UNDERSTANDING THE PURCHASE INTENTION OF CONSUMERS TOWARDS AIRBNB IN CHINA: DIVA
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Understanding the Purchase
Intention of Consumers
towards Airbnb in China:
From the Perspective of Liability of
Foreignness
Master’s Thesis 15 credits
Department of Business Studies
Uppsala University
Spring Semester of 2020
Date of Submission: 2020-06-03
Author： Jie Chen
Tong Lu
Supervisor: Cong Su
Lingshuang KongAbstract Purpose: The purpose of this thesis is to research the factors influencing the purchase intention of Airbnb in China from the perspective of liability of foreignness. Methodology: This thesis adopts a quantitative approach through a questionnaire- based survey among Chinese people. Based on a PLS-SEM analysis of the data from 241 online questionnaires, this thesis tests the effects of perceived risk, perceived higher price, lesser availability of listings, as well as lesser availability of advertising on consumers’ purchase intention towards Airbnb in China. Findings: The results illustrate that both perceived higher price and lesser availability of listings would reduce Chinese purchase intention towards Airbnb in China, whereas perceived risk and lesser availability of advertising do not significantly influence Chinese purchase intention towards Airbnb in China. Research implications: The study contributes to the literature on sharing economy and customer behavior by providing a new framework from the perspective of liability of foreignness in order to understand more completely the deterrents of the Chinese consumer’s purchase of Airbnb-related products and services in China. Specifically, this thesis invokes a relatively under-explored theoretical view (i.e., the liability of foreignness) in the research on the internationalisation of sharing economy, which enables us to enrich the research on sharing economy and international customer behaviors. This study also enhances the understanding of the barriers of international marketing of sharing economy and advances the research on the internationalisation of sharing economy by evaluating the four relevant factors (i.e., perceived risk, perceived higher price, lesser availability of listings, and lesser availability of advertising) in the host country. Practical implications: Our findings entail several practical implications for managers of Airbnb in conquering the Chinese market. For example, given that perceived higher price and lesser availability of listings appears to be the critical barriers of consumer intentions, we recommend that Airbnb China employ strategies to increase the
availability of listings, and devise relatively lower pricing strategies to attract Chinese
consumers.
Keywords: sharing economy; Airbnb; China; purchase intention; liability of
foreignnessTable of Contents
Chapter 1 Introduction................................................................................................ 1
1.1 The development of sharing economy and Airbnb .......................................... 1
1.2 Airbnb in China ................................................................................................ 2
1.3 Research objective ............................................................................................ 4
1.4 Research method and contributions .................................................................. 5
1.5 Thesis structure ................................................................................................. 5
Chapter 2 Literature review ....................................................................................... 7
2.1 Sharing Economy ............................................................................................. 7
2.1.1 The definition and importance of the sharing economy ......................... 7
2.1.2. Consumer purchase intention in the Sharing Economy ........................ 8
2.2 Liabilities of foreignness .................................................................................. 9
2.3 Factors influencing locals’ purchase intention of Airbnb China .................... 11
2.3.1 Perceived risk and perceived higher price............................................ 11
2.3.2 Lesser availability of listings and advertising ...................................... 13
Chapter 3 Methodology ............................................................................................. 16
3.1 Quantitative research and questionnaire design ............................................. 16
3.2 Data collection ................................................................................................ 17
3.3 Sampling ......................................................................................................... 19
3.4 Measurements ................................................................................................. 21
3.4.1 Dependent variables ............................................................................. 21
3.4.2 Independent variables ........................................................................... 21
3.4.3 Control variables .................................................................................. 22
3.5 Data analysis ................................................................................................... 23
Chapter 4 Results ....................................................................................................... 25
4.1 Reliability and validity .......................................................................................... 25
4.2 Hypothesis test ........................................................................................................ 27
Chapter 5 Discussion ................................................................................................. 30
5.1 Perceived risk ................................................................................................. 30
5.2 Perceived higher price .................................................................................... 30
5.3 Lesser availability of listings .......................................................................... 31
5.4 Lesser availability of advertising .................................................................... 32
Chapter 6 Conclusion ................................................................................................ 33
6.1 Main findings .................................................................................................. 33
6.2 Research contributions ................................................................................... 33
6.3 Managerial implication ................................................................................... 34
6.4 Research limitations ....................................................................................... 35References ................................................................................................................... 36 Appendix 1 Questionnaire (English) ........................................................................ 48 Appendix 2 Questionnaire (Chinese) ....................................................................... 51
Chapter 1 Introduction
1.1 The development of sharing economy and Airbnb
The development of internet technology has established an effective means of sharing
between providers, who own resources, and users who need them but do not want to
own them (Matzner, 2015). With the aid of information and communication technology,
a new economic model, which is defined as the sharing economy business model,
emerges (Liao et al., 2017). This new business model provides the possibility of
transferring the temporary right of use of idle resources from providers to demanders,
improving the efficiency of the existing resources, and thus promoting the sustainable
development of the social economy (Liao et al., 2017).
With the emergence of the sharing economy, tourism and hospitality have faced rapid
growth. Specifically, the sharing platforms provide opportunities for tourists and
residents to share their idle homes and expert local knowledge (e.g., locals being tour
guides) (OECD, 2016; Sigala, 2015; Lyons and Wearing, 2015). According to the
report of PwC (2016), in 2015, five leading platforms within the sharing economy-
related key segments in Europe, earned revenues of approximately 4 billion euros and
completed transactions of over EUR€28 billion. Moreover, the value of the sharing
economy will grow to US$335 billion by 2025, equaling the retail sector (PwC, 2015).
Because the sharing economy is a new phenomenon, the research on the sharing
economy is still under-explored. Hence, we need more knowledge to understand the
sharing economy.
Airbnb, one of the leading and most popular examples of a sharing economy in the
tourism and hospitality sector, can be traced back to 2007; when two recent university
graduates converted their home into an “Air Bed & Breakfast” by offering overnight
stays on air mattresses in San Francisco (Guttentag, 2013). Since then, Airbnb was
created as a commission-based web-platform for room sharers and travelers who turned
1the “inviting strangers to your home” concept into a for-profit model (Guttentag, 2013).
Through the online community marketplace Airbnb, consumers are able to capitalise
upon an alternative method of renting accommodation by providing short-term rentals
with various room types, ranging from entire homes to private rooms and shared rooms
(Zervas et al., 2017). Nowadays, Airbnb has become the world’s biggest
accommodation sharing platform (Akbar and Andrawina, 2019) that enables consumers
to participate in “collaborative consumption”, in which they jointly share empty rooms
(Botsman and Rogers, 2014). According to the annual report of Airbnb (2019), by 2020,
Airbnb has above 7 million listings in more than 220 countries and regions.
1.2 Airbnb in China
Many multinationals target China as a primary or even crucial market. China is a large
market for Airbnb with strong potential, especially since the tourism industry of China
is constantly developing with the continual and rapid growth of the country’s economy
(Guide, 2016). According to the China Tourism Industry Statistical Bulletin, in 2019,
the number of inbound and outbound tourists in China reached 1.45 and 1.62 billion,
respectively (Hinsdale, 2017). Meanwhile, China has become one of the most popular
tourist destinations and the third most-visited country worldwide (Pariona, 2017). The
World Tourism Organisation predicted that China would become the fourth most
significant source of outbound tourism and the most popular travel destination by 2020
(Guide, 2017). This would subsequently indicate that Chinese tourists are looking for
unique travel experiences which incorporate local cultures and cuisines (Hinsdale,
2017).
Airbnb entered the Chinese market in 2015 and has attracted the attention of Chinese
millennials with its unique and innovative rental model and cultural experience. With
the aggressive push into China, the number of homes listed on the Airbnb platform of
China reached 20,000 in 2017, with more than 8.6 million Chinese tourists choosing to
stay with Airbnb when traveling around the world (Marinova, 2017). Airbnb also
announced that China is targeted to be the most significant origin market for Airbnb
2and that it will invest US$ 2 million to support its innovative tourism projects in China
by 2020 (Choudhury, 2017). However, Airbnb is still facing numerous challenges in its
expansion in China. As Pascal (2015) indicates, Airbnb has experienced highly
skeptical responses from local institutions (such as legal, competitive, and cultural
institutions) in many of the foreign countries that they have entered, and China is no
exception. For instance, recent data from Forward-The Economist (2019) illustrates that
Airbnb does not perform well compared with other sharing short-term rental platforms
in China. The data indicates that by 2018, the number of listings that Airbnb attracts in
China is 150,000. In contrast, the number of available listings on Xiaozhu1 and Tujia2,
the two main competitors of Airbnb in China, has exceeded 300,000 and 800,000,
respectively.
The international expansion of Airbnb illustrates the tension between the “extraordinary
opportunities” created by the evolving digital environment (Palmisano, 2016) and their
foreign identities. Indeed, as a foreign firm, Airbnb has faced liability of foreignness
when expanding in the Chinese market. Liability of foreignness is described as a
liability that may result in additional costs and competitive disadvantages for firms
when entering a specific foreign market (Zaheer, 1995). As Marano et al. (2020) reveal,
the strict regulatory scrutiny, incumbent competitors’ opposition, and other societal
concerns pertaining to their impacts on employees, customers, and the communities
where they operate has hindered Airbnb’s international expansion. Yimin (2015)
further indicates that the most significant challenge for Airbnb China is how to
assimilate into the distinct Chinese market. Similarly, Qin et al. (2020) affirm that the
Airbnb service might not seamlessly fit into Chinese culture. A significant reason for
this is Airbnb’s lack of local knowledge when expanding in China, and the differences
in cultural do matter when influencing Chinese consumers’ purchase behavior. For
instance, Chinese consumers sometimes have a preference for choosing to stay in a
1 Xiaozhu: one of the largest sharing-rental platforms in China.
2 Tujia: one of the largest sharing-rental platforms in China.
3traditional hotel because they are accustomed to doing so, and this preference makes it
difficult for Airbnb to attract Chinese consumers.
1.3 Research objective
Purchase intention plays an essential role in marketing research, primarily in the field
of e-commerce (Thomson et al., 2005), organic food (Paul and Rana, 2012), and luxury
brands (Bian and Forsythe, 2012), among others. Studying the purchase intention can
help marketers to understand consumer’s shopping behavior mindset (Ahasanul, 2015).
With the boom of the sharing economy, many researchers have started to pay attention
to individuals’ purchase intention in the sharing economy and the sharing economy
reveals a new business model that may be influenced by different factors from
traditional business. However, most of the previous studies have focused mainly on one
aspect, that is exploring people’s motivations to participate in the sharing economy (e.g.,
Hamari et al., 2016; So et al., 2018). Many sharing economy firms have engaged in
internationalising, yet there is limited research on the attitudes and behaviors of
consumers in host countries. The same goes for Airbnb, a popular sharing economy
platform that has attracted the great attention of a number of scholars. However, there
is still a lack of research on the local consumers’ purchase intention when Airbnb is
conquering a new market.
A multinational company confronts various issues associated with the liability of
foreignness, which is considered to be the most significant barrier to operating in
foreign countries and attracting local consumers. Most of the previous literature focuses
on the liability of foreignness in the field of financial service (Zaheer and Mosakowski,
1997; Miller and Parkhe, 2002; Nachum, 2003; Bell et al., 2012) by using performance
measures (Zaheer, 1995; Sethi and Guisinger, 2002; Elango, 2009). Therefore, there is
a limited understanding of consumers’ purchase intentions concerning the liability of
foreignness in the sharing economy area. Besides, in comparison to many previous
studies that adopted firm-level data, focusing on consumers’ behavior can provide a
fine-grained perspective of overcoming the liability of foreignness (Maruyama and Wu,
42015). In a word, given that responding to the local market plays a crucial role in
understanding the local consumers’ purchase intention, this study, taking the
perspective of liability of foreignness, intends to investigate the factors influencing
Chinese consumers’ purchase intention towards Airbnb in the local context.
1.4 Research method and contributions
In this thesis, we adopt a quantitative research method for testing hypotheses through
an online survey in China. The study contributes to the literature on the sharing
economy and customer behavior by providing a new framework from the perspective
of liability of foreignness in order to understand more completely the deterrents of the
Chinese consumer’s purchase of Airbnb in China. Specifically, this thesis invokes a
relatively under-explored theoretical view (i.e., the liability of foreignness) in the
research on the internationalisation of the sharing economy, which enables us to enrich
the research on the sharing economy and international customer behaviors. Moreover,
due to the global nature of sharing economy firms, more research should be carried out
on the international consumer behavior of the sharing economy. Our study provides
valuable insights into the literature on consumer behaviors, which enhances the
understanding of sharing economy firms as relates to the local consumers’ behaviors.
Furthermore, this study also enhances the knowledge on the barriers of international
marketing in a sharing economy, as well as advancing the research on the
internationalisation of a sharing economy by evaluating the four relevant factors (i.e.,
perceived risk, perceived higher price, lesser availability of listings, and lesser
availability of advertising) in the host country.
1.5 Thesis structure
The remaining parts of this thesis are presented as follows: the literature-review section
presents the theoretical context to the research, including the relevant literature on the
sharing economy and the liability of foreignness, and develops the research hypotheses.
Subsequently, the research methods are described, including the approach of gathering
5and analysing data from an online survey, as well as the measurement, and analysis
techniques. The empirical results of the four hypotheses are then presented based on
the PLS data analysis technique, followed by a discussion of the findings. Finally, we
conclude the thesis by demonstrating the findings, research contributions, research
limitations, and highlighting a future research agenda.
6Chapter 2 Literature review
The short-term rental sharing platform, with Airbnb as its ancestor, has developed into
a globally adopted business platform. This , in turn, creates a valuable chance for future
research to extend current theories through exploring why, when, and how these firms
enter and manage in new countries (Cheng, 2016). In this chapter, we focus on
discussing the literature on the sharing economy and the liability of foreignness, aiming
to identify the significant factors from the perspective of the liability of foreignness,
which influence Chinese consumers’ purchase intention towards Airbnb in China.
2.1 Sharing Economy
2.1.1 The definition and importance of the sharing economy
Considering that current consumers are becoming more complex and are demanding
sustainable and integrated solutions over more standardised and homogeneous products
and services, the emergence of the sharing economy (Lessig, 2008) has spurred a trend
of rapid worldwide development and adoption (Parente et al., 2018; Hern, 2015). In this
thesis, we highlight Belk’s (2014) definition, that the sharing economy is “people
coordinating the acquisition and distribution of an idle resource for a fee or other
compensation” (p. 1597). Notably, the boom of the sharing economy has been further
accelerated by the increased popularity of mobile devices and the internet.
While the sharing economy is experiencing explosive growth, it has led to fierce
debates. The majority of participants believe that participation in the sharing economy
is highly ecologically sustainable (Prothero et al., 2011; Sacks, 2011). Similarly, many
researchers regard the new business model as one that may yield utopian outcomes -
empowerment of ordinary people, efficiency, and even lower carbon footprints (Hamari
and Alt, 2016). They argue that participating in a sharing economy can be rational,
utility-maximising behavior, wherein the consumer replaces exclusive ownership of
goods with lower-cost options from within a sharing service (Hamari and Alt, 2016).
However, critics denounce the participation in sharing economy activities, such as the
7use of sharing platforms, because they regard them as being focused on economic self-
interest rather than sharing, and the regulatory issue has become a cardinal problem.
Many people consider that it is not secure to stay in a private accommodation, and
there is much bad press resulting from crimes involving Airbnb accommodation.
Nevertheless, we cannot deny that the sharing economy promotes a more equitable and
sustainable distribution of resources by reducing the costs of accessing products and
services, as well as consumer demand for resources (Botsman and Rogers, 2010).
2.1.2. Consumer purchase intention in the Sharing Economy
Consumers’ purchase intention is the final consequence of several factors in a specific
business context, which may directly impact consumers’ shopping behavior (Arli et al.,
2018). Numerous determinants of purchase intention have been studied. For example,
the literature suggests that intervening constructs, including perceived price, perceived
quality, and perceived value, significantly affect consumers’ purchase intention (Olson
and Jacob, 1972; Parasuraman et al., 1988). Similarly, Kim et al. (2009) further propose
that perceived risks, perceived benefits, and trust have a direct effects on consumers’
purchase intention, while others apply the theory of planned behaviour (Lau and Chan,
2001; Akbar and Andrawina, 2019) to explain the antecedents of intention to purchase
products, including the variables of consumers’ attitudes, subject norms, and perceived
behaviour control.
However, as a new business model arising from the boom of information technology,
the sharing economy has a significant difference compared with other traditional
business models when considering the factors that impact on consumers’ purchase
intention, mainly due to their different business features. Previous studies have
contributed to the understanding of consumers’ purchase intention towards sharing
platforms from the consumer perspective. For instance, Mittendorf (2017) conducted
an empirical analysis of Uber, a ride-sharing platform, finding that trust is decisive in
the successful formation of customer intentions.
8In terms of the rental-sharing platform Airbnb, Guttentag and Smith (2017) conducted
a motivation-based segmentation study and posited that respondents are strongly
attracted to Airbnb through its practical attributes (e.g., cost, location, etc.), and less so
by experiential features. While in a recent survey of why consumers chose Airbnb again,
Mao and Lyu (2017) found that unique experience expectations, perceived value, and
perceived risk determined consumers’ attitudes towards the rental-sharing platform
Airbnb. Overall, travelers choose Airbnb, rather than other hotel chains, to fulfill the
purpose of better value for a lower cost, in addition to an authentic and fresh experience
in sustainable tourism products and social interaction (Forno and Garibaldi, 2015;
Sigala, 2015; OECD, 2016; Eckhardt and Bardhi, 2015).
In contrast to the traditional business model, the sharing economy has significantly
different factors pertaining to consumers’ purchase intention. For instance,
psychological constructs such as trust, perceived value, and perceived risk play a vital
role when motivating consumers to consider using sharing platforms. At the same time,
the lower price makes a difference when buying standard goods and services. However,
the review of the literature suggests that little research focuses on exploring the local
people’s purchase intention in the sharing economy in host countries, and most of the
sharing economy firms substantially commit to expanding foreign markets.
2.2 Liabilities of foreignness
Even though sharing economy firms are considered to be able to launch operations
across borders with a great advantage due to their lack of reliance on tangible
internalised assets across borders to create capture value, issues associated with the
liability of foreignness (Brouthers et al., 2016; Calhoun, 2002; Zaheer and Mosakowski,
1997) will still play an essential role in their success. To some extent, the liability of
foreignness arises from the unfamiliarity of the host countries’ environments, as well
as cultural, political, and economic differences (Hymer, 1976; Kindleberger, 1969).
Similarly, Parente et al. (2018) argue that the main inhibitors of sharing economy firms’
international expansion are the lack of complementary asset providers and local
9regulations, which play vital roles in this ecosystem. Furthermore, local emerging
sharing economy firms can rapidly copy successful concepts from abroad and
implement them in their home country with enhanced features that are customised to
the local market (Parente et al., 2018). Thus, in general, a foreign firm may be at a
competitive disadvantage relative to a local firm in a country, and this disadvantage, as
well as the additional costs that foreign firms cannot avoid, can be called liability of
foreignness (Hymer, 1976).
Much research focuses on how to overcome liabilities stemming from foreignness. The
typical notion is that the liability of foreignness can be overcome by the firm-specific
advantages that a particular firm possesses (Zaheer,1995; Nachum, 2003). Further,
several studies have examined how foreign firms respond to the challenge of the
liability of foreignness and have suggested ways of overcoming it. Methods such as
entry mode choices (Eden and Miller, 2001; Chen et al., 2006), adapting to the local
environment (Petersen and Pedersen, 2002), providing greater product variety (Elango,
2009), and acquiring market-based resources in the host country (Barnard, 2010) are
available to lessen the liability of foreignness. However, overcoming the liability of
foreignness is a long process that requires consideration of all the relevant aspects.
In contrast to traditional businesses, sharing economy firms offer a virtual service
instead of providing physical goods. Accordingly, sharing economy firms generally do
not possess the conventional firm-specific advantages (e.g., innovative technology,
patents), which is regarded as the most significant element in coping with the liability
of foreignness (Zaheer,1995; Nachum, 2003). Meanwhile, compared with local sharing
economy platforms, firms from overseas have disadvantages related to the lack of stable
relationships with key stakeholders in the ecosystem (Parente et al., 2018). Therefore,
the liability challenges stemming from foreignness faced by sharing economy firms
become even more complicated and challenging to solve when expanding abroad.
In some cases, foreign brands benefit from their foreign identities and tend to use them
directly to produce positive consumer purchase intention, especially in developing
countries (Maruyama and Wu, 2015). Since the Chinese government’s decision to open
10up the Chinese market to the world in 1978, Chinese consumers have had the
opportunity to access foreign brands, which they regard as innovative and advanced,
and this can be defined as an advantage of foreignness (Shi and Hoskisson, 2012).
Hence, foreign identity may bring intangible benefits to firms when conquering the
Chinese market. However, when it comes to firms who provides virtual services, the
advantage of foreign brands may not be fully utilized. Maruyama and Wu (2015) came
up with a concept named “the perceived importance of supporting domestic firms” to
express consumers’ intention to support domestic firms, which reflects consumers’
biases towards foreign firms. On the one hand, the Chinese prefer to support local firms
due to their perceptions that foreign firms lack moral legitimacy (Maruyama and Wu,
2015). On the other hand, the bias also reflects that local consumers regard products
from foreign countries as having higher prices and less value. Therefore, the liability of
foreignness can shape local individuals’ perceptions and attitudes towards
multinationals and their products and services, thus affecting their purchase intention.
However, little attention has been paid to investigating purchase intention while taking
the liability of foreignness into account.
2.3 Factors influencing locals’ purchase intention of Airbnb China
2.3.1 Perceived risk and perceived higher price
Bauer (1967) was one of the first to bring about the concept of perceived risk and
formally defined it as a combination of uncertainty plus the seriousness of the outcome
involved. Over the past decades, the experimental evidence on human behavior and the
considerable psychology literature have indicated that perceived risk is appropriate and
useful in predicting consumer behavior (Nguyen, 2016; Nicolau, 2011). That is,
perceived risk has a negative impact on consumers’ behavior (Chiu et al., 2014; Yang
et al., 2015).
When it comes to the sharing economy, Mao and Lyu (2017) define perceived risk as a
subjective expectation of a potential loss when pursuing the desired service, finding
11that perceived risk determines consumer’s attitude towards the short-term rental sharing
platform Airbnb, particularly applied in the Chinese market. Indeed, perceived risk is
arguably one of the most crucial travel inhibitors for consumers because of the high-
risk nature of the tourism industry (Nguyen, 2016), especially for unfamiliar places
such as Airbnb’s rental accommodations. Hence, Chinese consumers may regard that
they are more likely to perceive higher risk in staying in an Airbnb accommodation in
China. As Nguyen (2016) indicate, perceived risk hinders consumer’s intention to
purchase as individuals generally prefer to avoid hazards or loss. Therefore, we
hypothesise that perceived risk stifles local people’s motivation to consume and use
Airbnb in China when traveling.
H1: Perceived risk has a negative influence on Chinese purchase intention towards
Airbnb in China.
Another variable we intend to investigate is Chinese consumers’ perceived higher price
due to the fact that the majority of researchers recognise price as one of the determining
factors that influence consumers’ purchasing behavior (Chang and Wildt, 1994; Moon
et al., 2008). Pappas (2017) also points out that consumers want to garner the best
possible “value for money,” hence price plays a crucial role in selecting accommodation,
which also causes travelers to shift from traditional hotels to sharing economy
accommodations. According to Guttentag (2015), potential consumers who choose to
use a sharing platform are mainly money savers, and they are more sensitive to price,
aiming to fulfill their needs at lower prices.
However, Chinese consumers have a common belief that Airbnb provides a higher
priced service than domestic platforms in China due to its foreign identity. Hence, we
hypothesise that the perceived higher price stemming from foreign identity is a crucial
determinant to constrain local people’s willingness to use and consume Airbnb in China.
H2: Perceived higher price has a negative influence on Chinese purchase intention
towards Airbnb in China.
122.3.2 Lesser availability of listings and advertising
In addition to the foreign identity of Airbnb engendering bias from potential customers
in host countries, it also limits Airbnb’s marketing resources within the host countries.
As an online sharing platform that provides accommodation, the availability of listings
and advertising can be seen as two of Airbnb’s most significant marketing resources.
According to Guttentag and Smith (2017), consumers generally have a perceived
commitment to trust large companies that have the capabilities to provide plenty of
accommodation options. One reason for the liability of foreignness is that foreign firms
have a lack of understanding of the general business environment of the host country
(Sethi and Guisinger, 2002), therefore, Airbnb may face difficulty in gathering listings
as resources. On the other hand, the foreign identity results in a lack of network with
local hosts, which also hinders Airbnb’s capacity to absorb listings. Data from Forward-
The Economist (2019) illustrates that Airbnb has a lesser availability of listings than
other local rental sharing platforms in China. Specifically, by 2018, the number of
listings that Airbnb attracted in China was 150,000, while its main competitors, the
local short-term rental sharing platforms Xiaozhu and Tujia, had listings exceeding
300,000 and 800,000, respectively. Consequently, this lesser availability of listings may
result in Airbnb covering fewer cities in China, which may not meet consumers’
traveling needs and, thus, may reduce target consumers’ willingness to use Airbnb.
Hence, here we hypothesise that lesser availability of listings engenders an adverse
impact on Chinese purchase intention towards Airbnb in China.
H3: Lesser availability of listings has a negative influence on Chinese purchase
intention towards Airbnb in China.
Another factor is the advertisements that foreign firms place in the target market.
Advertisement is one of the most visible marketing activities and is well researched as
an essential factor in driving consumers’ purchase intention (Buil et al., 2013).
Generally, advertising can influence consumers’ purchase intention in several ways.
First, consumers generally perceive highly advertised brands as higher quality brands
13(Yoo et al., 2000; Bravo et al., 2007). Second, more advertising can increase the scope
and frequency of brand appearance, and as a consequence, the level of brand awareness
(Chu and Keh, 2006; Keller, 2007). As such, the more advertising, the higher the
awareness levels are likely to be (Yoo et al., 2000; Villarejo and Sanchez, 2005; Bravo
et al., 2007). Finally, advertising can also create favourable and unique brand
associations (Cobb-Walgren et al., 1995; Keller, 2007). Hence, the more advertising,
the stronger and more numerous the associations will be in the consumer’s mind (Bravo
et al., 2007). Overall, advertising creates brand awareness and links strong, favourable,
and unique associations to the brand in consumers’ memories, as well as eliciting
positive brand judgments and feelings, thereby enhancing consumers’ purchase
intention (Keller, 2007).
However, compared with local competitors, Airbnb is confronted with more
problematic issues when advertising in China. For instance, due to Airbnb’s foreign
identity, there are cultural differences when Airbnb advertises in China, which
sometimes results in the issue of the Chinese advertising regulator not approving a
particular advertisement. This is in line with Mezias’s (2002) elaboration on the
disadvantages presented by regulatory requirements in a foreign host country. In
contrast, it is easier for local competitors to comply with the local culture and they have
a stronger network with the local regulators. Therefore, we hypothesise that the limited
availability of advertisements of Airbnb in China hinders local people’s intention and
willingness to use Airbnb in China when traveling.
H4: Lesser availability of Advertising has a negative influence on Chinese purchase
intention towards Airbnb in China.
To summarise, based on the four research hypotheses we proposed above, we have
developed the research model as represented in Figure 1.
14Figure 1 Proposed research model
Perceived Risk
H1(-)
Perceived Higher H2(-)
Price
Purchase Intention
Lesser Availability of
Listings H3(-)
Lesser Availability of H4(-)
Advertising
15Chapter 3 Methodology
This section introduces the approach that was selected for the empirical findings and
analysis. We chose to use a quantitative research method through distributing an online
survey, with the aim of assessing the observed data to achieve the goals and fulfill the
purpose of this thesis. Furthermore, this section includes a discussion of the
questionnaire design, sampling, data collection, the measurement of the investigated
variables, and the data analysis techniques.
3.1 Quantitative research and questionnaire design
The quantitative method is developed from a deductive approach that involves
analysing data via statistical techniques to test a series of hypotheses and examine
relationships between variables (Saunders et al., 2009; Bryman and Bell, 2011).
Meanwhile, most of the mainstream sharing economy researchers use quantitative
analysis as a research method (Mao and Lyu, 2017). Hence, we apply the quantitative
research method via an online questionnaire-based survey to collect the data and test
the research hypotheses developed in chapter 2.
According to Saunders et al. (2009), the most scientific way of collecting data involves
two procedures: the first step is to review the existing research and further relate this to
the research topic; the second step is to collect the primary data related to the research
topic. Therefore, we designed the items in the questionnaire based on an intensive
literature review to establish a valid measurement of the findings.
The questionnaire was first designed in English and was then translated into a Chinese
version since our target respondents are Chinese. We then gathered feedback from two
bilingual teachers, given that arriving at an accurate translation is a complex and crucial
part of questionnaire development (Nemoto and Beglar, 2014). They also helped to
check the grammar and the understandability of the questions in the Chinese version.
In order to ensure that the original meaning has not been altered during the translation
process, we then produced a back-translation with the two bilingual teachers’ help. We
16then showed the questionnaire items to four individuals who are from the same
population group as the targeted respondents in order to gather their feedback
concerning wording and clarity of expression. We then continued the revisions until
there was a general agreement that the translations were accurate and understandable.
According to Nemoto and Bagler (2014), a survey is of limited effectiveness without
piloting because the actual performance of the items is unknown until they are tested.
Therefore, a pilot test survey was conducted before administering the formal
investigation. The pilot survey was sent out to eight respondents via online social tools.
We eventually received eight questionnaires from these respondents with different
sociodemographic characteristics. The outcome we obtained from the pilot test helped
us to adjust the questionnaire to ensure that each item was contributing to the
measurement. For example, we added a description of Airbnb in the head of the
questionnaire to avoid a scenario whereby the respondents felt confused and to enable
the respondents to answer the questions based on their actual perception and cognition.
After revising the questions by considering their feedback, the questionnaire was
perceived as being understandable and well structured.
The questionnaire (see appendix 1 and 2) is comprised of two parts as follows. Section
one consists of respondents’ sociodemographic characteristics such as age, gender,
income, educational level, etc. The second part consists of question items examining
the investigated independent and dependent variables. To reduce bias that may occur
due to the ignorance of the interview as pertains to Airbnb, we introduced it at the
beginning of the questionnaire.
3.2 Data collection
Data was gathered through an online survey in Chinese facilitated by the Wenjuanxing
website (https://www.wjx.cn/), which is a common questionnaire survey website in
China. Given that users can only book Airbnb accommodation through its website, the
online survey mode is the best option to reach the potential target group. Furthermore,
the Airbnb platform is interlinked with Chinese social media platforms, such as
17WeChat3 and Weibo4. This implies that users can be reached through those platforms
too (Varma et al., 2016; Pezenka et al., 2017). The questionnaire was designed in a way
that ensured that anonymity and confidentiality of the respondents, and there were no
participation incentives given to the respondents. Consequently, respondents were
invited via a link to the online questionnaire through the platforms mentioned above,
mainly WeChat.
We adopted convenience sampling to select the sampling respondents since it is an
efficient quantitative research method which reduces the limitations, such as
geographical proximity and the lack of sufficient time as it help researchers to access
the target sampling quickly (Etikan et al., 2016). However, due to the potential bias of
convenience sampling techniques, the sample is not representative of the entire
population, and consequently, it may be challenging to get the overall result (Win et al.,
2012). Hence, we adopted snowball sampling (Goodman, 1961) as an additional
method, which is defined as the initial contact with a small group of participants who
are then introduced to other new objectives in order to enlarge the sample size (Win et
al., 2012). To balance the sample distribution, we considered the male-female ratio and
sent surveys to our friends and previous colleagues living in different cities, asking
them to send the survey to other respondents they know. Eventually, we directly
distributed the questionnaire to around 400 people through social media platforms, and
291 surveys were retained for the analysis of this study, yielding a 72.5% response rate.
Common method variance bias (Chang et al., 2010) is a concern in a questionnaire-
based survey because the researchers would collect data on the explanatory and
dependent variables from a single source. Therefore, we took several steps to alleviate
such concerns. First, as arranging items in an appropriate order in a questionnaire can
reduce common method bias (Podsakoff et al., 2003), we placed the items in different
sections of the survey, which was useful for balancing the respondents’ consistency of
answers. Second, considering that reversed items are able to improve scale validity by
3 WeChat is one of the most popular social application in China with more than one billion users, has the same
function as Facebook.
4 Weibo is one of the most popular social application in China, has the same function as Twitter.
18broadening the sample upon which responses are based (Tourangeau et al., 2000), we
reverse-coded the constructs “perceived higher price”, “lesser availability of listings,”
and “lesser availability of advertising.” Third, as explained above, we adopted a pilot-
test to prevent the use of confusing, vague, or unfamiliar terms in the formulation of
the questions and indicators (Podsakoff et al., 2003). The pilot test also indicated that
the questionnaire needs at least two minutes to complete due to its length, and spending
less time means a high probability that the respondents would not read the questions
carefully. Hence, we removed the questionnaires which completed in less than two
minutes. Finally, we assured the respondents’ complete anonymity and confidentiality
in the survey process so that they could provide objective responses, thereby reducing
the chances of bias.
3.3 Sampling
The survey was conducted from April 10 to 15, 2020, and has returned 291
questionnaires. A total of 50 questionnaires were invalid due to the following issues:
(1) In order to judge whether the respondents fill in the questionnaire seriously, we set
opposite items such as item 26 (i.e., Compared with the local short-rental sharing
platforms, Airbnb’s advertisements in China is not interesting.) and item 28 (i.e.,
Compared with the local short-rental sharing platforms, Airbnb’s advertisements in
China are more attractive.). If these opposite questions had the same answers, then that
would be an indication that the interviewees had not taken care of the questions in the
questionnaire. Thus, we regarded these questionnaires as invalid in order to avoid bias.
(2) We removed the questionnaires which were filled out in less than two minutes. As
through the pilot test, it became evident that the questionnaire needs at least two minutes
to complete due to its length, and completing a questionnaire in less than two minutes
would mean a strong possibility that the respondent did not read the questions carefully.
Consequently, 241 questionnaires were valid, and the validity rate reached 82%.
According to Pallant (2016), the smallest sample size should be greater than 150. Thus,
the sample number is available for this study.
19Table 1 shows the demographics of the 241 sampled respondents. The demographics
data shows that the respondents were mainly aged between 21 and 40 years (90.87%),
which is consistent with Airbnb’s report (2019) that up to 78% of Chinese consumers
who choose to use online platforms to book accommodations are within the 20-40 age
group. According to the data, there were 129 female respondents, and 112 male
respondents, which means that the data was collected equally among genders. As
pertains to educational background, 98.76% of the respondents have a bachelor’s
degree or above, means that individuals with higher educational level consist of the
main part of consumers towards Airbnb. We regarded the group of respondents who
have an income between CNY 2800 to CNY 10200 to be the medium income group,
and we found that the proportion of low, medium and high-income individuals in the
sample is 70:96:75, where the sample appears to be well-distributed based on income.
Table 1 Sampling information
Items Number Percentage
Age Below 20 14 5.80
21-40 219 90.87
Above 41 8 3.33
Gender Female 129 53.53
Male 112 46.47
Income Below 2800 70 29.05
2800-10200 96 39.83
Above 10200 75 31.12
Educational level Below high school or equivalent degree 3 1.24
Bachelor degree or equivalent 141 58.51
Master degree or above 97 40.25
203.4 Measurements
The measures of dependent and independent variables (see Table 2) in our study are
mainly adapted from established studies, which have been modified to fit this study.
We adopted Likert-scale instruments (Messick, 1989) to measure items, which is the
most frequently used method to operationalise and measure a person’s effect or
cognition. The outcome space for Likert scales is made up of a limited range of possible
responses on continua, ranging from 1 (strongly disagree) to 5 (strongly agree).
3.4.1 Dependent variables
The purchase intention of Airbnb China, as the dependent variable, is measured to
reflect Chinese motivation or willingness to purchase Airbnb products and services in
China. Purchase intention is a construct that has been measured frequently in the
marketing context. Drawing on the existing research (Han et al., 2011), the adoption of
this perceptual measure allows us to account for the degree of respondents’ willingness
to use Airbnb in China. Four questions were designed to measure purchase intention.
Following a procedure similar to the one applied by Han et al. (2011), the respondents
were asked to evaluate the degree of willingness to purchase Airbnb products and
services and recommend them to others when traveling in China.
3.4.2 Independent variables
The “perceived risk” variable is adapted from previously validated inventories of
Pavlou and Gefen (2004) and Han et al. (2011), and is measured using three items. The
“perceived higher price” variable is also measured using three items, which are
developed based on measures by Irani and Hanzaee (2011). “Lesser availability of
listings” is measured through a two-item scale developed by Chen and Xie (2017).
While the “lesser availability of advertising” is measured using three items adapted
from McCoy et al. (2007).
21Table 2 Operationalisation of the constructs
Construct/Indicator Label
Dependent variable
Purchase intention PI
When the price is similar to the local short-rental sharing platforms (such as PI1
Tujia and Xiaozhu), I prefer to use Airbnb in China.
I will recommend Airbnb to my friends to book accommodation in China. PI2
Staying in an accommodation provided by Airbnb in China can fulfill my needs. PI3
I will consider using Airbnb when planning travels in China in the future. PI4
Independent variable
Perceived risk PR
When traveling in China, staying in an Airbnb home puts me at risk of losing PR1
my personal property.
Compared with the local short-rental sharing platforms (such as Tujia and Xiaozhu), PR2
using Airbnb China will leak my personal information.
Compared with the local short-rental sharing platforms (such as Tujia and Xiaozhu), PR3
paying online on Airbnb in China is risky.
Perceived higher price PHP
When traveling in China, compared with the local short-rental sharing platforms PHP1
(such as Tujia and Xiaozhu), I think I can save money by booking accommodation through
Airbnb. (reverse coded)
I think the price of Airbnb in China is reasonable. (reverse coded) PHP2
Compared with the local short-rental sharing platforms (such as Tujia and Xiaozhu), PHP3
I think Airbnb has price advantage in China. (reverse coded)
Lesser availibility of listings LAL
Even in a small city in China, I can also find accommodation I like through LAL1
Airbnb. (reverse coded)
Airbnb offers plenty of accommodations in China. (reverse coded) LAL2
Lesser availibility of advertising LAA
Compared with the local short-rental sharing platforms (such as Tujia and Xiaozhu), LAA1
Airbnb’s advertisements are more common in China. (reverse coded)
Compared with the local short-rental sharing platforms (such as Tujia and Xiaozhu), LAA2
Airbnb’s advertisements in China are more attractive. (reverse coded)
I have seen Airbnb’s advertisements on many media in China, such as subway, LAA3
newspapers, magazines, and WeChat platforms. (reverse coded)
3.4.3 Control variables
In order to examine the impact of the independent variables of the potential Chinese
consumers’ willingness to use Airbnb in China, we need to control for variables that
potentially influence purchase willingness. The control variables include the
measurements of “educational level” (EDU), “income” (INC), “purchasing experience”
(PE), and “degree of alternatives” (DOA). Generally, purchase intention is affected by
individuals’ sociodemographic characteristics (i.e., educational level and income).
22According to Arli et al. (2018), Chinese people with different educational levels and
income levels may have different attitudes towards purchase intention. We measured
the effect of educational level on purchase intention by using four dummy variables
(i.e., middle school or below, high school or equivalent degree, bachelor’s degree or
equivalent, and master’s degree or above), and divided the income level into six ranges.
On the other hand, purchase intention is influenced by purchasing experiences, and
individuals who have previously stayed with Airbnb may have formed different
attitudes and behavioral intentions compared to those without any prior experience of
using Airbnb (So et al., 2018). Purchasing experience in China is a dummy variable
that can be measured by asking “I have used Airbnb abroad.”, “I have used Airbnb in
China.” We also control for the degree of alternatives because strong local competitors
(such as Tujia, Xiaozhu) provide plenty of options for Chinese consumers in China,
which may influence the Chinese purchase intention towards Airbnb. The degree of
alternatives is measured by assessing, “When traveling in China, I have many
substitutions (such as Tujia and Xiaozhu).”
3.5 Data analysis
We used partial least squares path modeling (Chin and Newsted, 1999; Wold, 1982) to
analyse the data obtained from 241 valid questionnaires. Partial least squares (PLS) is
a powerful variance-based structural equation modeling (SEM) technique (Hair et al.,
2006) due to its minimal demands in terms of measurement scales, sample size, and
residual distributions. PLS has been frequently used by researchers in explaining the
residual variance of the latent variables and therefore predicting key constructs (Fornell
and Bookstein, 1982; Hair et al., 2006), which can provide much value for causal
inquiry in behavioral research fields (Lowry and Gaskin, 2014). As this thesis
investigates the effects of several factors on consumers’ purchase intention towards
Airbnb in China, the focus of our investigation lies in the evaluation of a set of
predictive relationships (Chin and Newsted, 1999; Hair et al., 2006). Moreover, a lack
of widely adopted theories in the fledgling sharing economy literature makes it difficult
23to impose any expected theoretical structure among the variables under our
investigation. Therefore, PLS was selected as an appropriate analytical technique for
our data analysis in this study. In this thesis, we use SmartPLS 3.0 to test the hypotheses.
24Chapter 4 Results
We tested and analysed the structure model by using partial least squares structural
(PLS) path modeling (Wold, 1982). SEM-PLS results are usually produced in a two-
step procedure. First, we need to evaluate the properties of the measurement model
before testing hypothesised relationships. If our measures are proved reliable and valid,
then we proceed to the second step, which means that it would be possible to assess the
significance of the hypothesised relationships based on a bootstrapping technique, the
variance explained by the constructs, and the predicted value of the model.
4.1 Reliability and validity
Table 3 reports the statistics for the individual item reliability loadings via the
measurement model. First, most of the correlations between constructs and sub-items
are significant, greater than the “ideal” cutoff point of 0.7 (Carmines and Zeller, 1979).
The only exception is the loading of “LAA1” (0.561), which is less than 0.700.
However, the acceptance threshold used in many studies is 0.5 (e.g., Hulland, 1999),
and considering its value of average variance extracted (AVE) is over the satisfied 0.5
level (Fornell and Larcker, 1981), it was our preference to retain the indicator “LAA1”
in the operationalisation. Second, construct reliability (presented in the third column of
Table 3), which is measured in terms of composite reliability (Werts et al., 1974),
demonstrates that all of the composite reliability measures are over the suggested 0.7
bound, ranging between 0.805 for the “lesser availability of advertising” and 0.898 for
the “perceived higher price.” Third, the values of all of the indicators’ average variance
extracted (AVE) (reported in the last column of Table 3), which tests the convergent
validity via the method of construct variance, are over the suggested 0.5 threshold
(Fornell and Larcker, 1981). Finally, Table 4 reports that the square root of each
construct’s AVE is greater than its correlation with the rest of the constructs, implying
that each construct meets the requirement of discriminant validity. Therefore, it is
reasonable to state that the measurement model is valid and reliable.
25Table 3. Item and construct reliability and average variance extracted (AVE)
Item reliability Construct reliability Convergent validity
Construct/indicator Loading Composite reliability Average variance
extracted (AVE)
Perceived risk 0.860 0.676
PR1 0.728
PR2 0.959
PR3 0.761
Perceived higher price 0.898 0.747
PHP1 0.855
PHP2 0.877
PHP3 0.860
Lesser availability of listings 0.812 0.684
LAL1 0.786
LAL2 0.866
Lesser availability of advertising 0.805 0.587
LAA1 0.561
LAA2 0.887
LAA3 0.811
Purchase intention 0.893 0.676
PI1 0.843
PI2 0.800
PI3 0.832
PI4 0.813
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