The Skill-Task Matching Model: Mechanism, Model Structure, and Algorithm

Abstract

We distinguished between the expected and actual profit of a firm. We proposed that, beyond maximizing profit, a firm's goal also encompasses minimizing the gap between expected and actual profit. Firms strive to enhance their capability to transform projects into reality through a process of trial and error, evident as a cyclical iterative optimization process. To characterize this iterative mechanism, we developed the Skill-Task Matching Model, extending the task approach in both multidimensional and iterative manners. We vectorized jobs and employees into task and skill vector spaces, respectively, while treating production techniques as a skill-task matching matrix and business strategy as a task value vector. In our model, the process of stabilizing production techniques and optimizing business strategies corresponds to the recalibration of parameters within the skill-task matching matrix and the task value vector. We constructed a feed-forward neural network algorithm to run this model and demonstrated how it can augment operational efficiency.