Agile Self-selecting Teams Foster Expertise Coordination

摘要

Aim/Purpose This paper aims to discuss the activities involved in facilitating self-selecting teams for Agile software development projects. This paper also discussed how these activities can influence the successful expertise coordination in Agile teams. Background Self-selecting teams enable Agile team members to choose teams based on whom they prefer to work with. Good team bonding allows Agile team members to rely on each other in coordinating their expertise resources effectively, litis is the focal point where expertise coordination is needed in Agile teams. Methodology This study employed Grounded Theory by interviewing 48 Agile practitioners from different software organizations mainly based in New Zealand. This study also carried out several sessions of observations and document analysis in conjunction with interviews. Contribution This study contributes to the body of knowledge by identifying the way self-selecting teams support expertise coordination. Findings Our findings indicated that the activities involved tend to influence the successful expertise coordination in Agile teams. Self-selecting teams are essential to supporting expertise coordination by increasing inter-dependencies between Agile team members, ensuring a diverse range of knowledge and skills in teams. Recommendations for Practitioners The self-selecting team activities can be used as a guideline for Agile software organizations in forming self-selecting teams in the fastest and most efficient way. It is vital for management to facilitate the process of self-selecting teams in order to optimize successful expertise coordination. Recommendations for Researchers There is potential for further Grounded Theory research to explore more activities and strategies involved in self-selecting teams. Impact on Society Self-selecting teams in Agile software developments projects tend to boost the productivity of software development. Future Research Several hypotheses can be tested through a deductive approach in future studies.