How to Enable the Financial Valuation of Intellectual Capital using an Explanatory Theory of Learning Curve Phenomena

This study proposes a theoretical justification, supported by rigorous empirical evidence, for employing the mathematical expression of the learning curve paradigm to rationalize the learning effects on production cost and, potentially, derive monetary information of the Intellectual Capital (IC) available in production environments. Despite the importance of learning curve phenomenon, as described in the literature, Intellectual Capital Reporting Frameworks (ICRFs) ignore the potential benefit of employing the mathematical expression of the learning curve paradigm for developing valuation approaches of team learning caused by Human Capital (HC). The above formulates the research problem of this study addressed within the context of software development processes. Initially, a learning curve explanatory framework is defined using a type of Lotka - Volterra differential equation, which allowed us to model the causal interaction of learning rate of developer teams (i.e. human capital) and the improvements on their average solving time (i.e. productivity) as a predator - prey interacting populations system. This provided the theoretical justification required and further supported by empirical evidence obtained from industry data, proving that the estimated learning curve rates capture the compound effect of single-loop, double-loop and deutero learning on the performance fluctuations; namely, the entire range of HC effects on team productivity. Our approach draws on the semantics of learning curve, emphasizing the cost savings realized by the learning capabilities of knowledge production workers, and therefore it is limited for wider use for IC reporting purposes outside the context of production environments.