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The global electronic equipment industry has evolved into one of the most innovative technology-based business sectors to transpire in the last three decades. Much of its success has been attributed to effective supply chain management. The purpose of this paper is to provide an examination of external risk factors associated with the industry’s key suppliers through the creation of Bayesian networks which can be used to benchmark external risks among these suppliers.

The study sample consists of the suppliers to seven of the leading global electronic equipment companies. Bayesian networks are used as a methodology for examining the supplier external risk profiles of the study sample.

The results of this study show that Bayesian networks can be effectively used to assist managers in making decisions regarding current and prospective suppliers with respect to their potential impact on supply chains as illustrated through their corresponding external risk profiles.

A limitation to the use of Bayesian networks for modeling external risk profiles is the proper identification of risk events and risk categories that can impact a supply chain.

The methodology used in this study can be adopted by managers to assist them in making decisions regarding current or prospective suppliers vis-à-vis their corresponding external risk profiles.

As part of a comprehensive supplier risk management program, companies along with their suppliers can develop specific strategies and tactics to minimize the effects of supply chain external risk events.

The purpose of this paper is to review Bayesian analysis in recent entrepreneurship research to assess how scholars have employed these methods to study the entrepreneurship process. Researchers in other business fields (e.g. management science, marketing, and finance) have increasingly employed Bayesian methods to study issues like decision making. To date, however, Bayesian methods have seen only limited use in entrepreneurship research.

After providing a general overview of Bayesian methods, this study examines how extant entrepreneurship research published in leading journals has employed Bayesian analysis and highlights topics these studies have investigated most frequently. It next reviews topics that scholars from other business disciplines have investigated using these methods, focusing on issues related to decision making, in particular.

Only seven articles published in leading management and entrepreneurship journals between 2000 and 2016 employed or discussed Bayesian methods in depth when studying the entrepreneurship process. In addition, some of these studies were conceptual.

This review suggests that Bayesian methods may provide another important tool for researchers to employ when studying decision making in high uncertainty situations or the impact of entrepreneurial experience on decision making over time.

This review demonstrates that Bayesian analysis may be particularly appropriate for entrepreneurship research. By employing these methods, scholars may gain additional insights into entrepreneurial phenomenon by allowing researchers to examine entrepreneurial decision making. Through this review and these recommendations, this study hopes to encourage greater Bayesian analysis usage in future entrepreneurship research.

This study examines the performance consequences of aligning the competitive strategy of quality differentiation with each of the four fundamental industry life cycle stages: introduction, growth, maturity, and decline. Reports results from a questionnaire survey of CEOs in a mid‐western state in the USA. The findings suggest that small manufacturing firms can achieve a sustainable competitive advantage vis‐à‐vis quality differentiation across three stages of industry evolution: introduction, growth, and maturity.

A review of the academic literature on managerial promotions reveals that there has been a limited number of studies conducted on this subject. This study aims to identify key determinants used by managers in making managerial promotion decisions via Bayesian networks. It also seeks to explore the effects these determinants have on managerial promotion outcomes.

The researchers surveyed MBA students with significant work experience to assess the effect levels for 13 managerial promotion factors derived from a research study by Service and Lockamy. The participants were asked to assign a percentage effect level to these factors. Factor analysis was used to determine the most influential factors, and Bayesian networks were constructed to determine the probability of receiving a promotion based on these factors.

The results indicate that there are five key determinants which have the most influence on managerial promotions. They also indicate that managerial promotion outcomes were not significantly influenced by either the promoting manager's years of work experience, or the number of promotions witnessed.

The paper focuses on managerial and professional career advancement research, managerial promotion processes, and personnel development.

The purpose of this paper is to examine the use of V‐A‐T analysis in the management of supply chain networks.

The paper provides a conceptual overview of V‐A‐T analysis as a procedure for categorizing manufacturing facilities, and explores the use of V‐A‐T analysis as a technique for the management of supply chain networks.

There are several challenges to the effective management of supply chain networks. However, organizations can overcome these challenges by understanding the nature of network control points as revealed through the application of V‐A‐T analysis on their supply chains.

The paper is conceptual in nature and must be validated through empirical research studies.

The concepts presented can be used by supply chain professionals to increase the likelihood of effective supply chain management within their organizations, and by supply chain researchers to further explore the use of V‐A‐T analysis as a tool for examining supply chain networks.

The paper makes a start in filling a void in the literature concerning how V‐A‐T analysis can be used as a tool to facilitate improved supply chain management.

The purpose of this paper is to provide a conceptual framework along with underlying propositions for the design and deployment of telemedicine projects which provide healthcare organizations with strategic benefits.

Field research conducted at four healthcare organizations along with academic literature in the areas of telemedicine and process management form the basis for the conceptual framework and propositions provided in this paper.

Telemedicine can be used as a process enabler for enhanced healthcare‐delivery systems. However, there are several challenges which must be considered prior to its implementation. The framework and propositions provided in the paper can be used to facilitate successful telemedicine project deployments.

The framework and propositions are derived from a small sample and must be validated through more rigorous empirical research studies.

The concepts presented in the paper can be used by healthcare planners to increase the likelihood of telemedicine deployment success within their organizations.

This paper begins to fill a void in the literature concerning how telemedicine can be used as a process enabler for improving healthcare‐delivery systems.

The purpose of this study is to identify the key determinants which inhibit intra-family business succession. The study also explores the effects these determinants have on preventing intra-family business succession. Finally, the study explores the probability that intra-family business succession does not take place based upon the effects of the identified determinants.

A total of 250 surveys were mailed to family business owners across the United States, yielding 68 usable responses (27.2% response rate). Factor analysis was used to determine the most influential factors which inhibit intra-family business succession, and Bayesian Networks were constructed to determine the probability that intra-family business succession does not occur based on these factors.

The study results indicate that there are four key constructs comprised of 23 variables which have the most influence on deterring intra-family business successions. The results also suggest managerial actions that can be taken to improve the probability of intra-family business succession.

A possible research limitation is that the survey respondents may not represent the entire cross-section of family-owned business in the United States. Additionally, the impact of company size, age, industry, and other demographic factors were not considered in the analysis of results. Finally, the selection of the key determinants was made based upon the highest value extracted from the principle components analysis. Combining these variables with other relatively high values may lead to different results.

The empirical findings contained in this study demonstrate that process, context, and governance factors have the largest effect on increasing the probability that intra-family succession does not take place. Additionally, the results of this study suggest several managerial actions that can be taken to improve the probability of intra-family business succession. Thus, the results of this study can be used by practitioners to assist them in intra-family business successions. The results can also be used by researchers as a basis for conducting additional empirical studies in this area.

Over the past decade, increased logistics costs, improved packaging technology, and enhanced environmental regulations have caused logistics managers to reevaluate their packaging decisions. The impact of packaging decisions on logistics costs illustrates the need for strategic thinking in the assessment of packaging options. In addition, the combination of more demanding technological and environmental requirements by customers and governmental legislation suggests that packaging decisions must be viewed strategically within the logistics planning process. This paper examines the relationship between strategic packaging elements and the competitive edges on which firms can compete in the marketplace. The effects of packaging associated costs, advances in packaging technology, and the environmental movement are explored to highlight their strategic impact. Finally, a conceptual framework for assessing strategic packaging decisions in relation to a firm's competitive edges is presented.

Since its inception, cost accounting has provided data to managers for the development of internal organizational performance measures. In the mid 1980s, Dr Eli Goldratt introduced a new management philosophy called the theory of constraints (TOC). This philosophy contained a new set of performance measures which linked together the strategic objectives and operational capabilities of the organization. This linkage allows for the maximization of profits. Since its introduction, there has been a growing amount of evidence documenting TOC’s ability to more tightly link local decisions to organizational performance than those of traditional cost accounting. This research used a simple Gedunken experiment to evaluate the difference between strategy driven product‐mix decisions based on TOC accounting and traditional cost accounting. In all cases, the constraint‐based approach to costing outperformed the traditional approach based on cost accounting.

As organizations increase their dependence on supply chain networks, they become more susceptible to their suppliers’ disaster risk profiles, as well as other categories of risk associated with supply chains. Therefore, it is imperative that supply chain network participants are capable of assessing the disaster risks associated with their supplier base. The purpose of this paper is to assess the supplier disaster risks, which are a key element of external risk in supply chains.

The study participants are 15 automotive casting suppliers who display a significant degree of disaster risks to a major US automotive company. Bayesian networks are used as a methodology for examining the supplier disaster risk profiles for these participants.

The results of this study show that Bayesian networks can be effectively used to assist managers in making decisions regarding current and prospective suppliers vis-à-vis their potential revenue impact as illustrated through their corresponding disaster risk profiles.

A limitation to the use of Bayesian networks for modeling disaster risk profiles is the proper identification of risk events and risk categories that can impact a supply chain.

The methodology used in this study can be adopted by managers to assist them in making decisions regarding current or prospective suppliers vis-à-vis their corresponding disaster risk profiles.

As part of a comprehensive supplier risk management program, organizations along with their suppliers can develop specific strategies and tactics to minimize the effects of supply chain disaster risk events.