Economies-of-scale, while beneficial in traditional manufacturing, present a complex challenge in the hospitality sector, particularly for homestay businesses which are vulnerable to market exits. Existing research primarily focuses on manufacturing, lacking direct applicability to the service-oriented nature of hospitality. This study aims to explore the nuanced impact of economies-of-scale on homestay business survival, distinguishing between personalized and professionalized models. Personalized homestays emphasize unique experiences and host-guest interaction, while professionalized homestays operate more like hotels, prioritizing standardization and efficiency. The rapid growth of the sharing economy, exemplified by Airbnb's success, highlights the importance of understanding sustainability in this sector. However, regulations and platform dynamics introduce constraints, influencing both individual and professional hosts. This research seeks to bridge this gap by examining how economies-of-scale affect the survival of different homestay business models, considering factors such as host-guest communication and product quality.

Previous research on survival analysis in the hospitality industry has largely focused on traditional hotels and restaurants, utilizing financial and non-financial factors to predict longevity. While some studies have examined factors impacting sharing accommodation survival, they lack the granularity needed to differentiate between professional and personalized businesses. Studies have shown that managing multiple listings can affect revenue, pricing, and guest satisfaction, but the specific impact on survival, particularly considering the heterogeneity of homestay businesses, remains unclear. This study builds upon this literature by explicitly differentiating between personalized and professionalized homestays, considering the unique contributions of social value and economies-of-scale respectively, and by incorporating the moderating effects of host-guest communication and product quality.

This study uses a panel dataset of 551,605 Airbnb listings in New York City, Los Angeles, Chicago, and Austin from February 2019 to June 2020. The study focuses on listings that entered the market during this period to mitigate left-truncation bias. The dependent variable is business survival (exit or continued operation). The key independent variables include: business type (professional vs. personalized), number of listings, communication quality (host response rate), product quality (hard attributes – price; soft attributes – Superhost certification), and a range of control variables (accommodation type, amenities, location, reviews, etc.). Panel survival analysis, specifically the Cox and exponential models, are employed to analyze the data, accounting for the temporal dependence and multiple observations per property. The models include interaction terms to examine the moderating effects of communication and product quality. Log transformations are used for variables with high standard deviations. Kaplan-Meier survival curves are used for non-parametric analysis. Robustness checks involve using alternative models (Accelerated Failure Time) and different definitions of professional homestay businesses (based on booking duration and number of listings).

Kaplan-Meier survival curves reveal intersecting survival rates for professional and personalized businesses, highlighting the need for time-specific analysis. The log-rank test confirms significant differences in survival rates across both short and long-term periods. Model 1 reveals that professional businesses have higher survival rates in the short term (within 3 months) due to economies-of-scale advantages. However, in the long term (beyond 3 months), personalized businesses demonstrate better survival due to their social value-adding capabilities. Model 2 indicates that increased listings negatively impact personalized business survival but positively affect professional business survival, showcasing the contrasting effects of economies-of-scale. Model 3 investigates the moderating effects. High-quality host-guest communication enhances survival for both business types. Higher prices (hard attribute) mitigate the negative impact of economies-of-scale on personalized businesses, but this effect isn’t present in professional businesses. Superhost certification (soft attribute) does not significantly moderate survival for personalized businesses but negatively moderates survival for professional businesses, suggesting that exceeding guest expectations is crucial for maintaining a good reputation. Robustness checks using the AFT model and alternative definitions of professional businesses yield consistent results. Interaction plots illustrate the complex interplay between multi-listing management, communication, pricing, and Superhost status on survival.

The findings challenge the simplistic application of economies-of-scale theory to the hospitality sector. The short-term advantage of economies-of-scale for professional businesses highlights the initial efficiency gains of standardization and professional management. However, the long-term dominance of personalized businesses underscores the importance of social value and the limitations of simply scaling up in a service-oriented industry. The moderating effects of communication and product quality demonstrate the importance of strategic management choices that balance efficiency with the unique aspects of the homestay experience. This study highlights the strategic tension between maximizing efficiency (economies-of-scale) and leveraging the social aspects of the homestay model. The findings emphasize the need for tailored strategies for different types of homestay businesses rather than a one-size-fits-all approach.

This study significantly contributes to the literature by highlighting the nuanced impact of economies-of-scale on the survival of personalized and professionalized homestay businesses. It reveals that the advantages of economies-of-scale are short-lived for professional businesses, while the social value added by personalized businesses is paramount in the long term. Effective communication and high-quality offerings are critical for optimizing survival in both models. Future research could explore the impact of platform regulations, host demographics, and dynamic pricing strategies on homestay business sustainability across a wider range of geographical areas and platforms.

Data limitations restrict the study's ability to definitively identify business failures (vs. migrations to other platforms). The definitions of professional homestay businesses, while based on existing literature, may influence the results. Future research could explore alternative definitions and incorporate data on actual business closures to enhance the validity of the findings. The study's timeframe is also limited to a specific period around the start of the COVID-19 pandemic and may not fully capture long-term trends.