ArticlePDF Available

Impacts of Peer-to-Peer Accommodation Use on Travel Patterns

October 2015
Journal of Travel Research

October 2015

DOI:10.1177/0047287515608505

Authors:

Iis Tussyadiah

University of Surrey

Juho Antti Pesonen

University of Eastern Finland

As a result of the phenomenal growth of the sharing economy in the travel industry, investigating its potential impacts on travelers and tourism destinations is of paramount importance. The goal of this study was to identify how the use of peer-to-peer accommodation leads to changes in travelers' behavior. Based on two online surveys targeting travelers from the United States and Finland, it was identified that the social and economic appeals of peer-to-peer accommodation significantly affect expansion in destination selection, increase in travel frequency, length of stay, and range of activities participated in tourism destinations. Travelers' desires for more meaningful social interactions with locals and unique experiences in authentic settings drive them to travel more often, stay longer, and participate in more activities. Also, the reduction in accommodation cost allows travelers to consider and select destinations, trips, and tourism activities that are otherwise cost-prohibitive. Implications for tourism planning and management are provided.

Characteristics of Respondents (N = 450).

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Peer-to-Peer Accommodation Use (N = 450).

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Correlation Matrix.

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. Ordinal Regression Models: Destination Selection and Travel Frequency.

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Ordinal Regression Models: Length of Stay and Activity Participation.

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Article PROOF

Impacts of Peer-to-Peer Accommodation Use

on Travel Patterns

Iis P. Tussyadiah1 and Juho Pesonen2

Abstract

As a result of the phenomenal growth of the sharing economy in the travel industry, investigating its potential impacts

on travelers and tourism destinations is of paramount importance. The goal of this study was to identify how the use

of peer-to-peer accommodation leads to changes in travelers’ behavior. Based on two online surveys targeting

travelers from the United States and Finland, it was identified that the social and economic appeals of peer-to-peer

accommodation significantly affect expansion in destination selection, increase in travel frequency, length of stay, and

range of activities participated in tourism destinations. Travelers’ desires for more meaningful social interactions with

locals and unique experiences in authentic settings drive them to travel more often, stay longer, and participate in

more activities. Also, the reduction in accommodation cost allows travelers to consider and select destinations, trips,

and tourism activities that are otherwise cost-prohibitive. Implications for tourism planning and management are

provided.

Keywords

collaborative consumption, peer-to-peer accommodation, sharing economy, travel pattern, travel behavior

Introduction

The sharing economy has emerged as a new socioeconomic

system that allows for shared creation, production,

distribution, and consumption of goods and resources among

individuals. Facilitated by online social network platforms,

people easily share access to resources sitting idle, such as

transportation (i.e., ride shares), accommodation (i.e., short-

term rentals), food (i.e., peer-to-peer dining), and skills (i.e.,

task shares), with one another. The sharing economy has

entered the travel and hospitality industry, giving ways to

successful startup businesses offering peer-to-peer

accommodation and peer-to-peer transportation, such as

Airbnb, 9Flats, Uber, and Lyft (Ferenstein 2014). These new

startup companies are starting to grow at a phenomenal rate

and change the travel industry. For example, for the full year

of 2014 alone, Airbnb served 18 million guests (100%

growth compared to the previous year), 75 million room

nights, and $5.5 billion in bookings (Melloy 2015),

indicating the disruptive force of the sharing economy. At

this rate, according to World Travel Market (WTM) London

(2014), alternative accommodation and peer-to-peer sharing

will continue to dominate the global travel trend in 2015.

In addition to the advancement of technology, the

emergence of sharing economy is believed to be driven by

economic and societal pressures (Botsman and Rogers 2010;

Owyang 2013). Literature suggests that because of the

economic recession, people are more mindful about their

spending and continuously try to be more resourceful

(Botsman and Rogers 2010; Gansky 2010). The practice of

collaborative consumption (Belk 2014), which implies

various forms of resource redistribution among individuals,

is viewed as an alternative consumption mode that offers

value with less cost (Botsman and Rogers 2010; Gansky

2010; Lamberton and Rose 2012; Sacks 2011). In the context

of travel, travelers use peer-to-peer accommodation rentals

as a low-cost alternative to hotels. Indeed, according to

Quinby and Gasdia (2014), better value for money was stated

as one of the top reasons for travelers to use peer-to-peer

accommodation along with more space. Likewise, Balck and

Cracau (2015) suggest that cost reduction was stated as the

main reason for consumers to choose peer-to-peer

accommodation instead of hotels. Additionally, the sharing

economy is also driven by people’s desire for a stronger

community (Botsman and Rogers 2010). Participating in

collaborative consumption allows people to create and

maintain social connections. That is, by using peer-to-peer

accommodation, travelers are able to have direct interactions

School of Hospitality Business Management, Carson College of

Business, Washington State University Vancouver, Vancouver, WA,

USA

Centre for Tourism Studies, University of Eastern Finland,

Savonlinna,

Finland

Corresponding Author:

Iis P. Tussyadiah, School of Hospitality Business Management, Carson

College of Business, Washington State University Vancouver, 14204

NE Salmon Creek Ave, CLS 308T, Vancouver, WA 98686 USA.

Email: iis.tussyadiah@wsu.edu

Journal of Travel Research

DOI: 10.1177/0047287515608505

Article PROOF

with hosts (i.e., local residents) and to connect with local

communities (Guttentag 2013). Therefore, peer-to-peer

accommodation appeals to travelers socially as it provides an

opportunity to have unique local experiences.

The exponential growth of peer-to-peer accommodation

calls for further investigation to assess the potential impacts

of this business model on the accommodation sector, the

travel industry in general, as well as tourism destinations.

While peer-to-peer accommodation has been shown to

positively impact local hosts (in income generation), local

neighborhoods, and tourism destinations (in tourism

spending), it is also believed to generate induced travels and

create changes in travel patterns and behaviors (e.g., see

Airbnb 2015b). That is, the advantages of using peer-to-peer

accommodation stimulate more people to travel, increase

travel frequency, and increase length of stay at the

destinations. Consequently, these might lead to further

rounds of economic, social, and environmental impacts (e.g.,

more spending, overcrowding, frictions with local residents,

etc.), prompting the need for policy development and

regulation. Additionally, the continued growth of peer-to-

peer accommodation affects the competitive landscape in the

accommodation sector, with budget hotels directly

competing for similar market segments (Economist 2014;

Zervas, Proserpio, and Byers 2014). Hence, in order to

estimate the broader impacts of peer-to-peer accommodation

on tourism destinations and the travel industry, it is important

to assess how the use of peer-to-peer accommodation affects

travel patterns among tourists. To that end, the goal of this

research is to assess the influences of the use of peer-to-peer

accommodation on travel patterns, which include destination

choice set, travel frequency, length of stay, and activity

participation. To accommodate the global phenomenon of

peer-to-peer accommodation, this study was designed to

capture responses from adult travelers residing in the United

States and Finland. The contrast between the United States

and Finland in terms of market penetration (i.e., Airbnb was

introduced to the U.S. market first and European market

later) and market sizes (i.e., U.S. population is 318.9 million,

Finland is 5.4 million) for peer-to-peer accommodation

provides opportunities to assess the potential impacts of

peer-to-peer accommodation on travel patterns that apply in

different contexts.

Collaborative Consumption

Collaborative consumption can be traced back to the well-

established form of resource exchanges in our

socioeconomic system. Leismann, Schmitt, Rohn and

Baedeker (2013) refer to the terms “new utilization concept”

and “product-service systems” (e.g., Baines et al. 2007;

Tukker 2004; Varian 2000) emphasizing “using rather than

owning” model as alternative modes of consumption. These

concepts highlight the shift toward resource-saving

consumption culture (Leismann et al. 2013), where

consumers put less value on ownership in favor of renting,

bartering, and exchange. Indeed, Chen (2009) suggests that

ownership is no longer considered the ultimate expression of

consumer desire, especially in experience consumption

contexts such as appreciation for art. Hence, as suggested by

Bardhi and Eckhardt (2012), consumers who could not afford

to own or choose not to own due to space or environmental

concerns are acquiring access to products and services and,

in cases of market-mediated access, willing to pay a price for

gaining that access. They refer to it as access-based

consumption, emphasizing that the market-mediated

transaction does not come with a transfer of ownership

(Bardhi and Eckhardt 2012). The alternative mode of

consumption is believed to provide an answer to economic

challenges for natural resource conservation and efficiency

(Leismann et al. 2013).

In order to formally define today’s sharing economy

practices, Belk (2014) challenges an early definition of

collaborative consumption suggested by Felson and Speath

(1978) that focused on joint activities involving consumption

(e.g., drinking beer with friends, a group of people watching

a sports game together), but not necessarily captured the

sharing aspects of the consumption (i.e., distribution of

resources to others for their use). He further asserts that a too

broad definition of sharing (e.g., sharing, bartering, lending,

trading, gifting, swapping, etc.) does not characterize the

new collaborative consumption practices either.

Collaborative consumption, he suggests, involves “people

coordinating the acquisition and distribution of a resource for

a fee or other compensation” (p. 1579). This definition

highlights the importance of market mediation (i.e., systems

of exchange) and the power of social network effects (i.e.,

peer-to-peer sharing enabled by social technologies) that

allow this type of consumption to grow in scale (Cusumano

2015). This translates well with Airbnb’s practices in

creating a seamless platform connecting supply and demand

in hospitality (Conley 2015; Zervas, Proserpio, and Byers

2015). Hence, while Airbnb and similar networked

hospitality exchange systems can be considered

collaborative consumption, couchsurfing and other free, non-

compensated peer-to-peer hosting models are excluded from

this definition.

As an alternative mode of accommodation, peer-to-peer

accommodation rentals have the potential to induce changes

in travel behavior. Indeed, Airbnb (2015a) reported

significant differences in length of stay and local spending

between Airbnb travelers and those staying at conventional

commercial accommodation. This is likely due to the

benefits of peer-to-peer accommodation offering lower cost

compared to hotels (Balck and Cracau 2015; Botsman and

Rogers 2010; Gansky 2010; Guttentag 2013; Lamberton and

Rose 2012; Owyang 2013; Sacks 2011) and opportunities to

meet people (Kohda and Matsuda 2013) and connect with

local communities (Botsman and Rogers 2010; Gansky

2010; Guttentag 2013). Furthermore, most of Airbnb listings

are located outside the central hotel districts and, thus,

providing access to what MacCannell (1973) refers to as

“back regions,” offering tourists with intimacy of

relationships and unique experiences in authentic settings

(Guttentag 2013). Airbnb (2015a) also reported that many of

the hosts use the rental income to pay their mortgage (i.e., to

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stay in their current property) and regular living expenses. As

a result, peer-to-peer accommodation systems contribute to

the local economy and generate income that is crucial to local

residents (Geron 2012).

While collaborative consumption has been suggested as a

more sustainable model of economic organization against the

backdrop of energy crises, environmental degradation, and

economic recession (Botsman and Rogers 2010), the

business model comes with considerable complexity that

potentially leads to negative impacts for the society at large.

For example, Zervas, Proserpio, and Byers (2014) suggest

that the rise of peer-to-peer accommodation presents

challenges to existing business models as well as the social

fabric that makes up the communities. They estimated that

the increase in Airbnb listing causes a decrease in quarterly

hotel revenues in the state of Texas, mainly with budget

hotels being affected. Further, they also assert that the

sharing economy might contribute to nonparticipant

externalities, where local residents subjected to noise,

cleanliness, and public safety issues resulting from the rise in

short-term rentals in their neighborhoods. Therefore, peer-to-

peer accommodation practices may contribute negatively to

the sense of community (Zervas, Proserpio, and Byers 2014).

Furthermore, the sharing economy continues to evolve in

legal grey areas, where laws concerning zoning, taxes,

insurance, health and public safety, and employment that

regulate commercial hotels are not fully considered as

barriers in peer-to-peer sharing systems. A better

understanding of the potential impacts of peer-to-peer

accommodation on traveler behavior will provide relevant

supports to “level the playing field” (Cusumano 2015) for

accommodation businesses and to assess further impacts on

the travel industry and tourism destinations.

Trends and Changes in Travel Patterns

Collaborative consumption is the latest addition to numerous

developments and trends in the marketplace that have

substantially transformed traveler behavior and disrupted the

industry dynamics. For example, the Internet changed the

landscape of travel distribution (Barnett and Standing 2001;

Novak and Schwabe 2009; Tse 2003) by causing changes in

the strategic practices among different players in the travel

distribution channels (Bitner and Booms 1982; Connolly,

Olsen, and Moore 1998; Law, Leung, and Wong 2004;

Werthner and Klein 1999). The internet also directly affected

traveler behavior, including the ways travelers search for

information and make purchase decisions. Further,

facilitated by the emergence of social media, the proliferation

of user-generated information containing personal tourism

experiences affected travelers’ choice of information sources

during trip planning processes as well as the evaluation and

sharing of experiences after the trip (Ayeh, Au, and Law

2013; Parra-López et al. 2011; Xiang and Gretzel 2010).

The boom of low-cost carriers as a result of the liberation

of air transport regulations was another development that

transformed traveler behavior and caused changes in the

travel industry. Low-cost carriers provide almost the same

services (about 80% of service quality) with drastically

reduced cost (about 50% of cost) (Franke 2004), thanks to

their operational efficiency achieved through a lean business

model (i.e., low cost structure with point-to-point network

and no frills services) and supported by internet technology

(i.e., online booking and e-ticketing). The reduction in

transportation cost stimulates travelers who would not have

otherwise traveled to fly, resulting in an increase in passenger

traffic (Bennett and Craun 1993; Windle and Dresner 1995).

Rebollo and Baidal (2007) stated that low-cost carriers

contributed positively to the growth of international

passengers to Spain, with a growth rate of 15.2% from 2001

to 2005. Additionally, as low-cost carriers often open new

routes and use secondary airports, they induce more travel to

destinations formerly not included in travelers’ consideration

set. However, studies also show that lower transportation

cost and access to more destinations encouraged travelers to

take multiple short vacations, the behavior associated with a

progressive decline in the overall length of stay at tourism

destinations (Mason and Alamdari 2007).

Similarly, the introduction of collaborative consumption

in the travel and hospitality industry has the potential to

induce changes in travel patterns. The reduction in

accommodation cost, which leads to reduction in the overall

trip cost, may yield similar impacts as those of low-cost

carriers. These may include induced travels (i.e., those who

would not have traveled otherwise), increase in travel

frequency, and longer stay. Indeed, previous studies suggest

that accommodation types typically associated with lower

cost, such as villas and apartments (Alegre and Pou 2007),

and campsites and rented homes (Martínez-Garcia and Raya

2008), lead to longer stays and eventually to the range of

activities they participate in the destinations. Further, the

experiential appeal of peer-to-peer accommodation (i.e.,

access to experiences in local neighborhoods not typically

exposed to tourists) opens up opportunities for travelers to

consider many more destinations to travel to. The following

subsections are dedicated to explore the potential impacts of

peer-to-peer accommodation on travelers’ destination choice

set, travel frequency, length of stay, and activity

participation.

Destination Choice Set

Destination selection is an important issue in tourism, and

destination choice set is a central component of destination

selection models (Crompton 1992; Sirakaya and Woodside

2005; Um and Crompton 1990). The concept of destination

choice set suggests that potential travelers develop an early

consideration set of possible destinations, reduce the number

of destinations to form late consideration set, and make a

final decision (Crompton 1992; Crompton and Ankomah

1993). In making destination selection, Crompton and

Ankomah (1993) further argue that travelers evaluate

alternatives in the early consideration set based on the

relative merits of the destination attributes and later use the

constraints of each destination alternative to evaluate those

in the late consideration set. According to Mansfeld (1992),

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while passing through these stages, potential travelers are

influenced by both the utilitarian (i.e., functional, such as

cost) and emotional (e.g., family and friends) elements.

Indeed, Nicolau (2011a) argues that price is one of the most

influential factors for consumers to make travel-related

decisions, including destination selection. However, he

further asserts that in a hedonic consumption context such as

tourism, high prices do not always act against demand

(Nicolau 2011a, 2011b). He found that tourists motivated by

cultural interests are less reluctant to pay more than expected

for the enjoyment of the cultural traits of destinations

(Nicolau 2011a).

According to literature (e.g., Botsman and Rogers 2010;

Gansky 2010; Guttentag 2013; Kohda and Matsuda 2013),

the advantages of peer-to-peer accommodation include low

cost and social experiences. These appeals can support

certain destinations to be included in travelers’ early and late

consideration sets and, finally, selected. That is, the

reduction in accommodation cost (i.e., low price) as well as

the opportunity to experience and interact with local

communities in neighborhoods outside of the typical tourism

settings (i.e., sociocultural attractions) will add to the

attributes of destinations for positive evaluation in the early

consideration set. Further, the use of peer-to-peer

accommodation has the potential to enable destinations in the

late consideration set that are otherwise cost-prohibitive (i.e.,

price as a constraint) to be selected. Therefore, it can be

argued that the use of peer-to-peer accommodation expand

travelers’ choice set to include destinations otherwise not

considered possible. The following hypothesis is suggested:

Hypothesis 1: The economic (1a) and social appeals (1b)

of peer-to-peer accommodation affect changes in

destination choice set.

Travel Frequency

Travel frequency (i.e., the number of trips individuals take in

a period of time) is a critical factor to predict tourism demand

(Alegre and Pou 2006; Alegre, Mateo, and Pou 2009). At a

macro level, travel frequency represents the number of trips

generated from areas of origin to destinations, which is

strategically associated with the management with regards to

flow of people (i.e., volume) and spending (i.e., value).

According to Eugenio-Martin’s (2003) five-stage process of

tourism decision, decisions on travel frequency and length of

stay are made after individuals have made decisions on travel

participation (i.e., whether or not to travel) and budget

constraint (i.e., how much to spend for travel). Hence, the

availability and size of tourism budget determine how many

trips to take in a period of time and how long to stay during

each trip. Following the model, given predisposed travel

budget, the reduction in the trip cost (e.g., due to lower

prices) may generate a larger trip frequency. More

specifically, the decisions on travel frequency and length of

stay depend on the combination of fixed cost (e.g., for

transportation) and variable cost (e.g., for accommodation

and activities) that make up the total trip cost. When

combined with high fixed cost (e.g., transportation cost for

international tourism), lower accommodation cost may result

in longer stay, but less frequent, trips. However, lower

accommodation cost also leads to a reduction in the total trip

cost (i.e., makes travel more affordable), allowing the

travelers’ budget to accommodate more trips. Therefore, it

can be suggested that the low prices of peer-to-peer

accommodation induce more travel.

An introduction of new tourism attractions and facilities

typically alerts potential tourists to their existence and,

eventually, generates visitation to the destinations. Previous

studies have emphasized this in the contexts of tourism resort

development (Prideaux 2000), the opening of new tourism

routes for rural development (Briedenhann and Wickens

2004), the sacralization of local heritage sites into cultural

theme parks (Teo and Yeoh 1997), and the development of

what Sharpley (1994) referred to as the selling of local places

to tourists. Considerably, as tourists are searching for new,

authentic experiences in areas of cultural riches

(Briedenhann and Wickens 2004), alternative attractions and

activities have great potentials to generate visitation.

Comparably, as the use of peer-to-peer accommodation

opens pathways to unique experiences with local social

landscapes, a certain extent of novelty, which is a basic

motive for leisure travel (Bello and Etzel 1985), is attached

to collaborative consumption experiences. Additionally,

staying in “common places” outside of the designated hotel

areas may appeal to tourists who seek variety in their

experiences. Therefore, it can be argued that the social appeal

of collaborative consumption has the potential to attract

interests, induce more travels, and lead to an increase in

travel frequency. The following hypothesis is suggested:

Hypothesis 2: The economic (2a) and social appeals (2b)

of peer-to-peer accommodation affect changes in travel

frequency.

Length of Stay

Length of stay is an important tourism indicator as a result of

its strategic policy and business implications for tourism

destinations and the travel industry. Length of stay represents

the “quantity” of vacation “purchased” by travelers as it has

direct implications to tourist spending and, consequently,

income generated for tourism destinations. The impacts of

accommodation types on length of stay have been suggested

in previous research (Alegre and Pou 2007; Barros, Butler,

and Correia 2009; Gokovali, Bahar, and Kozak 2006;

Martínez-Garcia and Raya 2008; Nicolau and Más 2009;

Woodside and Dubelaar 2002). Studying length of stay

among golf tourists, Barros, Butler, and Correia (2009) found

that the types of hotel affect tourists’ length of stay.

Consistent with Alegre and Pou (2007) as well as Woodside

and Dubelaar (2002), Martínez-Garcia and Raya (2008)

showed that nonhotel accommodation such as campsites, bed

and breakfasts, apartments, and rented homes lead to longer

stays. They further argued that this effect is associated with

the accommodation prices; travelers who stay at

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accommodation with lower prices stay significantly longer

than those staying at hotels. Likewise, Nicolau and Más

(2009) identified that travelers staying at rented apartments

or chalets (i.e., with lower price per day compared to hotels)

tend to stay longer in the destination. Staying in peer-to-peer

accommodation benefits travelers economically from

reduction in accommodation cost (Botsman and Rogers

2010; Guttentag 2013). Therefore, consistent with the

findings from previous research regarding the positive

effects of low-cost accommodation on length of stay, it can

be suggested that the use of peer-to-peer accommodation

leads to longer stay. Indeed, Airbnb (2015a) suggests that

Airbnb guests stay longer than hotel guests in San Francisco

(5.5 nights and 3.5 nights on average, respectively), New

York (6.4 nights and 3.9 nights, respectively), and Berlin (6.3

nights and 2.3 nights, respectively).

Furthermore, length of stay is also associated with

meaningful social interactions between tourists and local

residents. Previous studies show the relationship between

length of stay and the intensity of tourist–host social

interactions (e.g., Gomes de Menezes, Moniz, and Cabral

Vieira 2008; Seaton and Palmer 1997). For example,

travelers visiting friends and relatives tend to stay longer in

order to optimize their social “contact” (Gomes de Menezes,

Moniz, and Cabral Vieira 2008; Yang, Wong, and Zhang

2011). Studying social interactions among backpackers,

Murphy (2001) suggests that choosing backpacking as a

means of traveling is linked to its social aspects (e.g.,

opportunities to meet people, to obtain “real” experiences) as

well as the extension of trip length. Su and Wall (2010)

suggest that travelers interact with local residents in order to

understand local culture and local life, acquire more local

knowledge, and make friends. Staying at peer-to-peer

accommodation implies sharing personal experiences with

local residents who often possess rich knowledge of local

environments and attractions and have the experience and

ability to deal with local issues. Eventually, Su and Wall

(2010) found that guest–host interactions affect length of

stay. Therefore, it can be suggested that the experiential and

social appeal of peer-to-peer accommodation will lead to

travelers staying longer at the destinations to create and

maintain social connections with local communities.

Therefore, the following hypothesis is suggested:

Hypothesis 3: Economic (3a) and social appeals (3b) of

peer-to-peer accommodation affect changes in length of

stay.

Activity Participation

The economic and social appeals of peer-to-peer

accommodation potentially affect the range of activities that

tourists partake at destinations, ranging from dining out at

restaurants and bars to visiting museums, etc. Activity

participation is often associated with the level of tourist

expenditures during the trip (e.g., Kastenholz, Davis, and

Paul 1999; Loker and Perdue 1992; Masiero and Nicolau

2012; McKercher et al. 2002; Nicolau and Masiero 2013;

Perales 2002). Indeed, Masiero and Nicolau (2012) suggest

that while travelers obtain pleasure from leisure activities at

the destinations, they balance this pleasure with the amount

of money they need to spend for participating in these

activities. That is, price is considered a dissuasive factor in

the choice of activities, even though its effects vary among

travelers (Masiero and Nicolau 2012; Nicolau and Masiero

2013). Therefore, it can be suggested that the reduction in

accommodation cost due to the use of peer-to-peer

accommodation rentals allows for the distribution of

predisposed expenditures for other trip components,

including on-site activities.

Nicolau (2011b) further suggest the monetary and

nonmonetary efforts that travelers make in order to

participate in certain activities at the destination. He

identified significant relationships between accommodation

types and these efforts. While travelers staying at hotels

make higher monetary efforts (i.e., pay higher prices),

travelers staying at alternative accommodation make bigger

nonmonetary efforts (e.g., traveling further distances), driven

by their interest in taking part in specific activities at a

destination (e.g., visiting family and friends). In the context

of peer-to-peer accommodation use, staying with locals in

nontouristic areas offers new types of activities, potentially

leading to the attainment of niche tourism experiences,

which, according to Robinson and Novelli (2005), include

tourism activities in an authentic setting. Indeed, according

to Airbnb (2015a), besides wanting to live like locals, 80%

of guests visiting Paris, 85% of guests visiting London and

Edinburgh, as well as 96% of guests visiting Barcelona were

motivated to explore a specific neighborhood (outside of

tourist areas), often characterized with unique attractions and

activities. Also, about 98% of Airbnb hosts in Sydney

reportedly suggest local restaurants, cafes, bars, and shops in

their local neighborhoods to their guests, helping them

discover less-visited locales in tourism destinations.

Therefore, peer-to-peer accommodation is suggested to grow

and diversify tourism activities, appealing to tourists seeking

for authentic and personal experiences (Airbnb 2015a). The

following hypothesis is suggested:

Hypothesis 4: Economic (4a) and social appeals (4b) of

peer-to-peer accommodation affect changes in range of

tourism activities.

Peer-to-Peer Accommodation Use

While the practices of peer-to-peer sharing and renting are

not new (Belk 2014), present-day peer-to-peer

accommodation business models entered the market with the

introduction of Airbnb in 2008. Peer-to-peer accommodation

services are introduced as innovative business models

offering alternative solutions to travelers wanting

experiences unique to the standard hotel services and, hence,

are novel to most. However, the rapid growth of the business

model (i.e., in number of listings, number of guests served,

and revenues generated) indicates that the rate of adoption of

this alternative accommodation among travelers is relatively

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high. Indeed, according to PricewaterhouseCooper (2015),

44% of American adults are familiar with the sharing

economy. However, Travel Weekly (2014) also shows that

only about 8% of adults in North America (and 11% in

Europe) have rented peer-to-peer accommodation as of the

first quarter of 2014. Therefore, it is expected that there are

varying levels of use experience among peer-to-peer

accommodation users, including those who were new to the

services and those who are more experienced users.

The difference in the levels of use may cast a direct

influence on behavioral changes among travelers. That is, the

impacts of peer-to-peer accommodation on the behavior of

travelers who used it once are expected to be different from

those who have used it multiple times. With a higher level of

use of peer-to-peer accommodation (i.e., implying that users

become more experienced), travelers may recognize higher

cost-savings or heightened experiences and broadened social

connections, which, in turn, will influence their travel

behavior more. Therefore, it is suggested that the use levels

of peer-to-peer accommodation among travelers contribute

positively to the expansion in destination choice sets

(hypothesis 1c), increase in travel frequency (hypothesis 2c),

increase in length of stay (hypothesis 3c), and increase in

activity participation (hypothesis 4c).

Traveler Characteristics

Collaborative consumption is associated with the

sociodemographic characteristics of its users. For example,

studies suggest that the sharing economy appeals to younger

demographics. Based on a national survey in the United

States, Olson (2013) reported that 32% of Gen Xers and 24%

of Millennials find collaborative consumption “very

appealing,” in contrast to only 15% of Baby Boomers (65%

of both Gen Xers and Millennials find collaborative

consumption appealing, while 53% of Boomers do). A study

in San Francisco and Oakland, United States, also confirms

that younger respondents (25–30 years of age) are more open

to a peer-to-peer car sharing program (Ballús-Armet et al.

2014). Likewise, based on a study in Berlin and Trier,

Germany, it was found that younger respondents are more

willing to participate in ride sharing, peer-to-peer

accommodation, peer guided tours, etc. (Stors and

Kagermeier 2015). This is due to the tendency that younger

consumers, who were born in the era of social technology

and are accustomed to online sharing behavior, can easily

translate their online sharing behavior offline (Gaskins 2010;

John 2013). Further, Olson (2013) also demonstrates that

consumers with higher income levels are more likely to

participate in collaborative consumption, which is the

contrary to the view that the sharing economy appeals

primarily to low-budget consumers. This is consistent with

the findings from Mander (2014) that 60% of respondents in

the top 25% of income reported willing to rent rooms from

Airbnb, compared to about 47% of those in the bottom 25%

of income among internet users. PricewaterhouseCooper

(2015) found respondents with annual household income

between $50,000 and $75,000 (the U.S. national average is

$51,939) are most excited to use services such as Airbnb and

Uber. Finally, Mander (2014) also found that the proportion

of male and female respondents who reported interest in

renting from Airbnb-style platforms is comparable (51%

male, 47% female). Therefore, in analyzing the impacts of

peer-to-peer accommodation use, it is important to consider

travelers’ demographic characteristics as predictors of

changes in their travel patterns.

Indeed, previous studies suggest that destination selection

is influenced by personal characteristics of the travelers

(Lang, O’Leary, and Morrison 1997; Moscardo et al. 1996;

Um and Crompton 1990) in addition to trip characteristics,

destination-related attributes, and marketing variables. That

is, sociodemographic characteristics (i.e., age, income,

education, etc.) count for the individual differences in the

ways travelers evaluate alternatives and make destination

selection. For example, Guillet et al. (2011) found that

travelers’ age is a significant predictor of destination choice

among Hong Kong residents. Studying destination choice

among American college students, McIntosh and Goeldner

(1990) linked destination choice with income, suggesting

that students travel to nearby destinations due to income

restrictions. Lang, O’Leary, and Morrison (1997) identified

the influences of income and education levels on destination

choice of Taiwanese tourists, differentiating between within-

Asia and out-of-Asia destination choice groups. However,

the effects of age and gender were not found in their study.

Most recently, Park, Nicolau, and Fesenmaier (2013)

identified significant influences of age and income on

decisions to visit a destination. Previous studies also found

that cultural contexts (i.e., nationalities) influence tourist

behavior (e.g., Pizam and Jeong 1996; Pizam and Reichel

1996; Pizam and Sussman 1995), including destination

choice.

Travel frequency is also linked to the sociodemographic

characteristics of tourists in previous studies. For example,

Woodside, Cook, and Mindak (1987) identified that the

heavy traveler segment (i.e., those who travel very

frequently) in the United States can be distinguished from

less frequent travelers by their socioeconomic

characteristics. Also, Pearce and Lee (2005) identified that

travelers with high and low travel experience differ from

each other regarding sociodemographic characteristics such

as gender, education, age, and nationality. Littrell, Paige, and

Song (2004) found that senior tourists travel more frequently,

taking an average of 4.8 trips annually. Tsiotsou (2006)

identified income to play an important role in predicting ski

resort customers’ behavior and especially visit frequency.

Literature also shows that the demographic characteristics

of travelers influence length of stay, including nationality,

age, income, and education (Alegre and Pou 2007; Becken

and Gnoth 2004; Martínez-Garcia and Raya 2008). Fleischer

and Pizam (2002) found that level of income and age

significantly influence length of stay. Alegre and Pou (2007),

on the other hand, did not find age to be a relevant factor but

identified that nationality matters. Gokovali, Bahar, and

Kozak (2007) found the positive effects of nationality and

level of income on length of stay, as well as the negative

effect of level of education. However, they also did not find

Article PROOF

age as a relevant factor. Finally, Martínez-Garcia and Raya

(2008) identified nationality, age, and level of education as

relevant explanatory factors of length of stay, with older

travelers and those with lower levels of education showing a

tendency to stay longer.

Finally, sociodemographic characteristics of tourists have

been identified as factors affecting their participation in

activities while visiting a destination. While previous studies

segmenting tourists based on their activity preferences

argued that demographic characteristics are not the most

accurate predictors of activity participation (e.g., McKercher

and du Cros 2003; Perales 2002; Prentice, Witt, and Hamer

1998), researchers found age (e.g., McKercher et al. 2002;

Kastenholz, Davis, and Paul 1999), income (e.g.,

Kastenholz, Davis, and Paul 1999), education (e.g.,

McKercher et al. 2002), and tourist origins (e.g., McKercher

et al. 2002) as significant factors that distinguish activity-

based tourist segments. Although Perales (2002) did not

identify education and income to be significant in

distinguishing between modern and traditional rural tourists

to Spain, McKercher et al. (2002) found education to be

significant among culture tourists to Hong Kong. Also,

Kastenholz, Davis, and Paul (1999) showed that there are

differences in terms of expenditure per person per day, which

is associated with purchasing power, as well as the

nationalities among different rural tourist segments to

Portugal. Finally, McKercher et al. (2002) identified that

different tourist origins led to different culture tourism

segments to Hong Kong: Western tourists are likely to

engage in activities that include general cultural attractions,

as well as exploration of Colonial and Sino-Colonial

heritage, while Asian tourists are likely to be incidental

culture tourists engaging in exploration of iconic Chinese

heritage. Based on these findings from previous research,

this study proposes the variables of gender, age, education,

income, and nationality as predictors of expansion in

destination choice sets (hypotheses 1d–h ), increase in travel

frequency (hypotheses 2d–h), increase in length of stay

(hypotheses 3d–h), and increase in activity participation

(hypotheses 4d–h) among travelers due to the use of peer-to-

peer accommodation.

Methodology

This study was designed to identify if peer-to-peer

accommodation affects changes in traveler behavior. More

specifically, the study seeks to verify and test the impacts of

economic and social appeals of peer-to-peer accommodation

use on the expansion of destination choice sets (hypothesis

1), travel frequency (hypothesis 2), length of stay (hypothesis

3), and activity participation (hypothesis 4) among users

residing in the United States and Finland. To achieve the

objectives of the study, a questionnaire was designed to

capture respondents’ behavior with regard to the use of peer-

to-peer accommodation. First, respondents were given an

explanation of peer-to-peer accommodation following the

definition of collaborative consumption from Belk (2014):

“Peer-to-peer accommodation rentals are accommodation

services where you pay a fee to stay at someone’s property

(such as Airbnb), but excluding free accommodation services

(such as Couchsurfing).” The first part of the questionnaire

captures the patterns of peer-to-peer accommodation use,

including levels of use (i.e., how many times travelers have

used peer-to-peer accommodation before) and reasons for

using peer-to-peer accommodation. To measure the latter,

various motivations for collaborative consumption derived

from relevant literature (see Botsman and Rogers 2010;

Gansky 2010; Guttentag 2013; Kohda and Matsuda 2013;

Owyang 2013) were summarized into 12 statements

representing the appeals (i.e., advantages) of using peer-to-

peer accommodation. As this study is partly exploratory in

nature, bipolar scale was used to examine both negative and

positive aspects of the statements (Dolnicar 2013). The

statements were presented as a five-point Likert-type scale

(strongly disagree, disagree, neither agree nor disagree,

agree, strongly agree) (see Appendix). An exploratory factor

analysis was performed to identify the underlying factors that

explain these motivations, resulted in two factors: economic

and social appeals. In the second part of the questionnaire,

respondents were asked to rate their agreement on the

statements representing how peer-to-peer accommodation

has influenced their travel (i.e., in a five-point Likert-type

response format: strongly disagree, disagree, neither agree

nor disagree, agree, strongly agree). The statements include

impacts of peer-to-peer accommodation on expansion in

destinations they consider visiting, increase in travel

frequency, increase in length of stay, and increase in

activities participated at a destination. The last part of the

questionnaire captures sociodemographic characteristics of

travelers, including gender, age, education, and income

levels. Respondents’ origins (i.e., United States and Finland)

were used as a dummy variable representing nationality. It is

acknowledged that nationality can cause cross-cultural

differences in survey response patterns (Dolnicar and Grün

2007), but these limitations are addressed in data analysis by

examining nationality separately as a dummy variable.

In order to ensure readability and to test for face validity, two

experts in tourism and eight 3rd and fourth-year

undergraduate students enrolled in a hospitality management

program read and tested the English version of the

questionnaire. To gather responses from Finnish travelers,

two bilingual tourism experts translated the questionnaire

into Finnish language. First, the experts translated the

questionnaire from English to Finnish independently. Then,

the translated questionnaires were compared and once an

agreement was achieved, the Finnish questionnaire was

translated back into English to ensure that the meanings of

the questionnaire stayed the same through the translation

process. The questionnaire was distributed through Amazon

Mechanical Turk (mturk.com) to target adults residing in the

United States in August 2014 and sent to the M3 Online

Panel (m3research.com) members in Finland in December

2014. The data collection efforts resulted in 799 responses

from the United States (155 of them have used peer-to-

peer accommodation before) and 1,246 responses from

Finland (295 of them were users). To analyze the impacts

of peer-to-peer accommodation on travel patterns, only

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responses from those who have used peer-to-peer

accommodation were included in this study (a total of 450

respondents).

Table 1.

Characteristics of Respondents (

= 450).

Characteristics

United States

(

= 155)

Finland

(

= 295)

Total

(

= 450)

Gender

(0) Male

60.8

171

58.9

261

59.6

(1) Female

39.2

119

41.0

177

40.4

Age

(1) 24 years or younger

16.9

13.8

14.8

(2) 25–34 years

58.1

26.2

162

37.0

(3) 35–44 years

15.5

20.7

18.9

(4) 45–54 years

8.1

12.7

11.2

(5) 55–64 years

1.3

6.5

4.8

(6) 65 years or older

20.0

13.2

Education

(1) Less than high school

0.1

25.1

16.7

(2) High school

9.4

17.3

14.6

(3) Post–high school education

30.4

13.1

19.0

(4) Bachelor’s degree

37.8

26.8

132

30.6

(5) Master’s degree

18.9

15.9

16.9

(6) Doctoral degree

2.7

1.8

2.1

Income

(1) Under $20,000

(Under €15,000)

10.8

8.7

9.4

(2) $20,000–$39,999

(€15,000–€29,999)

45.9

22.9

131

31.0

(3) $40,000–$59,999

(€30,000–€44,999)

30.4

21.8

105

24.8

(4) $60,000–$79,999

(€45,000–€59,999)

12.8

15.6

14.6

(5) $80,000–$99,999

(€60,000–€74,999)

12.4

8.0

(6) $100,000–$119,999

(€75,000–€89,999)

8.2

4.0

(7) $120,000 or more

(€90,000 or more)

12.4

8.0

Peer-to-peer accommodation use

(1) Once

35.1

28.9

130

31.1

(2) 2–5 times

58.8

31.5

172

41.1

(3) More than 5 times

107

39.6

116

27.8

The characteristics of respondents are presented in

Table 1. Respondents from both countries are

predominantly male (60%). While American respondents

are mostly younger (i.e., with an overrepresentation of

respondents between the ages of 25 and 34 years [58%]

and underrepresentation of older respondents), the ages of

Finnish respondents are more evenly distributed with

more representation from senior travelers (20% of them

were 65 years or older), which is reasonable for age

distribution of the population in Finland. The majority of

respondents receive post–high school education (i.e.,

some college experiences in the United States and

vocational/university experiences in Finland). While the

majority of respondents earn less than US$60,000 in the

United States (88%) and less than €45,000 in Finland

(66%), around 20% of Finnish respondents are in higher

income levels, earning more than €60,000 annually.

In order to test the hypotheses, ordinal regressions with

polytomous universal model (PLUM) procedure were

identified for four dependent (outcome) variables:

Article PROOF

expansion in destination selection, increase in travel

frequency, increase in length of stay, and increase in

activities participated. Each dependent variable was

estimated by the factors of gender, age, levels of

education, levels of income, and nationality as well as

covariates representing social and economic appeals of

peer-to-peer accommodation. The regression analyses

were performed using IBM SPSS Statistics 19 software.

Table 2.

Peer-to-Peer Accommodation Use (

= 450).

Factors

Factor

Loadin

Eigenvalue

Cumulative

Percent

Cronbach’s

Alpha

Social Appeal (SA)

3.97

49.62%

.86

. . . I would like to

get to know

people from the

local

neighborhoods.

.86

. . . I would like to

have a more

meaningful

interaction with

the hosts.

.82

. . . I would like to

get insiders’ tips

on local

attractions.

.75

. . . I would like to

support the

local residents.

.74

. . . it was a more

sustainable

business model.

.73

Economic Appeal

(EA)

1.53

68.85%

.82

. . . it saved me

money.

.89

. . . it helped me

lower my travel

cost.

.89

. . . I would like to

have higher

quality

accommodation

with less

money.

.72

Results and Discussion

An exploratory factor analysis (i.e., principal components

analysis with varimax rotation) was utilized to explore the

reasons for travelers to use peer-to-peer accommodation. The

analysis revealed two factors that drive the use of peer-to-

peer accommodation among respondents: Social Appeal and

Economic Appeal (see Table 2). The two factors explain

68.85% of the total variance. The Kaiser–Meyer–Olkin

measure of sample adequacy (.83) and Bartlett’s test of

sphericity (χ2 = 1554.10, df = 28, significance = .00)

indicated that factor analysis is appropriate for this data. The

Cronbach’s alpha of .70 or more supports the reliability of

both scales (i.e., Social Appeal α = .86; Economic Appeal α

= .82). The two factors suggest that the use of peer-to-peer

accommodation among respondents was driven by (1) the

social motivation to get to know, interact, and connect with

local communities in a more meaningful way; to experience

tourism destinations as a local; and to contribute to local

residents, as well as (2) the motivation to get quality

accommodation with lower cost. These factors are consistent

with suggestions from literature regarding the societal

drivers and the low-budget appeal of collaborative

consumption (Botsman and Rogers 2010; Gansky 2010;

Guttentag 2013; Lamberton and Rose 2012; Owyang 2013;

Sacks 2011). In order to identify significant differences

between respondents from the United States and Finland in

terms of peer-to-peer accommodation use and travel

behavior variables, independent-samples t-tests were

conducted. A significant difference in means was found in

terms of economic appeal of peer-to-peer accommodation (t

= 7.04, significance = .00), with American travelers rated

significantly higher (mean = 4.24, SD = .58) on economic

appeal compared to their Finnish counterparts (mean = 3.71,

SD = .82). No significant difference was found in the social

appeal factor.

The correlation matrix between dependent and

independent variables used in this study is presented in Table

3. Among the independent variables, strong correlation was

observed between social appeal and economic appeal of

peer-to-peer accommodation (r = .428, p < .001) as well as

between nationality and age (r = –.498, p < .001). However,

the correlation coefficients were below the cutoff point of .80

to indicate concerns for multicollinearity in the subsequent

regression analyses. No other strong correlations were

observed among predictor variables.

Expansion in Destination Selection

The majority of respondents agreed that peer-to-peer

accommodation expands their selection on places to visit,

with 45.5% respondents selecting “agree” and 21.7%

“strongly agree” to the statement. Significant differences

were found between U.S. and Finnish respondents (χ2 =

50.84, df = 4, p < .001), with U.S. respondents showing a

larger proportion in agreement. Gender difference was also

significant (χ2 = 15.17, df = 4, p < .005), with female

respondents more in agreement. No significant differences

were identified among respondents in terms of their age,

levels of education, income, and use of peer-to-peer

accommodation. The results from ordinal logit regression

revealed significant chi-square statistic (χ2 = 193.99, df = 22,

p < .001), and the final model shows a significant

improvement over the baseline model, suggesting a good

model fit with the data. The Nagelkerke pseudo-R2 = .413

Article PROOF

suggests that predictor variables explain a significant

proportion (41.3%) of the variation between perceived

expansion in destination consideration set. To demonstrate

the relationship between the dependent and independent

variables, parameter estimates are presented in Table 4a.

The results show that the economic appeal of peer-to-peer

accommodation use significantly contributes to the

expansion of destinations to select from, the odds of

respondents selecting higher agreement rating increased by

4.96 (95% confidence interval [CI], 3.56 to 6.92) for every

unit increase in economic appeal (Wald χ2 = 89.60, df = 1, p

< .001), indicating significant effects. This suggests that the

lower accommodation cost allow travelers to expand

destination selection as more become more affordable. The

social appeal of peer-to-peer accommodation also

contributes to the expansion of destination selection. The

odds that respondents would select higher agreement ratings

on expansion of destination selection were 1.48 times (95%

CI, 1.14 to 1.92) higher for every unit increase in social

appeal (Wald χ2 = 8.61, df = 1, p < .005). This indicates that

the desire for social connection allows travelers to consider

more destinations in their choice set. In terms of

demographic characteristics, respondents in the age group of

55–64 years had 3.45 times (95% CI, 1.18 to 10.09) higher

odds compared to the reference age group of 65 plus to select

higher agreement ratings (Wald χ2 = 89.60, df = 1, p < .001).

Finally, in terms of levels of education, the odds of

respondents with some college experience perceiving that

peer-to-peer accommodation expands their selection of

destinations to visit were 3.84 times higher (95% CI, 1.08 to

13.60) than the reference group of those with doctoral

degrees (Wald χ2 = 89.60, df = 1, p < .001). No other

relationship is significant in the regression model.

Increase in Travel Frequency

A bigger proportion of respondents agreed that peer-to-peer

accommodation increases the frequency of their travel

(compared to those who disagreed), with 30% respondents

selecting “agree” and 11% “strongly agree” to the statement.

Significant differences were found between U.S. and Finnish

respondents (χ2 = 14.32, df = 4, p < .01), with a greater

proportion among U.S. respondents leaning toward

agreement. No significant differences were found among

respondents in terms of their gender, age, levels of education,

income, and use of peer-to-peer accommodation. The results

from ordinal logistic regression revealed a significant chi-

square statistic (χ2 = 165.41, df = 22, p < .001), suggesting

that the final model shows a significant improvement over

the baseline model, which indicates a good model fit with the

data. The Nagelkerke pseudo-R2 = .358 suggests that

predictor variables explain a significant proportion (35.8%)

of the variation between perceived increase in travel

frequency. To demonstrate the relationship between the

dependent and independent variables, parameter estimates

are presented in Table 4b.

Table 3.

Correlation Matrix.

Variables

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

Dependent variables

Expand Choice

Set (1)

Increase Travel

Frequency (2)

.426**

Longer Stay (3)

.303**

.589**

More Activities (4)

.413**

.509**

.553**

Independent variables

Social Appeal (SA)

(5)

.375**

.514**

.334**

.493**

Economic Appeal

(EA) (6)

.558**

.335**

.275**

.401**

.428**

Gender (7)

.165**

n.s.

.195**

Age (8)

n.s.

–.125**

.077**

Education (9)

n.s.

.107**

Income (10)

n.s.

.124*

–.092**

n.s.

.230**

Nationality (11)

.178**

n.s.

.101*

.323**

–.094**

–.498**

n.s.

.234**

P2P Use: Once

(12)

n.s.

–.139**

n.s.

–.164**

n.s.

–.103**

n.s.

P2P Use: 2–5

times (13)

n.s.

n.a.

n.s.

.099*

n.s.

–.050**

.095**

.086**

.073**

–.079**

P2P Use: More

than 5 times

(14)

n.s.

.118*

n.s.

–.157**

–.064**

–.075**

Note: Significant at **

< .01 level, *

< .05 level; n.s. = not significant.

Article PROOF

Table 4.

Ordinal Regression Models: Destination Selection and Travel Frequency.

Variables

a. Expansion of Destination Selection

b. Increase in Travel Frequency

Wald (df)

Sig.

Exp(B)

Wald (df)

Sig.

Exp(B)

0.39

0.13

8.61 (1)

0.00

1.48

1.30

0.14

86.02 (1)

0.00

3.67

1.60

0.17

89.60 (1)

0.00

4.96

0.35

0.15

5.89 (1)

0.02

1.42

[Gen=0]

n.s.

[Gen=1]

[Age=1]

n.s.

[Age=2]

n.s.

[Age=3]

n.s.

[Age=4]

n.s.

[Age=5]

1.24

0.55

5.11 (1)

0.02

3.45

n.s.

[Age=6]

[Edu=1]

n.s.

[Edu=2]

n.s.

[Edu=3]

1.35

0.65

4.35 (1)

0.04

3.84

n.s.

[Edu=4]

n.s.

[Edu=5]

n.s.

[Edu=6]

–

[Inc=1]

n.s.

1.16

0.49

5.68 (1)

0.02

3.19

[Inc=2]

n.s.

1.00

0.42

5.79 (1)

0.02

2.72

[Inc=3]

n.s.

0.87

0.41

4.43 (1)

0.04

2.38

[Inc=4]

n.s.

[Inc=5]

n.s.

1.20

0.49

5.96 (1)

0.02

3.33

[Inc=6]

n.s.

[Inc=7]

–

[Nat=0]

n.s.

[Nat=1]

–

[Use=1]

n.s.

[Use=2]

n.s.

[Use=3]

–

Note: Sig. = significance; EA = economic appeal; SA = social appeal; Gen = gender; Edu = education; Inc = income; Nat = nationality; Use =

frequency of use; n.s. = not significant.

Compared to the regression on choice set expansion, an

increase in travel frequency can be attributed mainly to the

social appeal of using peer-to-peer accommodation. The

odds of selecting higher agreement on the increase in

respondents’ travel frequency was 3.67 times (95% CI, 2.78

to 4.82) higher for every unit increase in social appeal (Wald

χ2 = 8.61, df = 1, p < .005). That is, travelers’ desire to

connect and develop meaningful relationships with local

communities drives travelers to take more trips. The appeal

of staying with locals in common places opens new

experience opportunities for travelers, stimulates interests,

and hence, generates more travels. While smaller compared

to the social appeal, the economic appeal of peer-to-peer

accommodation use also significantly contributes to the

increase in travel frequency. The odds of respondents

strongly agreeing on increase in travel frequency were 1.42

times (95% CI, 1.07 to 1.89) higher for every unit increase in

economic appeal (Wald χ2 = 5.89, df = 1, p < .05). The ability

to reduce trip expenditure (i.e., as a result of the cost savings

from accommodation) allows travelers to stretch their travel

budget to include more trips.

Importantly, the levels of income contributes to the

change in travel frequency with respondents in lower income

levels showing high odd ratios of agreeing to the statement

that they travel more often because of the availability of peer-

to-peer accommodation. Specifically, the odds of

respondents with an annual income less than $20,000

agreeing to increase in travel frequency were 3.18 times

(95% CI, 1.23 to 8.26) higher than the reference group with

an annual income of $120,000 or more (Wald χ2 = 5.67, df =

1, p < .05). The odds ratios gradually decreased as the annual

income increased, which can mean that travelers in the higher

income brackets are less sensitive to the reduction in trip

costs that would allow them to take multiple trips. However,

the income group of $80,000–$99,999 had an odds ratio 3.33

times (95% CI, 1.27 to 8.75) higher than the reference high-

income group (Wald χ2 = 5.96, df = 1, p < .05).

Increase in Length of Stay

About 29% respondents agreed that peer-to-peer

accommodation increases the length of stay at the destination

and 12% strongly agreed to the statement. A significant

Article PROOF

percentage of respondents (38%), however, stated that they

neither agreed nor disagreed to the statement. Significant

differences were found between U.S. and Finnish

respondents (χ2 = 14.24, df = 4, p < .01), with proportionally

higher tendency toward agreement among U.S. respondents.

No significant differences were identified among

respondents in terms of their gender, age, education, income,

and use of peer-to-peer accommodation. The results from

ordinal logistic regression revealed significant chi-square

statistic (χ2 = 87.65, df = 22, p < .001). The final model shows

a significant improvement over the baseline model,

suggesting a good model fit with the data. The Nagelkerke

pseudo-R2 = .208 suggests that predictor variables explain a

proportion (20.8%) of the variation between perceived

increase in length of stay, which is lower than the two

previous models. To demonstrate the relationship between

the dependent and independent variables, parameter

estimates are presented in Table 5a.

Table 5.

Ordinal Regression Models: Length of Stay and Activity Participation.

Variables

a. Increase in Length of Stay

b. Increase in Activity Participation

Wald (df)

Sig.

Exp(B)

Wald (df)

Sig.

Exp(B)

0.66

0.13

26.44 (1)

0.00

1.93

1.12

0.14

64.83 (1)

0.00

3.06

0.61

0.15

17.48 (1)

0.00

1.85

0.83

0.15

29.66 (1)

0.00

2.30

[Gen=0]

n.s.

[Gen=1]

–

[Age=1]

n.s.

[Age=2]

n.s.

[Age=3]

n.s.

[Age=4]

n.s.

[Age=5]

n.s.

[Age=6]

–

[Edu=1]

n.s.

[Edu=2]

n.s.

[Edu=3]

n.s.

[Edu=4]

n.s.

[Edu=5]

n.s.

[Edu=6]

–

[Inc=1]

n.s.

[Inc=2]

n.s.

[Inc=3]

n.s.

[Inc=4]

n.s.

[Inc=5]

n.s.

[Inc=6]

n.s.

[Inc=7]

–

[Nat=0]

n.s.

[Nat=1]

–

[Use=1]

n.s.

1.12

0.14

64.83 (1)

0.00

3.06

[Use=2]

n.s.

0.83

0.15

29.66 (0)

0.00

2.30

[Use=3]

–

n.s.

Note: Sig. = significance; EA = economic appeal; SA = social appeal; Gen = gender; Edu = education; Inc = income; Nat = nationality; Use =

frequency of use; n.s. = not significant.

The effects of both social and economic appeals of using

peer-to-peer accommodation on the increase in length of stay

are proportional; both appeals contribute almost equally to

travelers staying longer in the destinations. The odds of

respondents strongly agreeing on the increase in length of

stay were 1.85 times (95% CI, 1.38 to 2.46) higher for every

unit increase in economic appeal (Wald χ2 = 17.48, df = 1, p

< .001) and 1.93 times (95% CI, 1.50 to 2.48) for one unit

increase in social appeal of peer-to-peer accommodation

(Wald χ2 = 26.44, df = 1, p < .001). The cost-savings from

staying at peer-to-peer accommodation allow travelers to

stretch the trip budget to accommodate longer stays.

Additionally, the unique local experiences in atypical tourist

neighborhoods drive tourists to explore the destinations more

by staying longer. No other variables were found to have

significant effects on the dependent variables.

Increase in Activity Participation

Slightly more than 40% of the respondents agreed that peer-

to-peer accommodation increased the range of activities they

participate at the destination, while 13% stated they strongly

Article PROOF

agree with the statement. Significant differences were found

between U.S. and Finnish respondents (χ2= 14.41, df = 4, p <

.01), with a proportionally higher tendency toward

agreement among U.S. respondents. No significant

differences were found among respondents in terms of their

gender, age, levels of education, income, and use of peer-to-

peer accommodation. The results from ordinal logit

regression revealed significant chi-square statistic (χ2=

158.43, df = 22, p < .001); the final model shows a significant

improvement over the baseline model, suggesting a good

model fit with the data. The Nagelkerke pseudo-R2 = .350

suggests that predictor variables explain a proportion (35%)

of the variation between perceived increase in length of stay,

which is comparable to the effects in the first two models. To

demonstrate the relationship between the dependent and

independent variables, parameter estimates are presented in

Table 5b.

Similar to the regression model on the increase of travel

frequency, the increase in the range of activities participated

in the destinations was caused mainly by the social appeal

and slightly lesser by the economic appeal of using peer-to-

peer accommodation. The odds of selecting higher

agreement on the increase in the range of activities

participated at a destination is 3.06 times (95% CI, 2.33 to

4.01) higher for every unit increase in social appeal (Wald χ2

= 64.83, df = 1, p < .001). The social interactions with local

hosts as well as the authenticity of experiences outside of

touristic places allow tourists to engage in an array of

activities typically accessible only to locals. Insider tips and

local recommendation may direct tourists to visit local

restaurants, cafes, and bars as well as engage in local events

and festivities. Additionally, the odds of respondents

strongly agreeing on the increase in length of stay were 2.30

times (95% CI, 1.70 to 3.10) higher for every unit increase in

economic appeal (Wald χ2 = 29.66, df = 1, p < .001). That is,

the cost savings from staying at peer-to-peer accommodation

allow travelers to afford more activities in their travel budget.

Interestingly, the more respondents use peer-to-peer

accommodation, the less likely they are to perceive an

increase in activity participation. Travelers who used peer-

to-peer accommodation once have the highest odds of

strongly agreeing to the increase in activity participation with

3.06 times (Wald χ2 = 64.83, df = 1, p < .001). This may be

due to the diminishing value of the novelty and uniqueness

of the sharing economy as users become more familiar with

the service and exposed to varying experiences.

Based on the four regression models, it can be suggested that,

with varying degrees, peer-to-peer accommodation affects

changes in travel patterns of their guests. The motivations of

using peer-to-peer accommodation to save cost lead travelers to

consider more destinations in their choice set (i.e., as

destinations become more affordable), allow them to stay

longer, and participate in more activities. To a smaller extent,

the economic appeal of peer-to-peer accommodation also

influences travelers to take more trips as reflected in the increase

of travel frequency. The social appeal of peer-to-peer

accommodation contributes significantly to the increase in

travel frequency and range of activities participated in the

destinations. This signifies the suggestion that the experience of

staying with locals in an authentic setting induces more travels,

especially among those seeking for new, unique, and authentic

travel experiences. The availability of peer-to-peer

accommodation in common places (i.e., in neighborhoods

outside of tourist areas) also offers unique settings for a variety

of tourism activities to take place. This confirms the potentials

of collaborative consumption to generate diversified tourism

services and experiences that, eventually, support local

businesses and create vibrant local communities. Finally, the

social appeal of peer-to-peer accommodation affects length of

stay at the destination, confirming findings from Airbnb (2015a,

2015b), as well as the number of destinations considered in

travelers’ choice set, with more destinations becoming more

attractive as a result of their social experiences. Therefore, all

hypotheses pertaining the effects of peer-to-peer

accommodation use on changes in travel patterns are supported.

The demographic characteristics of travelers were not found

to be significant predictors of changes in travel patterns, except

for the effects of age and education on expansion of destination

choice set and the effects of income on increase of travel

frequency. Consistent with previous studies suggesting moving

away from using demographic variables in tourism

segmentation (e.g., McKercher and du Cros 2003; Perales 2002;

Prentice, Witt, and Hamer 1998), this result suggests that

demographic variables may not be accurate to predict traveler

behavior as a result of collaborative consumption trend in the

marketplace. Therefore, other variables that explain personal

characteristics from cognitive, psychographic perspectives,

such as values, lifestyle, and attitudes, may better explain their

behavior with regards to the use of peer-to-peer consumption in

the travel context.

Conclusion and Implications

Because of the explosive growth of tourism and hospitality

businesses adopting the sharing economy model, assessing

the impacts of collaborative consumption models will

provide relevant bases for the travel and hospitality industry

as well as tourism destinations to respond to the growing

trend with relevant management decisions and policies. The

results of this study show that the use of peer-to-peer

accommodation stimulates changes in travel patterns. First of

all, travelers use peer-to-peer accommodation largely

because of two factors: cost savings (i.e., economic appeal)

and desire for social relationships with local community (i.e.,

social appeal). Verified by the regression models in this

study, these factors are significant predictors of changes in

travel patterns, stimulating expansion in destination choice

set, increase in travel frequency, length of stay, and range of

activities participated in the destinations. It is also suggested

that demographic characteristics are not accurate to predict

changes in travel behavior in the context of sharing economy,

indicating that future studies should capture other personal

and behavioral characteristics to explain these behaviors.

First, the use of peer-to-peer accommodation leads to an

increase in the number of destinations in the choice set (i.e.,

expands travelers’ selections of places they could go to).

Specifically, the economic appeal of peer-to-peer

Article PROOF

accommodation contributes significantly to more

destinations being considered in the choice set, while social

appeal contributes in a smaller degree. Following the concept

of destination choice set (Crompton 1992; Crompton and

Ankomah 1993; Mansfeld 1992), it can be interpreted from

the results that the social and economic appeals of peer-to-

peer accommodation add to the overall merit of destinations

to be included in the consideration set. Additionally, the

reduction in accommodation cost leads to elimination of

price constraints in some destinations, which results in more

destinations being considered by travelers. However, it is not

just the reduction of prices that is changing the travel

behavior as low-cost accommodation has been available in

majority of destinations even before collaborative

consumption technology in the form of budget hotels and

hostels. Peer-to-peer accommodation platform such as

Airbnb is able to match a variety of different accommodation

services with customers that really value them by not only

providing tourists with budget options but efficiently

matching tourists with accommodation that best satisfies

their various needs (Zervas, Proserpio, and Byers 2015). It is

noted as a limitation that this study does not differentiate

between early and late consideration sets. Therefore, in order

to further elaborate the dynamics of destination selection

involving peer-to-peer accommodation, future studies should

address this issue.

The expansion of destination choice set as a result of peer-

to-peer accommodation use causes important implications to

tourism destinations. For less-developed tourism

destinations having limited accommodation facilities and

capacity, the availability of peer-to-peer accommodation

may support and strengthen their chance to attract potential

travelers. The impacts of collaborative consumption are

likely similar to those from opening new routes and hubs

(i.e., exposure of alternative destinations) in the case of low-

cost carriers. As long as carrying capacity is not a concern,

these destinations might benefit from collaborative

consumption in terms of attracting more visitors. On the

other hand, for well-established destinations that are

characterized with higher prices (i.e., price is a constraint),

induced travels due to lower accommodation cost will likely

result in spillover activities to neighborhoods that are not

zoned for tourism (e.g., residential areas). While the spillover

tourism activities may contribute economically to local

businesses, they may generate social issues, such as health

and public safety, likely from nonparticipant externalities

(Zervas, Proserpio, and Byers 2014). Future studies should

address this issue to explain further rounds of impacts of

peer-to-peer accommodation.

Second, the use of peer-to-peer accommodation also

affects travel frequency (i.e., allows travelers to take more

trips). The social appeal of collaborative consumption

contributes significantly to the increase in travel frequency,

confirming that perceived new ways of traveling (i.e., staying

with locals) stimulate more travels. Moreover, the cost

savings from this alternative accommodation, which results

in reduction of the total trip cost, makes taking more trips

more affordable. In other words, referring to Eugenio-

Martin’s (2003) decision model, travelers could fit more trips

into their budget constraint. The increase in travel frequency

(i.e., in volume) can be considered beneficial for tourism

destinations because it potentially leads to more tourism

spending (i.e., value). However, the main concerns

associated with travel frequency increase are the

environmental impacts of the induced travels. While the

general practice of collaborative consumption is viewed as a

greener, more sustainable consumption alternative that

promotes efficient use of resources (Leismann et al. 2013),

induced travels resulting from peer-to-peer accommodation

may cause more environmental pressures and lead to

resource exploitation and overcrowding in the destinations.

As tourism destinations may anticipate that an increase in

rental listings may generate more visitors, it is important to

have a set of regulations to ensure that the induced travels are

within the carrying capacity of the destination.

Third, the use of peer-to-peer accommodation leads to

longer stay. Staying at peer-to-peer accommodation implies

intense interactions between guests and local hosts. Because

local hosts have rich information regarding cultural traditions

and local environments, having access to this knowledge will

enable travelers to explore and stay longer in the destinations.

This confirms Su and Wall’s (2010) findings regarding the

effects of host–guest interactions on length of stay, with

social appeal of peer-to-peer accommodation identified as

significant in the regression models in this study. The

increase in length of stay is also influenced by the reduction

in accommodation cost, with travelers being able to spread

their trip budget to include more days. An increase in length

of stay, combined with more meaningful interactions with

local hosts (i.e., more than just brief exposure and superficial

image), is often associated with a deeper understanding and

result in travelers developing a strong emotional attachment

to the destinations. That is, the more travelers feel they are

integrated with the local community, the more they will

develop favorable attitude toward the community and the

destination (Pizam, Uriely, and Reichel 2000; Su and Wall

2010). This will eventually lead to satisfaction, positive

evaluation, and return intention (Pizam, Uriely, and Reichel

2000). Longer stay often translates into more spending,

which is beneficial for local businesses and the destination.

However, the potential negative consequences of travelers

staying longer include conflicts due to travelers’ use of

resources and facilities developed to accommodate residents,

crowding, and other nonparticipant externalities mentioned

before. Eventually, it is important for destination managers

and policy makers to ensure that collaborative consumption

practices are not threatening the social fabric of the local

communities.

Finally, the use of peer-to-peer accommodation causes

travelers to participate in more activities while experiencing

tourism destinations. Both economic and social appeals of

peer-to-peer accommodation lead to travelers participating in

more activities, with social appeal contributing in a higher

degree. The savings from lower accommodation cost can be

distributed to other activities, leading to increased intensity

and variety in activity participation. Additionally,

interactions with hosts and local community, where travelers

engage in casual conversations and various activities

Article PROOF

involving locals, can be considered new and unique

destination experiences. Therefore, the unique experiences

offered by staying at peer-to-peer accommodation diversify

tourism products and encourage niche tourism experiences.

Eventually, this will enrich destination attributes and add to

the competitiveness of destinations.

In summary, this study contributes to the better

understanding of the potential impacts of collaborative

consumption model in tourism by assessing how the different

motivations of using peer-to-peer accommodation affect

changes in travel patterns. The results of this study confirm

that the new trend has the potential to transform traveler

behavior, impacting the hospitality sector and tourism

destinations. This study provides support for better tourism

planning and management to anticipate further impacts of

this alternative accommodation. This study has several

limitations. First, this study does not consider the temporal

dimension of traveler behavior to assess if the impacts of

peer-to-peer accommodation use on travel behavior are

immediate (i.e., short-term) or prolonged. Therefore, in order

to differentiate between short-term and lasting impacts of

collaborative consumption, future studies should take the

temporal dimension of consumption and travel behavior into

consideration (e.g., time period, distance between first use,

and time of analysis). Second, this study captured changes in

travel behavior as perceived by the travelers (i.e., via self-

reported agreement rating) but did not capture the actual

behavior or the magnitude of these changes (e.g., increase in

length of stay by how many days, how many more activities,

etc.), as it would require a longitudinal study. Previous

studies have challenged the accuracy of results from self-

report measures in questionnaires due to memory errors (e.g.,

memory decay, lack of motivation to recall) and motivational

biases from leniency and social desirability (e.g., Podsakoff

et al. 2003; Tarrant et al. 1993). The latter can be influenced

by the cultural backgrounds (e.g., Chen, Lee, and Stevenson

1995; Hui and Triandis 1989) and personal characteristics of

respondents (e.g., Austin et al. 1998; Donaldson and Grant-

Vallone 2002), albeit small and insignificant in some cases.

However, despite these limitations, the use of self-report

measures in behavioral research is favored for its persuasive

advantages due to easy interpretability, information richness,

and practicality, thus continuing to yield important, useful,

and valid findings (e.g., Paulhus and Vazire 2007). In this

study, these concerns were addressed in the design of the

questionnaire by making its statements easy to comprehend

(i.e., easing the cognitive task) and in the data processing

through the detection and elimination of outliers from the

analysis. While it was consistently shown that there are

differences between U.S. and Finnish respondents in terms

of their agreement with the dependent variables, the

inclusion of nationality (a dummy variable) as a factor

variable in the regression models also assists in capturing the

potential cultural bias, which was found insignificant. In

light of the limitations from the study method, future studies

should capture actual travel behavior comparing between

those staying at hotels and peer-to-peer accommodation to

measure the actual impacts. Third, this study treats peer-to-

peer accommodation as an accommodation category by

contrasting it from hotels, but does not narrow down the

category to capture different types of peer-to-peer

accommodation services. For example, Airbnb and 9flats

allow hosts to offer three types of accommodation: entire

house or apartment, private room (often with shared

facilities), and shared room. The social and economic

appeals may vary according to these accommodation types.

Renting a shared room may yield more cost-savings and

more intense social interactions with the hosts when

compared to renting an entire house or apartment, even

though travelers may still enjoy the same benefits of staying

in a desired nontouristy neighborhoods and having authentic

tourism experiences. Therefore, future studies should

consider these different types of peer-to-peer

accommodation to capture its impacts.

Appendix

Measurement Items

1. Peer-to-peer accommodation:

“Peer-to-peer accommodation rentals are accommodation

services where you pay a fee to stay at someone’s property

(such as Airbnb), but excluding free accommodation services

(such as Couchsurfing).”

2. Reasons to use peer-to-peer accommodation:

“I used peer-to-peer accommodation rentals because . . .

. . . I would like to get to know people from the local

neighborhoods” (SA).

. . . I would like to have a more meaningful interaction

with the hosts” (SA).

. . . I would like to get insider tips on local attractions”

(SA).

. . . I would like to support local residents” (SA).

. . . it was a more sustainable business model” (SA).

. . . it saved me money” (EA).

. . . it helps lower my travel cost” (EA).

. . . I would like to have higher quality accommodation

with less money” (EA).

. . . the location was convenient” (did not converge).

. . . it saved me time to search for accommodation” (did

not converge).

. . . it was enjoyable to find the rental online” (did not

converge).

. . . I did not want to support hotel enterprises” (did not

converge).

3. Changes in Travel Patterns:

“The availability of . . . ”

“ . . . peer-to-peer accommodation rentals expands your

selection of places to go to.”

“ . . . peer-to-peer accommodation rentals increases the

frequency of your travel.”

Article PROOF

“ . . . peer-to-peer accommodation rentals makes you take

longer vacations.”

“ . . . peer-to-peer accommodation rentals makes you do

more activities while traveling.”

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect

to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research,

authorship, and/or publication of this article.

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Author Biographies

Iis P. Tussyadiah is associate clinical professor with the School of

Hospitality Management, Carson College of Business, Washington

State University Vancouver. Her research focuses on the roles of

information and communication technologies in travel and tourism

experiences.

Juho Pesonen is head of research (eTourism) at the Centre for

Tourism Studies, University of Eastern Finland. He is specialized

in market segmentation and online marketing in tourism.

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Tourism, Tourists and Society provides a broad introduction to the inter-relationship between tourism and society, making complex sociological concepts and themes accessible to readers from a non-sociological academic background. It provides a thorough exploration of how society influences or shapes the behaviours, motivations, attitudes and consumption of tourists, as well as the tourism impacts on destination societies. The fifth edition has been fully revised and updated to reflect recent data, concepts and academic debates: • New content on: mobilities paradigm and the emotional dimension of tourist experiences. • New chapter: Tourism and the Digital Revolution, looking at the ways in which the Internet and mobile technology transform both tourist behaviour and the tourist experience. • New end-of-chapter further reading and discussion topics. Accessible yet critical in style, this book offers students an invaluable introduction to tourism, tourists and society.

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