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The purpose of the paper is to note that customers are not necessarily human and to figure out how best to serve artificial intelligence (AI) customers. The authors also propose several major research streams, as examples, to help launch research on AI customers and how to serve them.

The current paper is a conceptual one that draws upon research from many areas to support the ideas proposed.

AI customer are proliferating. AI as customers can augment or replace human customers and can be the customer itself. Service providers may also be AI, which means that both humans serving AI customers and AI serving AI customers are relevant here. The authors show that even truly autonomous AI customers are likely to be more common in the future. The authors conclude that reverse engineering will probably not be successful in understanding AI customers and that an approach similar to how we research human consumer behavior is likely to be more useful.

Virtually, the entire literature on customers and how to serve them assumes that customers are human. With the rapid advancement of AI, purchase decisions are increasingly made by AI, suggesting that it is now important and necessary to consider the possibility of AI customers and how best to serve them. This paper opens the door for such research.

The purpose of this commentary is to explain that it is not useful to unnecessarily complicate a model. Striving for realism for its own sake does not advance understanding; however, making sure that a model provides valid insights is a useful goal.

The authors advocate that a standard should exist based on whether experts in a field think that a particular mechanism is necessary for the model to achieve the goals of validity and sufficiency.

The authors find that critiques that do not offer a more valid alternative model do not necessarily advance the production of science.

Decision makers need to understand the assumptions and limitations of the models that they are using, but they should also be educated on the basic concepts of modeling literacy, and develop an understanding that all models are necessarily incomplete, as to make a model a perfect reflection of the real world would not provide insightful generalizations.

Although the original paper provides some additional cases that should be explored in understanding the diffusion of information, the authors extend this paper by providing a standard that explains when it is necessary to examine additional extensions and when the original (less complex) model is sufficient.

Although there have been many research articles about how to measure service quality, how service quality perceptions are formed, what effect service quality has on behavior, and service quality’s financial impact, there has been little discussion to date of the potential impact of service quality on competitive marketing decisions. This paper considers directly the issue of how an analysis of the impact of comparative service quality can inform tactical marketing decisions in a competitive marketplace. We propose and empirically demonstrate a simple theoretical framework of how market share changes result from changes in service quality, by the focal firm and/or by a competitor. In addition we show how price changes trade‐off against changes in service quality, and how comparative customer value is affected by changes in service quality and/or price. Our framework enables us to evaluate the projected market share shifts produced by proactive changes in service quality and/or price, and also enables us to evaluate the projected effectiveness of reactions to competitors’ changes in service quality and price. For example, our framework suggests that a quickly‐implemented increase in service quality (rather than a matching price cut) may sometimes be an effective tactical response to a competitor’s price cut. We illustrate the implementation of our framework on actual longitudinal industry data. We show how the market share impact of changes in service quality and/or price can be projected, and how this information can be used to drive competitive marketing decisions.

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.

Many companies focus considerable resources on managing and enhancing positive word of mouth (WOM). WOM management, however, has become increasingly complex given the rise of online channels and the corresponding increasing breadth of connections giving and receiving WOM. Given the generally believed importance of WOM to business outcomes, managers seek to leverage key drivers that they believe will enhance positive and minimize negative WOM.

Implicit in these actions is the belief that leveraging key drivers to enhance positive (or minimize negative) WOM results in generally positive outcomes across channels and connections. This research investigates whether this belief is correct. We examined WOM behaviors from over 15,000 consumers from 10 different countries in eight industry categories, as well as consumer attitudes toward the various brands investigated. Our findings indicate that efforts to enhance positive WOM typically have mixed effects – enhancing positive WOM in some channels while decreasing it (or even enhancing negative WOM) in other channels. Therefore, managers need to have a greater understanding of the complexity of leveraging attitudinal key drivers when seeking to enhance WOM to minimize potential negative outcomes.

Argues that employee turnover is highest among employees who are not satisfied with their jobs. Because qualified employees are becoming more scarce and difficult to retain, organizations need to focus on increasing employee satisfaction. Suggests that one useful approach for increasing employee satisfaction is to view workers as customers. Based on the notion of employee as customer, illustrates how a customer satisfaction measurement approach can be applied to the measurement of employee attitudes. Suggests that the metaphor of employee as customer is indeed useful. Also demon‐strates how this approach yields actionable results that managers can implement to increase employee satisfaction and thereby retention.