基于粗集——自回归的高校图书馆服务质量评价

Service Quality Evaluation in University Library Based on Rough Set-Autoregressive

服务性是图书馆的本质属性之一,服务工作的好坏,服务质量的高低,都直接决定着一个图书馆的整体水平和实力。从目前国内外的研究情况来看,有关高校图书馆服务质量评价理论与方法的研究基本围绕单一因素来展开,如图书馆基础条件的建设、读者感受或馆员对图书馆氛围与多样化的满意度等方面。但图书馆服务质量评价是一个综合评价体系,对某一因素或某些因素进行的评价具有片面性,不能全面考证图书馆的服务质量。因此,文章建立一套包含条件、读者、馆员等主客观因素的图书馆服务质量评价体系,并确定每个因素在图书馆服务中的重要程度,进行实证研究。构建针对高校图书馆的粗集与自回归移动平均相结合的评价模型,应用到我国高校图书馆评价当中。实证研究结果表明,通过引入粗集理论可有效约简冗余数据信息,使得模型的可靠性、合理性和科学性得到更好保证,也提高了模型的分类效果,具有良好的学习推广能力和评价能力。

Service is one of the essential attributes of the library, so the service work and quality directly determine the overall level of the library. From the current research situation at home and abroad, the library service quality evaluation theory and methods expound substantially around a single factor, such as the library infrastructure construction, the feeling and satisfaction of librarians or readers to the environment and diversity of the library. But library service quality evaluation is a comprehensive evaluation system, so it is one-sided to evaluate only one or several factors without a comprehensive response to the quality of library services. Therefore, this paper builds a library service quality evaluation system including library construction conditions, readers, librarians and other subjective and objective factors. The degree of importance of each factor in the library service is calculated, and an empirical research is carried out. The paper constructs an evaluation model with the average combination of rough set--autoregressive moving based on theoretical and empirical analysis, which can be applied to university library evaluation. The empirical results show that the introduction of rough set theory can effectively reduce redundant information to support the reliability, rationality and scientificity as well as improve the classification performance model, which promotes learning and evaluation capacity greatly.