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Due to unceasing declination in environment, sustainable agro-food supply chains have become a topic of concern to business, government organizations and customers. The purpose of this study is to examine a problem associated with sustainable network design in context of Indian agro-food grain supply chain.

A mixed integer nonlinear programming (MINLP) model is suggested to apprehend the major complications related with two-echelon food grain supply chain along with sustainability aspects (carbon emissions). Genetic algorithm (GA) and quantum-based genetic algorithm (Q-GA), two meta-heuristic algorithms and LINGO 18 (traditional approach) are employed to establish the vehicle allocation and selection of orders set.

The model minimizes the total transportation cost and carbon emission tax in gathering food grains from farmers to the hubs and later to the selected demand points (warehouses). The simulated data are adopted to test and validate the suggested model. The computational experiments concede that the performance of LINGO is superior than meta-heuristic algorithms (GA and Q-GA) in terms of solution obtained, but there is trade-off with respect to computational time.

In literature, inadequate study has been perceived on defining environmental sustainable issues connected with agro-food supply chain from farmer to final distribution centers. A MINLP model has been formulated as practical scenario for central part of India that captures all the major complexities to make the system more efficient. This study is regulated to agro-food Indian industries.

The suggested network design problem is an innovative approach to design distribution systems from farmers to the hubs and later to the selected warehouses. This study considerably assists the organizations to design their distribution network more efficiently.

Information-facilitated product recovery system (IFPRS) has captivated industry attention and has developed into a matter of consideration among the researchers because of enhanced climate concerns, jurisdictive logics and societal liabilities. Although IFPRS implementation has become an essential aspect in manufacturing industries functional in the developed nations, still, limited consideration has been given in the literature to analyze the issues to IFPRS implementation for a circular economy (CE) in emerging and developing nations. Therefore, the objective of this study is to recognize issues to implementing IFPRS for a CE in context of select manufacturing industries in India.

In this study, 24 potential issues are established from the literature and from suggestions from the experts. The issues are clubbed under five different perspectives of technical, government, organization, policy and knowledge. Further, fuzzy VIKOR technique is applied on the results obtained to prioritize the identified issues. A sensitivity analysis has been carried out to check the robustness of the framework.

The present study shows that lack of skills and expertise in IFPRS implementation for a CE (I2), deficient capital to implement a CE in IFPRS (I₉), inadequate in adopting recent IT technology (I₁₈), feasibility of IFPRS employment for a CE (I₆) and no efficient training and program to CE adoption (I₂₁) are the top five potential issues in implementing IFPRS practices for a CE in Indian manufacturing industries.

In literature, limited study has been observed on determining issues to implementation of IFPRS for a CE. A more systematic method and statistical confirmation is necessary to establish further new confronting issues. This study is limited to Indian manufacturing industries.

The main contribution of this study includes identification of issues and later prioritizing them to reflect their severity. This would help the industry practitioners to formulate strategies for handling the issues conveniently.

Good quality input data is critical to developing a robust machine learning model for identifying possible money laundering transactions. McKinsey, during one of the conferences of ACAMS, attributed data quality as one of the reasons for struggling artificial intelligence use cases in compliance to data. There were often use concerns raised on data quality of predictors such as wrong transaction codes, industry classification, etc. However, there has not been much discussion on the most critical variable of machine learning, the definition of an event, i.e. the date on which the suspicious activity reports (SAR) is filed.

The team analyzed the transaction behavior of four major banks spread across Asia and Europe. Based on the findings, the team created a synthetic database comprising 2,000 SAR customers mimicking the time of investigation and case closure. In this paper, the authors focused on one very specific area of data quality, the definition of an event, i.e. the SAR/suspicious transaction report.

The analysis of few of the banks in Asia and Europe suggests that this itself can improve the effectiveness of model and reduce the prediction span, i.e. the time lag between money laundering transaction done and prediction of money laundering as an alert for investigation

The analysis was done with existing experience of all situations where the time duration between alert and case closure is high (anywhere between 15 days till 10 months). Team could not quantify the impact of this finding due to lack of such actual case observed so far.

The key finding from paper suggests that the money launderers typically either increase their level of activity or reduce their activity in the recent quarter. This is not true in terms of real behavior. They typically show a spike in activity through various means during money laundering. This in turn impacts the quality of insights that the model should be trained on. The authors believe that once the financial institutions start speeding up investigations on high risk cases, the scatter plot of SAR behavior will change significantly and will lead to better capture of money laundering behavior and a faster and more precise “catch” rate.

International Journal of Emerging Markets (IJoEM) is a leading journal that publishes high-quality research focused on emerging markets. In 2020, IJoEM celebrated its fifteenth anniversary, and the objective of this paper is to conduct a retrospective analysis to commensurate IJoEM's milestone.

Data used in this study were extracted using the Scopus database. Bibliometric analysis, using several indicators, is adopted to reveal the major trends and themes of a journal. Mapping of bibliographic data is carried using VOSviewer.

Study findings indicate that IJoEM has been growing for publications and citations since its inception. Four significant research directions emerged, i.e. consumer behaviour, financial markets, financial institutions and corporate governance and strategic dimensions based on cluster analysis of IJoEM's publications. The identified future research directions are focused on emergent investments opportunities, trends in behavioural finance, emerging role technology-financial companies, changing trends in corporate governance and the rising importance of strategic management in emerging markets.

To the best of the authors' knowledge, this is the first study to conduct a comprehensive bibliometric analysis of IJoEM. The study presents the key themes and trends emerging from a leading journal considered a high-quality research journal for research on emerging markets by academicians, scholars and practitioners.

This study aims to analyze the socio-ecological policy trade-off caused by technological innovations in the post-COVID-19 era. The study outcomes are utilized to design a comprehensive policy framework for attaining sustainable development goals (SDGs).

Study is done for 100 countries over 1991–2019. Second-generation estimation method is used. Innovation is measured by total factor productivity, environmental quality is measured by carbon dioxide (CO₂) emissions and social dimension is captured by unemployment.

Innovation–CO₂ emissions association is found to be inverted U-shaped and innovation–unemployment association is found to be U-shaped.

The study outcomes show the conflicting impact of technological innovation leading to policy trade-off. This dual impact of innovation is considered during policy recommendation.

The policy framework recommended in the study shows a way to address the objectives of SDG 8, 9 and 13 during post-COVID-19 period.

Policy recommendations in the study show a way to internalize the negative social externality exerted by innovation.

This study contributes to the literature by considering the policy trade-off caused by innovation and recommending an SDG-oriented policy framework for the post-COVID-19 era.

Considering the growing interests in managerial and accounting issues related to blockchain technology (BT), the study aims at identifying the main research venues in this specific field. In particular, the purpose is to understand the spatial and temporal production and distribution of research documents, highlighting the most relevant topics, the most influential authors and research.

This research carries out a bibliometric analysis of 189 research documents in the business, management and accounting areas. Data collection and refining is carried out from the Scopus database. The data analysis is based on a hybrid literature review approach using a descriptive bibliometric method, data analysis visualization (through VOSViewer software) and thematic analysis.

Results indicate that research studies focused on BT and accounting have been growing exponentially over the last three years, with authors who previously focused on generalist themes, and are now facing more specific issues. Through cluster analysis, the authors propose the framework of accounting domain and blockchain technology (ADOB) to systematize and visualize the map of current studies about the BT in the accounting domain.

The analysis highlights some aspects less investigated at the first research stage in the field of BT and accounting, such as the growing need of new accounting and control processes to address the practical issues of BT implementation and the need for education and training to stimulate a proper use of BT by accountants and practitioners.

This study is the first to adopt a bibliometric and thematic analysis to investigate BT in the accounting domain. The authors provide significant insights that could guide and foster the use of BT for accountants and practitioners, defining future research lines and a research agenda for academic researchers.