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849 items in total found

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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Journal Articles | 2023

Role of derivatives market in attenuating underreaction to left-tail risks

Sumit Sourav, Sobhesh Kumar Agarwalla, Jayanth R. Varma

The anomalous negative relationship between left-tail risk measures and future returns has recently attracted the attention of finance researchers. We examine the role of the derivatives market in attenuating left-tail risk anomaly in India, where derivatives trade only for a subset of stocks. We find that the negative association between left-tail risk measure and future return is absent only in stocks having derivatives, indicating that derivatives trading hastens the diffusion of negative information into the stock prices. We find evidence that the information generation role of derivatives markets plays a primary role compared to investor inattention and limits to arbitrage.

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Journal Articles | 2023

When Nash meets Stackelberg

Margarida Carvalho, Gabriele Dragotto, Felipe Feijoo, Andrea Lodi, Sriram Sankaranarayanan

This article introduces a class of Nash games among Stackelberg players (NASPs), namely, a class of simultaneous noncooperative games where the players solve sequential Stackelberg games. Specifically, each player solves a Stackelberg game where a leader optimizes a (parametrized) linear objective function subject to linear constraints, whereas its followers solve convex quadratic problems subject to the standard optimistic assumption. Although we prove that deciding if a NASP instance admits a Nash equilibrium is generally a Σ𝑝2Σ2𝑝-hard decision problem, we devise two exact and computationally efficient algorithms to compute and select Nash equilibria or certify that no equilibrium exists. We use NASPs to model the hierarchical interactions of international energy markets where climate change aware regulators oversee the operations of profit-driven energy producers. By combining real-world data with our models, we find that Nash equilibria provide informative, and often counterintuitive, managerial insights for market regulators.

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