Speaker
Description
The increasing prevalence of distributed software development has intensified the
need for robust models that explain and predict collaboration performance in virtual
IT teams. This paper proposes a theoretical framework for AI-driven prediction of
collaboration efficiency, integrating concepts from software engineering,
organizational behavior, and machine learning. The model conceptualizes
collaboration performance as a multidimensional construct influenced by
communication dynamics, task interdependencies, workload distribution, and team
topology. A structured feature space is defined, enabling the formal representation
of collaboration-related variables derived from digital trace data (e.g., version
control systems, issue tracking platforms, and communication tools). The study
outlines a predictive modeling architecture based on supervised learning
approaches, such as ensemble methods, without relying on empirical validation.
Additionally, the framework introduces a set of hypothesized relationships between
key variables and collaboration outcomes, providing a foundation for future
empirical testing. The proposed model contributes to the theoretical advancement
of AI applications in virtual team management and offers a systematic basis for
developing data-driven decision-support systems in distributed IT environments.