Purpose -Supply chain security (SCS), as a component of an organization's overall supply chain risk management strategy, has become a critical factor for businesses and government agencies since September 11, 2001, yet little empirical research supports policy or practice for the field. Therefore, this paper develops and presents a categorization of SCS based on existing research. This categorization of supply chain literature can help academics and practitioners to better understand SCS and also helps to identify a research agenda. Setting a research agenda for SCS will help academic and practitioner research focus on critical issues surrounding SCS. Design/methodology/approach -The researchers thoroughly reviewed the literature on SCS, including academic publications, white papers, and practitioner periodicals. The literature was then categorized according to the approach to SCS and the practical implications of this categorization are presented. In addition, this categorization was used to identify research gaps. Findings -This analysis found that SCS needs more attention from the academic community. Like earlier assessments of this literature, this analysis found it to be mainly normative, with little research based on primary data. This paper categorizes the literature into four approaches to SCS: intraorganizational, interorganizational, a combination of intraorganizational and interorganizational, and ignore. This study develops a focused agenda for future, primary, empirical research on SCS.Research limitations/implications -The sources of data for this literature review are secondary. The review sets a research agenda and calls for future empirical testing. Practical implications -Practitioners will benefit from the framework presented here by better understanding approaches to SCS. This comprehensive review discusses the characteristics of SCS in great depth. As other researchers follow the research agenda, practitioners will benefit from the empirical findings and theory building. Originality/value -This paper summarizes the literature on SCS to date, a topic that has grown in importance, yet received little attention from academics. This is the first comprehensive literature review of SCS. It includes a categorization of four possible approaches to SCS. It also distinguishes SCS from supply chain risk, while also recognizing their relationship. It identifies key issues in SCS research and calls for future research.

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Purpose The purpose of this paper is to gather the current definitions of supply chain management in practical and analytical usage, to develop standards for assessing definitions and to apply these standards to the most readily available definitions of the term. Design/methodology/approach In this research, the authors gathered the current definitions of supply chain management in practical and analytical usage from journals, textbooks, universities, and industry associations and online. Findings The research ends with proposed definitions for consideration. Discussion and areas for future research are included. Research limitations/implications Involved organizations, supply chain management programs in higher education, and professional and certifying organizations in the field need to meet and work together to research consensus on the final definition of the field, realizing that definitions can evolve, but also recognizing that a starting point is needed in this rapidly growing area. Practical implications The authors argue, quite simply, that a consensus definition of supply chain management is unlikely as long as we continue offering and accepting definitions that are technically unsound. Many of the current definitions violate several principles of good definitions. For these reasons, they are either empty, too restrictive, or too expansive. Until we come across or develop a definition that overcomes these limitations and agree on it, then we will still search for “the” definition without finding it. The field will become more crowded with definitions, but less certain, and progress will be restricted. Originality/value Theoreticians, researchers, and practitioners in a discipline require key terms in a field to share a nominal definition and prefer to have a shared real or essential definition. Yet in supply chain management, we find no such shared definition, real or nominal. Even the Council of Supply Chain Management Professional offers its definition with the caveat: “The supply chain management (SCM) profession has continued to change and evolve to fit the needs of the growing global supply chain. With the supply chain covering a broad range of disciplines, the definition of what is a supply chain can be unclear” (CSCMP, 2016).

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PurposeTraditional 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.Design/methodology/approachThis 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.FindingsThe 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.Practical implicationsFrom 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.Originality/valueMD 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.

I ndustry advancements are accelerating at phenomenal rates and changing the management of logistics and supply chain operations. Employers must develop supervision with advanced skills to manage and retain the most effective employees making up the new workforce of highly skilled and technologically advanced personnel. Emotional intelligence is a managerial competence leveraged by leaders to connect with subordinates on a psychologically emotional level. Our research evaluates and applies emotional intelligence within the context of managing logistics and supply chain employees. Recognizing that employees are critical to production and service delivery, logistics and supply chain managers must be able to cognitively analyze situations and connect with employees in a positive manner even during challenging times. We find that managers possessing higher levels of emotional intelligence are better equipped to help their employees manage emotions, build more positive working conditions for subordinates, increase retention of employees, and achieve more positive service outcomes for external customers.

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