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The purpose of this paper is to present a conceptual model that defines the essential components shaping the new Digital Supply Chains (DSCs) through the implementation and acceleration of Industry 4.0.

The scope of the present work exposes a conceptual approach and review of the key literature from 1989 to 2019, concerning the evolution and transformation of the actors and constructs in logistics and Supply Chain Management (SCM) by means of examining different conceptual models and a state-of-the-art review of Industry 4.0’s concepts and elements, with a focus on digitization in supply chain (SC) processes. A detailed study of the constructs and components of SCM, as defined by their authors, resulted in the development of a referential and systematic model that fuses the inherent concepts and roles of SCM, with the new technological trends directed toward digitization, automation, and the increasing use of information and communication technologies across logistics global value chains.

Having achieved an exploration of the different conceptual frameworks, there is no compelling evidence of the existence of a conceptual SCM that incorporates the basic theoretical constructs and the new roles and elements of Industry 4.0. Therefore, the main components of Industry 4.0 and their impact on DSC Management are described, driving the proposal for a new conceptual model which addresses and accelerates a vision of the future of the interconnectivity between different DSCs, grouped in clusters in order to add value, through new forms of cooperation and digital integration.

This research explores the gap in the current SCM models leading into Industry 4.0. The proposed model provides a novel and comprehensive overview of the new concepts and components driving the nascent and current DSCs. This conceptual framework will further aid researchers in the exploration of knowledge regarding the variables and components presented, as well as the verification of the newly revealed roles and constructs to understand the new forms of cooperation and implementation of Industry 4.0 in digitalized SCs.

While remanufactured products represent an increasingly researched phenomenon in the literature, not much is known about consumers' understanding and acceptance of such products. This study explores this issue in the context of the theory of perceived risk (TPR), investigating return policy leniency and distribution channel choice as potential factors to foster remanufactured products' sales.

This research utilizes an experimental design composed of a pre-test and a scenario-based main experiment to explore how return policy leniency might mitigate consumers' perceived risk and how their related purchase intention differs across two types of retail distribution channel structures (i.e. brick-and-mortar vs. online).

The investigation into the efficacy of return policy leniency within two retail distribution channel settings (i.e. brick-and-mortar vs. online) illustrates that providing a lenient return policy is an effective “cue” in increasing consumer purchase intention for remanufactured products. While prior literature has established that consumers value return policy leniency for new products, the authors provide empirical evidence that this preference also applies to remanufactured products. Notably, that return policy preference holds true in both channel settings (i.e. brick-and-mortar vs. online) under consideration. Additionally, and contrary to the authors’ predictions, consumers perceived remanufactured products sold via both channel settings as equally risky, thus highlighting that both are appropriate distribution channels for remanufactured products. Finally, while research on new products provides some initial guidance on consumer perceptions of quality and risk, the study provides empirical evidence into the difference of perceived risk with regard to new versus remanufactured products.

By employing the TPR, this research explored the role played by two supply chain management related factors (returns policy and channel structure) in reducing consumer's perceived risk and increasing purchase intention. In doing so, this study answers the call for more consumer-based supply chain management research in a controlled experimental research setting.

Research has demonstrated that varying degrees and combinations of individual traits, behaviors and characteristics influence the emergence of leaders in teams. While existing models have shown that such variables affect leader choice independently and interactively, the overall findings suggest that there is still a lack of research on a potentially significant factor, the impact of personal reputation on leader selection in autonomous work teams (AWTs), an increasingly ubiquitous team practice in organizations. This preliminary review thus aims to offer a general overview of personal reputation and its effects on leader emergence in the context of AWTs.

By surveying extant literature on team leader emergence, this review has identified that the potential impact of personal reputation on leader selection in teams is significant yet largely ignored.

Models of leader emergence in teams should incorporate the realistic assumption that teams formed in organizations are often comprised of individuals who have information on others either directly or indirectly. Personal reputation based on an individual’s history thus moderates how one’s behaviors or traits exhibited become contextualized in the overall assessment of leader desirability and selection in teams.

Based on a review of existing research on leadership emergence and personal reputation, this work contends that the external validity and predictive value of leader emergence models would greatly benefit from the inclusion of employee reputations as a moderating variable in the future assessment of the leader emergence process in AWTs.

By designing models that explain the potential effects of personal reputations on leader emergence in self-managed teams, team members and managers can be better elucidated and ultimately improve their understanding of the process of the evaluation and selection of team leaders.

Despite the prolific research on leader emergence, there is still a paucity of studies examining personal reputational effects on leader selection, especially in the context of AWTs. This work is the first review piece calling for the inclusion of personal reputation, a substantive factor overlooked and excluded in previous models, to enhance the current understanding of leader emergence in AWTs.

Social robotics is a rapidly growing application of artificial intelligence (AI) in society, encompassing an expanding range of applications. This paper aims to contribute to the ongoing integration of psychology into social robotics ethics by reviewing current theories and empirical findings related to human–robot interaction (HRI) and addressing critical points of contention within the ethics discourse.

The authors will explore the factors influencing the acceptance of social robots, explore the development of relationships between humans and robots and delve into three prominent controversies: deception, dehumanisation and violence.

The authors first propose design factors allowing for a positive interaction with the robot, and further discuss precise dimensions to evaluate when designing a social robot to ensure ethical design technology, building on the four ethical principles for trustworthy AI. The final section of this paper will outline and offer explicit recommendations for future research endeavours.

This paper provides originality and value to the field of social robotics ethics by integrating psychology into the ethical discourse and offering a comprehensive understanding of HRI. It introduces three ethical dimensions and provides recommendations for implementing them, contributing to the development of ethical design in social robots and trustworthy AI.