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Browsing by Author "Zhang, Huihui"

Now showing 1 - 5 of 5
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    Design standardization by Airbnb multi-unit hosts: Professionalization in the sharing economy
    Zhang, Huihui; Zach, Florian J.; Xiang, Zheng (Elsevier, 2023-01-01)
    Increased professionalism in the short-term rental market has enabled multi-unit hosts to replicate design features across their listings to increase efficiency; however, this standardization represents a huge risk caused by decreased flexibility. We identify the impacts of functional and aesthetic design standardization on guest experience and satisfaction using Airbnb as a case study. The findings show that design standardization impacts guest experience and satisfaction asymmetrically. The results provide implications for tourism place design by articulating the structural relationships of standardized design on guest experiences within the typically unstandardized home-sharing market. This study contributes to design literature by studying design from a strategic level and adds knowledge to standardization literature by testing customer-side outcomes within a micro-entrepreneurship context.
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    Multi-level differentiation of short-term rental properties: A deep learning-based analysis of aesthetic design
    Zhang, Huihui; Zach, Florian J.; Xiang, Zheng (Elsevier, 2023)
    This study aims to test the effects of differentiation on short-term rental performance along the dimension of aesthetic design. Online platforms display listing cover photos as search results, thus making aesthetic design a key element of differentiation. We hypothesize opposite impacts in two geographical scopes, local- and city-level, which answers an important question in differentiation literature of whom to compare to. Based on the assumption that localized competition has asymmetric influences, we introduce competition intensity as moderator. Hypotheses are tested with 96,196 listings from April 2021 to March 2022 in the Texas Airbnb market. We quantify aesthetic design by probability distribution scores over four design styles predicted by a pre-trained machine learning model. This study identifies differentiation benefits at local-level but discounts at city-level. Furthermore, it shows market intensity strengthens benefits and mitigates discounts regardless of the geographic scope. Finally, implications for aesthetic design as a strategic tool are discussed.
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    Optimal distinctiveness of short-term rental property design
    Zhang, Huihui; Zach, Florian J.; Xiang, Zheng (Elsevier, 2024-07)
    The short-term rental market remains highly competitive, requiring that hosts should identify effective strategies to position their products for desirable performance. This study investigates the optimal balance beyond dyadic choice between differentiating from or conforming to competitors, in the dimensions of properties’ functional and aesthetic design. We hypothesize U-shaped distinctiveness-performance relationships considering high legitimacy pressure and low strategy effectiveness in the short-term rental context. Moreover, the moderating effects of factors including online review volume and listing age are examined. Analyzing a sample of 99,757 Airbnb listings in Texas, the findings reveal different patterns of product positioning between functionality and aesthetics. The moderate degree of distinctiveness in functionalities leads to the worst performance while in aesthetics generating the best outcome. This study contributes to the hospitality literature by introducing and testing optimal distinctiveness within the short-term rental market. The findings also provide positioning guidance for short-term rental listings under different conditions.
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    Strategizing in Response to Environmental Uncertainty in the Hospitality Industry: A Data-Analytical Approach
    Zhang, Huihui (Virginia Tech, 2024-05-23)
    The hospitality industry confronts continuous challenges from external environments, such as the COVID pandemic, the proliferation of short-term rentals, and the disruptive innovations of Generative AI. For businesses, understanding these external conditions and adapting strategies accordingly is crucial yet challenging, especially considering environmental uncertainties. Therefore, this dissertation investigates the effectiveness of different strategies in navigating market, competitive, and technological uncertainties, through a big-data analytical approach. It incorporates three studies, each focusing on one specific strategy and its varying outcomes under environmental changes. These studies employ machine learning algorithms to quantify strategies and utilize econometric models to infer the causal relationships between strategies and their outcomes. The first study examines how standardization affects short-term rental unit survival across two market conditions: pre-COVID growth and during-COVID decline. The results indicate that the risks arising from standardization are heightened under market decline. In addition, the effectiveness of standardization varies with design attributes to which the strategy is applied. Standardizing functional design boosts unit survival in the growing market but leads to a higher failure rate during the decline. Aesthetic standardization, on the other hand, negatively impacts survival in both conditions, with a stronger effect in the declining market. The second study identifies the impacts of differentiation on unit performance in the short-term rental context in two competitive environments: local versus city-level. The findings suggest that the effectiveness of differentiation increases with competitive pressure. At the local level where firms face localized competition, differentiation enhances unit performance. Conversely, in city-level environments where direct competition diminishes, it yields negative outcomes. Moreover, competition intensity, as reflected by the number of competitors and the degree of market concentration, is found to amplify the benefits of and mitigate the drawbacks of differentiation. The third study explores if adopting Generative AI to hotel online review response can improve customer feedback, under varying technological settings. It finds that simulated AI adoption improves customer perceptions when Generative AI models operate at high temperatures, while models with low temperatures lead to negative outcomes. The findings further underscore the importance of task-technology fit, revealing that Generative AI's effectiveness varies with review valence. Specifically, high-temperature settings for positive reviews generate significant benefits, whereas low-temperature settings lead to adverse effects. Conversely, for negative reviews, AI adoption demonstrates more stable outcomes across temperature settings, indicating balanced benefits of both low and high temperatures. In short, this dissertation identifies that the effectiveness of standardization, differentiation, and AI adoption strategies is contingent on environmental conditions. It underscores the importance of strategic adaptation in navigating contemporary challenges.
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    Structure and properties of flax vs. lyocell fiber-reinforced polylactide stereocomplex composites
    Zhang, Huihui; Li, Qiao; Edgar, Kevin J.; Yang, Gesheng; Shao, Huili (Springer, 2021-07-28)
    A commonly used natural cellulose fiber (flax) and a regenerated cellulose fiber (Lyocell) were used at 20 wt% to reinforce polylactide stereocomplex (sc-PLA) composites. Composites were prepared by melt compounding cellulose fibers and an equivalent proportion of PLLA/PDLA, followed by injection molding. The structures and properties of these two kinds of cellulose fiber/sc-PLA composites were compared and evaluated. The results showed that the total crystallinity and stereocomplex crystallite content of composites could be increased by reinforcing with cellulose fibers, and Lyocell fibers were more effective in accelerating crystallinity and the formation of stereocomplex crystallites than flax fibers. Mechanical properties of Lyocell fibers were much poorer than those of flax fibers, and the interfacial adhesion values of Lyocell/sc-PLA composites were inferior to those of flax/sc-PLA composites. Lyocell/sc-PLA composites showed higher impact strength and similar tensile strength vs. flax/sc-PLA composites, but the Young’s modulus values of Lyocell/sc-PLA composites were lower than those of flax/sc-PLA composites. The Vicat softening temperatures of both flax/sc-PLA and Lyocell/sc-PLA composites were increased to nearly 100 °C higher than that of PLLA. Lyocell/sc-PLA composites showed the highest Vicat softening temperature of ~ 170 °C.