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This article examines the fiscal challenges the coronavirus pandemic poses in African countries, using Ghana as a case study and summarizes the country's immediate monetary and fiscal responses to the pandemic. The article also discusses the potential impacts of coronavirus-related shocks on the Ghana economy and policy options the national government may pursue to counteract the pandemic's adverse long-term effects.

The article uses daily and monthly economic indicators to assess the immediate impact of the pandemic on Ghana's economy. The article also uses latest data from the Ghana Living Standards Survey (GLSS) to simulate potential shocks to the economy related to the coronavirus crisis and examines the outcomes from a potential government response that expands spending on an existing direct social assistance program.

The authors find that the coronavirus pandemic is associated with a significant increase in Ghana's poverty measures over time, and an expansion in government spending under an existing cash transfer program would partly offset the economic shocks related to the crisis and improve outcomes for poverty and inequality. The authors also argue that other well-targeted expenditure and revenue policies will support long-term economic resilience.

The research suggests that a temporary expansion of the existing program of direct cash payments to poor households may be an effective social protection policy, as are well-targeted revenue and spending policies that support economic recovery and long-term fiscal sustainability.

The findings imply that while the pandemic might cause severe shocks in the economy, well-targeted spending and revenue policies that are anchored in sound macroeconomic management can promote economic resilience and long-term fiscal sustainability.

Public managers must ensure that national policy responses to the coronavirus pandemic consider socio-economic indicators, such as poverty and income inequality.

The authors present research that uses novel household-level data and an evidence-based microsimulation framework to articulate potential public policy strategies that can guide national responses to, and recovery from, the coronavirus pandemic.

This article examines the preliminary impact of the Sars-CoV-2 pandemic on India's economic and budgetary landscape – the most affected developing country from the first wave of the pandemic. It also includes a discussion of the monetary and fiscal responses adopted and the challenges faced in formulating the response to the pandemic.

Using high-frequency economic and fiscal indicators, this article evaluates the economic impact of the pandemic on the Indian economy. Further, it uses data from government sources and news to highlight the measures adopted at the national and subnational level in response to the pandemic.

The difficult economic conditions prior to the pandemic limited the fiscal space available to the government. As a result, the national and subnational governments have been cautious of accumulating excessive debt and have primarily responded with liquidity-enhancing measures, in addition to some fiscal measures for the most vulnerable. Overdependence on consumption taxes has led to unprecedented revenue shortfalls prompting the exploration of new avenues for revenue generation and implementation of austerity measures – some of which may be counterproductive in the long run.

The paper highlights the policy response of the largest democracy that has been hit hard by the pandemic. It also highlights various institutional and resource constraints that influenced the policies adopted. India's experience in responding to the virus could provide lessons for other developing countries.

Over the years, public sector reforms in emerging economies have focused on improving national budget systems and financial management practices to promote sustainable development. In the context of the COVID-19 crisis, this article examines whether the strength or effectiveness of national budget systems and related financial management practices moderates the impact of fiscal policy measures on economic recovery and resilience.

The article uses bivariate correlations and difference-in-difference analyses to examine the relationship between budget system effectiveness, government stimulus measures and forecasts of economic recovery and resilience. The analysis uses data from the Public Expenditure and Financial Accountability (PEFA) program, International Monetary Fund (IMF) and World Bank.

The article finds that estimates of economic recovery and resilience are higher in countries with more reliable budget processes and more transparent public finances. Also, the strength or effectiveness of the budget system before the pandemic appears to moderate the impact of government stimulus measures on economic recovery and resilience over a medium-term forecast horizon.

This is a prospective analysis based on economic forecasts from the IMF, which are subject to change in the coming years. In addition, the analysis uses subjective budget system indicators, which present measurement challenges that often influence this area of research. Better comparative data in the future, for example, large administrative datasets, will enable researchers to explore these issues with less estimation bias.

The findings are relevant for policymakers and budget officials in developing countries in Africa who are engaged in plans to improve national budget systems and enhance resilience to crises, such as the COVID-19-induced economic crisis. The findings also have implications for developing countries beyond Africa with similar economic and fiscal conditions.

The findings have implications for economic and budgetary planning for the social sector as well as the efficient delivery of public services in developing countries. Public managers have a critical role to play in adapting national budget systems and financial management reforms within complex and evolving economic circumstances even after the coronavirus pandemic.

The authors use novel and latest data on country responses to the COVID-19 pandemic as well as medium-term economic forecasts to examine the relationship between national budget systems and post-pandemic economic recovery and resilience in the African context. Previous research has only addressed these issues in the context of industrialized countries, and a limited number of empirical studies examine these relationships. The findings also have significant value for policymakers outside Africa who are facing similar challenges related to the coronavirus pandemic.

Writers present very different descriptions of what constitutes ‘ethnography’ and ‘case study’. While not attempting to review the literature, nor to comment on or endorse the overall quality of argument and presentation of research methodology made by each of the authors considered here, the following demonstrates how different the explanations and definitions offered can be.

Marketing-managing customer relationship, market positioning, target marketing, product strategy, distribution, retailing and supply chain management, integrated marketing communications.

Undergraduate and graduate students in international marketing; business administration; strategic decision making and general management courses.

The case study focuses on the current scenario within the Indian automotive lubricants industry, in order to provide an understanding of the marketing challenges, especially in retailing and distribution, faced by organisations within this highly competitive sector. The case examines the implementation of marketing strategies into practice and provides an insight into the importance of branding, market segmentation, market positioning, product and pricing strategies and customer relationship management (CRM).

The case study enables the students to understand and analyse: the current business environment and dynamics of emergence in the Indian automotive lubricants market; the critical success factors for doing business in the Indian automotive lubricants market and the associated opportunities and challenges; the importance of distribution and retailing strategies in the Indian context; Izo's growth and expansion strategy in India; and Izo's sales management and CRM systems and there importance to the success of the business.

Teaching note.

In this chapter, drone and vision camera technology have been combined for monitoring the crop product quality. Three vegetable crops such as tomato, cauliflower, and eggplant are considered for quality monitoring; hence, image datasets are collected for those vegetables only. The proposed method classified the vegetables into two classes as rotten and nonrotten products so the images were collected for rotten and nonrotten products. Three different features information such as chromatic features, contour features, and texture features have been extracted from the dataset and further used to train a Gaussian kernel support vector machine algorithm for identifying the product quality. The system utilized multiple features such as chromatic, contour, and texture features in classifier training which enhances the accuracy and robustness of the system. Chromatic features were utilized for detecting the crop while other features such as contour and texture features were utilized for further classifier building to identify the crop product quality. The performance of the system is evaluated based on the true positive rate, false discovery rate, positive predictive value, and accuracy. The proposed system identified good and bad products with a 97.9% of true positive rate, 2.43 % of false discovery rate, 97.73% positive predictive value, and 95.4% of accuracy. The achieved results concluded that the results are lucrative and the proposed system is efficient in agriculture product quality monitoring.

The growing incidence of precarious employment across many sectors is a serious challenge for a developing country like India. Neo-liberal arguments justify precarity as essential for the development of the free market economy and advocate realigning human resource practices with an ever-changing business environment and labor cost conditions. This chapter seeks to identify the determinants and dynamics surrounding precarity of workers engaged in temporary employment in India. It uses the unique Employment and Unemployment Survey data set published by the National Sample Survey Organisation of Government of India for two time periods 2009–2010 (66th Round) and 2011–2012 (68th Round) to bring out the dimensions of precarity and identify the determinants (both micro- and macro-levels) of participation in temporary employment. We find that precarious employment is most likely to affect the young, women, non-union members, those belonging to minority and socially deprived communities with low land holding and low educational status. Precarious employment is also most pronounced in states where labor-intensive industries are exposed to global import competition and where labor laws are rigid. The chapter concludes by discussing the implications of these findings for the economic and social policies that Indian governments have adopted in recent years.

Nonperforming assets in any banking system have stressed the economic health of nations. Resultantly, literature has given considerable impetus to predict failures and bankruptcy. Past studies have focused on the outcome of failures, while, there is a dearth of studies focusing on ongoing firms in bad shape. We plug this gap and attempt to identify underlying communication patterns for firms witnessing prolonged underperformance. Using text mining, we extract and analyze semantic, linguistic, emotional, and sentiment-based features in non-numeric communication channels of these poor-performing firms and their peers. These uncovered patterns highlight the use of vocabulary and tone of communication, in correspondence to their financial well-being. Furthermore, using such patterns, we deploy various Machine Learning algorithms to identify loser firm(s) way ahead in time. We observe promising accuracy over a time window of five years. Such early warning signals can be of critical importance to various stakeholders of a firm. Exploration of writing style-related features for any firm would help its investors, lending agencies to assess the likelihood of future underperformance. Firm management can use them to take suitable precautionary measures and preempt the future possibility of distress. While investors and lenders can be benefitted from this incremental information to identify the likelihood of future failures.

The general data protection regulation (GDPR) was designed to address privacy challenges posed by globalisation and rapid technological advancements; however, its implementation has also introduced new hurdles for companies. This study aims to analyse and synthesise the existing literature that focuses on challenges of GDPR implementation in business enterprises, while also outlining the directions for future research.

The methodology of this review follows the preferred reporting items for systematic reviews and meta-analysis guidelines. It uses an extensive search strategy across Scopus and Web of Science databases, rigorously applying inclusion and exclusion criteria, yielding a detailed analysis of 16 selected studies that concentrate on GDPR implementation challenges in business organisations.

The findings indicate a predominant use of conceptual study methodologies in prior research, often limited to specific countries and technology-driven sectors. There is also an inclination towards exploring GDPR challenges within small and medium enterprises, while larger enterprises remain comparatively unexplored. Additionally, further investigation is needed to understand the implications of emerging technologies on GDPR compliance.

This study’s limitations include reliance of the search strategy on two databases, potential exclusion of relevant research, limited existing literature on GDPR implementation challenges in business context and possible influence of diverse methodologies and contexts of previous studies on generalisability of the findings.

The originality of this review lies in its exclusive focus on analysing GDPR implementation challenges within the business context, coupled with a fresh categorisation of these challenges into technical, legal, organisational, and regulatory dimensions.

3D food printing technology is an emerging smart technology, which because of its inbuilt capabilities, has the potential to support a sustainable supply chain and environmental quality management. This new technology needs a supportive ecosystem, and thus, this paper identifies and models the enablers for adopting 3D printing technology toward a sustainable food supply chain.

The enablers were identified through an extensive literature review and verified by domain experts. The identified enablers were modelled through the hybrid total interpretive structural modelling approach (TISM) and the decision-making trial and evaluation laboratory (DEMATEL) approach.

It emerged that stakeholders need technical know-how about the 3D printing technology, well supported by a legal framework for clear intellectual property rights ownership. Also, the industry players must have focused and clear strategic planning, considering the need for sustainable supply chains. Moreover, required product innovation as per customer needs may enhance the stakeholders' readiness to adopt this technology.

The framework proposed in this research provides managers with a hierarchy and categorization of adoption enablers which will help them adopt 3D food printing technology and improve environmental quality.

This research offers a framework for modelling the enablers for 3D food printing to develop a sustainable food supply chain using the TISM and DEMATEL techniques.