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Empirical studies have shown strong evidence that a seller with a better customer feedback score is able to charge a higher price for his/her product or service. We study the problem faced by the seller of setting the price with the objective of maximizing expected revenue over a finite number of periods. Modeling the problem requires building a system of processes that includes: (1) the customer arrival and formation of customer reservation price; (2) the customer feedback collection and aggregation into a Seller Service Rating (SSR); and (3) determining how much to charge customers. Because of the technical difficulty in finding analytical solutions that fully reflect the closed-loop interconnections between these processes, we develop a simulation–optimization approach to solve the problem. We present a computational study and report on results of numerical experiments providing interesting insights on how the retailer could startegically align the price he charges with his service quality performance.

As an innovative public transport, the customized bus has rapidly grown. To improve the efficiency of customized bus and satisfy customers’ personal requirement, a mixed-integer programming model is proposed to optimize the stop assignment for customers and route scheduling for buses with walking distance constraints and travel time constraints. As an variants of vehicle routing problem, the formulated model on large-scale instances is intractable to solve for commercial solvers. Therefore, an exact algorithm based on branch and price is developed to solve the model, in which a labeling algorithm is designed for the pricing problems. Numerical experiments and a real-world case in Dalian are conducted to validate the effectiveness of the proposed model. The computational results show that the tailored algorithm can yield an optimal solution within a significantly shorter time than that of CPLEX. Some managerial implications are also obtained based on sensitivity analysis, which may be potentially useful for bus companies.

Motivated by applications in financial services, we consider a seller who offers prices sequentially to a stream of potential customers, observing either success or failure in each sales attempt. The parameters of the underlying demand model are initially unknown, so each price decision involves a trade-off between learning and earning. Attention is restricted to the simplest kind of model uncertainty, where one of two demand models is known to apply, and we focus initially on performance of the myopic Bayesian policy (MBP), variants of which are commonly used in practice. Because learning is passive under the MBP (that is, learning only takes place as a by-product of actions that have a different purpose), it can lead to incomplete learning and poor profit performance. However, under one additional assumption, a constrained variant of the myopic policy is shown to have the following strong theoretical virtue: the expected performance gap relative to a clairvoyant who knows the underlying demand model is bounded by a constant as the number of sales attempts becomes large. This paper was accepted by Gérard P. Cachon, stochastic models and simulation.

In this paper, we study an e-grocer’s tactical problem of differentiated time slot pricing in attended home delivery. The purpose of differentiating delivery prices is to influence customers’ choice behavior concerning the offered time slots, such that cost-effective delivery schedules on an operational level can be expected and overall profit is maximized. We present a mixed-integer linear programming formulation of the problem, in which delivery costs are anticipated by explicitly incorporating routing constraints, and we model customer behavior by a general nonparametric rank-based choice model. Concerning cost anticipation, we also propose a model-based approximation that enables application to real-world problem sizes. In a setup inspired by an industry partner operating in urban areas, we then perform a comprehensive computational study that reveals the value of the model-based approximation as a supporting instrument for an e-grocer’s pricing decisions in practice. In particular, we demonstrate the superiority of the model-based approximation for real-world problem sizes to several benchmark approaches applied in the scientific literature and in practice (e.g., a unit price approach and other standard pricing heuristics). The online appendix is available at https://doi.org/10.1287/trsc.2017.0738 .

Forward and reverse supply chains are one of the most important issues in supply chain management. These kinds of supply chain networks include a direct and reverse supply chain. In this paper, a multi-objective closed-loop supply chain network consisting of multi-level, multi-period, and multi-products is proposed under the set of parameter uncertainties. We formulate the problem as a mixed-integer linear programming model. The model assumes a shortage and a remaining inventory at the end of each period. The first objective function is to minimize the total costs of the network. The second one is to maximize the on-time delivery of the products purchased from suppliers to factories. The third objective is to maximize the quality according to the quality of the products produced in the forward supply chain and those that can be recovered in the reverse supply chain. Another point worth noting in this manuscript is selecting the best supplier. Because choosing the best supplier is one of the most critical decisions that purchasing managers have to make in a supply chain. It is based on different criteria, such as price, quality, customer service, and delivery, discussed in this article. Uncertainty is also considered in the model, and a scenario-based robust optimization approach is used to cope with it. Due to the problem’s multi-objective nature, four exact methods, namely LP-metric, sequential linear goal programming (SLGP), TH approach, and simple additive weighting are used to solve the objective functions. Finally, the most effective method for solving various numerical examples is selected as the best method by the least deviations compared to the other methods; in this paper, the SLGP method is chosen. To illustrate the response to a problem in more detail, some of the SLGP method outputs are presented. The results show the efficiency of the proposed model. Thus, it can be used in a variety of industries whose products are recycled and where the quality of products and the choice of appropriate suppliers are of great importance.

Purpose The effects of customer satisfaction on loyalty have been widely discussed by the academic community. Although the results of the studies reported in the literature are often contradictory, the existence of a relationship between satisfaction and loyalty is acknowledged, despite the influence of moderators and constraints of various kinds. The purpose of this paper is to discuss this relationship in the specific context of the retail sector, since this sector presents major challenges in terms of competition, and efforts placed on customer satisfaction and loyalty are more evident. Design/methodology/approach A survey based on the European Customer Satisfaction Index (ECSI) model was applied to a retail store in Portugal. This model has its roots in Switzerland, where in 1989, Claes Fornell developed a new complementary method for measuring the overall quality of companies’ output, through the calculation of an aggregated customer satisfaction index. The proposed model is based on a set of causal relationships established between a set of constructs. The ultimate goal is to calculate both satisfaction and loyalty indexes as well as to estimate the relationship between both constructs. Structural equation modelling, based on a partial least squares (PLS) estimation methodology, is the statistical technique used to estimate the model parameters, as well as to compare the aggregated indexes. PLS is based on the principles of linear regression and combines multiple regression aspects with factor analysis, in order to estimate a series of interrelated relationships. Findings The results confirm a positive influence of satisfaction on customers’ loyalty to the retail store. The study also shows the importance of the image construct, due to its strong direct effects on satisfaction, which makes it essential for influencing the loyalty index, both directly and indirectly. The impact of the image construct is also evident on the expectations, as the latter has shown a considerable direct effect on perceived quality. Research limitations/implications The study is based on a single case study of a Portuguese sports retail store. In future it would be interesting to study a representative sample of the whole retail sector. Practical implications The study is useful for the specific retail store where it was undertaken to help it devise better customer service, in order to increase satisfaction and loyalty. It is also useful for the entire network of stores for that retailer and other retail chains. Social implications The systematic application of customer surveys to whole sectors of the economy would improve competition, customer service and, ultimately, contribute to development and economic growth. Originality/value The ECSI has been applied to various industry sectors in different countries, including Portugal. It has never been used in the context of the Portuguese retail sector and it adds to the discussion on the relationship between satisfaction and loyalty, which is a pertinent topic of interest for researchers in quality management.

A new threshold stability problem in the context of facility location and discriminatory pricing is considered. In the statement of facility location and pricing problem, the company decides to open facilities and assign prices to each customer at each facility. The implementation of discriminatory pricing leads to a scenario where each customer is compelled to expend the maximum amount of their available financial resources, thereby ensuring the maximum revenue for the company. In the threshold stability problem, the available financial resources or budget of each consumer is a parameter with a known expected value. The objective is to maximize the deviation of the parameters from the expected value, provided that the company’s income remains above a given threshold. An algorithm based on variable neighborhood descent (VND) is proposed to solve the threshold stability problem. Numerical investigation of the algorithm is carried out on known instances and randomly generated ones. Various ways of constructing the starting facility location and different criteria for comparing the location vectors are analyzed.

Nowadays, the design of supply chain networks should be based on environmental issues as well as the needs of customers since the main driver of a supply chain network is customers. Continuous innovation of products requires understanding the features that are most important to customers, and product pricing should be carried out in a way that includes the satisfaction of both customers and manufacturers. This study uses the Kano model to classify product features into different categories. The design of the green supply chain network based on the Kano model has not been investigated in the literature so far. This study examines a green supply chain network including multiple manufacturers, product types, distributors, and carriers that is designed based on Kano’s conceptual model of multiple needs. In the proposed mathematical model of this paper, customer demand is a function of the selling price of the product, transportation pollution is minimized, and a solution based on the Cooperative Game Theory approach is used to solve the mathematical model using the GAMS software. One of the advantages of the proposed mathematical model in this research compared to other supply chain models is that the design needs of the supply chain network based on the Kano model (“must-be”, “one-dimensional”, “attractive” and “indifferent”) can be determined based on customer satisfaction. In addition, the price of the product can be determined according to the satisfaction of both customers and the manufacturers.

Scale economies are commonplace in operations, yet because of analytical challenges, relatively little is known about how firms should compete in their presence. This paper presents a model of competition between two firms that face scale economies; (i.e., each firm's cost per unit of demand is decreasing in demand). A general framework is used, which incorporates competition between two service providers with price– and time–sensitive demand (a queuing game), and competition between two retailers with fixed–ordering costs and pricesensitive consumers (an Economic Order Quantity game). Reasonably general conditions are provided under which there exists at most one equilibrium, with both firms participating in the market. We demonstrate, in the context of the queuing game, that the lower cost firm in equilibrium may have a higher market share and a higher price, an enviable situation. We also allow each firm to outsource their production process to a supplier. Even if the supplier's technology is no better than the firms' technology and the supplier is required to establish dedicated capacity (so the supplier's scale can be no greater than either firm's scale), we show that the firms strictly prefer to outsource. We conclude that scale economies provide a strong motivation for outsourcing that has not previously been identified in the literature.

Word-of-mouth (WOM) can be considered one form of public opinion, reflecting consumers’ views towards product or service quality. With the development of social media, WOM holds influence over investors and subsequently impacts the stock prices of companies. The purpose of this study is to uncover the dynamics encompassing the factors that influence WOM, its propagation mechanisms, and the consequential impact on reality illustrated through fluctuations in stock prices. The mathematical model to describe the whole process can be separated into two components: information propagation model and stock price fluctuation model. The cumulative abnormal return (CAR) is used to demonstrate fluctuations in stock prices. Utilizing a Lyapunov function, it is proven that the equilibrium point of the model is globally asymptotically stable. As for the real world, it suggests that WOM will eventually prevail online, leading to fluctuations in stock prices. Based on the event study method, the empirical examination of 43 real events shows that our model can effectively predict the CAR and reflect the influence on stock prices caused by WOM in real-world scenarios.