Customer satisfaction measurement in the private bank sector

Customer satisfaction represents a modern approach for quality in enterprises and organisations and serves the development of a truly customer-focused management and culture. Measuring customer satisfaction offers an immediate, meaningful and objective feedback about clients’ preferences and expectations. In this way, company’s performance may be evaluated in relation to a set of satisfaction dimensions that indicate the strong and the weak points of a business organisation. This paper presents an original customer satisfaction survey in the private bank sector. The implemented methodology is based on the principles of multicriteria analysis and preference disaggregation modelling. The most important results are focused on the determination of the critical service dimensions and the segmentation to customer clusters with distinctive preferences and expectations.     

Beyond comparisons—the role for the operational researcher in benchmarking

This paper explores the possible roles for the operational researcher in best practice benchmarking. It describes the range of activities which are carried out in the name of benchmarking, locates this approach in the broader context of performance improvement, and notes some critical concerns including the need to adopt a contingency approach to performance improvement. The paper concludes by encouraging operational researchers to contribute their professional skills to enhance the effectiveness of benchmarking.     

Preemptive benchmarking problem: An approach for official statistics in small areas

National Statistical Agencies and Autonomous Institutions are extremely interested in using information from those areas that are actually smaller than the actual areas for which a survey is initially designed. As such, small area estimation and its application are valuable when conducting research on Official Statistics. A wide range of different methods are available which provide estimations to small area levels, being reasonable to guarantee that they add up to the published design-based estimations in a large area that includes these small areas. This requirement is known as benchmarking. Different algorithms, all based on distances between original data and modified data, are introduced in this paper, with the intention of satisfying the benchmarking property. We provide rules to apply these proposed calibrated methods according to user criteria. Goal programming with priorities methodology is used to represent user preferences. The result is a collection of different interdependent network flow problems. Some of these problems require the development of ad hoc methods. The introduced methods are assessed by a Monte Carlo simulation study using the Spanish Labour Force Survey in the Canary Islands. The results also show that the consistency of the estimator is independent of the used calibrated methods, but it does depend on the benchmarking weights.     

Preference disaggregation for measuring and analysing customer satisfaction: The MUSA method

The MUlticriteria Satisfaction Analysis (MUSA) method for measuring and analysing customer satisfaction is presented in this paper. The MUSA method is a preference disaggregation model following the principles of ordinal regression analysis (inference procedure). The integrated methodology evaluates the satisfaction level of a set of individuals (customers, employees, etc.) based on their values and expressed preferences. Using satisfaction survey's data, the MUSA method aggregates the different preferences in unique satisfaction functions. This aggregation–disaggregation process is achieved with the minimum possible errors. The main advantage of the MUSA method is that it fully considers the qualitative form of customers' judgements and preferences. The development of a set of quantitative indices and perceptual maps makes possible the provision of an effective support for the satisfaction evaluation problem. This paper also presents the reliability analysis of the provided results, along with a simple numerical example that demonstrates the implementation process of the MUSA method. Finally, several extensions and future research in the context of the presented method are discussed.     

基于企业网络舆情的客户满意度分析及管理方法

社交网络平台的迅速发展,促使网络舆情成为企业获取商业情报、扩大竞争优势的重要信息来源。本文针对网络舆情环境下的企业客户关系管理问题展开研究。通过构建企业客户推动式信息反馈模型,描述了企业客户、网络用户与企业网络舆情间的联系,并依据信息反馈模型,提出变尺度聚类算法。该算法将传统聚类方法的求解过程由单一尺度分析扩展到多尺度分析,克服了实际数据聚类应用过程中的聚类结果特征不显著问题。本文选取新浪微博作为数据源,利用企业网络舆情数据集和企业客户数据集进行数据分析实验。实验结果表明,企业可以通过获取与其主营业务相关的网络舆情信息,实现客户满意度预测;同时,变尺度聚类算法结果能够为企业进一步制定销售战略和销售战术提供决策支持。     

Benchmarking with quality-adjusted DEA (Q-DEA) to seek lower-cost high-quality service: Evidence from a U.S.bank application

Benchmarking is a widely cited method to identify and adopt best-practices as a means to improve performance. Data envelopment analysis (DEA) has been demonstrated to be a powerful benchmarking methodology for situations where multiple inputs and outputs need to be assessed to identify best-practices and improve productivity in organizations. Most DEA benchmarking studies have excluded quality, even in service-sector applications such as health care where quality is a key element of performance. This limits the practical value of DEA in organizations where maintaining and improving service quality is critical to achieving performance objectives. In this paper, alternative methods incorporating quality in DEA benchmarking are demonstrated and evaluated. It is shown that simply treating the quality measures as DEA outputs does not help in discriminating the performance. Thus, the current study presents a new, more sensitive, quality-adjusted DEA (Q-DEA), which effectively deals with quality measures in benchmarking. We report the results of applying Q-DEA to a U.S. bank's 200-branch network that required a method for benchmarking to help manage operating costs and service quality. Q-DEA findings helped the bank achieve cost savings and improved operations while preserving service quality, a dimension critical to its mission. New insights about ways to improve branch operations based on the best-practice (high-quality low-cost) benchmarks identified with Q-DEA are also described in the paper. This demonstrates the practical need and potential benefits of Q-DEA and its efficacy in one application, and also suggests the need for further research on measuring and incorporating quality into DEA benchmarking. The review process of this paper was handled by the Edit-in-Chief Peter Hammer.     

Supply chain performance assessment and supplier and component importance identification in a general competitive multitiered supply chain network model

In this paper, we develop a multitiered competitive supply chain network game theory model, which includes the supplier tier. The firms are differentiated by brands and can produce their own components, as reflected by their capacities, and/or obtain components from one or more suppliers, who also are capacitated. The firms compete in a Cournot–Nash fashion, whereas the suppliers compete a la Bertrand since firms are sensitive to prices. All decision-makers seek to maximize their profits with consumers reflecting their preferences through the demand price functions associated with the demand markets for the firms’ products. We construct supply chain network performance measures for the full supply chain and the individual firm levels that assess the efficiency of the supply chain or firm, respectively, and also allow for the identification and ranking of the importance of suppliers as well as the components of suppliers with respect to the full supply chain or individual firm. The framework is illustrated through a series of numerical supply chain network examples.     

Team-based reconstruction for expanding organisational ability

Organisations can be interpreted as a collection of actors who produce value for clients. In order for organisations to stay competitive there is a need for organisations to continuously develop their ability. An organisation's ability is determined by its ability to effectively apply existing knowledge to create new knowledge and to take action that forms the basis for achieving competitive advantage from knowledge-based assets. One means for developing the organisational ability is to establish a mutually accepted understanding of the context in which the actors are acting. One important part of the organisation's knowledge creation processes is to facilitate the sharing of personal knowledge. A condition for developing the organisational ability is to expand the amount of shared knowledge to an appropriate level. One type of knowledge that needs to be shared among different actors within the organisation is the knowledge about how and in which order actions are performed (contextual knowledge) in order to satisfy client needs. In this paper an approach for team-based reconstruction, used for moving from personal to shared contextual knowledge, is presented. The approach bears on the foundation of theories about knowledge management for understanding the process of externalisation and theories about language action for understanding organisations. Experiences from three case studies are presented. Team-based reconstruction can be used for (1) moving from personal knowledge to shared contextual knowledge in an efficient way, (2) arriving at a foundation for efficient organisational coordination, (3) arriving at a base for development work, and (4) organisational learning.     

Impact of the industry on franchise chain efficiency: a meta-DEA and context-dependent DEA approach

A meta-DEA (Data Envelopment Analysis) approach is used to analyse the impact of the industry in which the franchisor operates its business on its chain efficiency by comparing chain efficiency between and across industries (retail versus services). To provide realistic short-term and long-term objectives to inefficient chains, a level-by-level benchmarking path is developed with the context-dependent DEA approach. Furthermore, the joint implementation of both approaches enables an identification of chains for which the industry influences their efficiency, an evaluation of the efficiency share that results from industry-specific factors rather than factors common to the franchise sector as a whole, and the characterization of an implementable and achievable benchmarking defining level-by-level improvements of chain efficiency and taking into account chain characteristics. Such an analysis can help franchisors to define their future strategic and managerial orientations.     

Efficient identification of independence networks using mutual information

Conditional independence graphs are now widely applied in science and industry to display interactions between large numbers of variables. However, the computational load of structure identification grows with the number of nodes in the network and the sample size. A tailored version of the PC algorithm is proposed which is based on mutual information tests with a specified testing order, combined with false negative reduction and false positive control. It is found to be competitive with current structure identification methodologies for both estimation accuracy and computational speed and outperforms these in large scale scenarios. The methodology is also shown to approximate dense networks. The comparisons are made on standard benchmarking data sets and an anonymized large scale real life example.     

一种新的在线调度算法竞争比分析方法——基于实例转换的方法

针对工件动态到达的在线调度模型提出了一种基于实例转换的竞争分析方法,该方法从问题的一个任意实例出发,逐步沿着性能比增加的方向修改工件的各种参数而得到结构更加简单特殊的实例,最后所导出的简单实例的性能比可以直接计算,且是算法竞争比的一个上界.该方法为在线调度算法的竞争比分析提供了一种新颖的、规律性的思路,以最小化总加权完工时间的单机在线调度问题为例,使用提出的分析方法为该问题一个已有的竞争分析结论提供了更加简洁明了的替代性证明.     

基于贝叶斯多元有序probit模型的休闲渔业游客满意度研究

游客满意度是休闲渔业景区制定管理决策的重要参考信息。通过探究游览过程中游客对各单一维度的满意程度与旅游总满意度之间的关系,管理者可以从中准确了解游客偏好、消费行为以及获取旅游产品设计等重要信息。据此,本文采取实地调研,获取11家全国休闲渔业示范基地1510份游客满意度信息,使用马尔科夫链蒙特卡洛法(Markov Chain Monte Carlo, MCMC),估算贝叶斯多元有序probit模型,并利用模型后验结果衡量休闲渔业游客7种分满意度对总体满意度的贡献。研究结果表明:渔憩体验满意度、景区环境满意度以及旅游餐饮满意度是对总体满意度影响程度最大的三种分满意度,所占比例分别为43.58%、24.67%、10.62%。     

Evaluation of Commodity Trading Advisors using fixed and variable and benchmark models

This paper examines the performance of Commodity Trading Advisors (CTAs) using fixed and variable benchmarking models. In order to avoid the troublesome passive and active commodity and managed futures benchmarks (indices) when examining the performance of CTAs, we innovate by using data envelopment analysis (DEA). Because this alternative class has non-linear returns due to long/short positions, and derivatives (i.e., dynamic trading strategies), DEA can alleviate the problems usually associated with these indices. The effectiveness of using benchmarking models in a DEA setting will provide investors with an alternative technique in assessing the performance and identifying efficient CTAs.     

Financial sustainability and poverty outreach within a network of village banks in Cameroon: A multi-DEA approach

Microfinance institutions face a double bottom-line. They perform financial tasks by giving microcredits to their customers and support projects aiming at reducing poverty. In doing so, they have to be financially self-sufficient and to target poor people excluded from the traditional financial systems. However, a trade-off may exist between financial sustainability and poverty outreach for these institutions. By using a multi-DEA approach, this paper shows that even if a trade-off exists for 15% of the MC² (Mutuelles Communautaires de Croissance) in Cameroon, there is no trade-off for 46% of them. In order to increase, without trade-off, financial and social performance of inefficient MC², a benchmarking approach combing DEA and performance indicators has been developed. DEA is used for identifying best-practices and setting benchmarking goals. Performance indicators are used for characterizing areas needing improvements and following the evolution of MC² toward their goals, i.e., for implementing benchmarking. Complementarity of both approaches provides a tool box for improving financial and social efficiency and reducing the trade-off between financial sustainability and poverty outreach of microfinance institutions.     

Evolutionary Selection of Socially Sensitive Preferences in Random Matching Environments

In this paper we study the evolutionary selection of socially sensitive preferences in the context of reference interaction settings such as coordination failure and cooperation. We refer to a specific class of socially sensitive preferences in which players weigh additively their own material payoff against the opponent with either a positive or negative coefficient (λ-players). Preference evolution is guided by replicator dynamics in a context of perfect observability of preferences types and stochastic pairwise matching. We take an indirect evolutionary approach, that is, the selection mechanism operates on the actual material payoffs earned by players, so that any instance of socially sensitive preference can be thought of as instrumentally maintained. We find that the evolutionary viability of socially sensitive preferences basically depends on whether or not they cause a substantial improvement in the achievement of socially efficient outcomes with respect to the case where only self-serving or unconditionally focused preference orientations are observed. Our results suggest that moderate pro-social preference orientations are likely to emerge from social selection even in the absence of an intrinsic motivational drive, whereas extremely pro-social orientations as well as competitive and anti-social ones may need a stronger motivational base.     

A hybrid model based on dynamic programming,neural networks,and surrogate value for inventory optimisation applications

This paper proposes a new approach to minimise inventory levels and their associated costs within large geographically dispersed organisations. For such organisations, attaining a high degree of agility is becoming increasingly important. Linear regression-based tools have traditionally been employed to assist human experts in inventory optimisation; endeavours; recently, Neural Network (NN) techniques have been proposed for this domain. The objective of this paper is to create a hybrid framework that can be utilised for analysis, modelling and forecasting purposes. This framework combines two existing approaches and introduces a new associated cost parameter that serves as a surrogate for customer satisfaction. The use of this hybrid framework is described using a running example related to a large geographically dispersed organisation.     

PAVER 2.0: an open source environment for automated performance analysis of benchmarking data

In this paper we describe PAVER 2.0, an environment (i.e. a process and a suite of tools supporting that process) for the automated performance analysis of benchmarking data. This new environment improves on its predecessor by addressing some of the shortcomings of the original PAVER (Bussieck et al. in Global optimization and constraint satisfaction, lecture notes in computer science, vol 2861, pp 223–238. Springer, Berlin, 2003. doi:10.1007/978-3-540-39901-8_17) and extending its capabilities. The changes serve to further the original goals of PAVER (automation of the visualization and summarization of benchmarking data) while making the environment more accessible for the use of and modification by the entire community of potential users. In particular, we have targeted the end-users of optimization software, as they are best able to make the many subjective choices necessary to produce impactful results when benchmarking optimization software. We illustrate with some sample analyses conducted via PAVER 2.0.     

A heuristic algorithm based on multi-assignment procedures for nurse scheduling

This paper tackles a Nurse Scheduling Problem which consists of generating work schedules for a set of nurses while considering their shift preferences and other requirements. The objective is to maximize the satisfaction of nurses’ preferences and minimize the violation of soft constraints. This paper presents a new deterministic heuristic algorithm, called MAPA (multi-assignment problem-based algorithm), which is based on successive resolutions of the assignment problem. The algorithm has two phases: a constructive phase and an improvement phase. The constructive phase builds a full schedule by solving successive assignment problems, one for each day in the planning period. The improvement phase uses a couple of procedures that re-solve assignment problems to produce a better schedule. Given the deterministic nature of this algorithm, the same schedule is obtained each time that the algorithm is applied to the same problem instance. The performance of MAPA is benchmarked against published results for almost 250,000 instances from the NSPLib dataset. In most cases, particularly on large instances of the problem, the results produced by MAPA are better when compared to best-known solutions from the literature. The experiments reported here also show that the MAPA algorithm finds more feasible solutions compared with other algorithms in the literature, which suggest that this proposed approach is effective and robust.     

消费者非均匀偏好条件下高校项目开发策略

为了探究教育项目消费者随机需求条件下的不同层次高校的竞优策略, 运用Stackelberg博弈方法, 构建了消费者偏好服从非均匀分布的不同层次高校开发同类教育项目的定位设计模型, 分析了消费者偏好行为对教育项目收益的影响规律以及双方的竞优策略。结果显示, H型高校(声誉较高)不受消费者偏好等市场因素影响, 始终选择最大差异化策略。L型高校(声誉较低)在消费者无差异偏好偏向自己一方时也不受市场因素影响采取最大差异化策略; 而在消费者无差异偏好偏向它方时, 若市场需求概率小于0.5则选择最大差异化策略, 否则其决策将受到自身初始位值、市场概率等因素的综合影响, 并适时选择最大差异化、有限差异化或有限趋同化策略。     

用因子分析法构建医院顾客让渡价值模型

使就医顾客满意对于加强医院的竞争力十分重要。本文选择十六项指标构建就医客户满意指标体系,通过因子分析法将其简化为六个主因子,它们分别是医师服务态度、就诊方便程度、医疗水平、候诊方便、收费高低、诊疗手续。基于这些主因子,我们建立医院顾客让渡价值模型。并根据该模型,分析医院的经营策略。