Article Preview
TopIntroduction
Police protective clothing serves to shield law enforcement personnel from harm posed by firearms, knives, and other dangerous weapons, bolstering their safety against various threats and hazards encountered during missions. As technology advances, demands for improved performance and comfort in such gear have increased. Consequently, there is a growing need among law enforcement officers for lighter, more comfortable, durable, and highly protective garments. This necessitates proactive engagement in research and development as well as product innovation by manufacturers of police protective clothing to align with evolving market demands and enhance the manufacturers' competitive edge (Jeonghwan et al., 2022; Siegenthaler, 2022). Typically comprising small to medium-sized enterprises, these manufacturers commonly grapple with inadequate technological prowess, limited innovation, issues concerning product quality, and a lack of brand influence. When public security authorities select manufacturers of protective clothing and procure police protective gear, a comprehensive consideration of the innovation capabilities of these enterprises is imperative. Hence, conducting a comprehensive assessment of the innovation capabilities of police protective clothing enterprises is beneficial not only for security agencies in selecting collaborative partners for protective gear but also for assisting enterprises in identifying their own innovation-related challenges; this furnishes directions for enhancement and improvement.
Numerous scholars have undertaken extensive evaluations of corporate innovation capabilities tailored to the unique characteristics of businesses. For instance, Xu et al. (2020) devised a framework for assessing green innovation capabilities in manufacturing enterprises, introducing a manufacturing enterprise, green innovation capability assessment model based on entropy weight TOPSIS(Technique for Order Preference by Similarity to Ideal Solution) and particle swarm optimization algorithms; the framework provides a comprehensive analysis and evaluation of green innovation capabilities. Furthermore, Peng and Dong (2023) integrated causal relationships among innovation system evaluation elements and proposed a participatory HF-EDAS (Hesitant Fuzzy Set Enterprise-level Distributed Application Services)method within the DSR(Drivingforce-State-Response) framework, offering incentive guidance and reflecting the causal weights of indicators. Huang et al. (2021) structured a quantitative basic index system for evaluating the innovation development of regional high-tech enterprises using dimensions such as innovation strength, cultivation potential, and regional contribution, employing quantitative empirical research through principal component analysis and the entropy value method.
Wang Shenglan et al. (2021) emphasized the importance of considering a company's sustained innovation capability and economic benefits in analyzing enterprise technological innovation capabilities, and they researched and constructed a new enterprise technological innovation capability assessment indicator system, encompassing two secondary indicators: innovation support and innovation subject. Liu and Mai (2024) put forward the evaluation of the technology application innovation industry enterprise's innovation power by using the cloud modeling method; this can reflect the current level of the ITAI enterprise's innovation power more realistically, which is helpful to understanding the innovation path of the ITAI enterprise. Zhang et al. (2023) proposed an Improving Particle Swarm Algorithm for Optimizing Attention-multi-scale convolutional neural network (IPSO-ATT-MSCNN)via Particle Swarm Optimization(PSO) from the swarm optimization algorithm and artificial neural network to evaluate enterprise innovation. This method uses multi-scale convolution to extract features of different scales, which improves the richness of features (Zhang et al., 2023;Javier et al,2022).