August 2024
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2 Citations
International Journal of Research in Marketing
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August 2024
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2 Citations
International Journal of Research in Marketing
January 2024
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12 Reads
June 2023
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202 Reads
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6 Citations
Journal of Marketing Research
The erosion of high-end fashion brands by fast-fashion copycats (e.g., Zara, H&M) has stirred controversies and unceasing legal attempts to copyright fashion designs. Despite the purported negative impact of copycats, the effect of fashion copycats on high-end brands remains empirically unclear. Research on this topic has been impeded by the absence of a modeling framework to quantify fashion and by the lack of consumer-level data on fashion choices. The authors collect data on the posting behaviors of consumers on a fashion-specific social media platform and develop a dynamic structural model with deep learning image analytics to characterize consumers’ choices of brands and styles. Results suggest that fast-fashion copycats can both harm high-end brands (a cannibalization effect) and help them (a market expansion effect). The authors also identify both static and dynamic mechanisms that contribute to the market expansion effect: The affordability of mixing copycats with high-end brands boosts the number of high-end items featured in posts by financially constrained consumers (a static mechanism). In addition, good styles from copycats enable users to build their popularity on social media over time, which may increase the users' valuation of high-end brands and reduce the users' future costs via sponsorship opportunities (dynamic mechanisms). The results could inform policy makers about the potential consequences of prohibiting fashion copycats.
January 2023
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20 Reads
SSRN Electronic Journal
July 2022
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60 Reads
We utilize an offline reinforcement learning (RL) model for sequential targeted promotion in the presence of budget constraints in a real-world business environment. In our application, the mobile app aims to boost customer retention by sending cash bonuses to customers and control the costs of such cash bonuses during each time period. To achieve the multi-task goal, we propose the Budget Constrained Reinforcement Learning for Sequential Promotion (BCRLSP) framework to determine the value of cash bonuses to be sent to users. We first find out the target policy and the associated Q-values that maximizes the user retention rate using an RL model. A linear programming (LP) model is then added to satisfy the constraints of promotion costs. We solve the LP problem by maximizing the Q-values of actions learned from the RL model given the budget constraints. During deployment, we combine the offline RL model with the LP model to generate a robust policy under the budget constraints. Using both online and offline experiments, we demonstrate the efficacy of our approach by showing that BCRLSP achieves a higher long-term customer retention rate and a lower cost than various baselines. Taking advantage of the near real-time cost control method, the proposed framework can easily adapt to data with a noisy behavioral policy and/or meet flexible budget constraints.
January 2022
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7 Reads
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1 Citation
SSRN Electronic Journal
December 2021
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69 Reads
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17 Citations
Journal of Marketing Research
Consumers’ choices about health products are heavily influenced by public information, such as news articles, research articles, online customer reviews, online product discussion, and TV shows. Dr. Oz, a celebrity physician, often makes medical recommendations with limited or marginal scientific evidence. Although reputable news agencies have traditionally acted as gatekeepers of reliable information, they face the intense pressure of “the eyeball game.” Customer reviews, despite their authenticity, may come from deceived consumers. Therefore, it remains unclear whether public information sources can correct the misleading health information. In the context of over-the-counter weight loss products, the authors carefully analyze the cascading of information post endorsement. The analysis of extensive textual content with deep-learning methods reveals that legitimate news outlets respond to Dr. Oz's endorsement by generating more news articles about the ingredient; on average, articles after the endorsement contain higher sentiment, so news agencies seem to amplify rather than rectify the misleading endorsement. The finding highlights a serious concern: the risk of hype news diffusion. Research articles react too slowly to mitigate the problem, and online customer reviews and product discussions provide only marginal corrections. The findings underscore the importance of oversight to mitigate the risk of cascading hype news.
January 2021
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56 Reads
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3 Citations
SSRN Electronic Journal
January 2021
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73 Reads
SSRN Electronic Journal
January 2021
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25 Reads
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8 Citations
SSRN Electronic Journal
... In comparison, while projects like CryptoPunks have innovated in terms of artistic style, Yuan Yuan, Xiao Liu, Shunyuan Zhang, and Kannan Srinivasan have noted that female CryptoPunks typically trade at a 36.8% lower price than male CryptoPunks, and Black CryptoPunks at a 30.7% lower price than their White counterparts [3]. This contrast offers a unique perspective on how digital art challenges social norms. ...
August 2024
International Journal of Research in Marketing
... Indeed, luxury products are characterised by scarcity, high quality and price, and (even more importantly) by the ability to create a unique and desirable identity that consumers aspire to reach (Oc et al., 2023;Pantano, 2021;Pantano & Stylos, 2020). Moreover, luxury fashion brands are challenged by the frequent copycats and dupes provided by fast fashion (Shi et al., 2023). Thus, the debate on using GAI led by LLMs as an opportunity or threat for the luxury industry still requires further investigation. ...
June 2023
Journal of Marketing Research
... One of the reasons we inferred is that there is a single sales channel for the product in the US. "Within-channel Direct Selling is far inferior to Cross-channel Spillover Effects" [5]. The single sales channel is reflected in all parts of the sales chain. ...
January 2022
SSRN Electronic Journal
... The characteristics are denoted as Sentiment, Subjectivity, Readability, Diversity, and Length. Following Shi et al. (2021), we computed Sentiment and Subjectivity concurrently via a widely used NLP tool, TextBlob, built using the Natural Language Toolkit library. TextBlob first tokenized the review text into tokens and then removed stop words and tagged token parts with significant signals of sentiment and subjectivity. ...
December 2021
Journal of Marketing Research
... Emotion analysis is conducted using image processing techniques, with studies directly focusing on the human face. In a study by [14], they attempted to calculate the charisma score of the human face. They aimed to determine the visual attractiveness of celebrities by using both celebrities and noncelebrities. ...
January 2021
SSRN Electronic Journal
... Though applications using automated approaches to extract features from videos are still very limited, it shows a promising and growing area (Schwenzow et al., 2021). A recent practical example is investigating consumer reactions to influencers in sponsored and non-sponsored videos (Hwang,Liu,and Srinivasan, 2021). video data can furthermore shed light on organizational phenomena, as Gylfe et al. (2016) have shown. ...
January 2021
SSRN Electronic Journal
... Not all consumers are receptive to this trend. Researchers have argued that the sweet spot for organizations turning to automation is to combine it with human insights (Proserpio et al., 2020). Speciesists may respond optimally to this combination. ...
December 2020
Marketing Letters
... Other studies such as [9] demonstrated the use of both types of matrices to compute complementarity between two products. Again, the authors failed to provide explanation on why this method works in finding the complementary relationship. ...
July 2020
... A variety of large-scale discrete choice models have been proposed and made tractable through regularization (Bajari et al., 2015), random projection (Chiong & Shum, 2018), and the use of auxiliary data on consumer consideration sets and search (Amano et al., 2019;Morozov, 2020). A second strand has focused on modeling demand for wider assortments spanning multiple categories and has exploited the flexibility of deep learning (Gabel & Timoshenko, 2021) and representation learning methods such as word embeddings (Ruiz et al., 2020) and matrix factorization (Chen et al., 2020;Donnelly et al., 2021). 1 There is also a well-established literature on economic models of multicategory demand (e.g., Manchanda et al., 1999;Song & Chintagunta, 2006;Mehta, 2007;Thomassen et al., 2017;Ershov et al., 2021). However, with the exception of Ershov et al. (2021), microfounded multi-category models become intractable at scale and have only been estimated on data with relatively small choice sets spanning a few categories. ...
January 2020
SSRN Electronic Journal
... For this, technology brands must know their (potential) customers. In this context, the characterization of potential consumers, e.g., with the help of sociodemographic or psychographic variables, is key to deriving perfectly tailored strategic and operational marketing strategies to attract customers (Mari et al., 2020(Mari et al., , 2024Sun et al., 2021). ...
January 2019
SSRN Electronic Journal