Authors:Manojit Chattopadhyay; Subrata Kumar Mitra Pages: 451 - 474 Abstract: Measuring performance of microfinance institutions (MFIs) is challenging as MFIs must achieve the twin objectives of outreach and sustainability. We propose a new measure to capture the performance of MFIs by placing their twin achievements in a 2 × 2 grid of a classification matrix. To make a dichotomous classification, MFIs that meet both their twin objectives are classified as ‘1’ and MFIs who could not meet their dual objectives simultaneously are designated as ‘0’. Six classifiers are applied to analyze the operating and financial characteristics of MFIs that can offer a predictive modeling solution in achieving their objectives and the results of the classifiers are comprehended using technique for order preference by similarity to ideal solution to identify an appropriate classifier based on ranking of measures of performance. Out of six classifiers applied in the study, kernel lab-support vector machines achieved highest accuracy and lowest classification error rate that discriminates the best achievement of the MFIs’ twin objective. MFIs can use both these steps to identify whether they are on the right path to attaining their multiple objectives from their operating characteristics. PubDate: 2017-12-01 DOI: 10.1007/s10588-016-9237-x Issue No:Vol. 23, No. 4 (2017)

Authors:Wei Zhong Pages: 475 - 495 Abstract: Individual responsive behavior to an influenza pandemic has significant impacts on the spread dynamics of this epidemic. Current influenza modeling efforts considering responsive behavior either oversimplify the process and may underestimate pandemic impacts, or make other problematic assumptions and are therefore constrained in utility. This study develops an agent-based model for pandemic simulation, and incorporates individual responsive behavior in the model based on public risk communication literature. The resultant model captures the stochastic nature of epidemic spread process, and constructs a realistic picture of individual reaction process and responsive behavior to pandemic situations. The model is then applied to simulate the spread dynamics of 2009 H1N1 influenza in a medium-size community in Arizona. Simulation results illustrate and compare the spread timeline and scale of this pandemic influenza, without and with the presence of pubic risk communication and individual responsive behavior. Sensitivity analysis sheds some lights on the influence of different communication strategies on pandemic impacts. Those findings contribute to effective pandemic planning and containment, particularly at the beginning of an outbreak. PubDate: 2017-12-01 DOI: 10.1007/s10588-016-9238-9 Issue No:Vol. 23, No. 4 (2017)

Authors:Peter van Woensel; Dick de Gilder; Peter van den Besselaar; Peter Groenewegen Pages: 496 - 523 Abstract: The emergence of a shared attitude in organizations can be regarded as a self-organizing complex process in which a majority attitude emerges from the ensemble of interactions among individuals. Almost by definition, emerging processes seem beyond the control of management, which is in conflict with the task of management to steer an organization. By modeling the emergence of a shared attitude in organizations, we were able to demonstrate that management had a distinct influence on this process. Furthermore, the first round of interactions was decisive for the outcome. The key to influencing the emergence of a shared attitude is to reduce resistance against the preferred attitude. High levels of group conformity inhibited conversion to the preferred attitude. Although the emergence of a shared attitude can be influenced by management, there remains an intrinsic uncertainty in the outcomes of attitude development processes. PubDate: 2017-12-01 DOI: 10.1007/s10588-016-9239-8 Issue No:Vol. 23, No. 4 (2017)

Authors:Feiqiong Chen; Yao Chen; Fangfang Zhong Pages: 524 - 545 Abstract: Post-merger integration plays a key role in the success of mergers and acquisitions, but how to choose the appropriate integration mode to achieve potential synergies in mergers and acquisitions (M&As) still lacks widely accepted theoretical support and practical experience. Therefore, this article establishes a mathematical model considering the similarity and complementarity of resources in an attempt to explore the optimal integration strategy (including the choices of integration degree and autonomy granted to the target) under different conditions of resources similarity and complementarity, thus building a comprehensive framework that considers pre- and post-merger factors together to jointly explain innovation realization in technology-sourcing cross-border M&As. The results show that the higher the resource similarity, the higher the degree of integration and the lower the degree of target autonomy. Conversely, the higher the resource complementarity, the lower the degree of integration and the higher the degree of target autonomy. Resource similarity and resource complementarity have negative interactions for integration and target autonomy. The higher the resource similarity, the faster the convergence of optimal degree of integration, while the higher the resource complementarity, the slower the convergence. The study results add to our knowledge of M&A integration management, and provide some implication for practitioners. PubDate: 2017-12-01 DOI: 10.1007/s10588-016-9241-1 Issue No:Vol. 23, No. 4 (2017)

Authors:Harish Garg Pages: 546 - 571 Abstract: Pythagorean fuzzy set, an extension of the intuitionistic fuzzy set which relax the condition of sum of their membership function to square sum of its membership functions is less than one. Under these environment and by incorporating the idea of the confidence levels of each Pythagorean fuzzy number, the present study investigated a new averaging and geometric operators namely confidence Pythagorean fuzzy weighted and ordered weighted operators along with their some desired properties. Based on its, a multi criteria decision-making method has been proposed and illustrated with an example for showing the validity and effectiveness of it. A computed results are compared with the aid of existing results. PubDate: 2017-12-01 DOI: 10.1007/s10588-017-9242-8 Issue No:Vol. 23, No. 4 (2017)

Authors:Janusz Łyko; Radosław Rudek Pages: 572 - 586 Abstract: In this paper, we show that operations research methods can be successfully applied to support decision-making in politics on the case study of the apportionment of seats in the European Parliament. The related political constraints and assumptions are quantitatively described and the optimization problem is formulated. On this basis, it is revealed that the current composition of the European Parliament as well as some intuitive propositions do not respect degressive proportionality as far as it was assumed. Nevertheless, our algorithm allows us to find better solutions, and among them, there is only one best allocation, which respects degressive proportionality as far as possible, according to the well known and often applied measures. Namely, over 9 thousands allocations consistent with the political requirement “nobody gains and nobody loses more than one” are referred to over 5.4 millions degressively proportional solutions, and only one allocation is revealed to be the best for all defined criteria under given populations of countries. PubDate: 2017-12-01 DOI: 10.1007/s10588-017-9243-7 Issue No:Vol. 23, No. 4 (2017)

Authors:Carlos M. Fernández-Márquez; Francisco Fatas-Villafranca; Francisco J. Vázquez Pages: 319 - 346 Abstract: We present an agent-based market model in which social emulation by consumers and the adaptation of producers to demand play a significant role. Our theoretical approach considers boundedly-rational agents, heterogeneity of agents and product characteristics, and the co-evolution of consumers’ desires and firms’ adaptation efforts. The model reproduces, and allows us to interpret, statistical regularities which have been observed in the evolution of industrial sectors, and that seem to be also significant in the case of discretionary consumption activities. Thus, we suggest new determinants and explanations (from the consumer-side) for these stylized facts, and we obtain new theoretical patterns which may be of help to better understand the dynamics of discretionary goods markets. This model and results may contribute to guide future research on the field of consumer market. PubDate: 2017-09-01 DOI: 10.1007/s10588-016-9230-4 Issue No:Vol. 23, No. 3 (2017)

Authors:Andrés Mora-Valencia; Trino-Manuel Ñíguez; Javier Perote Pages: 347 - 361 Abstract: This article proposes a three-step procedure to estimate portfolio return distributions under the multivariate Gram–Charlier (MGC) distribution. The method combines quasi maximum likelihood (QML) estimation for conditional means and variances and the method of moments (MM) estimation for the rest of the density parameters, including the correlation coefficients. The procedure involves consistent estimates even under density misspecification and solves the so-called ‘curse of dimensionality’ of multivariate modelling. Furthermore, the use of a MGC distribution represents a flexible and general approximation to the true distribution of portfolio returns and accounts for all its empirical regularities. An application of such procedure is performed for a portfolio composed of three European indices as an illustration. The MM estimation of the MGC (MGC-MM) is compared with the traditional maximum likelihood of both the MGC and multivariate Student’s t (benchmark) densities. A simulation on Value-at-Risk (VaR) performance for an equally weighted portfolio at 1 and 5 % confidence indicates that the MGC-MM method provides reasonable approximations to the true empirical VaR. Therefore, the procedure seems to be a useful tool for risk managers and practitioners. PubDate: 2017-09-01 DOI: 10.1007/s10588-016-9231-3 Issue No:Vol. 23, No. 3 (2017)

Authors:Meysam Alizadeh; Claudio Cioffi-Revilla; Andrew Crooks Pages: 362 - 390 Abstract: In this paper, we propose a class of models for generating spatial versions of three classic networks: Erdös-Rényi (ER), Watts-Strogatz (WS), and Barabási-Albert (BA). We assume that nodes have geographical coordinates, are uniformly distributed over an m × m Cartesian space, and long-distance connections are penalized. Our computational results show higher clustering coefficient, assortativity, and transitivity in all three spatial networks, and imperfect power law degree distribution in the BA network. Furthermore, we analyze a special case with geographically clustered coordinates, resembling real human communities, in which points are clustered over k centers. Comparison between the uniformly and geographically clustered versions of the proposed spatial networks show an increase in values of the clustering coefficient, assortativity, and transitivity, and a lognormal degree distribution for spatially clustered ER, taller degree distribution and higher average path length for spatially clustered WS, and higher clustering coefficient and transitivity for the spatially clustered BA networks. PubDate: 2017-09-01 DOI: 10.1007/s10588-016-9232-2 Issue No:Vol. 23, No. 3 (2017)

Authors:Víctor G. Alfaro-García; Anna M. Gil-Lafuente; Gerardo G. Alfaro Calderón Pages: 391 - 408 Abstract: In recent years, a trend for accelerating the economic, social and environmental development of cities through associations, organization and creation of synergies has been identified. Our investigation applies a grouping model in order to identify municipalities that could create optimal synergies towards the construction of competitive advantages. In order to achieve this task, we use tools of Fuzzy Logic to evaluate subjective and qualitative characteristic elements of different municipalities under Galois’ group theory. Results conclude on 32 different groups ordered in 7 different levels, relating 12 municipalities of a specific region according to 8 competitive variables. This work seeks to shed light in the conformation of groups under uncertain conditions and the deep examination of the characteristic competitive elements in a specific region for further policy and decision-making processes. PubDate: 2017-09-01 DOI: 10.1007/s10588-016-9233-1 Issue No:Vol. 23, No. 3 (2017)

Authors:Che-Jung Chang; Jan-Yan Lin; Peng Jin Pages: 409 - 422 Abstract: Product life cycles have become increasingly shorter because of global competition. Under fierce competition, the use of small samples to establish demand forecasting models is crucial for enterprises. However, limited samples typically cannot provide sufficient information; therefore, this presents a major challenge to managers who must determine demand development trends. To overcome this problem, this paper proposes a modified grey forecasting model, called DSI–GM(1,1). Specifically, we developed a data smoothing index to analyze the data behavior and rewrite the calculation equation of the background value in the applied grey modeling, constructing a suitable model for superior forecasting performance according to data characteristics. Employing a test on monthly demand data of thin film transistor liquid crystal display panels and the monthly average price of aluminum for cash buyers, the proposed modeling procedure resulted in high prediction outcomes; therefore, it is an appropriate tool for forecasting short-term demand with small samples. PubDate: 2017-09-01 DOI: 10.1007/s10588-016-9234-0 Issue No:Vol. 23, No. 3 (2017)

Authors:Stefania Monica; Federico Bergenti Pages: 423 - 450 Abstract: In this paper opinion dynamics in multi-agent systems is investigated analytically using a kinetic approach. Interactions among agents are interpreted as collisions among molecules in gases and opinion dynamics is described according to the Boltzmann equation. Starting from a microscopic description of single interactions, global properties of the opinion distribution are derived analytically. The proposed analytic model is general enough to allow reproducing features of real societies of agents, such as positive and negative influences and bounded confidence, which are typically used to study opinion distribution models. Analytic results relative to emergent and global characteristics of considered multi-agent systems are verified by simulations obtained via direct implementation of the proposed microscopic interactions rules. Simulations confirm analytic results. PubDate: 2017-09-01 DOI: 10.1007/s10588-016-9235-z Issue No:Vol. 23, No. 3 (2017)

Authors:Hernan Mondani Abstract: In this article, we use Swedish longitudinal register data to study the effect that similarity in organizational properties has on the interaction between organizations. We map out the social space of large organizations in the Stockholm Region and the interplay between social distance and the network communities of employee movements between organizations. We firstly use homogeneity analysis to describe the dynamics of organizations in terms of the time evolution of their similarity. Our results show that most categorical variables are quite stable over time. Organizations linked through employee movement edges have a lower average distance in social space than non-linked organizations. Secondly, we look at network community dynamics in social space. Employee flows between organizations in different communities exhibit a so-called gravity law from spatial statistics, decaying more slowly than observed geographical networks, meaning that employees reach out regions of social space further than of physical space. Finally, the rate of change of distance in homogeneity space exhibits a statistical distribution similar to the ones found in various other growth processes in natural and man-made systems. PubDate: 2017-11-13 DOI: 10.1007/s10588-017-9260-6

Abstract: The strategy of integration known as vendor-managed inventory (VMI), which allows the coordination of inventory policies between producers and buyers in supply chains, has long been considered a strategy for inventory cost reduction. Although the literature acknowledges the importance of understanding the dynamics of VMI implementation through evolutionary games, research in this topic still remains scarce. This paper studies the dynamics of strategic interaction of a producer–buyer supply chain under a newly developed VMI scheme, which makes use of a synchronization mechanism between the buyer and the producer replenishment cycles. By using this alternative VMI representation, we obtain the mathematical conditions that determine the degree of stability of evolutionarily stable strategies. As other evolutionary game theoretical approaches, we also find a lower bound for penalty costs that ensures a VMI contract, but most importantly, we also find how a VMI implementation might depend on the difference between production and demand rates, regardless of any penalty costs. PubDate: 2017-09-08 DOI: 10.1007/s10588-017-9259-z

Authors:Bàrbara Llacay; Gilbert Peffer Abstract: The use of agent-based models (ABMs) has increased in the last years to simulate social systems and, in particular, financial markets. ABMs of financial markets are usually validated by checking the ability of the model to reproduce a set of empirical stylised facts. However, other common-sense evidence is available which is often not taken into account, ending with models which are valid but not sensible. In this paper we present an ABM of a stock market which incorporates this type of common-sense evidence and implements realistic trading strategies based on practitioners literature. We next validate the model using a comprehensive approach consisting of four steps: assessment of face validity, sensitivity analysis, calibration and validation of model outputs. PubDate: 2017-08-29 DOI: 10.1007/s10588-017-9258-0

Authors:Debdatta Pal; Subrata K. Mitra Abstract: This article examines the question of whether the inclusion of problem loans leads to any variation in the technical efficiency of microfinance institutions (MFIs). This question has become pertinent as MFIs, which are well known for their excellent asset quality, have been vulnerable to a delinquency crisis worldwide. Traditionally, the efficiency of MFIs has been measured through non-parametric data envelopment analysis (DEA) or parametric stochastic frontier analysis. As both methods are not flexible enough to cover undesirable outputs, we have instead used the method of directional distance function (DDF) that accounts for the joint production of both desirable and undesirable outputs. Using data from 64 large MFIs, this study reveals corroborative evidence that, with the inclusion of at-risk portfolios as undesirable outputs in the efficiency analysis, the scores and rankings of sample MFIs differ significantly from the results of conventional DEA after the use of DDF. MFIs whose numbers of at-risk portfolios are comparatively high have exhibited lower efficiency scores and vice versa. It is therefore critical that MFIs also include problem loans in their efficiency assessment. This would help MFIs get a more accurate picture of their performance as compared to their peers. PubDate: 2017-08-14 DOI: 10.1007/s10588-017-9257-1

Authors:R. Krishankumar; K. S. Ravichandran Abstract: Social behaviors are an integral part of team building. In this context, we propose a novel classification model that chooses an optimal classifier from the pool of classifiers for predicting the overall performance (OP). Secondly, the chosen classifier is used to investigate the impact of trust and personality on OP. To achieve these goals a pilot study with real time data from 442 respondents are collected from cross functional teams (CFTs) in India using an E-Questionnaire system. The results indicate that the adaptive neuro fuzzy inference system (ANFIS) method is an optimal classifier (A = 89.14%) with respect to other classifiers. We also infer that the predictors, trust and personality are most suitable for predicting OP with a direct relationship to OP and play an indispensable role; as a catalyst; for boosting OP. PubDate: 2017-07-04 DOI: 10.1007/s10588-017-9256-2

Authors:Matthew Benigni; Kenneth Joseph; Kathleen M. Carley Abstract: The ability of OSNs to propagate civil unrest has been powerfully observed through the rise of the ISIS and the ongoing conflict in Crimea. As a result, the ability to understand and in some cases mitigate the effects of user communities promoting civil unrest online has become an important area of research. Although methods to detect large online extremist communities have emerged in literature, the ability to summarize community content in meaningful ways remains an open research question. We introduce novel applications of the following methods: ideological user clustering with bipartite spectral graph partitioning, narrative mining with hash tag co-occurrence graph clustering, and identifying radicalization with directed URL sharing networks. In each case we describe how the method can be applied to social media. We subsequently apply them to online Twitter communities interested in the Syrian revolution and ongoing Crimean conflict. PubDate: 2017-06-20 DOI: 10.1007/s10588-017-9255-3