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Rising crude oil prices are likely to have an asymmetric and nonlinear negative impact on GDP growth. The purpose of this paper is to ask the following questions: Does the effect of a crude price shock depend on the position of crude price cycle, i.e. is the effect of price shock larger/smaller in periods of already elevated crude price? And, does the effect of crude price shock depend on the position of the economy in the business cycle, i.e. does the crude price shock affect growth differentially in periods of low/high growth?

The authors use a local linear projection (LLP) model to examine the asymmetric impact of crude price on GDP growth in an environment of high crude price. Next, a quantile regression model is used to account for differential impact on growth around high and low growth periods.

Results from the LLP model show that when oil price is above $70, each additional percentage point of increase in oil price results in a 20 basis point (bps) drop in quarterly GDP growth rate on average. The impact is felt between the third and sixth quarters. When oil prices rise above $80, the impact is similar, with a sharper drop in growth (30 bps). The exercise with quantile regression shows that the impact of an increase in crude prices on growth is almost double at lowest quantiles of growth compared with the median.

There is a growing literature that evaluates the impact of oil price in developing economies. However, nonlinearities in crude price-GDP growth dynamics have not received enough attention, especially during phases of elevated crude price or a growth downcycle. The authors believe that accounting for such effects is especially relevant in the present economic scenario of high oil prices because of geopolitical crises and a period of vulnerable growth because of supply chain issues arising out of the pandemic. Using recent data from oil-importing emerging market economies such as India, this paper fills a crucial gap in the literature.

This study aims to explore the long-run equilibrium relationship between India’s real exchange rate and sectoral productivity trends using internationally comparable KLEMS databases on productivity for India, China, Euro area, the USA, the UK and Japan.

This study uses pooled mean group estimations for panel data suggested by Pesaran et al. (1999). This method is chosen because of the presence of variables with different orders of integration.

The results find support for an “extended” Balassa–Samuelson (BS) hypothesis which allows labour market frictions that does not allow for wage equalisation between traded and non-traded sectors within a country. This mechanism continues to find some support when we separate out distribution sector that comprises wholesale and retail trade in the domestic services sector. The empirical evidence suggests that India’s real exchange rate is anchored to domestic fundamentals and is closely aligned to its fair value over a medium to long-time horizon.

To the best of the authors’ knowledge, unlike the available literature, which uses aggregate per-capita income as proxy for a country’s productivity growth, this paper perhaps makes the first attempt to validate the BS hypothesis by accounting for productivity differential at the sectoral levels using KLEMS data across countries. Moreover, this study takes the country’s productivity improvement rather than using a basket of countries, a prevalent practice in the literature. While this paper uses India’s data, which witnessed a prolonged appreciation in its real effective exchange rate and rapid technological progress, the authors believe its findings and policy implications could be applicable to the similar emerging market economies.

This study aims to optimize blood donation drive efficiency by addressing operational bottlenecks and improving resource deployment, focusing on enhancing donor experience and reducing camp duration.

The research uses a mixed-method approach, combining qualitative insights from blood banking officer interviews with quantitative data from 58 camp observations. “Simio” simulation software models various operational configurations, while mathematical techniques like queuing theory analyze key performance metrics. The process involves creating a baseline model, proposing optimizations and validating recommendations through real-world implementation.

The study achieved significant improvements: Reduced average donor time from 1.79 to 0.79 hours (56.4% improvement); Shortened camp operation time from 7.98 to 4.85 hours (39.2% improvement); Decreased waiting times at the medical check station from 45.23 to 0.60 minutes; Improved service rates across all stations, notably at registration (233.79% increase); and Streamlined processes through digitization and health check consolidation.

The study provides actionable recommendations for blood bank managers, including digital pre-registration and optimized staff allocation, leading to substantial time and resource savings while enhancing donor experience.

By improving donation camp efficiency and experience, this research could increase donor retention rates and lead to a more stable blood supply, crucial for medical care.

This research presents a novel approach by combining discrete event simulation with real-world implementation and validation, offering an innovative solution to common bottlenecks in blood donation drives.

This chapter intends to give insights into the use of technologies such as virtual reality (VR), augmented reality (AR) and artificial intelligence (AI) in tourism and hospitality. Thus, the objectives are to: (1) give an overview about the meaning of luxury the use of technologies in tourism and hospitality, (2) provide examples about the use of such technologies (e.g., VR, AR, AI in practice) and (3) point out suggestions about further research in the interface of luxury and technologies in tourism and hospitality. The chapter is of interest to both academics and practitioners since it presents luxury, VR, AR and AI concepts, gives examples of technologies applications and points out critical areas for future research.

The case study is based on a social entrepreneurial journey where the authors have used an interview method to get the insights from the protagonists and the employees. Rigorous interviews were conducted online and in person for deep analysis of the protagonist’s strategies and decisive dilemma.

Secondary data was collected from company’s website for facts and figures.

This case study is a story of indigenous tribes of Odisha from the eyes of a woman who, with her co-founder, empathized with their vulnerable life and took on the challenge of creating sustainable livelihoods by establishing Millet Magic Foundation. The Millet Magic Foundation was established in 2021 by Shyama and her cofounder to uplift the indigenous tribe of Mayurbhanj by providing them livelihood through millet-based products. The foundation launched their millet-based snack products with the brand name WOWMOM. Millet Magic Foundation created social impact for the tribals by providing them with employment, fair wages, health care and social well-being. The specialty of the Millet Magic was reverse positioning and focusing on the bottom of the pyramid. The success of the Millet Magic Foundation relied on its mission to uplift the life of these indigenous tribal, especially the women, by overcoming the challenges with the strategies to establish Millet Magic as a social enterprise.

The case study is primarily suitable for postgraduate programme to teach the concept of social entrepreneurship in the entrepreneurship module. The case study can also be used for highlighting the role of social enterprise in sustainable economic development of emerging economies.

A stochastic technique for solving interval non-linear problems using generalized Hukuhara (GH)-difference is proposed. The non-linear programming problem in interval form is transformed into an equivalent non-linear programming problem with real coefficients by associating a Gaussian random variable to the interval and the six-sigma rule. The conceptualized idea eliminates the decision maker’s instinctive selection of weight functions and provides an alternative to the order relation method, max-min criteria-based methods and bi-level approaches for representing intervals as real numbers. To demonstrate a coherent understanding, numerical examples have been used.

A stochastic approach has been used to develop a solution technique for solving interval nonlinear programming problems which arise in the modeling of scientific and engineering problems under uncertain environments.

The proposed idea eliminates the decision maker’s instinctive selection of weight functions and provides an alternative to the order relation method, max-min criteria-based methods and bi-level approaches for representing intervals as real numbers. This method provides specific results rather than in the interval form, which are more practical and implementable by the decision maker.

This is to certify, that the research paper submitted is an outcome of original work. I have duly acknowledged all the sources from which the ideas and extracts have been taken. This article has not been submitted elsewhere for publication.

This research presents a comprehensive explanation of unethical pro-organisational behaviour (UPB), an emerging phenomenon in organisational behaviour and especially in moral behaviour research. The authors tested the fit of Culture-Identification-Ideology-UPB moral behaviour model. The results indicate that individuals having strong organisational identification and high relativism ethical ideology may indulge in the practice of UPB. Interestingly, our study also reveals that strong ethical organisational culture may not restrain, rather may facilitate UPB. The authors concluded with suggestions for the practitioners and future scope of research.

This study develops an integrated approach combining data envelopment analysis (DEA) and structural equation modeling (SEM) for estimating the working capital management (WCM) efficiency and evaluating the effects of diverse exogenous variables on the WCM efficiency and firms' performance.

DEA is applied for deriving WCM efficiency for 212 Indian manufacturing firms over a period from 2008 to 2019. Also, the effect of human capital (HC), structural capital (SC), cost of external financing (CEF), interest coverage (IC), leverage (LEV), net fixed asset ratio (NFA), asset turnover ratio (ATR) and productivity (PRD) on the WCM efficiency and firms' performance is examined using SEM.

The average mean efficiency scores ranging from 0.623 to 0.654 highlight the firms operating at around 60% of WCM efficiency only, which is a major concern for Indian manufacturing firms. Further, IC, LEV, NFA, ATR revealed direct effect on the WCM efficiency as well as indirect effect on firms' performance, whereas CEF had only a direct effect on WCM efficiency. HC, SC and PRD had no effects on WCM efficiency and firms' performance.

The findings offer vital insights in guiding policy decisions for Indian manufacturing firms.

This study is the first to identify the endogenous nature of the relationship of HC, SC, CEF, IC altogether with firms' performance, compounded by the WCM efficiency, by applying a comprehensive methodology of DEA and SEM and provides an efficiency performance model for better decision-making.

This paper aims to explore how sub-national or regional cultural differences influence backers’ willingness to crowdfund projects. The paper also explores how migrant transnationalism influences the impact of backer’s sub-national culture and crowdfunding relationships.

This paper is based on the experimental design technique using analysis of covariance methods. The authors tested the study hypotheses on a sample of 790 respondents.

The study results suggest that individuals differ in their intent to crowdfund product campaigns depending on value congruence between their cultural values derived from the region to which they belong and the nature of the product category, such as environmentally friendly or happiness-enhancing products.

This paper explores the role of regional cultural differences in determining the intention to crowdfund different campaigns based on the nature of the product. Value congruence, as driven by regional cultural differences with crowdfunding campaigns, has not been explored before.

The purpose of this paper is to study the key determinants of capital structure for Indian manufacturing firms and which theory implications, i.e. trade off vs pecking order are more applicable in current Indian manufacturing sector scenario.

A sample size of 422 listed Indian manufacturing companies on Bombay Stock Exchange has been considered to do the empirical evaluation. A ten year period from 2003-2004 to 2012-2013 and annual financial standalone data have been considered for study. Ratio analysis and panel data approach have been applied to perform the empirical evaluation. Total debt to total capital and total debt to total assets are used as the proxy for firm financial leverage.

It was empirically found that size, age, asset tangibility, growth, profitability, non-debt tax shield, business risk, uniqueness and ownership structure are significantly correlated with the firm financial leverage or key determinants of capital structure in Indian manufacturing sector. Also, other variables like dividend payout, liquidity, interest coverage ratio, cash flow coverage ratio (CFCR), India inflation and GDP growth rate are empirically found to be insignificant to determine the capital structure of Indian manufacturing sector. There is no single theory implications, i.e. trade off vs pecking order which can explain the capital structure nature of Indian manufacturing sector and rather it is a mix of both the theories.

The findings of the study would enhance the literature on capital structure and is significant for the Indian manufacturing firm’s decisions as it includes the most recent data and covers the period of both pre- and post-recession of 2008-2009.