Keep IT Together: Behavioral Aspects of Teams' Location in Enhancing Motivation to Adopt Complex Work Plans

Keep IT Together: Behavioral Aspects of Teams' Location in Enhancing Motivation to Adopt Complex Work Plans

Mor Brokman Meltzer (Bar-Ilan University, Israel), Dikla Perez (Bar-Ilan University, Israel) and Roy Gelbard (Bar-Ilan University, Israel)
Copyright: © 2021 |Pages: 13
DOI: 10.4018/IJITPM.2021010105
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Abstract

Prior research shows that project managers tend to avoid following the optimal work plan, and that this tendency is negatively influenced by the perceived complexity of the Gantt chart. This research introduces a central factor moderating this effect: the level of communication effort required by PMs (project managers) when managing IT (information technology) team members. To test this prediction, the researchers followed an experimental approach and manipulated the level of communication effort by defining two group types, which differed in the number of team members and the geographical distance between them (together or not). Research results show that the complexity level of the Gantt chart negatively affects PMs' motivation to adopt an optimal work plan, and importantly, findings suggest that the group type (together or not) attenuates this effect. Research findings also offer practical implications for managers seeking to influence PM's behavior and attenuate the negative impact of Gantt chart complexity without changing other aspects of the work plan.
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Introduction

Former research pointed to behavioral aspects that influence PM's (project manager) decisions and showed that PMs do not necessarily choose the optimal Gantt chart (Wauters and Vanhoucke, 2016, Brokman et al., 2018a, Brokman et al., 2018b). It has been suggested that such decisions tend to be biased by the PM’s perception of Gantt chart complexity, which has a negative effect on their motivation to adopt optimal work plans (Brokman et al., 2018a, Brokman et al., 2018b). This effect of perceived complexity is specifically relevant to companies that mostly build their production and development process on the human resource. In such companies, the “machines” are actually people and the production or development process might be affected by behavioral aspects. It is important to first define two important terms that are extremely relevant to this research: (a) an optimal work plan and (b) an optimal Gantt chart. An optimal work plan is defined as one that minimizes project costs while meeting deadline requirements (Kelley and Walker,1959, Walker and Sawyer, 1959 and Kelley,1961). A Gantt chart is a graphical representation of a project structure, that shows the time dependency of several tasks of a project within a calendar (Sharp et. Al, 2009). The optimal Gantt chart is a graphical illustration that represents the optimal work plan-i.e. under the given constraints, it allows the completion of the project in the shortest time (Blazewicz et al., 1983).

Former research points to several factors that influence work plan complexity, such as the number of trade-offs between the different activities and their schedules (Wauters and Vanhoucke, 2016), poor information flow and communication efforts (Austin et al., 2002). The latter is timely and relevant for projects based on the human resource because communication between team members takes a significant part of the process. For example, research shows that outside of machine-based industries, complex Gantt charts consist mainly of complex communication and “handshaking” within the development team (Park et. al, 2003, de Souza et. al, 2007). In addition, an optimal plan typically includes coordinating and integrating activities in an efficient and effective manner, using limited resources (Turner, 2000). Finally, it has been suggested that communication and coordination are critical to meet deadlines set for the project and avoid delays or priority conñicts (Espinosa et. al, 2007). These studies examine the effect of communication efforts on performance and incorporate objective measures for complexity and for performance. In contrast, behavioral aspects of communication efforts and their possible effects on PM's motivation to adopt the optimal plan, remain unclear. Thus, this research’s main goal is to uncover the role of communication efforts required to complete a work plan on influencing a PM's motivation to adopt complex work plans.

The researchers build their predictions on previous results suggesting that complexity has a negative effect on PM's motivation to adopt the optimal work plan, and on findings that show that communication efforts reflect one of the complexity dimensions. This research argues that intervening with this one aspect might attenuate the effect of complexity without changing other aspects of the optimal work plan. In order to decide how to operate and manipulate different levels of communication efforts, this research followed Espinoza and colleagues’ (2007), who suggested that geographical dispersion and team size are key factors in communication between teams, and used these two factors to represent different group types reflecting different communication efforts. Then, the researchers combined them into an operational measure of togetherness, which was used to define small IT (Information Technology) teams that share a location as “kept together” teams, while large IT teams that worked from different locations were defined as “not kept together” teams. This method was designed to manipulate the level of communication efforts required by IT team members. Thus, communication efforts will be higher in a “not kept together” team than in a “kept together team.” The researchers predicted that the group type (kept together or not) will moderate the effect of complexity on PM's motivation to adopt optimal work plans.

To test research predictions, an experimental approach was followed while a pre-test and an experiment were conducted. We specifically chose this approach that follows a recent tendency in PM research, incorporating behavioral aspects and methods in project management research (Müller et al. 2011; Maqbool et al. 2017; Zaman et al. 2019; Georg Gemünden 2014).

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