Luke Williams

Service Failure Severity, Customer Satisfaction, and Market Share An Examination of the Airline Industry

The generally accepted view among managers and researchers is that the greater the severity of a service failure, the greater the resulting impact on customer satisfaction and business outcomes, such as lost customers and revenue. The research used to defend this viewpoint, however, does not typically address the severity of service failures, like those that result in injury or death (i.e., product-harm crises). This research addresses this issue by examining both minor incidents (i.e., failures that do not result in physical harm) and major incidents (i.e., failures that result in injury or death) in the U.S. airline industry, and the corresponding impact on the customer satisfaction and market share of the firms affected. Our results indicate that minor incidents are more strongly (negatively) related to future market share than are major incidents. Moreover, our findings indicate that only minor incidents are significantly linked to customer satisfaction. We argue that these findings occur for two reasons: First, most customers believe major incidents to be low probability events that are less salient when compared to more probable failures. Second, consumers impacted by major incidents most likely defect and are therefore not captured in future customer satisfaction surveys. Consequently, managers can delude themselves that things have “returned to normal” after a major incident when relying on customer satisfaction scores alone.

Why Loyalty Matters in Retailing

Retailers have long known that their long-term success depends upon customer loyalty. In fact, legendary retailers were the first businesses to champion customer satisfaction as a source of competitive differentiation. In 1875, Montgomery Ward differentiated his mail order catalog by promising “satisfaction guaranteed or your money back.” By the early 1900s, Chicago’s Marshall Field’s department store and London’s Selfridges department store were championing “the customer is always right” (although it is not clear whether Mr. Field or Mr. Selfridge was first to coin the phrase). Today, these catchphrases and the ideals that they convey are ubiquitous throughout the business world (although some might question the degree to which most businesses actually adhere to these principles).

A roadmap for driving customer word-of-mouth

Managers seeking to manage customer word-of-mouth (WOM) behavior need to understand how different attitudinal drivers (e.g. satisfaction, positive and negative emotion, commitment, and self-brand connection) relate to a range of WOM behaviors. They also need to know how the effects of these drivers are moderated by customer characteristics (e.g. gender, age, income, country). The paper aims to discuss these issues.,To investigate these issues a built a large-scale multi-national database was created that includes attitudinal drivers, customer characteristics, and a full range of WOM behaviors, involving both the sending and receiving of both positive and negative WOM, with both strong and weak ties. The combination of sending-receiving, positive-negative and strong ties-weak ties results in a typology of eight distinct WOM behaviors. The investigation explores the drivers of those behaviors, and their moderators, using a hierarchical Bayes model in which all WOM behaviors are simultaneously modeled.,Among the many important findings uncovered are: the most effective way to drive all positive WOM behaviors is through maximizing affective commitment and positive emotions; minimizing negative emotions and ensuring that customers are satisfied lowers all negative WOM behaviors; all other attitudinal drivers have lower or even mixed effects on the different WOM behaviors; and customer characteristics can have a surprisingly large impact on how attitudes affect different WOM behaviors.,These findings have important managerial implications for promotion (which attitudes should be stimulated to produce the desired WOM behavior) and segmentation (how should marketing efforts change, based on segments defined by customer characteristics).,This research points to the myriad of factors that enhance positive and reduce negative word-of-mouth, and the importance of accounting for customer heterogeneity in assessing the likely impact of attitudinal drivers on word-of-mouth behaviors.

WAR: What Else Is It Good For? A Comparison of Maximum Difference Scaling, Adaptive Choice-based Conjoint, Constant Sum Scaling and Wallet Allocation Rule Utilities

Abstract Purpose – This research compares the results of Maximum Difference Scaling (MaxDiff), Adaptive Choice-Based Conjoint (ACBC), and constant sum scaling with the results of a Wallet Allocation Rule (WAR) approach for identifying the features of smartphones deemed most valuable by consumers. Design/methodology/approach – The authors examine the responses of 554 recent purchasers and 598 intended purchasers of smartphones about the features of smartphones that they have considered/will consider in their purchase decisions and the relative value they assign to each feature. MaxDiff, ACBC, constant sum scaling and WAR are used for quantifying the importance of these features and correlation analysis is used to compare the results of the four methods. Findings – The authors find that constant sum scaling and WAR provide nearly identical results at both the aggregate and individual levels, while MaxDiff and ACBC produce similar results to WAR at the aggregate level only. Originality/value – This research validates the feasibility of a simple method for estimating the utility of product features by demonstrating the similarity of results to more established and/or complex methods. The availability of a simpler method creates value by allowing more firms to engage in this kind of co-creative research while reducing costs for customers to participate.