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The case study will help to learn about the importance of pre-sanction precautionary measures before lending to self-help groups (SHGs), to learn about the potential lapses and errors while sanctioning SHG finance and to learn about the importance of bank’s guidelines and compliance before sanctioning loans.

This case study details the tenure of Seema in a rural branch of Safe Bank of India located in Haryana which she joined as a manager in the year 2016. She overachieved the target given by the district collector office, and going by the tide, she kept her reliance on the references provided by non-government organization (NGO) without complying the bank’s instructions. She committed errors while sanctioning the loans, which led towards the upsurge of non-performing assets of the branch. Later on, after investigation it was discovered that she did not follow fundamental bank’s instructions. In wake of those lapses and errors, how she could have avoided those lapses and secure the public money? What were the most important documents while granting agriculture finance and what due diligence she should have taken? How did she treat calls from the government departments? Was she right in trusting the suggestions of the NGO?

This case study caters to students of various streams, namely, management, business administration and law, and can be targeted at both undergraduate and postgraduate students. It could be suitable for several types of courses and students. Furthermore, this case study can also be targeted for various training programmes for bank employees and employees of various lending institutions engaged in agriculture finance and credit linkage programmes.

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CSS 1: Accounting and finance.

Using the lens of crisis innovation and strategic alignment, this study explores how a segment of the restaurant sector that may be less agile than others—Michelin-starred restaurants—perceives and aligns with the challenges brought about by the COVID-19-pandemic.

The study collected data from 19 Michelin-starred restaurants in Spain using a qualitative interview method. The data were analyzed qualitatively and organized thematically.

Four key categories of strategic challenges were identified: human resources, uncertainty, control and economic challenges. In response, chefs displayed both behavioral and organizational strategies. Those organizational strategies were new human resource management, reorganization, product and service innovation and marketing. While the new human resource management actions adopted to align with the human resource challenges identified, a misalignment remains between some of the other strategic actions, such as product and service innovation, marketing and economic and uncertainty challenges.

The findings offer new insight into Michelin-starred restaurant chefs' challenges and (mis)alignment strategies, an area that has been understudied in the current literature on innovative responses in the hospitality sector post-pandemic.

This paper aims to examine the dynamics of house prices in metropolitan cities in an emerging economy. The purpose of this study is to characterise the house price dynamics and the spatial heterogeneity in the dynamics.

The author explores spatial heterogeneity in house price dynamics, using data for 35 Indian cities with a million-plus population. The research methodology uses panel econometrics allowing for spatial heterogeneity, cross-sectional dependence and non-stationary data. The author tests for spatial differences and analyses the income elasticity of prices, the role of construction costs and lending to the real estate industry by commercial banks.

Long-term fundamentals drive the Indian housing markets, where wealth parameters are stronger than supply-side parameters such as construction costs or availability of financing for housing projects. The long-term elasticity of house prices to aggregate household deposits (wealth proxy) varies considerably across cities. However, the elasticity estimated at 0.39 is low. The highest coefficient is for Ludhiana (1.14), followed by Bhubaneswar (0.78). The short-term dynamics are robust and show spatial heterogeneity. Short-term momentum (lagged housing price changes) has a parameter value of 0.307. The momentum factor is the crucial dynamic in the short term. The second driver, the reversion rate to long-term equilibrium (estimated at −0.18), is higher than rates reported from developed markets.

This research applies to markets that require some home equity contributions from buyers of housing services.

Stakeholders can characterise stable housing markets based on long-term fundamental value and short-run house price dynamics. Because stable housing markets benefit all stakeholders, weak or non-existent mean reversion dynamics may prompt the intervention of policymakers. The role of urban planners, and local and regional governance, is essential to remove the bottlenecks from the demand side or supply side factors that can lead to runaway prices.

Existing literature is concerned about the risk of a housing bubble due to relaxed credit norms. To prevent housing market bubbles, some regulators require higher contributions from home buyers in the form of equity. The dynamics of house prices in markets with higher owner equity requirements vary from high-leverage markets. The influence of wealth effects is examined using novel data sets. This research, documents in an emerging market context, the observations cited in low-leverage developed markets such as Germany and Japan.

This study aims to gain insight into the motivations behind the decision to use high-cost payday loans by households who possess mainstream credit and to determine whether this behavior has changed over time.

Using data from Statistics Canada’s Surveys of Financial Security, probit models are used to examine the sociodemographic and financial indicators associated with payday loan use.

The analysis uncovers the sociodemographic and financial characteristics of payday loan-user households with access to lower-cost short-term loans. The findings indicate that the likelihood of payday loan use has risen over time. Additional analysis reveals that indicators of financial instability are positively associated with payday loan use among this group.

This research highlights the dichotomy of payday loan users and recommends policymakers tailor solutions to the specific needs of different types of payday loan users.

This research highlights the distinguishing sociodemographic and financial characteristics of payday loan user households and recommends policymakers tailor solutions to the specific needs of different types of payday loan users.

This is the first study, to our knowledge, to focus analysis on payday loan use of those with access to lower-cost short-term credit alternatives in Canada and to include measures of financial instability in the analysis. This research is timely given the current economic environment of high interest rates and high levels of household debt.

This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.

This paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.

The machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.

The main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.

The purpose of this study is to explore the determinants of investment decisions of Estonian farms after the transition to market economy and accession to the European Union (EU), in the period 2006–2019.

The paper employs Estonian Farm Accountancy Data Network (FADN) individual farm-level data from the period 2006–2019, and standard and augmented accelerator investment models. Generalised methods of moments (GMM) and bias-corrected least-squares dummy variables (LSDVC) regressions were used to estimate parameters of these models.

In the considered period, farm investments were positively affected by sales growth, investment subsidies and the cash flow. Decomposition of cash flow into volatile, market income related part, and more stable, farm subsidies related part indicated that investments do not depend on market income part of cash flow. Instead, the stable part of the cash flow (farm subsidies) had a significant and positive effect on investments. This suggests that credit rationing could be present in the EU agriculture, and it depends on the farm subsidies not market income of farms.

Despite the wealth of literature on the investment behaviour of farmers, this article is the first attempt to decompose farm cash flow into stable (farm subsidies) and volatile (market income) parts to explain the role of subsidies as a part of cash flow in credit rationing.

India launched Smart City Mission in 2015 with an objective of development of 100 smart cities with a completion deadline in 2019 that was extended till June 2023. Smart City Mission is an important mission in the backdrop that urban population in India is projected to be 67.55 crore in 2035 from 48.30 crore in 2020. Further, by 2035, the percentage of population in India at mid-year residing in ‘urban area’ will be 43.2% as per the United Nations – Habitat's World Cities Report 2022 and it will be just next to China's urban population in 2035 that is projected at 1.05 billion. A recent World Bank report (2022) estimated that India will need to invest US (United States) $840 billion over the next 15 years, i.e. US $55 billion per annum – into urban infrastructure if it has to effectively meet the needs of its fast-growing urban population.

This chapter focuses on financing of sustainable smart cities in India. This chapter summarises financing options explored by the government in the beginning, challenges faced in financing of Smart City Mission in India over a period due to various developments such as pandemic, delay in execution of projects under the Smart City Mission, among others. Finally, suggestions have been given for making financing means effective and sustainable. These suggestions are based on the gaps between the ‘financing means thought of’ in the beginning and ‘financing means actually applied’ while executing Smart City Mission in India. Financing part is worth exploring in the background that India had the fiscal deficit at 3.9% of Gross Domestic Product (GDP) in 2015–2016 and most recently, the country had the fiscal deficit at 6.71% of GDP in FY22. And the country also dealt with the pandemic like other economies and provided COVID-19 vaccine free of cost to all citizens. Insights are useful for any other economy with a similar sustainable and smart city mission while facing resource constraints.