熵权优化模型在设施管理绩效评价中的应用研究

绩效评价是设施管理过程中的关键环节,文章在研究香港学校设施管理的绩效评价时,提出了将指标的客观熵权作为评价指标的权重系数,避免了传统的专家打分法的不确定性和随意性,并与TOPSIS法整合,形成多指标决策的熵权优化模型,用该模型来评价教室和办公室的设施管理绩效,取得了科学、合理的结果。并且发现其中一所学校没有必要重建却仍被重建,这是一种不经济和不合理的做法。     

Cost Efficiency Benchmarking for Operational Units with Multiple Cost Drivers

We consider the activity‐based costing situation, in which for each of several comparable operational units, multiple cost drivers generate a single cost pool. Our study focuses on published data from a set of property tax collection offices, called rates departments, for the London metropolitan area. We define what may be called benchmark or most efficient costs per unit of driver. A principle of maximum performance efficiency is proposed, and an approach to estimating the benchmark unit costs is derived from this principle. A validation approach for this estimation method is developed in terms of what we call normal‐like‐or‐better performance effectiveness. Application to longitudinal data on a single unit is briefly discussed. We also consider some implications for the more routine case when costs are disaggregated to subpools associated with individual cost drivers.     

自然评价：人工智能驱动下的学术成果评价模式重构

学术成果评价作为科研项目评审、人才评价、期刊评价、机构评价等活动的基础，是学术评价体系的重中之重，也是科研管理的关键环节之一。当前，我国学术成果评价模式存在“同行评议黑箱”“引文动机模糊”“评价数据间接且片面”等弊端，阻碍了学术创新，也与当下建构自主知识体系的战略相悖。而近年来人工智能等技术在数据、算法、算力上的突破性发展，特别是判别式模型和生成式模型在机器翻译、文本分类、文本摘要、情感分析、问答系统等领域日益成熟的应用，为传统学术成果评价模式的革新带来契机，为实现更加科学、多元与智能的学术成果评价提供了充分可能。基于对现有学术成果评价模式不足与人工智能应用契合度的分析，文章提出学术成果评价新模式——自然评价。自然评价模式是人工智能与学术成果评价的深度融合，既是对以往同行评议、文献计量、网络计量、替代计量等传统评价模式的批判性继承与发展，也是一种突破与革新。具体而言，自然评价是基于学术共同体在各种学术活动中自然产生的全量化的学术痕迹大数据，将人工智能技术应用于学术成果知识内容和学术共同体学术痕迹数据的语义理解与自动分析中，从而动态形成评价判断，并服务于知识创新与学术进步的一种评价模式。...     

APPLICATION OF A PRODUCTIVITY MODEL FOR COMPUTER SYSTEMS*

The performance evaluation of computer systems as they impact on final user requirements for services is a complex task. A management-oriented approach to the issue based on conventional economic theory was described in a previous report as an outgrowth of a joint university-industry research project. In this sequel we report our experience in estimating, testing, and applying this conceptual model.     

Understanding Patterns of Publishing Activity in Academic Research Occupations*

While it is widely understood that faculty in various disciplines tend to publish at different rates and in different forms, knowledge of these differences is too limited to facilitate systematically differentiated performance appraisal and reward systems. In this study, theory concerning knowledge production system characteristics as influences on individual performance is applied to academic research occupations using a classification scheme developed by Biglan [4]. Regression analysis is applied to a general (industry-wide) sample of United States faculty, with publishing patterns as performance measures. Each dimension of the classification scheme is found to have predictive validity. Output patterns are consistent with a conceptualization of research occupations in terms of (1) transformational/technological processes, (2) research mission, and (3) input/subject matter characteristics. The results offer a basis for generating disciplinary publishing norms and differentiated reward systems.     

Replication of Measures in Information Systems Research: The Case of IS SERVQUAL*

The Information Systems (IS) discipline should recognize potential pitfalls in replication of research studies. This response to a note appearing in this issue by Van Dyke, Prybutok, and Kappelman (1999) aims to serve two purposes. First, this note will address the problems we have with the Van Dyke et al. (1999) attempted replication of our own previous work. Second, we hope it serves to establish more generally an awareness in the IS field for the potential to create and accept the “illusion of replicability” when this has not really been established.     

Evaluating and Refining Expert Critiquing Systems: A Methodology*

Expert critiquing systems are a rapidly growing class of intelligent decision support systems that apply artificial intelligence techniques to criticize a user's proposed solution to a problem. Critic programs now exist in the medical, engineering, programming, knowledge acquisition, word processing, and other domains. Critic refinement is a nontrivial activity that, even when done well, consumes a sizable fraction of the complete effort to build and deploy a critiquing system. To ease that effort, it is important to adopt a rigorous approach that allows one to reproducibly measure the degree of success of the current critic version and to predict which refinements will improve the critic further. The current article presents one such approach with actual case studies that illustrate its usage and elaborate selected aspects of the refinement process.