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3818 items in total found

Journal Articles | 2023

Can biofuels help achieve sustainable development goals in India? A systematic review

Prantika Das, Chandan Kumar Jha, Satyam Saxena, Ranjan Kumar Ghosh

Biofuels are expected to play a pivotal role in developing economies' transition towards net-zero emissions. However, their promotion can cause multifaceted sustainability concerns. National biofuel policies often align with the optimistic discourse surrounding biofuels but may lack comprehensive measures to simultaneously address all sustainability risks. This study conducts a systematic review to evaluate the sustainability performance of biofuels and examines their implications for advancing the Sustainable Development Goals (SDGs). A total of 12 sustainability indicators were identified as economic, social, and environmental priorities. Biofuel linkages with 8 SDGs, 21 targets, and 22 indicators were mapped. The analysis revealed a wider coverage of sustainability impacts associated with biodiesel compared to ethanol feedstocks for India. Notably, the sustainability effects of biofuels exhibited considerable variability across different spatial scales. Irrespective of the biofuel types, negative sustainability outcomes were found to be associated with socio-economic indicators related to food security, livelihood, and income, and environmental indicators like land use. Positive sustainability effects were observed for environmental indicators like water and soil quality, biodiversity, and ecosystem services. The study identifies policy gaps in addressing localized adverse effects of biofuels, emphasizing the need to align biofuel strategies with SDGs for more comprehensive and sustainable biofuel development in developing countries.

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Journal Articles | 2023

What money couldn’t buy: Social protection for migrants in India’s lockdown

Karan Singhal, Ankur Sarin, Advaita Rajendra

We analyze findings from a large-scale survey of over 11,000 respondents across 64 districts in India, conducted between December 2020 and January 2021 to examine the impact of the lockdown on internal migrants in India. We find that compared to the households without migrants, households with migrants were relatively advantaged in income levels before the pandemic but faced more severe food and financial vulnerability even nine months after the first lockdown. In addition, governmental social security support was more difficult to access for households with migrants. The paper joins several scholars in arguing for greater policy attention and social protection for migrants.

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Journal Articles | 2023

Doing “Reputation” in the Indian context: An employee perspective

Avani Desai, Asha Kaul, Vidhi Chaudhri

perceptions of employees, a critical group of stakeholders, within the Indian context and examines factors that inform an understanding of reputation from an employee perspective and shares the consequences of the same. Building on existing research conducted in developed countries, the study reveals similarities and dissimilarities with existing reputation conceptualizations. Results reveal three new factors, namely stakeholder connect, customer centricity, and company ethos, which are critical to an understanding of reputation from the perspective of Indian employees. Based on factors and attributes emerging from employee perceptions, the study proposes the Loyalty, Engagement, Emotional Connect, and Commitment model, which highlights the consequences of a good reputation in the Indian context.

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Journal Articles | 2023

The distortion in the EU feed market due to import constraints on genetically modified soy

Shyam Kumar Basnet, Ranjan Kumar Ghosh, Mattias Eriksson, Carl-Johan Lagerkvist

Feed importers in some EU member states face constraints on imports of genetically modified (GM) soy, a practice that may compromise the interests of EU livestock farmers. Using the cases of Sweden and Austria, we analyzed price transmission in the soy supply chain originating from Brazil, applying an asymmetric non-linear auto-regressive distributed lag (ARDL) model to identify short-run and long-run asymmetries. The results revealed significant asymmetric effects in how positive and negative price changes are absorbed within the feed industry. Notably, increases in the cost of Brazilian soy swiftly affect the prices for EU farmers, while cost reductions fail to trigger corresponding price decreases. Consequently, stronger constraints on GM soy imports are likely to exacerbate the competitiveness challenges faced by livestock farmers, primarily due to their reliance on non-GM soy. This implies that the restrictions on GM imports need to be relaxed or that low-cost local protein alternatives need to be developed.

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Journal Articles | 2023

Transitioning diets: a mixed methods study on factors affecting inclusion of millets in the urban population

Suruchi Singh, Vidya Vemireddy

The increasing health challenge in urban India has led to consumers to change their diet preferences by shifting away from staple cereals and making way for healthier foods such as nutri-cereals like millets and other diverse food groups. Taking the case of millets, this study seeks to uncover the exact drivers for this shift of consumers away from a traditional cereal dense diet to a nutritionally more diverse diet that includes nutri-cereal. We also look at deterrents that dissuade consumers from shifting to millets.

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Journal Articles | 2023

Understanding the impact of augmented reality product presentation on diagnosticity, cognitive load, and product sales

Pratik Tarafdar, Alvin Chung Man Leung, Wei Thoo Yue, Indranil Bose

Augmented reality (AR) enhances consumers’ sensory responses to online product presentations, providing a more immersive experience. In online marketplaces, the utilization of various sensory modalities for product representation proves valuable for consumers’ evaluations. To investigate the impact of AR interfaces on human cognition, we developed a mobile AR app and conducted an experiment. Subjects tested the app, equipped with AR capabilities, alongside traditional two-dimensional (2D) representations for various product types. Our findings reveal that, in comparison to conventional 2D presentations, AR affordances significantly enhance consumers’ perceived product diagnosticity. Notably, this effect is more pronounced for technology products. Additionally, our research indicates that AR interfaces may contribute to an increased perceived cognitive load. In a second study, we conducted a natural experiment using AR-enabled Amazon products to explore the influence of AR interfaces on purchase decisions. For technology products, we observed a substantial increase in product sales when utilizing AR for online presentations. This research makes a valuable contribution to the mobile commerce literature, offering insights to retailers about the efficacy of AR interfaces in the realm of mobile shopping.

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Journal Articles | 2023

Zoning strategies for human–robot collaborative picking

Kaveh Azadeh, Debjit Roy, René de Koster, Seyyed Mahdi Ghorashi Khalilabadi

During the last decade, several retailers have started to combine traditional store deliveries with the fulfillment of online sales to consumers from omni-channel warehouses, which are increasingly being automated. A popular option is to use autonomous mobile robots (AMRs) in collaboration with human pickers. In this approach, the pickers' unproductive walking time can be reduced even further by zoning the storage system, where the pickers stay within their zone periphery and robots transport order totes between the zones. However, the robotic systems' optimal zoning strategy is unclear: few zones are particularly good for large store orders, while many zones are particularly good for small online orders. We study the effect of no zoning (NZ) and progressive zoning strategies on throughput capacity for balanced zone configurations with both fixed and dynamic order profiles. We first develop queuing network models to estimate pick throughput capacity that correspond to a given number of AMRs and picking with a fixed number of zones. We demonstrate that the throughput capacity is dependent on the chosen zoning strategy. However, the magnitude of the gains achieved is influenced by the size of the orders being processed. We also show that using a dynamic switching strategy has little effect on throughput performance. In contrast, a fixed switching strategy benefiting from changes in the order profile has the potential to increase throughput performance by 17% compared to the NZ strategy, albeit at a higher robot cost.

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Journal Articles | 2023

Zoning strategies for human–robot collaborative picking

Kaveh Azadeh, Debjit Roy, René de Koster, Seyyed Mahdi Ghorashi Khalilabadi

During the last decade, several retailers have started to combine traditional store deliveries with the fulfillment of online sales to consumers from omni-channel warehouses, which are increasingly being automated. A popular option is to use autonomous mobile robots (AMRs) in collaboration with human pickers. In this approach, the pickers' unproductive walking time can be reduced even further by zoning the storage system, where the pickers stay within their zone periphery and robots transport order totes between the zones. However, the robotic systems' optimal zoning strategy is unclear: few zones are particularly good for large store orders, while many zones are particularly good for small online orders. We study the effect of no zoning (NZ) and progressive zoning strategies on throughput capacity for balanced zone configurations with both fixed and dynamic order profiles. We first develop queuing network models to estimate pick throughput capacity that correspond to a given number of AMRs and picking with a fixed number of zones. We demonstrate that the throughput capacity is dependent on the chosen zoning strategy. However, the magnitude of the gains achieved is influenced by the size of the orders being processed. We also show that using a dynamic switching strategy has little effect on throughput performance. In contrast, a fixed switching strategy benefiting from changes in the order profile has the potential to increase throughput performance by 17% compared to the NZ strategy, albeit at a higher robot cost.

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Journal Articles | 2023

Celebrity co-creator or celebrity endorser? Exploring mediating and moderating factors in Marcom decision

Subhadip Roy, Aditya Shankar Mishra, Ainsworth Anthony Bailey

The present research delves into the concept of celebrity co-creation from the consumer behavior perspective. It explores the impact of the degree of a celebrity's involvement with a brand (celebrity as an endorser vs. celebrity as a co-creator) on consumers' advertisement and brand-based evaluations (Study 1) and purchase behavior (Study 2). The research subsequently incorporates the mediating effects of consumers' perceived risk (Study 3) and the moderating effect of celebrity expertise (Study 4) in the relationships. Three of the four studies were controlled experiments among nonstudent samples (combined n = 486), while one was a field study. Major findings indicate that a celebrity co-creator is more effective than a celebrity endorser, but both cases of celebrity presence are more effective than the control (Studies 1 and 2). This effect is observed to be mediated by the consumers' perceived risk (Study 3) and moderated by the celebrity's expertise (Study 4). The present research provides a new direction to value co-creation research from the communications perspective and adds to the literature on celebrity endorsements.

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Journal Articles | 2023

A gradient-based bilevel optimization approach for tuning regularization hyperparameters

Ankur Sinha, Tanmay Khandait, Raja Mohanty

Hyperparameter tuning in the area of machine learning is often achieved using naive techniques, such as random search and grid search. However, most of these methods seldom lead to an optimal set of hyperparameters and often get very expensive. The hyperparameter optimization problem is inherently a bilevel optimization task, and there exist studies that have attempted bilevel solution methodologies to solve this problem. These techniques often assume a unique set of weights that minimizes the loss on the training set. Such an assumption is violated by deep learning architectures. We propose a bilevel solution method for solving the hyperparameter optimization problem that does not suffer from the drawbacks of the earlier studies. The proposed method is general and can be easily applied to any class of machine learning algorithms that involve continuous hyperparameters. The idea is based on the approximation of the lower level optimal value function mapping that helps in reducing the bilevel problem to a single-level constrained optimization task. The single-level constrained optimization problem is then solved using the augmented Lagrangian method. We perform extensive computational study on three datasets that confirm the efficiency of the proposed method. A comparative study against grid search, random search, Tree-structured Parzen Estimator and Quasi Monte Carlo Sampler shows that the proposed algorithm is multiple times faster and leads to models that generalize better on the testing set.

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