Understanding the Interactions between the Scrum Master and the Development Team: A Game-Theoretic Approach

Abstract

Scrum methodology is widely used in the information technology (IT) industry for the purposes of team-based iterative software development. However, limited studies have been conducted to explore the nature of interactions between a Scrum Master and other team members and the effect of these interactions on team effectiveness. The aim of this study is to understand the interactions between the Scrum Master and other team members in an educational setting and propose and demonstrate an application of cooperative game theory for the same. Cooperative game theory can model scenarios where other team members can benefit from cooperating. Through the lens of the cooperative game-theoretic model, we investigated the strategies employed by the Scrum Master and other team members when involved in a semi-capstone IT project. Specifically, the study explored the team interaction between a Scrum Master and other team members at three different levels of team effectiveness: least effective, partially effective, and most effective. Our results indicate that a Scrum Master should be active to maximize their payoff as well as the teams’ overall payoff. Contrary to this, other team members should be active in the most and partially effective teams, while being passive in the least effective teams at higher costs of interpersonal relations and the processes. The results of the study represent a novel application of game-theoretic modeling for understanding the Scrum Master and other team member interactions. These results are applicable not just in an educational setting but also to the wider area of software development by identifying the right set of strategies by the Scrum Master, and other team members in order to help IT professionals to maximize their payoff.

Characterizing Team Orientations and Academic Performance in Cooperative Project-Based Learning Environments

“…The apriori thematic analysis intends to obtain an in-depth knowledge of how teams made the strategic decision in terms of setting goals, assigned roles, implemented coordination processes, and developed interpersonal interactions when they worked on each milestone. Rubrics were developed to score each code per team (see Table 4, [51]). As the data were qualitative in nature, two researchers manually coded it.…”

“…The study characterizes teamwork skills in terms of setting goals, assigning roles, implementing coordination processes, and developing interpersonal relationships. For this, we will use the lens of the GRPI model, also known as the goals, roles, process, and interpersonal relationships model [49,[51][52][53][54]. The model proposes that setting goals, assigning roles, implementing coordination processes, and developing interpersonal relationships are essential teamwork skills that are integral to team effectiveness [53].…”

“…The team leader was responsible for facilitating communication over the course of each milestone in terms of defining the collective goals that the team wants to achieve and the exact tasks to be performed by each team member. The team leaders could take more active or passive approaches to leadership depending on specific team dynamics and individual tendencies [51]. The instructional team would actively monitor group performance and provide feedback on deliverables.…”

“…The team retrospectives consisted of written reflections that revealed each team's goals, roles, processes, and interpersonal relations for every milestone. Table 3 illustrates the questions related to all the four sub-constructs of team effectiveness, the corresponding definition for each of the themes, and the alignment between the GRPI themes and the team retrospective questions [51].…”

“…This section presents the results focused on identifying the collective orientations in terms of patterns associated with the implemented teamwork skills. The rubric scores [51] were obtained for each construct, and each team was used to perform the clustering. As the optimum number of clusters that could be generated from the data was two, we divided the results of the clusters into two orientation types.…”