Browsing by Author "Liu, Xianwei"

Now showing 1 - 13 of 13
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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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    How do hotel managers react to rating fluctuation?
    Xu, Yukuan; Zhang, Zili; Nicolau, Juan Luis; Liu, Xianwei (Elsevier, 2020-08-01)
    Rating fluctuation is inevitable for hotels listed on hotel booking platforms, which induces potential consumers’ perception of uncertainty and risk. Managerial response is expected to be effective in enhancing the interaction between hotels and consumers. However, how hotel managers react to rating fluctuation remains unclear. In order to fill this gap in the literature, we collect customer reviews and managerial responses from a leading hotel booking platform and build a panel dataset (hotel*month). The empirical results suggest that (1) rating fluctuation induces more managerial responses and requires more response time; (2) upscale hotels are more likely to conduct frequent and timely responses when facing rating fluctuation; and (3) hotels tend to respond more frequent and timely once rating fluctuation is observed by a larger audience. This study concludes by presenting theoretical contributions to the literature and practical implications for operators of hotel booking platforms and hotel managers.
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    Insights into managerial responses to repeat customers: The moderating role of social influence and revisit intention
    Ji, Xiaoxian; Nicolau, Juan Luis; Liu, Xianwei (Emerald, 2023-04-10)
    Purpose: Repeat customers play an important role in the restaurant sector. Previous studies have confirmed the positive effect of managerial responses on customer relationship management. However, the practice of managerial response strategies toward repeat customers in the restaurant sector remains unclear. This study aims to explore how social influence and the revisit intention of customers affect the responding behavior of restaurant managers. Design/methodology/approach: This study collects information of 251,944 customer reviews and managerial responses from 1,272 restaurants on Yelp (a leading restaurant review website around the world) and builds four econometric models (with restaurant and month fixed effects) to test the hypotheses empirically. Findings: The empirical results show that restaurant managers are less likely to respond to reviews posted by repeat customers (10% lower than that of new customers). This effect is moderated by customer social influence, which entails that repeat customers with great social influence are more likely to receive managerial responses. Moreover, reviews from repeat customers who have had a longer time since their last consumption are also more likely to receive managerial responses. Practical implications: The results present implications for restaurant managers in business practice regarding managerial response. Managers should take advantage of platform designs and tools (i.e. customer relationship management programs to keep track of repeat customers) to locate repeat customers and avoid the potential negative effects caused by their selected response strategies. Originality/value: To the best of the authors’ knowledge, this study is among the first attempts to examine empirically how restaurant managers respond to reviews generated by repeat customers in real business practice and reveals what drives such activities from the perspectives of social influence and revisit intention.
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    Long rating scales trigger positive ratings: When referent thinking beats relative thinking
    Ji, Xiaoxian; Nicolau, Juan Luis; Liu, Xianwei (Pergamon-Elsevier, 2023-09)
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    OFDI and inbound tourism: a perspective of reverse country-of-origin effect
    Yu, Yiqing; Nicolau, Juan Luis; Liu, Xianwei; Chen, Zheshi (Routledge, 2021-04-05)
    This study is to analyze the effect of the investor country’s outward foreign direct investment (OFDI) on its inbound tourism from the investee country. Although overseas expansion is a prevalent strategy, this study fills a gap in the literature: inbound tourism as a potential spillover of OFDI remains unexplored. Accordingly, this research proposes a conceptual model based on the reverse country-of-origin effect and conducts an empirical application with data from 202 countries/regions. The main findings show that countries with a higher number of OFDIs tend to attract more inbound tourism from the investee countries. Theoretical and practical implications are discussed.
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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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    Repeat Customers and Satisfaction: Uncovering New Intricacies Through Restaurant Reviews
    Ji, Xiaoxian; Nicolau, Juan Luis; Law, Rob; Liu, Xianwei (Sage, 2022-12-14)
    Repeat customers are crucial for business success. Previous studies have mainly focused on those factors that affect repeat patronage but ignored how repeat customers reevaluate the same service provider after consumption. We obtained a dataset containing 637,748 reviews of restaurants in New York City and used a generalized difference-in-differences design to further explore the rating behavior of local repeat customers. The results of this study contribute to theories of customer satisfaction, repeat patronage, and customer location in the context of user-generated content as repeat customers are found to be sensitive to quality variations. Such sensitivity is even accentuated by local customers. Relevant practical implications for restaurant managers are also drawn from the results.
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    The search value model: Detecting abnormal searching behavior
    Nicolau, Juan Luis; Kim, Hyoeun; Liu, Xianwei (Pergamon-Elsevier, 2021-04-20)
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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 saturation effect in hotel managerial response
    Liu, Xianwei; Ye, Qiang; Nicolau, Juan Luis; Xu, Yukuan (Elsevier, 2022-04-01)
    Hotel booking platforms widely adopt managerial response. The literature supports its positive effect on product/service evaluations, but the question of a potential saturation effect and its implications are yet to be analyzed. The present study fills this gap by empirically analyzing 4,888 hotels and over two million hotel reviews and finds that 1) managerial response enhances future ratings of hotels with low rating with a diminishing marginal utility, 2) the effect of managerial response on reducing rating fluctuation mainly works for hotels with high variance, and 3) the effectiveness of managerial response in enhancing the rating valence and reducing the rating variance of a hotel weakens when dealing with experienced consumers. These findings provide direct implications for hotel booking platforms and hotel managers.
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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.