Introduction
Inter-organizational collaboration is a central preoccupation for today's top managers and researchers. Competitive advantage, resource diversification, risk mitigation, and access to complementary competencies and new markets are among the underlying motivations for developing various forms of collaborative relationships (Borgatti & Foster, 2003; Kodama, 2005; Todeva & Knoke, 2005). The nature and type of inter-organizational collaborations are also extremely diversified, as reported in the literature. While some firms claim to collaborate at a strategic level, as in alliances or joint ventures, others pursue collaboration at a more micro-level, as in projects (Ghosh & Varghese, 2004; Staudenmayer, Tripsas, & Tucciet, 2005).
In most cases, collaboration at the project level involves a network of dispersed teams and individuals who are actively involved in common activities and who share resources, know-how, services, and artifacts (by-products). In high-tech industries, such as aeronautics, automotive, and telecommunications, extended teams with specialized expertise have become the norm (Iansiti & Levien, 2004; Johansen, Comstock, & Winroth, 2005; O'Sullivan, 2003). For example, Lasser and Heiss (2005) report that Siemens, the large German electronic systems and components producer, handles more than 1,000 projects in a distributed mode, that is, with teams dispersed all over the world including Asia, Europe, and North America. Nidiffer and Dolan (2005) observe the same patterns in the software industry: “the growing need for larger and more complex software-intensive systems, the customer's desire to place more risk with the developer, and the need for companies to be more competitive in the marketplace are the principal forces driving project development teams to become increasingly dispersed.” (p. 63)
Clearly, this illustrates the fact that organizations must now perceive projects as distributed endeavors with specific inherent characteristics that differ from those found in traditional, co-located environments. This represents a new development in the way firms interact and create value. The increasingly complex nature of technology and the availability of more specialized capabilities around the world have favored more intrusive types of collaboration between organizations involved in devising and conducting projects. From a project management perspective, this also creates new challenges as current tools and processes are considered to be ill-adapted and ineffective at facilitating distributed, collaborative work (Qureshi, Liu, & Vogel, 2006). Some of the challenges inherent in this form of collaboration are related to the very nature of the project environment. Unlike other forms of long-term, ongoing activities such as manufacturing, projects are often unique, time-constrained, and dependent on temporarily assigned people. In addition, project sponsors need to set up teams, organize work processes, and make proper technologies available in a context where organizations do not necessarily share the same capabilities, structure, culture, and systems. Sharing highly interdependent tasks—such as engineering—at a distance also adds to the challenge compared to other types of coordination (Kumar & van Dissel, 1996; Lakemond, Berggren, & van Weele, 2006).
In this paper, the focus is mainly on decision-making processes from the perspective of distributed teams. This exploratory research aims to initiate a reflection process on what has become a widespread phenomenon (i.e., conducting projects with team members spread over a large number of organizations), but one for which very little empirical, ground-based research has been—to date—carried out.
Research Background
This section highlights some of the key concepts used in the research and pinpoints some theoretical contributions from the literature
Distributed project teams
In recent years, many researchers have studied aspects of these organizational problems, as witnessed by the impressive number of papers published on teams distributed, dispersed, decentralized, and virtual (Cramton & Webber, 2005; Gibson & Cohen, 2003; Lipnack & Stamps, 1997; Mortensen & Hinds, 2001; Zolin, Hinds, Fruchter, & Levitt, 2004). Although these studies show some differences in what they designate as distributed project teams, they generally refer to a group of people who must collaborate despite geographical and time boundaries, by using information and communication technologies at varying degrees of intensity (Hertel et al., 2005). For instance, McDonough, Kahn, and Barczaket (2001) deal with the concepts of global teams and co-located teams, whose members are centralized in one place, in opposition to decentralized teams. The distinctions between various levels of virtuality stem from geographical distance and cultural differences. In their study, virtual teams are made up of individuals who are geographically distributed and culturally similar. Indeed, members of virtual teams may be co-located in the same building but on different floors. Child (2005) defines a virtual or dispersed team as a group of geographically and temporally dispersed individuals who are assembled by means of information and communications technology (ICT) to accomplish an organizational task. Activities of a global scope are both dispersed (carried out at different locations) and asynchronous (carried out at different times). Considering that technology is now available to enable team-working on a global basis, the challenge now appears to be how to organize and manage this approach more effectively.
Studies such as that of Maznevski and Chudoba (2000) shed some light on the conditions required for positive interaction within such teams. In their research with a United States (US) industrial technology company and two of its European strategic partners, they found that one requirement is the fit between the form chosen for interaction (medium and duration) and the decision-making process and complexity of the communication required within the team. For example, to build commitment, team members must be highly involved in the decision-making process, an act which necessitates the exchange of complex messages. Consequently, effective interaction in this situation requires rich communication media. Less rich communication media—such as emails and instant messaging— would not be adequate in that case. Trying to discuss complex strategic issues via emails proved to be an inappropriate fit and was unsuccessful (Child, 2005; Maznevski & Chudoba, 2000). In addition, face-to-face meetings were found to be particularly important in the early stages of a team's life. Other challenges of working with geographically distributed members include building relationships to increase trust and creating an identity as a team to compensate for members’ isolation. Indeed, past research has identified several determining factors associated with team performance, such as trust, group cohesion, and information channels (Jarvenpaa & Leidner, 1999; Massey, Montoya-Weiss, & Hung, 2003; Maznevski & Chudoba, 2000).
More recently, Martins et al. (2004) observed that each team belongs to a certain level of virtuality and needs appropriate structures. The factors that influence how virtual teams operate and perform include, among other, group size, individual competences, the technology in place, and the nature of tasks. Empirical studies conducted by Paul, Seetharama, Samarah, & Mykytyn (2004) show that a collaborative style of conflict management improves virtual team members’ satisfaction with decision-making, perceived quality, and member participation, no matter how homogeneous or heterogeneous the team may be. Evaristo, Scudder, Desouza, & Sato (2004) carried out case studies on companies in the United States, Japan, and Europe to understand the impact of distributedness on project management as it relates to such various dimensions as structure (control, communication), perceived distance, complexity, level of dispersion, and types of stakeholders, to name a few. Evaristo et al. found that, depending on the type of industry, particular dimensions of distributedness were more or less prevalent. For instance, the structure dimension is more important in the software industry than in manufacturing. Despite the value of these results for the successful management of distributed teams, most authors call for more research, as several key aspects have not yet been defined, especially from a project management perspective (Evaristo et al.; Powell, Piccoli, & Ives, 2004). This is particularly true of the concepts related to decision-making processes.
Concepts related to decision-making processes
Like many other management activities, project management can be seen as including a strong decision-making component. Designing and executing projects in constrained timeframes generally results in pressure to make timely decisions concerning—among other factors—activities, resources, and technology.
From the perspective of limited rationality, a decision involves choosing between several alternatives. Any responsible choice implies an anticipation of the results of this choice (Mekhilef & Cardinal, 2005). In every project, stakeholders who take part in conducting a project are compelled to make decisions in order to carry out their duties and meet their objectives. Doing so involves asking several relevant questions: Who should this task be given to? Who should be called to provide this information? In what previous project will relevant information be found? Which technologies should be used to satisfy this need? Which technical principle should be chosen for optimal reliability, while respecting the cost objective? (Mekhilef & Cardinal, 2005)
The effectiveness of group decision-making is an increasingly vital concern for organizations (Brodbeck, Kerschreiter, Mojzisch, & Schulz-Hard, 2007). But the act of defining and understanding decision-making models is not an easy task (Pennings, 1985). Thanks to the seminal work of researchers, such as Simon (1960), March (1988), and Mintzberg et al. (1976), we now understand the process and the mechanisms underlying decision-making, whether these decisions are strategic or operational in nature. Although the management literature has a plethora of publications on the topic that have come out in the last decades, it is rather intriguing that the project management literature has not kept pace. Authors who have published on this topic focus, for instance, on research and development (R&D) project selection as an important task for organizations engaged in R&D project management (Tian, Ma, Liang, Kwok, & Liu, 2005), or on the extent of involvement of the key parties in project planning, focusing on the anatomy of decision-making within the planning team that they create (Shapira, Laufer, & Shenhar, 1994). Most researchers and practitioners would readily agree that projects are particularly sensitive to how decisions are made within organizations. The short-term, one-time, specific nature of projects, as opposed to organizations’ ongoing activities, often make the decisions that are made by project actors appear particularly critical and irreversible. In today's fast-changing economic and technological environment, this phenomenon seems even more significant. When these factors are combined with the geographical and organizational distribution of project actors, decision-making becomes a real challenge for practitioners, and authors such as Nidiffer and Dolan (2005) call for increased efficiency in that regard:
“The evolution toward distributed project management drives the need for improved processes, methods, and tools to input and share common data. The need applies across the project life cycle and among all or selected elements of the team. In our global economy, there's a growing need to decrease the time it takes to make an informed decision, to improve the team's decision velocity.” (p. 68)
Such observations give rise to several questions that ought to be studied more thoroughly than they have been. In view of the increasing number of project teams acting simultaneously on several sites and with different cultural and organizational backgrounds, decision-making needs to attract more attention as a central concept in projects, more specifically in the context of distributed project teams.
Decision-making within distributed project teams
Past research trying to understand decision-making within distributed project teams focused on the decision-making effectiveness of individuals, face-to face teams, and virtual teams. For instance, through an empirical study of graduate students placed in a decision-making context related to new product development (NPD), Schmidt et al. (2001) demonstrated that decision-making teams, both face-to-face and virtual, make project review decisions more effectively than individuals acting alone. It appears that teams are less likely than individuals to continue projects whose outcomes appear dubious (Schmidt, Montoya-Weiss, & Massey, 2001). As noted by these authors, teams are better than individuals at evaluating the feasibility and success of a project, so their members are less inclined to participate in risky projects. The team develops better control over the project because of the experiences, knowledge, and points of view contributed by each team member. Another important result of the Schmidt et al. study is that virtual teams appear to make more effective NPD decisions than face-to-face teams. Two key factors help explain this finding. First, the authors posit that in virtual teams, the traditional social cues and mechanisms that facilitate human interaction and decision-making are altered by the communication technology. Indeed, they indicate that the leanness and low social presence of the asynchronous communication environment (imposed in that case by Lotus Notes technology) contributed to more focused and objective decision-making. Since decisional errors can be partially attributed to a breakdown in rationality as a result of social power or group dynamics (Shaw, 1981), the communication environment served to reduce the escalation of commitment behavior. In sum, these authors suggest that teams are more effective for making NPD project continuation decisions, and their effectiveness can be heightened if the members communicate via technology rather than face-to-face. Conversely, authors such as Potter and Balthazar (2002) conclude that virtual teams show the same interaction styles as traditional teams (co-located teams, face-to-face) and that these interaction styles have the same impact on tasks and performance. They suggest that individuals do not give up their personalities when they enter virtual teams; on the contrary, they display them in virtual teams as well as in co-located teams.
Other authors, such as Duarte and Snyder (2001), find that a virtual team is more likely to succeed in a non-hierarchical, less authoritarian culture. This is supported by Kock (2000). However, Kahai, Snyder, & Carr (2001) found that a virtual team is more successful with clearly defined and structured workflows and goals. In addition, Toney's (2001) benchmark data indicate, and Frame (1995) emphasizes, that a virtual project succeeds most often when team members have confidence in their leader and in each other, since virtual teams normally do not have the ability to assess each other's working habits visually. Finally, Hedlund, Ilgen, & Hollenbeck (1998) compared groups of students organized in hierarchical decision-making teams with distributed expertise. In these teams, members do not share the same expertise and the leader must coordinate the decisions made to perform a task or find a solution. Consensus is not desirable or even possible because each member possesses different levels of knowledge. The authors suggest that, in the first phases of project development, the leader must initiate a flow of information that is useful for making individual decisions. However, when decisions depend on other members’ expertise, social pressure prevents decision-making from being efficient. Teams then take advantage of electronic communication tools to reduce this social pressure. While we recognize the value of the existing decision-making literature, and the literature on virtual or distributed team management, we find that more attention is still required at the project level and in the specific context of decision-making within distributed project teams.
Conceptual Model and Hypotheses
This study aims to investigate how formal decision-making processes and team autonomy in distributed project teams are related to better decision quality and teamwork effectiveness. A theoretical model was developed, drawing on the literature on the decision-making process and distributed project teams. The model links team autonomy and formalization of the decision-making process to quality of decisions and teamwork effectiveness with different levels of geographical dispersion (see Figure 1). We will briefly discuss these concepts and identify the working hypotheses to be tested.
Figure 1. Theoretical model
Quality of the decision-making process (DMP) as antecedent of teamwork effectiveness
In all kinds of political, economic, and societal contexts, groups often make important decisions. One of the reasons for this is that groups possess larger informational resources (Clark & Stephenson, 1989) and thus are expected to make better decisions than individuals (Vroom & Jago, 1988). As Brodbeck et al. (2007) argued, the higher costs (with regard to time, money, and effort) of group decision-making, as compared with individual decision-making, can only pay off in decision quality if the exchange of information during the discussion has the potential to help them find the best solution.. The alleged advantages provided by team or group decision-making are essentially of two kinds. On one hand, groups or teams can be perceived as a vehicle for identifying and integrating individual viewpoints. This function permits members to participate in decision-making, which has the beneficial effects of higher acceptance and better implementation of a decision. On the other hand, teams can be viewed as a vehicle for combining and integrating different knowledge, ideas, and perspectives in high-quality decisions and innovations. Compared to individual decision-makers, groups have access to more information due to the unique knowledge distributed among team members (Brodbeck et al.; Clark & Stephenson; Hollenbeck, Ilgen, Tuttle, & Sego, 1995). Overall, it is therefore assumed that for project teams, there is a link between the quality of the decision-making process and their effectiveness in conducting their duties. Thus, we made the following hypothesis:
H1: The quality of the decision-making process is related positively to the teamwork effectiveness in distributed teams.
Formalization as an antecedent of DMP quality
The structuring of decision activities is recognized in the literature of one of the most important components of the decision-making process (Abualsamh, Carlin, & McDaniel, 1990; Korhonen, 1997, Mintzberg, Raisinghani, & Theoret, 1976; Perry & Moffat, 1997). Wright and Goodwin (1999) recognized that, in general, improved decision structuring will improve the quality of the decision outcome.
In recent years, researchers and professional organizations have teamed up to develop standards for implementing projects from a process perspective (Cooper, Edgett, & Kleinschmidt, 1999). These standards (for example, stage-gate) have helped shape a common understanding among practitioners, notably with regard to information gathering and sharing to facilitate decision-making. Within distributed teams, a lack of proximity can lead to additional coordination difficulties (Hoegl & Proserpio, 2004). Cramton and Webber (2005) show that, despite the use of technology, distributed teams experience difficulties in sharing information caused by the loss of verbal signals, such as those that occur during face-to-face encounters. In such a context, project teams welcome a sense of coordination through formal processes since this improves how a team collaborates and communicates. Powell and Buede (2006) state that when a team lacks a rational and explicit communication process, the team is unable to make well-structured decisions: Furthermore, these decisions are not discussed widely and not well documented, and thus dysfunctions will most likely occur. This necessity motivates our work in looking at the existing decision-structuring approaches used by project actors within distributed teams. The point is that project actors can make better decisions if they formalize and control the decision process. Though formalism tends to create some kind of rigidity (Khatri, 1994), authors such as Hambrick (1990) claim that formal approaches to decision making are most likely to maximize organizational performance. This leads us to propose the following hypothesis:
H2a: The formalization of the decision-making process is related positively to the perceived quality of the decision-making process.
H2b: The formalization of the decision-making process is related positively to the teamwork effectiveness in distributed teams.
Team autonomy as an antecedent of DMP quality
In team and project management literature, team autonomy is recognized as an important success factor (Hoegl & Parboteeah, 2006). Thus, decentralization of decisions within the hands of distributed team members should prevail, especially in contexts where a quick response to changing technologies and environments is necessary (Zabojnik, 2002). Despite the importance from a team's perspective of preserving its autonomy, organizations tend to interfere with team autonomy for many reasons. Sometimes, team decision-making discretion is withdrawn because top management does not share an understanding of the product development process (Clark & Wheelwright, 1992). This interference may take the form of higher levels of management requesting that they be consulted for major or minor operational decisions. Other reasons are that managers may not buy into the concept of team autonomy (Gerwin & Moffat, 1997a, 1997b). While some forms of management interference can be beneficial because these provide feedback to help project completion or encourage creativity within the team by discouraging groupthink, some researchers argue that team-external influence over project decisions is detrimental to teamwork in projects (Hoegl & Parboteeah, 2006). Distributed projects involve high levels of uncertainty and ambiguity, along with a crucial need to solve problems (Sicotte & Langley, 2000). Such information sharing and task coordination within the team is likely reduced when top management interferes with project decisions. Drawing from the information processing perspective on the organizational level (Daft & Lengel, 1986; Nadler & Tushman, 1988), such hierarchical structures linking team members and top management decrease collaborative processes within the team, as communication increasingly flows vertically (from top management to the team) rather than horizontally (within the team). If a team has a high degree of autonomy over project decisions, team members rely upon themselves for task decisions, which will likely increase the sharing of information and the coordination of task activities horizontally within the team. Top management influence on project decisions may signal to team members that management does not buy into the team autonomy idea (as mentioned earlier) or does not trust the team to be able to make such decisions. Both cases are likely to result in team members’ being less satisfied with the team (Kirkman & Rosen, 1999) and less committed to the team (Wall, Kemp, Jackson, & Clegg, 1986), thereby undermining their feelings of authority, responsibility, accountability, and consequently reducing the quality or effectiveness of their teamwork. Team members who can influence the decisions that affect them are more likely to value the outcomes, which in turn reinforces satisfaction (Black & Gregersen, 1997). The highest satisfaction comes with high-level involvement, as occurs when team members are involved in generating alternatives, planning processes, and evaluating results. Given all the above features, team autonomy over project decisions should lead to better effectiveness and decision-making quality. Such evidence should be emphasized in the particular context of distributed projects, as the nature of these projects involves higher levels of uncertainty and ambiguity.
H3a: Team autonomy is related positively to the perceived quality of the decision-making process.
H3b: Team autonomy is related positively to the teamwork effectiveness in distributed teams.
Effect of geographical dispersion on the model
The recent literature has confirmed the impact of dispersion (or distributedness) on various aspects of project management and teamwork. In fact, numerous reviews of the literature have been done in recent years, highlighting the key dimensions, including communication, conflict, and trust. While some authors have broadened the concept and talk about discontinuities (i.e., geography, time zones, culture, and work practices) (Chudoba, Mei Lu, & Watson-Manheim, 2005), others have studied proximity instead of distance. Hoegl and Proserpio's (2004) analyses show that team member proximity (the degree to which all team members are in the same vicinity over the project's duration) positively relates to teamwork quality. This evidence suggests that the quality of teamwork improves the performance of innovative projects (Hoegl & Gemuenden, 2001) and also that the quality of teamwork decreases as member proximity decreases (Hoegl & Proserpio, 2004). In a more recent study, Hoegl et al. (2007) go farther, showing dispersion to be a determinant of teamwork quality, which in turn affects team performance. They argue that teamwork affects team performance more strongly as member dispersion increases. Two main reasons for this are discussed. First, high-quality teamwork can leverage the increased knowledge potential of dispersed teams; second, team members in more dispersed teams have little possibility of compensating for low-quality teamwork through hands-on leadership. Thus, geographical proximity affects team processes and performance. In this study, we propose to assess the impact of team dispersion on the dimensions that make up our main model (H1 to H3). Thus, we also propose the following hypotheses:
H4a: The effect of DMP quality on teamwork effectiveness is stronger within highly distributed teams than in moderately distributed teams.
H4b: The effect of formalization on DMP quality is stronger within highly distributed teams than in moderately distributed teams.
H4c: The effect of formalization on teamwork effectiveness is stronger within highly distributed teams than in moderately distributed teams.
H4d: The effect of autonomy on DMP quality is stronger within highly distributed teams than in moderately distributed teams.
H4e: The effect of autonomy on teamwork effectiveness is stronger within highly distributed teams than in moderately distributed teams.
Teamwork effectiveness
Research on virtual teams’ outcomes primarily focuses on the effectiveness of the team and, to a lesser extent, on satisfaction (Powell et al., 2004). Studies of satisfaction mostly rely on student teams, whereas field research examines effectiveness (Saunders & Ahuja, 2006). As this research area matures, Saunders and Ahuja recognized that a more encompassing and finer-grained view of outcomes was necessary. Measures of team performance are traditionally based on specific criteria, such as sales revenues produced by marketing teams (Gladstein, 1984), number of technical papers produced by R&D teams (Smith, 1970), or quality ratings by management on project teams (Keller, 1994). Team evaluation criteria identify important dimensions of effectiveness that take the type of team into account and are specific and meaningful to the organization studied (Keller; Sundstrom De Meuse, & Futrell, 1990). Saunders and Ahuja define effectiveness as the degree of task completion. They add that, for temporary teams such as project teams, effectiveness is not only task-oriented but would also tend to focus on accomplishing their goals on a one-time basis. In a more recent study, Staples and Webster (2007) used social cognitive theory to study the effectiveness of members of teams in traditional and two types of virtual teams: hybrid and distributed. To do this, they developed a self-efficacy (SE) teamwork measure based on virtual team best practices identified in the literature and from six case studies. In that study, perceived effectiveness was defined as the intention to remain on the team, the ability to cope, perceived individual performance, perceived team performance, and satisfaction with the team. For the purpose of our research, teamwork effectiveness refers to the perceived performance by team members on items such as task completion, goal achievement, sharing information, conflict resolution, problem solving, and the team's ability to create and sustain a good working environment. Effectiveness is therefore not driven only by goals or oriented by task completion; it also considers practices that are important for improving the effectiveness of individual distributed team members such as supporting other team members, good working environment, and effective communication, among others.
Research Method
Data collection
This model was tested using data from the field (professional settings) as suggested by numerous authors, who tend to be very critical of traditional research investigating distributed teams in controlled environments (Martins, Gilson, & Maynard, 2004). More specifically, we collected data during the last six months of 2006 using an online survey instrument.1
The instrument was developed by a research team led by the first two authors. The team spent several weeks conducting interviews with managers who deal with distributed projects on a day-to-day basis. This first phase of the study enabled us identify the key concepts which were ultimately used in developing the questionnaire. We created the online questionnaire from scratch to ensure a fully customized format, which can still be difficult to achieve when using publicly available Internet tools for developing online surveys. The online questionnaire was pre-tested with at least 10 respondents to ensure the technical reliability of the system and adequate comprehension of the questions. Minor changes were made following this pre-test. All the respondents had to meet the criterion of working in a distributed environment. The questionnaire included several measures that help classify the respondents along a continuum of dispersion. Most of the questions ask respondents to answer on a 7-point Likert scale.
We contacted over a thousand potential respondents, most of whom were project managers based in Canada and involved in technical projects. Of these, we found that 149 surveys provided responses that we could use for data analysis. Our response rate of nearly 15% is quite comparable with similar surveys today (Jugdev, Mathur, & Shing Fung, 2007; Wu et al., 2006). This final, usable sample included diversified profiles of project managers and projects. The average project duration was 16.5 months and the average project budget was C$42.2 million; the average number of team members was 24.5.
We also found other interesting team and project characteristics: Approximately 28.2% of the respondents were PMP-certified practitioners and their average experience in the field of project management was 8.4 years. The nature of the projects on which these professionals based their responses included new product/service development, existing product/service improvement, implementation of technology, and R&D.
Data analysis
We used a three-step procedure to investigate our hypotheses H1 to H4. This process follows that of similar studies published recently in the field of virtual teams, project management and small group research (Jugdev et al., 2007; Mijias, 2007).
As a first step, we conducted a principal component analysis to aggregate items and identify interpretable factors, using a cut-off level of 0.5 for each loading. We found that a clear solution emerged with the following factors: formalization of decision processes (4 items; α = 0.8960; variance = 38.38%), team autonomy (2 items, α = 0.7133; variance = 19.82%), quality of decision-making process (2 items, α = 0.8663; variance = 21.72%), and teamwork effectiveness (6 items, α = 0.9188; variance = 71.15%).
In this model, we found that formalization of decision processes characterizes the extent to which key decisions made within distributed teams are governed by firms’ internal structured processes. Respondents were invited to determine—among other things—whether formal processes existed for establishing who had to be involved in decision-making within the team, whether a formal process existed for determining how key decisions were to be made, and whether key decisions were all subject to a formal decision-making mechanisms such as stage-gate. The second group of independent variables relates to team autonomy, namely those dimensions that relate to the distributed team's capacity to make decisions, the project manager's authority to manage projects, the team's decision autonomy regarding project budgets, and the team's functioning. As for dependent variables, quality of decisions refers to various items characterizing dimensions such as evaluation of different alternatives or options, time constraints (i.e., decisions were made in a reasonable time), team cooperation and consensus in supporting decisions made, and variations and changes in final decisions (i.e., once made, decisions did not usually change). Thus, decision-makers must have the ability to process and analyze information quickly and efficiently. They should have autonomy in their decision-making processes in order to act in a diligent manner. (Lundäck & Hörte, 2005). Teamwork effectiveness refers to a group of items examining the team members’ perception of activities such as setting common objectives, planning and organizing tasks, holding meetings, information sharing, problem solving, and creating and sustaining a good working environment. In addition to the main dimensions of the model, we developed a dispersion index using various measures. We found that team dispersion can be measured from different perspectives, as the number of practitioners, the number of sites, and the distance between sites vary substantially. Chudoba et al. (2005) suggest various measures of dispersion including geographical distance, time zone differences, cultural diversity, and diversity in the level of technology use. For this study, we only considered geographical dispersion and used three main dimensions: the number of sites that the team is spread over; the number of hours between the most distant time zones within the team; and the average distance (natural log.) between each project site and the project manager's site. The final usable index is the arithmetic sum of these three project characteristics, which provided the necessary variance for the subsequent analyses (min = 2.00; max = 31.24).
The second step of the analysis consisted in assessing the unidimensionality and the convergent validity of the measures through a confirmatory factor analysis (CFA), as suggested in the literature (Anderson & Gerbing, 1988; Hair, Anderson, Tatham, & Black, 1998). From the model fit statistics, we concluded that the dimensions have strong convergent validity (χ2: 75.208; p: 0.205; Df: 66; χ2/df: 1.140; GFI: 0.914; IFI: 0.991; CFI: 0.990; RMSEA: 0.036). The average variance extraction (AVE) coefficients were also estimated and found to be acceptable (above 50%), thus confirming the unidimensionality of the dimensions. Additional examination of the dimensions allowed us to assess their discriminant validity in accordance with Bagozzi and Yi's (1988) recommended procedure (constrained and non-constrained models). The results show that chi-square measures are significantly lower for the non-constrained than for the constrained models (φ=1), which confirms the discriminant validity of the dimensions.
The third and final step of the analysis consisted in assessing hypotheses H1 to H3 using a full structural equation model performed on EQS software version 6.1. Further analysis (H4) was carried out using two sub-models based on geographical dispersion (highly vs. moderately dispersed teams) using the multiple group analysis method as suggested by Byrne (1994). The results are provided in the next section.
Results
We developed a first structural model (Model 1) for the entire sample and for the purpose of assessing H1 to H3. The overall statistics indicate an acceptable model fit for this model (χ2: 75.208; p: 0.205; Df: 66; χ2/df: 1.140; GFI: 0.914; IFI: 0.991; CFI: 0.990; RMSEA: 0.036). Table 1 presents the correlation coefficients between each pair of factors included in Model 1.
Table 1 Correlation coefficients between factors
| Dimension | Mean | SD | Formalization | Autonomy | Quality of DMP | Teamwork effectiveness |
| Formalization | 4.685 | 1.470 | 1.000 | |||
| Autonomy | 5.111 | 1.220 | -0.057 | 1.000 | ||
| Quality of DMP | 3.648 | 1.674 | 0.373 **** | 0.115 | 1.000 | |
| Teamwork effectiveness | 5.038 | 1.337 | 0.357 **** | 0.230 *** | 0.654 **** | 1.000 |
Significance levels: * p ≤ .10; ** p ≤ .05; *** p ≤ .01; **** p ≤ .001
Table 2 shows the standardized path coefficients and the fit statistics. Two sets of relationships can be analyzed in this first model. The first set concerns the group of dimensions that hypothetically impact the effectiveness of teamwork in distributed teams. A fairly high level of R2 (63.88%) suggests that the three dimensions considered have strong explanatory power. In particular, the quality of the decision-making process has a very significant positive effect on the effectiveness of teamwork (β = 0.689; t = 6.437; p ≤ .001). So does autonomy (β = 0.208; t = 2.476; p ≤ .01). The formalization dimension is much weaker in its explanatory power ((β = 0.131; t = 1.584; p ≤ .10) regarding the effectiveness of teamwork. Conversely, its impact is much stronger when we consider the second set of relationships, namely the hypothetical links between autonomy and formalization and the quality of decision-making process. In this case, it seems clear that formalization is associated with the quality of the process (β = 0.406; t = 3.764; p ≤ .001). The autonomy dimension also shows opposite behavior as its relationship with the quality of decision-making process is not significant. This second set of relationships is also weaker than the first one (R2 = 17.74%).
Overall, this model provides a strong support for H1, H2a and H3b but not for H3a. The support of H2b is rather weak, suggesting that formalization alone is not perceived very strongly by project professionals when evaluating the effectiveness of their team's work. However, it is certainly an important dimension of the quality of the decision-making process, as suggested by Table 2. Table 2 also makes it clear that the quality of the decision-making process has a very strong impact on how teamwork effectiveness is perceived by project professionals who work in distributed settings. More specifically, formalization appears to play a very important role.
Table 2 Statistics for structural model 1
| β | t-value | R2 | ||
| Autonomy | 0.208 *** | 2.476 | 63.88 | |
| Formalization | 0.131 * | 1.584 | ||
| Quality of DMP | 0.689 **** | 6.437 | ||
| Autonomy | 0.131 | 1.186 | 17.74 | |
| Formalization | 0.406 **** | 3.764 |
χ2: 75.208; p: 0.205; df: 66; χ2/df: 1.140; GFI: 0.914; IFI: 0.991; CFI: 0.990; RMSEA: 0.036
In order to assess the impact of the geographical dispersion of project professionals on the model (H4), it was necessary to build two additional sub-models (Model 2 and Model 3) which are depicted in Figures 3. Fit statistics for this step are considered satisfactory (χ2: 167.74; p: 0.019; df:132; χ2/df: 1.271; GFI: 0.882; IFI: 0.963; CFI: 0.961; RMSEA: 0.053)
In moderately dispersed teams (see “M” on Fig. 3), called Model 2, the quality of the decision-making process is only impacted by formalization, which parallels the relationship found in Table 2 when the whole sample was considered, that is, when no discrimination is made regarding dispersion. In the same way, formalization does not have any effect on teamwork effectiveness for the moderately dispersed teams. However, the quality of decision-making process is still a strong predictor of teamwork effectiveness with a highly significant beta (β).
In the case of highly dispersed teams (see “H” on Fig. 3), called Model 3, all the relationships between the dimensions appear to be significant though at various levels. Although formalization continues to have a strong effect on the quality of decision-making process, its effect on the effectiveness of teamwork is also strongly significant. In these teams, autonomy also has much stronger explanatory power as it proves to be highly significant in its effect on teamwork effectiveness (β = 0.286 ***), and to a lesser degree, in its effect on the quality of the decision-making process (β = 0.193 *). When comparing the results for the two models (moderately distributed versus highly distributed), only two instances of significant differences between beta coefficients can be found (see superscripted letter a in Figure 3). In the first instance, the Quality of DMP Teamwork effectiveness relationship is significantly lower for highly dispersed teams; in the second, the Formalization
Teamwork effectiveness relationship is significantly stronger for the highly dispersed teams.
Table 4 provides an overall summary of the results obtained with structural models 1 to 3; it shows the level of support for all of the hypotheses considered in this study. Generally speaking, dispersed teams’ effectiveness is clearly impacted by the quality of decision-making process, as indicated by Models 1, 2, and 3. Hypothesis 1 is therefore supported. However, it was predicted that geographical distance would change this relationship so that the relationship between the quality of DMP and the teamwork effectiveness would be stronger in the case of highly dispersed teams (H4a). Our results do not support this hypothesis.
Table 4 Summary of structural model hypothesis testing
| Hypothesis | Model 1 (full sample) | Model 2 (moderately dispersed) | Model 3 (highly dispersed) | (p value) | Supports hypothesis | |
| H1: | QDMP | 0.689 **** | Yes | |||
| H2a: | F | 0.406 **** | Yes | |||
| H2b: | F | 0.131 * | Weak | |||
| H3a: | A | 0.131 | No | |||
| H3b: | A | 0.208 *** | Yes | |||
| H4a: | QDMP | 0.900 **** | 0.489 **** | 0,018 | No | |
| H4b: | F | 0.447 *** | 0.419 *** | 0,983 | No | |
| H4c: | F | - 0.121 | 0.364 *** | 0,016 | Yes | |
| H4d: | A | 0.127 | 0.193 * | 0,741 | No | |
| H4e: | A | 0.190 * | 0.286 *** | 0,712 | No |
A: autonomy; F: formalization; QDMP: quality of decision-making process; TWQ: effectiveness of teamwork; Significance levels: *p ≤ .10; ** p ≤ .05; *** p ≤ .01; **** p ≤ .001
Of the two main dimensions considered as predictors of the quality of DMP, only formalization shows consistent predictive power in Models 1, 2, and 3. This confirms Hypothesis 2a but, as in the previous case, it was not possible to validate hypothesis 4b since formalization remains a strong predictor for both moderately and highly dispersed teams. In other words, distance does not seem to play a role in changing the impact of formalization on the quality of DMP. Formalization also shows a certain level of predictive power for teamwork effectiveness (H2b weakly supported), and particularly so in the case of highly dispersed teams (Model 3). H4c is therefore supported
Team autonomy was thought to be a predictor of the quality of DMP but the results suggest that this is not the case. Except for Model 3, which suggests a slightly significant value of the beta coefficient (0.193 *), autonomy does not appear to be a very significant dimension with regard to the decision-making process. However, it does play an important role in explaining the effectiveness of teamwork. Beta coefficients are highly significant in Models 1, 2, and 3, which confirms H3b. Like formalization, autonomy remains a strong predictor for both moderately and highly dispersed teams, which means that H4e cannot be supported as originally formulated.
Discussion and Managerial Implications
The first purpose of this study was to assess the extent to which team autonomy and formal decision processes predict quality of decision-making and teamwork (Model 1). It also aimed to evaluate the impact of the geographical dispersion of teams over these dimensions. Three important findings emerged.
First, the results strongly support the benefits of a quality decision-making process in the context of distributed teams. Because of the numerous discontinuities that characterize them, such a process is probably seen as a way to avoid and/or iron out obstacles, and achieve successful results. In the context of distributed teams, putting such a process in place is certainly challenging, and much remains to be investigated in this regards, but project managers need to pay attention to this factor.
Second, autonomy is clearly an important characteristic for successful dispersed teams. In fact, it is an important dimension no matter what the degree of dispersion may be. To meet stakeholders’ expectations, most project managers will recognize the importance of providing the team with as much autonomy as possible. While one can argue that this has long been accepted in project management literature (at least, from a North American perspective), it is certainly a contribution with regard to the specific case of distributed teams. It also has important implications for top managers, who play a central role in deciding to what extent a dispersed team will be empowered. The results of this study clearly indicate the way to go, despite the fear of out of sight, out of control syndrome that may be present in some organizations.
Third, formalization does add value to teamwork, and this is particularly true as the distributedness of the team increases. It is inherently part of the explanation of a high-quality decision-making process and therefore teams would need to highlight it as a key part of their strategies. Formal decision-making would seem to play a less important role than team autonomy and decision quality in determining a distributed team's effectiveness. In reality, it can be interpreted as having a more indirect influence than the other dimensions.
These results extend the literature on teamwork effectiveness in the particular context of distributed project teams. They shed light on the contradictory results found in the literature comparing the performance of virtual teams to that of co-located teams. For example, McGrath (1991) suggests that teams with no past history, engaged in a dispersed task, are less likely to be effective than co-located teams, whereas Schmidt et al. (2001) show that dispersed teams may well make better new product decisions than co-located ones. The results of the present study clearly suggest that the effectiveness of distributed teamwork is strongly impacted by decision quality and team autonomy.
Team effectiveness is therefore not only driven by goals or task completion (Saunders & Ahuja, 2006) but it is also influenced by practices such as team autonomy that satisfy team members’ feelings of authority, responsibility, and accountability. Team members who can influence decisions that impact on them are more likely to value the outcomes, which in turn increases satisfaction (Black & Gregersen, 1997). Teams can also be viewed as vehicles for combining and integrating different knowledge and ideas in high-quality decisions and innovations, which in turn lead to effective teamwork (Brodbeck et al., 2007).
Limitations and Future Research
A few limitations of this study should be noted, along with questions for future research. The results of this study offer clear indications to managers and researchers on some successful approaches that may be beneficial in the context of distributed project teams. However, this study was conducted with firms and respondents based in North America, raising the question of whether the results can be transferred to other cultures such as those in Europe and Asia. Further research involving other countries, with multiple respondents per team, is encouraged in order to increase our understanding of the possible influences of country context on the relationships investigated here. The next step in this research is to consider these limitations, combined with a deeper understanding of the impact of distributed specificities on decisions and effective teamwork. One can argue that, with additional dimensions (such as number of sites and time difference between locations of distributed teams), relationships between variables may differ. Similarly, firms from different industries will have different relationships between variables. For instance, firms in the aeronautical industry have a natural tendency to formalize their decision processes as a result of product complexities and institutional constraints. In our future research, we intend to extend our framework and include other types of relationships (such as a focus on project success combined with teamwork effectiveness) and add other characteristics (such as project manager experience and its impact on team autonomy).
Conclusion
This study focused on the decision-making process in a distributed team environment. The purpose of this exploratory, empirical investigation was to examine the links between key dimensions such as autonomy and formalization and the quality of teamwork and the decision-making process. Using data from the field, provided by project managers, we found that the quality of the decision-making process is strongly associated with effective teamwork within distributed teams. The data suggest that the effects of autonomy and formalization are mixed. Both have a significant effect on teamwork effectiveness in the case of highly distributed teams.
As the number of distributed teams is constantly rising in most firms today, there is certainly a need to better understand how project management processes need to be adapted. In the case of decision-making processes, there is a shortage of studies despite the fact that decisions are central to carrying out projects. It is hoped that more studies like this one will contribute to a better understanding of the issues involved, and provide some relevant insights for managers.
