International Journal of Applied Decision Sciences (13 papers in press)
Efficiency measurement to identify the best efficient unit in the presence of dual-role factors
by Bohlool Ebrahimi
Abstract: Efficiency measurement is crucial to management for performance evaluation of several decision-making units (DMUs) in order to find the most efficient unit. Data envelopment analysis (DEA) is a practical and popular method that has been used widely for efficiency measurement in many applications. Thus, in the literature of DEA, several mixed integer DEA models are developed to efficiency measure and find the best efficient unit. In some situations, some factors named dual-role factors may be simultaneously considered as both inputs and outputs. However, a little attention has been paid in the literature of DEA to these types of factors. The current paper develops a new mixed integer linear DEA model to identify the best efficient DMU in the presence of dual-role factors. The basic DEA models consider different roles for each dual-role factor, and also solve at least one model for each DMU to determine the best unit. However, the developed approach in this paper considers a unique role for each dual-role factor, and also determines the best unit through by solving just one mixed integer DEA model. Two numerical examples are used to illustrate the profits and applicability of the developed approach.
Keywords: data envelopment analysis (DEA); efficiency measure; best unit; dual-role factors; supplier selection.
Institutional environment, internal control and corporate social responsibility disclosure quality: evidence from China.
by Lijun WU, Hua Bu
Abstract: This study examines the association between institutional environment, internal control and CSR disclosure quality in China. The institutional environment is divided into legal governance environment and regional governance environment. We find that the perfect legal governance environment, regional governance environment and high-quality internal control can significantly improve the quality of CSR disclosure. The weak regional governance environment can inhibit the positive regulating effect of internal control on the CSR disclosure quality. However, the strong legal governance environment does not significantly improve the positive regulating effect of internal control on the CSR disclosure quality. After a series of robustness tests, the above conclusions are still valid. Our findings have some significant implications for CSR disclosure in China.
Keywords: Legal Governance Environment; Regional Governance Environment; Internal Control; Quality of CSR Disclosure.
Designing Information Ecosystems to Process Citizen Input and Improve Public Sector Decision Making
by Ram Gopalan
Abstract: Recent advances in technology and social media have enabled average, individual citizens to forcefully voice their opinions on various public-sector issues. To fully harvest this new tide of technology-enabled activism, information ecosystems must be proactively designed and scientifically managed to receive and process citizen input. In this paper, an activist is a term broadly used to connote any citizen interested in engaging in participative government, usually mediated by the use of technology. The collective set of measures that need to be rigorously undertaken to bring citizen activism to fruition is referred to as activism science in this paper. This position paper examines five case studies wherein citizen activism has already proved successful. The learnings from these case studies leads to a synthetic proposal for managing activism scientifically, incorporating citizen inputs, management science models, technology and data architectures and suitable participative process design. The proposed activism science framework (ASF) is applied to envision improvements in the delivery of online education and urban services.
Keywords: Smart government; Open data portals; Public sector decision making.
Probability of Informed Trading: a Bayesian Approach
by Pedro Henrique Albuquerque, Yaohao Peng, Eduardo Nakano, Cibele Da-Silva, Leonardo Bosque
Abstract: One of the most popular models for measuring information asymmetry of financial assets is the probability of informed trading model (PIN). Its theoretical foundation and its wide possibility of application made PIN a benchmark in insider trading studies. In view of the interpretability of PIN and its parameters, this study aims to evaluate and propose a Bayesian version for the probability of informed trading model. The proposed approach brings the possibility to include expert opinions about PIN parameters and represents a new contribution to the theoretical scope of market microstructure models.
Keywords: Probability of Informed Trading; PIN; Bayesian Inference; Market Microstructure Model; Private Information; Information Asymmetry.
Locating and Sizing Electric Vehicles Charging Stations in New Markets
by SAFA BHAR LAYEB, MOHAMED AZIZ JEGHAM
Abstract: In countries where there are a handful of electric vehicles (EV), installing appropriately charging stations could promote the usage of this emergent technology. In this work, we address the problem of locating and sizing EV charging stations in the specific context of a virgin market. First, we forecast the percentage of electric vehicles using a Bass diffusion model (BDM). Once the EV demand properly predicted, we adopt a set-covering-based approach through the formulation of linear programming models to locate and size the potential EV charging stations. The objective is to reduce weighted installation and access costs while ensuring that the stations are within a comfortable reach for the EV users. Without any loss of generality, we apply the overall scheme on the real case study of Tunis City, Tunisia, to propose the appropriate charging infrastructure deployment for the year 2025.
Keywords: Transportation; Electric Vehicles; Charging Stations Locations; Linear Programming; Bass Diffusion Model.
Production Trade-off and Weight Restrictions in Two-stage Network Data Envelopment Analysis
by Reza Kazemi Matin, Hamid Kiaei, S.Hadi Nasseri
Abstract: The use of weight restrictions in data envelopment analysis (DEA) is considered an appropriate method to avoid zero inputs and outputs weights. Due to a number of problems associated with the use of weight restrictions, the production trade-off method in DEA is often preferred. An important issue is that in most common DEA models, the internal structure of the production units is ignored and the units are often considered as black-boxes. The current study aims to estimate the production trade-offs in two-stage network data envelopment analysis (NDEA) to observe its likely impact on efficiency evaluation and discrimination of units and subunits. Also, the probable effect of such the trade-offs are shown on the overall efficiency decomposition to divisional efficiencies and production possibility set(PPS) in two-stage NDEA. Finally, a numerical example is used to explain the results and compare the possible effects of different production trade-offs scenarios in two-stage NDEA with standard DEA models.
Keywords: Network data envelopment analysis (NDEA); Two-stage production; Production trade-off; Weight restrictions; Ranking.
An Integrated Fuzzy Multi-criteria Decision Making Approach for Evaluating Suppliers Co-design ability in New Product Development
by Detcharat Sumrit
Abstract: There are many methods for managing the complexity and challenges of new product development (NPD) under faster emerging technology. Most firms use them to collaborate with external organisations for their co-design on new products. This study purposes to provide a measurement model by applying multi-criteria decision making (MCDM) approach to evaluate suppliers co-design capabilities in NPD in terms of product structural design and engineering, product concept and functional design, and process design. This MCDM includes fuzzy step-wise weight assessment ratio analysis (SWARA) to determine the relative weights of criteria and fuzzy technique for order preference by similarity to an ideal solution (TOPSIS) to rank the results of suppliers capabilities regarding to their co-design in NPD. Given the result, supplier with the highest collaboration performance rating is selected at first. The evaluation of suppliers collaboration is regarded as an essential starting point for management challenge to develop suppliers capabilities.
Keywords: MCDM; multi-criteria decision making; Fuzzy SWARA; step-wise weight assessment ratio analysis; Fuzzy TOPSIS; technique for order preference by similarity to an ideal solution; new product development.
A Latent Semantic Analysis Based Image Tag Optimization Method
by Aiping Cai
Abstract: According to the nominal tags, the adverb and adjective high-level semantic tags which is described human emotion are easily to exceed the handling scope. Furthermore, the majority of optimisation methods exists many problems (e.g., although inputting a high application cost, it cannot get a satisfactory effect). This paper proposed an effective image tag optimisation algorithm which was composed of two important parts: firstly, a random walk model was used to process the initial image tag information, the image relationship diagram and the tag relationship diagram were structured based on image vision similarity and tag relevancy respectively, then, the image and tag information was spread through a dual-diagram (image relationship diagram and tag relationship diagram) walk mode randomly; secondly, a new image tag optimisation model was built according to three factors, i.e., semantic uniformity, noise sparsity and result matrix sparsity. In the experimental stage, the effectiveness of this model and the superiority of pre-processing were verified by experiment. Compared to other methods, the experimental result indicates that this algorithm is more reasonable and efficient.
Keywords: Semantic analysis; Image tag optimization; Tag information; society tags.
Attribute Rank based Weighted Decision Tree
by Benjamin David, RAJA S. P, A. Suruliandi
Abstract: Decision tree is a renowned classifier reformed by innumerable tree generation techniques to construct efficient trees. In this paper, a novel framework for constructing decision tree classifiers using ranked attributes and weights is presented. The tree is fabricated using proposed framework hierarchically from root to leaf using ranks and weights of attributes assigned to branches based on their contribution towards classification accuracy. The weights are perpetually updated until the tree furnishes maximum classification accuracy. This framework is experimented with cuckoo search, firefly search, wolf search and proposed limited lazy wolf search for ranking attributes and the experimental results illustrates the framework achieving excellent performance than the antecedent. For demonstrating the stability of proposed framework in all problems, numerous experiments are carried out using prominent benchmark datasets and a proposed crime propensity prediction dataset. The crime propensity prediction accuracy accomplished by fine tuning the proposed algorithm was 99.3617% directing crime free society.
Keywords: Data Mining; Classification; Decision Trees; Crime Prediction; Crime Propensity.
A bi-level programming model of rough stochastic MRCPSP in large-scale hydropower construction project
by Zhe Zhang, Xiaoling Song
Abstract: This paper focuses on developing a bi-level programming model for rough stochastic multi-mode resource constrained project scheduling problem (RS-MRCPSP/bi-level). The metal structure installation project (MSIP) in Wudongde Hydropower Station is considered as the prototype, and then it is extended to be a generalised bi-level MRCPSP. In the upper level, the construction contractor is responsible for the investment on the activity, while the outsourcing partner is in charge of construction and implementation in the lower level. In order to deal with the rough stochastic parameters, the rough stochastic expected value operator is employed. Subsequently, in order to obtain the optimal schedule, an interactive method-based passive congregation particle swarm optimisation (IM-based PCPSO) is designed. Finally, a practical application for MSIP is presented to highlight the practicability of proposed model and solution method.
Keywords: bi-level programming; multi-mode; RCPSP; rough stochastic parameters; PSO.
ANALYSING THE INFLUENCE OF DEMOGRAPHICS ON DEPOSITOR BEHAVIOUR
by Zandri Dickason-Koekemoer, Sune Ferreira
Abstract: Banks are exposed to operational risk events on a daily basis and constitute a large portion of a bank's risk exposure. The most important role of a bank is to establish who their key stakeholders are and to prioritise responsibilities according to these stakeholder characteristics, needs, perceptions and behaviour. This paper made use of hypothetical operational risk events within a bank in order to test how likely depositors will be to withdraw funds from their bank accounts. A non-parametric Spearman correlation and analysis of variation was used to test the relationship between demographical factors and depositors' likelihood to withdraw funds. Concerning the level of education, a negative relationship exists between the level of education and depositors' likelihood to withdraw funds. A negative relationship also exists between the income level and depositors' likelihood to withdraw funds.
Keywords: demographics; behaviour; depositors; South Africa.
Multifactor Capital Asset Pricing Model in Emerging and Advanced Markets using Two Error Components Model
by Woraphon Yamaka, Songsak Sriboonchitta, Wachirawit Puttachai, Radamanee Noppasit, Paravee Maneejuk
Abstract: We aim to explore the macroeconomic factors that are able to influence the return of the stock market in emerging and advanced markets. To find such factors, we analyse the data from three emerging countries and three developed countries. The multifactor capital asset pricing model (CAPM), which includes the overall market return and the selected macroeconomic variables, is employed to achieve our study. However, the assumption of CAPM, which displayed only one error term, has been concerned in our analysis as one error term may fail to capture and define the whole erroneousness in the model. Thus, we prove the existence of the second error components of the CAPM. In addition, the concept of a seemingly unrelated regression (SUR) model is also applied to join the CAPM of each country. As a consequent, we propose a new SUR model which compounds two errors to investigate the multifactor CAPM.
Keywords: Emerging and Advanced Markets; Multifactor CAPM; Copula Approach; Seemingly Unrelated Regressions; Two Errors Component Model.
A Bi-level Optimal Scheduling Model for Virtual Power Plants and Conventional Power Plants Considering Environmental Constraints and Uncertainty
by Linpeng Nie, Huijuan Huo, Jun Dong
Abstract: To effectively realise the distributed energy resources participating in the system scheduling and reduce pollutant emissions from conventional plants, a bi-level stochastic optimal model for conventional plants and VPPs was built considering the uncertainty and environmental constraints. Firstly, a method of scenario generation and reduction is proposed to simulate the output of WPP and PV based on interval method and Kantorovich distance. Secondly, a bi-level stochastic optimal scheduling model for VPPs and conventional plants in a day-ahead plan is constructed. Finally, different simulation scenarios are designed to verify the effectiveness of the proposed model. The results illustrate that the model can overcome the influence of uncertainty and realise optimal economic jointly dispatch for VPPs and conventional plants under environmental constraints, through VPP technology, distributed power generation resources can be integrated effectively and have good environmental effect. Plants with high environmental performance will gain a larger share and generate more power.
Keywords: Environment economic dispatching; Virtual power plant; Stochastic programming; bi-level Model; Uncertainty.