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The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.

The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.

The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.

Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.

First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.

As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.

This paper explores how financial technology (FinTech) organisations address poverty-related challenges when providing digital financial services. Employing the conceptual foundation of the liability of poorness (i.e. literacy gaps, a scarcity mindset, intense non-business pressures and a lack of financial slack), this paper explores the innovative strategies that FinTechs use to address these liabilities and promote entrepreneurship.

The paper uses detailed case data collected from three FinTech organisations operating in one South Asian country.

FinTech organisations' innovative strategies reflect a combination of “high touch” (human) vs “low touch” (digital) solutions. All the organisations simplified internal systems or procedures to accommodate customers. The degree to which the three organisations adopted each of the identified strategies shows an emerging typology of FinTechs; that is, innovators with high digital interactions, a mix of digital-human interactions and high human interactions.

The paper develops a typology which categorises FinTech innovative strategies. The typology highlights strategies pro-poor FinTechs use and explains the types of entrepreneurial support innovative organisations provide for their customers. Both the typology and the innovative strategies contribute to enhanced financial inclusion and entrepreneurial promotion amongst the poor.

The originality of the paper comes from its focus on FinTechs' innovative pro-poor strategies. Existing studies typically address the technology-side of innovations. In contrast, this paper combines innovative strategies with the liability of poorness to identify issues associated with financial inclusion.

Warehouse receipt-based financing (WRF), an innovative instrument with its structure embedded in the agricultural value chain can potentially address farmers' concerns about timely credit access and accessible remunerative markets. However, studies indicate farmers' exclusion from currently practiced WRF mechanisms across developing countries. Transaction cost and lack of assured remunerative markets post storage are the challenges thwarting farmers' participation. The study explores how these challenges can be addressed by analyzing a case study. The finding will help in coming up with a farmer-inclusive WRF mechanism.

The study uses a case study as an analysis tool. Primary data is gathered through farmers. Descriptive statistics and partial least squares (PLS) approach to structural equation modeling methodology has been adopted for empirical testing of the hypothesis of the study. The study uses SMART PLS 3.0 for analysis of data.

Single window offering of multiple value chain operations and technological intervention in physical handling substantially reduces transaction costs for farmers. Sustained farmers' participation in the case supports this finding. The presence of an assured market (PAM) is found to have a positive and significant relationship with WRF in the case of beneficiary farmers. The PAM is found to have a negative yet significant relationship with WRF in the case of nonbeneficiary farmers. Critical success factors of the entity KisanMitra stated in the case substantiates a farmer-inclusive WRF mechanism.

The study analyzes a case study of specific geography. However, similarities enlisted across developing countries in the introduction section provide a scope of generalization of findings across developing countries. The identified factors for a farmer-inclusive WRF mechanism will enable the governments, policymakers and development institutions to ascertain and align their WRF implementation measures to inculcate and upgrade these factors to the prospective WRF agents. Future studies can explore the replication of farmer-inclusive WRF mechanisms across other geographies. The studies also explores the role of technological interventions in further reducing the transaction cost and suitable policy modifications to encourage replication of the study in other geopgraphical context.

The study on WRF and the methodology adopted is first of its kind to identify factors for a farmer-inclusive WRF mechanism.