Abstract: In order to solve the problems in dynamic incentive evaluation, such as the lack of systematization, theory basis, normalization, maneuverability and scalability, a methodological model of dynamic incentive evaluation is constructed by introducing reinforcement theory, equity theory and contingency theory. Firstly, using process incentive theories mainly of reinforcement theory as the internal basis, and the data analysis method as the external support, the principal functional module “Generic Reinforcement Incentives” operator is proposed; secondly, each parameter of the operator is explained in detail, and the related properties are discussed; finally, the representational expression, application route and attentions are illustrated respectively. Through the parameters’ universality test and comparative analysis of numerical example, it shows that a feasible methodology system is provided by this model with significant universal ability, so as to help decision-maker to explore the specific dynamic characteristics and development potential of the evaluated object in specific background broadly and thoroughly, effectively widen the grades between the evaluated objects and bring about a more comprehensive classified selection.

Key words: dynamic incentive evaluation; reinforcement theory; methodology; data analysis; contingency