Forthcoming articles


Asian Journal of Management Science and Applications


These articles have been peer-reviewed and accepted for publication in AJMSA, but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.


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Asian J. of Management Science and Applications (6 papers in press)


Regular Issues


  • Relative Incentive Rate in a Multi-Period and Multi-Task Agency   Order a copy of this article
    by Junwook Yoo 
    Abstract: This study explicitly calculates the relative incentive rate in an N-period contract with multiple tasks. The inter-temporal covariance risk, as well as the within-period risk-premium, prevents the first-best allocation of effort from being endogenously achieved even if the first-best allocation is feasible. The inter-temporal covariance risk reduces the effective sensitivity of a performance measure, and thus the performance measure with a bigger inter-temporal covariance risk is assigned a weaker relative incentive rate. From these results, an empirical prediction is derived that a performance measure with larger positive (negative) inter-temporal covariances is assigned a weaker (stronger) relative incentive rate in multi-period contracts.
    Keywords: relative incentive rate; performance measures; multi-period; multi-task; inter-temporal covariance.

  • The Analysis Based on Principal Matrix Decomposition for 3-Mode Binary Data   Order a copy of this article
    by Haruka Yamashita, Masayuki Goto 
    Abstract: Recently, principal points for a multivariate binary distribution (Yamashita and Suzuki , 2014, 2015) have been proposed as the binary vectors that optimally represent a distribution, in terms of the average Eulidian squared distance between a multivariate binary distribution and the vectors. In this paper, we proposes a new analysis procedure for 3-mode binary data, based on principal points for a multivariate binary distribution (Yamashita and Suzuki , 2014, 2015). Moreover, we propose a method that decomposes principal matrixes for 3- mode binary data into a small number of vectors based on vector products . In order to investigate our methods applicability to real-world data, we use the method to analyze 3-mode structured data from annual all-star games for Japanese professional baseball.
    Keywords: principal points; 3-mode data; binary data; clustering; data analysis.

  • Random Assignment under Ordinal Preferences: A separation characterization   Order a copy of this article
    by Ping Zhan 
    Abstract: Assignment problems include allocating a set of objects among agents, here only ordinal preferences are revealed. In this paper, we establish a condition of feasible solutions for deterministic assignments. Related to it we show then a separation characterization for probabilistic serial (PS) mechanism, based on sd-efficiency, sd-envy-freeness and the definition of PS (where "sd" stands for first-order stochastic dominance). An application to recent result about PS is also described. Models here are suitable for assignment problems in various fields, such as fair sharing of resources in industry. The separation structure proposed here provides a possibility to divide a large scale problem into several sub-problems.
    Keywords: Deterministic and random assignment; Serial rule; Bipartite-graph.

  • Hedging financial and environmental risk in portfolios: Constructing and evaluating eco-funds   Order a copy of this article
    by Kan Nakayashiki, Wei Zang, Satoshi Kumagai 
    Abstract: Reducing their environmental load has recently become a key concern to firms. Many financial products that invest in companies with a strong environmental consciousness, such as the Nikko Eco-fund, have been released. However, choosing an ecological fund based on an interview or questionnaire is a qualitative process, as an ecological fund does not reflect a firms environmental performance, nor reduces environmental risk. This study calculates an environmental beta value (environmental risk) using emissions data on environmental load material, applying eco performance and economic screening. We, then, decide on investment brands and ratios using the environmental beta value and efficient frontier. Finally, we propose an environmental portfolio that reduces environmental and financial risks. This methodology enables to assess environmental risk in a quantitative manner. Two brands of company that simultaneously reduce environmental and financial risks are identified.
    Keywords: Environmental risks; Beta-value; Portfolio management; Eco-funds; Economic screening; Eco performance screening.

  • Data pair selection for accurate classification based on Information-Theoretic Metric Learning   Order a copy of this article
    by Takashi Maga, Kenta Mikawa, Masayuki Goto 
    Abstract: Data classification is one of the main technique in data analysis which has become more and more important in various fields of business. Automatic classification is the problem that classification category label is learned from training data. One of the effective approaches for automatic classification is the k-Nearest Neighbor (kNN) method based on distances between data pairs, combining with the well-known Distance Metric Learning. In this study, we focus on Information-Theoretic Metric Learning (ITML) method. In ITML, the optimization problem is formulated as learning metric matrix so that the distance between each pair of data belonging to the same class becomes smaller than a constant, while the distance between each pair of data belonging to different classes becomes larger than the other constant. In this study, we propose an improved procedure by choosing the data-pairs which affect clarifying the boundaries effectively. We verify the effectiveness of our proposed method by conducting the simulation experiment with benchmark dataset.
    Keywords: Automatic classification; Distance Metric Learning; Mahalanobis distance; Information-Theoretic Metric Learning.

  • Designing insert buffers for mixed-model assembly lines   Order a copy of this article
    by Sho Matsuura, Haruki Matsuura, Akiko Asada 
    Abstract: To meet a diverse range of manufacturing specifications and keep pace with rapid changes in consumer requirements, mixed-model assembly lines must become more efficient and flexible than they are at present. This study examines the characteristics of an insert buffer that uses the curved portion of a mixed-model assembly line to provide guidelines for designing the buffer, and then analyzes the number of sequences generated with the buffer. Also analyzed is the effect of the insert buffer on the working time and line length. Based on these results, a procedure for designing an insert buffer is proposed.
    Keywords: mixed-model line; insert buffer; line length; flexibility.