted from Jaworski and Kohli [59], which are consistent with their technology and market turbulence. Four questions were asked for each variable. Technology turbulence refers to the change associated with new product technologies, and market turbulence indicates the change in the composition of customers and their preferences. Team size was selected as a control variable. Team size is measured as the number of persons in the team. In an NPD team context, process effectiveness (e.g., team learning and speed-tomarket), for instance, is influenced by the size of team. Also, knowing who knows what may not be clear in a large team, because people may not know each other and be familiar with the degree of their expertise. C. Measure Validity and Reliability After data collection, measures were subjected to a purification process to assess their reliability, unidimensionality, and discriminant validity [9], [16]. To assess the unidimensionality, measures were divided into three subsets of theoretically related variables: the two interindividual connection measures (i.e.,


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TABLE I DESCRIPTIVE SCALES AND CONSTRUCT CORRELATIONS, AND RELIABILITY ESTIMATES

TMS and collective mind), three outcome measures (i.e., team learning, speed-to-market, and NPD), and two environmental turbulence measures (i.e., market and technology turbulence), as recommended by Moorman and Miner [81] and Bentler and Cho [19], due to small sample size. After eliminating the problematic items in a step-by-step procedure (one item from speed-to-market question was discarded due to nonsignificant loading score), results indicated that three models fit adequately: the two interindividual connection variables , , ), three outcome ( , , ), and two variables ( environmental turbulence variables ( , , ).3 To assess the discriminant validity, a series of two-factor models, recommended by Bagozzi et al. [16] were estimated in which individual factor correlations, one at a time, were restricted to unity. The fit of the restricted models was compared with that of the original model. In total, 21 models were evaluated using AMOS 4.0. The chi-square change in each model, constrained and unconstrained, were significant, , which suggests that constructs demonstrate discriminant validity. Further, the measures were subjected to confirmatory factor analysis (CFA) using AMOS 4.0. All factors, excluding the team size measure, were included in one CFA model. During the CFA analysis, subscales or parcels (a method aggregating or taking the mean of several items that purportedly measure the same construct as indicators of a latent variable) were used for the CFA instead of individual items as recommended by Drasgow and Kanfer [32], and Schmit and Ryan [98], due to small sample size. These researchers noted that goodness-of-fit measures are affected when the number of items used to identify a small number of factors is relatively large. Consistent with this approach, two subscores or parcels for each scale were created, 3Confirmatory factor analysis item-construct loadings can be obtained from authors.

each consisting of a randomly divided subset of the items in the scale. The CFA produced a good fit with an incremental fit index (IFI) of 0.94 and a comparative fit index (CFI) of 0.93 , ). (also, Table I shows the correlation among all eight variables. The relatively low to moderate correlations provide further evidence of discriminant validity. Also, all reliability estimates, including coefficient alphas, average variance extracted for each construct, and AMOS-based composite reliabilities, are well-beyond the threshold levels suggested by Nunnally [87] and Fornell and Larcker [39]. Also, the squared correlations (ranging from 0.0004 to 0.45) did not exceed the average variance extracted (ranging from 0.64 to 0.74) suggesting discriminant validity [39]. It is, therefore, concluded that measures are unidimensional and have adequate reliability and discriminant validity. Further, skewness ranged from 0.96 to 0.09, and kurtosis ranged from 0.83 to 2.51. These results denote that the variables are well below the level requiring transformation of variables, skewness of 2 and kurtosis of 5 as indicated by Ghiselli et al. [44]. VI. ANALYSES AND RESULTS After validating the measures, a series of multiple linear regression models were performed to test the hypotheses using the Baron and Kenny [18] procedure. According to Baron and Kenny [18], a variable ( ) mediates the relationship between an independent variable ( ) and a dependent variable ( ) if: a) is significantly related to —shown in model A; b) is significantly related to —shown in model B; c) after is remains significantly related to —shown in controlled for, is controlled for, the - relationship model C; and d) after is zero. Steps b) and c) are the essential steps in establishing mediation and Step d) is only necessary to prove a fully mediated effect. In order to test our hypothesis 1, we regressed the team learning and speed-to-market on the TMS, respectively. As

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TABLE II REGRESSION RESULTS FOR TEAM LEARNING

TABLE III REGRESSION RESULTS FOR SPEED-TO-MARKET

seen in the model A of Tables II and III, TMS is positively associated with both team learning and speed-to-market, sup. Specifically, the findings demonstrate that a TMS, porting team members’ knowledge of who knows what and who has a specific skill and expertise: 1) enhances the team’s ability to find and correct the product related problems; 2) facilitates incorporating lessons learned during the project to product development process; and 3) helps to launch the product faster than planned in the original schedule and industry standard. Tables II and III also show the mediating role of a collective mind between a TMS and process effectiveness; team learning and speed-to-market. Tables II and III indicate that the TMS is positively associated with team learning and speed-to-market (in model A), and with collective mind (in model B). Further, model C show that when process effectiveness measures regressed on the TMS and collective mind, collective memory denotes positive relation with process effectiveness, indicating that collective mind mediates the relationships between TMS is supported. and team learning and speed-to-market. Thus, However, it should be noted that when the collective mind is

controlled for, the relationship between the TMS and team , indicating that we learning is very close to zero have a strong evidence of the mediation of the collective mind (in Table II). However, the relationship between the TMS and , demonstrating speed-to-market is not close to zero that collective memory is a partial mediator between the TMS and speed-to-market (in Table III). These results demonstrate the effect of team members’ knowledge about who knows what on solving product related problems and on achieving quick product development when team members: 1) have a global perspective that includes each other’s decision and the relationship among them; 2) carefully interrelate actions to each other in this project; 3) carefully make their decisions to maximize an overall team performance; and 4) develop a clear understanding of how each function should be coordinated. Table IV shows the regression results between the TMS and process effectiveness under the different environmental conditions. A moderated multiple hierarchical regression analysis was used [57]. Because of the possibility of multicollinearity, the TMS and market and technology turbulence measures were


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TABLE IV ENVIRONMENTAL TURBULENCE AND TRANSACTIVE MEMORY SYSTEM

mean-centered before performing the linear regression model. Variance inflation factors (VIF) were estimated to examine multicollinearity levels and results (VIFs 2) were found to be below a harmful level [84]. The findings demonstrate that both market and technology turbulence variables moderate the relation between a TMS and team learning.4 As hypothesized , the effect of the TMS on the team learning changes quadratically (an “ ” shape) with respect to the environmental turbulence (for instance, the coefficient of quadratic variable, , is negatively significant) [18]. Specifically, a TMS has a positive influence on team learning with the increasing rate of market turbulence and with emerging new product technologies. However, given the above, an increasing rate of turbulence in time weakens the team members’ knowledge and expertise, leading the TMS to exacerbate team was supported. learning. Thus, Table IV also shows that the impact of a TMS on speed-to-market is weakened by market and technology . Especially, the increasing rate of turbulence, supporting technology/market turbulence reduces the impact of a TMS on: 1) developing and launching a new product on or ahead of the original schedule developed at initial project go-ahead and 2) completing the NPD project in less time than what was considered normal and customary for the industry that project team performing in. Table V demonstrates the regression result of testing team learning and speed-to-market as mediators of the relationship between a TMS and NPS (Hypotheses 5). As seen in Table V, a TMS significantly affected the dependent variable, NPS, in model A. The TMS significantly affected the mediator

R

4Following the suggestion of Anderson [10], we compared the regression equation without the cross-product to the of the regression equation with the due to the interaction cross-product to determine if the incremental is significant. Results show that the inclusion of the interaction terms on the hierarchical regression added significant variance explanation p < : in our four models in Table IV.

R

R (1R )

(

0 1)

variables, team learning, and speed-to-market in model A, as demonstrated in Tables II and III. The mediators, team learning, and speed-to-market, significantly affected the NPS when the TMS is controlled in Model C. These results provide support for a mediated effect of team learning and speed-to-market, . However, the relationship between TMS and supporting NPS is not close to zero , demonstrating that process effectiveness including team learning and speed-to-market is a partial mediator between the TMS and NPS. This finding indicates that NPS can be improved by solving product related problems during the process and shortening the product development process with an effective TMS. VII. DISCUSSION AND IMPLICATIONS This paper investigated the TMS in new product development teams. Even though the TMS has been investigated in the group behavior literature, a specific study of the theory of a TMS in the field of project management broadens the understanding of this subject. In addition, because product development teams involve multimental models—due to different knowledge, expertise, backgrounds, and personalities in various subgroups of an organization—the collective memory, in general, and a TMS, in particular, are also critical issues for effective team and project management [30], [62]. This study found that a TMS facilitates the team-learning process and expands the notion of “team memory” in the NPD literature. Specifically, team memory has been assessed as mechanistic memory or documentation system of the project team. That mechanistic perspective of memory, however, makes the team’s activities slower or insufficient due to recalling and manipulating wrong, invalid, and insufficient information, and/or interpreting the information differently than what was intended [107]. On the other hand, using a TMS with a mechanistic memory leverages the effectiveness of the project team and offers a safeguard to the misuse of knowledge and information. The managers we interviewed also confirmed this:

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TABLE V REGRESSION RESULTS FOR NEW PRODUCT SUCCESS

“We are not always able to solve system problems by just looking at the system manuals. If a team member comes across a problem that is not listed in the manuals, then he or she notifies us. That person knows that I have experience or expertise to solve that problem, or someone may recommend me to that person. I can then answer his or her questions on the phone. However, if he cannot solve the problem again, we then meet, and try to solve problem together. Then, he or/she learns from me. I don’t lose any knowledge, but he or she is filled with new knowledge.” “In our industry, electronics, there are many factors that affect the development of a new system. And it is impossible to compile all critical factors into manuals. We, thus, ask if there is a person who worked in similar projects before. We then talk with that person. He tells his experience to us based on the previous projects. His experiences give us new ideas, and we then experiment with new things based on his or her experience to solve problems.” This paper also demonstrated that a TMS enables project teams to develop and launch new products faster. Team members’ knowledge of their colleague’s expertise and skills and the successful application of that knowledge stimulates effective and quick decision making on product and process related issues and alternatives. For instance, mechanistic memory (e.g., databases, files, and records) brings the past into the present to aid in decision making for future events. However, a TMS does not only convey the past to present, it also bridges the past, present, and future during the decision-making process due to its dynamic nature. Indeed, as writers in the area of knowledge management pointed out, while a mechanistic memory is objective and created in the “then and there,” a TMS is subjective, experiential and created in the “here and now” (see, [66] and [86]). The managers we interviewed helped us put this issue in perspective: “If you want to solve the problems using documented information, you have got to find all resources, books, Internet, files and records, and then you should read all

the resources. All those take time, and you lose valuable time by reading all these materials. But customers don’t tolerate waste of time. Even though you know that you will find the solution to a problem in the documents, asking someone to get information and knowledge is always a shortcut way to solve the problems.” “At the beginning of the project, it seems that databases are more advantageous, because databases are composed of filtered information obtained from the experiences of many people, and also you don’t bother anyone for any information. But later you realize that it takes time to read and understand all that information. Also, you need to relate the information with your project and problems. And with the progress of the project, you don’t have much time for searching, understanding, and relating the information in databases. Thus, it is very advantageous to ask someone for information, because it is faster.” This paper also investigated the contingencies of the TMS on the team learning and speed-to-market. Most of the studies in the group behavior literature promoted the useful side of the TMS, while omitting the negative side of the TMS on group functioning. Even though a TMS is useful for group functioning, e.g., group learning, decision making, the effect of a TMS on group functioning, in essence, changes tentatively with respect to environmental changes in real organizational work groups. A TMS hinders team learning and speed-to-market when there is an extensive amount of environmental turbulence. Knowing who knows what thwarts the efforts of the team to solve product related problems and to develop the product faster under extremely turbulent conditions. Studies on organizational/team improvization and unlearning—a subprocess of learning (see [3], for more detail) noted the barrier role of the “memory” for team learning and speed-to-market at the edge of chaos and under extreme turbulence. For instance, Akgün et al. [2] noted that team memory may produce rigid project routines that facilitate fixed responses to many issues and promotes incorrect beliefs or mindsets that tend to assimilate


errors in judgment and actions. Indeed, a TMS: 1) becomes the project’s routines over time and a source of inertia because team members’ knowing who knows what and receiving information from that person becomes a habit in the project [42] and 2) encourages team members to use others’ knowledge without critically evaluating their reliability due to an established appreciation of team members’ expertise. In this vein, a TMS, in essence, should also be updated for effective group functioning when there is an increased rate of market and/or technology turbulence. Specifically, team members should receive knowledge from the people that have recent and more reliable information on the subject, and should update team’s knowledge-base on “who knows what” constantly. In addition to the TMS, the collective mind has also been investigated in an NPD team context. A collective mind fosters a joint action or “seamless coordination” [55, p. 169] during the project as team members act in a coordinated fashion by using a TMS [22]. A TMS provides a means for transcending the different and individualistic mental models in anticipation of an organized action in NPD teams. Managers also mention that: “At the beginning of the project, we, team members, didn’t know each other very well. However, with the progress of the project, we start to know each other. During the meetings, dialogues, after work meeting in tea houses, we realized the expertise and skills of each of us. Then, we start to behave jointly and interrelate our actions during the project.” “With the progress of the project, our development process started to become automatic and routine. Our team members asked less questions to others, because a collective knowledge emerged with the progress of the project. That knowledge is never written down on the pages; instead, it is in people’s memory. And people start to act via that collective knowledge.” “In a software development project, I was working alone at night. Whenever I came across with any problem, I was not able to ask someone at that time. So due to lack of communication, we were not able to act together and launched a product with a lot of bugs.” “After we learned about each other’s expertise, we develop a common understanding and meaning. We start to produce similar ideas to solve problems. Our behaviors started to resemble each other. And when I forgot how to solve a problem that we had solved before, my teammates help me to recall my memory back. And, they prevented me from making any mistakes by acting as a check point.” From this research, the implications for managers are threefold. First, managers should encourage and enhance the TMS during the project. If people cannot gain timely and unobstructed access to others who have the needed experience and skills, knowing who knows what just remains idle information. Developing and sustaining a TMS is thus imperative for project performance. Specifically, managers should list people and their respective skills, expertise, experience, and practical and theoretical knowledge at the beginning of the project. In a sense, managers should use mechanistic memory to begin the development of a TMS. In this way, a general framework

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of knowledge, skills, and expertise of team, as a whole, is established. Then, managers should inform people about who knows what in the project. However, in some cases, team members hesitate or do not share information/knowledge due to emotional-based disagreements, such as disliking each other or jealousy, especially caused by wage differences or inequality of status. In this case, managers should provide rewards and motivations to enhance the relations among the team members for an effective TMS. Also, managers should provide team members access rights to others in the organization and establish a psychologically safe environment to exchange knowledge during the interactions. Baer and Frese [15, p. 50] defined a climate for psychological safety as “formal and informal organizational practices and procedures guiding and supporting open and trustful interactions within the work environment.” In this sense, a climate for psychological safety provides a work environment where team members are safe to interact with each other without feeling rejected or punished. Specifically, people: 1) feel safe to make errors and honestly discuss what they think and know; 2) feel confident to give information/knowledge to others without fear of negative consequences, such as not to be blamed or embarrassed for the giving wrong information unintentionally; and 3) have right to protect the ownership of the their knowledge, that is others appreciate them for bringing diverse viewpoints. Consequently, managers should develop supportive and trust-enhancing relations among its employees by encouraging them to solve project-related problems together; demonstrating integrity between project’s requirements and the teams’ knowledge and actions; fostering communication among everyone; and emphasizing the importance of the success of the project for the organization. Next, managers should also: 1) develop TMS specialties, by assigning responsibility for certain information/experiences to team members in the project and 2) coordinate the TMS, by establishing, at a base level, which team members may best assist others and encourage an ongoing dialog between and among all team members as the needs of the project progresses. Finally, as was found in this study, an inflexible TMS is not always useful for project-process effectiveness and should be managed carefully for team learning and speed-to-market when project teams performing in a turbulent environment. In this sense, managers should continue to observe and update the TMS during the project. Second, managers should facilitate a TMS at the beginning of the project in order to promote rapid development of a collective mind for the NPD process, which facilitates the successful launching of products. For example, newer advanced technology companies, e.g., many biotechnology firms, address this issue on a company-wide basis by having weekly “parties,” where the company buys the snacks, beer, wine, etc., and all of the scientists are invited to talk with each other; a great source of shared information and development for the TMS. Third, managers should tie mechanistic memory and the TMS together for effective project management. Documents and files must not be dismissed completely in project teams, especially, in those teams performing in virtual environments, because the physical distance between team members constrains the development and maintenance of a TMS [5]. In this sense, in virtual NPD teams, where individuals work across space, time, and

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organizational boundaries to execute interdependent tasks, it is helpful to establish the communication links through information technologies in general and mechanistic memory in particular. Also, an information technology, (IT)-based, system developed to support and enhance team knowledge management processes develops and supports a TMS by creating searchable repositories of codified knowledge and computerized yellow pages of employees skills and experience [5]. VIII. LIMITATIONS AND FUTURE RESEARCH There are some methodological limitations to this study—notably, single sourcing, self-reporting, and retrospective reporting. Scholars (e.g., [14] and [48]) have argued that studies employing single-source methodology can be biased by artificially high intercorrelations produced by an overall response tendency. Even though, as noted, project managers are able to see the bigger picture of project activities, collecting data from team members as respondents in each of the NPD projects will add new insights about these complex constructs, such as TMS, collective memory, and collective mind. Also, collecting data from single respondents using self-reports may reflect common method bias. Project team managers might provide answers that they feel are logically consistent. Further, because there was a time lag between the times when the projects were completed and when data were collected, there might be a recall issue in survey questions. Utilizing a cross-sectional design with questionnaires was also the one of the limitations of this study. Even though “surveying is a large and growing area of research in the natural environment” [41], the method used (only questionnaire) may not provide objective results about the flow of knowledge, which is an inherently dynamic phenomena in NPD teams. A research strategy that may overcome this limitation is one that involves longitudinal studies, in which flow of knowledge can be followed over time. In this sense, a longitudinal research method could be used to get more objective results about the flow of knowledge. Also, using objective measures, archival data for some variables, such as speed and success, may give results that are more objective. In addition to the nature of data, the generalizability of sampling is another limitation of this study. The study has been conducted in a specific national context, Turkish NPD teams. For instance, Turkish culture demonstrates more collectivist (less individualistic) tendencies than many Western cultures and social belonging to groups and teams is high in Turkish culture. It is important to note that readers should be cautious when generalizing the results in different cultural contexts. Also, small sample size is another limitation of this study that hindered the generalizability of the findings to the broader contexts, e.g., industries and project types. The TMS will lead to many future research studies in the NPD literature. One should further investigate how a TMS can be developed and improved during specific projects. For instance, group level studies indicate the importance of communication, familiarity of group members with each other, team members’ stability, trust among group members, co-location of group members, interpersonal interactions among team members, and training together for facilitating a TMS [27], [53], [69],

[82], [93]. Thus, one can empirically test the influence of the above factors as antecedents of the TMS in NPD teams. Also, special types of work groups (e.g., new software development teams), and industries (e.g., telecommunication) can be investigated in the context of antecedents and consequences of a TMS. In addition to antecedents of the TMS, how a TMS facilitates tacit connections among team members and interfunctional integration can be investigated using in-depth case studies and a longitudinal design. Also, a comparison of mechanistic memory and the TMS, as well as their joint effect on NPD performance, can be tested in future studies by using environmental (e.g., uncertainty and turbulence), team process (e.g., team size, team tenure, and project type), and product innovativeness (e.g., incremental, radical) moderators, as well as organizational factors, such as organizational culture. IX. CONCLUSION The existing research on NPD teams has overlooked the term of “collective memory.” Especially, researchers have long examined a project team’s collective memory in the perspective of mechanistic or hard-data view. However, with the increasing importance of tacit knowledge dissemination and human connections in NPD activities, NPD teams are also required to have a socially constructed memory, which is another aspect of the collective memory. Especially, a TMS is needed if team members are to perform the processes of teamwork effectively, and if they are to increase the performance of the project. In this paper, we empirically investigated the social aspect of the collective memory, the TMS, in NPD project teams. The results show that a TMS is associated positively with team learning, speed-to-market, and new product success. Further, that a TMS is less critical for team learning and speed-to-market under high turbulence in the market or technology. Also, this study demonstrated that a TMS is appropriated with a collective mind in NPD teams. It was found that a collective mind mediates relations between a TMS and NPD process effectiveness. This research just scratches the surface of this important, but understudied, subject. Future researchers will find the area of collective memory, collective mind, and the TMS rich and fruitful. APPENDIX MEASURES Transactive memory system (Adapted from Yoo and Kanawattanachai [109]). • The team has a good “map” of each others’ talents and skills. • Team members know what task-related skills and knowledge they each posses. • Team members know who on the team has specialized skills and knowledge that is relevant to their work. Collective mind (Adapted from Yoo and Kanawattanachai [109]). • Our team members had a global perspective that includes each other’s decision and the relationship among them. • Our team members carefully interrelated actions to each other in this project.


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Ali E. Akgün received the M.S. degree in engineering management from Drexel University, Philadelphia, PA, and the Ph.D. in technology management from Stevens Institute of Technology, Hoboken, NJ. He is an Associate Professor of Science and Technology Studies in the School of Business Administration, Gebze Institute of Technology, Gebze-Kocaeli, Turkey. His research has been published in the Human Relations, Journal of Engineering and Technology Management (JET-M), Journal of Product Innovation Management, the IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, Research Technology Management, Industrial Marketing Management, International Journal of Technology Management, among other journals. His research areas are new product/technology development, organizational learning, and cognitive and social psychology in innovation management.

John C. Byrne received the Ph.D. degree in technology management from Stevens Institute of Technology, Hoboken, NJ, and the M.B.A. degree from Pace University, New York. He is an Assistant Professor of Management at Lubin School of Business, Pace University. Prior to his academic career, he spent more than 30 years in several technology fields: biotechnology, electronic instrument manufacturing, and precision optics. He has managed capital biotechnology projects for many of the world’s leading biotechnology companies, and acted as a corporate liaison with facilities in France, Switzerland, and England. He maintains a consulting practice addressing the pharmaceutical and biotechnology industries. He has published on organizational learning, emotional intelligence, and peer feedback. Dr. Byrne is a member of the American Physical Society (APS) and the Society for Industrial and Organizational Psychology (SIOP).

Halit Keskin received the Ph.D. in management and organization from the Gebze Institute of Technology, Gebze-Kocaeli, Turkey. He is an Assistant Professor of Science and Technology Studies in the School of Business Administration, Gebze Institute of Technology. His research interests include technology and innovation management, knowledge management and human resource management in high-tech firms.

Gary S. Lynn received the B.S. degree in mechanical engineering from Vanderbilt University, Nashville, TN, the M.S. degree in management from the J. L. Kellogg Graduate School of Management, Northwestern University, Evanston, IL, and the Ph.D. in technology marketing from Rensselaer Polytechnic Institute (RPI), Troy, NY. He is an Associate Professor in the Wesley J. Howe School of Technology Management, Stevens Institute of Technology, Hoboken, NJ. He has authored or coauthored 50 books and refereed publications on the intersection of technology, innovation, and marketing. His latest book, Blockbusters: The Five Keys to Developing Great New Products (New York: Harper Business, 2002) identifies the critical success factors of over 700 new product development teams. Dr. Lynn was named one of “Today’s Leading Business Thinkers” by Business 2.0 Magazine, October 2002.

Transactive Memory System in New Product Development Teams

Transactive Memory System in New Product Development Teams

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