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743 items in total found

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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Journal Articles | 2023

A framework for fair decision-making over time with time-invariant utilities

Andrea Lodi, Sriram Sankaranarayanan, Guanyi Wang

Fairness is a major concern in contemporary decision problems. In these situations, the objective is to maximize fairness while preserving the efficacy of the underlying decision-making problem. This paper examines repeated decisions on problems involving multiple stakeholders and a central decision maker. Repetition of the decision-making provides additional opportunities to promote fairness while increasing the complexity from symmetry to finding solutions. This paper presents a general mathematical programming framework for the proposed fairness-over-time (FOT) decision-making problem. The framework includes a natural abstraction of how a stakeholder’s acquired utilities can be aggregated over time. In contrast with a natural, descriptive formulation, we demonstrate that if the aggregation function possesses certain basic properties, a strong reformulation can be written to remove symmetry from the problem, making it amenable to branch-and-cut solvers. Finally, we propose a particular relaxation of this reformulation that can assist in the construction of high-quality approximate solutions to the original problem and can be solved using simultaneous row and column generation techniques.

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Journal Articles | 2023

The impact of consumer expectations and familiarity on deceptive pricing in advertising: A view from drip pricing practice

Somak Banerjee, Sujay Dutta, Abhijit Biswas, Hyokjin Kwak

Drip pricing practice, which involves promoting a seemingly low initial price and then introducing add-on price components without upfront disclosures, is commonly seen as a deceptive advertising practice that can lead to negative associations. Here, we provide an alternative perspective by investigating that whether consumers form positive or negative opinions about drip pricing depends on their expectation of encountering it and their overall familiarity with this practice. That is, this research reveals that when consumers’ general familiarity with drip pricing is low, higher expectations of drip pricing create greater perceptions of price fairness and purchase intentions. Moreover, our findings indicate that higher consumer expectations of drip pricing lead to positive attributions and evaluations of the detailed pricing information, resulting in higher perceptions of price fairness, which, in turn, increases purchase intentions. Further, we show that when consumers have high expectations of encountering drip pricing, they evaluate ­pricing information more positively and deception less harshly, resulting in greater purchase intentions than when they do not expect it.

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Journal Articles | 2023

Understanding the relationship between reviews, search and sales: A study of the Indian car market

Madhuri Prabhala, Indranil Bose

While there has been extensive research on understanding the effects of online reviews on product sales, there is not enough investigation of the inter-relationships between online reviews, online search and product sales. The study attempts to address this gap in the context of the Indian car market.

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