Key Performance Indicators for Measuring Efficiency and Effectiveness in an Agile Software Development Team : Development of a KPI Dashboard for Measuring Individuals’ Performance Through an Entrepreneurial Study within Extenda Retail

Sammanfattning

Measuring individual performance in agile environments presents unique challenges, yet it holds the potential to drive growth. This study aims to identify and apply Key performance indicators (KPIs) to offer a clearer picture of individual contributions and areas for improvement within agile software development. The main purpose of this bachelor’s thesis in Information Systems is to identify and describe key performance indicators for measuring efficiency and effectiveness in an agile software development team, from individual team members’ perspective. The sub-purpose is to develop a KPI dashboard that measures individuals’ performance based on data from a project management tool. Using a mixed-method approach that integrates an entrepreneurial approach with an action research approach, iteratively a KPI dashboard is developed. SPIN interviews were conducted to gather insights into the degree of performance measurement challenges and degree of needs of different stakeholders, while the action research approach guided the prototyping process through iterative cycles of diagnosing, planning, action taking, and evaluation. The first conclusion of this study is that individual Velocity, Cycle time, and Change failure rate are, to a higher degree, effective KPIs for measuring the degree of efficiency and degree of effectiveness in agile teams. Cycle Time, in particular, helps identify workflow bottlenecks that are often missed when relying solely on Velocity. The second conclusion is that a balanced approach, combining both individual and team-level KPIs, is preferred by participants. The third conclusion is that the SPACE framework, while more useful in theory, requires adjustments when applied in practice due to challenges with the degree of data availability and quality, particularly for objective KPIs. The fourth conclusion highlights the importance of a higher degree of data quality for more reliable performance insights.