دانلود مقاله ISI انگلیسی شماره 4524
عنوان فارسی مقاله

بررسی تاثیر پیچیدگی پروژه بر ارتباط تیم با استفاده از شبیه سازی مونت کارلو

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
4524 2011 19 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
An investigation of project complexity's influence on team communication using Monte Carlo simulation
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Engineering and Technology Management, Volume 28, Issue 3, July–September 2011, Pages 109–127

کلمات کلیدی
- ارتباطات تیم - عملکرد - پیچیدگی پروژه - شبیه سازی مونت کارلو - آزمایش های مجازی
پیش نمایش مقاله
پیش نمایش مقاله بررسی تاثیر پیچیدگی پروژه بر ارتباط تیم با استفاده از شبیه سازی مونت کارلو

چکیده انگلیسی

Research using empirical methods has established a curvilinear relationship between team communication and performance. We conduct virtual experiments to examine team communication and performance when teams work under varying types and levels of project complexity. Data samples, generated using Monte Carlo simulation, are based on the statistical characteristics of empirical data collected from 60 cross-functional project teams that communicated over multiple media (email, phone, and face-to-face) and were completing projects of varying complexity. Regression analysis indicates that project complexity influences the communication–performance relationship. Optimization shows that the communication frequencies at which teams maximize or minimize their performance are dependent upon media used.

مقدمه انگلیسی

Cross-functional teams are commonplace in today's work environment. Understanding how to facilitate effective team behaviors is critical for successful project and engineering management. Herein, we focus on team communication, as it plays a crucial role in a cross-functional project team's ability to complete its work. Indeed, communication among team members promotes team processes such as cooperation (e.g., Pinto and Pinto, 1990), coordination (Hauptman, 1990), information processing (e.g., Hinsz et al., 1997), and decision making (e.g., Poole and Hirokawa, 1996). As well, communication may enhance team member attitudes about their work relationships (Oh et al., 1991) and knowledge sharing (de Vries et al., 2006). Research indicates, however, that the link between team communication and performance may not be straightforward. Evidence suggests that too little communication may decrease performance over time (Katz and Allen, 1982) whereas too much may lead to information overload that hinders a team's task achievement (Fussell et al., 1998). These findings indicate that communication may have a more nuanced relationship with outcomes than at first perceived. The relationship between team communication and performance, tested through empirical research, has been found to be curvilinear (Hoegl and Wagner, 2005, Leenders et al., 2003 and Patrashkova-Volzdoska et al., 2003). Specifically, the performance of product development teams (Hoegl and Wagner, 2005) and creative teams (Leenders et al., 2003) is adversely impacted when team communication frequency is too low or too high. Patrashkova-Volzdoska et al. (2003) advanced this research stream by examining the communication–performance relationship across several media types. They found an inverted U-shape relationship between communication frequency and performance, but the amount of communication for email and face-to-face at optimal performance was different. Indeed, the amount of email associated with optimal performance was much lower than the amount of face-to-face communication. While these studies make an important contribution to our understanding of the relationship between team communication and performance, they do not take into account the conditions under which the communication transpires. To further this line of inquiry, we incorporate the team's task into our analyses. The task is a logical extension as it is the impetus for all team activities, including communication. Moreover, the nature of the task may drive the communication requirements (Daft and Lengel, 1984 and Daft and Lengel, 1986) and the media used (Lewis, 1998). Herein, we operationalize the task by examining project complexity, including the approaches and endstates a team must consider as well as the conflict and ambiguity associated with decisions they must make (McComb et al., 2007). Complexity may intensify the role of team communication because of the increased need for coordination and decision making (Marks et al., 2001). Moreover, since media may be differentially effective in transmitting the information necessary to diffuse uncertainty and equivocality about the task (Daft and Lengel, 1986), the use of multiple media types may be influenced by a project's complexity. Thus, the purpose of this study is to extend our understanding of the curvilinear team communication–performance relationship across various media by studying the way in which it is influenced by project complexity. To accomplish our purpose, we design a factorial experiment to reveal the effects of project complexity on the team communication–performance relationship. Instead of controlling for project complexity in regression analyses, we treat multiplicity and ambiguity as three-level factors (i.e., not controlled, high, and low) and investigate the communication–performance relationship by replicating Patrashkova-Volzdoska et al. (2003) model under eight combinations of these factors. Their dependent variables assessed team performance, namely goal achievement (the ability of the team to meet technical objectives of the project and business goals of the organization) and efficiency (the team's ability to achieve cost and schedule goals). The independent variables in their model include three communication media (i.e., email, phone, face-to-face and a squared term for each) and four control variables (i.e., task significance, team size, and co-location (requiring two dummy-coded variables)) for a total of 10 independent variables in each regression equation. To acquire the large sample necessary for our experiment we employ Monte Carlo simulation. Our study achieves several scholarly, practical, and methodological contributions. First, we extend previous research by testing the curvilinear team communication–performance relationship in the presence of project complexity using regression analysis. Our investigation lends insight into the dynamics of this relationship when teams use various media to exchange information and face varying types and levels of project complexity. Second, we examine the differences in communication frequencies at optimal performance. Previous research suggests that optimal performance occurs at lower communication frequencies for email than for face-to-face interactions (e.g., Patrashkova-Volzdoska et al., 2003 and Patrashkova and McComb, 2004). We investigate relative communication frequencies across email, phone, and face-to-face at optimal performance. Further, we examine the changes in communication frequencies across media under varying types and levels of project complexity to ascertain how optimal performance may be facilitated when teams must address complexity. Third, we apply and validate a Monte Carlo method for simulating virtual datasets, thereby demonstrating its applicability to behavioral research. We believe this method allows us to establish potentially interesting research domains and provides direction for further laboratory and field research. Finally, our results provide practical implications for managing communication across multiple media and varying types and levels of project complexity to best influence performance. This paper is organized into the following sections. In Section ‘Theory and hypotheses’, the fundamental theory and hypotheses driving our study are presented. Outlined in Section ‘Approach’ are our analytical approach and experimental design. In Section ‘Results’, the multiple regression results run on simulated data are presented. Finally, we discuss our results and offer conclusions in Section ‘Discussion’.

نتیجه گیری انگلیسی

Overall, this investigation of the interplay among communication frequency, communication media and project complexity contributes to both research and practice in a number of ways. In terms of research, we advance the field by demonstrating the use of Monte Carlo simulation to our behavioral investigation as discussed in the previous section. Further, little is known about the way multiple media address team communication needs simultaneously. Using media richness theory and previous empirical research we hypothesized and tested communication frequencies from three media that are at different points along the media richness continuum. As such, we extend research in this area by showing that differences in communication frequencies exist at optimal performance and that the richness of the media may explain these differences. Additionally, the examination of complexity extends previous research about the curvilinear team communication–performance relationship. By testing such a complicated relationship we were able to bring new insights to the literature. For example, we demonstrate that under different complexity conditions the relationship curve may be U-shaped rather than inverted U-shaped. This unanticipated finding suggests that the dynamics of the relationship may depend upon the conditions under which the communication occurs. Future researchers may endeavor to test other types of conditions (e.g., task significance) to uncover the dynamics of the relationship further. The results also contribute to practice by indicating that the relationships between communication and outcomes create a challenging problem for devising policies and managing team communication technology. Managing communication appears to be especially pertinent when efficiency is the outcome of interest. This effect may be due to the nature of goal achievement and efficiency performance. Whereas, goal achievement is focused on the team's ability to attain specific technical objectives and business goals, efficiency is focused on meeting cost and schedule targets. Thus, the role of communication in achieving efficiency may be more influenced by day-to-day activities and the impact more immediate. Indeed, communication takes time, time is money, and therefore, excessive communication may lead to inefficiencies in budget and schedule. For managers, our results demonstrate the importance of email across complexity types and performance objectives. Indeed, the more time and energy a team spends communicating via email, the less likely it is to meet its performance objectives. If the team is facing highly ambiguous projects, regardless of multiplicity, too much or too little email can jeopardize the team's ability to achieve its technical objectives and business goals. Too little email may not provide enough information to reduce the ambiguity, whereas too much email may exacerbate the ambiguity and cause the team to lose sight of what they are trying to achieve. Regarding efficiency, email is not a significant factor when multiplicity and ambiguity are both high or both low; it is only significant when one is high and the other is low. Projects low on both factors may be easily completed regardless of media type and their corresponding frequencies of use. Alternatively, email may not provide enough bandwidth to realistically address issues when both complexity dimensions are high because the amount of exchange required for sufficient information processing may overload the medium. When email is important, our findings suggest that email communication needs to be used in moderation. In contrast to the above situation regarding email, managing the level of communication across face-to-face and phone exchanges under different project complexity conditions may be more complicated. On the one hand, a team can communicate more frequently using these media than they can using email because performance optima occur at higher communication frequencies. On the other hand, the relationships are not always in the same direction. That is, under certain project complexity conditions, the face-to-face and phone communication–performance relationships are optimized at a maximal point and under other conditions, a minimal point. Therefore, communication policies considering the combination of face-to-face and media technology are dependent upon the project complexity facing the team. As project complexity can change over the life span of a project, communication policies may also need to change. In other words, communication in the project environment needs to be dynamically monitored. Nevertheless, our research suggests that performance gains can be made from such active management of team communication.

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