innovation projects conducted by distributed teams: the impact of key team characteristics on...

INNOVATION PROJECTS CONDUCTED BY DISTRIBUTED TEAMS:THE IMPACT OF KEY TEAM CHARACTERISTICS ONCOLLABORATION Mario Bourgault et Jaouad Daoudi De Boeck Supérieur | Journal of Innovation Economics & Management 2014/1 - n°13pages 37 à 72

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--------------------------------------------------------------------------------------------------------------------Bourgault Mario et Daoudi Jaouad, « Innovation projects conducted by distributed teams: the impact of key team

characteristics on collaboration »,

Journal of Innovation Economics & Management, 2014/1 n°13, p. 37-72. DOI : 10.3917/jie.013.0037

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n° 13 – Journal of Innovation Economics & Management – 2014/1 37

INNOVATION PROJECTS CONDUCTED BY DISTRIBUTED TEAMS: THE IMPACT OF KEY

TEAM CHARACTERISTICS ON COLLABORATION

Mario BOURGAULT Département de mathématiques et de génie industriel

École Polytechnique de Montréal, [email protected]

Jaouad DAOUDIDépartement des sciences administratives

Université du Québec en Outaouais, Canada [email protected]

This article presents an empirical study on the conduct of innovation pro-jects by distributed teams in a high-tech environment. More specifically, we attempt to understand the impact of key team characteristics on the collabo-rative dynamics within these groups, variously called “dispersed”, “distrib-uted”, or “virtual” teams. These teams are usually temporary working groups whose primary characteristic is that they do not have a main or predominant physical site where they perform their tasks. Unable to meet face-to-face on a regular basis, the teams must offset this “relational deficit” with an assembly of mechanisms, processes, and tools, particularly information technology, in order to fulfil their mandate. These practices have become ubiquitous, both in multinational firms as well as networks of independent firms.

This phenomenon has grown exponentially since Internet technologies fostered the emergence of highly sophisticated communication tools. Thus, in principle, geographic distance should not constitute a handicap to the dis-tribution of work to locations that offer competitive advantages. However, according to the large body of recent research in this area, it seems that the efficiency and effectiveness of these project teams are regularly questioned. More specifically, the climate of collaboration that company directors wish to foster is not that easy to create.

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Mario BOURGAULT, Jaouad DAOUDI

38 Journal of Innovation Economics & Management – 2014/1 – n° 13

The analysis presented here relates certain tangible and intangible team characteristics and examines their explanatory power for three key dimen-sions of collaboration: communication, coordination, and participation in decision making. In addition to the relationships between the variables, the main proposal of this article is that rivalry between sites (intersite rivalry) wields a significant moderating effect on these relationships. Because the data were collected from a large-scale survey of more than 253 innovation project actors, the results have high external validity. Moreover, this study responds to repeated calls by the scientific community to undertake real-life research in distributed project teams.

LITERATURE REVIEW AND THEORETICAL FRAMEWORK

This section presents the theoretical background for the study and a review of the recent literature. A conceptual model is then developed, presenting the relationships to be tested in the empirical section, below.

Background

It is practically impossible to talk about innovation today without referring to collaboration as a mechanism that facilitates the sharing of ideas, resources, and people. Trends such as globalization, growing technological content in prod-ucts, and new disciplines developed at the intersections of traditional and new business models have all contributed, to varying degrees, to a transformation in the way that companies innovate. For Gassmann (2006), this transforma-tion is expressed in many ways, but the “distributed” or “collaborative” nature of innovation activities is certainly one of the most striking. The two concepts (innovation and collaboration) are also closely entwined within the emerg-ing phenomenon of open innovation (Bensebaa, Béraud, 2012). First initiated by Chesbrough (2003) and Chesbrough, Vanhaverbeke and West (2006), this type of innovation requires much stronger collaboration than in traditional inter-organisational relationships, such as subcontracting. Collaboration is also viewed as a key factor for success in many project-based industries such as engi-neering, construction, and consulting (Hobday, 2000). In this type of environ-ment, Dietrich et al. (2010) contend that the challenges are greater, because projects have a limited duration by definition, and collaboration needs time to be built and sustained before the expected benefits can be felt.

This notion of social construction was taken up by Calamel et al. (2011) in a recent publication on collaborative projects in French innovation

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Innovation projects conducted by distributed teams


clusters. Among other things, these researchers demonstrate that collabora-tion takes place only if the stakeholders are truly willing to act in concert. This open-mindedness to collaboration must also be supported by manage-ment, who, over and above adapted modalities and practices (Drouin et al., 2009), must demonstrate exemplary behaviour. In a recent issue of the Harvard Business Review on this topic, collaboration was identified as a key competency for companies, both large and small, that undertake various types of innovation. Placing more emphasis on management’s role, Ibarra and Hansen (2011) state that, “A hyperconnected business world, spurred on by social media and globalization, demands a leadership style that can harness the power of connections. Leaders need to shed the command-and-control and consen-sus style in favor of collaborative leadership”. The authors add that this type of leadership is required to bring together individuals, groups, and entities that are not always keen to work with each other.

Collaboration in distributed project teams

Creating and maintaining strong relationships between disparate stakehold-ers in order to achieve a concrete goal is a repeated theme in numerous stud-ies of collaboration down the years. Over twenty years ago, Wood and Gray (1991) proposed the following definition of collaboration: “Collaboration oc-curs when a group of autonomous stakeholders of a problem domain engage in an interactive process, using shared rules, norms and structures, to act or decide on issues related to that domain”. Since then, many studies have attempted to understand and measure this process, as well as the factors that could explain success or failure in collaborative arrangements between economic entities, including networks, companies, and teams. The common perspective across all these studies is the positive view of collaboration between various eco-nomic entities. Of particular note, the word “collaboration” has recently evolved from a noun to an adjective, underscoring the fact that stakehold-ers must work together closely to achieve their objectives. Thus, we now talk about “collaborative work” (Hoegl, Gemuenden, 2001; Baldwin, von Hippel, 2011) and “collaborative projects” (Barnes et al., 2006; Calamel et al., 2011). Although the work that is accomplished at the workplace is usu-ally collective by definition, the recent emphasis on collaboration suggests a higher degree of interaction between individuals and groups, resulting in part from increasing task fragmentation and specialisation. In the past two decades, as more borders open and goods, people, and information circulate freely between economies, this trend has intensified.

The attempts to define collaboration are as varied as they are numerous. In the field of organisation and management, the seminal works of Wood

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and Gray (1991) propose the first authoritative concept of collaboration. Their perspective highlights commitment and the sharing of practices in order to act or decide together. Other authors following in the wake of Wood and Gray explored various aspects of collaboration in real situations. Table 1 presents a selective review of recent empirical studies on collaboration, including some other proposed definitions.

Table 1 – Some definitions of collaboration

Authors Definition

Czajkowski (2006, p. 131)

“Collaboration as a process of joint decision-making. If joint decision-making is not taking place, perhaps the partners are coordinating or cooperating but not collaborating”.

Himmelman (1997, p. 5)

“Collaboration is defined as exchanging information, altering activities, sharing resources, and enhancing the capacity of another for mutual benefit and to achieve a common purpose… collaboration is a relationship in which each person or organization wants to help their partners become better at what they do”.

Kanter (2003, p. 245)

“…the scope for collaboration is more open, understanding grows between specific individuals, communication is frequent and intensive, and the interpersonal context is rich. The best intercompany relationships are frequently messy and emotional, involving feelings like chemistry or trust”.

Keyton and Stallworth (2003, p. 235)

“A collaborative group is formed when representatives from different organizations come together to share decision-making responsibilities directed toward solving a mutual problem”.

Kumar, van Fenema and von Glinow (2004, p. 3)

“Collaboration normally translates into coordination mechanisms requiring intense communication and information processing”.

Peters and Manz (2007, p. 119)

“Collaboration, in general, is a purposive process that results from a desire or need to solve a problem, create, or discover something. It involves decision making among interdependent parties that involves joint ownership of decisions and collective responsibility for outcomes”.

Sandow and Allen (2005, p. 9)

“…collaboration is the social coordination of action, and occurs in a social system of relations wherein everyone in the network is accepted by everyone else in the network as a contributor toward a shared purpose”.

Tabaka (2006, p. 4)

“When teams declare a collaborative imperative in their work, it is their pledge to employ consensus-based decision approaches through participatory decision-making. They apply high-bandwidth information gathering coupled with well-formed and well-articulated priorities”.

Thomson and Perry (2006, p. 23)

“Collaboration is a process in which autonomous actors interact through formal and informal negotiation, jointly creating rules and structures governing their relationship and ways to act or decide on the issues that brought them together”.

These various definitions reveal at least three major dimensions of col-laboration, which we identify as participation in the decision-making pro-cess, coordination, and communication.

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In a work team, active participation in the decision-making process is the primary feature of a collaborative situation (Keyton, Stallworth, 2003; Peters, Manz, 2007). According to Czajkowski (2006), this dimen-sion is crucial for real collaboration. Without it, the interaction is limit-ed to simply information exchange or coordination. In order to assess the quality of collaboration, the ways in which decisions are made within a team must be observed, among other things. Many studies show that par-ticipation in the decision-making process is a determining factor for pro-ject team performance (DeLuca, Valacich, 2008; Garland, 2009). This impact is essentially due to the complexity of the process, in which di-versified viewpoints are generated and explored, thoughts and ideas are shared and pooled, and a considered and coherent decision is then made (Kaner et al., 2007).

Nevertheless, this approach poses challenges in strategic projects with tight deadlines. Relational problems may also surface, as Kaner et al. (2007, p. 14) stresses: “In practice, it’s hard for people to shift from expressing their own opinions to understanding the opinions of others. And it’s particularly challenging to do so when a wide diversity of perspectives are in play. In such cases people can get overloaded, disoriented, annoyed, impatient – or all of the above. Some people feel misunderstood and keep repeating themselves. Others push for closure”. Such problems are accentuated in distributed teams. For example, cultural dif-ferences can wield a considerable impact on the members’ involvement in the decision-making process (Bourgault et al., 2009; Müller, Spang, Ozcan, 2008). In addition, the size of the project team may hinder participation (Garland, 2009). Finally, although the decision-making process has been extensively studied, the specific dynamics of participation in the process re-main unclear. Nevertheless, the literature on collaboration considers par-ticipation in the decision-making process an integral part of collaboration: collaboration cannot be envisaged without the members’ involvement in the decision-making process (e.g., Gray, 1989; Butterfield, Reed, Lemak, 2004; Czajkowski, 2006). On the other hand, the project management lit-erature provides mixed results, on the grounds that the effectiveness of col-laboration in temporary project teams, which operate under certain specific constraints, is debatable, and the benefits of involvement in the decision-making process have been questioned.

Another dimension of the collaborative climate is communication be-tween members, including its quality. Although this concept has been in-vestigated for decades, the growing complexity of projects has brought com-munication issues to the forefront of managers’ concerns, especially because new organisational environments (i.e., multicultural and multifunctional teams) introduce greater uncertainty (Badiru, 2008). In a project situation,

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communication is understood primarily in terms of information exchange: team members share information according to a set of conventions apply-ing to the content, processes, and support tools (Adenfelt, 2010; Kliem, 2008; Cleland, Ireland, 2002). In order to communicate effectively, these conventions must ensure a common understanding of the shared informa-tion (Pritchard, 2004). Moreover, information exchange is just one aspect of the quality of communication within a team. In order to describe it, the ways in which the members interact, organise themselves, resolve conflicts, and create a climate that fosters exchanges of ideas and viewpoints must be assessed. These may be considered the mechanisms by which many, if not all, the members integrate into the team. This cannot happen unless the members trust each other (Kliem, 2008; Diallo, Thuillier, 2005; Cleland, Ireland, 2002). In the opposite case, when trust and information are with-held, unfortunate situations may result, as Cleland and Ireland (2002, p. 490) argue: “Team members are reluctant to share information, which might be critical to the success of the project. They want to protect their territory and per-haps their jobs”. Communicating does not mean just exchanging informa-tion; it also means taking a position with respect to the task at hand, the project, and the team. For Scheid (1980), communicating is also a way to achieve a goal: “communicating is always associated with a search for an influ-ence. By communicating, person A seeks to influence the behavior of person B” (Scheid, 1980, p. 193).

A number of seminal studies over the years show that the quality of com-munication is influenced by the complexity of the task, which is the cen-tral object of the information exchange between the various team members (Lawrence, Lorsch, 1967; Katz, Tushman, 1979; Duncan, 1973; Galbraith, 1973). Crozier (1963, p. 238) shares this view: “The inflexibility with which are defined the contents of tasks, the relationships between them and the network of human relations needed for them to be accomplished renders communication be-tween groups and with the environment more difficult”. Furthermore, Pritchard (2004) and Lipman-Blumen and Leavitt (1999) explain that the quality of communication also depends on the size of the team. Finally, we should add that, in distributed teams, the communication problem is even more acute, necessitating considerable efforts to adapt systems and processes and to strike a balance between virtual and real-life interactions (Bourgault et al., 2010).

Coordination is the third dimension of collaboration that has been proposed in the literature (see Table 1). Coordination refers mainly to the mechanisms put in place to manage the interdependencies of the planned tasks (Thompson, 1967; Malone, Crowston, 1990). Coordinating team tasks involves ensuring that individual contributions are effectively

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and efficiently integrated in order to meet established objectives (Nizet, Pichault, 2012). In a project environment, Badiru (2008) views coordina-tion as a determining factor for success (p. 41): “Coordination facilitates har-monious organization of project efforts (…) Project coordination is a balanced choreography of teamwork across the various elements of a project organization and among several members of the project team”. However, coordinating and integrating activities that require the resources and skills of far-flung actors can be problematic (Ancona, Caldwell, 1992; Faraj, Sproull, 2000; Hoegl, Proserpio; 2004). Fortunately, these problems can often be overcome by a clear division and allocation of project tasks and effective integration mechanisms.

Theoretical framework, research questions, and model

We have outlined above the strong link between the concepts of collab-oration and innovation. This link has frequently been empirically tested, particularly at the industrial and interorganizational level (e.g. Beaudry, Shiffauerova, 2009). In the present study, we focus on a more microscopic level: the team. We are specifically interested in the characteristics liable to explain the quality of collaboration in the case of real-life distributed teams working on technological innovation projects. This focus on the team (group level) is important, because it allows pinpointing the actions of various actors on the ground who are directly involved in innovation practices.

Empirically, we wish to answer the following question: what links exist between key team dimensions and the quality of team collaboration? We attempt to answer this question in the specific case of innovative projects involving distributed teams. Our theoretical model (Fig. 1) establishes relationships between dimensions that emerged from an extensive literature review of the research on the determinants of collaboration (Daoudi, 2010). Here, collaboration (with its various components) is considered as an explained variable (dependent). The aim was not to verify the link between col-laboration and successful innovation projects, which has been well es-tablished in numerous previous studies, but rather to understand certain conditions that are liable to foster a collaborative climate in distributed teams.

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Figure 1 – Theoretical Model

Two sets of characteristics are considered: intrinsic team characteristics that are related to the circumstances of the team’s “distributedness”, and team dimensions in terms of the team’s overall “attitude” and that of its managers with respect to the distributed setting. We briefly present each dimension along with its description, as developed by Daoudi and Bourgault (2012, 2011).

Today, geographically distributed teams are organized in a range of differ-ent configurations. Although they have been variously defined over the past twenty years, the consensus among the scientific community (Hertel, Geister, Konradt, 2005; Bourgault et al., 2008; Caya, Mortensen, Pinsonneault, 2009) is that they generally have at least three dimensions in common: they are teams (i) whose members are physically separated from each other, (ii) whose members cooperate in order to achieve common objectives, and (iii) for which information and communication technologies (ICT) provide es-sential operational support. Of course, before the Internet and Web business technologies became so prevalent, it was necessary to mention ICT as a dimension, and even though this has become self-evident today, most cur-rent definitions retain this dimension. In addition, in the era of economic globalization, this geographic distribution often presents two more specific characteristics: differences in culture and work practices.

Cultural differences are among the most striking features of geographic distribution. Nam et al. (2008) and Shore et al. (2009) find that linguistic and national differences between team members are sufficiently indicative

Figure 1 – Theoretical Model

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to deduce the presence of cultural “discontinuity” in a team. This discon-tinuity refers to the differing value systems of the team members, which frequently lead to differences of opinion, conflicts, and consequently anxi-ety within the team (Lim, Liu, 2006). Individuals who are culturally dif-ferent have different (a) ways of communicating and decision making, as well as (b) verbal and non-verbal communication styles (Shachaf, 2008). Furthermore, cultural diversity may cause the team to disperse, whereby the members coalesce into culturally homogenous subgroups (Shore et al., 2009). When team members become attached to subgroups, interpersonal tensions may then arise with members of other subgroups (Staples, Zhao, 2006). In such cases, cultural diversity hinders the development of trust, which negatively impacts communication and the decision-making process (Shachaf, 2008).

On the other hand, Nam et al. (2008) contend that teams with fewer cultural differences demonstrate (a) better communication and (b) more constructive cohesion and better social relationships. Staples and Zhao (2006) qualify this negative view of cultural discontinuity, asserting that the repercussions of cultural differences within a team fade with time. In a newly formed team, the members must deal with ignorance or lack of knowledge about the values, norms, and identities of other members. Such situations would favour the formation of culturally homogeneous subgroups. However, once the team members start to interact, the richness of the cul-tural diversity begins to be appreciated. This richness gains in value when there is leadership that can (a) anticipate and handle conflicts, and (b) en-courage the team to participate in conflict resolution and decision-making (Lim, Liu, 2006).

Besides cultural differences, geographic distribution usually implies dif-fering work practices: different ways of thinking and resolving problems, dif-ferent norms to follow, different methods and tools, and so on (Chudoba, Mei Lu, Watson-Manheim, 2005; Knoben, Oerlemans, 2006). This can gen-erally be explained by the different skills (Giuri et al., 2010) and expertise (Akgün, Dayan, Di Benedetto, 2008) of each team member. Furthermore, according to the literature, this discontinuity has a mixed effect on team performance. For instance, when perceived as added value, this variety of perspectives is a positive factor for team productivity (Giuri et al., 2010). However, the same diversity can be a source of disputes and conflicts among members, which would negatively affect team performance (Harvey, Novicevic, Garrison, 2004). Similarly, opposing visions and alternative views make decision making process and overall collaboration even more complex (Harvey, Novicevic, Garrison, 2004; Akgün, Dayan, Di Benedetto, 2008). Moreover, a variety of expertise combined with a diversity of cultures

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can lead to misunderstandings within teams. Therefore, in order to miti-gate this effect, companies must develop coherent and harmonious work practices and implement them at all the sites involved (Chudoba, Mei Lu, Watson-Manheim, 2005).

The above discussion leads to the formulation of two hypotheses:H1: Cultural differences influence the perceived quality of collaboration within

a distributed project team undertaking an innovation project.

H2: Differences in (work) practices influence the perceived quality of collabo-ration within a distributed project team undertaking an innovation project.

The differences observed in distributed project teams are important con-cerns for managers. They may constitute either strength or weakness, as explained by Ibarra and Hansen (2011): “Differences in convictions, cultural values, and operating norms inevitably add complexity to collaborative efforts. But they also make them richer, more innovative, and more valuable. Getting that value is the heart of collaborative leadership”. This “effective use” of differences within teams necessitates a particular attitude by the team and its leader. Gray (1989), Powell (2000) and Butterfield, Reed, and Lemak (2004) un-derscore, among others, the importance of commitment in order to ensure this essential condition for collaboration.

Commitment is not a new concept, as it was developed during the sev-enties. Traditionally, it referred to employees’ attitudes (or engagement) towards their place of employment. An early study by Porter et al. (1974) defines commitment as the “relative strength of an individual’s identification with and involvement in a particular organization”. These authors defined three determinants of this concept: (i) a strong belief in and acceptance of the organization’s goals and values, (ii) a willingness to contribute consider-able effort to the organization they work for, and (iii) a determination to maintain their adhesion to the organization, or loyalty. Many studies have demonstrated the explanatory potential of commitment for collaboration, job performance, productivity, satisfaction, and lack of absenteeism (Meyer, Allen, 1997). In addition, a number of studies have positively correlated commitment with other organizational factors, notably participation in decision making (e.g., Jermier, Berkes, 1979), team cohesion (e.g., Klein, Mulvey, 1995; Wech et al., 1998), and consideration and respect among team members (e.g., DeCotiis, Summers, 1987).

As mentioned above by Ibarra and Hansen (2011), leadership is also a key factor for establishing a climate of collaboration in a distributed team. Leadership is a continuously reformulated concept that has been studied by a wide range of management researchers, including Bennis and Nanus (1985), Kotter and Heskett (1992), Collins (2001), Goleman, Boyatzis,

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and McKee (2002), Westley and Mintzberg (1989), and more. Although these researchers have developed relatively heterogeneous definitions and concepts of leadership, there is some consensus on certain of the leader’s responsibilities. The leader has the responsibility to: (a) clarify objectives and priorities, (b) develop a climate of sharing and trust between team mem-bers, and (c) motivate stakeholders to fulfil their tasks in accordance with previously defined objectives (Bell, 2008). In sum, the leader ensures a cli-mate that allows the team members to work and operate optimally (Hill, 2005). The research on collaboration generally agrees on a traditional con-ceptualization of leadership (Mattessich, Monsey, 1992; Czajkowski, 2006, Butterfield, Reed, Lemak, 2004; Huxham, Vangen, 2000; Fedor et al., 2003; Harris, 2005; Clark, 2008; Tabaka, 2006). However, in a collaborative envi-ronment, additional efforts are required to establish leadership. Leaders must cope with (i) the team members’ diversity of skills, experiences, cultures, and so on, (ii) complex task interdependencies, (iii) resistance to sharing information and knowledge, and (iv) little or no social interaction between team members (Lerner, 2008). In short, in addition to the traditional leader-ship role, a leader in a collaborative situation must also handle collaboration issues and stakeholders. Thus, the leader plays a pivotal role in the collabo-rative dynamics (Gratton, Erickson, 2007).

Finally, the literature on collaboration underscores the importance of flexibility for effective teamwork. Flexibility is defined as the team’s ability to adapt, adjust, and change rapidly in the face of contingencies, while re-taining what is important and a priority (e.g., meeting deadlines and require-ments for deliverables, customer satisfaction) (McComb, Green, Compton, 2007; Harris, 2005). In the literature, flexibility is considered a key factor: it (i) lessens the frequency and intensity of conflicts between collaborating members (Sparrow, Braun, 2008; Milliman, von Glinow, Nathan, 1991), and it (ii) improves collaboration among members (Clarck, 2008; Mattessich, Monsey, 1992; DeFillippi, 2002; Wageman, 1997). Furthermore, flexibility is considered as a factor that facilitates collaboration among all members so that they can integrate their ideas and experience (Holland, 2009). It is considered an asset, the absence of which can seriously hinder the quality of interactions between members (Lee, Holmquist, 2009). Flexibility therefore constitutes a determinant factor in the establishment and maintenance of a collaborative relationship (Lane, Maznevski, Mendenhall, 2004).

Even though the importance of team characteristics (leadership, com-mitment, and flexibility) for collaborative dynamics has been stressed in the literature on collaboration, this notion remains to be empirically confirmed, given that most studies to date are exploratory. Accordingly, we propose the following three hypotheses:

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H3: There is a positive relationship between the quality of leadership demon-strated by the project manager and the perceived quality of collaboration within a distributed project team undertaking an innovation project.

H4: There is a positive relationship between the members’ commitment to the project team and the perceived quality of collaboration within a distributed project team undertaking an innovation project.

H5: There is a positive relationship between the flexibility of a distributed pro-ject team and the perceived quality of collaboration within a distributed project team undertaking an innovation project.

Despite the concerted efforts of the various members and leaders of a distributed team, some empirical studies have shown that rivalry between sites (intersite rivalry) is a potential mitigating factor for a collaborative climate. According to the literature, this rivalry would have a mixed impact. For instance, rivalry within a distributed project team could negatively af-fect collaboration (Cool, Dierickx, 1993; Luo, Slotegraaf, Pan, 2006) and overall team performance (Beckhard, 1969). However, Brandenburger and Nalebuff (1996), Lado, Boyd, and Hanlon (1997), and Luo, Slotegraaf, and Pan, (2006) noted that rivalry can positively affect collaboration, thereby contributing to constructive interactions. In fact, when team members real-ize (1) the urgent need for critical information to be shared between “rivals” and (ii) the importance of using that information wisely, and more impor-tantly, rigorously, rivalry becomes a potentially positive factor that can foster team synergy (Luo, Slotegraaf, Pan, 2006; Tsai, 2002). However, few empiri-cal studies have thoroughly investigated this concept in relation to collabo-ration: is it a restraining factor or is it a catalyst that helps improve collabo-ration? Accordingly, rivalry between members from distributed sites should moderate the relationship between team characteristics and the quality of team collaboration, as proposed in the following hypothesis:

H6: Rivalry has a moderating effect on collaborative dynamics.

RESEARCH DESIGN AND METHOD

The above-presented hypotheses were tested using data obtained on profes-sionals working on innovation projects in various fields such as aeronautics, telecommunications, and software. We contacted project engineers, man-agers, and experts directly and through local professional associations and several leading-edge companies. We present below the main findings of the empirical study, the data characteristics, and analysis methods.

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Sample

The sample includes data on engineering project teams working on various types of innovation projects. More specifically, the notion of team refers to a large group of individuals engaged in an innovation project, including individuals from different companies such as subsidiaries and subcontractors (Vanegras, 2006; Pineda, Lerner, 2006).

Theoretically, the population addressed in this study is representative of project teams with distributed members. Because such groups of professionals are almost impossible to describe, we opted to use non-probability sampling, whereby the considered elements were selected on the basis of a reasoned de-cision (Beaud, 1992). This approach is consistent with that of Babbie and Benaquisto (2002), who suggest that: “Sometimes it’s appropriate for you to select your sample on the basis of your own knowledge of the population and the purpose of the study. This type of sampling is called purposive or judgmental sampling” (p. 165). Three criteria were used to select the study participants: (i) the respondent participated in the project; (ii) the project was technological and/or innova-tive; and (iii) the project team members were geographically distributed.

Data was collected on a final sample of 253 teams operating mainly in the telecommunications and electronics industries. The project teams were working mainly on innovation projects such as product development (50%) or improving products, services, and systems (24%). In addition, 19% of the sample consisted of project teams involved in equipment or ICT installation, production process or management improvement, product deployment, or research projects. The remaining projects (7%) were not precisely identi-fied by the respondents. However, the method used to identify respondents (e.g., contacts with companies operating in high-tech sectors) ensured that all the projects involved technology1. All teams that participated in the study included members who collaborated remotely with members in other countries, including Canada, the United States, Japan, Australia, Sweden, Romania, Switzerland, Great Britain, and other European countries.

Selection of respondents

The empirical data analysed for this study were collected from the team mem-bers. The respondents were chosen according to their role and their degree of influence on team dynamics and project implementation. Respondents were mostly managers and specialists (99%): 52% were specialists or technical

1. Results of the statistical analyses showed no significant differences between these four project types in terms of the model’s output variables (participation in decision making, communica-tion, and coordination).

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experts (i.e., engineer, IT specialist, analyst, scientist), 21% were mid-level managers (work package managers), and 4% were administrative officers (plan-ning managers, finance managers, accountants, contract managers). Managers who were solely responsible for project management and control accounted for 22% of the sample. Overall, the respondents’ profile was functionally ho-mogeneous: 56% of respondents were responsible for technical or administra-tive aspects, and 43% acted as project managers or work package supervisors. Despite the large number of questions, the pre-test questionnaire took only about 30 minutes to complete, which was deemed reasonable, and consistent with similar surveys. Most questions were answered on a 7-point Likert scale. The questionnaire also included nominal scale and descriptive questions on issues such as project type and the respondent’s position in the firm.

In order to reduce respondent bias in the questionnaire, respondents were asked about the dynamics of two innovation project teams in which they had participated: one that, in their opinion, was successful, and another that was less successful. This approach allowed us to both maximise the sample size and to en-courage the respondents to be more discerning in their responses in order to lessen respondent bias (Rose et al., 2007; Blindenbach-Driessen, van den Ende, 2006).

Besides the on-going and simultaneous assessment of two projects, other techniques were applied to reduce common method variance (CMV), as rec-ommended by many researchers (Podsakoff et al., 2003). Because the question-naire items used to build constructs were distributed across sections, the re-spondents were unable to identify the items of interest (e.g., explaining factors and outcomes). Accordingly, the respondents were assumed to be unaware of the dependent and independent variables. We also used various types of scales to reduce CMV bias (Podsakoff et al., 2003). Similarly, we initially eliminated some questionnaires following a detailed testing of how respondents filled them out, which revealed single response bias and respondent fatigue (respondents become less motivated and begin to give more perfunctory answers).

We also applied a series of appropriate statistical methods. We used prin-cipal component analysis (PCA) on each dimension and obtained mono-factors in every case. As recommended by Bagozzi and Yi (1989), we also used CFA to assess convergent validity to ensure unidimensionality of the measures used. Finally, we demonstrated strong discriminant validity in the constructs. All dependent and independent measures were strongly discrimi-nated indicating that our measures well capture the various concepts. All variables used show satisfactory reliability (Cronbach’s α >0.7), except for work practice discontinuity and recorded complexity, with Cronbach’s α of 0.678 and 0.681, respectively. These may be acceptable for two reasons: first, because the two numbers are very close to the recommended benchmark of

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0.7; and second, due to the novelty of the two concepts. In fact, there is al-most no literature in support of the operationalisations considered here. The theoretical treatment and in-depth development of these two constructs would therefore constitute a promising research direction. Table 2 summa-rizes the measurement parameters and reliability (see Appendix).

This study also controls for the dimension of project complexity. Complexity is taken as a proxy that can involve a wide variety of dimen-sions (size, technicity, scope, etc.). Complexity varies widely across projects, and can also be considered as a subjective dimension (Baccarini, 1996). Moreover, it has a temporal dimension: whereas complexity may be much anticipated at project outset, situations may turn out very differently as the project unfolds. Our model includes complexity at two different project times: the beginning (anticipated complexity) and after project completion (recorded complexity). Table 2 (see Appendix) presents the two complexi-ties estimated on a 7-point Likert scale.

Once the variables were statistically tested, the hypotheses were tested with linear regressions using SPSS software (IBM SPSS Statistics 19).

RESULTS

Using the compiled data, the hypotheses were tested by examining the rela-tionships between the main input factors and the three collaboration dimen-sions: communication, coordination, and participation in decision-making.

First, descriptive statistics and a correlation matrix were generated to determine the distribution of variables as well as potential collinearity ef-fects that could limit the analysis and the hypotheses verification. Results are presented in Table 3.

The results show that all variables have a mean between 3.17 and 5.39 with a standard deviation ranging from 1.14 to 1.64. Taken together, the variables show near-normal skewness and kurtosis (near 0). This observa-tion enabled us to conclude that the model was analysed based on relevant data. However, in order to test for redundancy among independent variables (Acx, Rcx, L, F, C, R, δCt, δWP), we used a correlation matrix. According to the literature, even if a correlation is above 0.5, it is considered a collin-earity that limits the parameter estimation of the multivariate analysis only when it exceeds 0.902. No correlation exceeds 0.67 amongst the independ-ent variables; therefore, all these variables were used in the analysis.

2. “The presence of high correlations, generally 0.90 and higher, is the first indication of substantial collinearity” (Hair et al., 2009, p. 200).

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Tabl

e 3

– C

orre

lati

on m

atri

x

A

cxR

cxL

FC

PCr

Co

R∆C

t∆W

P

Acx

1

Rcx

0.3

67****

1

L0.0

32

0.0

10

1

F-0

.048

0.0

37

0.6

71****

1

C0.0

87

0.0

36

0.5

60****

0.5

22****

1

P-0

.019

-0.0

27

0.7

59****

0.6

36****

0.5

26****

1

Cr

-0.1

21*

-0.0

97

0.7

16****

0.6

87****

0.5

75****

0.6

14****

1

Co

-0.0

98

-0.0

76

0.6

26****

0.5

99****

0.4

85****

0.5

66****

0.7

34****

1

R-0

.035

0.2

02***

-0.1

16*

-0.1

27*

-0.1

33**

-0.1

07

-0.0

77

-0.1

34**

1

∆Ct

0.1

29**

0.1

95***

0.0

11

0.1

20*

0.1

04

-0.0

34

0.0

71

0.0

68

0.1

60**

1

∆WP

0.2

52****

0.2

78****

-0.2

97****

-0.3

16****

-0.2

49****

-0.2

21***

-0.4

44****

-0.3

47****

0.2

18***

0.2

52****

1

Mea

n4

.46

93

.75

15

.11

24

.88

65

.38

65

.19

24

.90

95

.04

03

.17

35

.02

24

.01

5

Std

dev

.1

.42

61

.47

71

.23

31

.14

11

.46

81

.15

71

.25

31

.14

51

.57

91

.64

01

.49

6

Ske

wne

ss-0

.52

00

.23

0-0

.64

0-0

.38

0-0

.68

0-0

.73

0-0

.40

0-0

.41

0-0

.40

0-0

.76

0-0

.15

0

Kur

tosi

s-0

.35

0-0

.65

0-0

.06

0-0

.21

0-0

.44

00

.08

0-0

.44

0-0

.53

0-0

.80

0-0

.34

0-0

.86

0

All

variab

les

are

mea

sure

d w

ith p

erce

ptua

l Lik

ert

scal

es (1 t

o 7). S

igni

fican

ce le

vels

: * p

<.10,

**p<

.05,

*** p

<.01,

**** p

<.001 (bi

late

ral t

est).

Acx

: Ant

icip

ated

Com

plex

ity;

Rcx

: R

ecor

ded

Com

plex

ity;

L: L

eade

rshi

p; F

: Fl

exib

ility

; C:

Com

mitm

ent;

P:

Par

ticip

atio

n in

Dec

isio

n M

akin

g; C

r: C

oord

inat

ion;

Co:

co

mm

unic

atio

n; R

: riva

lry;

∆Ct:

Cul

tura

l dis

cont

inui

ty; ∆W

P:

Wor

k pr

actic

e di

scon

tinui

ty.

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To verify the hypotheses, we next tested for relationships between the independent variables (leadership, commitment, flexibility, cultural discon-tinuity, and work practice discontinuity) and outcome variables (participa-tion in decision making, coordination, and communication). An additional analysis was run to test the moderating effect of rivalry on these relation-ships. According to Sharma et al. (1981, p. 291), “A moderator variable has been defined as one which systematically modifies either the form and/or strength of the relationship between a predictor and a criterion variable”. The objective was therefore to test the impact of rivalry on the direction and intensity of the relationships in the model. Following Sharma et al.’s (1981, p. 291) recommendation, a cross-product analysis was performed between the po-tential moderating variable and the basic explanatory variables. In practi-cal terms, the parameters of three regression lines were calculated: the first considered leadership, commitment, flexibility, cultural discontinuity, and work practice discontinuity as the only explanatory variables; the second introduced another potential moderating variable (rivalry); and the third regression calculated, in addition to the previous explanatory variables, the cross effects between rivalry and the basic entry point variables in the base model. The hierarchical regression analysis results are presented in Table 4.

The finding is that, according to the first regression parameters, project manager’s leadership, members’ flexibility, and members’ commitment to the project team have a significant positive effect on communication. In de-scending order, leadership contributes the most (significant at p≤0.001) to the quality of communication within the project team, with a standardised beta of 0.34. Flexibility comes next (p≤0.01), with a beta of 0.25, followed by members’ commitment (p≤0.05), with a beta of 0.14. Work practice dis-continuity trails behind, with only a slight negative effect (p≤0.10) and a standardised beta of –0.11.

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Tabl

e 4

– H

iera

rchi

cal r

egre

ssio

n fa

ctor

s (ou

tput

var

iabl

e: c

omm

unic

atio

n)

Reg

ress

ion

1R

egre

ssio

n 2

Reg

ress

ion

3

Std

B

eta

tSig

(1)

VIF

(2)

Std

B

eta

tSig

(1)

VIF

(2)

Std

B

eta

tSig

(1)

VIF

(2)

(Con

stan

t)5.1

19

****

5.0

98

****

4.8

64

****

Acx

: A

ntic

ipat

ed

Com

plex

ity

-0.0

65

-1.1

58

n.s.

1.2

35

-0.0

69

-1.2

26

n.s.

1.2

65

-0.0

51

-0.9

17

n.s.

1.2

79

Rcx

: R

ecor

ded

Com

plex

ity

-0.0

39

-0.6

97

n.s.

1.2

46

-0.0

33

-0.5

74

n.s.

1.2

98

-0.0

33

-0.5

80

n.s.

1.3

18

∆Ct:

Cul

tura

l di

scon

tinu

ity

0.0

54

1.0

09

n.s.

1.1

38

0.0

57

1.0

64

n.s.

1.1

55

0.1

59

1.4

52

n.s.

4.9

78

∆WP: W

ork

prac

tice

di

sc.

-0.1

12

-1.8

84

*1.4

11

-0.1

08

-1.7

88

*1.4

38

-0.2

22

-1.9

23

*5.5

42

F: F

lexi

bilit

y0.2

51

3.4

07

***

2.1

63

0.2

50

3.3

86

***

2.1

64

-0.0

80

-0.4

86

n.s.

11.4

32

C: Com

mitm

ent

0.1

37

2.1

54

**

1.6

00

0.1

35

2.1

14

**

1.6

06

0.1

64

1.0

85

n.s.

9.5

13

L: L

eade

rshi

p0.3

38

4.6

12

****

2.1

40

0.3

38

4.6

06

****

2.1

40

0.2

51

1.4

12

n.s.

13.1

50

R: ri

valr

y-0

.029

-0.5

38

n.s.

1.1

36

-0.8

07

-2.4

10

**

46.7

79

(R *

∆Ct)

-0.1

67

-0.8

35

n.s.

16.6

16

(R *

∆W

P)

0.1

58

0.8

40

n.s.

14.7

21

(R *

F)

0.7

14

2.1

18

**

47.3

92

(R *

C)

-0.0

73

-0.2

59

n.s.

32.7

46

(R *

L)

0.2

27

0.5

78

n.s.

64.6

46

R2

0.4

796

0.4

804

0.5

183

Adj

uste

d R

20.4

620

0.4

602

0.4

871

p0.0

000

0.0

000

0.0

000

(1) Sig

nific

ance

leve

ls: n.

s.: no

t si

gnifi

cant

, * p

<.10, **p<

.05, *** p

<.01, **** p

<.001 (bi

late

ral t

est).

(2) Var

ianc

e In

flatio

n Fa

ctor

.

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Rivalry is a pure moderator for flexibility only. This finding is justified by two results: rivalry does not show a significant effect on communication in regression 2, and the cross product between rivalry and flexibility in re-gression 3 shows a significant effect (p≤0.05). To gain a deeper understand-ing of this interaction, we adapted the approach followed by Simsek et al. (2007). We performed two simple regressions of communication on flex-ibility with condition values of high and low rivalry (mean +/- 1 s.d.). As seen in Figure 2, we found a stronger relationship between flexibility and communication for high than for low rivalry.

Figure 2 – Relationship between flexibility and communication

A similar analysis was run to test the effect of the different variables on coordination and to test the moderating effect of rivalry. Results are pre-sented in Table 5.

Figure 2 – relationship between flexibility and communication

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Tabl

e 5

– H

iera

rchi

cal r

egre

ssio

n fa

ctor

s (C

oord

inat

ion)

Reg

ress

ion

1R

egre

ssio

n 2

Reg

ress

ion

3

Std

Bet

at

Sig

(1)

VIF

(2)

Std

Bet

at

Sig

(1)

VIF

(2)

Std

Bet

at

Sig

(1)

VIF

(2)

(Con

stan

t)3.8

91

***

3.3

83

****

3.0

56

***

Acx

: A

ntic

ipat

ed

Com

plex

ity

-0.0

67

-1.4

76

n.s.

1.2

23

-0.0

53

-1.1

57

n.s.

1.2

59

-0.0

46

-0.9

95

n.s.

1.2

78

Rcx

: R

ecor

ded

Com

plex

ity

-0.0

38

-0.8

17

n.s.

1.2

48

-0.0

54

-1.1

48

n.s.

1.2

94

-0.0

54

-1.1

42

n.s.

1.3

14

∆Ct:

Cul

tura

l di

scon

tinu

ity

0.0

75

1.7

15

*1.1

33

0.0

66

1.4

97

n.s.

1.1

49

0.0

67

0.7

28

n.s.

4.9

20

∆WP: W

ork

prac

tice

di

scon

tinu

ity

-0.1

83

-3.7

18

****

1.4

23

-0.1

97

-3.9

71

****

1.4

58

-0.2

56

-2.6

22

***

5.6

37

F: F

lexi

bilit

y0.2

54

4.1

83

****

2.1

71

0.2

57

4.2

48

****

2.1

73

0.1

60

1.1

43

n.s.

11.6

08

C: Com

mitm

ent

0.1

65

3.1

06

***

1.6

55

0.1

69

3.2

01

***

1.6

58

0.1

88

1.4

81

n.s.

9.4

69

L: L

eade

rshi

p0.3

92

6.4

74

****

2.1

52

0.3

93

6.5

28

****

2.1

53

0.3

10

2.0

29

**

13.7

31

R: ri

valr

y0.0

79

1.8

11

*1.1

42

-0.3

43

-1.2

23

n.s.

46.4

70

(R *

∆Ct)

0.0

21

0.1

24

n.s.

16.5

41

(R *

∆W

P)

0.1

02

0.6

47

n.s.

14.7

82

(R *

F)

0.1

96

0.6

88

n.s.

47.7

70

(R *

C)

-0.0

51

-0.2

14

n.s.

32.8

37

(R *

L)

0.2

17

0.6

47

n.s.

66.5

35

R2

0.6

508

0.6

564

0.6

623

Adj

uste

d R

20.6

389

0.6

429

0.6

402

P0.0

000

0.0

000

0.0

000

(1) Sig

nific

ance

leve

ls: n.

s.: no

t si

gnifi

cant

, * p

<.10, **p<

.05, *** p

<.01, **** p

<.001 (bi

late

ral t

est).

(2) Var

ianc

e In

flatio

n Fa

ctor

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The factors that influence communication also influence coordination: leadership, members’ flexibility, and members’ commitment all have a sig-nificant positive effect. However, the effect of magnitudes of the explanatory variables on coordination differs from those for communication: leadership and team members’ flexibility (adjusting to changes in instructions and con-tingencies) have a strongly significant effect (p≤0.001) on team coordina-tion, with standardised betas of 0.39 and 0.25, respectively. Members’ com-mitment also has a significant effect on coordination (p≤0.01), with a beta of 0.16.

Comparing the two forms of discontinuity, work practice discontinuity has a negative effect (similar to communication), but at higher significance (p≤0.001) and with a beta of –0.18. In contrast, cultural discontinuity shows a more positive effect than does communication, but with only slight signifi-cance (p≤0.10) and a low standardised beta of 0.07. Results of the modera-tion analysis show that rivalry has no moderating effect on the coordina-tion model. However, in regression 2, rivalry may be considered as simply a predictor variable, with slight significance (p≤0.10) and a low effect on coordination, with a beta of 0.08.

Finally, a third analysis was performed to test the hypotheses concern-ing participation in decision making. Results are presented in Table 6. Leadership, members’ flexibility, and members’ commitment are again influ-ential factors on participation in decision-making. Leadership and flexibility have a strongly significant (p≤0.001) influence on participation in decision-making, with standardised betas of 0.56 and 0.24, respectively. Members’ commitment shows an equally significant contribution at (p≤0.01) with a beta of 0.11. However, work practice discontinuity has low significance (p≤0.10), with a standardised beta of 0.09, along with cultural discontinuity at (p≤0.10) and a beta of –0.09.

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Tabl

e 6

– H

iera

rchi

cal r

egre

ssio

n fa

ctor

s (Pa

rtic

ipat

ion

in d

ecis

ion

mak

ing)

Reg

ress

ion

1R

egre

ssio

n 2

Reg

ress

ion

3

Std

Bet

at

Sig

(1)

VIF

(2)

Std

Bet

at

Sig

(1)

VIF

(2)

Std

Bet

at

Sig

(1)

VIF

(2)

(Con

stan

t)2.9

73

***

2.9

33

***

1.9

65

*

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: A

ntic

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ed C

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exity

-0.0

50

-1.0

63

n.s.

1.2

37

-0.0

52

-1.0

79

n.s.

1.2

67

-0.0

51

-1.0

79

n.s.

1.2

81

Rcx

: R

ecor

ded

Com

plex

ity

-0.0

05

-0.1

03

n.s.

1.2

52

-0.0

03

-0.0

62

n.s.

1.3

03

-0.0

04

-0.0

80

n.s.

1.3

24

∆Ct:

Cul

tura

l dis

cont

inui

ty-0

.089

-1.9

75

*1.1

38

-0.0

88

-1.9

33

*1.1

55

-0.2

55

-2.7

26

***

4.9

79

∆WP: W

ork

prac

tice

di

scon

tinu

ity

0.0

88

1.7

46

*1.4

07

0.0

89

1.7

52

*1.4

35

0.2

10

2.1

30

**

5.5

17

F: F

lexi

bilit

y0.2

45

3.9

36

****

2.1

57

0.2

44

3.9

20

****

2.1

59

0.1

06

0.7

49

n.s.

11.3

92

C: Com

mitm

ent

0.1

14

2.1

32

**

1.5

89

0.1

13

2.1

11

**

1.5

95

0.0

70

0.5

44

n.s.

9.4

50

L: L

eade

rshi

p0.5

62

9.0

98

****

2.1

25

0.5

62

9.0

77

****

2.1

25

0.7

11

4.7

09

****

12.9

77

R: ri

valr

y-0

.009

-0.1

98

n.s.

1.1

36

-0.1

52

-0.5

29

n.s.

46.7

80

(R *

∆Ct)

0.3

35

1.9

58

*16.6

19

(R *

∆W

P)

-0.2

56

-1.5

95

n.s.

14.7

06

(R *

F)

0.3

53

1.2

23

n.s.

47.3

60

(R *

C)

0.0

88

0.3

69

n.s.

32.7

10

(R *

L)

-0.3

55

-1.0

56

n.s.

64.4

74

R2

0.6

307

0.6

308

0.6

486

Adj

uste

d R

20.6

182

0.6

164

0.6

258

p0.0

000

0.0

000

0.0

000

(1) Sig

nific

ance

leve

ls: n.

s.: no

t si

gnifi

cant

, * p

<.10, **p<

.05, *** p

<.01, **** p

<.001 (bi

late

ral t

est).

(2) Var

ianc

e In

flatio

n Fa

ctor

.

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The tests of the moderating effect of rivalry on the dynamics of the deci-sion-making process clearly show a pure moderation: in regression 2, rivalry has no significant effect on team members’ participation in the decision-making process, and in regression 3, the cross product between rivalry and cultural diversity shows a significant contribution (p≤0.1). This means that cultural discontinuity, which previously (regression 1) showed a clearly low contribution to participation in decision-making, now shows a substantial negative effect (p≤0.01), with a beta of –0.25, with increasing intersite ri-valry between the various project sites.

To further explore the interaction between cultural discontinuity and rivalry, a similar plot to that shown in Figure 2 is proposed, following the same procedure (Simsek et al., 2007). Figure 3 suggests that in a low-rivalry situation, cultural discontinuity has a much stronger (negative) relationship with participation in decision making than in a high-rivalry situation. This somewhat counterintuitive result suggests that rivalry does play a moderat-ing role, and that in a situation of high rivalry, there may be another factor at play that remains to be understood.

Figure 3 – Interaction between cultural discontinuity and rivalry

Briefly, the main findings of this analysis can be summarised in the fol-lowing four points:

i) The project manager’s leadership, the project team members’ flexibility, and their commitment to the project team have sub-stantial positive explanatory power for collaboration and its three

Figure 3 – interaction between cultural discontinuity and rivalry

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components: communication, coordination, and participation in the decision-making process (H3, H4, and H5 are confirmed).ii) Work practice discontinuity has an influence on the three compo-nents of collaborative dynamics: negative in the case of coordination and communication; positive in the case of PDP (H2 confirmed).iii) Cultural discontinuity also has a slight influence on collaborative dynamics –coordination and PDP (H1 weakly confirmed).iv) Strong rivalry between project sites (intersite rivalry) affects the relationship between certain dimensions: between flexibility and communication, and between cultural discontinuity and participa-tion in decision making (H6 partly confirmed).v) Finally, it is worth noting that neither type of complexity (antici-pated or recorded) shows any explanatory power over the collabora-tion variables (tables 4, 5, and 6).

DISCUSSION

Review of the results

The literature on collaboration in distributed teams has grown rapidly in the past fifteen years, along with the number of studies attempting to understand the interrelationships between collaboration and innovation (e.g., Beaudry, Shiffauerova, 2009). The objective of the present study was to contribute to this vast body of research by offering a different and complementary perspec-tive. In the wake of past research (i.e., Bourgault et al., 2009), we sought to test hypotheses in real business settings. It is clear that this approach has certain methodological limitations, as in similar studies using experimental protocols with students (i.e., Turel, Connelly, 2012), but we felt it was im-portant to raise the number of empirical studies conducted directly in the field in order to make progress towards greater realism.

With respect to the determining factors for collaboration, our results clearly show the explanatory power of team leadership, concurring with pre-vious studies using different methodological strategies (Gratton, Erickson, 2007; Ibarra, Hansen, 2011). The impact of strong leadership is particu-larly marked on the decision-making process, probably because it is usu-ally crucial for project conduct. Frequently investigated and demonstrated in the project management literature (e.g., Müller, Geraldi, Turner, 2012), the strong explanatory power of team leadership for effective distributed teamwork is reconfirmed in the present study, with various implications for

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practice. Even in cases where technologies facilitate exchanges between in-dividuals (“virtual” environments), the team leader retains a central role in the development of an effective collaborative climate. In this sense, many of our results are in agreement with a comment by Abele (2011), who suggests that, “True collaboration requires more than handy applications. The availability of so much software doesn’t so much solve our need for collaboration as heighten the difficulty of managing it (…) for all these reasons, it’s more critical than ever to understand the complex soft elements (…) that are the makings of a strong collabo-rative culture”. Our results also clearly show that team attitudes, measured here in terms of flexibility and commitment to the project team, constitute a determining factor for collaboration among team members. This finding has practical implications in that it provides a promising direction for staffing innovation project teams.

Our study also contributes to the empirical research that aims to more ac-curately define the “distributed” nature of project teams, particularly by distin-guishing two types of “distribution”: cultural diversity and work practice diver-sity. It should be kept in mind that geographic distribution is already taken into account in the sample. Indeed, all the projects examined in this study were carried out by distributed teams (according to the selection criteria). Results indicate that the two types of diversity influence collaboration, especially when teams show intersite differences in internal practices. However, this influence is difficult to identify, because the effects vary across the observed dimensions (communication, coordination, and the decision-making process). Further re-search is needed to explore the behaviour of this characteristic to gain a deeper understanding of the effects on innovation project teams.

Our study also investigates an important aspect of the reality of distrib-uted project teams: intersite rivalry, which arises in numerous situations. Surprisingly, this concept has been largely neglected in studies on intersite collaboration. Nevertheless, rivalries do occur, as reported in the literature on multinationals (Dunning, 2008). Moreover, although confirmations and explanations of the moderating effect of rivalry on distributed project teams are proposed in the literature, they remain exploratory. Therefore, we be-lieve that further studies are called for to better understand how rivalry oper-ates in project teams.

Limitations of the study

All studies have limitations, and this study is no exception. In terms of the methodology, using a questionnaire to gather data implies certain constraints when studying collaboration in an innovate community. Direct interviews with respondents would add complementary responses, but would also be

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costly in both time and money, especially when examining distributed teams. Moreover, this study does not consider the various project phases. A longitudinal study across the entire duration of various projects would intro-duce additional response items and provide a more realistic view of the col-laboration dynamics over time. Furthermore, our study focuses on only one control variable (project complexity) to indirectly capture the specificity of the projects examined. However, this variable was limited in order to con-duct a more detailed analysis of the various model contributions. Taking into account other control variables, notably respondent type, industry sector, the company’s role in the project (e.g., prime contractor vs. subcontractor), project size, and other industry sectors involved, would produce more precise results. These aspects constitute promising research directions for the future.

CONCLUSION

The management of distributed innovation projects in a distributed setting is a risky undertaking, and subject to a multitude of internal and external in-fluences. Rather than treating it as “business as usual”, enterprisers engaged in such projects should pay close attention to the context. Although certain characteristics of distributed teams, such as cultural diversity, may be per-ceived as a given, and that cannot be changed, our results show that, on the contrary, distributed teams may actually benefit from them. In this respect, the team’s attitude, and especially the leader’s, is a determining factor for collaborative dynamics within project teams.

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APPENDIX

Table 2 – Construction of variables and measures of reliability

Code Leadership (1)

L1 The project manager clarified project priorities.

L2The project manager communicated the goals and let the project team members figure out how to achieve them.

L3The project manager facilitated on-going information exchange within the project team.

L4The project manager effectively dealt with conflicts and disagreements within the project team.

L5The project manager gave the project team members the freedom to take reasonable risks without fear of being punished for failure.

L6The project team members shared the leadership in the execution of tasks (e.g., directing tasks, making decisions based on their expertise).

Cronbach’s α = 0.902

Code Flexibility (1)

F1 Project team members were open to change/review requests.

F2 New ideas were always being taken into account during the project.

F3 The project team was always trying to improve its ways of doing things

F4Members at one site generally considered/took into account local constraints at other sites.

F5Overall, the project team could be described as flexible and willing to deal with change.

Cronbach’s α =0.855

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Code Commitment (1)

C1 I felt a strong sense of belonging to my team.

C2 I would work on future projects with the same team members.


Code Participation in decision making (1)

P1Project team members had the abilities (e.g., competences, skills) to decide how to do their job.

P2Operating decisions were made by the project team member(s) located closest to the work.

P3Project team members had a say in, or influence on, what went on in their work group.

P4Project team members had a say in, or influence on, decisions that affected their job.

P5Ideas and suggestions from project team members were listened to and well received.


Code Coordination (1)

Cr1 Activities were well coordinated between project team members.

Cr2 Duplicate activities were avoided.

Cr3 Project team members had no problems coordinating with each other.

Cr4Coordination problems between different project team members were settled quickly.

Cr5 People working on the project were encouraged to work together as a unified team.


Code Communication (1)

Co1 There was frequent communication within the project team.

Co2 Project-relevant information was shared openly by all project team members.

Co3The project team members were satisfied with the timeliness of the received information.

Co4The project team members were satisfied with the accuracy of the received information.

Co5The project team members were satisfied with the usefulness of the received information.


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Code Cultural discontinuity (2)

During the project, how often did you experience the following?

∆Ct1 Working with members who had different nationalities.

∆Ct2 Working with members from different cultural backgrounds.

∆Ct3 Working with members who spoke different languages or dialects from yours.

∆Ct4 Working with members in different time zones.

∆Ct5Working longer days or adjusting your work time in order to communicate with team members located in different time zones.


Code Work practice discontinuity (2)

During the project, how often did you experience working with members whose

∆WP1 Objectives and/or priorities differed from yours.

∆WP2 Ways of handling technical issues differed from yours.

∆WP3 Decision-making processes differed from yours.

∆WP4 Working conditions differed from yours.


Code Rivalry (1)

R1Members from different sites regularly competed with each other for larger budgets (e.g., worked hours).

R2Members at different sites regularly competed for recognition from senior managers.

R3 Members at different sites regularly competed to take on more responsibility.

R4Members at different sites tried to gain strategic importance and/or power within the project.

R5 I was afraid of leaking my knowledge to members at other sites.


Code Anticipated complexity (3)

AT THE PROJECT START-UP, how difficult did the project team believe it would be to meet the following targets?

Xp1 The budget

Xp2 The schedule

Xp3 The specifications/requirements

Xp4 The client’s/end user’s expectations


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Code Recorded complexity (1)

AFTER THE PROJECT WAS COMPLETED, to what extent do you agree with the following statements?

Xa1 The project team had to use non-routine processes to achieve the project.

Xa2 The project team was much larger than previous project teams.

Xa3The project team had a much broader variety of expertise than previous project teams.


(1) All items are measured with perceptual Likert scales (from 1 “I disagree completely” to 7 “I agree completely”).(2) All items are measured with perceptual Likert scales (from 1 “Never” to 7 “Frequently”).(3) All items are measured with perceptual Likert scales (from 1 “Not very difficult” to 7 “Very difficult”).

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innovation projects conducted by distributed teams: the impact of key team characteristics on...

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