Howard Feiertag Department of Hospitality and Tourism Management

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Browsing Howard Feiertag Department of Hospitality and Tourism Management by Subject "Airbnb"

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    A motivation-based study to explain accommodation choice of senior tourists: Hotel or Airbnb
    Nicolau, Juan Luis; Rodríguez-Sánchez, Carla; Ruiz-Moreno, Felipe (Elsevier, 2024-10-01)
    Senior tourists, traditionally known for using offline methods and staying in hotels, are increasingly using new technologies and opting for alternative accommodation forms. Based on the push and pull theory of motivation, the generational cohort and lifespan development theories, this study fills a gap in the literature and examines senior tourists’ choice between Airbnb and hotels. A random parameter binomial logit applied to data from six European countries finds that the same motivation can act differently in this decision choice process. Rather than assuming that a motivation has an exclusive effect on each accommodation type, we propose the notion of a “differential fulfillment paradigm” to reflect the idea that two accommodation types can relate to the same motivation with different approaches. While both hotels and Airbnb can fulfill a common motivation, they do so through distinct features or attributes, offering consumers diverse avenues to achieve their desired outcome.
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    Airbnb vs hotel? Customer selection behaviors in upward and downward COVID-19 trends
    Nicolau, Juan Luis; Sharma, Abhinav; Shin, Hakseung; Kang, Juhyun (Emerald, 2023-04-04)
    Purpose: To provide a dynamic view on accommodation choice behaviors during the pandemic, this study aims to examine the impact of recent trends on prospective travelers’ preferences for hotels and Airbnb. Design/methodology/approach: The paper adopts a mixed methods approach that incorporates three independent studies (experimental analysis, online search pattern analysis and an econometric event study) to understand customer decision-making behaviors. Findings: The findings indicate that travelers prefer Airbnb entire flats/apartments to hotels when the pandemic is trending upward. This result externally validates travelers’ preference toward Airbnb during periods of high risk. Interestingly, when the trends go downward, however, the same behavioral pattern was not identified. Research limitations/implications: This study provides important empirical insights into how the evolution of health crises influence customer decision-making for hotels and Airbnb. Future research needs to consider the role of socio-demographic factors in accommodation selection behaviors and examine how travelers react to cleanliness levels between Airbnb and hotels. Originality/value: As one of initial studies that empirically examine Airbnb customers’ decision-making behaviors in the context of the COVID-19 pandemic’s trends, this study provides a dynamic view on how the evolution of the pandemic influences accommodation choice behaviors.
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    Disruptor Recognition and Market Value of Incumbent Firms: Airbnb and the Lodging Industry
    Bianco, Simone; Zach, Florian J.; Singal, Manisha (Sage, 2022-04-03)
    Although Airbnb debuted in 2008, incumbent lodging firms did not fully recognize it as a legitimate competitor for several years. However, as Airbnb made inroads into the accommodation business, hotel firms and their investors started to take notice and to legitimize its disruptive role. In this paper, we investigate investors’ awareness of the disruptor Airbnb as a competitor of incumbent lodging firms. Specifically, we assess the effect of awareness on incumbent hotel management and hotel property owner firms. Employing an event study methodology, our analysis finds that Airbnb performance milestones negatively affect incumbents’ market value. This research contributes to our understanding of the role played by investors and financial analysts in shaping competitive markets by legitimizing an industry disruptor and by spurring competitive action among incumbent firms.
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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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    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.