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The purpose of this paper is to assess the combined impact of the Industry 4.0 blockchain and industrial internet of things technology (IIoT) technologies on the development of supply chain linkages associated with power, benefits and risk reduction and the ultimate impact of the linkages on supply chain performance.

A structural model with blockchain and IIoT as antecedents to the supply chain power, benefits and risk reduction linkages and the linkages as antecedent to supply chain performance is theorized. Data collected from 303 US manufacturing managers are analyzed using a covariance-based structural equation modeling (CB/SEM) methodology.

The CB/SEM results indicate that blockchain technology does not directly impact implementation of the linkages. Rather, implementation of blockchain technology supports implementation of IIoT technology, which strengthens supply chain linkages, thereby improving supply chain performance.

To the best of the authors’ knowledge, this study is one of the first to provide empirical evidence that Industry 4.0 technologies such as blockchain and IIoT strengthen linkages among supply chain partners related to power, benefits and risk reduction and that those stronger linkages lead to improved supply chain performance. It should be noted that this study is based on data from managers representing only one sector (manufacturing) and one country (USA). Replication based on data from other sectors and countries is needed to support generalization of the results.

Practitioners are provided with empirical evidence that the implementation of Industry 4.0 technologies such as blockchain and IIoT support supply chain management. These technologies facilitate data and information sharing among supply chain partners, enabling the integration and coordination of business processes throughout the entire supply chain.

The ultimate customers of supply chains benefit when supply chain partners work together efficiently and effectively. The implementation of blockchain and IIoT digital technologies lead to improve linkages among supply chain partners driving improvements in both efficiency and effectiveness, thus benefiting customers and society.

Industry 4.0 technologies are relatively new with the promise of improved supply chain performance. The efficacy of Industry 4.0 technologies as mechanisms to enhance information sharing is demonstrated based on the results of this study.

The purpose of this study is to determine whether the implementation of a performance management system comprising some traditional management practices (management process and organizational behavior modification) lead to an ethical organizational environment and improved employee performance.

A structural model is theorized and assessed using data from samples of full-time employees in the USA and Brazil. Partial least squares–structural equation modeling is used.

The findings of this study suggest that management process and organizational behavior modification directly and positively impact the ethical environment, and the ethical environment directly and positively impacts employee performance. The management process and organizational behavior modification indirectly impact employee performance through an ethical environment.

Although this theorized model was tested and provided significant results for implementing the management practices suggested, it is strongly recommended that other random data samples be used to analyze the theorized model and assess to reconfirm the results. In addition, incorporating the ethical environment construct within a larger model that includes other potential antecedents, such as management principles, and other potential outcomes, such as organizational commitment, job satisfaction and workplace optimism, is recommended.

This study provides management practitioners with empirical evidence that implementing a performance management system consisting of the management process and organizational behavior modification will enhance both the ethical environment and organizational trust, which, in turn, will lead to improved individual employee performance. Based on the theoretically and statistically supported framework, managers can improve the performance of their subordinates. The results further support the assertions that managers must implement the management process along with organizational behavior modifications to improve employee performance through an ethical environment and organizational trust

The general conclusion from this study is that good management practices in the form of the management process and organizational behavior modification are inherently ethical. Furthermore, when implemented and consistently maintained by managers, these practices will result in an organizational environment that supports ethical behavior and engenders a high level of trust. The results of this study demonstrate a significant contribution to the existing literature, in that good management is tied, in fact, directly to ethics and trust.

The results provide evidence that good management in the form of the management process and organizational behavior modification yields both a positive ethical environment and improved employee performance. Practitioners are provided with evidence that reaffirms the need to define expectations for employees and to provide the necessary resources and positive reinforcement to fulfill the expectations. This study is one of the first to directly assess the impact of traditional management practices on an ethical environment.

The purpose is to replicate and extend Ambulkar et al.’s (2015) work testing resource reconfiguration as a mediator of the supply chain disruption/firm resilience relationship and testing risk management infrastructure as a moderator. This study extends the work of Ambulkar in that it uses analysis of survey data gathered from manufacturing firms during an actual disruption event (COVID-19). The previous work is also in extended in that the authors include a pandemic disruption impact variable and supply chain performance is an expanded model.

Partial least squares structural equation modeling techniques were used to analyze data gathered from 184 US manufacturing managers during the height (Summer 2021) of the COVID-19 pandemic.

Two of four of Ambulkars et al.’s (2015) hypotheses were confirmed as relevant to firm resilience during the pandemic while two were not confirmed. Results also show that supply chain disruption orientation, risk management infrastructure and resource reconfiguration combine to improve firm resilience, which in turn improves supply chain performance while mitigating the disruption impact of COVID-19.

Previous work is replicated and extended, using data from an actual disruption event (COVID-19). This study presents a more comprehensive model using a newly developed and validated scale to measure pandemic impact and including supply chain performance.

The objective of this paper is to investigate the effect of supply chain quality integration (SCQI) and green supply chain practices and their impact on the Triple bottom line (TBL) in driving sustainability performance.

Structural equation modeling of partial least squares was used to study the relationships between SCQI and green supply chain practices (GSCPs) in driving sustainable performance and is empirically tested using data from 167 manufacturing firms.

This study reveals that supply chain quality integration (SCQI) enhances the green purchasing activity as well as the implementation of internal environmental management (IEM) of the firm. However, IEM is a crucial factor that contributes to sustainability performance.

This study is one of the few relevant researches that would benefit manufacturing organizations to further improve their triple bottom line by integrating supply chain quality with green practices.

This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.

This study aims to proposed that blockchain helps the organization improve supply chain (SC) performance by improving integration, agility and security through real-time information sharing, end-to-end visibility, transparency, data management, immutability, irrevocable information and cyber-security platforms.

This study has made an initial effort toward proposing a framework that shows the problems and challenges for the O&G SC under its segments (upstream, midstream and downstream) and provides the interlink among blockchain properties for SCM problems. SC managers were selected for survey questionnaires from the Pakistan O&G industries.

This study analyzes the impact of blockchain-enabled SC on firm performance with an understanding of the SC robustness capabilities as a mediator. The result revealed that the SC manager believes that the blockchain-enabled SC has a positive and significant on firm performance and robustness capabilities.

Blockchain technology is reflected as high-tech to support the firm process, responses and methods. The technology helps eliminate bottlenecks, avoid uncertainties and improve decision-making, leading to improved SC functions. This study guides managers about the potential problems of existing SC and how blockchain solves SC problems more effectively.

The oil and gas (O&G) sectors are neglected by researchers, and there are limited studies on O&G supply chain management (SCM). Additionally, no empirical evidence suggests implementing blockchain for O&G as a solution for potential problems. Furthermore, present the roadmap to other industries those having complex SC networks for the implication of blockchain to improve the SC performance.

The purpose of this paper is to evaluate the green building certification systems. In doing so, a validation process, using confirmatory factor analysis (CFA), was undertaken.

An extensive literature review was conducted to highlight the paramount aspects of green building certification systems. Then, using a grounded theory, key findings of the initial literature review were explored. This was necessary to form a broad theoretical framework. Finally, CFA was performed to evaluate various green building certification systems.

Initially, it was noted that accreditation process was the central key for effective building certification systems, particularly at the international level. Further, using CFA, it was also determined that to increase the sustainable performance of the green building certification, meeting the increasing expectation of the system user is paramount.

When evaluating the green building certification systems, it is recommended to focus on a specific aspect of the programmes holistically. This needs to be done particularly towards delivering a clear message to the stakeholders globally; doing so may alleviate the many challenges of green building certifications.

Currently, various green building certification systems have been developed for local purposes. This raises the question of the adaptability of these systems across varying climates and geography in differing regions. Using CFA, this research will examine various green building certification systems to evaluate their applicability at a global level.

Supply chain management (SCM)-embedded valuable resources, such as capital, raw-materials, products, partners, customers and finished inventories, where the evaluation of environmental texture and flexibilities are needed to perceive sustainability. The present study aims to identify and evaluate the directory of green and agile (G-A) attributes based on decision support framework (DSF) for identifying dominating measures in SCM.

DSF is developed by exploiting generalized interval valued trapezoidal fuzzy numbers (GIVTFNs). Two technical approaches, i.e. degree of similarity approach (DSA) and distance approach (DA) under the extent boundaries of GIVTFNs, are implicated for data analytics and for recognizing constructive G-A measures based on comparative study for robust decision. A fuzzy-based performance indicator, i.e. fuzzy performance important index (FPII), is presented to enumerate the weak and strong G-A characteristics to manage knowledge risks in allied business environment.

The modeling is illustrated from the insights of decision-makers for augmenting business value based on cognitive identification of measures, where the best performance score is identified by the “sustainable packaging” under the traits of green supply chain management (GSCM). “The use of Web-based applications” under the traits of agile supply chain management (ASCM) and “Outsourcing flexibility” under traits of ASCM is found as the second and third most significant performance characteristics for business sustainability. Additionally, the “Reutilization (recycling) and reprocessing” under GSCM in manufacturing and “Responsiveness and speed toward customers needs” under ASCM are found difficult in attainment.

The G-A evaluation will assist in attaining performance excellence in day-to-day operations and overall functioning. The outcomes will help executives to plan strategic objectives and attaining success.

To reinforce the capabilities of SCM, wide extent of G-A dimensions are presented, concept of FPII is reported to manage knowledge risks based on identification of strong attributes and two technical approaches, i.e. DSA and DA under GIVTFNs are presented for attaining robust decision and directing managerial decision-making process.

This short case could be handed out at the end of class discussion on “J&L Railroad” [UVA-F-1053] in preparation for the following class, or if students are more experienced with hedging and option pricing, the instructor may choose to cover both cases in a single class period. It is the companion case to “J&L Railroad” [UVA-F-1053], and presents more technical issues regarding the hedging problem by requiring students to understand option-pricing principles. The board likes the CFO's hedging recommendations, but it wants a more careful analysis of the bank's prices for its risk-management products: the caps and floors. Besides demanding an understanding of option pricing, this case puts particular emphasis on the calculation and use of implied volatility.