Airbnb Development and Impact on the Hospitality Industry

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Background of the Study

The growth of multifaceted technology platforms has revolutionised how people do business (Turban et al. 2017). For example, technology-aided sharing platforms such as Airbnb and Uber have allowed people to exploit underutilised assets by making transportation and hospitality businesses more efficient. Their contribution could be largely summed as developments in the “sharing economy” (Meyer 2016; Zhu 2013). Although these innovative additions to the global economy are largely commendable and impressive, their impacts on the global economy are best analysed through a segmented framework.

In the hospitality industry, Airbnb has had the greatest disruption on the traditional mode of doing business. The business is a virtual service that allows people to list their homes for short-term lease in the same way hotels do. The business thrives on the basis that it gives homeowners a platform to get extra income by renting out a portion of their houses or apartments (Tranton 2016). At the same time, customers benefit from having “a home experience away from home” because unlike the conventional hotel business, most listings on the Airbnb platform are actual homes.

Since Airbnb was founded in 2008, more than 30 million people have used its services (Fauvel 2017). This type of accommodation model has dramatically changed how people conduct business in the hospitality industry because it offers a substitute for the traditional hotel business where people check into a standard hotel room and order room service or eat at designated points (Kroft & Pope 2014). The challenge posed by Airbnb to the hotel business is partly explained by the fact that many people have enthusiastically adopted the service on a peer-to-peer platform that has allowed the hospitality industry to scale exponentially and in a seamless manner (Tranton 2016). The array of goods and products available on the Airbnb platform has also increased the variety of available accommodation products. The success of Airbnb can be seen through recent market valuations, which have shown that the business is worth more than $10 billion (Meyer 2016). This figure means that the company is now worth more than traditional and established hotel brands such as Hyatt (Meyer 2016). Despite this interesting dynamic in business valuation, many traditional hotel brands continue to downplay the impact of Airbnb on their businesses. This paper proposes a study to evaluate the development of Airbnb in London and quantify its impact on the city’s hospitality industry.

Research Problem

Although many hotels are aware of the possible impact of Airbnb on their businesses, few can quantify their effects or accurately predict how the virtual business influences specific aspects of their operations (Kozak & Kozak 2016). Furthermore, many of them are unaware of the extent that their businesses are affected by the growth and development of Airbnb. The problem is further compounded by the skewed distribution of the impact of Airbnb on different cities around the world because each market has unique dynamics that would moderate the influence of the California-based company on their businesses. The presence of these dynamics in the industry means that most hotels are blind to the effects of Airbnb on their operations. Additionally, those that are aware of its possible impacts cannot quantify the same (Ivanov 2014). The proposed study would provide a contextualised analysis of the impact of Airbnb operations in the London hospitality market.

Research Aim

To investigate the development of Airbnb and its impact on the London hospitality industry

Research Objectives

To investigate how Airbnb operations have affected the occupancy rates of London-based hotels
To investigate the effects of Airbnb operations on the prices charged by London-based hotels
To examine the development of Airbnb operations and its effects on hotel revenues in London

Research Questions

How have Airbnb operations affected the occupancy rates of London-based hotels?
What is the effect of Airbnb operations on the prices charged by London-based hotels?
Has the development of Airbnb operations affected hotel revenue in London?

Hypotheses

H1: Airbnb operations have caused a decline in the occupancy rates of London-based hotels
Ho: Airbnb operations have had no effect on occupancy rates of London-based hotels
H2: Airbnb operations have caused a decline in the cost of accommodation in London-based hotels
Ho: Airbnb operations have had no effect on the cost of accommodation in London-based hotels
H3: The development of Airbnb operations has affected hotel revenue in London
Ho: The development of Airbnb operations has had no effect on the revenues of London-based hotels

Purpose of Study

Airbnb is a direct threat to the traditional hotel business model because it has changed the dynamics of how people seek accommodation. Instead of using customer-to-business models to book rooms, Airbnb promotes individual-to-individual accommodation arrangements (Wood 2013; Turban et al. 2017). Therefore, traditional hotels are isolated from the lodging business. The purpose of this study is to understand the impact of such a paradigm shift in the industry through a contextual review of the effects of the Airbnb virtual platform in the London hospitality sector.

Significance of Study

Although some hotels have dismissed the impact of Airbnb on their businesses, they fail to realise that the new business model introduced by the online marketplace and hospitality service is here to stay and it affects how guests spend their money (Stephens 2016). It is important to study the impact of Airbnb on different markets and submarkets of the hospitality industry. The findings of the proposed study would add to the growing literature on peer-to-peer competition and the development of the sharing economy on the global hospitality business (Turban et al. 2017). Much of the analysis will exemplify the economic theory of two-sided markets. For example, it would add to the understanding of theoretical and structural models that explain the price structure of goods and services in the hospitality industry, as well as the use of the same products in the sector (Somervuori 2014). By studying the findings of the proposed study, it would also be easier for stakeholders to comprehend models that connect innovations in service design and re-examine how they connect with contemporary service platforms. Thus, the findings of the proposed study would help stakeholders in the hospitality industry to better understand firm behaviour in two-sided markets and comprehend how labour supply issues integrate with globally accepted technological developments.

Literature Review

Many analysts consider Airbnb as a threat to traditional hotels because some guests prefer to book with listed hosts as opposed to the traditional hotel booking method (Bowie et al. 2016). The commission for Hotels in New York recently published a report to ascertain the impact of Airbnb on the hospitality industry and established that its impact varies across different cities (Bowie et al. 2016). However, the report affirms that the US industry as a whole is greatly affected by the business and so are other countries and cities outside of it (Bowie et al. 2016). A deeper assessment of the facts published in the report reveals that hotels lose up to $450 million in annual revenues through lost businesses. The figure is further expected to increase as Airbnb entrenches itself in different markets.

Few studies have comprehensively explored the nature of competition between peer-to-peer markets. In one line of study, Pandian and Kalaivanthan (2016) explored the nature and structure of peer-to-peer markets by advancing several theoretical frameworks to understand their effects on incumbent firms. A key finding they made in the analysis is that peer-to-peer competition could significantly decrease price variability among competitors (Pandian & Kalaivanthan 2016). Empirical evidence on peer-to-peer platforms has mostly focused on Uber as the most notable example of a successful company in the sharing economy. Using the example, they have demonstrated a decline in price volatility through a decrease in gas consumption, miles travelled, and car ownership numbers (Meyer 2016; Zhu 2013). There is little empirical evidence in the hospitality industry regarding the effect of peer-to-peer platforms on incumbent businesses. Instead, the available literature is mostly comprised of opinion pieces and blogs, most of which are not academic in nature.

Airbnb-commissioned studies provide the closest analysis regarding the impact of the business on the hospitality industry that could be considered credible. The studies show that the online marketplace complements traditional hotel businesses (Schneider 2017). However, it is important to point out the possible bias in this point of view because the studies argue for Airbnb and seem to advocate for its adoption in various cities by highlighting the economic benefits Airbnb could offer local economies and hosts alike. Although the proposed study relates to some of these studies, the methodology chosen for analysis is more contextualised, sophisticated and adopts a segmented overview of the research topic. Therefore, the conclusions are expected to be more detailed, credible, reliable and nuanced.

Summary

Many researchers have outlined general descriptions of the impact of Airbnb on hotel businesses. However, many markets have unique dynamics that moderate the effect of the technology platform in the hospitality business. For example, some cities do not have the proper infrastructure to allow guests to access listed residences. In other markets, there are inadequate properties listed on Airbnb because of the lack of awareness of the business. Some of these dynamics (among others) make it difficult to generalise the effects of Airbnb on the hospitality industry. Therefore, there is a need to provide a contextual analysis of the research problem. Based on this need, this paper seeks to understand the development of Airbnb in London and examine its impact on the city’s hospitality industry.

Methodology

Research Method

The two main research methods used in academic studies are quantitative and qualitative approaches. The qualitative research method is often used in studies that involve human subjects and on topics that have a subjective appeal (Creswell 2014). Comparatively the quantitative research technique is often used in studies that involve numerical data (Creswell 2014). The proposed study will be based on the quantitative research approach. This technique will be employed in the study because most of the data collected will be in the form of numbers and statistical results. The quantitative research method is appropriate for the study because the analysis is based on a highly structured data collection method as described below.

Data Collection

The data collection process would be based on a survey questionnaire that would be sent out to several hotel employees in London (see appendix). Since it would be physically impossible to collect data through site visits, the data collection instruments would be administered online. The respondents would answer standard questions that would help in meeting the research objectives. The researcher deliberately chose the online platform for data collection because it is more convenient to collect data this way (considering the geographical dispersions of the hotels to be sampled) (Helen 2015).

Secondary data would also be included in the study to complement the primary information obtained from the research participants. The main purpose of including this data collection approach in the proposed study is to compare the primary research findings with what other researchers have written about peer-to-peer businesses. The researcher would collect published data from several sources, including the Airbnb website (listing data), the Census Bureau, industry reports, and hotel reviews from Trip Advisor.

Sample Population

The desired sample population would be comprised of 140 employees of various London-based hotels. The research participants should have served in their positions for at least five years because the researcher would want engage participants who have a long-term view of the hotel business.

Sampling Strategy

The respondents would be sampled randomly from a selected group of hotels operating in London. The researcher would seek permission from their superiors before engaging any employee. The simple random sampling method is deliberately selected for the proposed study because it provides the most unbiased view of the impact of Airbnb operations on London-based hotels. A request for participating in the study would be sent to the participants via email, while follow-up meetings would be done through mobile communication.

Data Analysis

The information collected in the proposed study would be analysed using the Excel software. This data analysis technique would be adopted because it is instrumental in helping the researcher to visualise and gain insight into the information presented by the respondents (Watkins & Gioia 2015). Some of the main tools that would be used in the data analysis process include pivot charts and slicers that are crucial in providing a visual representation of the data collected (Leavy 2017). The Excel software is justifiably proposed for use in the analysis because it aligns with the research approach (quantitative research) because they both focus on quantifiable information (McGuire 2016). Therefore, the use of statistical assessments to answer the research questions aligns with the Excel software.

Ethical Issues

Some of the main ethical issues that could emerge in the proposed study include privacy issues, consent, and treatment of data (Walliman 2015). To address privacy concerns, the researcher would transcribe the data collected from the respondents anonymously. This way, it would be difficult to attribute the responses to a specific person, or hotel. Additionally, all the respondents would participate in the study voluntarily. The researcher will also allow participants to withdraw from the study without any repercussions. At the same time, the researcher would not coerce them or offer incentives to secure their cooperation. Lastly, all the information collected in the study would be stored in a computer and secured using a password that would only be privy to the researcher.

Reference List

Bowie, D, Buttle, F, Brookes, M & Mariussen, A 2016, Hospitality marketing, Taylor & Francis, London.

Creswell, J 2014, Research design: qualitative, quantitative, and mixed methods approaches, SAGE, London.

Fauvel, E 2017,101 Tips to become Airbnb superhost: get more bookings, choose the right price, increase revenue, get 5 stars reviews, attract the best guests, be a remote host and more, My Publishing Company, New York, NY.

Helen, K 2015, Creative research methods in the social sciences: a practical guide, policy press, New York, NY.

Ivanov, S 2014, Hotel revenue management: from theory to practice, Zangador, Varna.

Kozak, M & Kozak, N (eds) 2016, Tourism and hospitality management, Emerald Group Publishing, London.

Kroft, D & Pope, G 2014, ‘Does online search crowd out traditional search and improve matching efficiency? Evidence from craigslist’, Journal of Labour Economics, vol. 32, no. 2, pp. 259-303.

Leavy, P 2017, Research design: quantitative, qualitative, mixed methods, arts-based, and community-based participatory research approaches, Guilford Publications, New York, NY.

McGuire, K 2016, The analytic hospitality executive: implementing data analytics in hotels and casinos, John Wiley & Sons, London.

Meyer, J 2016, Uber-positive: why Americans love the sharing economy, Encounter Books, New York, NY.

Pandian, V & Kalaivanthan, M 2016, Handbook of research on holistic optimization techniques in the hospitality, tourism, and travel industry, IGI Global, New York, NY.

Schneider, H 2017, Creative destruction and the sharing economy: Uber as disruptive innovation, Edward Elgar Publishing, London.

Somervuori, O 2014, ‘Profiling behavioural pricing research in marketing,’ Journal of Product & Brand Management, vol. 23, no. 6, pp. 462-474.

Stephens, D 2016, Essentials of consumer behaviour, Taylor & Francis, London.

Tranton, P 2016, Airbnb: the basics and tips for beginners, Conceptual Kings, New York, NY.

Turban, E, Outland, J, King, D, Lee, J, Liang, T & Turban, D 2017, Electronic commerce 2018: a managerial and social networks perspective, Springer, New York, NY.

Walliman, N 2015, Social research methods: the essentials, SAGE, New York, NY.

Watkins, D & Gioia, D 2015, Mixed methods research, Oxford University Press, Oxford.

Wood, R 2013, Key concepts in hospitality management, SAGE, London.

Zhu, F 2013, ‘Responses to entry in multi-sided markets: the impact of

craigslist on local newspapers’, Management Science, vol. 60, no. 2, pp. 476-493.

Appendix: Survey Questionnaire

Part A: Demographic Information (please tick on the appropriate box)

What is your gender?

Male
Female

What is your Age?

Younger than 20
21 years – 30 years
31 years – 40 years
41 years – 50 years
years – 60 years
Older than 60 years

Work Experience. How long have you worked as an employee of in your organisation

Between 5-10 years Between 11- 15 years
Between 16-20 years Between 20 – 25 years
More than 25 years

Educational Experience

What is your highest educational qualification?

High school Undergraduate
Master’s PHD
None of the above

Part B: Please state whether you agree or disagree with the following statements

1. Airbnb is a threat to the operations of my organisation

Strongly Agree	Agree	Neither agree or disagree	Disagree	Strongly disagree

1. Hotel revenue has declined because of the loss of customers to Airbnb

Strongly Agree	Agree	Neither agree or disagree	Disagree	Strongly disagree

1. We have lost some our customers because of Airbnb operations

Strongly Agree	Agree	Neither agree or disagree	Disagree	Strongly disagree

1. Occupancy rates have declined because of the loss of customers to Airbnb operations

Strongly Agree	Agree	Neither agree or disagree	Disagree	Strongly disagree

1. Managers need to adjust their prices downwards because of competition from Airbnb

Strongly Agree	Agree	Neither agree or disagree	Disagree	Strongly disagree

1. Airbnb operations will negatively affect the operations of my organisation in the future

Strongly Agree	Agree	Neither agree or disagree	Disagree	Strongly disagree

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