Market segmentation: A multiple criteria approach combining preference analysis and segmentation decision

We propose a new multiple criteria decision aiding approach for market segmentation that integrates preference analysis and segmentation decision within a unified framework. The approach employs an additive value function as the preference model and requires consumers to provide pairwise comparisons of some products as the preference information. To analyze each consumer’s preferences, the approach applies the disaggregation paradigm and the stochastic multicriteria acceptability analysis to derive a set of value functions according to the preference information provided by each consumer. Then, each consumer’s preferences can be represented by the distribution of possible rankings of products and associated support degrees by applying the derived value functions. On the basis of preference analysis, a new metric is proposed to measure the similarity between preferences of different consumers, and a hierarchical clustering algorithm is developed to perform market segmentation. To help firms serve consumers from different segments with targeted marketing policies and appropriate products, the approach proposes to work out a representative value function and the univocal ranking of products for each consumer so that products that rank in the front of the list can be presented to her/him. Finally, an illustrative example of a market segmentation problem details the application of the proposed approach.     

An extension of the Electre I method for group decision-making under a fuzzy environment

Many real-world decision problems involve conflicting systems of criteria, uncertainty and imprecise information. Some also involve a group of decision makers (DMs) where a reduction of different individual preferences on a given set to a single collective preference is required. Multi-criteria decision analysis (MCDA) is a widely used decision methodology that can improve the quality of group multiple criteria decisions by making the process more explicit, rational and efficient. One family of MCDA models uses what is known as “outranking relations” to rank a set of actions. The Electre method and its derivatives are prominent outranking methods in MCDA. In this study, we propose an alternative fuzzy outranking method by extending the Electre I method to take into account the uncertain, imprecise and linguistic assessments provided by a group of DMs. The contribution of this paper is fivefold: (1) we address the gap in the Electre literature for problems involving conflicting systems of criteria, uncertainty and imprecise information; (2) we extend the Electre I method to take into account the uncertain, imprecise and linguistic assessments; (3) we define outranking relations by pairwise comparisons and use decision graphs to determine which action is preferable, incomparable or indifferent in the fuzzy environment; (4) we show that contrary to the TOPSIS rankings, the Electre approach reveals more useful information including the incomparability among the actions; and (5) we provide a numerical example to elucidate the details of the proposed method.     

The Interplay between Information Acquisition and Quality Disclosure

This study investigates the interactions between a manufacturer's information acquisition and quality disclosure strategies in a supply chain setting in which the manufacturer privately knows his product quality but is uncertain about consumer preferences. We argue that the manufacturer should treat his information acquisition and quality disclosure decisions as an integrated process because these decisions can significantly influence a retailer's rational inferences about product quality and can have conflicting effects on his own profitability. Although information acquisition helps a manufacturer subsequently craft better pricing and quality disclosure strategies, it also leaks certain product information to the retailer, thus helping the retailer better estimate product quality. Therefore, in equilibrium, a manufacturer may choose not to acquire any consumer information, even when such acquisition is costless. Moreover, we find that this adverse effect of acquisition is highly dependent on the cost of disclosure and consumers’ preference differentiation. Increased consumer preference differentiation may have a non‐monotonic relationship with the manufacturer's profit, and information acquisition can become detrimental to the manufacturer once the disclosure cost is sufficiently high.     

Robust Ordinal Regression and Stochastic Multiobjective Acceptability Analysis in multiple criteria hierarchy process for the Choquet integral preference model

The paper deals with two important issues of Multiple Criteria Decision Aiding: interaction between criteria and hierarchical structure of criteria. To handle interactions, we apply the Choquet integral as a preference model, and to handle the hierarchy of criteria, we apply the recently proposed methodology called Multiple Criteria Hierarchy Process. In addition to dealing with the above issues, we suppose that the preference information provided by the Decision Maker is indirect and has the form of pairwise comparisons of criteria with respect to their importance and pairwise preference comparisons of some pairs of alternatives with respect to some criteria. In consequence, many instances of the Choquet integral are usually compatible with this preference information. These instances are identified and exploited by Robust Ordinal Regression and Stochastic Multiobjective Acceptability Analysis. To illustrate the whole approach, we show its application to a real world decision problem concerning the ranking of universities for a hypothetical Decision Maker.     

Screening alternatives graphically by an extended case-based distance approach

Screening is a helpful process of multiple criteria decision aid (MCDA) to reduce a larger set of alternatives into a smaller one containing the best alternatives; thereby, decision makers are able to concentrate on evaluating alternatives within a smaller set. Therefore, determining how to assist decision makers in screening is an important issue for MCDA. This study proposes an extended case-based distance approach incorporating the advantages of a case-based distance method, a mixed-integer programming approach of discriminant analysis, and a multidimensional scaling technique to help decision makers screen alternatives visually in MCDA. The proposed approach can screen alternatives by evaluating sets of cases selected by decision makers, providing visual aids to observe decision context, reducing the number of misclassifications, and improving multiple solution problems. An interactive screening procedure is also developed to provide flexibility so that decision makers can check and adjust screening results iteratively.     

Predicting the Incremental Benefits of Online Information Search for Heterogeneous Consumers

Consumer information search (CIS), the process by which a consumer browses and inspects a shopping environment for appropriate information to select a product or service from available options, has been a research focus in the context of online business. One of the key questions related to CIS is how to determine how much information to search (i.e., when to stop searching). Extensive literature on behavioral science has revealed that consumers often search either “too little” or “too much,” even with the help of existing consumer decision support systems (CDSSs). To address this issue, this article introduces a new method of CDSSs that provides effective estimation of incremental search benefits. The method, called the personalized distribution‐based prediction method (PDM), is developed from the perspective of machine learning and utilizes consumer preference information generated by collaborative filtering (CF) algorithms. In contrast to related methods that assume that all consumers follow the same distribution function in terms of product preference, the PDM method is designed to consider the diversified search behaviors of consumers through the incorporation of heterogeneous preference distribution functions. Experiments based on data provided by Netflix illustrate that the proposed method is effective and advantageous over existing applicable techniques. Theoretical analyses are also provided to explain the advantageous performance of PDM.     

电商平台信息披露策略研究

电商平台模式下，供应商决定产品价格将产品直接出售给消费者，并与平台企业共享收益。通常，消费者与产品供应商/平台企业在产品属性(匹配)信息上存在不对称性。本文基于博弈理论，研究电商平台(如：亚马逊、京东等)的产品信息披露策略，比较两种信息披露方式：“平台披露”与“供应商披露”。首先考虑由一个平台企业和一个供应商组成的垄断结构。研究表明，信息披露策略取决于产品属性(匹配)的相对重要性。同时，与平台披露相比，供应商更愿意披露全部产品信息。然后考虑一个平台企业和两个供应商组成的竞争结构。研究发现，与垄断结构不同，平台披露下，平台总是披露全部产品信息；而供应商披露下，信息披露策略取决于产品属性(匹配)信息的相对价值。此外，平台会偏好由供应商负责信息披露。     

Extreme ranking analysis in robust ordinal regression

We extend the principle of robust ordinal regression with an analysis of extreme ranking results. In our proposal, we consider the whole set of instances of a preference model that is compatible with preference information provided by the DM. We refer to both, the well-known UTA^GMS method, which builds the set of general additive value functions compatible with DM's preferences, and newly introduced in this paper PROMETHEE^GKS, which constructs the set of compatible outranking models via robust ordinal regression. Then, we consider all complete rankings that follow the use of the compatible preference models, and we determine the best and the worst attained ranks for each alternative. In this way, we are able to assess its position in an overall ranking, and not only in terms of pairwise comparisons, as it is the case in original robust ordinal regression methods. Additionally, we analyze the ranges of possible comprehensive scores (values or net outranking flows). We also discuss extensions of the presented approach on other multiple criteria problems than ranking. Finally, we show how the presented methodology can be applied in practical decision support, reporting results of three illustrative studies.     

A robust ranking method extending ELECTRE III to hierarchy of interacting criteria,imprecise weights and stochastic analysis

A great majority of methods designed for Multiple Criteria Decision Aiding (MCDA) assume that all assessment criteria are considered at the same level, however, decision problems encountered in practice often impose a hierarchical structure of criteria. The hierarchy helps to decompose complex decision problems into smaller and manageable subtasks, and thus, it is very attractive for computational efficiency and explanatory purposes. To handle the hierarchy of criteria in MCDA, a methodology called Multiple Criteria Hierarchy Process (MCHP), has been recently proposed. MCHP permits to consider preference relations with respect to a subset of criteria at any level of the hierarchy. Here, we propose to apply MCHP to the ELECTRE III ranking method adapted to handle three types of interaction effects between criteria: mutual-weakening, mutual-strengthening and antagonistic effect. We also involve in MCHP an imprecise elicitation of criteria weights, generalizing a technique called the SRF method. In order to explore the plurality of rankings obtained by the ELECTRE III method for possible sets of criteria weights, we apply the Stochastic Multiobjective Acceptability Analysis (SMAA) that permits to draw robust conclusions in terms of rankings and preference relations at each level of the hierarchy of criteria. The novelty of the whole methodology consists of a joint consideration of hierarchical assessments of alternatives performances on interacting criteria, imprecise criteria weights, and robust analysis of ranking recommendations resulting from ELECTRE III. An example regarding the multiple criteria ranking of some European universities will show how to apply the proposed methodology on a decision problem.     

Combining analytical hierarchy process and Choquet integral within non-additive robust ordinal regression

We consider multiple criteria decision aiding in the case of interaction between criteria. In this case the usual weighted sum cannot be used to aggregate evaluations on different criteria and other value functions with a more complex formulation have to be considered. The Choquet integral is the most used technique and also the most widespread in the literature. However, the application of the Choquet integral presents two main problems being the necessity to determine the capacity, which is the function that assigns a weight not only to all single criteria but also to all subset of criteria, and the necessity to express on the same scale evaluations on different criteria. While with respect to the first problem we adopt the recently introduced Non-Additive Robust Ordinal Regression (NAROR) taking into account all the capacities compatible with the preference information provided by the DM, with respect to the second one we build the common scale for the considered criteria using the Analytic Hierarchy Process (AHP). We propose to use AHP on a set of reference points in the scale of each criterion and to use an interpolation to obtain the other values. This permits to reduce considerably the number of pairwise comparisons usually required by the DM when applying AHP. An illustrative example details the application of the proposed methodology.     

本土品牌逆袭与消费者偏好逆转的纵向扎根研究

  如今的中国市场,诸多行业本土品牌崛起甚至逆袭,消费者对全球品牌与本土品牌的偏好相应逆转。已有研究较为全面地揭示了全球品牌和本土品牌偏好的驱动因素,但本土品牌逆袭过程中全球品牌与本土品牌偏好的驱动因素是否相同、本土品牌逆袭与消费者偏好逆转的互动影响路径是怎样的,对这些问题缺乏探讨。         在梳理相关研究的基础上,以手机行业为研究对象,采用扎根理论的研究方法,通过对网络讨论数据的初始编码、聚焦编码和理论编码,首先研究全球品牌与本土品牌偏好的驱动因素的异同,然后结合对讨论数据的纵向统计结果,研究本土品牌逆袭过程中品牌逆袭与消费者偏好逆转的互动影响路径。         研究结果表明,本土品牌逆袭中,全球品牌与本土品牌偏好的驱动因素模型具有相似的结构,功能价值和社会价值的测量受个人因素调节而影响消费者偏好,但测量功能价值或社会价值的具体因素在全球品牌与本土品牌中存在差异。品牌逆袭有助于提升综合国力、促进消费者成熟,进而驱动消费者偏好逆转;消费者偏好逆转则通过反馈路径帮助品牌进一步逆袭。具体的,消费者对产品特质更加重视,全球品牌的原产国效应和符号价值表现力减弱,本土品牌的口碑效应和符号价值表现力增强,同时消费者的民族主义增强而爱国绑架感减弱,这些因素通过感知价值带来消费者偏好逆转。综合而言,本土品牌逆袭和消费者偏好逆转是互动提升的关系。         研究结果丰富了全球品牌和本土品牌的相关研究,为全球品牌和本土品牌的建设提供了实践启示。产品特质是影响消费者偏好的主要因素,并对其他驱动因素有支撑作用。所以全球品牌和本土品牌都要重视产品的创新和研发,还要根据各驱动因素的变化调整其营销策略。     

Stock Evaluation Using a Preference Disaggregation Methodology

This paper presents a real application of a multicriteria decision aid (MCDA) approach to portfolio selection based on preference disaggregation, using ordinal regression and linear programming (UTADIS method; UTilités Additives DIScriminantes). The additive utility functions that are derived through this approach have the extrapolation ability that any new alternative (share) can be easily evaluated and classified into one of several user-predefined groups. The procedure is illustrated with a case study of 98 stocks from the Athens stock exchange, using 15 criteria. The results are encouraging, indicating that the proposed methodology could be used as a tool for the analysis of the portfolio managers' preferences and choices. Furthermore, the comparison with multiple discriminant analysis (either using a stepwise procedure or not) illustrates the superiority of the proposed methodology over a well-known multivariate statistical technique that has been extensively used to study financial decision-making problems.     

考虑产品质量和利他偏好关切的电商供应链模型研究

考虑产品质量的影响,文章构建了电商供应链的决策模型,包括不考虑关切的分散决策、电商平台利他偏好关切的分散决策和供应链集中决策模型。然后,求解各种模型的最优决策,探讨电商平台的利他偏好行为对供应链决策的影响。接着,结合实际案例阐述电商平台利他偏好的影响,最后,用数值算例对结论进行验证。研究表明：（1）消费者对产品质量重视程度的增加能够大幅度提高制造商、电商平台和供应链整体的利润。（2）分散决策下电商平台的利他偏好能够提高制造商利润,降低自身利润,但对供应链系统利润的影响是先增加后减少;而且当电商平台利他偏好时,分散决策与集中决策下产品售价的大小关系不确定,这与传统供应链中"集中决策下售价最低"的结论不同。（3）电商平台服务水平,制造商销售价格和产品质量随着利他偏好程度的增加而增加,且增加速率一直变快,但是与集中决策下的最优决策存在较大的差距。（4）电商平台的利他偏好行为主要表现为电商平台提升自身的服务水平,虽然有助于供应链的稳定运行,但现实中,电商平台的利他偏好程度一般比较小。文章的研究结论有助于补充完善ECSC的理论基础。     

How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy

Decision making is a complex task that involves a multitude of perspectives, constraints, and variables. Multiple Criteria Decision Analysis (MCDA) is a process that has been used for several decades to support decision making. It includes a series of steps that systematically help Decision Maker(s) (DM(s)) and stakeholders in structuring a decision making problem, identifying their preferences, and building a decision recommendation consistent with those preferences. Over the last decades, many studies have demonstrated the conduct of the MCDA process and how to select an MCDA method. Until now, there has not been a review of these studies, nor a proposal of a unified and comprehensive high-level representation of the MCDA process characteristics (i.e., features), which is the goal of this paper. We introduce a review of the research that defines how to conduct the MCDA process, compares MCDA methods, and presents Decision Support Systems (DSSs) to recommend a relevant MCDA method or a subset of methods. We then synthesize this research into a taxonomy of characteristics of the MCDA process, grouped into three main phases, (i) problem formulation, (ii) construction of the decision recommendation, and (iii) qualitative features and technical support. Each of these phases includes a subset of the 10 characteristics that helps the analyst implementing the MCDA process, while also being aware of the implication of these choices at each step. By showing how decision making can be split into manageable and justifiable steps, we reduce the risk of overwhelming the analyst, as well as the DMs/stakeholders during the MCDA process. A questioning strategy is also proposed to demonstrate how to apply the taxonomy to map MCDA methods and select the most relevant one(s) using real case studies. Additionally, we show how the DSSs for MCDA method recommendation can be grouped into three main clusters. This proposal can enhance a traceable and categorizable development of such systems.     

Can Carbon Nanomaterials Improve CZTS Photovoltaic Devices? Evaluation of Performance and Impacts Using Integrated Life‐Cycle Assessment and Decision Analysis

In emergent photovoltaics, nanoscale materials hold promise for optimizing device characteristics; however, the related impacts remain uncertain, resulting in challenges to decisions on strategic investment in technology innovation. We integrate multi‐criteria decision analysis (MCDA) and life‐cycle assessment (LCA) results (LCA‐MCDA) as a method of incorporating values of a hypothetical federal acquisition manager into the assessment of risks and benefits of emerging photovoltaic materials. Specifically, we compare adoption of copper zinc tin sulfide (CZTS) devices with molybdenum back contacts to alternative devices employing graphite or graphene instead of molybdenum. LCA impact results are interpreted alongside benefits of substitution including cost reductions and performance improvements through application of multi‐attribute utility theory. To assess the role of uncertainty we apply Monte Carlo simulation and sensitivity analysis. We find that graphene or graphite back contacts outperform molybdenum under most scenarios and assumptions. The use of decision analysis clarifies potential advantages of adopting graphite as a back contact while emphasizing the importance of mitigating conventional impacts of graphene production processes if graphene is used in emerging CZTS devices. Our research further demonstrates that a combination of LCA and MCDA increases the usability of LCA in assessing product sustainability. In particular, this approach identifies the most influential assumptions and data gaps in the analysis and the areas in which either engineering controls or further data collection may be necessary.     

产品责任、企业社会责任偏好与供应链中的质量-数量协调

在产品质量和产品数量（订货量）内生化的情形下，同时实现其质量和数量的协调将成为供应链管理的一个重要问题。进一步，在产品低质量对消费者造成伤害的情形下，制造商将面临着产品责任（表征制造商对消费者产品伤害的补偿），从而导致"产品责任如何影响供应链中的质量-数量协调"这一问题。最后，如果核心企业具有对消费者的企业社会责任（CSR）偏好，则这种CSR偏好又如何影响供应链中的质量-数量协调？针对这三个问题，首先，利用批发价合同构建了一个由上游制造商和下游零售商组成的两级供应链运作博弈模型；其次考察了制造商产品责任、CSR偏好程度和质量改进效率对其产品质量决策、批发价合同和相应的供应链节点企业利润的影响；最后，研究了供应链质量-数量协调问题。结果表明：（1）产品责任不影响产品质量、订货量、供应链节点企业经济利润和消费者剩余，但制造商批发价随产品责任的增加而增加；（2）随着CSR偏好程度的增加，产品质量、订货量、零售商经济利润、供应链系统经济利润和消费者剩余随之增加，而制造商经济利润随之减小；（3）质量改进效率的提高，有利于产品质量、订货量、零售商经济利润、供应链系统经济利润和消费者剩余的增加；（4）一个由数量折扣契约和质量改进成本分担契约共同构成的协调机制，可以有效的实现供应链系统的协调，其中，产品责任将促使数量折扣契约中产品批发价的增加和质量改进成本分担比例的减小，而CSR偏好程度的增加将促使数量折扣契约中产品批发价的减小，但不影响质量改进成本分担比例。     

基于制造商网络渠道选择的双渠道供应链定价 与服务决策研究

本文考虑由线下零售商实体销售与制造商网络销售构成的网络直销,以及由线下零售商实体销售与线上零售商网络销售构成的网络分销两种制造商双渠道模式,研究“搭便车”行为下制造商模式选择和供应链最优定价与服务决策。研究发现:制造商的渠道选择策略与“搭便车”行为程度、消费者偏好网络渠道程度、价格敏感系数等有关。尤其当消费者受到的服务且对价格的敏感性相同时,制造商会选择网络直销双渠道模式。进一步分析消费者“搭便车”行为和网络渠道偏好对制造商渠道选择和定价决策影响发现,制造商在消费者偏好网络渠道程度比较低时应选择网络分销双渠道模式,而当消费者网络渠道偏好和“搭便车”行为程度都比较高时,制造商应选择网络直销双渠道模式。消费者网络渠道偏好或“搭便车”行为程度越大,两种模式下批发价格应设置的更低。网络直销渠道模式下“搭便车”行为程度越大,网络渠道销售价格应设置的更低,而消费者网络渠道偏好程度越大,网络渠道销售价格应设置得更高。     

A Model of Consumer Inertia with Applications to Dynamic Pricing

This paper introduces a decision model of consumer inertia. Consumers exhibit inertia when they have an inherent bias to delay purchases. Inertia may induce consumers to wait even when it is optimal to buy immediately. We embed our decision model within a dynamic pricing context. There is a firm that sells a fixed capacity over two time periods to an uncertain number of both rational and inertial consumers. We find that consumer inertia has both positive and negative effects on profits: it decreases demand (in period one) but intensifies competition among consumers for the product (in period two). We show that our model of inertia is consistent with well‐established behavioral regularities, such as loss aversion and probability weighting in the sense of prospect theory, and hyperbolic time preferences. We offer practical recommendations for firms to influence the level of consumer inertia. These include offering returns policies (to mitigate potential consumer losses), providing decision aids (to avoid perception errors), and offering flexible payment options (to lower transaction costs).     

社交网络情境下基于同侪影响的产品线规划研究

依托社交网络优化产品线是实现“匹配消费者需求带动产品设计-制造-营销全流程协调发展”的重要途径,尽管已经引起实业界的重点关注,相应的学术研究却十分匮乏。鉴于此,本文基于同侪影响理论与前景理论,结合社交网络情境下消费者的产品需求、偏好及决策,构建涵盖产品线营销层面和设计层面的双层规划模型;然后结合智能手机产品线实例,运用嵌套灰狼算法对该双层规划模型进行求解验证。本研究旨在提出社交网络与产品线规划之间耦合作用的联合优化框架,为面向社交网络的产品线规划提供理论依据。