The circular economy (CE) has become one of the prominent topics in both natural science and management literature over the last few decades. CE is a dual-loop regenerative system that focuses on the effective and efficient utilization of resources in the ecosystem, which is beneficial to environmental and economic performance optimization. Dual CE initiatives allow firms to increased resource eco-efficiency, as well as resource effectiveness. CE has profound consequences for economic and operational advantage. This reinforces the need for reflection on the definition that may provide guidelines to assess and advance the depth and diversity of the field. We aim to provide a definitional analysis of the CE and suggest future research streams to advance the existing literature. For this purpose, we employed a systematic literature review to collect related publications in the CE. As a result of this, a total of 91 papers were selected, studied, and analyzed. We proposed a sound definition of a circular economy that includes the main identified elements, organizational planning processes, customers and society, utilization of the ecosystem, and economic resource flows. Moreover, future direction agenda, in CE research, is suggested considering three research streams: (1) circular design as value creation and capture, (2) antecedents of key activities, and (3) consequences of key processes. There is limited empirical research conducted on CE, and much of the existing research focuses on theoretical, conceptual, and normative. A few empirical research studies are mainly cross-sectional in their focus and are confined to developing and emerging economies. We hope this study’s findings will extend the field of CE, in which some of the most influential information regarding CE literature is provided. This study suggests that the development of CE initiatives plays an important role in the growing digital transformation in the value chain. There have been limited research studies in the interface of circular economy and Industry 4.0. Future research studies may investigate the extent to which digital transformation can increase the implementation of CE, and their influence on digital performance management.

Purpose: The purpose of this paper is to focus on tracing GHG emissions across the supply chain industries associated with the US residential, commercial and industrial building stock and provides optimized GHG reduction policy plans for sustainable development. Design/methodology/approach: A two-step hierarchical approach is developed. First, Economic Input-Output-based Life Cycle Assessment (EIO-LCA) is utilized to quantify the GHG emissions associated with the US residential, commercial and industrial building stock. Second, a mixed integer linear programming (MILP) based optimization framework is developed to identify the optimal GHG emissions’ reduction (percent) for each industry across the supply chain network of the US economy. Findings: The results indicated that “ready-mix concrete manufacturing”, “electric power generation, transmission and distribution” and “lighting fixture manufacturing” sectors were found to be the main culprits in the GHG emissions’ stock. Additionally, the majorly responsible industries in the supply chains of each building construction categories were also highlighted as the hot-spots in the supply chains with respect to the GHG emission reduction (percent) requirements. Practical implications: The decision making in terms of construction-related expenses and energy use options have considerable impacts across the supply chains. Therefore, regulations and actions should be re-organized around the systematic understanding considering the principles of “circular economy” within the context of sustainable development. Originality/value: Although the literature is abundant with works that address quantifying environmental impacts of building structures, environmental life cycle impact-based optimization methods are scarce. This paper successfully fills this gap by integrating EIO-LCA and MILP frameworks to identify the most pollutant industries in the supply chains of building structures.

Purpose: A major frustration for most policy researchers in the small to medium‐sized enterprise (SME) area is the virtual non‐existence of scientific data on this sector in the country. This paper aims to provide some data.Design/methodology/approach: A survey of 651 SMEs from the manufacturing sector of Pakistan was conducted in 2003 and statistical analysis was carried out to analyze the data. This paper focuses on the relationship between the health indicators namely, sales/employee, increase/decrease in sales, and investment plans and the different personal characteristics of the owner/entrepreneur.Findings: This analysis suggests that education, generation setting up the business, and number of partners have a significant relationship with the health of SMEs. Health of the firm is also dependent on owner habits like watching television, reading newspapers and using computers for office work. Other factors like caste of the owner and occupation of the owner's relatives were also analyzed, but no significant relationship with the health of an SME was observed.Practical implications: The implications of this study are far reaching in understanding the profiles of owners of SMEs in Pakistan and how these profiles impact the profitability of the firms.Originality/value: To date no other such study has been carried out in Pakistan. Similar studies on owner profiles have previously been carried out in other countries such as the UK and USA.

Purpose: This paper conducts a Systematic Literature Review (SLR) of Machine Learning (ML) in Supply Chain Management through bibliometric and network analysis, the authors are able to grasp key features of the contemporary literature. The study makes use of state-of-the-art analytical framework based on a unified approach to reveal insights from the present body of knowledge and the potentials for future research developments.Design/methodology/approach: Unlike standard literature reviews, in SLR, a structured approach is followed. The approach enables utilizing contemporary tools and software packages such as R-package “bibliometrix” and Gephi for exploratory and visual analytics. A number of clustering methods are employed to form clusters. Later, multivariate analysis methodologies are adopted to determine the dominant clusters for the influential co-cited references.Findings: Using contemporary tools from Bibliometric Analysis (BA), the authors identify in an exploratory analysis, the influential authors, sources, regions, affiliations and papers. In addition, the use of network analysis tools reveals research clusters, topological analysis, key research topics, interrelation and authors’ collaboration along with their patterns. Finally, the optimum number of clusters computed for cluster analysis is decided using a systematic procedure based on multivariate analysis such as k-means and factor analysis.Originality/value: Modern-day supply chains increasingly depend on developing superior insights from large amounts of data available from diverse sources in unstructured and semi-structured formats. In order to maintain a competitive edge, the supply chains need to perform speedy analysis of big data using efficient tools that provide real-time decision-making insights. Such an analysis necessitates automated processing using intelligent ML algorithms. Through a BA followed by a detailed data visualization in a network analysis enabled grasping key features of the contemporary literature. The analysis is based on 155 documents from the period 2008 to 2018 selected using a systematic selection procedure.