Optimizing the credit term decisions in supply chain finance

We study a credit term determination problem in the context of a supplier-buyer supply chain. The supplier's credit term decision is simultaneously made with its production and inventory decisions, and most importantly, it is impacted by the buyer's order quantity. We present a new game-theoretic framework to model this problem, which captures the interaction between the supplier's credit term decision and the buyer's order decision in a multi-period setting. An exact method based on nonlinear programming is implemented to obtain the optimal solutions. We apply our methodologies on a real world case. The computational results show that our approach significantly outperforms the heuristics with fixed credit terms, and either a short or a long credit term can be sub-optimal for the supplier in profitability. Our work offers the first data-driven model and solution approach that assists purchasing and supply managers to make optimal dynamic credit term decision in conjunction with production, ordering and inventory decisions in a game-theoretic setting.     

Switch or persevere? The effects of experience and metacognition on persistence decisions

This paper extends the scholarly understanding of entrepreneurial persistence decisions by identifying individual-level constructs that moderate which decision criteria have the most influence on entrepreneurs’ persistence decisions. Prior research demonstrates that contextual factors, such as feedback through adversity and the attractiveness of opportunities in an entrepreneur’s environment, determine whether or not an entrepreneur will persist with their current venture. We contribute to this literature by theoretically proposing and empirically testing how individual differences in entrepreneurial experience, metacognitive experience, and metacognitive knowledge moderate which aspects of environmental information entrepreneurs pay the most attention to when deciding whether or not to persist. We test our proposed model using a conjoint experiment that allows for monitoring actual persistence decisions of 124 entrepreneurs. Results suggest that metacognitive knowledge influences persistence decisions primarily through altering the impact that probability of expected outcomes associated with potential alternatives has on entrepreneurs’ persistence decisions. Furthermore, the results provide evidence that more experienced entrepreneurs weigh financial returns and switching costs more heavily when making persistence decisions.     

Smoothing and Benchmarking for Small Area Estimation

Small area estimation is concerned with methodology for estimating population parameters associated with a geographic area defined by a cross-classification that may also include non-geographic dimensions. In this paper, we develop constrained estimation methods for small area problems: those requiring smoothness with respect to similarity across areas, such as geographic proximity or clustering by covariates, and benchmarking constraints, requiring weighted means of estimates to agree across levels of aggregation. We develop methods for constrained estimation decision theoretically and discuss their geometric interpretation. The constrained estimators are the solutions to tractable optimisation problems and have closed-form solutions. Mean squared errors of the constrained estimators are calculated via bootstrapping. Our approach assumes the Bayes estimator exists and is applicable to any proposed model. In addition, we give special cases of our techniques under certain distributional assumptions. We illustrate the proposed methodology using web-scraped data on Berlin rents aggregated over areas to ensure privacy.     

Developing integrated management systems using an AHP‐Fuzzy VIKOR approach

Integrated management systems (IMSs) involve a strategy to manage multiple systems while meeting the needs and expectations of stakeholders. There are various management standards used for the development of IMS. This study aims to undertake research focused on how to develop an IMS. In this context, management standards, including the International Organization for Standardization (ISO) and Global Reporting Initiatives (GRI), consider alternatives and integration potential at multiple levels (i.e., strategic, tactical, and operational). We assess criteria including continuous improvement, systematic management, integration, organizational learning, standardization, and ability to cut through bureaucracy before a deeper dive into 28 subcriteria. We then use an analytical hierarchy process (AHP) approach to prioritize the main criteria and subcriteria. Next, we include the use of fuzzy VlseKriterijuska Optimizacija I Komoromisno Resenje (F‐VIKOR) methods to prioritize alternatives. The contributions of this study reveal that systematic management and standardization are the most influencing criteria among six guiding principles. The ISO standard and GRI are the most suitable standards for the development of dynamic IMS. This study is the first of its kind to prioritize guiding principles of IMS. The outcomes of this study will assist business managers, organizations, and policymakers in their decision making regarding management standards for IMS development as well as improve sustainable business practices. Researchers will find new IMS insights and constructs for further empirical investigation.     

Self‐employment among women: Do children matter more than we previously thought?

This paper presents an estimation approach that addresses the problems of sample selection and endogeneity of fertility decisions when estimating the effect of young children on women's self‐employment. Using data from the National Longitudinal Survey of Youth 1979, 1982–2006, we find that ignoring self‐selection and endogeneity leads to underestimating the effect of young children. Once both sources of biases are accounted for, the estimated effect of young children roughly triples when compared to uncorrected results. This finding is robust to several changes in the specification and to the use of a different dataset.     

Rational and intuitive decision-making in sourcing teams: Effects on decision outcomes

According to dual process theory, individual decision-making can be based on rational procedures and experience-based intuition, and the decision-making approach can influence decision outcomes. We investigate how the application of rational procedures and experience-based intuition affects the outcomes of supplier selection decisions taken by cross-functional sourcing teams. Specifically, we examine whether the selected supplier׳s cost and quality/delivery/innovativeness performance is higher when more team members use a highly rational and/or a highly experience-based decision-making approach. From data on 54 teams, we find that the use of rational procedures enhances cost performance. Conversely, when sourcing team members use their experience-based intuition, the decision is more likely to result in satisfactory supplier performance along all tested performance dimensions.     

IDENTIFYING THE RESPONSE OF FERTILITY TO FINANCIAL INCENTIVES

While using financial incentives to increase fertility has become relatively common, the effects of such policies are difficult to assess. We propose an identification strategy that relies on the fact that the variation in wages induces variation in benefits and tax credits among ‘comparable’ households. We implement our approach by estimating a discrete‐choice model of female participation and fertility using individual data from the French Labor Force Survey and a detailed representation of the French tax–benefit system. Our results suggest that financial incentives have had a significant effect on fertility decisions in France. As an example, we simulate the effects of an additional, unconditional child credit of 150 euros per month. The effects are strongest for the third child. Copyright © 2013 John Wiley & Sons, Ltd.     

An Empirical Test of Cognitive Style and Strategic Decision Outcomes*

abstract We examine how cognitive style, as measured by the MBTI, affects strategic decision outcomes. Executives participated in a simulated strategic decision making environment that allowed controlled collection of decision outcomes, including manager decisiveness, decision quality, and perceived effectiveness. We found that iNtuiting/Thinking managers used their intuition to make cognitive leaps based on objective information to craft more decisions of higher quality than other managers. In contrast, Sensing/Feeling types used time to seek socially acceptable decisions, which led to the lowest number of decisions and the lowest perceived effectiveness of all. We found no effect on decisiveness or perceived effectiveness based on a manager's preference for Perceiving or Judging. However, we found that others perceived Extraverted managers as being more effective than Introverted managers when, in fact, the Extraverts were no more decisive than Introverts. Thus, cognitive style influences actual decision outcomes as well as how others perceive one's decision performance.     

A Social Interactions Model with Endogenous Friendship Formation and Selectivity

This paper analyzes the endogeneity bias problem caused by associations of members within a network when the spatial autoregressive (SAR) model is used to study social interactions. When there are unobserved factors that affect both friendship decisions and economic outcomes, the spatial weight matrix (sociomatrix; adjacency matrix) in the SAR model, which represents the structure of a friendship network, might correlate with the disturbance term of the model, and consequently result in an endogenous selection problem in the outcomes. We consider this problem of selection bias with a modeling approach. In this approach, a statistical network model is adopted to explain the endogenous network formation process. By specifying unobserved components in both the network model and the SAR model, we capture the correlation between the processes of network and outcome formation, and propose a proper estimation procedure for the system. We demonstrate that the estimation of this system can be effectively done by using the Bayesian method. We provide a Monte Carlo experiment and an empirical application of this modeling approach on the friendship networks of high school students and their interactions on academic performance in the Add Health data. Copyright © 2015 John Wiley & Sons, Ltd.     

Accounting for heterogeneous returns in sequential schooling decisions

This paper presents a method for estimating returns to multiple schooling levels taking into account that returns may be heterogeneous among agents and that educational decisions are made sequentially. A sequential decision model explicitly considers that the level of education is the result of previous schooling choices and so, the variation of supply-side instruments over time will emerge as a source of identification of the desired parameters. A test for heterogeneity in returns from sequential schooling decisions is developed and expressions for Marginal Treatment Effects are obtained in this context. Returns are estimated and tested from cross-sectional data from a Spanish household survey that contains rich family background information and useful instruments. This methodology is used to analyze possible effects of the 1970 reform of the Spanish education system.     

Sequential optimization three-way decision model with information gain for credit default risk evaluation

To categorize credit applications into defaulters or non-defaulters, most credit evaluation models have employed binary classification methods based on default probabilities. However, while some loan applications can be directly accepted or rejected, there are others on which immediate accurate credit status decisions cannot be made using existing information. To resolve these issues, this study developed an optimized sequential three-way decision model. First, an information gain objective function was built for the three-way decision, after which a genetic algorithm (GA) was applied to determine the optimal decision thresholds. Then, appropriate accept or reject decisions for some applicants were made using basic credit information, with the remaining applicants, whose credit status was difficult to determine, being divided into a boundary region (BND). Supplementary information was then added to reevaluate the credit applicants in the BND, and a sequential optimization process was employed to ensure more accurate predictions. Therefore, the model’s predictive abilities were improved and the information acquisition costs controlled. The empirical results demonstrated that the proposed model was able to outperform other benchmarking credit models based on performance indicators.     

Fuzzy multi-linguistic preferences model of service innovations at wholesale service delivery

In group decision making, most researches often assume the linguistic ways with personal preferences have been given and ignore the linguistic evaluated formats involving their knowledge and experience. In practice, people contributing to the judgment tend generally to give ratings about their personal preferences depending on their background. Thus, problems in multiple linguistic preferences go undetected, resulting in the evaluation process not satisfying with decisions’ expectations. In this study, we provide a fuzzy multiple preference integrated model with two stages to better reduce the bias for group decision makings. The first stage focuses on making the information unify on the alternatives according to the individual linguistic preferences, then we compute collective performance values and solve the problems lacking on the integration of respective fuzzy choice subsets. The second stage, we choose the alternatives of retailing service innovations according to the collective performance values by stage one. The goal of the decision process is to reach the subjective fuzzy cognitions in terms of the preference values of all the decision makers. Finally, the survey data of the chain wholesale using multiple preference formats in service innovations determination is verified.     

Adjusting inconsistency through learning in group decision-making, and its application to China's MBA recruiting interview

In group decision-making, because of limitations on individual knowledge and information bases, or because of the existing decision rule, an individual decision maker may not be capable of evaluating selected alternatives. Such circumstances can lead to inconsistencies across group decision matrices. These inconsistencies are difficult to remedy under existing approaches. Based on Rough Set Theory, we thus propose a new approach that integrates two types of learning techniques. It first applies a machine-learning procedure that extracts possible alternatives from other decision makers that are currently not included in a given decision maker's alternative set. It then applies a group knowledge-learning model to determine corresponding attribute values of those newly learned alternatives in meeting a group's consistency requirement. Efficacy of the approach is illustrated by its application to China's MBA recruiting interview.     

A Predictive Estimator of the Mean with Missing Data

One of the most difficult problems confronting investigators who analyze data from surveys is how treat missing data. Many statistical procedures can not be used immediately if any values are missing. This paper considers the problem of estimating the population mean using auxiliary information when some observations on the sample are missing and the population mean of the auxiliary variable is not available. We use tools of classical statistical estimation theory to find a suitable estimator. We study the model and design properties of the proposed estimator. We also report the results of a broad-based simulation study of the efficiency of the estimator, which reveals very promising results.     

An experiment of social learning with endogenous timing

In this paper, we experimentally investigate a social learning model with endogenous timing. Specifically, we focus on a model, in which two subjects are supposed to make a binary decision. One alternative is a safe action with a fixed payoff, while the other alternative is a risky action. The subjects can make their decisions in three stages. The safe action is reversible, but the risky action is not. A subject who delays his decision can observe the decision of the other subject in the earlier stages, and as a result, acquire more information. We show that players do delay their decisions in order to obtain more information. Furthermore, they delay especially when their private information does not particularly support the risky action. We also find evidence which suggests that risk aversion plays an important role in timing decisions, often leading to ex post inefficient outcomes.     

Social enterprise factory location and allocation model: Small scale manufacturing for East Africa

The manufacturing location decision for social enterprises that work in the context of sustainable development is rarely based on a quantitative, analytical process. As a result, decisions may be far from profit-maximizing. Location and allocation optimization models have the potential to improve decisions and thus enable such enterprises to scale up their business as well as their impact. We develop and explain a single-period single-factory model, and also a two-echelon location and allocation model, to provide enterprises with information about optimal factory locations, and with future demand allocations and capacity-changing decision information. We apply the models to a company that manufactures and distributes solar cookers in East Africa. Our results illustrate that quantitative location and allocation models can significantly affect social enterprises by improving profitability. The case study shows strong cost-reduction potential of local manufacturing in developing countries due to high transportation costs for small production volumes. We discuss this model-recommended decision by weighing it against associated opportunities and risks. This paper aims to enable and encourage social and sustainability-oriented manufacturing enterprises to apply operations research methods in their strategic factory location decision-making.     

公司管理层权力与银行信贷融资决策

运用中国上市公司2010—2013年相关数据,考察公司管理层权力与公司融资结构及银行信贷资源配置,结果显示：公司的管理层结构及高管层权力是公司融资政策选择和银行信贷资源配置的重要动因,公司管理层权力越大,管理层向银行借入短期借款越多,公司管理层权力与短期借款结构正相关。在债务约束效应下,管理层可能会出于个人任期和控制权私利的原因做出影响公司融资决策的行为,在公司银行信贷融资中表现为银行信贷融资规模与公司管理层权力呈现负相关关系,即管理层权力越大,公司配置的银行信贷资源越少;同时,公司管理层权力中结构权力与所有者权力对公司信贷决策有重要影响。     

COLLATERAL AND CREDIT RATIONING: A REVIEW OF RECENT EMPIRICAL STUDIES AS A GUIDE FOR FUTURE RESEARCH

Abstract.  The relationship between firms and banks often suffers from informational opacity that may result in credit rationing. In theory, providing collateral to the bank can have a mitigating effect on these informational asymmetries and thus solve the credit-rationing problem. Even though collateral is already a widespread debt contract feature, recent trends predict that, in the future, collateral will become even more important for informationally opaque firms. The aim of this paper is twofold. First, we provide a review of the recently growing empirical research on collateral as a remedy for credit rationing. Second, we would like to pinpoint gaps and limitations in current empirical research. Most studies contend with a flawed research design by not distinguishing between business and personal collateral and excluding other information opaqueness reducing tools such as the strength of the relationship between borrower and lender, loan maturity and covenants. We also discuss the limitations of using a single equation estimation method and the usefulness of incorporating interaction effects into the estimation models. Finally, we provide suggestions for fruitful research avenues that would fill these gaps and enrich the empirical knowledge in this research domain.     

退休计划中养老年金购买决策的建模与分析

随着人类寿命的不断延长,社会个体面临越来越大的长寿风险,养老年金是规避长寿风险的有效工具。本文讨论个体退休计划中有关养老年金购买的重要决策问题,构建了一个能规避长寿风险的多期消费与投资决策模型框架,该框架把最优年金购买决策和传统的消费与投资选择问题有机地结合起来,以求达到规避长寿风险并获得消费和遗产的最大效用的双重目的。本文最后展示了特定参数设置下的计算结果以说明特定的社会个体应该如何构建能规避长寿风险的消费和投资策略,并分析了寿命的不确定性等因素对个体年金计划的影响。