Customer Goodwill Research Articles

Discount or Speed?: An Analysis of Reward Offer in Exchange for No-Rush Shipping

This research investigates how a no-rush shipping reward program, which offers a discount or a reward to a customer who chooses no-rush shipping for her online purchased items, can be utilized by an online seller to reduce shipping and expediting costs. In particular, we consider a setting in which an online seller has to ship two items to a customer. Each item can be shipped either on time or late, depending on its uncertain lead time. If an item is delivered late, the seller will incur a loss in customer goodwill unless the customer opts in to a no-rush shipping reward program and agrees to take a reward in exchange for potentially receiving an item late. To avoid shipping an item late, the seller also has an option to expedite an item at an extra cost. The seller’s optimal decisions regarding whether to offer a no-rush shipping reward program on any items and whether to ship each item separately or together at a normal or expedited speed are characterized. Our findings reveal that the number of items that the seller should offer the no-rush shipping reward program decreases as the required reward to entice customer participation increases. If the no-rush shipping reward program is offered on both items, then consolidating them into a single shipment always emerges as the optimal strategy. However, in cases where the program applies to none or only one item, it would be optimal for the seller to consolidate the shipment if and only if the shipping cost is sufficiently large. Through numerical demonstration, we underscore the noteworthy cost savings attainable via the no-rush shipping reward program, especially when the expected wait time to receive an item is long, the expediting cost is large, but the normal shipping cost is small. This study suggests that implementing a no-rush shipping reward program empowers online sellers with enhanced shipping management flexibility, which in turn effectively reduces operating and shipping costs as well as the environmental impact, while also meeting customers’ expectation.

Exploring market orientation versus finance orientation effects on perceived CSR motivations and outcomes using resource-advantage (R-A) theory

Drawing on resource-advantage (R-A) theory, this paper examines the association between market orientation versus finance orientation and different potential CSR engagement motivations. It also examines if the different CSR motivations, in turn, are associated with differences in fiscal competitiveness and internal customer focus (employee morale). We analyze data from 257 companies. The results indicate that more market-oriented companies engage in CSR due to (a) ‘beneficence’ motivation (it’s the right moral thing) and/or (b) ‘effectiveness’ related motivations (to generate employee goodwill, customer goodwill, and/or competitive advantage). The relationships are somewhat reversed for firms that are more finance-oriented—they are more focused on (a) effectiveness motivation and/or (b) efficiency motivation (everyone else is doing it, avoiding legislation, and/or rising energy costs), but not on (c) beneficence motivation. Last, beneficence and effectiveness motivations are associated with employee morale improvements, making these valuable internal marketing tools, while efficiency motivations are associated with fiscal competitiveness improvements.

Improving effort estimation of software products by augmenting class point approach with regression analysis

Software products are essential parts of many organizations on-going business up to a large extent. The main factors contributing to the successful delivery of a software product are its timely completion within the allocated budget and its quality compliance. Customer goodwill and profitability are very important for a software organization’s continued business. A large proportion of software products are delivered late or go over-budget causing significant inconvenience to the customers. This work proposes an accurate development effort estimation approach for software products. The Class Point (CP) approach with regression analysis method has been used for estimation of the development effort. This work uses a two step estimation approach. In the first step, an enhanced CP approach is used to evaluate the development effort of the system. In the second step, regression analysis models are utilized to refine the estimated effort accuracy. The results derived by applying the proposed two step approach confirmed the validity and the accuracy of this approach. It was observed that the SVR with RBF kernel is providing the best accuracy compared to other approaches.

An algorithm for a no-wait flowshop scheduling problem for minimizing total tardiness with a constraint on total completion time

We consider a no-wait m-machine flowshop scheduling problem which is common in different manufacturing industries such as steel, pharmaceutical, and chemical. The objective is to minimize total tardiness since it minimizes penalty costs and loss of customer goodwill. We also consider the performance measure of total completion time which is significant in environments where reducing holding cost is important. We consider both performance measures with the objective of minimizing total tardiness subject to the constraint that total completion time is bounded. Given that the problem is NP-hard, we propose an algorithm. We conduct extensive computational experiments to compare the performance of the proposed algorithm with those of three well performing benchmark algorithms in the literature. Computational results indicate that the proposed algorithm reduces the error of the best existing benchmark algorithm by 88% under the same CPU times. The results are confirmed by extensive statistical analysis. Specifically, ANOVA analysis is conducted to justify the difference between the performances of the algorithms, and a test of hypothesis is performed to justify that the proposed algorithm is significantly better than the best existing benchmark algorithm with a significance level of 0.01.

Geometric imperfect preventive maintenance and replacement (GIPMAR) model for aging repairable systems

PurposeThis work seeks to develop a geometric imperfect preventive maintenance (PM) and replacement model (GIPMAR) for aging repairable systems due to age and prolong usage that would meet users need in three phases: within average life span, beyond average life span and beyond initial replacement age of system.Design/methodology/approachThe authors utilized the geometric process (GP) as the hazard function to characterize the increasing failure rate (IFR) of the system. The GP hazard function was incorporated into the hybridized preventive and replacement model of Lin et al. (2000). The resultant expected cost rate function was optimized to obtain optimum intervals for PM/replacement and required numbers of PM per cycle. The proposed GIPMAR model was applied to repairable systems characterized by Weibull life function and the results yielded PM/replacement schedules for three different phases of system operation.FindingsThe proposed GIPMAR model is a generalization of Lin et al. (2000) PM model that were comparable with results of earlier models and is adaptive to situations in developing countries where systems are used across the three phases of operation depicted in this work. This may be due to economic hardship and operating environment.Practical implicationsThe proposed model has provided PM/Replacement schedules for different phases of operation which was never considered. This would provide a useful guide to maintenance engineers and end-users in developing countries with a view to minimizing the average cost of maintenance as well as reducing the number of down times of systems.Social implicationsA duly implemented GIPMAR model would ensure efficient operation of systems, optimum man-hour need in the organization and guarantee customer's goodwill in a competitive environment.Originality/valueIn this work, the authors have extended Lin et al. (2000) PM model to provide PM/replacement schedules for aging repairable systems which was not provided for in earlier existing models and literature.

The Relationship between LGBT Executives and Firms’ Value and Financial Performance

Drawing on resource-based theory, we analyze the relationship between having LGBT executives in a firm’s leadership positions and its value and financial performance. The existence of LGBT executives is considered to be associated with employee and customer goodwill towards LGBT-friendly policies and practices and to lead to human capital and reputational benefits. Our findings suggest that there is a positive effect of the presence of LBGT executives on a firm’s value, both directly and indirectly, through its effect on the firm’s financial performance. We interpret this as suggesting that besides the direct effect of the existence of LGBT executives on a firm’s value, an indirect effect also exists, mediated through financial performance, presumably through the effect that this has on employee and customer goodwill towards LGBT-friendly policies and practices. As far as we are aware, our study is the first to examine the impacts of the presence of LGBT executives, as well as distinguish between its direct and indirect effects on firm value.

Workforce training and operations planning for maintenance centres under demand uncertainty

Companies that provide repair & overhaul services to the users of complex technical systems are confronted with uncertain volume of demand when making tactical decisions such as workforce training and planning of repair operations over an annual planning horizon. Given the high importance of equipment availability (e.g. gas turbines) to the users (e.g, power plants), any delay in the delivery of repaired equipment caused by demand uncertainty would lead to significant penalties and loss of customer goodwill. In this paper, a two-stage stochastic programming model is proposed to obtain the optimal number of items to repair, spare part inventory, and the number of operators to train with the goal of minimising the total expected cost of maintenance operations and late delivery. Outsourcing and borrowing strategies are adopted as corrective measures to reduce the probability of late delivery in the emerge of demand uncertainty. Numerical findings illustrate the importance of integrating uncertainty into these operations planning decisions as well as the mitigation strategies in handling the cost of the system.

Revenue Management with Repeated Customer Interactions

Motivated by online advertising, we model and analyze a revenue management problem where a platform interacts with a set of customers over a number of periods. Unlike traditional network revenue management, which treats the interaction between platform and customers as one-shot, we consider stateful customers who can dynamically change their goodwill toward the platform depending on the quality of their past interactions. Customer goodwill further determines the amount of budget that they allocate to the platform in the future. These dynamics create a trade-off between the platform myopically maximizing short-term revenues, versus maximizing the long-term goodwill of its customers to collect higher future revenues. We identify a set of natural conditions under which myopic policies that ignore the budget dynamics are either optimal or admit parametric guarantees; such simple policies are particularly desirable since they do not require the platform to learn the parameters of each customer dynamic and only rely on data that is readily available to the platform. We also show that, if these conditions do not hold, myopic and finite look-ahead policies can perform arbitrarily poorly in this repeated setting. From an optimization perspective, this is one of a few instances where myopic policies are optimal or have parametric performance guarantees for a dynamic program with nonconvex dynamics. We extend our model to the cases where supply varies over time and where customers may not interact with the platform in every period. This paper was accepted by Chung Piaw Teo, optimization.

Voluntary overbooking in commercial airline reservations

This paper studies a voluntary overbooking model under rational expectation equilibrium to promote cooperation between customers and airlines, maintain goodwill of customers, and maximize the expected total returns to airlines. A decision tree analysis is constructed for both customers and airlines. Sensitivity analysis is conducted in both realistic and simulated no-show random variables for validation. The findings suggest considerable mutual benefits associated with a ‘voluntary overbooking’ policy that emphasizes mutual cooperation between passengers and commercial airlines. The main underlying assumption of the paper is that customers are willing to provide valuations to airlines seeking volunteers for overbooking. The originality of the proposed model is the incorporation of elements of the Rational expectations hypothesis into classical overbooking models gleaned from the literature.

The impact of airline service quality on passengers’ behavioral intentions using passenger satisfaction as a mediator

This research study measures the perception of airline service quality based on data collected from Pakistan International Airline (PIA) passengers using SERVQUAL. It investigates the impact of service quality dimensions on passenger's behavioral intentions in presence of mediator (passenger satisfaction). The data is collected through an adopted SERVQUAL instrument from the respondents in the domestic and International waiting lounges of PIA. The data is analysed using reliability statistics, correlation analysis and through hierarchal regression analysis. A systematic random sampling technique is used to analyze the study sample and PROCESS macros was used to run mediation analysis. The results reveals that passenger's satisfaction mediates the relationship between airline service quality and behavioral intentions. Practically, PIA should not only improve on Service Quality but should build up quality consciousness among on ground and inflight employees; pay attention to reliable factors and establish customer goodwill through enhanced services; regard reliability factors and enhance employee accomplishment. Implications of these results for management policy and practice are highlighted as guidelines. PIA managers can now better understand the importance of service quality and its long-term benefits in the form of customer loyalty.

An approach to estimate the back order penalty cost of a manufacturing company

The classical inventory models solely rely on accurate estimate of the back order cost, with a view to establish economic order quantity (EOQ) that must be placed by a customer. Recognizing and quantifying the adverse effects of loss of customer goodwill owing to the inability of a raw material or product supplier organisation to meet customer demands should not only focus on direct penalty cost computation, but should also incorporate change in customers’ future demand owing to this backordering phenomenon. A lot of classical and mathematical approaches focused on the computation of the back order penalty cost coefficient; which gives an organisation a clue of the customer disappointment index, and not the estimated back order cost required for EOQ computation. In light of this, this paper proposes an approach that could be utilized to accurately compute the back order penalty cost of an organisation. The approach considers: (i) the number of times backordering phenomenon have occurred in an organisation, (ii) the decision a customer takes when backordering occur once or couple of times during the ordering phases of an organisation and (iii) myriads of penalties that a customer bestow on a raw material or product supplier organisation for backordering its order, to establish the backorder cost of this organisation. The approach proposed in this study serve as a useful information to suppliers in ascertaining the raw material backorder cost based on their customer responses to backordering, with a view to ensure sustainable raw material supply.

A System Dynamics Model for Systems Security Engineering Analysis of Internet Service Provider Customer Modem Cyber Defense

AbstractInternet service providers seek to increase revenue by reducing costs and increasing subscriptions. We take a systems security engineering approach to evaluate ways of enhancing customer modem cybersecurity, using a system dynamics model to show the impact of different solutions for improving customer modem bandwidth. The model is extensible and modifiable to test other stakeholder goals. We demonstrate this by modelling customer goodwill and solution cost. When used as a tool for planning system changes, the model can be compared to the live system before and during incremental system changes. This helps improve model validity, control risk, evaluate return on investment, and provide a platform for testing proposed system changes. Businesses with similar cyber vulnerabilities can benefit from using this system engineering approach.

A Decision Support Tool (DST) for Inventory Management

Loss of customer goodwill is one of the greatest losses a business organization can incur. One reason for such a loss is stock outage. In an attempt to solve this problem, an overstock could result. Overstock comes with an increase in the holding and carrying cost. It is an attempt to solve these twin problems that an economic order quantity (EOQ) model was developed. Information on fifteen items comprised of 10 non-seasonal and 5 seasonal items was collected from a supermarket in Ikot Ekpene town, Nigeria. The information includes the quantity of daily sales, the unit price, the lead time and the number of times an item is ordered in a month. Based on this information, a simple moving average and y-trend method of forecasting were used to forecast the sales quantity for the following month for the non-seasonal and seasonal items. The forecast value was used to compute the EOQ for each of the items. Different scenarios were created to simulate the fuzzy logic EOQ after which the result of the conventional method, EOQ method, and fuzzy EOQ methods were obtained and compared. It was revealed that if the EOQ method is adopted, savings of 43% of holding and carrying cost would be made. From the scenarios of a fuzzy EOQ, a savings of 35.65% was recorded. It was however observed that in a real-life situation, the savings on a fuzzy EOQ is likely to be higher than that of an EOQ considering the incessant public power outages and the increase in transportation fares due to the high cost of fuel and the bad state of roads in Nigeria. To this end, a Decision Support Tool (DST) was developed to help the supermarket manage its inventory based on daily predictions. The DST incorporates a filter engine to take care of some emotional and cognitive incidences within the environment.

Optimisation of identical parallel machine scheduling problem

Scheduling is allocating the resources optimally over a period of time. The productivity and customer goodwill of the company increases by proper scheduling. This work focuses on scheduling of identical parallel machines (IPM) with an objective to makespan minimisation by using Grey Wolf Optimiser (GWO) algorithm. Makespan is the maximum completion time of all the jobs. In this work, the different jobs in different identical parallel machines are classified as experiments E1, E2, E3 and E4. These experiments were computationally solved by new metaheuristic GWO algorithm. The experimental computational results of GWO were compared with GA to obtain near optimal solution in all experiments.

Production planning incorporating issues of reliability and backlogging with service level constraint

To stay competitive in present-day turbulent business environment, managers of manufacturing firms must achieve crucial operational goals, such as maintaining product quality, reducing operating costs, and attaining customer satisfaction. In real manufacturing systems, allowing backordering sometimes can be an effective strategy to help smooth manufacturing schedules and lower operating costs. However, the consequence of excessive shortages is the undesirable service level which reduces customer satisfaction and goodwill. To cope with this unwanted result, a maximal permitted backlogging level is set as an operating constraint to achieve the minimum required service level. Also, production of scrap items and stochastic breakdown are both inevitable in the manufacturing processes. Motivated by addressing aforementioned issues and providing insight information to managers for supporting their operational decision makings, this paper investigates the optimal runtime for a fabrication system with backordering, service level constraint, stochastic breakdown, and scrap. Mathematical model was developed and optimization methods are used to decide the optimal runtime that minimizes total system costs. Various key insights relating to joint impacts of backlogging, service level, stochastic breakdown, and scrap on the optimal runtime decision are revealed through a numerical illustration along with sensitivity analyses.

Revenue Management with Repeated Customer Interactions

Motivated by online advertising, we model and analyze a revenue management problem where a platform interacts with a set of customers over a number of periods. Unlike traditional network revenue management, which treats the interaction between platform and customers as one-shot, we consider stateful customers who can dynamically change their goodwill towards the platform depending on the quality of their past interactions. Customer goodwill further determines the amount of budget that they allocate to the platform in the future. These dynamics create a trade-off between the platform myopically maximizing short-term revenues, versus maximizing the long-term goodwill of its customers to collect higher future revenues. We identify a set of natural conditions under which myopic policies that ignore the budget dynamics are either optimal or admit parametric guarantees; such simple policies are particularly desirable since they do not require the platform to learn the parameters of each customer dynamic and only rely on data that is readily available to the platform. We also show that, if these conditions do not hold, myopic and finite look-ahead policies can perform arbitrarily poorly in this repeated setting. From an optimization perspective, this is one of a few instances where myopic policies are optimal or have parametric performance guarantees for a dynamic program with non-convex dynamics. We extend our model to the cases where supply varies over time and where customers may not interact with the platform in every period.

Sampling and pricing strategy under competition

Consumers are often uncertain about how products fit their individual preferences. In this situation, free samples can be offered to allow consumers to resolve such uncertainty before purchase. Product samples thus can build up customer goodwill for the products by reducing consumers’ risk of product fit uncertainty. However, product samples may also have negative effects, because consumers who realize poor fits after sampling trials may switch with a certain probability to competing products. With consideration of these tradeoffs, we study the sampling and pricing strategies for sellers of competing products in an oligopoly market. We formulate this problem as a Hotelling game and characterize the equilibrium solution. We first discuss the situation when competing retailers simultaneously make the decisions. We show that the intensity of product competition (i.e., the degree of product differentiation) and consumer switching behavior play important roles in determining equilibrium sampling strategy. When product competition is strong and no consumer switching behavior occurs, competing retailers always adopt symmetric sampling strategies. However, if consumer switching behavior exists and/or the product competition is relatively weak, retailers may begin to adopt asymmetric sampling strategies. Counter-intuitively, consumer switching behavior can soften price competition and thus benefit both retailers. This paper also sheds light on how the retailers’ equilibrium sampling strategy is affected by the probability of realizing a good fit with a product and the magnitude of goodwill effect. We further extend the study to the case when retailers sequentially make the decisions. Different from the simultaneous game, we find that there exists a second mover advantage in a sequential game. Thus, competing retailers may adopt asymmetric sampling strategies even if the intensity of competition is strong and consumers do not switch between retailers. In addition, both retailers may be better off in a sequential game than in a simultaneous game.

Addressing a product management's orphan: How to externally implement product eliminations in a B2B setting

While product eliminations (PEs) may help suppliers reduce unprofitable products and the cost of increasingly complex portfolios, they often have deleterious consequences for customer–supplier relationships. This dilemma even increases as a supplier's attempt to mitigate deleterious consequences for customers through customer-oriented PE implementation may at the same time hinder optimal internal adjustments and related cost-saving potential, thus running counter to the actual purpose of PEs. This study investigates whether and how a supplier should act in the customer's interest to maximize gains from implementing PEs. We identify key approaches of customer-oriented PE implementation and performance outcomes. Using a multiple-informant supplier sample and a customer validation sample, we show that, depending on the availability of alternatives to customers and the type of PE implementation activity, customer-oriented PE implementation can either pay off considerably or be disadvantageous to a supplier. While PE compensation is always detrimental to overall PE performance, both PE communication and PE support are mostly beneficial. By contrast, PE participation is ambiguous to overall PE performance, as it generally helps retain customer goodwill but also decreases supplier cost-savings from PEs.

Exploring critical attributes during air traffic congestion with a fuzzy DEMATEL\u2013ANP technique: a case study in Ninoy Aquino International Airport

The constant growth of air travel in the Philippines has brought about significant consequences to air traffic congestion. Given limited resources, major airports seek to address this issue while considering various attributes generally affecting air transportation. This paper adopts fuzzy decision-making trial and evaluation laboratory–analytic network process (DEMATEL–ANP) to identify the most critical attributes in the commercial aviation industry. A case study participated by key experts of Ninoy Aquino International Airport was conducted to illustrate the proposed approach. The fuzzy DEMATEL–ANP model performed satisfactorily as it was able to extract the global priority vectors of attributes under a fuzzy environment. The results showed that aviation safety is most prioritized, as can also be seen from the significant influence it brings on other attributes. Following next to safety in terms of priority are attributes that address the general air transportation system such as economic value, environmental value, social value, equitable treatment of competing airline, customer goodwill, and utilization of runway and terminal. Then, attributes relating to passenger cost, fuel cost, extra crew cost, landing/take-off fee, and cost of using flight routes are of last priority. Given the order of priorities and criticality of each attribute, short-term and long-term policies can be framed accordingly to propose air traffic flow management actions that can best address the issue on congestion.

Understanding the value of upstream inventory information sharing in supply chain networks

• We developed CTMC models to study the effect of upstream inventory information sharing in SCNs. • A two-echelon SCN with one retail store and two production facilities was used as a test bed. • The marginal benefits of sharing additional inventory information were examined. • The impact of backorder limits on SCN performance was also assessed. • The study reveals that inventory information can be an effective substitute for physical inventory. Global competition has caused a paradigm shift in a firm’s outlook from a product-centric to a customer-centric view. With this shift, the availability of inventory at a store is a prime concern for firms in a supply chain network (SCN) as it affects customer goodwill and market share. Information sharing among the SCN partners is a key strategy to address this issue. In this study, we focus on the impact of sharing upstream inventory information in a SCN on its overall performance. A two-echelon SCN configuration with one retail store and two production facilities was used as an experimental test bed. To assess the marginal benefits of sharing additional information, three levels of information sharing were considered along with the base case of no information sharing. The information shared ranged from stock-out information at the lowest level to inventory and backorder levels at the highest level. Continuous Time Markov Chain models of the SCN were developed and analyzed to gain insights into the value of inventory information sharing. Numerical experiments were conducted to show that inventory information can be an effective substitute for physical inventory and to assess the impact of backorder limits on the SCN performance.