Sharing economy and dynamic pricing: Is the impact of Airbnb on the hotel industry time-dependent?

Roma, Paolo; Panniello, Umberto; Vasi, Maria; Nigro, Giovanna Lo

doi:10.1016/j.jhtm.2021.09.013

Cited by 12 publications

(4 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, setting different lengths of stay results in different spatial patterns of listings (Oses Fernández et al , 2018). When running multiple queries is not possible, a common solution is to perform this during the high season when more listings are available (Adamiak, 2020; Roma et al , 2021). For the same reason, either weekend days are chosen for check-in (Roma et al , 2021) or separate sets are collected for week days (Gyódi and Nawaro, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…When running multiple queries is not possible, a common solution is to perform this during the high season when more listings are available (Adamiak, 2020; Roma et al , 2021). For the same reason, either weekend days are chosen for check-in (Roma et al , 2021) or separate sets are collected for week days (Gyódi and Nawaro, 2021). Finally, the limited number of listings displayed by the Airbnb site must be considered.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Addressing data quality in Airbnb research

Prentice

Pawlicz

2023

IJCHM

View full text Add to dashboard Cite

Purpose This paper aims to examine the primary supply data sources that have been used for research into the sharing economy, and the advantages and limitations of these sources in the literature. Design/methodology/approach To address the research aims, this study conducted a systematic literature review and content analysis of all relevant articles. Following the review, the methodological sections of the selected papers were examined to identify the characteristics and limitations of all data sources used in the papers. Findings This study revealed several limitations of the use of three major data sources, namely, web scraping with self-made bots, inside Airbnb and AirDNA, for sharing economy research. The review shows that the majority of the selected papers did not acknowledge any limitations, nor did they discuss the quality of the data sources. Research limitations/implications The findings of this paper can serve as guidelines for selecting appropriate data sources for research into the sharing economy and cautions researchers to address the limitations of the data sources used. Originality/value To the best of the authors’ knowledge, this is the first study that explores the advantages and limitations of data sources used in short-term rental market research.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Addressing data quality in Airbnb research

Prentice

Pawlicz

2023

IJCHM

View full text Add to dashboard Cite

show abstract

“…The tourism and hospitality industry has experienced significant changes due to the development of the sharing economy (Zhu et al , 2017). P2P accommodation platforms, which represent the sharing economy, have emerged as a substantial change by offering an alternative channel for delivering products and services traditionally provided by the conventional hospitality industry (Dolnicar, 2019; Jiang and Tian, 2018; Roma et al , 2021). This emergence of P2P accommodations has introduced a novel pattern of tourism consumption, leading to changes in travel behaviors such as the length of the average stay and the regional scope of tourism (Tussyadiah and Pesonen, 2016).…”

Section: Conceptual Backgroundmentioning

confidence: 99%

Estimation of an attribute value peer-to-peer accommodation platform using a choice experiment: focus on Airbnb

Jung,

Lee,

Moon

2024

JHTT

View full text Add to dashboard Cite

Purpose The purpose of this study is to identify individuals’ preferences for the information attributes of Airbnb, a representative peer-to-peer (P2P) accommodation platform. In the sharing economy, platforms are considered to be the principal intermediaries in supply and demand, and these platforms have distinctly different attributes from traditional accommodation reservation systems. Design/methodology/approach The present study used a choice experiment, which is a method for systematically identifying attributes’ preferences in the form of marginal willingness-to-pay (MWTP). Amazon Mechanical Turk, a crowdsourcing marketplace, was used for data collection, and 243 respondents ultimately participated in the survey. Findings Results showed that respondents’ choices were positively affected by the number of pictures of an accommodation, host experience, cancellation policy and local information but negatively affected by cost. Regarding MWTPs, host experience had the highest value (US$84.25), followed by cancellation policy (US$40), photos (US$26.67) and local information (US$10.92). Originality/value These study results could provide strategic guidance for guiding the development of P2P accommodation platforms by providing a prioritized list of preferred attributes for Airbnb.

show abstract

“…Not surprisingly, many tourism and hospitality researchers have begun to pay attention to the Airbnb phenomenon. As a result, numerous studies have explored the role of Airbnb and its influence on the tourism and hospitality industry [2,3,5,[7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Cross-Cultural Study of Value Priorities between U.S. and Chinese Airbnb Guests: An Analysis of Social and Economic Benefits

Tosun

Eck

2022

Sustainability

View full text Add to dashboard Cite

Guest value priorities in relation to online peer-to-peer accommodation are an underexamined area. This study examined social and economic benefits among Airbnb guests. The relationships between guests’ benefit priorities were tested in relation to satisfaction and behavioral intention. A total of 693 Airbnb guests were recruited from the U.S. and China. A framework to examine how cross-cultural differences moderate the associations between constructs was employed to examine the influences of the two cultures, one characterized by collectivism (China) and the other by individualism (U.S.). Confirmatory factory analysis and partial least-squares structural equation modeling (PLS-SEM) were used to test variable relationships. PLS-SEM analysis indicated that social and economic benefits both significantly influenced satisfaction and behavioral intention (satisfaction also influenced behavioral intention). Multigroup analysis was employed to test a framework examining cultural differences. It was found that social and economic benefits influenced behavioral intention differently for Chinese and U.S. Airbnb guests. The results suggest the importance of social and economic benefits in a peer-to-peer accommodation setting, as well as the need to understand cultural differences in the sharing economy.

show abstract

Sharing economy and dynamic pricing: Is the impact of Airbnb on the hotel industry time-dependent?

Cited by 12 publications

References 79 publications

Addressing data quality in Airbnb research

Addressing data quality in Airbnb research

Estimation of an attribute value peer-to-peer accommodation platform using a choice experiment: focus on Airbnb

A Cross-Cultural Study of Value Priorities between U.S. and Chinese Airbnb Guests: An Analysis of Social and Economic Benefits

Contact Info

Product

Resources

About