Improving performance evaluation based on the balanced scorecard with data envelopment analysis and game theory approaches: a case study

IF 1.8 Q3 MANAGEMENT
Mansour Abedian, Hadi Shirouyehzad, Sayyed Mohammad Reza Davoodi
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引用次数: 0

Abstract

Purpose

This paper aims to propose an integrated use of balanced scorecard (BSC), data envelopment analysis (DEA) and game theory approach as an enhanced performance measurement technique to determine and rank the importance of manufacturing indicators of a steel company as a real case study.

Design/methodology/approach

An efficiency change ratio is defined to examine the characteristic function of each coalition which is super-additive. Then, the Shapley value index is used as the solution of the cooperative game to determine the importance of the BSC indicators of the company and rank order them.

Findings

The results reveal that “profitability rate” is the most important BSC indicator, whereas “customer satisfaction” is the least significant one. The ranking order of the importance of all BSC indicators makes it possible for the senior managers of the organization to realize the importance of each index separately and to improve the profitability and the number of customers by presenting programs according to the budget and time constraints.

Originality/value

The main contribution of this paper lies in the adoption of a game theory approach to performance measurement in the industrial sector that determines and ranks the importance of manufacturing indicators.

利用数据包络分析和博弈论方法改进基于平衡计分卡的绩效评估:案例研究
目的 本文旨在提出一种综合利用平衡计分卡(BSC)、数据包络分析(DEA)和博弈论方法的增强型绩效衡量技术,以某钢铁公司为实际案例,确定其生产指标的重要性并对其进行排序。结果结果表明,"利润率 "是最重要的 BSC 指标,而 "客户满意度 "是最不重要的 BSC 指标。所有 BSC 指标的重要性排序使组织的高级管理人员能够分别认识到每个指标的重要性,并根据预算和时间限制,通过提出方案来提高盈利能力和客户数量。原创性/价值本文的主要贡献在于采用博弈论方法对工业部门的绩效进行衡量,从而确定制造业指标的重要性并对其进行排序。
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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
期刊介绍: Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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