No abstract

Multiple regression analysis assumes that one model or theory is relevant for the entire population, yet research has shown that this assumption is often false and may severely limit valid theory development and testing. Latent class regression analysis overcomes this limitation and allows the researcher to identify and develop regression models that are relevant for different segments within the same population. Latent class regression analysis is introduced and is used to analyze truck drivers' intentions to stay with the same firm. This article demonstrates the advantages of testing logistics theory with latent class regression analysis and provides numerous applications for practitioners.

Research on truck driver retention addresses how traditional variables impact drivers’ decisions to stay with a particular carrier, yet many of the traditional research methods have been called into question. Additionally, research is sparse on understanding whether unique driver need‐based segments exist. Therefore, the authors present a theoretical framework and examine an exploratory study for incorporating numerous constructs that pertain to a driver’s intention to drive for a firm. Empirical results indicate that drivers’ value pay, personal safety, and time at home are most important when deciding to remain with a firm. In addition, three unique truck driver need‐based segments were identified. The authors offer retention recommendations based on the specific needs of each segment.

To increase the relevance of logistics and supply chain academic research, this paper recommends the development and testing of middle‐range theory and practice‐level theory. Yet, there are a number of research issues that arise when academic researchers test middle‐range and practice‐level theory, both in measuring constructs and in testing theoretical relationships between constructs. Concerning the measurement of constructs, this paper recommends that academic researchers pay more attention to content validity and undertake rigorous processes to ensure content validity. In addition, academic researchers need to more explicitly define constructs as either reflective or formative. If the construct is defined as formative, then the traditional statistical approaches to validate these measurement scales are not recommended. The appropriate use of employing single‐item measures for concrete constructs is discussed. In regard to conducting hypothesis tests, research issues associated with multicollinearity and omitted variable bias are discussed. Relative weight analysis is ideal for testing theoretical models and research hypotheses when survey data are obtained, multicollinearity is present, and there are a large number of independent variables predicting a dependent variable. Thus, relative weight analysis is ideal for testing research hypotheses in logistics and supply chain management.

Purpose -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. Design/methodology/approach -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. Findings -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.Research limitations/implications -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. Originality/value -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.