No abstract

Replication studies are important for the empirical research process. Yet, while there is an increased awareness of the need for replication in management research, it appears that such studies are rarely published in leading management journals. Importantly, we lack a comprehensive overview of replication studies in the top management journals that spans all sub-disciplines. Our systematic review closes this gap and provides an overview of the prevalence, types, outcomes, and impact of replication studies in management journals. We find that differences in the prevalence of replications between sub-disciplines exist and that most replications are wide replications. With regard to the replication outcome, our review shows that the share of non-confirming replications is low. Moreover, such replications are cited less often than confirming replications pointing towards a confirmation bias in management research. We discuss the implications of our results for authors, reviewers, and editors of management journals.

Nowadays, artificial intelligent (AI) is becoming a more effective digital domain promised to facilitate immediate access to information and effective decision making in ever-increasing business environments. The researchers understand the extensive use of artificial intelligence among firms as an essential and necessary tool for shaping the future of supply chain 4.0 industry. This chapter discusses the role of AI applications for the success of a supply chain in the big data era. From a holistic perspective, today, manufacturers, particularly those with global operations and presence, are under enormous pressure to keep up with the continuous growth of disruptive innovative procurement models. This has open doors for the firms to aggressively seek out big data management capabilities to improve operational efficiencies and to innovate the process. This chapter provides a better understanding related to the application of data analytics in the supply chain context. The research issues are classified into different categories, including big data management and machine learning, a business case for the supply chain and innovation in supply using data. This study also present machine learning data analysis steps.

This chapter aims to identify and analyze the published definitions of circular economy (CE). Twenty-eight definitions were gathered through intensive critical literature review, using both Scopus and Web of Science. The definitions developed from peer-reviewed literature analysis covered a period from 1999 through 2019, although most definitions were published from 2011 onwards. CE received significant attention in the early 90s and now is considered a mainstream strategy for product design and social, economic and environmental sustainability. Given that research is still relatively new in the sustainable circular economy. CE offers a reverse resource regenerative idea to eliminate the linearity of production and consumption system to support sustainability objectives. The CE definition analysis revealed that so far, resilience and stakeholder perspective is not explicitly included in the definition, although stakeholder is considered to be part of a natural and ecology system. CE has an impact on different aspects of the business throughout the entire supply chain. The concept of a CE is a value-orientated resource transformational process. CE considers both upstream and downstream production and consumption patterns to promotes the resilience orientation of resources. Currently, CE practices are carried out -meso, micro and macro. This chapter highlights that suggest that it is imperative to consider exosystem and Chronosystem to better move away from linear to circular economy.

Biophilic thinking is an approach to solve problems of nature and humankind by applying creative approaches of biophysics and biomimicry designs for conceptualization and developing innovative solutions to improve human well being and sustainability. Biophilia, meaning "love of life or living system" (Fromm 1964), following the term eco-philia translate to as, love of organizations to their natural environment. Eco-philia thinking identifies and improves purposefully design process with overall organizational sustainability including planning for both human and ecology systems. Through the lense of eco-philia design thinking, the creation of sustainable innovation can keep up the corporate sustainability agenda in a larger biophysical, ecological, and human ecosystem.