Suneel Sharma

This study explores the determinants of the intention to adopt blockchain technology for sustainable supply chain management in Indian micro, small, and medium enterprises. Different from existing studies that advocate the use of socio-technical theory for blockchain technologies, we develop a new theoretical framework, called “SOS,” based on a review of the existing literature. This is an adaptation of the technology–organization–environment framework that examines the measures and scales from socio-technical, organizational, and sustainability contexts. We use ADANCO 2.3.2 for variance-based structural equation modeling. The results show that two of the nine hypotheses are negatively significant, while the rest are positive. In our context, social sustainability and computer self-efficacy are strongly negatively significant for the adoption intention of blockchain technology in our context. Software quality and environmental sustainability are strongly positively significant. Meanwhile, collaboration, economic sustainability, and relative advantage mediated by experience are positively significant. Our study contributes to the literature by offering a new theoretical framework, fresh insights from the Indian industry, and several recommendations to practitioners.

Rural credit is one of the most critical inputs for farm production across the globe. Despite so many advances in digitalization in emerging and developing economies, still a large part of society like small farm holders, rural youth, and women farmers are untouched by the mainstream of banking transactions. Machine learning-based technology is giving a new hope to these individuals. However, it is the banking or non-banking institutions that decide how they will adopt this advanced technology, to have reduced human biases in loan decision making. Therefore, the scope of this study is to highlight the various AI-ML- based methods for credit scoring and their gaps currently in practice by banking or non-banking institutions. For this study, systematic literature review methods have been applied; existing research articles have been empirically reviewed with an attempt to identify and compare the best fit AI-ML-based model adopted by various financial institutions worldwide. The main purpose of this study is to present the various ML algorithms highlighted by earlier researchers that could be fit for a credit assessment of rural borrowers, particularly those who have no or inadequate loan history. However, it would be interesting to recognize further how the financial institutions could be able to blend the traditional and digital methods successfully without any ethical challenges.

Through a systematic review of publications in reputed peer-reviewed journals, this paper investigates the role of blockchain technology in sustainable supply chain management. It uses the What, Who, Where, When, How, and Why (5W+1H) pattern to formulate research objectives and questions. The review considers publications since 2015, and it includes 187 papers published in 2017, 2018, 2019, and the early part of 2020, since no significant publications were found in the year 2015 or 2016 on this subject. It proposes a reusable classification framework—emerging technology literature classification level (ETLCL) framework—based on grounded theory and the technology readiness level for conducting literature reviews in various focus areas of an emerging technology. Subsequently, the study uses ETLCL to classify the literature on our focus area. The results show traceability and transparency as the key benefits of applying blockchain technology. They also indicate a heightened interest in blockchain-based information systems for sustainable supply chain management starting since 2017. This paper offers invaluable insights for managers and leaders who envision sustainability as an essential component of their business. The findings demonstrate the disruptive power and role of blockchain-based information systems. Given the relative novelty of the topic and its scattered literature, the paper helps practitioners examining its various aspects by directing them to the right information sources.

There is an increasing influence of machine learning in business applications, with many solutions already implemented and many more being explored. Since the global financial crisis, risk management in banks has gained more prominence, and there has been a constant focus around how risks are being detected, measured, reported and managed. Considerable research in academia and industry has focused on the developments in banking and risk management and the current and emerging challenges. This paper, through a review of the available literature seeks to analyse and evaluate machine-learning techniques that have been researched in the context of banking risk management, and to identify areas or problems in risk management that have been inadequately explored and are potential areas for further research. The review has shown that the application of machine learning in the management of banking risks such as credit risk, market risk, operational risk and liquidity risk has been explored; however, it doesn’t appear commensurate with the current industry level of focus on both risk management and machine learning. A large number of areas remain in bank risk management that could significantly benefit from the study of how machine learning can be applied to address specific problems.