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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 Kong

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Abstract

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

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availability of listings, and devise relatively lower pricing strategies to attract Chinese consumers.

Keywords: sharing economy; Airbnb; China; purchase intention; liability of

foreignness

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Table 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 ... 35

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References ... 36

Appendix 1 Questionnaire (English) ... 48

Appendix 2 Questionnaire (Chinese) ... 51

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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

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the “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

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and 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 Xiaozhu 1 and Tujia 2 , 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.

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traditional 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,

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2015). 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

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and 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.

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Chapter 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

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use 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.

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In 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

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regulations, 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

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up 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

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that 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.

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2.3.2 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

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(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.

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Figure 1 Proposed research model

H1(-)

H2(-)

H3(-)

H4(-)

Perceived Risk

Perceived Higher Price

Lesser Availability of Listings

Lesser Availability of Advertising

Purchase Intention

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Chapter 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

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then 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

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WeChat 3 and Weibo 4 . 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.

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broadening 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.

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Table 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

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3.4 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).

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Table 2 Operationalisation of the constructs

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).

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)

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According 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

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to 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.

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Chapter 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.

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Table 3. Item and construct reliability and average variance extracted (AVE)

Item reliability Construct reliability Convergent validity 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

Construct/indicator

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Table 4. Discriminant validity a : correlations and the square root of the average variances extracted (AVE)

Notes: Correlations and the square root of the average variance extracted (AVE).

ᵃDiagonal values in bold are the square roots of the variance shared between the reflective constructs and their measures. For discriminant validity to be established, the diagonal elements must be greater than the off-diagonal elements in the corresponding rows and columns.

4.2 Hypothesis test

Regarding the structural model, a 5,000-sample with the one-tailed bootstrapping technique was used. This supports a valuable number of random samples based on the original dataset through sampling with replacement (Efron and Tibshirani, 1993) and is expected to draw a good picture, which can assess the samples through an accuracy method. This bootstrapping technique indicates that two of the four hypothesised relationships are statistically significant in our analyses (as shown in Figure 2 and Table 5), while the other two hypotheses are not confirmed. In particular, the path coefficient between the “perceived higher price” and the “purchase intention” (β=-0.201, p<0.05) is significantly negative, which supports Hypothesis 2. Meanwhile, the path coefficient between “lesser availability of listings” and the “purchase intention” (β=-0.423, p<0.001) is also significantly negative. Hypothesis 3 is, therefore, empirically supported. However, the path coefficients between “perceived risk” and the “purchase intention” (β=0.043, p >0.05) and between “lesser availability of advertising” and

1 2 3 4 5 6 7 8 9

1 DOA 1.000

2 EDU 0.060 1.000

3 LAA -0.032 0.108 0.766

4 LAL -0.150 0.141 0.548 0.827

5 PR 0.042 -0.057 -0.249 -0.151 0.822

6 PHP -0.120 0.123 0.509 0.723 -0.223 0.864

7 PI 0.189 -0.126 -0.453 -0.661 0.166 -0.603 0.822

8 PE -0.065 -0.090 0.195 0.194 0.073 0.250 -0.273 0.780

9 INC 0.088 0.307 0.103 0.043 0.085 0.059 -0.071 0.105 1.000

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“purchase intention” (β=-0.074, p>0.05) are insignificant. Hence, Hypothesis 1 and Hypothesis 4 are not empirically supported.

Concerning the control variables, two of them (i.e., purchasing experience, degree of alternative) have significant effects (see Table 5). Notably, the degree of alternatives of Airbnb is positively related to “purchase intention” (β = 0.093, p<0.05). As for the purchasing experience, the results show that previous experiences have a negative impact on Chinese consumers’ purchase intention (β = -0.125, p<0.05).

As the primary objective of PLS-SEM is prediction, the most crucial criterion is R 2 (Ringle et al., 2010). As revealed in Table 5, the R 2 value for all independent variables is 0.500, which is over the 0.26 threshold suggested by Cohen (1988), indicating that the structural model predicts well.

The Stone–Geisser Q 2 statistics (Geisser, 1975; Stone, 1974) describe a positive value

for reflective dependent variables. After calculation, we found the value of Q 2 to equal

0.313, which represents that the observed values are well reconstructed, and the model

has predictive relevance (Hensler et al., 2009). Overall, the results support the

predictive power of the paths, thereby substantiating the structural soundness of the

proposed model.

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Table 5. Dependent variables: direct effects, t value, and explained variances

Notes:

* p < 0.05; ** p < 0.01; *** p < 0.001 (based on a Student t(4999) distribution with one tail).

n.s.: not significant.

Figure 2. The resulting model

n.s.: not significant

Effects on dependent variables t value

(bootstrap) Variance explained

Independent variables

Perceived risk (H1) 0.043 n.s. 0.494 0.007

Perceived higher price (H2) -0.201 * 2.111 0.121

Lesser availability of listings (H3) -0.423 *** 5.571 0.280 Lesser availability of advertising (H4) -0.074 n.s. 1.018 0.034 Control variables

Degree of alternatives 0.093 * 1.719 0.018

Educational level -0.042 n.s. 0.950 0.005

Income -0.019 n.s. 0.419 0.001

Purchasing experience -0.125 * 2.273 0.034

Direct effect

H1:β=-0.043,n.s.

H2:β=-0.201,p<0.05

H3:β=-0.423,p<0.001 R 2 =0.500

H4:β=0.074,n.s.

Perceived Risk

Perceived Higher Price

Lesser Availability of Listings

Lesser Availability of Advertising

Purchase Intention

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Chapter 5 Discussion

Based on the results, this chapter presents a discussion of the findings regarding the factors influencing Chinese consumers’ purchase intention towards Airbnb in China.

5.1 Perceived risk

Our study finds that perceived risk significantly reduces Chinese consumers’ likelihood of booking accommodation on Airbnb when they plan to travel. Prior studies have found support for the negative relationship between perceived risk and consumers’

attitudes (Campbell and Goodstein, 2001; Herrero and Martin, 2012). Travelers usually feel insecure while living in unfamiliar places. However, our results indicate that perceived risk does not affect Chinese consumers’ purchase intention towards Airbnb in China. This result aligns with So et al.’s (2018) investigation which found that perceived risk had no significant relationship to consumers’ attitudes or behavioral intentions. Amaro et al. (2019) also identified that perceived risk has no impact on consumers’ intention to book accommodation on Airbnb. Two potential explanations can be given. First, the majority of Airbnb’s potential consumers are young people, and they are more likely to be risk-takers and be careless in relation to hazards such as losing personal information. Therefore, perceived risk is not a crucial factor that affects the target consumers’ willingness to use Airbnb in China. Second, the development of the consolidated trust mechanism, including the purchase return system, third party supervision, and privacy-protecting mechanism, provides a more effectible and efficient system to protect consumers, which gives them more confidence to ignore the perceived risk.

5.2 Perceived higher price

The critical role of price has been recognised by many studies investigating the

determining factors that influence consumers’ purchasing behavior (Chang and Wildt,

1994; Kim and Kim, 2004). Our results are consistent with the findings of these studies,

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and a strong negative relationship between the perceived higher price and Chinese consumers’ purchase intention towards Airbnb in China is confirmed in our study. That is, Chinese consumers believe that compared with other Chinese rental sharing platforms, Airbnb provides higher-price products, which results in consumers’

unwillingness to use Airbnb in China.

Interestingly, it is worth mentioning that as a disruptive innovation, Airbnb’s potential consumers are those who chase low prices when booking accommodation since that disruptive innovation initially appeals to the group of customers in the low-end of the market (Guttentag, 2015). However, the majority of Chinese consumers believe that Airbnb provides higher-price products compared to Chinese platforms. We investigated the prices on Airbnb in China as well as local platforms such as Xiaozhu, and found that Airbnb charges an extra service fee while local platforms do not. Furthermore, Chinese consumers have a common prejudice against Airbnb due to its foreign identity, believing that Airbnb provides a higher-priced service than domestic platforms in China.

Hence, Airbnb should use the concept of “low cost” to attract customers in media stories and reduce their prices.

5.3 Lesser availability of listings

In line with prior studies, our research reveals that the limited listings result in Chinese

consumers’ unwillingness to use Airbnb. Specifically, our study indicates that the factor

of lesser availability of listings is found to be significant and has a negative relationship

with Chinese consumers’ purchase intention towards Airbnb in China. This falls in-line

with the issue of liability of foreignness that Airbnb is facing. Firstly, as an essential

resource, the number of listings plays a critical role in conquering an overseas market

such as China. The more listings Airbnb has in China, the more competitive the

platform is. Secondly, consumers generally have a perceived commitment to trust large

companies that have the capabilities to provide accommodation options everywhere

(Guttentag and Smith, 2017). However, Airbnb has a lesser availability of listings in

China, which hinders its capture of the Chinese market.

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Overall, the results of our studies encourage Airbnb to deeply engage in investigating the real demand of Chinese customers and the Chinese market, and not only cover more cities than competitors but also provide varieties of styles of accommodations.

5.4 Lesser availability of advertising

The findings of this study reveal that the lack of ability to advertise in China is not proven to hinder customers’ willingness to book rooms through Airbnb. Many potential reasons, combined with the liability of foreignness, could be used to analyse this result.

First, as mentioned above, many researchers have found that consumers are usually

attracted to the higher priced products and services through viewing their

advertisements (Yoo et al., 2000; Bravo et al., 2007). However, while using sharing

platforms to book accommodation, Chinese consumers are more sensitive to price and

usually chase low prices to save money (Guttentag, 2015). Second, in contrast to the

local competitors in China, as a fresh foreign company, Airbnb has limited

acknowledge about Chinese culture and their potential customers and the local

platforms have much greater expertise in dealing with the local culture and Chinese

consumers,F which hinders Airbnb’ advertising creating brand awareness and unique

associations to the brand in consumers’ memories. Third, since the target consumers

are millennials who focus on personal experience and are careless of traditional

advertising, which means that advertising is not as significant as it used to be.

References

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