Browsing by Author "Li, Chunhong"

Now showing 1 - 6 of 6
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    Applying image recognition techniques to visual information mining in hospitality and tourism
    Liu, Xianwei; Nicolau, Juan Luis; Law, Rob; Li, Chunhong (Emerald, 2022-10-31)
    Purpose: This study aims to provide a critical reflection of the application of image recognition techniques in visual information mining in hospitality and tourism. Design/methodology/approach: This study begins by reviewing the progress of image recognition and advantages of convolutional neural network-based image recognition models. Next, this study explains and exemplifies the mechanisms and functions of two relevant image recognition applications: object recognition and facial recognition. This study concludes by providing theoretical and practical implications and potential directions for future research. Findings: After this study presents different potential applications and compares the use of image recognition with traditional manual methods, the main findings of this critical reflection revolve around the feasibility of the described techniques. Practical implications: Knowledge on how to extract valuable visual information from large-scale user-generated photos to infer the online behavior of consumers and service providers and its influence on purchase decisions and firm performance is crucial to business practices in hospitality and tourism. Originality/value: Visual information plays a crucial role in online travel agencies and peer-to-peer accommodation platforms from the side of sellers and buyers. However, extant studies relied heavily on traditional manual identification with small samples and subjective judgment. With the development of deep learning and computer vision techniques, current studies were able to extract various types of visual information from large-scale datasets with high accuracy and efficiency. To the best of the authors’ knowledge, this study is the first to offer an outlook of image recognition techniques for mining visual information in hospitality and tourism.
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    Face recognition of profile images on accommodation platforms
    Liu, Xianwei; Li, Chunhong; Nicolau, Juan Luis; Han, Meini (Routledge, 2022-08-13)
    Visual information plays a critical role on peer-to-peer (P2P) accommodation platforms. Recent studies have found that attractive hosts possess advantages in alluring potential guests and charging high prices, highlighting the beauty premium effect from the perspective of hosts. Are attractive guests more likely to receive better service from their hosts, thus producing a beauty premium effect from the perspective of guests? To answer this undocumented research question, we collect data from Airbnb accommodations listed in Los Angeles, New York City, and Orlando in the US. By virtue of deep learning techniques, face recognition, and text-mining, our empirical results reveal a beauty premium effect from the perspective of guests that attractive guests are more satisfied with their accommodations and receive more interactions from hosts. These findings illustrate the application of face recognition in the context of P2P accommodation platforms and provide direct implications for the operation of accommodation platforms.
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    Online engagement and persistent reactions to social causes: The black-owned business attribute
    Liu, Xianwei; Han, Meini; Nicolau, Juan Luis; Li, Chunhong (Elsevier, 2022-02-01)
    The persistence of COVID-19 exerts an unprecedented impact on the tourism and hospitality industry and the Black-owned businesses had been hit disproportionately harder than any other racial group. Many platforms (e.g., Airbnb and Yelp) have run a series of campaigns to support Black-owned businesses. Determining whether these campaigns are effective in attracting supports from consumers or just merely promotional is key. Based on the theoretical framework of Hennig-Thurau et al. (2004) and Aldous et al. (2019) and data collected from Yelp, this study reveals that higher levels of engagement produce persistent reactions. Specifically, the rating support lasts for one month and then vanishes; the review support increases for three months and then gets reversed; and the verbal support lasts for three months but does not get reversed. The findings of this study contribute to theories of online engagement and provide direct implications for platforms involving social campaigns in business practice.
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    Racial discrimination in online booking: how profile pictures affect host behaviors and platform actions
    Li, Chunhong; Nicolau, Juan Luis; Liu, Xianwei (Informa UK Limited, 2024-08-13)
    Research has shown that racial discrimination is detrimental to the ethnic minorities in accommodation and hospitality sectors. However, whether racial discrimination happens during the stage of online booking before check-in remains unclear. Leveraging a natural experiment of the anti-discrimination policy implemented on Airbnb and face recognition techniques to identify the racial information of hosts and guests, this study reveals that racism from hosts against ethnic guests indeed exists during the stage of online booking. The results indicate that the monthly proportion of ethnic guests increases 5% after the launch of anti-discrimination policy. We also find that discrimination exists between ethnic hosts and ethnic guests, which was not documented in previous studies. Moreover, the anti-discrimination policy in relieving racism plays a bigger role among hosts with severe racial discrimination. These findings are critical for online booking platforms to set anti-discrimination policies.
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    Smiley guests post long reviews!
    Li, Chunhong; Ye, Qiang; Nicolau, Juan Luis; Liu, Xianwei (Elsevier, 2021-07-01)
    The inclusion of a photo in users’ profile provides information about them and shows a higher sense of self-expression and potential engagement. On peer-to-peer rental platforms, profile images may be useful for hosts and guests to infer individual characteristics and expectations. We try to fill a gap in the literature by inferring guests’ posting behavior through their profile image. Using Airbnb data and deep learning techniques, our empirical analysis reveals that guests who upload profile images—especially profile images displaying happy emotions—are more involved in posting long reviews. As theoretical implications, these results add knowledge to the application of the Five Factor Model of Personality, deep learning, image recognition, and emotion recognition in hospitality. As managerial implications, the prediction of posting behavior through the mining of visual information can be a relevant tool in the age of big data.
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    The value of rating diversity within multidimensional rating system: Evidence from hotel booking platform
    Liu, Xianwei; Li, Chunhong; Nicolau, Juan Luis; Han, Meini (Elsevier, 2023-04-01)
    An increasing number of hotel booking platforms and review websites deploy multidimensional rating systems to encourage users to provide additional evaluations of a product/service besides the overall rating. However, providing the itemized ratings of a product/service requires extra effort which makes users tend to post identical ratings (default setting). According to the information transfer theory, we postulate that rating diversity enhances review usefulness within the multidimensional rating system and; based on the loss aversion phenomenon, we also posit that the positive effect of rating diversity on review usefulness should be greater among negative reviews. Using a sample of 1,720,429 hotel reviews, this study reveals that while rating diversity enhances review usefulness, its effect varies across the valence of each review; specifically, the positive effect of rating diversity mainly functions among negative reviews. These findings yield direct implications for hotel booking platforms or review websites that deploy multidimensional rating systems.