Abstract:

With the growing complexity of evaluation issues， fully excavating dynamic temporal information characteristics and guiding the evaluated objects toward benign development through effective dynamic incentive mechanisms have become critical steps and essential components for enhancing evaluation scientificity and guidance. Furthermore， in practical evaluation problems， evaluation indicator systems tend to be multi-dimensional， while indicators are often interdependent and may exhibit interactions， making it particularly crucial to consider inter-indicator relationships during evaluation.To address current limitations in dynamic incentive evaluation research—particularly insufficient attention to coupling coordination among indicators and lack of differentiation and appropriateness in incentive methods—reinforcement theory and equity theory are introduced to propose a novel dynamic dual-drive evaluation method integrating “quantity-quality dual incentives” （Q₂-DI） with coupling coordination. First， development prediction points of evaluated objects at various time nodes are determined based on temporal data， gain information， and time factors， which are then connected to form an incentive reference line for implementing "quantity" incentives reflecting current states. Second， by comprehensively considering the evaluated objects’ opportunity pressure and development velocity， "quality" incentives targeting trends are applied from both absolute and relative dimensions. Subsequently， the Q₂-DI dynamic evaluation model is constructed by integrating "quantity" and "quality" incentives to derive dual-incentive composite values. Building on this， a coupling coordination model is introduced to obtain coupling coordination values. The SCD-Ward method clusters data based on dual-incentive composite values and coupling coordination values respectively， with group weights and contribution degrees aggregating information to produce comprehensive evaluation results driven by both Q₂-DI and coupling coordination.Applied to business environment evaluation， case studies demonstrate the rationality and effectiveness of the proposed method. Comparative analyses with existing incentive methods further highlight its superiority： it thoroughly exploits dynamic temporal data to achieve differentiated and appropriately calibrated incentives while incorporating indicator coupling coordination into dynamic incentive mechanisms， thereby enhancing comprehensiveness and accuracy. This enriches dynamic incentive evaluation methodologies and provides support for evaluation challenges in various practical scenarios.

Key words: dynamic incentive evaluation, Q₂-DI model, coupling coordination, clustering