Millennials’ behavioral intention to book a room or an apartment on Airbnb

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Millennials’ behavioral intention to book a room

or an apartment on Airbnb

MSc. Dissertation

Marco Di Betta – S3205150\B7058871

Double Degree Master International Business Management & Marketing University of Groningen, Newcastle University

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Abstract

The sharing economy has experienced an important expansion in recent years especially thanks to the development of online platforms. Notably, Airbnb, a home sharing platform, has drastically grown and it has now an overall value of 31 billion dollars. Considering the importance and

popularity of Airbnb, it is fundamental to investigate which factors affect intentions to book on the home sharing platform. Therefore, this thesis analyzed which factors affect millennials’ behavioral intention to book a room or an apartment on Airbnb and how perceived risk moderates this

relationship. The relationship between influencing factors and millennials behavioral intention will be explained drawing on the extended theory of planned behavior.

The data for this study has been collected via an online survey, which lead to a final sample of 211 respondents. To test the hypotheses, factor analysis, Pearson’s correlation and multiple regression analysis were conducted. The overall results show that perceived environmental benefits, attitude toward Airbnb and subjective norms positively influence millennials’ behavioral intention to book a room or an apartment on Airbnb. The strongest relationship was found for perceived environmental benefits. No significant correlation has been found for the moderating role of perceived risk. A possible explanation for this finding is that millennials perceived lower risk when purchasing online than other generations.

This thesis adds a significant contribution to the existing literature by using the extended theory of planned behavior combined with other constructs. The findings show how millennials are more concerned about the perceived environmental benefits than the perceived economic benefits. This thesis has some practical implications. Firms are provided with better information on which factors millennials value the most which might help them in making better decisions regarding the marketing strategy.

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List of Figures

Figure 1. Sharing economy and related forms of platform economy ... 11

Figure 2. Conceptual Model ... 23

Figure 3. Normal P-P Plot of Regression Standardized Residual- Intention ... 39

Figure 4. Histogram of millennials intention with perfect normal distribution curve ... 40

Figure 5. Scatterplot of Standardized Millennials Intention to test homoscedasticity ... 41

List of Tables

Table 1. Overview of the variables used in this study ... 28

Table 2. Measurement Scales and Factor Loadings ... 32

Table 3. KMO and Barlett's test ... 33

Table 4. Sample characteristics (n=211) ... 35

Table 5. Sample nationalities (n=211) ... 36

Table 6. Correlation between variables ... 38

Table 7. Descriptive Statistics ... 41

Table 8. Multiple regression analysis ... 44

Table 9. Cronbach Alpha ... 45

Table 10. Alternative multiple regression analysis ... 47

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List of Abbreviations and Acronyms

• C2C Consumer to Consumer

• GDP Gross Domestic Product

• ICT Information and Communication Technology

• KMO Kaiser-Meyer-Olkin

• P2P Peer to Peer

• PBC Perceived Behavioral Control

• PCA Principal Component Analysis

• TAM Technology Acceptance Model

• TPB Theory of Planned Behavior

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1. Introduction

In the last century, especially thanks to technological advancement, the sharing economy has been trying to question the fundamental economic assumptions, such as resource allocation and scarcity, that rest upon our economic system. Sharing is not a new practice; the concept has been around since the 1960s, but it has recently experienced a revival thanks to the development of information and communication technology (ICTs). The idea behind the sharing economy is simple; instead of owning a good such as a car, accommodation or even an idea, it is easier to share the usage of it. In the sharing economy, the traditional assumption of ownership is challenged by the access of underutilized and private resources for a monetary benefit.

Although the ideas at the base of the sharing economy might seem quite understandable and straightforward, there is not a single agreed definition of the concept. Several terms, including

collaborative consumption, on-demand economy, product-service economy, second-hand economy and access-based consumption, have been used to describe it. Additionally, Internet and online

peer-to-peer platforms have made communication more comfortable and faster; the nature of the relationship between users and providers of the service have challenged the existing business model of buyers and sellers operating in the market. If we look at sharing economy platforms like Airbnb for accommodation or Uber for ridesharing, we can see they have been growing at an incredible rate. PwC (2015) states that the five key sectors of the sharing economy, (travel, ride sharing, finance, staffing, music and video streaming) could reach global revenue of 335 billion by 2025; this is an increase of 22 times their current value of 15 billion dollars. The sharing economy is undoubtedly expanding its boundaries in the tourism and hospitality sectors, so it is of fundamental importance to better understand the factors that drive consumers behavior to engage in this new type of

accommodation service (Tussyadiah, 2016).

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31 billion dollars, it made 2.3 billion dollar revenue in 2018 and it is likely to be quoted on the stock exchange market in 2019 (Bosa, 2019).

Although Airbnb can be considered a business success, there have been claims labelling it as an extremely controversial business activity. Some studies have shown that Airbnb is responsible for raising housing prices and expelling residents from tourist areas (Dogru, 2017; Oskam, 2016). It can be argued that Airbnb also has a negative effect on local hotel room revenue (Zervas, 2017).

Furthermore, since Airbnb is challenging to regulate, there are cities and countries worldwide where it is considered illegal (Guttentag, 2015). Additionally, in many neighborhoods, residents have filled in many complaint reports about the bother of living next to an Airbnb host, protesting about loud noises and safety issues (Kobie, 2016). On the other hand, Airbnb also presents positive aspect. For instance, some authors have shown that while Airbnb might decrease hotels’ revenues by taking clients away, it can also create new demand (Forgacs, 2016). Coyle (2016) and Dogru (2017) proved that Airbnb activities are not as harmful as demonstrated to the lodging industry because Airbnb guests are different from usual hotel guests. Some important economic, financial and social benefits can also be attributed to Airbnb. For instance, Deloitte (2017) highlights the positive impact of Airbnb on the Australia Gross Domestic Product (GDP) and employment. Moreover, Airbnb also creates additional profits for hosts in particular neighborhoods that usually are not visited by tourists (Dogru, 2017).

Considering the rapid growth and importance of Airbnb as well as the controversy surrounding it, it would seem interesting to analyze which factors affect behavioral intentions to book an

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question I will try to answer with this study is: “What factors drive millennials behavioral intentions to

book a room or an entire apartment on the home sharing platform Airbnb?”

The rest of the thesis is organized as follows: in the next section, the literature on the sharing economy, the advent of the internet and the characteristics of the millennials will be reviewed. Section three discusses the methodology used to conduct the analysis, including data collection methods, questionnaire design, variables description and factor analysis. Results from the data analyses are then presented, followed by a discussion of important insights as well as

recommendations for future research.

2. Literature review and Hypotheses Development

2.1 The practice of Sharing

As a society, we conceive our economy as a linear model; new resources need to be put into the system to increase consumption continually. Axelsson (cited in Edbring, 2016) states that if the world’s 7 billion people consume goods as the Swedish population does today, 3.25 Earths would be needed. This clearly asserts that Western consumption habits are unsustainable and need to be changed. Nowadays, the commercial use of sharing economy services allows people to share resources in creative new ways (Cohen, 2014). For example, people can access rooms (AirBnB, Roomorama), cars and bikes (Relay Rides, Wheelz), and taxi services (Uber, Lyft)

(Malhotra, 2014).

To understand the underlying meaning of the sharing economy and what it stands for, is not an easy task. First, the concept of sharing must be analyzed, detached from the word economy. Sharing is not a new practice, it has been around for centuries. People have always shared, as sharing creates social relations and strengthens cultural practices (Belk, 2009). Belk (2007, p.126) defines sharing as “the

act and process of distributing what is ours to others for their use and the act and process of receiving or taking something from other for our use”. The act of sharing is a reason for survival (Fine, 1980)

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family and friends, while the latter means sharing among strangers, for example providing someone with spare change, directions or the time of the day. It is clear that the degree of intimacy is

different, and already it can be said that there is a difference between non ownership-based sharing, like providing directions, transfer of ownership like giving spare change, and reciprocal exchange like gift-giving. It must be noted that none of these acts of sharing involve any creation of debt (Belk, 2013).

However, problems come up when we speak about borrowing and lending, which represent extreme cases of sharing because they create the expectation that the goods or object exchanged will be returned (Belk, 2013). We need to differentiate of course between borrowing a sheet of paper and borrowing someone’s phone to make a call. In the first example, we do not expect the sheet of paper to be returned, while in the second one we would certainly expect the person to give back the phone. The latter is also an example of “sharing out” as it is sharing a ride.

2.2 The advent of the Internet

To give a complete definition of what sharing economy means Frenken’s (2015) defines it as “consumers granting each other temporary access to under-utilized physical assets for money”. The most common goods being exchanged are usually cars and homes. Under-utilization is a key concept in the sharing economy because it separates the practice of sharing goods from the practice of on-demand personal service (Frenken, 2017). There is a distinction between calling a taxi with Uber or Lyft and sharing a ride with BlaBlaCar or another carpooling platform. In the case of Uber, the user creates new capacity by calling the taxi, and this is an example of on-demand economy. In contrast, in the example of carpooling the trip would have been done anyway so this is ride sharing as part of the sharing economy (Benkler, 2004). The concept of under-utilization is of fundamental importance, also in home sharing platform like Airbnb (Frenken, 2017). If a house owner goes on holiday or simply has a spare room, the asset is underutilized, so we speak about the sharing economy. On the other hand, if someone decides to buy a house just to host tourists, it is considered running a commercial lodging business like a B&B or hotel.

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the access to the product or service, merely sell it. Product-service economy is defined as consumers renting out products or services from a company, rather than from other consumers. An example is the car rental service Zipcars, a “car sharing” company that rent out automobiles in American and European cities. Consumers pay a yearly fee to use the service, yet they need not worry about fuel, insurance, parking fees and maintenance.

Furthermore, the term on-demand economy is used when we speak about a P2P service delivery like Uber or Lyft. It can be noticed from Frenken’s framework (2015) (figure 1) that the sharing economy has three main characteristics: consumer to consumer interaction (C2C), temporary access, and physical goods. This definition of sharing economy and the framework provided, incorporates the historical features of the act of sharing itself, and the new economies derived from it. Before the rise of Internet platforms, people already exchanged goods with each other, but mainly with family members and friends, as there were established relationships of trust. Nowadays, what is different is that people share products and services with complete strangers because the internet has decreased transaction costs and it has transformed what once were local markets to international markets (Frenken, 2017).

Figure 1. Sharing economy and related forms of platform economy

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enable us to understand the advantages and disadvantages of this phenomenon. Napster is the first example of a peer to peer (P2P) file sharing website that allows users to download and upload music and films for free (Hennig-Thurau, 2007). This had a negative effect on the music and movie

industries because they started losing sales of CDs and DVDs, and consequently tried to enforce their intellectual property rights through legal actions (Giesler, 2008). The resulting famous “war on sharing” was pointless; in the end Napster was shut down but many alternatives sites started popping up everywhere (Aigrain, 2012). It must be noticed that there are also websites like iTunes, Pandora, Spotify and Netflix, which offer legal downloads and streaming of music and movies. However, the most significant proportion of film, music, software, e-books and gaming downloads are illegal, especially among young people (Belk, 2014).

Although illegal downloads of music and movies have received most of the attention from the media and the public, several other sharing platforms have been on the rise, thanks to the development of the Web 2.0. This refers to social media sites such as Facebook, Twitter and YouTube; interest sharing sites such as Pinterest; pictures sharing sites such as Flickr; rating sharing sites such as TripAdvisor and blogs that provide reviews of movies, products and books. This is not to say that these websites are non-profit because there are individuals who earn money through advertising and online selling, but most of the users on these platforms share information, ratings, pictures, videos without being remunerated. There are also examples of open source software like Linux Kernel, where users can freely download the software and contribute to its development, and even free useful information websites like Wikipedia.

The exchange of material goods among users is another crucial example, and it represents an essential type of internet-facilitated sharing (Belk, 2012). EBay, Craigslist and Kijiji are sites that offer products for sale, yet there are also other websites that promote the exchange of free goods

between users, including sites such as Freecycle and Really Really Free Stuff. If we look at the past, people did not share with strangers or those outside their social networks. With the rise and

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2.3 Collaborative consumption and sharing economy

So far, I have explained the concept of sharing and how, thanks to the internet, we have been able to create an economy out of it. It should be clear now that the sharing economy is a new concept, and for this reason, it is still hard to give a general definition of it. What also has to be addressed is the term collaborative consumption, which also includes the concept of sharing economy. To explain what it means and why it is essential, I will use the words of Botsman and Rogers (2010, p. 10) when they describe how Joe Gebbia, Brian Chesky and Nathan Blecharcyzk created the idea of Airbnb:

“On a whiteboard in their apartment, they drew a spectrum. On one side they wrote hotels and on the other they scribbled rental listing such as craigslist, youth hostel and non-monetary travel exchanges such as CouchSurfing that help people travel by creating a network of couches available to sleep from free”

The center of the spectrum drew on the whiteboard by Joe Gebbia, Brian Chesky and Nathan Blecharcyzk is what collaborative consumption is; people managing acquisition and distribution of a resource which it can be a product or a service for a fee (Belk,2014). Collaborative consumption has elements of both sharing and marketplace exchange. Bardhi and Eckhardt (2012) link together the concepts of sharing economy and collaborative consumption in their concept of “access-based consumption”. They point out how “instead of buying and owning things, consumers want access to

goods and prefer to pay for the experience of temporarily accessing them” (p.881). Collaborative

consumption is here defined as a subcategory of access-based consumption, that they call market mediated access.

The rapid growth of what I call the sharing economy, in the past decade is correlated to social and economic reasons; consumers are looking for better value distribution of the supply chain

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2.4 Different views on the concept of sharing economy

After a review of the current literature, I identify two main streams of research which are highly correlated with each other. The first refers to the studies on the collaborative consumption as a technological phenomenon, while the second refers to the perspective of consumer culture behavior. Hamari (2016) studies why people participate in collaborative consumption. The study investigated the influence of intrinsic and extrinsic motivations on attitudes and behavioral intentions. The research was based on self-determination theory, which states that human behavior can be divided in intrinsic and extrinsic motivations (Ryan, 2000). Motivations are placed on a spectrum; on the left-hand side there is a complete lack of motivation. In the center there are extrinsic motivations, which means that people behave in a certain way because they aim at obtaining something that goes beyond the behavior itself (e.g. gifts and rewards). On the right-hand side there are intrinsic

motivations, which means that the individual finds an activity rewarding in itself and is likely to take part in it (Ryan, 2000). First, Hamari (2016) measures and analyses 254 collaborative consumption platforms from which he distinguishes two main groups: access over ownership and transfer of ownership. Second, primary data is gathered through 168 surveys from users on the P2P trading service, “Sharetribe”. Hamari (2016) tested the model and found a significant correlation between factors of sustainability, enjoyment, economic benefits and behavioral intention to participate in a peer to peer sharing option. The research is collocated in the literature on technology participation and adoption, as the author’s view is that information system technologies moderate all sharing economy activities.

On a similar line of research, Mohlmann (2015) analyzed the determinants of satisfaction and the likelihood of using a sharing option again. She based her research on two surveys; the first with consumers of a car sharing platform Car2go and the second with consumers of the sharing

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Both authors, Hamari and Mohlmann, posit their studies in the literature on technology participation and adoption. The Sharing economy heavily relies on the development of internet access and

information systems technology. That is why the first researchers have focused on technology acceptance model (TAM); considering technology acceptance as a main determinant of technology use. TAM model has been widely used to understand user acceptance of information technology in different situations (Morosan, 2008, 2012), including online contexts (Gefen, 2003; Moon, 2001). Originally introduced by Davis (1989), TAM is derived from Fishbein’s theory of reasoned action (TRA) (Fishbein, 1980) and Ajzen’s theory of planned behavior (Ajzen, 1991). TAM explains user adoption of a technology by linking an individual’s beliefs to attitude and intention to use that technology. The question of whether to adopt a particular technology or not is not relevant anymore. The focus should be more on the reasons why millennials choose a specific application and what factors drive their behavioral intention to participate. Millennials were raised in a technology-friendly

environment, which means they are high-tech savvy, absorbed by the online atmosphere and users of social media networking sites. The use of internet applications to move around a city, to book a home to stay in, and even to find someone to go out on a date with are all practices almost taken for granted. This means there has been a shift in cultural values and beliefs that is not fully captured by TAMs.

To better capture this change I will use the theory of planned behavior for this research design, which is what TAM is based on to study millennials behavioral intentions. That is why I will try to posit my thesis in the middle of the two streams of research; namely that of technology adoption and that of consumer culture behavior. Ajzen’s Theory of Planned Behavior (TPB) (Ajzen, 1985, 1991), investigates psychological and sociological determiners in online sharing behavior. The theory is built on Bandura’s theory of self-efficacy and Ajzen and Fishbein’s Theory of Reasoned Action (Fishbein, 1980). TPB states that to understand why an individual behaves in a certain way, we need to look at the individual’s intention to perform that behavior. Individual intentions are influenced by individual attitudes toward the behavior, the subjective norms regarding the behavior and the individual’s perception of control over the behavior (Ajzen, 1985, 1991). In a meta-analysis comparing TPB with other behavior change theories, Webb et al (2010) found “TPB to be the most effective as a

foundation for the design of internet-based behavior change interventions” (Bellotti, 2015 p.1086).

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2.5 Millennials

Young consumers also defined as “Millennials” are the people born between 1981 and 1999. They have different behaviors and lifestyles in comparison to other generation groups such as Baby Boomers and Gen X. Nowadays they represent more than a third of the workforce population in U.S., Europe and China. Millennials are more concerned about environmental issues, and they better embrace information and communication technologies (ICTs) than Baby Boomers and Gen X (Polzin, 2014). They prefer to live in cities and are more inclined to use share mobility services (Garikapati, 2016). Generation theory might help explain these differences in lifestyle and travel behavior; the theory states that different age cohorts like Baby Boomers, Generation X and Millennials have different travel behavior (Keeling, 2003). These differences exist mainly because of two reasons: direct age effects and cohort effects. Direct age effect means that older travelers might perceive the natural and physical environment in different ways than younger travelers. Cohort effects refers to the fact that travel behavior can be explained through historical, social, economic, cultural and technological contexts, thus each generation has to be analyzed within these dimensions. Millennials, for instance, have socio-demographic traits, lifestyles habits and a rate of ICTs adoptions that define them as the generation who drive less and use public transport, bike, car and home sharing at a higher rate than previous generations (Klein, 2017). It is also claimed that millennials own fewer vehicles and drivers’ licenses, as well as delay marriages and childbearing, choose to follow higher education and struggle to find a job in the present job market (Ralph,2017). For instance, a recent study demonstrates how millennials prefer on-demand services like Uber to traditional car

ownership. The change of values and beliefs could explain this; in the past having a car was somehow explicative of the identity of the individual while nowadays millennials do not feel that way anymore and prefer experiences over ownerships (McDonald, 2015; Garikapati, 2016). The same point can be made for Airbnb when compared to traditional hotels; the unique cheaper experience offered by the home sharing platform cannot be replicated in the lodging industry. Although altruistic and

environmental motives may drive millennials behavioral intention the importance of economic benefits as a reason to participate in the sharing economy has been stated several times (Tussyadiah, 2015; Mohlmann, 2015, Lamberton and Rose, 2012; Hamari, 2016). Economic benefits can be

considered an appealing advantage for people to take part in the sharing economy (Tussyadiah, 2015; Guttentag, 2015). Researches have highlighted how economic motives drive consumers behavior (Lamberton and Rose, 2012; Tussyadiah, 2016) ; users are persuaded by lower price

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2.6 Extended theory of planned behavior

The purpose of this thesis is to study the factors influencing millennials behavioral intentions to use a P2P home sharing option, namely Airbnb. The idea is to build a theoretical framework that can predict what drives millennials’ behavioral intention to participate in the service. The framework will be based on the existing theory of planned behavior, including other validated constructs, mentioned below.

The theory of planned behavior provides the ground for the argument that users’ perceptions influence users intention to participate in P2P sharing option. Intention is the central element in the theory of planned behavior (Ajzen, 1991). The theory states that motivational factors influence intentions which affect an individual’s behavior. Intentions are the expression of ‘‘how hard people

are willing to try, of how much of an effort they are planning to exert, in order to perform the behavior (Ajzen, 1991, p.188). The stronger a person’s intention to participate in a specific behavior,

the higher is the likelihood that this person will perform the behavior. However, this thesis studies millennials’ intentions and not actual use so the behavior construct will not be part of the

framework. Internal relationships between attitude, subjective norms and perceived behavioral control will also be excluded from the framework.

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2.6.1 Subjective norms

The first determinant, subjective norms, refers to ‘‘the perceived social pressure to perform or not to

perform the behavior’’ (Ajzen, 1991, p.188). Recent research has produced conflicting results on the

role of subjective norms. For instance, Lee (2007) demonstrated that subjective norms has a positive effect on people’s intention to travel and purchase online while San Martin’s study (2012) found the opposite. However, as Burnkrant (1975) states the influence of others is an important factor in predicting an individual’s behavior. Subjective norms are easily linked to the social pressure that millennials experience nowadays when they need to conform to their group of peers. In today’s society, where everyone is always connected and can see other people’s life thanks to social media, individuals feel obliged to display their consumption habits in order to feel accepted by another group of peers (Kim, 2014). Millennials are also very sensitive and easily influenced by other people’s beliefs and ideas. The internet has its own rules; for instance, when something new and shocking appears on a social media platform people tend to believe it rather than checking its validity. This effect is called network effect and it is described as the pressure that users feel to conform to other people’s ideas; this means that a small number of users can drag the intentions of many others. Nowadays, it is common practice that businesses spend a part of their marketing budget to hire popular social media figures called “influencers”. These individuals are in charge of promoting the specific company, product or service through pictures and videos on social media platforms. Influencers also use “social media contests”, where people have, for instance, to tag friends under specific posts in order to get a product or a service for free. It is in the influencers’ own interest not to publicize services that do not work, or dishonest products, because their reputation and income heavily depend on user’s trust and consideration. Millennials are likely to support and believe the products and services influencers promote and advertising them to friends and family. Therefore, the social pressure experienced by millennials is linked to subjective norms and it is likely to affect their behavioral intention positively.

I hypothesize that subjective norms are major predictors for behavior intention toward the participation in Airbnb.

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2.6.2 Perceived behavioral control

The second determinant of intention is perceived behavioral control (PBC), which refers to ‘‘the

perceived ease or difficulty of performing the behavior’’ (Ajzen, 1991, p.188). The TPB differs from

TRA in its addition of this construct. PBC plays an vital role in the TPB , and it can be considered of greater psychological interest than the actual control (Ajzen, 1991). Although PBC can be compared to several others constructs of control, like Rotter’s perceived locus of control or Atkinson’s

perceived probability of succeeding at a given task, the most compatible one is Bandura’s (1982) concept of perceived self-efficacy which “is concerned with judgments of how well one can execute

courses of action required to deal with prospective situations” (Bandura, 1982, p.122) (Ajzen,1991).

Bandura demonstrates how people’s behavior is strongly affected by people’s confidence in their ability to perform it. Self-efficacy beliefs can affect how people engage in a certain activity and how much effort they are willing to put in it (Bandura, 1982). According to Ajzen (1991), PBC can be utilized directly to predict behavioral use. For example, if two people want to learn how to ride a bike equally and they have the same strong intentions, the one who is more confident and persists the most is more likely to succeed than the person who doubts himself. However, since in this thesis, I do not consider the construct of behavioral use, the relation between PBC and actual use will also not be considered.

H2: Perceived behavioral control demonstrates increasing levels of millennials’ intentions to participate in Airbnb.

2.6.3 Attitude toward Airbnb

The third determinant of intention, attitudes, refers to ‘‘the degree to which a person has a favorable

or unfavorable evaluation or appraisal of the behavior in question’’ (Ajzen, 1991, p.188). The theory

of planned behavior states that the more favorable the attitude, the stronger a person’s intention to perform the behavior (Ajzen, 1991). TPB is widely used in research especially to study the

relationship between purchase intention and purchase behavior. In the context of online travel, several studies discovered that attitudes toward online shopping is a crucial factor that drives intention to buy travel online (Amaro, 2015; Lee, 2007). Hamari (2016) also found that attitude is a major determinant to behavioral intention to participate in collaborative consumption.

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To the three original constructs of the TPB, I will also add three new constructs: perceived economic benefits, perceived environmental benefits and perceived trust in Airbnb business. In this way the framework presents a more precise overview which in turn will allow for a better understanding millennials’ behavioral intention.

2.6.4 Perceived economic benefits

Generally, the literature has shown that economic motives are a fundamental reason for people to share (Belk,2009). Lamberton and Rose (2012) demonstrate that there is significant correlation between economic benefits and willingness to share. Research has also shown that one of the main reasons why travelers decide to book houses or hotel rooms online is for financial motives (Amaro, 2015). The same concept could be applied to home sharing application like Airbnb. Many academic papers have demonstrated that Airbnb users are stimulated to participate by lower prices

(Tussyadiah, 2015; Guttentang, 2015; Tussyadiah, 2016). Mohlmann (2016) also highlights that users are very interested in the fact that collaborative consumption helps them save money.

H4: Millennials’ perceptions of perceived economic benefits demonstrate higher level of millennials’ intentions toward Airbnb participation

2.6.5 Perceived environmental benefits

The sharing economy offers environmental benefits because it improves efficiency and avoids waste (Botsman, 2010). Taking part in the sharing economy satisfies consumers’ philosophy and the desire to be a responsible and active citizen. Adopting a sharing behavior will help avoid excessive

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concentrate on idealist motivations that promote sustainability. In the case of Airbnb, studies show that adopting a home sharing application to travel is more sustainable because of the significant savings on water and energy and avoids the waste of chemicals (Airbnb,2018). Airbnb claims that 88 percent of providers of home sharing now adopt green practices when hosting, such as: the use of green cleaning products, practice of recycling, encouragement of guests to use public transports, or in some cases even providing bicycles and installing solar panels (Airbnb, 2018). Furthermore, due to a 2017 survey it was discovered that 66 percent of Airbnb guests take environmental benefits into consideration (Airbnb,2018). In 2014, Airbnb joined Cleantech Group in order to do an environmental analysis to see how its impact was compared to that of traditional hotels. The results proved that the effect Airbnb has on the environment is lower than that of traditional hotels. Another 2018 analysis, which also used the Cleantech model, discovered that when consumers use the Airbnb service, the consumption of energy and water is reduced, and less greenhouses gases are diffused and waste is reduced (Airbnb,2018). In Europe in 2017, the energy savings achieved were equal to 826,000 houses, water reduction was equivalent to 13,000 Olympic sized pools, and the reduced greenhouse gas emissions were equal to 2.38 million cars and waste reduction equal to 107,047,799 kg (Airbnb, 2018).

Therefore, I propose the following hypothesis:

Hypothesis 5: Millennials’ perceptions of perceived environmental benefits demonstrate increasing levels of millennials’ intentions to participate in Airbnb.

2.6.6 Perceived trust in Airbnb business

Trust does not have a general or universally accepted definition because it is a complicated concept with many different nuances. Previous research demonstrated that trust is valuable for building relationships, both of interpersonal and commercial nature (McKnight, 2001). Additionally, in contexts where uncertainty and risk are present, trust is critical to overcome them (Schoorman, 2007). This especially applies to socially distant relationships like an online environment where there is a constant complexity and uncertainty (Kim, 2011). Online interactions that are not entirely administered by laws and policies need a strong base of trust to achieve accomplishment (McKnight, 2002). Therefore, many scholars point out the importance of trust in several fields such as

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business behaves in a positive way in order to make consumers comfortable to use the website and helps them avoid perception of risk and insecurity (Kim, 2011; McKnight, 2002).

H6: Millennials’ perceptions of perceived trust in Airbnb business demonstrate increasing levels of millennials’ intentions to participate in Airbnb

2.6.7 The role of perceived risk as moderator

In the consumer behavior literature, perceived risk was first conceived as fraud and product quality, but in recent times it has been described in connection with financial, physical, psychological and social risks of operating in a non face-to-face online environment (Hanafizadeh, 2014). When engaging in online purchases, people often perceive risks concerns. Since it is not easy to address an objective measure of risk, the concept of perceived risks is preferred. In this thesis I defined

perceived risk as users’ beliefs about the potential negative outcomes to book Airbnb. This definition seems in line with the one provided by Featherman and Pavlou (2003, p.454), which defined

perceived risks as “the potential for loss in the pursuit of a desired outcome of using an e-service”. In the context of online transactions Featherman and Pavlou’s interpretation of perceived risk is

generally agreed upon (Yang, 2015; Martins, 2014). Internet security is a crucial factor that influences consumers to purchase online. Several online users do not think of the web as a safe place; many fear the potential unauthorized access to their data and information (Zimmer, 2010). The high dependence on online payment increments perceived risks in online shopping. Perceived risks are also responsible for decreasing intentions to transfer data and complete purchases online. However, research on perceived risk has produced ambiguous conclusions. A few researches have

demonstrated a significant negative effect of perceived risk on behavior intentions (Martins, 2014), attitude (Lim, 2014) and trust (Yang, 2015). But other studies have not discovered any significant correlation between perceived risk and intention (De Kerviler, 2016), so the need to analyze the role of perceived risk in an online environment in more depth appears crucial. Specifically, in the context of a home sharing platform like Airbnb perceived risk has been overlooked; people might have privacy and payment concern (Guttentag, 2015), so it is important to study the role of perceived risk and its effect on the other variables of the framework.

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H7b: Millennials’ perceptions of perceived risk negatively moderate the relation between perceived behavioral control and intention to Airbnb

H7c: Millennials’ perceptions of perceived risk negatively moderate the relation between attitude toward Airbnb and intention to book Airbnb

H7d: Millennials’ perceptions of perceived risk negatively moderate the relation between economic benefits and intention to book Airbnb

H7e: Millennials’ perceptions of perceived risk negatively moderate the relation between perceived environmental benefits and intention to book Airbnb

H7f: Millennials’ perceptions of perceived risk negatively moderate the relation between perceived trust in Airbnb business and intention to book Airbnb

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3.Methodology

3.1 Sample and Data Collection

This study will be conducted among young European consumers aged 18 or older, that have engaged at least once in an Airbnb service. Young consumers, or more specifically millennials, are more willing to engage in online purchasing and internet services. Although every person is allowed and able to book a room or a house through Airbnb, the network through which I will deliver the surveys is mainly composed of young international consumers. Quantitative research will be adopted to test the hypotheses previously proposed. Data will be gathered through online surveys, which will be distributed through social media and email. Since the sample will consist of both men and women of various nationalities, the questionnaire will be distributed in English. Considering that English is an international language; one of the most studied all over the world and spoken in 101 countries, it seems the appropriate choice as the language for the survey (Noak, 2015).

This research analyses whether a group of factors influence millennials’ intention to adopt a sharing option, so the focus is if there is or is not a positive effect on the intention to participate and not the size of the effect, so a non-probability sample will be used (Blumberg, 2014). Taking the small-time frame and the specific characteristics of the target group into consideration, the non-probability sample that will be used is a convenience sample. A convenience sample has the advantage to allow everyone to take part in the study which means that family, friends and colleagues can be included (Blumberg, 2014).

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3.2 Questionnaire design

The purpose of this study is to understand the effect of subjective norms, perceived behavioral control, attitudes toward Airbnb, perceived economic benefits, perceived environmental benefits and perceived trust in Airbnb business on millennials’ behavioral intention to book Airbnb. Web-based questionnaires are chosen because they are less time consuming and more cost effective compared to traditional paper questionnaires (Wright, 2005). The questionnaires will be distributed through personal social media platforms and emails. I will send the surveys as private messages on Facebook to my personal network of friends, students or alumni of Groningen University, Newcastle University and Milan University.

Although an online sample is not always representative of the total population because it contains only the people with Internet access, it can be argued that it is appropriate, as the target population is composed of Airbnb users that undoubtedly have internet access (Brace, 2008). Online surveys are more adequate for this type of research because without an interviewer people feel free to answer questions in an honest way and the overall experience of providing answers is more enjoyable (Brace, 2008). Online surveys are faster to complete than face to face or telephone surveys.

However, the lack of a direct interviewer may also generate some issues creating misunderstandings among the respondents and leave some unanswered questions (Brace, 2008).

3.3 Pilot test

A pilot test was conducted to verify that the questions in the survey were precise and

comprehensible for the consumers to answer. Five people were selected to participate in the pilot test to check the survey. Questions about the structure, length, format and accuracy of the survey were asked. After the conclusion of the test, respondents were also asked about their experience. Feedbacks from the consumers were to make changes where required. Due to the feedback, some minor adjustments were made to the survey. Some respondents claimed that a few statements were not clear; others claimed that some statements were similar to each other. As a result, the

statements were reviewed, and some small changes were applied to some of the statements. The complete survey is displayed in Appendix 1.

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suitable to be used in the survey question. This is because all five people selected knew what the sharing economy was and they could name a few examples as well, including Airbnb.

3.4 Ethical Considerations

This research will consider the potential ethical issues and it will be designed while conforming to the ethical guidelines of the University. These ethical directions include elements such as data collection and financial inducements. First, respondents will be assured that anonymity and confidentiality will be maintained in their answers. Second, the collection of their data will be used only for the purpose of the research. Third, no monetary incentive will be proposed to complete the survey. Last,

consumers that take part in the survey will be free to stop at any moment.

3.5 Variables

3.5.1 Independent variables

The independent variables concerning the different factors that influence millennials’ behavioral intentions are derived from different articles and papers and are displayed in table 1. Subjective

norms are measured using two items adapted from Ajzen (2002a). Perceived behavioral control is

measured using three items adapted from Ajzen (2002). Attitude is measured using three items adapted from Nunkoo and Ramkissoon (2013)and two from Hamari (2016). Perceived economic

benefits are measured using four items adapted from Hamari (2016). Perceived environmental benefits are measured using four items from Mohlmann (2015). Perceived trust in Airbnb business is

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3.5.2 Dependent variable

Several measurements can be used to measure the dependent variable behavioral intention. In this study, to measure the dependent variable, millennials behavioral intention to book on Airbnb, a 5-point Likert scale questions will be adopted from Nunkoo and Ramikissoon (2013) and two from Hamari (2016). The dependent variable is measured with five items. As presented in table 2, all five items have high factors loadings and the Cronbach’s alpha (α) is 0.85, which indicates that the construct intended to measure is valid.

3.5.3 Moderating variable

The moderating variable perceived risk is measured using three items adapted from Im, Kim and Han (2008). As presented in table 2, all the three items have high factors loadings and the Cronbach’s alpha (α) is 0.83 which indicates that the construct is valid.

3.5.4 Control variable

Four control variables are included in this study, namely: gender, age, education and Airbnb experience.

The control variable gender is a discrete nominal variable. The variable will equal 1 when the

respondent is male and equal 2 when the respondent is female. For the regression analysis, a dummy variable is created.

The control variable age is a discrete ordinal variable. This variable will equal 1 when the consumer is between 18 and 24 years old, equal 2 when the consumer is between 25 and 29 years old, equal 3 when the consumer is between 30 and 34 years old and equal 4 when the respondent is between 35 and 39 years old. Due to the low response rate from the age group 35 to 39 years old (see table 4) it was decided to merge it with the age group 30 to 34 years old.

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respondent holds a bachelor degree, equal 5 when the consumer has a master degree and equal 6 when the respondent possesses a PHD. Due to the low response rate for the education group PHD (see table 4) it was decided to merge it with the education group master degree.

The control variable Airbnb experience is a discrete ordinal variable. The variable will equal 1 when the consumer has never heard of Airbnb, it equals 2 when the consumer has heard of Airbnb but never visited the website, it equals 3 when the consumer has visited the website but never booked and it equals 4 when the consumer is an Airbnb user.

All the variables I use in this research are presented in the table below. Table 1. Overview of the variables used in this study

Constructs Items Type Adapted

from

Subjective norms

-The people I trust would, in my opinion, rent a room or an apartment on Airbnb. - People whose opinions I value the most would convince me to rent a room or an apartment on Airbnb.

Independent Ajzen (2002a)

Perceived behavioral control

-All the indispensable means (e.g. laptop, internet connection, time) for renting a room or an apartment online on Airbnb are available to me.

-I have the necessary financial means (e.g. credit card, PayPal) to book Airbnb. -I am confident I can easily book a room or an apartment on Airbnb.

Independent Ajzen(2002)

Attitude toward Airbnb

-The use of Airbnb to book a room or an apartment from local hosts is a good idea. -All things considered, I think participating in Airbnb is a positive thing.

- For me the idea to rent a room or an apartment on Airbnb would be pleasant.

Independent Hamari (2016) Nunkoo and Ramkissoon (2013) Perceived economic benefits

-Booking accommodations from local hosts on Airbnb could help me save money. -Airbnb allows me to book a room or an apartment at a lower price than other travel accommodation website. -I can find affordable accommodation thanks to Airbnb.

-My participation on Airbnb benefits me financially

Independent Hamari (2016)

Perceived

environmental benefits

-With the use of Airbnb, I display environmentally friendly consumption behavior.

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-Renting a room or an apartment on Airbnb is energy efficient.

-Renting a room or an apartment on Airbnb helps reduce the use of natural resources. Perceived Trust in

Airbnb business

-Airbnb business has integrity. -The Airbnb online website is reliable. -The Airbnb online website is trustworthy.

Independent Kim (2011)

Intention to participate in Airbnb

-In future, Airbnb will be the preferred medium through which I will rent a room or an apartment.

-Overall, I can see myself renting a room or an apartment on Airbnb in the future. -My willingness to rent (or continue renting) a room or an apartment on Airbnb is very high.

-In the near future, I could see myself joining the Airbnb community as a host. -In the near future, I could see myself joining the Airbnb community as a guest.

Dependent Hamari (2016) Nunkoo and Ramkissoon (2013) Perceived Risk

-Booking accommodations from local hosts on Airbnb is hazardous.

-Sending personal information on Airbnb is risky.

-Making online payments on Airbnb is dangerous.

Moderator Im, Kim, and Han (2008)

Gender -Male

-Female

-Prefer not to say

Control

Age -18-24 years old

-25-29 years old -30-34 years old

Control

Level of education

-Less than high school graduate -High school graduate

-Some college but no degree -Bachelor degree

-Master degree

Control

Airbnb experience -Never heard of it

-Heard of Airbnb but never visited the website

-Visited the website, but never booked -Airbnb user

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3.6. Plan of Analysis

All the responses to the surveys were collected through the program Qualtrics after that the data was downloaded as an SPSS Statistics Data document. To analyze the data, the software IBM SPSS Statistics 24 was used. The validity and reliability of the variables were tested through an exploratory factor analysis and Cronbach Alpha test. In section four, Pearson correlation table and descriptive statistics were outlined. Lastly, in order to conduct an effective multiple linear regression analysis, all five assumptions for the regression were tested.

3.7 Validity and Reliability of Measurement Instruments

In order to run an efficient multiple regression analysis, it is fundamental to test the validity and reliability of the constructs that are used in this study. The validity of the different constructs has been tested through the use of an exploratory factor analysis. For the exploratory factor analysis, a principal component analysis (PCA), was conducted on the 26 items with a direct oblimin rotation. The results of the PCA are displayed in the table 2. Factor loadings should be above 0.3 in order to be acceptable for the PCA (Field, 2009).

After the PCA was run only 6 constructs were extracted out of the expected 8. The 3 items intended to measure the variable perceived behavioral control, loaded on the same construct as the 3 items intended to measure the variable attitude towards Airbnb. Same for the 3 items intended to measure perceived trust in Airbnb business which were loading on the 3 items intended to measure perceived environmental benefits. The pattern matrix for the factor analysis is displayed in Appendices 2. To avoid problems of cross loading factors, the two variables explaining less variance, namely, perceived behavioral control and perceived trust in Airbnb business were dropped out of the analysis, which implies that the related hypotheses 2 and 6 cannot be tested. It could be noticed that in this thesis the variable attitude toward Airbnb was adapted from two different sources namely Hamari (2016) and Nunkoo and Ramkissoon (2013). Hamari (2016) derived the variable attitude from the work of Ajzen (1991) while Nunkoo and Ramkissoon derived it from the work of Davis (1989) which is what the TPB is based upon. TPB was proposed by Ajzen (1991) and it includes both perceived behavioral control and attitude. Therefore, it could be argued that the reason why perceived behavioral control and attitude overlap is because the items come from the same source.

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ethics. Likewise, it could be argued that the diving line between perceived behavioral control and attitude toward Airbnb is not that important in an online context because the user has most of the control over the service. These different interpretations of the results will be tested in an alternative multiple regression analysis.

Overall, all the other items had a sufficient factor loading on the construct to which they were assigned. To the test the reliability of the variables, a reliability analysis was conducted. In table 2, the Cronbach’s alpha (α) for each variable are presented. All variables were valid: subjective norms (α =0.686), attitude toward Airbnb (α=0.806), perceived economic benefits (α=0.822), perceived environmental benefits (α=0.804), intention to participate (α=0.859) and perceived risk (α=0.831). The Kaiser-Meyer-Olkin (KMO) score was also analyzed and it was found to have a value of 0.851 which is above the recommended threshold of 0.6 (Kaiser,1974).

Based on the PCA and the reliability analysis, new variables were computed for the items that measured the same construct. The new variables were calculated using the mean of the items that were measuring the same variable. The two items with sufficient factor loadings for the subjective norm construct were computed into the variable subjective norm. The three items with sufficient factor loading for the attitudes toward Airbnb construct were computed into the variable attitude toward Airbnb. The four items with sufficient factor loading for the perceived economic benefits construct were computed into the variable perceived economic benefits. The three items with sufficient factor loading for the perceived environmental benefits were computed into the variable perceived environmental benefits. The five items with sufficient factor loading for the intention to participate in Airbnb dimension were computed into the variable Intention. Finally, the three items with sufficient factor loading for the perceived risk dimension were computed into the variable perceived risk.

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Table 2. Measurement Scales and Factor Loadings

Constructs Measurement items Factor

Loadings Eigenvalue/ variance explained Cronbach’ s alpha (α) Subjective norms

The people I trust would, in my opinion, rent a room or an apartment on Airbnb.

0.764 1.285/ 6.42%

0.686 People whose opinions I value the most would

convince me to rent a room or an apartment on Airbnb.

0.867

Attitude toward Airbnb

The use of Airbnb to book a room or an apartment from local hosts is a good idea.

0.708 1.048/ 5.24%

0.806 All things considered, I think participating in

Airbnb is a positive thing.

0.838 For me the idea to rent a room or an apartment

on Airbnb would be pleasant.

0.763

Perceived economic benefits

Booking accommodations from local hosts on Airbnb could help me save money.

0.775 1.844/ 9.22%

0.822 Airbnb allows me to book a room or an

apartment at a lower price than other travel accommodation website.

0.792

I can find affordable accommodation thanks to Airbnb.

0.805 My participation on Airbnb benefits me

financially.

0.768

Perceived environmental benefits

With the use of Airbnb, I display

environmentally friendly consumption behavior.

0.843 1.069/ 5.34%

0.804 Renting a room or an apartment on Airbnb is

energy efficient.

0.843 Renting a room or an apartment on Airbnb

helps reduce the use of natural resources.

0.747

Intention to participate in Airbnb

In future, Airbnb will be the preferred medium through which I will rent a room or an

apartment.

0.770 6.362\ 31.81%

0.859

Overall, I can see myself renting a room or an apartment on Airbnb in the future.

0.714 My willingness to rent (or continue renting) a

room or an apartment on Airbnb is very high.

0.848 In the near future, I could see myself joining the

Airbnb community as a host.

0.677 In the near future, I could see myself joining the

Airbnb community as a guest.

0.811

Perceived risk

Booking accommodations from local hosts on Airbnb is hazardous.

0.874 2.625/ 13.12%

0.831 Sending personal information on Airbnb is risky. 0.893

Making online payments on Airbnb is dangerous.

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Table 3.KMO and Barlett's test

KMO and Barlett’s test Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.841

Bartlett’s test of Sphericity Approx. Chi Square 1917,830

Df 190

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4. Results

4.1 Sample description

A detailed overview of the sample characteristics is presented in table 4. The final sample consists of 211 respondents. 116 of all respondents were male (54.98%) and 94 were female (44.55%). Only one respondent decided not to answer the question concerning gender. The majority of the respondents were between the two age groups 18 to 24 years old and 25 to 29 years old, with 43.13% and 39.34% respectively. 15.64% of the respondents were between 30 and 34 years old and only 1.9% of the respondents were between 35 and 39 years old.

Concerning the level of education, the majority of the respondents possessed a bachelor and a master’s degree with 33.65% and 38.86% respectively. 14.22% of the respondents went to some college but had no degree and 10.9% were high school graduate. Additionally, only 1% of the respondents were less than high school graduate and only 1% had a PHD.

Furthermore, a total of 126 respondents (59.72%) were Airbnb users and out of them 102 have been guests (80.95%), 10 have been host (7.94%) and 14 have been both guests and hosts (11.11%). The majority of the Airbnb users have used the home sharing platform only once (50%), 26.19% of the users used Airbnb from 2 to 4 times and 23.81% over 4 times. Moreover, 41 of the respondents (19.43%) visited the Airbnb website, but never booked, 34 of the respondents (16.11%) heard of Airbnb, but never visited the website and only 4.74% had never heard of it.

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Table 4. Sample characteristics (n=211)

Characteristics Frequency Percentage

Total sample size 211

Gender

Male 116 54.98

Female 94 44.55

Prefer not to say 1 0.47

Total 211 100 Age 18-24 91 43.13 25-29 83 39.34 30-34 33 15.64 35-38 4 1.90 Total 211 100 Level of education

Less than high school graduate 3 1.42

High school graduate 23 10.90

Some college but no degree 30 14.22

Bachelor’s degree 71 33.65 Master’s degree 82 38.86 PHD 2 0.95 Total 211 100 Airbnb experience Never heard of it 10 4.74

Heard of Airbnb, but never visited the website

34 16.11

Visited the website, but never booked 41 19.43

Airbnb users 126 59.72

Total 211 100

How many times have you used Airbnb

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Table 5. Sample nationalities (n=211)

Nationality Frequency Percentage

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4.2 Pearson correlation

To assess the relationship between variables, a bivariate Pearson’s correlation analysis was conducted. A moderate positive correlation was found between attitudes toward Airbnb and

intention (r = 0.490, p<0.001). Weak positive correlations were found between subjective norms and intention (r = 0.349, p<0.001), perceived economic benefits and Intention (r = 0.385, p<0.001) and perceived environmental benefits and intention (r = 0.384, p<0.001). Very weak positive correlations were found between perceived risk and Intention (r = 0.150, p<0.005) and gender and intention (r = 0.152, p<0.005). The correlation between the variable age and intention (r = -0.091) was statistically non-significant. The correlation between the variable education and intention (r = 0.025) was also statistically non-significant. Lastly, the correlation between Airbnb experience and intention (r = 0.076) was also statistically non-significant.

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Table 6. Correlation between variables

Variables 1 2 3 4 5 6 7 8 9 10

1. Intention ---

2. Subjective norms 0.349** ---

3. Attitude toward Airbnb 0.490** 0.369** ---

4. Perceived economic benefits 0.385** 0.358** 0.513** ---

5. Perceived environmental benefits 0.384** 0.242** 0.294** 0.381** ---

6. Perceived risk 0.150* 0.182** 0.46 0.164* 0.425** ---

7. Age -0.091 -0.037 -0.100 -0.061 0.002 0.084 ---

8. Education 0.025 0.92 -0.184** -0.042 -0.004 0.077 0.207** ---

9. Gender 0.152* 0.010 0.197** 0.123 0.053 0.044 -0.020 0.014 ---

10. Airbnb Experience 0.076 0.137* -0.024 0.042 -0.086 -0.099 -0.173* 0.223** 0.050 --- **. Correlation is significant at the 0.01 level.

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4.3 Assumptions

First of all, the data was checked for abnormalities, missing values and distribution pattern in order to test the validity and to be able to perform a multiple regression analysis. There are five key assumptions that need to be checked before conducting a multiple regression analysis, namely: no multicollinearity, linearity, multivariate normality, no auto correlation and homoscedasticity (Newbold, 2013). As previously discussed, no multicollinearity was found between the variables in the Pearson correlation table. The second assumption relates to the dependent variable (intention toward book Airbnb) which has to be a linear function of the independent variables (Newbold, 2013). Several scatterplots were created in which the standardized residuals were plotted against the standardized predictors (Saunder et al., 2012). Due to these plots, it was concluded that there is a linear relationship between the independent and dependent variable. The third assumption, multivariate normality, demands that the dependent variable follows a normal distribution (Newbold, 2013). The condition of multivariate normality is tested with a P-P-Plot (Figure 3). The normal probability plot is a graphical technique to see if the data is normally distributed. The data is plotted against a theoretical normal distribution line and the points should construct an approximate straight line (Saunder et al., 2012). The performed analysis thus confirms approximate normality; the dependent variable is approximately normally distributed (figure 4).

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Figure 4. Histogram of millennials intention with perfect normal distribution curve

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Figure 5. Scatterplot of Standardized Millennials Intention to test homoscedasticity

It is concluded that all the five key assumptions for a multiple regression analysis are met. Before creating the moderator terms, the variables were mean centered. Table 7 summarizes the descriptive statistics for all the variables used in the multiple regression analysis.

Table 7. Descriptive Statistics

Descriptive Statistics

Variables Mean Std. Deviation N

Subjective norms 3,9739 ,87043 211

Attitude toward Airbnb 3,8073 ,94993 211

Perceived economic benefits 3,7666 ,89660 211

Perceived environmental benefits 3,5877 ,94900 211

Perceived risk 3,2670 1,06499 211

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4.4 Multiple regression analysis

Multiple regression analysis was conducted to predict millennials intention to book a room or an apartment on Airbnb. The independent variables included in the analysis were subjective norms, attitude toward Airbnb, perceived economic benefits and perceived environmental benefits with the potential moderating role of perceived risk. Seven different models were analyzed. The results are displayed in table 8.

Model 1 tested the control variables. The regression model was significant R²= 0.039, F(2.097)=0.042. The results show that gender has a positive impact on millennials behavioral intention B= 0,162, t= 2.370 p< 0.001. Thus, being a female has positive significant effect on millennials intention. No significant results were found for the other control variables.

Model 2 tested hypotheses 1, 3, 4 and 5. The regression model was significant, R²= 0.345,

F(13.292)=0.001. R-square is a statistical measure of how close the data are to the regression line. The value of 0.345 means that the multiple linear regression explains 34% of the variance in the data. The linear regression’s F-test refers to the null hypothesis meaning that if R²=0 than the model explains zero variance in the dependent variables. In this case the F-test is significant (p<0.001) and therefore it can be claimed that the model explains a significant amount of the variance in millennials behavioral intention to book a room or an apartment on Airbnb.

The regression model showed significant correlation for the variable Attitude B = 0.335, t = 4.694, p < 0.001. Thus, positive attitude toward Airbnb has a positive effect on millennials’ intention to book a room or an apartment on Airbnb. Therefore, hypothesis 3 was supported. The regression model showed also a marginally significant correlation for the variable Perceived Environmental benefits B = 0.225, t = 3.549, p < 0.001. Thus, perceived environmental benefits are found to have a positive effect on millennials intention to book a room or an apartment on Airbnb. Therefore, support for hypothesis 5 was found. The regression model shows weak significant correlation for the variable subjective norms B = 0.128, t = 1.985, p < 0.001. Thus, subjective norms is found to have a positive effect on millennials intention to book a room or an apartment on Airbnb. Therefore, hypothesis 1 was supported. No other significant results were found so there is no support for the other hypotheses.

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43 Marco Di Betta

4.5 Multiple regression analysis with moderator

Find whether a moderator significantly influence the relationship between an independent and a dependent variable is not easy therefore the interaction effects have been added individually in separated model before putting them altogether in model 7.

Model 4 tested the moderating role of perceived risk on subjective norms. The regression model was not significant R²= 0.346, F= 10.579 =0.573. Therefore, there is no support for the hypothesis 7a. Model 5 tested the interaction effect between attitude toward Airbnb and perceived risk. The regression model was not significant R²= 0.349, F= 9.699 =0.335. Thus, there is no support for hypothesis 7c.

Model 6 tested the interaction effect between perceived economic benefits and perceived risk. The regression model was not significant R²= 0.359, F= 9.258 =0.074. Thus, there is no support for hypothesis 7d.

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Note. *p<0.001

Table 8. Multiple regression analysis _{Dependent Variable: Millennials behavioral intention}

Independent Variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Control Variables Gender 0,162*(0,126) 0.072 (0.107) 0,072 (0.107) 0,067 (0.108) 0,068 (0.108) 0,075 (0.108) 0,078 (0.108) Level of education 0,030 (0,063) 0.061 (0.055) 0,061 (0.055) 0,063 (0.055) 0,064 (0.055) 0,058 (0.055) 0,053 (0.055) Age 0,087 (0,086) -0.051 (0.072) -0,051 (0.072) -0,054 (0.072) -0,060 (0.073) -0,044 (0.073) -0,042 (0.073) Airbnb experience 0,048 (0,074) 0.058 (0.062) 0,058 (0.063) 0,055 (0.063) 0,047 (0.064) 0,057 (0.064) 0,049 (0.06) Moderator effect Perceived risk 0,007 (0.057) 0,008 (0.057) 0,008 (0.057) 0,012 (0.056) 0,006 (0.057) Main effects Subjective norms 0,128* (0.069) 0,127 (0.070) 0,113 (0.075) 0,108 (0.075) 0,113 (0.075) 0,114 (0.075)

Attitude towards Airbnb 0,335* (0.070) 0,336 (0.071) 0,333 (0.071) 0,340 (0.072) 0,324 (0.072) 0,322 (0.072) Perceived economic benefits 0,070 (0.073) 0,070 (0.074) 0,068 (0.074) 0,054 (0.075) 0,037 (0.076) 0,044 (0.076) Perceived environmental benefits 0,225* (0.062) 0,222 (0.068) 0,218 (0.068) 0,217 (0.068) 0,213 (0.068) 0,198 (0.069)

Interaction effects

Perceived risk x Subjective norm -0,038 (0.054) -0,006 (0.060) 0,017 (0.061) 0,018 (0.061)

Perceived risk x Attitude -0,068 (0.054) 0,011 (0.063) 0,033 (0.065)

Perceived risk x Perceived economic benefits

-0,145 (0.069) -0,122 (0.067) Perceived risk x Perceived

Millennials’ behavioral intention to book a room or an apartment on Airbnb