Leveraging project team expertise for better project solutions
Kevin P. Grant, University of Texas at San Antonio
Michael R. Baumann, University of Texas at San Antonio
One of the most important and prevalent goals of project management researchers is to help project managers to succeed. As many studies have reported, project failure remains all too common in the dynamic and contemporary project management environment. Studies that have addressed the causes of project failure have identified a variety of culprits. Among the frequently cited causes are inadequate communication, inadequate planning, bad estimating and incorrect scheduling (Nicholas, 2004). In each of these cases, the root cause lies in the planning effort conducted by the project team.
Naturally, researchers and practitioners have focused considerable attention on tools, techniques, and best practices to help project managers and teams to develop robust and reliable project plans. A crucial ingredient to the success of the project planning effort is to introduce and apply relevant expertise to the task at hand. From an organizational perspective, there are many techniques that firms have adopted to make expertise available to the project planning process. Many firms are leveraging information technologies to make project-relevant information easily accessible to project teams; for example, to digitize printed information and create relevant online directories and databases on the company intranet (Hoegl, Parboteeah, & Munson, 2003). Organizations have also encouraged the development of informal social networks to open communication within and across teams. Frequently, informal contacts may have the expertise needed to solve a particular problem (Griffin & Hauser, 1996). To facilitate the development of these networks, organizations may conduct organization-wide information sessions, organize social gatherings, hire individuals with established networks and measure network development (Hoegl et al., 2003).
One of the most significant approaches adopted by organizations to apply expertise to project solutions is through the use of cross-functional or multidisciplinary teams. The importance of involving experts from many disciplines on a project team has been long recognized as a characteristic of project management. Writing in 1959, Gaddis observed that the project team is composed of specialists who represent the disciplines needed to bring the project to a successful conclusion (Cleland, 1999). The premise that motivates the use of cross-functional teams is that when the appropriate collection of experts are assembled, team decisions and actions are more likely to reflect the full range of perspectives essential to project success (Van der Vegt & Bunderson, 2005). Since the team members possess knowledge about non-overlapping aspects of the project, they will view project information differently (Uhl-Bien & Graen, 1998). These differences in perspective can lead to varying interpretations that need to be communicated and combined in order for the team to perform effectively (Foushee, 1984). Therefore, members of project teams must find ways to communicate regularly if they are to achieve success (Bly, Harrison, & Irwin, 1993). When effective communication is achieved, cross-functional project teams use information better, make better decisions and resolve conflicts (Griffin & Hauser, 1996). They also solve integration problems (Griffin & Hauser, 1996) and promote learning and innovation through the cross-fertilization of ideas (Van der Vegt & Bunderson, 2005). For these reasons, multidisciplinary teams are a particularly attractive vehicle to leverage organizational expertise when expertise is distributed among various individuals (Van der Vegt & Bunderson, 2005). Organizations can support the performance of cross-functional teams through purposeful consideration of which functions should be involved during various aspects of the project, thus ensuring that critical information is available when needed (Griffin & Hauser, 1996).
An important stream of research that focuses more specifically upon the mechanisms used within teams to leverage expertise involves the study of Transactive Memory Systems (TMS)(Wegner, 1987). Team members can possess knowledge personally, or team members can possess knowledge that is held externally in the minds of other team members or in physical storage media such as documents or databases (Alavi & Tiwana, 2002). The storage and retrieval of the knowledge that is possessed externally by teammates is facilitated by rich and iterative communication interactions or transactions; hence, the term “transactive memory” (Anand, Manz, & Glick, 1998). Numerous studies have shown that team member knowledge contributions and, indeed, task performance in complex task environments are enhanced through effective transactive memory systems (Faraj & Spruill, 2000; Moreland, 1999; and Yoo & Kanawattanachai, 2001).
As members of a project team embark upon a collective task, the transactive memory enables the individual team members to pool their tacit knowledge. It also gives team members the ability to search for the complementary knowledge that is needed to complete the task. In short, individual team members can use the transactive memory to access and retrieve knowledge that they do not possess personally, but which they recognize exists elsewhere on the team (Alavi & Tiwana, 2002). A TMS facilitates an awareness of who knows what and who possesses important expertise (Akgun, Byrne, Keskin, & Lynn, 2006). When knowledge is distributed in a group, this awareness is essential for team members to make effective use of that knowledge (Akgun et al., 2006).
The challenge for project managers interested in leveraging the expertise of team members is twofold. First, the project manager must facilitate the acquisition and recognition of expertise on the project team. Second, the project manager must facilitate the effective sharing and use of information possessed by the experts. But what are the specific mechanisms that are essential to leverage expertise effectively? How are these mechanisms related and what factors are likely to mediate these relationships? In this paper, we present a theoretical model of the relationships between variables important to this process. Next, we recommend future research to contribute to our understanding of these relationships. We also describe the research test bed that we have developed to conduct this stream of research and conclude by offering some practical recommendations for leveraging expertise.
A Proposed Model of Recognition of Expertise and Performance in Teams
Many factors influence performance in teams. Central to the current paper is the role of recognition of expertise. The existence of a positive relationship between recognition of member expertise and performance is well documented in the literature on groups and teams (e.g., Baumann & Bonner, 2004; Bottger & Yetton, 1988; Libby, Trotman, & Zimmer, 1987; Littlepage & Sibliger, 1992). However, the factors influencing the recognition of expertise and the mechanism through which recognition of expertise influences performance are less well understood. In this section, we propose a model addressing these issues. In proposing this model, we draw on research from several domains. Our goal is to provide a conceptual framework to help guide attempts to leverage expertise and improve performance in project teams. We present a graphical representation of the model in Figure 1. Please note that Figure 1 is not a process flow model. Rather, each box represents a theoretical variable and arrows between boxes represent the influence of one variable on the other.
Figure 1. A proposed model of recognition of expertise and performance in teams
Factors Affecting Recognition of Expertise
For the purposes of this model, we define recognition of expertise as the correct identification of differences in member expertise. At the team level, recognition of expertise also involves a perception shared across team members. Thus, when we use the term recognition of expertise, we are referring to a shared mental model of differences in member knowledge, skills, and abilities. In the proposed model, factors expected to affect the recognition of expertise are grouped into those that affect whether or not team members make an attempt to determine member expertise (“expertise-seeking behaviors”), the extent of the differences in expertise present (“functional diversity / variability”), and the information available to members when attempting to assess each other's expertise (expectations and performance).
Any team may discover differences in expertise. However, the probability of a team discovering expertise should be greater when members are engaging in expertise-seeking behaviors. Expertise-seeking behaviors may include such simple activities as asking members about their background and education, or a team leader encouraging everyone to contribute everything they know to the conversation. Expertise-seeking behaviors may also include more complex activities such as actually testing members with respect to various abilities. A project manager who creates a list of team member skills and knowledge domains and provides that list to team members (Akgun et al., 2006) is engaging in expertise-seeking behaviors.
Regardless of whether team members are attempting to determine expertise, the degree of difference between members will affect the likelihood that team members accurately identify (i.e., recognize) their experts (Baumann & Bonner, 2004; Libby et al., 1987). In the context of cross-functional teams, it should be easier for members to recognize differences when each has a very different area of expertise (i.e., the team has high functional diversity). On a team in which members have very similar areas and levels of expertise, it should be more difficult for members to recognize these differences.
People rely on several types of information when assessing expertise. Research suggests that stereotypes (Hollingshead & Fraidin, 2003; Kite & Johnson, 1988; Biernat & Kobrynowicz, 1997) and reputation (Yaniv & Kleinberger, 2000) are often used to form an initial perception of expertise. Because expectations can influence information processing (Lord, Ross, & Lepper, 1979; Snyder & Swann, 1978), the effect of expectations on perceptions of expertise can be very strong under certain conditions. However, research in group problem solving suggests that group members update their perceptions of expertise using performance information accumulated during the task. Performance information can reinforce the initial perception when performance information is consistent with initial expectations, refute the initial perception when inconsistent with initial expectations, or create a new perception in the absence of expectations (Baumann & Bonner, 2004).
Variables Mediating the Relationship between Recognition of Expertise and Performance
In identifying variables that mediate the relationship between recognition of expertise and performance, it is useful to consider the behavior of groups in which members are not aware of each other's areas of expertise. For this, we turn to laboratory experiments of information sharing in groups. These studies often involve tasks on which some information is held in common by all members while other information is held uniquely by one member. When each member has unique information relevant to a particular part of the task, each member is in effect the team's expert at that part of the task. Existing research shows that teams often fail to fully discuss unique information (Stasser & Titus, 1985, 1987) and, when they do discuss it, weight it less heavily than they should (Chernyshenko, Miner, Baumann, & Sniezek, 2003; Gigone & Hastie, 1993). This suggests that teams failing to recognize expertise face at least two problems: (1) the unique information of the expert may fail to be shared with the team and (2) the team may fail to appropriately weight that information if it is shared. Recent research in this domain suggests that making group members aware of differences in member expertise increases the likelihood that members will discuss unique information (Stasser, Stewart, & Wittenbaum, 1995; Stasser, Vaughan, & Stewart, 2000) and give more weight to unique information provided by an expert than non-expert (Baumann, 2005). Furthermore, discussion of unique information (Stasser et al., 1995; Stasser et al., 2000) and increased weighting of the opinion of the expert (Baumann & Bonner, 2004) are associated with higher levels of group performance, at least in a laboratory environment. Thus, it is likely that the relationship between recognition of expertise and performance is mediated in part by the influence of recognition of expertise on information sharing and information weighting.
We believe it is likely that recognition of expertise influences performance in part by influencing information sharing and weighting. However, this presents a new question: how does recognition of expertise influence information sharing and information weighting? In short, what mediates these relationships? To answer this question, we turn to the literature on Transactive Memory Systems. In teams with transactive memory systems, members develop a shared representation of each other's expertise. Moreland and colleagues (Liang, Moreland, & Argote, 1995; Moreland, Argote, & Krishnan, 1996, Moreland & Myaskvosky, 2000) and Lewis (2003) treat transactive memory systems as having three components. These components are theoretically distinct, although correlated with each other in practice. The first component, specialization, is the degree to which team members believe they differ from each other in terms of knowledge, skills, or abilities relevant to the task. More specifically, it is the belief that each member has important knowledge that others do not. Although this concept is related to recognition of expertise, there is an important difference. Recognition of expertise, as we have used the term, is the correct identification of differences in member expertise. In contrast, specialization is the belief that differences exist and does not necessarily imply correct identification. The second component, credibility, is the degree to which the team members trust the knowledge offered by other members of the team. The third component, coordination, is the degree to which team members are able to integrate their efforts. As typically measured (e.g., Lewis, 2003), coordination also captures the degree to which team members have the same representation of the task and how to execute it.
The literature on transactive memory suggests a possible mediator for the influence of recognition of expertise on sharing of unique information. As argued by Stasser and colleagues (Stasser et al., 1995; Stasser et al., 2000), a person believing himself or herself to be the expert on a particular topic should also believe that he or she possesses information on that topic that no other member possesses. Thus, the person should have a motive to discuss that unique information. Specialization is the belief that different members have different areas of expertise important to the task at hand. Recognition of actual differences in expertise is likely to contribute to the belief that members differ in task-relevant expertise (i.e., specialization). Thus, recognition of expertise should lead to specialization, which, in turn, should lead to the discussion of unique information. It is worth noting that by the preceding logic, incorrect identification of expertise leading to specialization inconsistent with actual expertise would not increase the sharing of task-relevant unique information. Only the person identified as the expert on a particular topic gains a motive to share unique information on that topic. If this is not the true expert, then the true expert will not be motivated to share unique information on that topic.
The transactive memory literature also suggests a likely mediator for the influence of recognition of expertise on weighting. One possible reason why groups underweight unique information is that members are unsure whether to trust that information (Chernyshenko et al., 2003). The number of members holding a piece of information may be used as a cue to the likelihood that the information in question is true. This notion is similar to the idea of “social proof” in the persuasion literature (e.g., Cialdini, 2001), in which the greater the number of people we are told believe something, the more likely we are to accept the belief (e.g., “a million happy customers can't be wrong”). However, the literature on persuasion (e.g., Cialdini, 2001) suggests that people also have a tendency to “trust the expert.” This is similar to the notion of credibility in the transactive memory literature. Furthermore, the advice-taking literature suggests that people give more weight to the information provided by a perceived expert than a perceived non-expert (e.g., Yaniv & Kleinberger, 2000). Thus, we believe it is likely that recognition of the expert leads to an increase in trust in the expert, which in turn increases the weight given to information provided by the expert. It is again worth noting that this relationship implies that an incorrect identification of expertise could have negative effects. Unique information provided by the person identified as the expert receives additional weight. However, identifying a person as a non-expert may reduce the weight given to information provided by that person. Therefore, identifying the wrong member as an expert would lead to overweighting of information provided by that member and underweighting of information provided by the true expert.
In addition to suggesting mediators for the influence of recognition of expertise on discussion and weighting of unique information, the transactive memory literature suggests another influence of recognition of expertise on performance. The third component of transactive memory, coordination, may also be affected by recognition of expertise. Coordination as a construct includes such things as creating an appropriate division of labor and the ease with which member contributions are re-integrated. This is neither information sharing nor information weighting per se. Research in transactive memory (e.g., Lewis, 2003) and shared mental models (e.g., Mathieu, Heffner, Goodwin, Salas, & Cannon-Bowers, 2000) suggests that coordination has a positive association with performance. Although not directly tested in the literature, it stands to reason that recognition of expertise would allow for better coordination in the form of more efficient divisions of labor and easier reintegration of member contributions. Therefore, we propose that part of the influence of recognition of expertise on performance is due to an influence of recognition of expertise on coordination.
Thus far, we have focused on the relationships between variables that we believe influence recognition of expertise or mediate the relationship between recognition of expertise and performance. However, we believe that environmental factors can also influence all of these variables and the relationships between them. We represent this in Figure 1 by embedding the entire model in a box labeled “environmental factors.” Research suggests that recognition of expertise, information sharing, and information weighting can all be influenced by the environment created by the project manager. For example, the project manager aids team members in discovering each other's expertise (Akgun et al., 2006). Additionally, information sharing should be greater in teams with a collective sense of identity (Van der Vegt & Bunderson, 2005), in psychologically safe environments (Akgun et al., 2006), and when the project manager has an attitude that motivates team members to share information (Newell, 2004). The extent to which project managers encourage team members to ask each other questions, challenge their own assumptions, and seek alternative explanations is also likely to affect information sharing and weighting (Van der Vegt & Bunderson, 2005). Although the factors listed so far affect the social or cultural environment, environmental factors also include the extent to which the physical environment supports or inhibits communication and information sharing (Allen, 1986).
The proposed model is consistent with existing research. Each link in the model has at least some empirical support, and the mechanisms that we propose are plausible. However, these mechanisms are not the only ones possible. Furthermore, because the model integrates findings from disparate domains, studies testing more than one link of the model are rare. Thus, additional research will be needed to determine whether the proposed mediations are correct. To guide this future research, we will now summarize which of the links in the model have been tested and which need additional research.
Previous research has shown both recognition of expertise (e.g., Baumann & Bonner, 2004; Bottger & Yetton, 1988; Libby et al., 1987; Littlepage & Sibliger, 1992) and specialization (Lewis, 2003) influence performance. Recognition of expertise has also been linked to sharing of unique information (Stasser et al., 1995; Stasser et al., 2000). However, to the best of our knowledge, the relationships between recognition of expertise (i.e., correct identification of the expert) and specialization and the relationship between specialization and sharing of unique information have not been tested. The few studies examining recognition of expertise and information sharing (Stasser et al., 1995; Stasser et al., 2000) have not examined the case of incorrect identification of expertise. Thus, it is unclear whether specialization in the absence of recognition of expertise would lead to an increase in sharing of unique information. It is possible that the increase in sharing of unique information that occurs when members are made aware of differences in expertise is the result of a general increase in motivation to share unique information rather than motivation to share expertise-specific unique information. If so, specialization would lead to an increase in the sharing of unique information even in the absence of recognition of expertise. Although we believe this to be unlikely based on our interpretation of existing data, it cannot be ruled out.
Research recommendation 1: Evaluate the relationships among recognition of expertise, specialization, information sharing, and performance.
The notion that beliefs about a person's expertise affect the weight given to opinions or recommendations provided by that person is supported by the literature (Bauman & Bonner, 2004; Bonner, Baumann, & Dalal, 2002; Yaniv & Kleinberger, 2000). Similarly, past research has shown a positive relationship between the weight given to the expert's opinion and performance in group problem-solving tasks (Baumann & Bonner, 2004). However, these studies have not explicitly examined trust, nor have they dealt with the weighting of unique information per se. Therefore, additional research is required to evaluate these propositions. If, as we have proposed, the weighting of unique information is influenced by the trust members have in the source of that information, and trust is affected by recognition of expertise, then identifying the wrong member as an expert could have extremely negative consequences for performance. Therefore, evaluating these proposed mediators is of significant practical, as well as theoretical, importance.
Research recommendation 2: Evaluate the relationships among recognition of expertise, trust, information weighting, and performance.
Studies of transactive memory (Lewis, 2003) and shared mental models (Mathieu et al, 2000) support the notion that coordination influences group performance. However, the notion that recognition of expertise leads to improved coordination, while plausible, has not yet been tested. Research on transactive memory has found specialization, coordination, and credibility to be correlated (Lewis, 2003). Given that transactive memory is conceptualized as a division of labor based on differences in member expertise and our previous assertions about the influence of recognition of expertise on specialization and trust, it is logically consistent to assert that recognition of expertise will influence coordination. We have proposed a direct influence between recognition of expertise and coordination. However, we acknowledge there are other plausible forms for this relationship. For example, it is possible that recognition of expertise is related to coordination, but that said relationship is fully mediated by specialization and trust. It is plausible that a group that believes members differ in task-relevant expertise is more likely to develop an efficient division of labor and that a group whose members trust each other more is better able to reintegrate member contributions.
Research recommendation 3: Evaluate the relationships among recognition of expertise, coordination and performance, including the possibilities that the relationship between recognition of expertise and coordination is mediated by specialization, trust, or both.
The Projects in Space Learning Experience & Research Test Bed
To pursue our research recommendations and to test the validity of the proposed model, we have developed a research test bed and learning experience. Our goal was to create a controlled environment conducive to measuring the variables in the proposed model that was also sufficiently information-rich and engaging that the group processes occurring in the environment would reflect those occurring in actual project teams. The result was the Projects in Space for Professionals Learning Experience. The Projects in Space for Professionals Learning Experience is a highly interactive team training simulation that includes a web-based learning program, a planning exercise, a project execution exercise, and an extended post-mission debriefing.
Each session of the Projects in Space for Professionals Learning Experience involves two five-person project teams designated “NAV” (Navigation) and “Probe.” Each of these teams is challenged to develop a risk assessment plan in preparation for a space mission during which each team will complete its assigned project. The members of each team are distributed between a simulated mission control center at Mars Base and a Mars transport vehicle. The Mars transport vehicle serves as a launch platform for probes. It also transports samples collected on Mars back to Earth for further analysis. The NAV team must ensure that the Mars transport reaches Mars, lands safely, successfully takes off from the Martian surface and returns to Earth with its cargo. The Probe team must assemble and launch a probe to examine several anomalies on the Mars moon, Phobos.
Figure 2. The Projects in Space for Professionals Learning Experience
The members of each team are randomly assigned to one of five roles. These include a Project Manager, Control Officer, Mission Analyst, Systems Operator, and Status Officer for the NAV team; and a Project Manager, Control Officer, Mission Analyst, Assembly Officer, and Inventory Specialist for the Probe team. The Projects in Space for Professionals Learning Experience includes four major activities, as illustrated in Figure 2.
The learning experience begins with a web-based training program during which participants complete learning modules that address risk management and collaboration on project teams. Participants are also expected to study their individual role descriptions. Information and duties in the role descriptions are assigned such that each team member receives some common information and some unique information. The unique information that each member receives focuses on a particular aspect of the project, creating a different area of expertise for each member of the team. Controlling the initial distribution of information facilitates the assessment of information sharing and weighting.
Once participants have completed their study of the modules and their roles, they complete a web-based “flight certification” exam. This exam will test the participants’ mastery of the modules and their roles. Each time a participant gives an incorrect response, the exam provides the correct answer and a brief explanation of why the answer provided was correct. Thus, in addition to testing participants’ mastery of their roles, the exam also reminds participants of information that they may have missed.
After completing the flight-certification exam, the participants report to the Challenger Learning Center (CLC) of San Antonio at a designated time. Once at the CLC, participants first complete pre-mission surveys. The participants then work within their assigned team to develop a risk plan for their upcoming mission. All planning is conducted face to face and videotaped for analysis of information sharing. Once the planning session is complete, teams are asked to submit their risk plan to the researchers. After a short break, teams enter the CLC simulation facilities to conduct a simulated mission. During this mission, teams face risks that they should have anticipated from information provided in the role descriptions, as well as risks that they are unlikely to have anticipated. While the teams are busy completing their projects, the researchers evaluate the risk assessments to determine if the uniquely seeded information was used correctly by each team.
Upon completion of the mission, each team's project manager conducts a short debriefing for his or her team. Teams then complete post-mission questionnaires to assess recognition of expertise, elements of transactive memory (specialization, credibility, and coordination), member influence, and perceived information sharing. Upon completion of these surveys, participants receive an extensive debriefing facilitated by the researcher. The debriefing is conducted to increase the educational value of the experience and to collect valuable research data. During this debriefing, the researcher makes participants aware of mistakes they made or difficulties they encountered that resulted from failures in information sharing and utilization. During this process, the researcher also queries team members as to why they think failures in information sharing and utilization occurred. Finally, participants are asked to describe obstacles to information sharing and utilization in their home organizations.
Application and Value of Test Bed
Leveraging expertise is vital to team performance. However, to make recommendations on how to leverage expertise and improve team performance, we must first understand the mechanisms affecting recognition of expertise and those through which recognition of expertise influence team performance. For a number of reasons, there are limits to what can be learned from studying project teams in the workplace. For example, concerns about interfering with team process limit the ways in which data can be collected. For ethical reasons, performance concerns also require caution when assessing the effectiveness of untested interventions. It is simply unacceptable to introduce a team process intervention that has unintended negative effects on performance and causes the organization to lose money, personnel, or materials. A research test bed such as Projects in Space for Professionals allows us to collect additional data in a controlled environment. This increases our ability to assess hypothesized relationships between variables, and allows for more detailed analyses of those relationships. Such test beds are also safe places to test team process interventions before testing them in the workplace. In so doing, unexpected negative side effects can be identified without loss of money, personnel, or materials. Finally, the fact that the test bed is integrated with a learning experience will make it easy to take the information we learn and use it to train project managers / project teams in techniques that increase the effective leveraging of expertise and, ultimately, performance.
For the results of a simulator to be applicable to the real world, the exercise must faithfully replicate the psychological aspects of the workplace (e.g., Salas, Bowers, & Rhodenizer, 1998). In the current context, this translates into an environment which is engaging and requires team members to share expertise. Data from proof-of-concept testing suggests that we have been largely successful in creating such an environment. Participants have reported that the overall learning experience is highly engaging and has increased their awareness of the importance of recognizing expertise and information sharing in teams. Thus, we believe that this test bed will facilitate the conduct of research on various aspects of leveraging expertise. We also believe that this research will facilitate the development of practical recommendations and specific interventions for leveraging expertise in project teams.
Practical Recommendations for Leveraging Expertise
The motivation for the research that we propose is to help project managers to succeed. While considerable research remains to be conducted, many prior studies have already developed findings that project managers can use to leverage expertise in project teams and ultimately develop better project plans and solutions. It is with our central goal in mind that we draw from the extant literature to provide nine practical recommendations to help project managers succeed.
1. Pursue and acquire team members with the expertise you need.
The formation of a project team frequently requires a compromise between what is needed and who is available (Foust, 2004). The project manager intent upon acquiring all the expertise necessary for the project must be clear about the specific expertise that is needed and diligent in soliciting support from the leaders in the functional organizations who will contribute team members to the project. In those situations when the project manager will have the latitude to screen the candidates, it may prove particularly advantageous to verify the expertise of the prospective team member. Direct observation of the candidate may provide some very instructive insights. Do others frequently solicit their advice? Have they established a sound reputation? Do they adopt a logical approach to problem solving? Do they use the tools of their trade effectively (Foust, 2004)? In addition to direct observation an interview can be also be structured to ensure that the candidate possesses the requisite expertise. Specific and focused questions can shine a light on the depth of knowledge held by the candidate. True experts should be able to demonstrate their points by using specific examples and simple demonstrations that help others to understand (Foust, 2004).
2. Create a sense of collective identity.
There are several actions that project managers can take to achieve this sense of identity among team members. One tactic is to conspicuously show support for the team and to recognize team accomplishments. Another approach is to allow team members to develop a shared history together (Van der Vegt & Bunderson, 2005). Securing long-term commitments and team stability can be helpful in this regard. Finally, project managers can help the team to achieve a collective sense of identity by encouraging team members to interact regularly and by facilitating contact among team members (Van der Vegt & Bunderson, 2005).
3. Create a psychologically safe environment.
In a psychologically safe environment, team members exchange knowledge freely because they feel safe to make mistakes and discuss their thoughts. Moreover, they feel confident that they can share their ideas without incurring blame or ridicule. The safe environment will also protect team member's ownership rights to the knowledge they share. Ultimately, when team members participate in a safe environment, they will enjoy the sense that their teammates appreciate them for bringing their diverse viewpoints to support team efforts to develop project solutions (Akgun et al., 2006). To establish a safe, supportive and trust-enhancing environment, project managers can encourage the members of the team to solve project-related problems together. Additionally, the project manager can demonstrate integrity between the project requirements and the teams’ knowledge and actions. Finally, the project manager can emphasize the importance of project success in an effort to promote collaboration and trust (Akgun et al., 2006).
4. Enable team members to assess each other's expertise.
Once the stage is set, project managers should work to enable all team members to recognize the expertise that is resident on the project team. One good idea is to develop a list of the team members that explicitly describes their skills, knowledge domains and expertise (Akgun et al., 2006). The project managers can share this information with the members of the team to help them to know “who knows what” in the project. Project managers, equipped with the knowledge of who possesses particular expertise, can work to actively encourage this vital participation on the part of the expert. Finally, project managers should adopt procedures that promote logical analysis in problem solving and the explanation of related ideas. The recognition of expertise is enhanced when the experts demonstrate their reasoning in a structured manner (Littlepage & Mueller, 1997).
5. Align incentives with information sharing.
Project managers should also place particular attention on the system of rewards and motivators used within the project team. Research suggests that the perception of fairness and equity among team members is associated with enhanced sharing of knowledge and information (Akgun et al., 2006). Project managers should reward team members for knowledge-sharing behavior and the contributions that they make over the course of the project (Bartol & Srivastava, 2002). Similarly, they should adopt team metrics that motivate team members to focus on group goals and performance (Bartol & Srivastava, 2002). Finally, they should acknowledge their experts, who can take great satisfaction in knowing that they are recognized and appreciated for their contributions (Foust, 2004)
6. Facilitate interpersonal information sharing and communication.
Several scholars have underscored the importance of information-sharing communication as a key mechanism by which the diversity of expertise on a project team can promote performance (Bantel & Jackson, 1989; Bunderson & Sutcliffe, 2002). In order to effectively promote the requisite information sharing, project managers should concentrate on developing their learning and interpersonal skills. Project managers can also encourage team members to ask questions, challenge assumptions, seek different perspectives, evaluate alternatives and reflect on past actions (Van der Vegt & Bunderson, 2005). These activities are essential for team members to succeed in sharing, refining and combining task-relevant knowledge (Argote, Gruenfeld, & Naquin, 1999). Project managers may also find it helpful to facilitate this ongoing dialog between and among team members, by coordinating the transactive memory system and establishing which team members may best assist others (Akgun et al., 2006). Further, the team can develop sharing routines to help achieve efficient sharing of expertise.
7. Design the physical environment to enhance communication.
In addition to encouraging interpersonal sharing, project managers can increase communication by dedicating their attention to the physical environment. Research has shown increases in communication when teams are collocated and when the teams work in non-territorial spaces (Allen, 1986). Additionally, the creation of strategically located informal meeting places equipped with white boards and coffee has also proven effective (Allen, 1986). The crux of the matter is to provide team members with rich and multiple communication channels and a shared space in order to facilitate the real-time collaboration essential to share expertise (Alavi & Tiwana, 2002). It is also important to complement efforts to provide physical proximity by providing groups with techniques that they can use to build cross-functional relationships and to encourage open-door policies (Souder, 1987).
8. Exploit technology to augment information sharing.
In the contemporary project environment, information technology systems can be used effectively to support and enhance team knowledge management processes (Alavi & Tiwana, 2002). Key ingredients of the technology support can include searchable repositories of codified knowledge and computerized yellow pages of employee experience, skills and expertise (Alavi & Tiwana, 2002). Additional technology aids may include the adoption of peer-to-peer collaboration tools, and creation and notification profiles to disseminate contextual knowledge to all team members (Alavi & Tiwana, 2002).
9. Monitor performance and update recognition of expertise.
As the team performs, the project manager should continue to observe and update his or her beliefs about member expertise (Akgun et al., 2006). Particular attention should be directed to the performance of the experts on the team. Do their ideas and recommendations really work? Project managers should monitor performance and consider the long-term track record of their experts (Foust, 2004).
Project failure remains an all-too-common occurrence in the contemporary project management environment. Often, the root cause is a failure in leveraging member expertise. For example, sometimes projects fail because a critical deadline is agreed to despite the concerns of the team members most familiar with the expected duration of the critical tasks in the project. Sometimes the solution fails because the constituent components were not properly integrated, or key pieces of information were not shared. In each of these cases, the root cause lies in the failure of the team to leverage the expertise resident in its members. It is this insight that frames the critical issue that we tackle in this paper. In order to achieve success, project managers and teams must leverage team member expertise effectively. Additional research is needed to support project managers and teams as they confront this challenge. To that end, we propose a model that illustrates the relationship between recognition of expertise and team performance. We also identify several topics requiring additional research, and describe an elaborate research test bed to conduct this research. Recognizing that project managers urgently need practical techniques for leveraging expertise we integrate existing research to present techniques that can be used today. In closing, leveraging member expertise is critical for team performance. We urge project managers to attend to this issue, and strongly recommend further research to help project managers effectively leverage expertise. Ultimately, these efforts will bear fruit in the form of better project plans and successful project solutions.
Akgun, A. E., Byrne, J. C., Keskin, H., & Lynn, G. S. (2006). Transactive memory system in new product development teams. IEEE Transactions on Engineering Management, 53, 95-111.
Alavi, M., & Tiwana, A. (2002). Knowledge integration in virtual teams: The potential role of KMS. Journal of the American Society for Information Science and Technology, 53, 1029-1037.
Allen, T. J. (1986). Managing the flow of technology. Cambridge, MA: MIT Press.
Anand, V., Manz, C., & Glick, W. (1998). An organizational memory approach to information management. Academy of Management Review, 23, 796-809.
Argote, L., Gruenfeld, D., & Naquin, C. (1999). Group learning in organizations. In M. E. Turner (Ed.), Groups at work: Advances in theory and research (pp. 369-413). New York: Erlbaum.
Bantel, K. A., & Jackson, S. E. (1989). Top management and innovations in banking: Does the composition of the top team make a difference? Strategic Management Journal, 10, 107-124.
Bartol, K. M., & Srivastava, A. (2002). Encouraging knowledge sharing: The role of organizational reward systems. Journal of Leadership and Organization Studies, 9(1), 64-71.
Baumann, M. R. (2005). Determining when teams will and won't listen to their expert. Invited presentation, David Eccles School of Business Management Department Seminar Series, University of Utah, November 18, 2005.
Baumann, M. R., & Bonner, B. L. (2004). The effects of variability and expectations on utilization of member expertise and group performance. Organizational Behavior and Human Decision Processes, 93, 89-101.
Biernat, M., & Kobrynowicz, D. (1997). Gender- and race-based standards of competence: Lower minimum standards but higher ability standards for devalued groups. Journal of Personality and Social Psychology, 72, 544-557.
Bly, S. A., Harrison, S. R., & Irwin, S. (1993). Media spaces: Bringing people together in a video, audio and computing environment. Communications of the ACM, 36(1), 28-48.
Bonner, B. L., Baumann, M. R., & Dalal, R. S. (2002). The effects of member expertise on group decision making and performance. Organizational Behavior and Human Decision Processes, 88, 719-736.
Bottger, P. C., & Yetton, P. W. (1988). An integration of process and decision scheme explanations of group problem solving performance. Organizational Behavior and Human Decision Processes, 42, 234-249.
Bunderson, J. S., & Sutcliffe, K. M. (2002). Comparing alternative conceptualizations of functional diversity in management teams: Process and performance effects. Academy of Management Journal, 45, 875-894.
Chernyshenko, O. S., Miner, A. G., Baumann, M. R., & Sniezek, J. A. (2003). The impact of information distribution, ownership and discussion on group member judgment: The differential cue weighting model. Organizational Behavior and Human Decision Processes, 91, 12-25.
Cialdini, R. B. (2001). Influence: Science and practice. Boston: Allyn & Bacon.
Cleland, David I. (1999). Project management: Strategic design and implementation (3rd ed.). New York: McGraw-Hill.
Faraj, S., & Sproull, L. (2000). Coordinating expertise in software development teams. Management Science, 46, 1554-1568.
Foushee, H. C. (1984). Dyads and triads at 35,000 feet: Factors affecting group process and aircrew performance. American Psychologist, 39, 886-893.
Foust, J. A. (2004). Leading experts: One manager's experience. Research-Technology Management, 47, 12-19.
Gigone, D., & Hastie, R. (1993). The common knowledge effect: Information sharing and group judgment. Journal of Personality and Social Psychology, 65, 959-974.
Griffin, A., & Hauser, J. R. (1996). Integrating R&D and marketing: A review and analysis of the literature. Journal of Product Innovation Management, 13, 191-215.
Hoegl, M. K., Parboteeah, P., & Munson, C. L. (2003). Team-level antecedents of individuals’ knowledge networks. Decision Sciences, 34, 741-770.
Hollinshead, A. B., & Fraidin, S. N. (2003). Gender stereotypes and assumptions about expertise in transactive memory. Journal of Experimental Social Psychology, 39, 355-363.
Kite, M. E., & Johnson, B. T. (1988). Attitudes toward older and younger adults: A meta-analysis. Psychology and Aging, 3, 233-244.
Lewis, K. (2003). Measuring transactive memory systems in the field: Scale development and validation. Journal of Applied Psychology, 88, 587-604.
Liang, D. W., Moreland, R. L., & Argote, L. (1995). Group versus individual training and group performance: The mediating role of transactive memory. Personality and Social Psychology Bulletin, 2, 384-393.
Libby, R., Trotman, K. T., & Zimmer, I. (1987). Member variation, recognition of expertise, and group performance. Journal of Applied Psychology, 72, 81-87.
Littlepage, G. E., & Mueller, A. L. (1997). Recognition and utilization of expertise in problem-solving groups: Expert characteristics and behavior. Group Dynamics: Theory, Research and Practice, 1, 324-328.
Littlepage, G. E., & Silbiger, H. (1992). Recognition of expertise in decision-making groups: Effects of group size and participation patterns. Small Group Research, 23, 344-355.
Lord, C. G., Ross, L., & Lepper, M. R. (1979). Biased assimilation and attitude polarization: The effects of prior theories on subsequently considered evidence. Journal of Personality and Social Psychology, 37, 2098-2109.
Mathieu, J. E., Heffner, T. S., Goodwin, G. F., Salas, E., & Cannon-Bowers, J. A. (2000). The influence of shared mental models on team process and performance. Journal of Applied Psychology, 85, 273-283.
Moreland, R. L., Argote, L., & Krishnan, R. (1996). Socially shared cognition at work: Transactive memory and group performance. In J. L. Nye and A. M. Brower (Eds.), What's social about social cognition? (pp. 57-84). Thousand Oaks, CA: Sage Publications.
Moreland, R. L. (1999). Transactive memory: Learning who knows what in work groups and organizations. In L. L. Thompson, J. M. Levine and D. M. Meseick (Eds.), Shared cognition in organizations (pp. 3-31). Hillsdale, NJ: Lawrence Erlbaum Associates.
Moreland, R. L., & Myaskovsky, L. (2000). Exploring the performance benefits of group training: Transactive memory or improved communication? Organizational Behavior and Human Decision Processes, 82, 117-133.
Newell, S. (2004). Enhancing cross-project learning. Engineering Management Journal 16(1), 12-20.
Nicholas, J. M. (2004). Project management for business and engineering (2nd ed.). Burlington, MA: Elsevier.
Salas, E., Bowers, C. A., & Rhodenizer, L. (1998). It is not how much you have but you use it: Towards a rational use of simulation to support aviation training. International Journal of Aviation Psychology, 8, 197-208.
Snyder, M., & Swann, W. B., Jr. (1978). Hypothesis-testing processes in social interaction. Journal of Personality and Social Psychology, 36, 1202-1212.
Souder, W. E. (1987). Managing new product innovations. Lexington, MA: Lexington Books.
Stasser, G., Stewart, D. D., & Wittenbaum, G. M. (1995). Expert roles and information exchange during discussion: The importance of knowing who knows what. Journal of Experimental Social Psychology, 31, 244-265.
Stasser, G., & Titus, W. (1985). Pooling of unique information in group decision making: Biased information sampling during discussion. Journal of Personality and Social Psychology, 48, 1467-1478.
Stasser, G., & Titus, W. (1987). Effects of information load and percentage of common information on the dissemination of unique information during group discussion. Journal of Personality and Social Psychology, 53, 81-93.
Stasser, G., Vaughan, S. I., & Stewart, D. D. (2000). Pooling unshared information: The benefits of knowing how access to information is distributed among group members. Organizational Behavior and Human Decision Processes, 82, 102-116.
Uhl-Bien, M., & Graen, G. B. (1998). Individual self-management: Analysis of professionals’ self-managing activities in functional and cross-functional work teams. Academy of Management Journal, 41, 340-351.
Van Der Vegt, G. S., & Bunderson, J. S. (2005). Learning and performance in multidisciplinary teams: The importance of collective team identification. Academy of Management Journal, 48, 532-547.
Wegner, D. M. (1987). Transactive memory: A contemporary analysis of the group mind. In B. Mullen & G.T. Gothals (Eds.), Theories of group behavior (pp. 185-208). New York: Springer-Verlag.
Yaniv, I., & Kleinberger, E. (2000). Advice taking in decision making: Egocentric discounting and reputation formation. Organizational Behavior and Human Decision Processes, 83, 260-281.
Yoo, Y., & Kanawattanachai, P. (2001, August). Development of transactive memory and collective mind in virtual teams. Paper presented at the Annual Meeting of the Academy of Management, Washington DC.
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