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While most practitioners are familiar with traditional customer satisfaction surveys, research findings suggest that best practice companies use multiple tools to bring the voice of the customer inside the organization. The purpose of this study is to examine how best practice companies use various tools to listen to customers. The primary contribution of this article is in discussing a variety of different customer listening tools used by practitioners, along with introducing new customer listening tools to the literature. Furthermore, this article puts forth a framework that captures essential characteristics of each tool, depicting when their use is most appropriate. Finally, this article depicts how customer listening tools are linked together and synthesized into a customer performance model.

To use customer value and satisfaction data effectively, the company culture must embrace, support, and encourage customer value and satisfaction initiatives. The purpose of this article is to discuss the nature of a customer value and satisfaction culture and to model how best practice companies transform their cultures. The research results make a contribution to the literature by revealing unique insights into the nature of a customer value and satisfaction culture and by offering practitioners a model of how to manage such a culture.

The purpose of this paper is to put forth maximum difference scaling to more accurately identify importance scores for customer requirements, which will also allow need‐based segments to be recognized and utilized within the QFD process.

An overview of research methods to explore customer requirements are discussed, followed by survey data analysis of customer requirements which compares and contrasts stated importance ratings to maximum difference scaling results.

The results from this study suggest that maximum difference scaling offers some advantages compared to traditional stated importance ratings, as well as other traditional methods for determining importance ratings. Providing significantly more discriminating power among customer requirements, maximum difference scaling allows researchers to have a more accurate and valid view of the relative importance of customer requirements as well as the ability to form need‐based segments.

Limitations of all research methods are discussed, including those limitations of maximum difference scaling.

The method put forth in this article provides practitioners with an improved methodology for determining the importance of customer requirements.

While maximum difference scaling has been proposed and tested in other fields, this is the first article of maximum difference scaling being applied to a QFD project.

Traditional methods of capturing and determining logistics attribute importance have serious research limitations. The purpose of this paper is to introduce maximum difference (MD) scaling as a new research methodology that will improve validity in measuring logistics attribute importance, overcoming many of the limitations associated with traditional methods. In addition, this new research method will allow logistics researchers to identify meaningful need‐based segments, an important goal of logistics research.

This paper provides an overview of MD scaling along with important research advantages, limitations, and practical applications. Additionally, a detailed research process is put forth so that this technique can be implemented by logistics researchers. Finally, an application of this technique is presented to illustrate the research method.

The importance of truck driver satisfaction attributes was analyzed using bivariate correlation analysis as well as MD scaling analysis. The two sets of results are compared and contrasted. The resulting rank order of attributes is very different and MD scaling results are shown to possess important advantages. As a result of this analysis, MD scaling analysis allows for meaningful, need‐based segmentation analysis, resulting in two unique need‐based driver segments.

From a practitioner viewpoint, knowing which attributes are most important will help in investing scarce resources to improve decision making and raise a firm's ROI. Although a number of relevant applications exist, the most important may include examining: the importance of customer service attributes; the importance of logistics service quality attributes; and the importance of customer satisfaction attributes.

MD scaling is a relatively new research technique, a technique that has yet to be utilized or even explored in existing logistics and supply chain literature. Yet, evidence is mounting in other fields that suggest this technique has many important and unique advantages. This paper is the first overview, discussion, and application of this technique for logistics and supply chain management and creates a strong foundation for implementing MD scaling in future logistics and supply chain management research.

The influence of security practices is increasingly common in the supply chain management and logistics literature. However, an under-researched area exists within the logistics service provider (LSP) selection process. The purpose of this paper is to introduce a security capability into the LSP selection process. Specifically, this research seeks to understand partner willingness to compensate and collaborate with service providers that possess a security capability.

Adaptive choice modeling is adopted to assess the influence of a security capability in the LSP selection process. This study represents the first use of this method in supply chain management and logistics research. Cluster analysis is also performed to uncover specific buyer segments along with traditional regression-based significance testing and counting analysis.

The findings indicate that security can have an important influence on the LSP selection process. In particular, the findings note a willingness to pay for a security capability in LSP selection. Applying segmentation techniques to the findings, three LSP buying segments are determined, each placing different importance and value on LSP capabilities.

This research notes an ongoing provider deficiency in security offerings. Partner firms sometimes maintain a cost focus, but others show a willingness to pay higher prices for access to partners with a security capability. Key practitioner findings include the need to include security with other traditional selection variables. The study walks the researcher and manager through the development of segments based on LSP capabilities.

This manuscript investigates logistic service provider selection. The authors detail an advanced form of conjoint analysis, adaptive conjoint modeling, for first time consideration. Additionally, this is the first study to integrate security into the LSP selection process. This is also the first study to identify a willingness to pay for a security capability.

Owner operators are an important group of truck drivers, yet have been the subject of very little academic research. Specifically, no research has explored retention issues among owner operators. Thus, this paper aims to employ a segmentation approach to owner operator retention, examining important independent variables that include: pay and compensation, top management support, time at home, and safety.

Data were collected from a large truckload (TL) carrier. The drivers surveyed were irregular route long‐haul drivers. A total of 239 responses were obtained, for a response rate of 74.6 per cent. Latent class regression (LCR), a technique new to logistics and supply chain management (SCM) research, was utilized for data analysis.

The results show there are four different need‐based driver segments, which means that specific driver retention programs must be developed for each segment.

The major limitation of this paper is that an attitudinal measure (intent to stay) was used to assess turnover instead of actual behavior. Additionally, only one demographic variable was collected. Implications of this paper include demonstrating the existence of unique segments within the same sample; the specification of the major attitudinal drivers for each segment; and the need to further study a largely neglected but very important group in the supply chain.

From a practitioner viewpoint, knowing that different groups of owner operators exist will help in developing programs to improve driver retention. Specific recommended actions for each segment are presented. These results will help managers with the retention of owner operators.

Owner operators are an important group when it comes to making the global supply chain function smoothly and efficiently. Yet, little research in logistics and SCM address this group. Driver turnover continues to plague the TL industry and understanding owner operators, and their segments, is critical to improve driver retention, cost structure, and profitability. Also, to determine if segments of this under‐researched group exist, LCR was deployed.

Supply chain practitioners have made great strides in forming collaborative partnerships, yet the end consumer is often left out of these plans. Being an important member of the supply chain, we propose that supply chains need to get closer to the end consumer ‐ forming collaborative relationships that center around demand planning. Developing subscription relationships with end consumers will lead to increased planning time, which will result in dramatic cost reductions and superior consumer value and satisfaction through lower costs, increased convenience, and improved availability of supply. Being a new and unique strategy, not all consumers will adopt subscription supply chains. However, we argue that some consumer segments will receive tremendous value and satisfaction purchasing certain types of products and services through subscriptions. Tremendous opportunities await those supply chains who target the right consumers, marketing the right products and services through a subscription supply chain.

Much of the research conducted in logistics/SCM has focused on satisfaction/retention of customers. This has left a critical gap for managers: before customers can be satisfied and ultimately retained, a purchase choice of logistics services has to occur. To date, very little research has addressed how logistics customers make purchase choice decisions about logistics services. The purpose of this paper, using logistics research methods, is to introduce adaptive choice modelling (ACM) to address this gap and put forth a research method that is useful for academic researchers and logistics/SCM managers.

This paper provides an overview of ACM, along with a discussion of its important research advantages, limitations, and practical applications. Additionally, an empirical demonstration of this research technique is provided to illustrate how academic researchers and logistics managers can use ACM to better understand the decision‐making process of customers when selecting logistics services.

In order to demonstrate this research technique, a research project was designed and implemented that analyzed the choice process of consumers selecting parcel carriers to ship a textbook. The results show that price, speed of delivery, and tracking are the three most important variables in the selection decision. The results also show that consumers are not homogeneous, but can be divided into five distinct need‐based segments. Recognizing and understanding the nature of these segments should help managers better meet the needs of parcel shippers.

The main research limitation with this study is that it is based on a convenience sample; thus future research will need to replicate this study to confirm the research findings. However, the ultimate purpose of the study is to present a new research method and discuss how to apply this method, so that logistics/SCM practitioners and academic researchers can better understand customers of logistics/SCM services. Thus, while the nature of the sample is a limitation, it should be viewed in this context.

While conjoint analysis has existed for decades, this technique has rarely been implemented by logistics/SCM researchers and practitioners. Instead, logistics/SCM researchers and practitioners have focused more on retention methods and have virtually ignored modelling the actual purchase choice of logistics/SCM services. New advancements in conjoint analysis, specifically the ACM approach, have many important and unique advantages and applications for logistics/SCM researchers and practitioners. ACM has not been used in a logistics/SCM context.

Volume 25 celebrates the 25th year of publication for the American Journal of Business (AJB). Launched by eight MAC schools of business in March 1986, the Journal has featured more than 700 authors who have contributed more than 330 research articles at the intersection of theory and practice. From accounting to marketing, management to finance, the Journal prominently covers the breadth of the business disciplines as a general business outlet intended for both practitioners and academics. As the Journal reaches out beyond the MAC in sponsorship, authorship, and readership, we assess the Journal’s first quarter century of impact.