International Journal of Applied Decision Sciences (9 papers in press)
Measuring the Productivity of the Bank Branches using Data Envelopment Analysis and Malmquist Index
by Kaveh Khalili-Damghani, Batoul Rahmani, Melfi Alrasheedi
Abstract: Productivity measurement is assumed as one of the main guideline to assess the effectiveness and efficiency of organisations. Productivity of the bank branches as main players of financial systems should be measured periodically. In this paper, the productivity and its components (i.e., technical efficiency change and technological frontier change) in 42 profit-making branches of a private bank in Tehran province, Iran, are analysed during the period 2014-2016. Inefficient bank branches are determined. The projection of inefficient bank branches toward efficient frontier is also discussed. Productivity and technical efficiency of production elements of branches of a private bank are investigated by Malmquist productivity index (MPI) and an input-oriented data envelopment analysis (DEA) in constant returns to scale (CRS) and variable returns to scale (VRS) conditions. Based on results, scale inefficiency had the greatest impact on technical efficiency in the case study.
Keywords: Bank performance; Malmquist productivity index; Data envelopment analysis; Technical efficiency; Technological Frontier.
A Uniqueness-Driven Similarity Measure for Automated Competitor Identification
by Adam Fleischhacker, Xin Ji, Yi-Lin Tsai
Abstract: Uniqueness is an important source of competitive advantage and a salient aspect for firms identifying competitors and market structure. While marketing research often includes uniqueness as an important aspect of product positioning and product strategy, the existing literature has offered little guidance on operationalizing this notion for use in the competitor identification process. This paper proposes a probabilistic similarity measure to quantify a competitive landscape where uniqueness is a key driver of competition. The proposed measure, when used with readily available data and combined with existing clustering algorithms, enables automation of the competitor identification process. Empirical experiments are used to validate the proposed measure. These experiments show that marketers can use readily available data, including social media tags and geographical proximity data, to reveal the same insight as is gathered when using the more laborious and time-consuming approach of traditional consumer surveys.
Keywords: Uniqueness; Similarity; Competitor Identification; Related Social Tags.
Lights on the shadows: exploring the need for regulation in shadow banking
by Valentina Lagasio, Marina Brogi
Abstract: Since the outbreak of the economic and financial crisis of 2007-2008, the shadow banking system gained attention and caused concerns among standard setters, policy makers, and academics. This research is aimed at analysing the growth of the shadow banking system and assessing whether and how shadow banking entities should be further regulated. Using an instrument-based definition we infer the need for regulation in the shadow banking system by directly investigating the time series of asset backed commercial paper (ABCP) and securitised real estate loans (SREL). By means of several advanced and refined econometric tests, we explore time series data and find a non-stationary trend. This provides support for the need to regulate shadow banking. Further policy implications are discussed in detail.
Keywords: shadow banking system; financial intermediation; Asset Backed Commercial Paper; Securitized Real Estate Loan; time series analysis.
Application of Artificial Neural Networks to Assess Student Happiness
by Gokhan Egilmez, Nadiye Erdil, Mana Vahid, Omid Arani
Abstract: The purpose of this study is to develop an analytical assessment approach to identify the main factors that affect graduate students' happiness level. The two methods, multiple linear regression (MLR) and artificial neural networks (ANN), were employed for analytical modelling. A sample of 118 students at a small non-profit private university constituted the survey pool. Various factors including education, school facilities, health, social activities, and family were taken into consideration as a result of literature review in happiness assessment. A total of 32 inputs and one output variables were identified during survey design phase. The following survey conduction, data collection, cleaning, and preparation; MLR and ANNs were built. ANN models provided better classification performance with over 0.7 R-square and a smaller standard error of estimate compared to MLR. Major policy areas to improve student happiness levels were identified as career services, financial aid, parking and dining services.
Keywords: student happiness; data analytics; neural networks; regression; higher education policy.
A hierarchical SBM-Tobit approach for examining the influencing factors of industrial CO2 emission efficiency in the Yangtze River Delta
by Jie Zhang, Mei Yang, Zhencheng Xing
Abstract: As the most developed region, the Yangtze River Delta (YRD) has been one of the largest CO2 emitters in China. Chinese Government has proposed the concept of YRD urban agglomeration to improve its competitiveness. To this end, we evaluate industrial CO2 emission efficiency (ICEE) of 26 cities in YRD and its space-time distribution during 2006-2015 by applying SBM-undesirable model and the method of GIS visualisation respectively. Moreover, we used Tobit model to study factors influencing ICEE. The results are as follows: 1) ICEE of YRD increases in fluctuation during our study period; 2) there is a spatial cluster in the distribution of ICEE; 3) the proportion of industrial research and development funds to regional GDP (R&D), population size (PS) and the degree of opening up to the outside world (OPEN) positively influence the ICEE, while industrial energy structure (IES) and the actual use of foreign direct investment (FDI) negatively do.
Keywords: Yangtze River Delta; YRD; industrial CO2 emission efficiency; ICEE; influencing factors; SBM-undesirable model; Tobit regression.
Price coordination in closed-loop data supply chain
by Xinming Li, Huaqing Wang, Lei Wen, Yu Nie
Abstract: By focusing on new features of data products and, based on game theoretical models, we study three pricing mechanisms' performance and their effects on the participants in the data industry from the data supply chain perspective. A win-win pricing strategy for the players in the data supply chain is proposed. We obtain analytical solutions in each pricing mechanism, including the decentralised and centralised pricing, Nash bargaining pricing, and revenue sharing mechanism. Our findings show that: 1) the decentralised pricing has the lowest performance; 2) although Nash bargaining pricing can achieve the centralised channel performance, the upstream data provider and downstream application provider can only equally divide the total channel profit; 3) revenue sharing mechanism, in which the data provider subsidises the application provider, can achieve the first best performance and divide the maximum profit arbitrarily. Accordingly, end-users benefit mostly from the bargaining pricing and revenue sharing.
Keywords: price coordination; data pricing; Nash bargaining; revenue sharing; channel coordination.
Consistency formation of fuzzy multi-attribute group decision making based on alternative adjustment
by Bingjiang Zhang
Abstract: Analytic hierarchy process (AHP) is a method usually used in group decision making. The process of group decision making using AHP is essentially an individual preference of the decision maker incorporating process. In this process, it becomes one of the cores for studying theory and method of group decision making, how we incorporate effectively different forms of preference information. Therefore, in this paper, we propose a solution which makes consistency preference of decision maker group form finally by decision alternative adjustment. We can make clear the deference of decision preference of each division on basis of group division by means of decision information of the decision maker and put forward to employ flexibly AHP for consistency formation of fuzzy multi-attribute group decision making. Thus, an effective dynamic group decision making process is formed. A group decision making example of market approves of new tissue product shows the feasibility of the proposed method.
Keywords: operations research; fuzzy multi-attribute group decision making; analytic hierarchy process; AHP; judge matrix; clustering analysis.
Game theory in supply chain management: current trends and applications
by Neelesh N. Vasnani, Felixter Leone S. Chua, Lanndon A. Ocampo, Lance Brandon M. Pacio
Abstract: As game theory continues to be a relevant approach in evaluating supply chain coordination and competition, this paper explores the various applications and current trends of game theory to supply chain management through an exhaustive review on the literature. This work reviews more than 200 papers which cover several supply chain formations, structures, environments, and decisions together with different game theoretic concepts and applications. First, the paper elaborates on the basic concepts of Nash and Stackelberg games to allow further understanding of their applications in supply chain analysis. The review is then presented and divided into three segments: 1) the development of game theory; 2) the use of Nash and Stackelberg solution concepts in analysing supply chain management; 3) the integration of game theory to different supply chain structures, decisions, and present conditions. Moreover, descriptive analytics are also provided to highlight the distribution of trending topics in the domain field. Finally, the paper provides a summary of the review and recommendation for future works in line with using game theory for supply chain analysis.
Keywords: game theory; supply chain management; current trends; applications; literature review.
A goal programming embedded genetic algorithm for multi-objective manufacturing cell design
by Barnali Chaudhuri, R.K. Jana, Dinesh K. Sharma, P.K. Dan
Abstract: In this paper, a multi-objective manufacturing cell design problem is studied. A goal programming (GP) embedded real-coded genetic algorithm (GA) is designed for solving this problem. Initially, the GA is used to obtain the individual minimum of each objective. Thereafter, utilising the concepts of GP, an equivalent problem is derived, and the sum of deviation variables associated with the objectives are minimised. The GA is used further to obtain the optimal cell design. A software toolkit is developed based on the proposed technique using C Sharp.net to ensure its use in a larger scale. The effectiveness of the technique is judged based on a set of test problems of different sizes. The proposed technique is found to be better in terms of the performance measure over the existing ones.
Keywords: manufacturing cell design; multi-objective optimisation; goal programming; genetic algorithm.