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Professionalizing Sharing Platforms for Sustainable Growth in the Hospitality Sector: Insights Gained through Hierarchical Linear Modeling

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  • Emeka Ndaguba

    (Department of Applied Management, College of Economic and Management Sciences, University of South Africa, Theo van Wijk Building, Preller Street, Muckleneuk Ridge, P.O. Box 392, Pretoria 0003, South Africa)

  • Cina Van Zyl

    (Department of Applied Management, College of Economic and Management Sciences, University of South Africa, Theo van Wijk Building, Preller Street, Muckleneuk Ridge, P.O. Box 392, Pretoria 0003, South Africa)

Abstract

The sharing economy relating to e-hospitality is threatened globally with sanctions and closure owing to incessant noise and partying complaints, as well as complaints relating to reckless driving, tax evasion, and its social and economic effect on residents and accommodation vendors of longer stay rentals. Because the government is seeking a balance in regulating the e-hospitality sector, we sought to explore how professionalism of the e-hospitality platforms could potentially contribute to the sustainable growth of the sector in local and regional communities. In our study we developed a conceptual narrative that distinguishes two dimensions of professionalism for the sharing economy, namely the ticket clipper and end-to-end model. Data for the research was obtained from Vacation Rental Data (Airdna). Airdna provides a databank for both Airbnb and VRBO/Stayz. For the study a dataset from Airdna for HomeAway, also popularly known as Stayz, was utilized as a representative sample from a tourism town in Western Australia. For analysis of the dataset, path/panel regression was utilized, with a hierarchical linear model subsequently adopted for cross-section and multi-sectional analysis. Findings in the study demonstrate that professionals tend to improve the overall rating, and where the overall rating mediates the relationship between management firm (property/apartment/accommodation venue) and price. It was further observed that no relationship exists between overall rating and the number of HomeAway supply types; nevertheless, professionals promote the image and reputation of the property. Contrary, bad, or negative e-hospitality reviews lead to avoidance by prospective visitors. Lastly, results from the study took the form of two theoretical contributions, namely the ticket clipper model and the end-to-end model. More complaints were received concerning ticket clippers and it was noted that this model has caused severe shutdown in several cities and regions. The end-to-end model appears to be more sustainable. Moreover, literature suggests that there are more complaints from residents concerning ticket clippers and it was noted that this model has caused severe shutdown in several cities, nonetheless the end-to-end model appears to be more sustainable.

Suggested Citation

  • Emeka Ndaguba & Cina Van Zyl, 2023. "Professionalizing Sharing Platforms for Sustainable Growth in the Hospitality Sector: Insights Gained through Hierarchical Linear Modeling," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8267-:d:1150591
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    References listed on IDEAS

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    1. Zahyah H. Alharbi, 2023. "A Sustainable Price Prediction Model for Airbnb Listings Using Machine Learning and Sentiment Analysis," Sustainability, MDPI, vol. 15(17), pages 1-19, September.

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