Managing stakeholders in team‐based innovation

Managing stakeholders inteam-based innovation

The dynamics of knowledgeand trust networks

Bettina BuchelStrategy and Organization, International Institute for Management Development,

Lausanne, Switzerland

Levi NieminenDenison Consulting, LLC, Ann Arbor, Michigan, USA

Heidi Armbruster-DomeyerDomeyer GmbH & Co. KG, Bremen, Germany, and

Daniel DenisonOrganization and Management,

International Institute for Management Development, Lausanne, Switzerland

Abstract

Purpose – Team-based innovation requires a balance of creative and pragmatic processes both withinteams and between teams and their organizational stakeholders. However, prior research has focusedprimarily on the internal team dynamics that facilitate innovation, paying comparatively littleattention to team-stakeholder dynamics. The purpose of this study is to address this limitation bystudying the impact of team-stakeholder networks and shared cognition on the effectiveness ofinnovation teams.Design/methodology/approach – This study investigates the knowledge and trust linkagesbetween 51 new product development (NPD) teams and their organizational stakeholders using amixed methods design that combines network analysis, surveys, and qualitative interviews. Multipleindicators of team effectiveness were collected at various stages of the innovation process.Findings – The results show that effective NPD teams establish knowledge ties with manynon-redundant organizational stakeholders and foster a high level of agreement among stakeholdersabout team innovation factors. Conversely, effective NPD teams also establish highly centralized trustnetworks that are focused on only a few key stakeholders in the organization.Research limitations/implications – This study focuses on NPD teams in chemical andpharmaceutical manufacturing. Future studies should seek to replicate the findings using largersamples of teams involving diverse innovation tasks.Practical implications – These results have implications for the most effective way to build andmanage innovation teams, considering both pre-existing stakeholder linkages and networkingstrategies for the future.Originality/value – The results suggest that the optimal characteristics of team-stakeholderknowledge and trust networks differ and highlight the unique importance of shared understandingabout risk-taking and creativity beyond higher overall levels.

Keywords Innovation, Teams, Knowledge sharing, Social networks, New product development,Trust

Paper type Research paper

Innovation is the process of generating new ideas and putting those ideas into practice(Caldwell and O’Reilly, 2003). The ability to innovate successfully is critical to thegrowth and competitiveness of organizations (Amabile, 1988; Brown and Eisenhardt,

The current issue and full text archive of this journal is available atwww.emeraldinsight.com/1460-1060.htm

European Journal of InnovationManagementVol. 16 No. 1, 2013pp. 22-49r Emerald Group Publishing Limited1460-1060DOI 10.1108/14601061311292841

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1998; Hargadon, 1998; Nonaka and Takeuchi, 1995). Teams serve as an importantvehicle for organizing the creative and pragmatic aspects of innovation, particularlyas organizations become flatter and more dynamic in response to highly complexoperating environments (Ancona et al., 2002; Hargadon, 1998; McAdam andMcClelland, 2002). Prior studies uncover a number of challenges facing modernwork teams as they strive to deliver innovate solutions and products but focusprimarily on the set of factors related to internal team structures and processes(Edmondson and Nembhard, 2009; Mathieu et al., 2008). In contrast, less attention hasbeen paid to the linkages that successful teams establish with their externalstakeholders in the organization, with whom they build partnerships, secure resources,and exchange knowledge (Choi, 2002; DeChurch and Mathieu, 2009; Glynn et al., 2010;Marrone, 2010).

Team boundary spanning refers to the range of actions implemented by teams tobridge internal team processes to external stakeholders (Ancona and Caldwell, 1992).Although prior research supports the general importance of boundary spanningwithin and across the organization (e.g. Burke et al., 2006), a number of questionsremain unanswered regarding the specific mechanisms through which boundaryspanning impacts the innovation effectiveness of teams (Edmondson and Nembhard,2009; Marrone, 2010). This study was concerned with the following overarchingquestions related to new product development (NPD) teams’ boundary spanning effortswith organizational stakeholders external to the team. First, how do successful NPDteams manage the exchange of knowledge and trust beyond the internal team? Andsecond, is there a benefit of shared knowledge (or cognition) that extends beyondthe internal team? Throughout this manuscript, we use the term internal to refer to thestructures and processes of team members and external to refer to non-team membersfrom the host organization (e.g. individuals from production).

The overarching questions for this study follow from different theoreticalperspectives. Social networking theory suggests that the interconnectivity amongindividuals and groups defines the patterns of communication and interdependencethat exist (Brass et al., 2004; Kilduff and Tsai, 2003). Prior studies have demonstratedthe link between the network characteristics of teams and team effectiveness outcomes(e.g. Balkundi and Harrison, 2006; Balkundi et al., 2007; Reagans and Zuckerman,2001). Marrone (2010) identified social networking as a key perspective that shouldguide future work on team boundary spanning. However, as with prior scholars, thefocus of this discussion was on the network characteristics within the internal team.For example, Marrone (p. 930) proposed:

[y] supportive internal team network structures are likely to be essential for effectivelydisseminating information obtained through boundary spanning to the team’s members.

Yet, a more fundamental question remains regarding how the teams’ external socialnetwork facilitates or impedes the actual boundary spanning that must take place first,before the outside information can be brought inside and shared among internal teammembers.

From a slightly different perspective, theories of team cognition suggest thatteams are most effective when team members have a shared understanding ofperformance-relevant knowledge (DeChurch and Mesmer-Magnus, 2010). For example,closely held team mental models – defined as shared understandings of thefoundations of the performance context (Cannon-Bowers and Salas, 2001) – enhanceeffectiveness at the collective level by aligning the expectations and behaviors of

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Managingstakeholders

individuals (Kozlowski and Ilgen, 2006; Mohammed et al., 2010). Despite that teamsalso need to manage alignment and coordination with external individuals, littleattention has been paid to whether teams benefit from shared cognitions that extendbeyond the internal team. Specific to the performance context of interest here, wefocussed our examination of team-stakeholder agreement on several foundationalelements of successful innovation: customer focus, entrepreneurial culture, and goalorientation.

In the sections that follow, we more fully introduce the team boundary spanningliterature, focussing on social networking and shared cognition as two theoreticalperspectives that have the potential to offer new insights into the mechanismsunderlying effective team boundary spanning. We then offer specific hypothesesrelating team-stakeholder networks and shared cognition to innovation effectiveness.Next, we describe more fully the mixed methods design and NPD teams that served asthe basis for our empirical investigation. The results of hypothesis testing arepresented next, followed by a discussion of insights gained from the qualitative portionof the study. Finally, we discuss key practical implications, address the mainlimitations of our study, and offer directions for future research.

Team boundary spanningEarly studies on team-based innovation demonstrated the importance ofcommunication outside the team, particularly the exchange of task- and market-related information (Gladstein, 1984; Tushman, 1977). Subsequent studies by Anconaand Caldwell (1988, 1990, 1992) helped to differentiate among several major types ofboundary spanning behaviors, including actions to protect the team’s interests andpersuade others in the organization (ambassadorial), coordinate design and technicaltasks with other functional areas (task coordinator), scouting for information about themarket and competition (scouting), and guarding the flow of external information andcommunication (guarding). Research by Ancona and Caldwell with NPD teams fromhigh-tech firms found that the most effective teams engaged more frequently in theambassadorial and task coordinator types of boundary spanning and engaged lessfrequently in isolation tactics and prolonged scouting (e.g. beyond the early stages ofthe innovation process). Importantly, these studies were the first to demonstrate thatthe type of external team behavior matters, not just the frequency of the team’sexternal communication (Ancona et al., 2002).

Since the publication of Ancona and Caldwell’s research, a number of studies haveinvestigated external team processes, but few have focussed specifically on NPD teamsand the factors that influence innovation effectiveness. Marrone’s (2010) recent reviewof the team boundary spanning literature points to the special importance of generalinformation searching outside the team that can bring new knowledge and technicalexpertise inside to support innovation (Hargadon, 1998). At the same time, no newempirical studies were cited as evidence of this relationship (i.e. since Ancona andCaldwell’s research). Thus, Marrone’s conclusions underscore that, despite progress inseveral areas (e.g. understanding the antecedents of team boundary spanning), there isa clear need for additional studies to elucidate “at a finer-grained level how a team canmost effectively carry out critical boundary spanning processes” (p. 929).

One area in particular need of additional exploration within the team innovationdomain involves the transfer of knowledge and trust across team boundaries (Nonakaand Takeuchi, 1995; Sheremata, 2000). To effectively engage others in the organization –whether through ambassadorial, task coordinator, or general information searching

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tactics – teams need effective strategies for determining who to engage as stakeholdersfor various kinds of exchanges and how best to develop and manage thoserelationships. Two perspectives that are particularly relevant for addressing theseissues are social networking and team cognition.

The social networking perspectiveThe structural characteristics of networks are typically described in terms of thepattern of “ties,” where a tie refers to an interconnection between two individuals or“nodes” within the network (Scott, 2000; Wasserman and Faust, 1994). Dense networksare characterized by a pattern of intense interconnectivity, with ties establishedbetween many or most of the nodes in the network. On the other end of a continuum,a highly “centralized” network is one in which fewer ties are active, and those tiesthat are active tend to be concentrated on a small subset of nodes in the network(Brass et al., 2004; Burt, 2000).

As implied by the boundary spanning literature, but perhaps less well developed inthe teams literature, a team’s network includes nodes inside and outside the team (e.g.external stakeholders), as well as the ties that connect nodes across team boundaries.Applying this broader perspective, teams with dense team-stakeholder networks haveties to many external stakeholders, whereas teams with centralized team-stakeholdernetworks have concentrated linkages with fewer external stakeholders. Figure 1depicts this shift in perspective, contrasting the traditional (internal) view of teamnetworks with the alternative view that was taken in this study, focussing on team-stakeholder linkages. Both the dense and centralized networks shown illustrate a highdegree of redundancy in the ties with external stakeholders; however, the crucialdifference is in the number of external stakeholders that are engaged by the team aswell as the pattern of linkages with those stakeholders.

A longstanding area of research has investigated the unique benefits andtradeoffs of different network structures or patterns. One key finding emerging fromthis literature is that the content of the linkages matters when considering theconsequences of various structural arrangements (Brass et al., 2004; Harrington, 2002).For example, although dense communication patterns have been associated with anumber of benefits, Sparrowe et al. (2001) found that, when exchanges involvedhigh levels of interpersonal conflict, higher network density was associated with adecrease in team performance. Building on this rationale below, we consider the typesof network configurations that are likely to be most beneficial for team-basedinnovation when it comes to the exchange of knowledge v. trust across teamboundaries. Knowledge linkages are characterized by the exchange of explicit andtacit project-related information, whereas trust reflects the parties’ perceptionsof benevolence and competence in the exchange and willingness to share sensitiveor confidential information (Costa et al., 2001; Levin and Cross, 2004; Tsai andGhoshal, 1998).

Knowledge linkages with external stakeholdersThe unique benefits afforded by access to non-redundant resources and knowledgewere established by Granovetter’s (1973) seminal research on the strength of weak tiesand reflected closely in Burt’s (2001) structural holes theory. Granovetter’s researchdemonstrated that, for job seekers weak ties (e.g. distant acquaintances) were morebeneficial than strong ties (e.g. close personal friends), leading to more and better jobopportunities. This is because of the greater potential that weak ties carry when it

25


comes to providing the individual with new and diverse information. In contrast,individuals who are closely connected are likely to already share access to similarinformation and resources. Burt’s research extended this work by discussing theadditional value that holding a “structural hole” position entails. When an individualis tied to two or more individuals who are not linked to one another (a structuralhole position), the individual is empowered as a “broker” between otherwisedisconnected parties.

Although the importance of non-redundant ties was established in reference toindividuals and their interpersonal networks, subsequent research has applied asimilar rationale when studying the social networks of and among groups (Brass et al.,2004). At the firm level, Hargadon (1998, p. 210) describes the most successful

Traditional (internal only) perspective on team networks

Team A: High density Team B: High centralization

Team D: High centralizationTeam C: High density

Team-stakeholder perspective on team networks

Notes: Individuals (or “nodes’’) are depicted by dots and “+” symbols. Dots representinternal team members and are encircled by a dotted line to indicate the internal team’sboundary.“+” symbols represent external stakeholders to the team. Lines indicate ties that areactive. Team A illustrates high density because all possible ties among team members areactive. Team B illustrates high centralization because all active ties are concentrated on twoteam members (i.e. the inner-most dots in the figure). Team C illustrates high densitybecause all possible ties between team members and external stakeholders are active. Team Dillustrates high centralization because all active ties are concentrated on two externalstakeholders

Figure 1.Traditional versusteam-stakeholderviews of dense andcentralized networks

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innovators as those that are able to continuously broker “knowledge from whereit is known to where it is not,” such as across diverse technological and industryboundaries. A recent study by Bergenholtz (2011) supports this proposition,demonstrating the positive effects of weak ties when conducting inter-organizationsearches at the beginning stages of innovation. This ethnographic study describeda Danish firm’s strategy to bring outside scientists, potential customers, and evencompetitors inside the firm to interact with new products and the employees whodesign them. Other researchers have also emphasized linkages to customers as theprimary source for new idea generation, particularly early on in the innovation process(e.g. Dougherty, 1990). Importantly, Reid and de Brentani (2004) note that this externalperspective can also be achieved vis-a-vis mediating linkages to market-facingfunctions within the organization.

Within-organization knowledge transfer has been identified as a key factor insuccessful innovation. Research by Tsai and colleagues (Tsai, 2001; Tsai and Ghoshal,1998) has shown that the transference of new knowledge between groups withinorganizations (e.g. across functional areas) is a critical factor in learning andknowledge creation. As such, groups that are positioned to exchange knowledge withmany other groups within the organization have the best opportunity for learningand innovation. The social capital view of networks reinforces the value ofinterconnections that bring diverse perspectives and functional backgrounds intothe innovation process, such as when “outside expertise” leads to new insights or waysof thinking about an innovation task (Inkpen and Tsang, 2005; Reagans andZuckerman, 2001).

Extended to team-based innovation, this literature suggests that teams may benefitmost directly from widely dispersed and diverse knowledge networks with manynon-redundant linkages outside the team (e.g. weak ties). One additional reason thatweak ties may facilitate innovation has to do with the team’s ability to bring in externalknowledge without relinquishing too much control over the innovation process. Alongthese lines, Reuf (2002) demonstrated that weak ties can be more beneficial toentrepreneurs because they strike an appropriate balance between outside informationand outside influence. In contrast, strong ties carry a greater degree of obligationand can eventually constrain the entrepreneur from acting in truly innovativeways. Although no studies that we are aware of have focussed squarely on theteam-stakeholder networks of NPD teams, building our rationale from the indirectevidence cited here (e.g. studies of interorganizational and individual networks), wepropose that NPD teams will be most successful innovating when they are able to drawon a diverse set of perspectives and project-related information from outside the team.Therefore, we hypothesize the following in reference to team-stakeholder knowledgenetworks:

H1. Increasing non-redundant knowledge ties to external stakeholders has apositive effect on NPD teams’ innovation effectiveness.

H2. Increasing centralization of knowledge ties on fewer external stakeholders hasa negative effect on NPD teams’ innovation effectiveness.

Trust linkages with external stakeholdersIn comparison to project-related knowledge, trust ties are characterized by a morerelationally intensive exchange, and therefore typically require a greater investment

27


of energy and resources to develop and sustain them over time (Larson, 1992).Accordingly, a number of studies have linked centralized networks, and morespecifically, strong ties to the development of trust relationships (Brass et al., 2004).For example, Levin and Cross (2004) demonstrated that strong ties promote theformation of trust, which in turn, accounts for individuals’ perceptions that theinformation they receive in network exchanges is useful. These findings help toexplain the unique benefits of strong ties when the information exchanged is complex(Hansen, 1999), suggesting that strong ties, while suboptimal from a knowledgediversity perspective, are nevertheless key channels for protecting the team and occurwithin trusted relationships.

One such exchange involves the transmission of confidential or potentially sensitiveinformation across NPD team boundaries. As described by Larsson et al. (1998), thecorresponding increase in openness, accessibility, and transparency that follows fromclose strategic partnerships also presents several unique risks that need to be managedappropriately. In an NPD setting, one clear risk involves maintaining the team’sintellectual property. In doing so, the team faces a number of difficult decisions,including who to confide in, when to confide in them, and what type of information canbe shared v. held inside the team. A second risk involves the potential for negativeconsequences when strong ties dissolve (Seabright et al., 1992). The most apparent riskinvolves whether the dissolved parties choose to exploit the team’s interests. Anotherreason is that achieving a high level of trust may require building ties to tertiaryindividuals either inside or outside the team (e.g. gaining credibility with one’s peers),such that the potential for negative spillover effects is greater. And finally, trustis undergirded by a higher degree of reciprocal influence between the connectedparties (Uzzi, 1997). Returning to Reuf’s (2002) discussion of the critical balancebetween cooperation and conformity, this suggests that trust relationships ought to bedeveloped selectively in a way that wins external support without compromisingthe creative force of the team. In contrast, trust relationships that are dispersed widelyacross many external stakeholders may have the effect of pulling the team in too manysimultaneous directions.

For these reasons, we propose that confidential information accorded by trustrelationships is best exchanged across team-stakeholder networks that are centralizedon a select number of external stakeholders rather than dispersed widely. Therefore,we hypothesize the following in reference to team-stakeholder trust networks:

H3. Increasing non-redundant trust ties to external stakeholders has a negativeeffect on NPD teams’ innovation effectiveness.

H4. Increasing centralization of trust ties on fewer external stakeholders has apositive effect on NPD teams’ innovation effectiveness.

Shared team-stakeholder cognitionsInkpen and Tsang (2005) suggest that a number of the risks associated with externalpartnerships can be alleviated when the parties share a common language thatincludes “accepted but tacit codes of conduct” (p. 158). These agreed upon codes ofconduct follow closely from a shared understanding (or shared cognitions) of thework and cultural context, particularly the behaviors and values that are normative forthe group (Choi, 2002). In this way, shared cognition facilitates performance in groupsby allowing the members to anticipate and respond to one another’s actions in an

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efficient and coordinated manner (DeChurch and Mesmer-Magnus, 2010; Mohammedet al., 2010).

Because of the intense interdependence of NPD teams with external stakeholders, itis further likely that these same risks and benefits operate across team boundaries aswell (Marrone, 2010). As NPD teams shepherd their products through the multiplestages of creativity and implementation, coordinated actions with externalstakeholders may be partly dependent on the team’s ability to make their internalmindset known to others outside the team. According to Edmondson and Nembhard(2009), failure to transcend mindsets in this manner can result in “misunderstandingsand misattributions of behaviors and motives,” which can ultimately delay or derail theteam’s progress.

Still, an important question remains regarding what needs to be jointly understoodby NPD teams and their external stakeholders. The logic advanced within the teammental models literature holds that those factors which are most centrally and directlyrelated to effective NPD performance are also likely the ones for which closely heldmental models are most beneficial (Cannon-Bowers and Salas, 2001; Kozlowski andIlgen, 2006). In the present study we investigated shared team-stakeholder cognitionsaround three team-level factors that have been found to support team-basedinnovation. The three themes are customer focus, entrepreneurial culture, and goalorientation. Beyond the innovation success attributable to higher overall or mean levelson each of these factors, we were particularly interested in the contribution ofagreement among team members and organizational stakeholders.

Customer focusThis factor represents the external orientation of the NPD team (Brown andEisenhardt, 1995; Leonard-Barton, 1995). Externally oriented teams are more likely toengage the customer as a source for new product ideas and seek customer input in thedesign, development, and testing of new products. As a result, externally focussedNPD teams should be better able to capture the needs of customers throughout theinnovation process and use outside perspectives to spark new ideas and ways ofthinking (Cooper and Kleinschmidt, 1995). A recent meta-analysis by Hulsheger et al.(2009) found support for the positive effects of time spent communicating externally onteam innovation effectiveness. At the same time, Bergenholtz’s (2011) aforementionedstudy reveals a number of challenges associated with bringing the customer “inside”the organization. As a result, agreement on the degree and manner in which to engagethe customer would appear crucial in the team’s ability to avoid missteps and retainstakeholder buy-in. Therefore, we hypothesized the following:

H5a. Team customer focus (mean) is positively linked to NPD teams’ innovationeffectiveness.

H5b. Shared team-stakeholder cognitions on customer focus is positively linked toNPD teams’ innovation effectiveness.

Entrepreneurial cultureA related construct that has been assessed within the innovation literature isentrepreneurial culture. Entrepreneurial values and behavioral norms can supportinnovation by reinforcing the types of risk-taking and creative behaviors that lead

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to new product ideas (Cooper and Kleinschmidt, 1995; Jaskyte and Dressler, 2005).As noted by Caldwell and O’Reilly (2003), some organizational cultures provide “socialapproval to activities such as trying new ways of doing things, tolerating mistakes,taking action, and other attitudes and behavior associated with innovation” (p. 502).Other organizations may support innovation only nominally or have fragmented,ambiguous, or conflicting cultural support (Martin, 1992). In either case, acting innon-normative ways at the NPD team-level (i.e. in comparison to the broaderorganizational culture) is likely to result in intergroup conflict and could eventuallyresult in a loss of support. Alternatively, shared understanding at the team-stakeholderlevel likely signals that there is cultural alignment and that the team’s internalnorms and values are understood by key members of the organization. Therefore, wehypothesized the following:

H6a. Team entrepreneurial culture (mean) is positively linked to NPD teams’innovation effectiveness.

H6b. Shared team-stakeholder cognitions on entrepreneurial culture is positivelylinked to NPD teams’ innovation effectiveness.

Goal orientationSheremata (2000) describes the contradictory forces that organizations must negotiateto balance the creative and pragmatic aspects of the innovation process. Centrifugalforces allow the organization to develop new knowledge and capabilities bydiversifying structures and processes, whereas centripetal forces create the focus andenergy needed to coordinate efforts and deliver new products on time and withinbudget by integrating structures and processes. Reflecting on Sheremata’s model, thepreviously described team innovation factors of customer focus and entrepreneurialculture can be described as centrifugal, diverting the organization’s attention andresources toward its customers and surrounding environment while supportingnew and potentially risky strategies. In balance, goal orientation can be described ascentripetal, helping to create structure and accountability around the team’s workprocess, objectives, and timeline.

Research supports the general importance of goal-setting for team performance andsheds light on several ways in which goals impact team effectiveness. For example, thedevelopment of specific and challenging goals improves team performance byfocussing attention and commitment, increasing goal commitment and goal striving,and clarifying the path to goal attainment (Locke and Latham, 1990; O’Leary et al.,1994). These same benefits are anticipated for NPD teams. In addition to theimportance of having a strong goal orientation within the NPD team, it is alsoimportant that the individuals involved are aligned in their understanding of goals andthe work practices that support their implementation in the innovation context(Caldwell and O’Reilly, 2003; Edmondson and Nembhard, 2009). This provides acommon basis and framework for organizing how individuals work together towardtheir shared objective. Extending beyond the team’s internal members, we anticipatesimilar positive effects due to aligned goal orientation with external stakeholders.Therefore, we hypothesized the following:

H7a. Team goal orientation (mean) is positively linked to NPD teams’ innovationeffectiveness.

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H7b. Shared team-stakeholder cognitions on goal orientation is positively linked toNPD teams’ innovation effectiveness.

This study used a mixed methods design combining social networking, surveymethodologies, and qualitative interviews. This approach allowed for unique insightsinto the potential processes underlying the effects of team-stakeholder networking andshared cognition, as well as the potential interplay between these processes over time.In contrast, prior studies have treated these topics separately without considering hownetworking might shed light on the development of shared cognitions, and vice versa,how shared cognition could influence the exchange of knowledge and trust withinnetworks. A second way that our study contributes to the literature is by focussingon a specific and understudied team performance context – team-based innovation.The study sample included 51 NPD teams from six chemical and pharmaceuticalmanufacturing companies as they progressed through various stages of the innovationprocess. And finally, as laid out above, our study addresses an important gap byextending research on networking and shared cognition across internal teamboundaries.

MethodStudy designA mixed methods design was used, combining social network analysis, team 3601surveys, and qualitative interviews (for a general discussion of the advantages/tradeoffs of mixed methods designs, see Creswell, 2009 and Jick, 1979). Threeconsiderations guided our choice of methods: first, the methods that were mixedfollowed from the diverse theoretical perspectives that guided out study; second, theaddition of qualitative interviews provided further insights into the potentialmechanisms underlying our quantitative findings; and third, the design accommodatedthe practical constraints of the participating teams and organizations.

Due to these constraints, not all teams were able to complete all aspects of the study.Instead, partially overlapping subsamples were specified. Figure 2 provides aschematic of the overall study design, depicting the methods, study variables, andtiming of data collection for each subsample of teams. NPD team effectiveness wasassessed with rating instruments, interviews with managers, and financialperformance outcomes. The timing of these assessments varied across subsamples,from concurrent ratings of performance at baseline to financial performanceinformation collected 36 months later. During the study, teams progressed throughvarious stages of the innovation process, from concept sharing to concept analysis,validation, development, and implementation.

SampleThe overall sample included 51 NPD teams from six large organizations in thechemical and pharmaceutical manufacturing industry. This sample resulted from alarger set of teams that were invited to participate in the study, having satisfied thefollowing criteria: served a primary NPD role in their organization; were comparablein terms of industry type; and had small to moderate team sizes ranging from 4 to 20members. After potential teams were identified, the research project was described tosenior management and team leaders, with 51 teams in total agreeing to participate.

Subsample A (n¼ 28) provided the basis for testing the social networkinghypotheses. Completed network questionnaires were obtained from 203 team members

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and team leaders, representing an average response rate per team of 90 percent, andranging from 71 to 100 percent[1]. The mean size of teams in subsample A was 8.21members (SD¼ 3.17). Subsample B (n¼ 31) provided the basis for testing the sharedcognition hypotheses. Completed 3601 surveys were obtained for a total of 226 teammembers and team leaders (M¼ 7.29, SD¼ 3.69 per team), as well as 262 external teamstakeholders (M¼ 8.42, SD¼ 4.90 per team).

Subsample C (n¼ 8) served as the basis for a qualitative analysis exploring the jointeffects of stakeholder networks and shared perceptions. These teams were also partof subsamples A and B, thus representing the overlap among the three subsamples.The mean size of teams in subsample C included 9.13 team members (SD¼ 7.32) and20.88 external stakeholders (SD¼ 7.79). Table I provides a brief description of eachteam’s core purpose. Unlike the other subsamples, these teams were all from a singleorganization, specifically a large North American chemical manufacturing companywith annual sales of over $23 billion.

MeasuresSocial network questionnaire. Team leaders were asked to nominate the externalstakeholders whose names would be listed on the questionnaires. Specifically, theywere asked to list at least four or five people from each of the departments that theirteams typically interact with, including research and development, engineering,production, sales and marketing, as well as two or three of the team’s externalmanagers who had been particularly important in helping the team achieve its goals.On average, the team leaders identified 11.4 external stakeholders (SD¼ 3.43).

Total sample: 51 teamsData collection methodsand team-stakeholder variables

Innovation team effectivenessoutcomes and timing

Subsample A28 teams

Subsample B31 teams

Subsample C8 teams

Quantitative: Social networkquestionnaires

• Non-redundancy and centralization of team- stakeholder knowledge and trust networks

Quantitative: 360° surveys

• Team customer focus, entrepreneurial culture, goal orientation

• Shared stakeholder perceptions of team variables

Qualitative: Mixed methodsa

• Team intemal and external focus

• Team-stakeholder linkages

• Team contextual variables

0 monthsb 24 months 36 months

Financialperformance

Effectivenessinterviews

Continuedfunding

Effectivenessratings

Compositeperformance

ratings

Notes: aMethods included interviews, 360 surveys, network analysis; b0 monthsdenotes that criteria were collected concurrently with predictor measures(e.g. social network questionnaires)

Effectivenessinterviews

Figure 2.Overview ofmixed methodsresearch design

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Questionnaires were completed by team members, team leaders, and externalmanagers.

Separate sections assessed external knowledge and trust linkages. The knowledgesection asked respondents the following question in reference to each nominated teammember and external actor: “How often do you communicate with each person in orderto exchange information or knowledge specifically related to the project? For example,this might include market data or NPD information about the proposed project.” Usingthe same format, the trust section presented the following question: “How often do youconfide in each person? For example, how often do you discuss confidential issues,whether related to the project or not?” Respondents indicated a frequency from7¼ daily to 1¼ almost never or never. Network data were prepared for analysis usingUcinet 6.0 software (Borgatti et al., 2002).

Social network analysis requires a high response rate because missing data cause“holes” in the network data matrix, not only for the missing individual but also for theindividual’s relations with others in the network. However, some missing data can bereplaced using the reconstruction method described by Stork and Richards (1992).This method assumes that if individual A describes a relationship with individual B(in-degree for node B), a relationship does in fact exist between them without requiringconfirmation by individual B (out-degree for node B). As a result, the in-degrees can beused for individuals who did not complete the questionnaire in order to reconstructtheir relationships with others. After applying this reconstruction method, 71.4 percentof teams had no missing data.

Indices for non-redundant ties and centralization were derived separately forknowledge and trust. The index for non-redundant ties was based on Everett andBorgatti’s (1999) concept of group degree centrality. Group degree centrality is thenumber of ties from individuals in a group to external individuals, where ties tothe same individual are counted only once. Therefore, group degree centrality capturesthe non-redundant ties from members of the NPD teams to their external stakeholders.With binary data, multiple ties to the same external actor are counted only once. Withvalued data, as was used here, only the single highest frequency score for each external

TeamTeamsize

Number ofstakeholders Team purpose

C1 4 16 Developed technology to enable a new business model and a movedownstream

C2 8 16 Developed technology to improve the performance of an end-useapplication

C3 8 15 Focused on selling technology and solutions to companies to improvetheir efficacy

C4 26 37 Developed technology to improve the performance of downstreamcustomer products

C5 4 16 Developed technology to allow downstream customers to measuretheir results

C6 5 18 Sold raw materials to improve the performance of end-use productsC7 3 18 Developed technology and end-use products to allow a new business

model and participation downstreamC8 15 31 Developed technology to improve the performance of end-use

applications

Table I.Description of

subsample C(qualitative

analysis) teams

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tie was retained. Based on the valued data, we derived normalized group centrality asthe ratio of actual group degree centrality to the maximal possible group degreecentrality. Therefore, values ranged from 0 to 1, where 1 indicates that a team has tieswith all possible stakeholders in the organization.

Centralization represents the extent to which ties are focussed on a single externalnode as opposed to being evenly distributed. The index of centralization reflects thesummed differences between the most centralized external stakeholder and all otherstakeholders in the organization. This was calculated as the ratio of the existing sum ofdifferences to the sum of all possible differences based on the following formula(Wasserman and Faust, 1994).

Pi 6¼j ðmj � diÞðn� 1Þmj

where mj is equal to the valued degree of the most centralized stakeholder j, and di isthe valued degree of all other external stakeholders i. Thus, centralization scores foreach team were between 0 and 1, where 1 indicates that all external ties are centralizedon j and 0 indicates that external ties are equally distributed across all externalstakeholders. Finally, we note that non-redundancy and centralization arecomplementary indices. Increasing the number of non-redundant external ties isaccompanied by a decrease in centralization and vice versa.

3601 surveys. Team innovation factors were measured using a team 3601 survey.Surveys were completed by team members, team leaders, and external stakeholders,including managers and employees working upstream (e.g. sales and marketing)or downstream (e.g. research and development). The survey was adapted fromthe Denison Leadership Development Survey, a valid and reliable developmentalinstrument designed to assess key behaviors and skills of highly effective leaders(Denison et al., 2012). Item modifications involved a referent shift from leaders(individuals) to teams, such that respondents provided their ratings of team attributes(Klein et al., 2001). For example, the item, “Holds individuals and teams accountable forachieving goals and objectives,” was modified to read, “This team is accountable forachieving its goals and objectives.” Items used a seven-point Likert-type scale rangingfrom 1¼ strongly disagree to 7¼ strongly agree.

Customer focus included six items (a¼ 0.92) examining team understanding ofpresent and future customer needs, the degree of ongoing input from the customer, andthe extent to which all team members are driven by a concern to satisfy the customer(e.g. this team has a deep understanding of customer wants and needs; this teamactively seeks feedback from customers). Entrepreneurial culture included six items(a¼ 0.91) examining team emphasis on creativity and innovativeness, as well astolerance of associated risk-taking behaviors (e.g. this team encourages creativethinking; this team generates innovative ideas and solutions to problems). Goalorientation included six items (a¼ 0.91) examining the degree to which teams encouragedaccountability in setting and accomplishing goals (e.g. this team sets clear goals that areambitious, but realistic; this team establishes high standards of performance).

Principal components analysis revealed a unidimensional factor structure for eachsix-item measure, with the first extracted component accounting for 70.7 percent(customer focus), 68.5 percent (entrepreneurial culture), and 68.4 percent (goalorientation) of the total item-level variance. Team factor scores were derived by taking

34

EJIM16,1

the mean across items and respondents, including all internal team respondents andexternal stakeholders from the organization. Shared cognition among team membersand external stakeholders was computed using an index of inter-rater agreement(Lindell et al., 1999). Inter-rater agreement measures “the extent to which differentjudges tend to make exactly the same judgments about the rated subject,” as opposedto inter-rater reliability, which assesses the similarity of rank-ordered judgments acrossraters. We chose inter-rater agreement because we were ultimately interested in thecloseness of ratings by team members and external stakeholders.

Team effectiveness. Multiple criteria were used to assess teams’ effectivenessthroughout the innovation process. The first involved performance ratings by teammembers, team leaders, and external managers collected concurrently with the socialnetwork analysis questionnaire for the teams in subsample A. The performance ratingscale included ten items grouped into three dimensions. Items were adapted from thescales used by Hoegl and Gemuenden (2001). Effectiveness measured the quality ofinnovation products (a¼ 0.96). Efficiency assessed the team’s adherence to schedulesand budgets (a¼ 0.86). Satisfaction measured the extent to which team members weresatisfied with the team’s dynamics (a¼ 0.93). Items used a seven-point Likert-typescale, ranging from 1¼ very strongly disagree to 7¼ very strongly agree. Analyses arebased on a composite index of team performance, derived as the mean across sourcesand dimensions.

The second criterion was based on a five-item measure of team effectiveness thatwas administered concurrently with the team 3601 surveys in subsample B. Ratingswere provided by key external stakeholders and senior managers from theorganization. An example item is: overall, this team is one of the most effectiveteams in our organization. Items used a seven-point Likert-type scale, ranging from1¼ very strongly disagree to 7¼ very strongly agree (a¼ 0.92). Principal componentsanalysis revealed a unidimensional factor structure, with the first extracted componentaccounting for 76.9 percent of the total item-level variance.

At the end of each stage, the team’s progress and results were evaluated by businessdirectors who decided whether to continue funding or terminate the team. Thus, a thirdcriterion involved whether teams received continued funding or not (1¼ active,0¼ terminated). For the teams in subsample B, continued funding was determined at24 months following the administration of surveys. At that time, 19 of the 31 teamshad been terminated. The surviving teams had moved through the first two stages ofthe stage-gate innovation process and were either in the concept analysis (stage 2)or validation stages.

The final two criteria were collected as part of the qualitative analysis withsubsample C. Interviews with external managers and team leaders were conducted atbaseline and 24 months later. These interviews were used to gather evaluativefeedback regarding team effectiveness and to gain deeper insights into team dynamicsand the context surrounding team performance. The majority of interviews wereattended by two researchers, who created communication logs that were latertranscribed. Consensus meetings were used to solve any discrepancies. Teams werethen clustered into high (n¼ 3), medium (n¼ 2), and low (n¼ 3) effectiveness. Thesegroupings were determined by ratings made by managers of the NPD teams during theinterviews, where low reflects below average ratings on team effectiveness, mediumreflects average ratings, and high reflects above average ratings. In addition, financialperformance was based on teams’ actual sales after 36 months compared to forecastedsales at baseline. Teams were categorized as high performing if they exceeded sales

35


projections, medium if they generally met projections, and low if their sales levelswere clearly below projections. These categories were created to allow broadcomparisons of teams based on their general effectiveness levels. At the conclusion ofthe study, six teams had progressed to implementation (stage 5), one team hadprogressed to product development (stage 4), and one team had been terminated by theorganization.

ResultsThe effect of team-stakeholder knowledge and trust networksTable II shows the descriptive statistics and correlations for the variables involved intesting H1-H4. Hypotheses were tested using regression analysis with SPSS version17.0. Predictors were entered into the model simultaneously, including four controlvariables and the four substantive predictors. We controlled for team size (i.e. thenumber of team members, team leaders, and external stakeholders), the number ofout-degrees for knowledge and trust ties (the sum of outgoing ties to external actors),and centralization on the team leader (i.e. with the team leader specified as j inthe centralization formula presented previously). As with the non-redundancy andcentralization indices described above, out-degrees and centralization on the teamleader were normalized by computing the proportion of actual ties relative to allpossible ties, such that values ranged from 0 to 1. Team size was controlled becausestudies have generally found negative relationships between network size and networkdensity, such that it is important to account for their non-independence when studyingeffects on third variables (e.g. Reagans and Zuckerman, 2001). Similarly, we controlledfor the number of out-degrees when examining the unique effects of non-redundancy,as out-degrees and non-redundancy were moderately to strongly positively correlated(r¼ 0.55 for trust ties; r¼ 0.69 for knowledge ties).

Results of the regression analysis are shown in Table III. The overall model wassignificant (F¼ 3.17, po0.05) and accounted for 53 percent of the variance incomposite performance ratings. H1, which proposed a positive effect of non-redundancy of stakeholder knowledge ties on team performance, was supported(B¼ 0.86, po0.05), demonstrating that many knowledge ties to non-redundantstakeholders outside the NPD team facilitated higher team performance. H2, whichproposed a negative effect of centralization of stakeholder knowledge ties on teamperformance, was not supported (B¼ 0.11, p¼ ns). H3, which proposed a negativeeffect of non-redundant stakeholder trust ties on team performance, received weaksupport as evidenced by a trend-level effect (B¼�0.59, po0.10). H4, which proposed apositive effect of centralization of stakeholder trust ties on team performance, wassupported (B¼ 0.45, po0.05), indicating that the most effective teams werecharacterized by more highly centralized trust networks – that is, where adisproportionate number of external linkages focussed on one trusted stakeholderfrom the organization. In summary, the overall pattern of findings indicates different,and in fact opposite, effects for knowledge and trust networks with organizationalstakeholders, such that the most effective NPD teams were characterized by a highdegree of non-redundancy in knowledge linkages and a high degree of centralization intrust linkages.

The effect of team-stakeholder shared cognitionTable IV shows corresponding descriptive statistics and correlations for the variablesinvolved in testing H5-H7. Hypotheses were tested using separate regression analyses

36

EJIM16,1

Var

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78

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Table II.Descriptive statistics

and correlationsfor subsample A

(n¼ 28) variables

37


for effectiveness ratings (model 1) and the continued funding criterion (model 2).Predictors were entered into each model simultaneously, including mean levels andagreement indices (rwg). In addition, we controlled for team size, as previous researchhas demonstrated a negative relationship between sample size and the level ofagreement in perceptions of climate/culture (e.g. Koene et al., 1997). Similarly, ourfindings indicated that team size was negatively correlated with shared customer focus(r¼�0.36, p40.05) and shared goal orientation (r¼�0.42, po0.05) but not sharedentrepreneurial culture (r¼�0.07, p¼ ns).

Results of the regression analyses are shown in Table V. The overall results formodel 1 were significant (F¼ 3.53, po0.05) and indicated that the predictors as a setaccounted for 59 percent of the variance in effectiveness ratings. The overall results formodel 2 were also significant (F¼ 1.60, po0.05) and indicated that the predictors as aset accounted for 49 percent of the variance in continued funding at a 24-month follow-up. H5a, which proposed a positive effect of the mean level of customer focus, was notsupported in either model (B¼�0.04, p¼ ns; B¼ 0.01, p¼ ns). H6a, which proposed apositive effect of the mean level of entrepreneurial culture, was supported in model 1(B¼ 0.56, po0.05) and model 2 (B¼ 2.76, po0.05). H7a, which proposed a positiveeffect of the mean level of goal orientation, received weak support, with a trend-level

Variable M SD 1 2 3 4 5 6 7 8

1. Team size 15.71 7.502. Customer focus 5.58 0.62 �0.083. Entrepreneurial culture 5.16 0.50 �0.31 0.51**4. Goal-orientation 5.35 0.40 �0.17 0.54** 0.67**5. Shared customer focus 0.97 0.01 �0.36* 0.53** 0.44* 0.296. Shared entrepreneurial culture 0.94 0.03 �0.07 �0.08 0.11 0.18 0.237. Shared goal orientation 0.97 0.02 �0.42* 0.14 0.12 0.18 0.24 0.298. Team effectiveness 5.34 0.74 �0.08 0.24 0.55** 0.40* 0.11 0.43* 0.48*9. Continued funding 0.61 0.49 0.34 �0.17 �0.18 0.00 �0.26 0.34 0.20 0.34

Notes: **po0.01; *po0.05

Table IV.Descriptive statisticsand correlations forsubsample B(n¼ 31) variables

Variables B SE

Control variablesTeam size �0.32 0.05Out-degrees knowledge ties �1.25* 2.53Out-degrees trust ties 0.72 5.45Centralization on team leader �0.04 0.24Team-stakeholder networkNon-redundancy external knowledge network 0.86* 1.91Non-redundancy external trust network �0.59** 1.92Centralization external knowledge network 0.11 1.09Centralization external trust network 0.45* 1.03R2 0.53F 3.17*

Notes: n¼ 28. Standardized regression weights and standard errors are shown. *po0.05; **po0.10

Table III.Subsample A regressionresults for team-stakeholder knowledgeand trust networks aspredictors of innovationteam effectiveness

38

EJIM16,1

effect observed for model 2 (B¼ 3.14, po0.10) but no effect observed for model 1(B¼ 0.93, p¼ ns).

H5b, which proposed a positive effect of shared team-stakeholder cognition ofcustomer focus, received weak support, with a trend-level effect observed for model 2(B¼ 2.11, po0.10) but no effect observed for model 1 (B¼�0.23, p¼ ns). H6b, whichproposed a positive effect of shared entrepreneurial culture, received partial support,with a significant effect observed for model 2 (B¼ 2.91, po0.05) but no effect observedfor model 1 (B¼ 0.26, p¼ ns). And finally, H7b, which proposed a positive effect ofshared entrepreneurial culture, received partial support, with a significant effectobserved for model 1 (B¼ 0.45, po0.05) but no effect observed for model 2 (B¼ 1.45,p¼ ns).

In summary, the findings for model 1, with effectiveness ratings as the criterion,demonstrated significant positive effects of mean levels of entrepreneurial cultureand shared cognition on goal orientation. In other words, higher mean levels ofentrepreneurial culture and stronger team-stakeholder agreement about the team’sgoal orientation were associated with higher effectiveness ratings. The findings formodel 2, with continued funding at 24-month follow-up as the criterion, demonstratedsignificant positive effects of mean levels of entrepreneurial culture and sharedcognition on entrepreneurial culture. In other words, higher mean levels of entrepreneurialculture and stronger team-stakeholder agreement about the team’s entrepreneurial culturewere associated with a higher likelihood of continued funding.

Qualitative analysisThe qualitative analysis provided an opportunity to study the combined effects ofteam-stakeholder knowledge linkages and shared perceptions within a subset of eightteams (subsample C) over an extended time frame. Table VI shows the effects of non-redundant team-stakeholder knowledge linkages and team-stakeholder sharedperceptions on team effectiveness based on interviews with team leaders andmanagers and financial performance at 36-month follow-up based on a comparison ofactual v. projected financial performance. As with the previous analyses, the mostsuccessful teams – both in terms of interview judgments of effectiveness and financialperformance at 36 months – tended to be those with a greater number of non-redundant stakeholder knowledge linkages and a higher degree of team-stakeholder

Model 1: team effectiveness Model 2: continued fundingVariables B SE B SE

Control variableTeam size 0.24 0.03 2.23 0.19Team process variablesCustomer focus �0.04 0.43 0.01 1.60Entrepreneurial culture 0.56* 0.45 2.76* 2.74Goal orientation 0.93 0.60 3.14** 2.42Shared customer focus �0.23 17.77 2.11** 55.74Shared entrepreneurial culture 0.26 10.05 2.91* 49.84Shared goal orientation 0.45* 11.97 1.45 56.37R2 0.59 0.49F 3.53* 1.60*

Notes: n¼ 31. Standardized regression weights and standard errors are shown. *po0.05; **po0.10

Table V.Subsample B

regression results forteam-stakeholder shared

perceptions as predictorsof innovation team

effectiveness andcontinued funding

at 24 months

39


agreement. The combination of these two factors also appears important, as thetwo most successful teams (C6 and C4) were those categorized as high-high onnon-redundancy of stakeholder knowledge linkages and shared perceptions,respectively. In contrast, the next two teams shown (C5 and C2), each of which wasless successful than C6 and C4, were categorized as high on one but not both factors(e.g. medium-high). The remaining four teams had medium to low performance overalland generally reflected various combinations of medium and low categorizations interms of non-redundancy and shared cognition. Not surprisingly, the lowestperforming team over the study period was categorized as low-low.

These findings are important because they provide preliminary evidence that theinfluence of the team-stakeholder interface can be observed through later stages ofthe innovation process as evidenced by actual financial performance of NPD teams.The qualitative analysis also provides unique insight into the possible relationshipbetween team-stakeholder knowledge networks and shared cognition. For the eightteams comprising subsample C, the number of non-redundant knowledge ties tostakeholders in the organization is seen to covary positively with the degree of team-stakeholder shared cognition. In other words, teams that had wider knowledgenetworks, and thereby share more information between the team and organizationalstakeholders, also exhibited stronger team-stakeholder agreement.

Interviews with team leaders and managers shed further light on the importance ofNPD teams’ knowledge networks with external stakeholders in the organization. Forexample, the leader of an unsuccessful team (C1) stated, “The number of team memberswith external contacts was two. We spent 40% of our time outside and 60% inside.”Teams composed of many non-redundant linkages to individuals from marketing,manufacturing, and research and development areas of the organization were able to

TeamNumber of non-redundantstakeholder knowledge linkagesa

Team-stakeholdershared cognitionb Effectivenessc

Financialperformanced

C6 High High High HighC4 High High High HighC5 Medium High High No salese

C2 High Medium Medium Terminatedf

C8 Medium Low Medium MediumC7 Medium Low Low MediumC3 Low Medium Low LowC1 Low Low Low Low

Notes: n¼ 8. aNon-redundant team-stakeholder knowledge linkages were based on the results ofsocial network analysis, where low indicates fewer than average knowledge linkages, mediumindicates average knowledge linkages, and high indicates above average knowledge linkages; bteam-stakeholder shared cognition were based on the results of team 3601 surveys, where low indicatesbelow mean, medium indicates mean, and high indicates above mean; ceffectiveness levels weredetermined by interviews with managers of the NPD teams, where low indicates below averageratings on a scale from 1 to 7, medium indicates average ratings, and high indicates above averageratings; dfinancial performance was based on the comparison of actual v. projected sales, where lowindicates substantial underperformance relative to projected levels, medium indicates that projectionswere generally met but not exceeded, and where high indicates that actual sales exceeded projections;ethis team was not yet at the implementation phase of the five-stage innovation process at the36-month follow-up, and therefore had no sales; fthis team was terminated during the 36-monthstudy window

Table VI.Subsample C qualitativeanalysis showingjoint effects of team-stakeholder networksand shared perceptionson innovation teameffectiveness

40

EJIM16,1

learn more from these groups and disseminate information more rapidly. Thus,having many non-redundant ties was clearly related to more effective informationsharing and knowledge transfer. Moreover, although the focus of the presentstudy has been on external stakeholders within the organization, interviews alsohighlighted the importance of networks that extend beyond organizational boundaries.For example, the leader of a highly successful team (C6) stated, “100% of teammembers were talking to different customers. Even the process research guy had tomake a first sales call. He got some mentoring but it was crucial to get an outsideperspective.”

Comments from several managers emphasized the unique role of external trustnetworks and provided further support for the previously described findingsassociating higher centralization and fewer (not more) non-redundant trust ties withhigher effectiveness. Specifically, the importance of having a small number of“champions” with whom the team could confide was an emergent theme. One effectiveteam leader described it as follows: “We have been very assertive about the people whoneed to be informed. Others who would like to know but were not as important wereonly periodically updated by selected team members.” In other words, having a fewkey (senior) trust partners within the company was perceived as more important thankeeping a large number of stakeholders as close partners. Similarly, one team leaderindicated, “We had engaged and supportive management to the right point.” Thesecomments support the idea that selectively engaging the right stakeholders as trustedpartners is crucial.

Several additional comments indicated that these key trust partners must also beinfluential within the organization. According to the team leaders, a high level of trustby key sponsors from upper management translated into support for the team in othermeetings: “Inside [the company], we were perceived to manage our project successfully.I can’t imagine how you’d be successful without having the buy-in from the key internalstakeholders. They are a key set that influences your team.” Along the same lines, asponsor of one of the projects indicated: “Obtaining continued funding was a matter ofmanaging perceptions. If you could show how the project was adding financial value inthe long run, you had bosses within functional areas that differentiate the successfulfrom the less successful teams.”

DiscussionDougherty and Hardy (1996) argued that sustainable innovation cannot occur untilNPD teams are able to resolve “innovation-to-organization problems.” A central aspectof navigating these issues involves managing interdependencies with members ofthe organization who fall outside the team’s internal boundaries. Our study extends theearlier work by Ancona et al. (2002) by examining the exchange of knowledge and trustacross this team-stakeholder membrane. Guided by social networking and sharedcognition theories, our study contributes a unique and finer-grained perspective thanprior studies, which focussed primarily on the frequency of outside communication orthe types of various boundary spanning activities.

Our results suggest that the optimal strategies for managing the team’s externalnetworking with organizational stakeholders differ according to whether the linkagesare focussed on exchanging knowledge v. trust. Indeed, external knowledge and trustties were observed to have an opposite effect on innovation outcomes. The NPD teamsthat were most effective had more non-redundant knowledge ties to externalstakeholders and fewer non-redundant trust ties. In other words, NPD teams benefitted

41


the most from widely dispersed knowledge networks and tightly centralized trustnetworks.

These findings have practical implications for the types of strategies that NPDteams could benefit from when constructing teams (based on individuals’ pre-existingsocial networks) and developing strategies for communicating across team boundaries.Overall, our study points to the need for teams to think and plan their actionsdifferently when it comes to building external knowledge and trust relationships(Gulati and Westphal, 1999; Harrington, 2002; Podolny and Baron, 1997). For manyteam and organizational contexts, this will involve a shift away from simply focussingon more network connections toward the appropriate network connections for differentpurposes. Our findings regarding shared team-stakeholder cognitions and externalteam champions provide further insights into why it might be the case that teams areoptimally configured when they exchange knowledge with many and trust with few.

Knowledge linkages and shared cognitionsBurt’s (2001) structural holes theory describes how teams need to bridge gaps to keystakeholders in order to transfer knowledge outwardly and pull external informationand resources inwardly. If the majority of individuals comprising the team have similarexternal ties – such as by reporting to the same people within the organization,communicating with the same customers, and occupying the same cross-functionalroles – the breadth of knowledge sharing outwardly and inwardly will be rather limitedand unlikely to meet the informational and resource demands needed to support theteam innovation process. In other words, the diversity of team members’ linkages toothers in the organization is what appears to be crucial in terms of knowledge creationand transfer. As a result, teams that are either constructed with heterogeneousstakeholder networks in place, or which take concerted efforts to diversify theirnetworks after the group is formed, will be in a better position to manage the flow ofknowledge across the team-stakeholder interface.

One explanation for how diverse knowledge networks might facilitate theinnovation process is through the creation of shared team-stakeholder cognitions.Our study provides initial evidence that shared cognition between the team andstakeholders is important, particularly with respect to the team’s entrepreneurialculture. Teams with entrepreneurial cultures have strong values and norms thatpromote creativity, risk-taking, and innovation. Consistent with past studies, thoseteams with higher levels of entrepreneurial culture – more intensely heldentrepreneurial values and behavioral norms – had higher performance and weremore likely to receive continued funding by their organizations (Caldwell and O’Reilly,2003). Importantly, a unique effect of team-stakeholder agreement also emerged on thisfactor. NPD teams with higher levels of agreement between team members andexternal stakeholders regarding the entrepreneurial values and norms of the team weremore likely to receive continued funding. This suggests that NPD teams are likely to bemost effective when their team values and norms support strong entrepreneurshipand when there is alignment between the internal (i.e. team) and external (i.e.organizational stakeholders) understanding of these team norms and values. Morebroadly, this finding has potentially important implications for theory and research onshared cognition because prior studies have limited their scope to the understanding ofinternal team members. Our study suggests that the theorized benefits of sharedcognition on team performance might extend across team boundaries to include thoseexternal stakeholders that NPD teams must interact with to be successful.

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EJIM16,1

One reason to interpret these findings cautiously is the somewhat more mixedresults observed with respect to goal orientation and customer focus. For goalorientation, a positive effect of shared team-stakeholder cognition emerged on teameffectiveness ratings but not continued funding, whereas for customer focus, atrend-level effect emerged on continued funding but not team effectiveness ratings.These findings are somewhat difficult to interpret given that significant mean leveleffects were not observed for either factor, but do point to several possible explanationsthat warrant future investigation. Perhaps, the importance of shared cognitions withinand outside teams systematically differ across factors (i.e. the content of the cognitions)and across effectiveness criteria. For example, it is interesting to note that theeffectiveness criteria are not as strongly correlated as one might assume (r¼ 0.34),such that it is possible to consider how some factors may contribute to judgments ofteam effectiveness but not whether funding was ultimately continued (e.g. sharedperceptions of goal orientation).

Certainly, the team goal-setting literature is on solid footing in the organizationalsciences (Locke and Latham, 1990), but perhaps goal-to-performance effects are not asstraightforward in the context of innovation where teams can manage internalperformance behaviors effectively but fail on other fronts such as judging marketreadiness. It also seems plausible, and partially fits our pattern of our findings, thatthe most important factors on which team-stakeholder agreement is beneficial arealso those which are most directly linked to innovation outcomes in terms of level(Cannon-Bowers and Salas, 2001). Future studies should examine these possibilities, inaddition to attempting to replicate the relationships observed here for entrepreneurialculture.

Trust linkages and team championsOur results indicate that the optimal structure for trust networks involvedcentralized ties to fewer non-redundant individuals outside of the team.Although this finding may seem somewhat counterintuitive, our rationale focussedon the tradeoffs that exist when building intense relational linkages outside the team(Bond et al., 2004). For example, confiding in too many stakeholders in theorganization might erode the team’s ownership over the creative process (Reuf, 2002).The ability of the team to manage the exchange of sensitive information is also crucial,especially for NPD teams that are exploring business models that challenge theorganization or designing innovative products and solutions that entail intellectualproperty.

Prior research has linked the presence of a small number of outside champions toteam performance (e.g. Howell and Higgins, 1990; Howell and Shea, 2001; Markhamand Griffin, 1998). These studies describe champions as individuals who provide activeand enthusiastic support from outside the team, particularly early on in the innovationprocess (Reid and de Brentani, 2004). They can be called on at critical times to secureorganizational resources and when necessary, to defend the team. They also inspireand motivate by sharing confidence in the team’s creative process (Howell andHiggins, 1990).

Adding to this list, we argue that champions also serve the crucial role of receivingand exchanging confidential information with the team. Both the quantitative resultsand interviews support this idea, indicating that the most effective teams selectivelylocated the right champions and developed redundant linkages with these fewindividuals (i.e. strong ties). In contrast, a diffuse external trust network likely signals

43


that the team lacks real champions, and consequently, the appropriate channelsthrough which to exchange sensitive information.

Limitations and future directionsSeveral of the challenges encountered in this research are not unique to the presentstudy, but have been described more generally within the teams literature. Two suchchallenges involve moving to a team-level of analysis and accurately reconstructingsocial networks in the presence of missing data (Rulke and Galaskiewicz, 2000;Stasser, 1992). Both limitations, but particularly the relatively small number of teamsstudied, highlight the importance of replicating our findings in future research.Another challenge involves the generalizability of these results to innovation teams inother organizations and industries. Importantly, our focus on NPD teams within aparticular industry context helped to rule out several possible confounds in theanalyses presented here, but simultaneously underscores the need for additionalstudies to consider how team-stakeholder knowledge and trust linkages may impactteam success differently in other settings.

A similar point involves the stages of innovation that these particular teamsprogressed through during the study period (i.e. stages 2-4 of a five-stage innovationprocess). Within a longitudinal framework, it is possible that the structure andcontent of external network linkages and shared team-stakeholder perceptions aredifferentially important to innovation effectiveness at different stages. To furtherexamine this possibility, future studies might investigate the importance of team-stakeholder networks and shared cognition as teams progress through other stages ofthe innovation process.

Future research should also investigate potential mediators of the beneficial effectsfound here for non-redundant knowledge networks and centralized trust networks.The present research suggests two important directions for this work. First, althoughwe were not able to quantitatively assess the relationship between the characteristicsof knowledge networks and the degree of shared team-stakeholder perceptions, theresults of our study suggest that these two variables may be linked. Consequently, it isinteresting to speculate that shared cognition of team processes partially mediate theeffect of dense knowledge networks on team effectiveness. In other words, perhapsdense knowledge networks benefit NPD team success because they facilitate sharedunderstanding across a wider range of external organizational stakeholders. Ourqualitative analysis, which included survey and network data for a subset of teams,tentatively supports this interpretation. Related to the positive effect of centralizedtrust networks, future research should also test explicitly whether the number of teamchampions mediates this relationship. In addition to ruling out alternativeexplanations for our findings, studying the role of team champions could provideadded insights into why fewer (not more) trust linkages is beneficial for team-basedinnovation.

ConclusionOrganizational stakeholders from a variety of functional backgrounds support theNPD team by providing access to resources, knowledge, and trusted partnershipsthroughout the innovation process. Our results suggest that the most effective NPDteams are able to exchange knowledge and information with a wide array oforganizational partners, while establishing trust relationships with a strategic few.The most effective teams also tend to have strong entrepreneurial cultures, with

44

EJIM16,1

external stakeholders whose perceptions of the team’s risk-taking and creativity matchthose of internal team members.

Note

1. Previous network studies have had team level response rates ranging from 55 (Cummingsand Cross, 2003; Hansen et al., 2001) to 96 percent (Sparrowe et al., 2001).

References

Amabile, T. (1988), “A model of creativity and innovation in organizations”, Research inOrganizational Behavior, Vol. 10, pp. 123-67.

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About the authors

Bettina Buchel is Professor of Strategy and Organization at the International Institute forManagement Development (IMD) in Lausanne, Switzerland. Since receiving her PhD from theUniversity of Geneva, she has authored numerous books and journal articles describing herresearch on cooperative links and performance.

Levi Nieminen is the Director of the Research and Development group at Denison Consulting,LLC in Ann Arbor, Michigan. He completed his doctorate in Industrial and OrganizationalPsychology from Wayne State University in Detroit, Michigan. His research focuses on theintersection of organizational culture and leadership as interrelated drivers of organizationaleffectiveness. Levi Nieminen is the corresponding author and can be contacted at:[email protected]

Heidi Armbruster-Domeyer is Executive Assistant of Domeyer GmbH & Co. KG in Bremen,Germany, which is a leading specialized trading company and manufacturer for fire preventionand safety equipment. She completed her doctorate in Business Administration from theUniversity of Geneva and worked several years as Project Manager and Deputy Head ofdepartment for a German research institute.

Daniel Denison is Professor of Organization and Management at the International Institutefor Management Development (IMD) in Lausanne, Switzerland and Chairman of DenisonConsulting, LLC. Since receiving his PhD in Organizational Psychology from the University ofMichigan, he has authored numerous books and journal articles describing his research andconsulting linking organizational culture to bottom-line business performance.

To purchase reprints of this article please e-mail: [email protected] visit our web site for further details: www.emeraldinsight.com/reprints

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