Understanding Repurchase Intention of Airbnb Consumers: Perceived Authenticity, EWoM and Price Sensitivity by Lena Jingen Liang A Thesis presented to The University of Guelph In partial fulfilment of requirements for the degree of Master of Science in Tourism and Hospitality Guelph, Ontario, Canada © Lena Jingen Liang, May, 2015 ABSTRACT UNDERSTANDING REPURCHASE INTENTION OF AIRBNB CONSUMERS: PERCEIVED AUTHENTICITY, EWOM AND PRICE SENSITIVITY Lena Jingen Liang University of Guelph, 2015 Advisors: Professor HS Chris Choi Professor Marion Joppe The main purpose of this paper is to extend the research on consumer repurchase intention (RI), perceived value (PV) and perceived risk (PR) into the realm of the peer-to-peer economy, specifically in the context of Airbnb. A series of research propositions were proposed built on the prospect theory and means-end chain theory. Three antecedents: perceived authenticity (PA), electronic word-of-mouth (eWoM) and price sensitivity (PS) were identified through a content analysis, producing an extended model. 395 surveys were collected via a panel based in North America. The results showed that PR negatively impacts Airbnb consumers’ PV and RI while PV positively enhances their RI. Interestingly, PS was found not to reduce customers’ PR but improve their PV and promote the intention to repurchase. PA was found to have significant effects in reducing Airbnb consumers’ PR and positively influences PV. Theoretical and managerial implications are discussed and future study direction was offered. Acknowledgements To the University of Guelph, the College of Business and Economics, and the School of Hospitality, Food and Tourism Management: without your provision of space, funding, resources and help from all of you, my completion of this degree is impossible. I have all of you to thank. To my two advisors, Dr Chris Choi and Dr Marion Joppe: we would argue about a research question, we would argue about a research method, but I know deep in my heart, it is unarguable that you are my family, and will be so for all my life. To my committee Dr Stephen Smith: thank you for always asking me numerous questions. All those questions had made me see more about my research and always give a second thought of every word I used. To all my friends, Shuyue, Joe, Lenka, Jay, Bixian, Brittany, Chanel, Chi-wei, Warren, Derek, Drew, Tammy and everyone in MINS 210: your supportiveness and listening really help me survive. I will always remember the days we talked, we hugged, we worked, we travelled, we played, we ate, we laughed and cried. To my dearest families, dad Zhikun and brother Haixing, I always love you even though you are thousand miles away; mum Lijuan in heaven, thank you for making me strong and I will not forget all the things you taught me. And I wish all the best for everyone in my family, especially my grandpa and grandma. iii Table of Contents Abstract .................................................................................................................................... ii Acknowledgements ................................................................................................................ iii Table of Contents .................................................................................................................... iv List of Tables, Figures and Appendices ................................................................................... v CHAPTER 1: INTRODUCTION......................................................................................................1 CHAPTER 2: LITERATURE REVIEW..............................................................................................4 2.1 Online Repurchase Studies ............................................................................................... 4 2.2 Peer-to-Peer Economy ................................................................................................................ 9 2.3 Studies on Airbnb ................................................................................................................. 10 CHAPTER 3: THEORETICAL FRAMEWORK & MODEL DEVELOPMENT ....................................12 3.1 Theoretical Background ....................................................................................................... 12 3.2 Proposed Model ................................................................................................................... 15 3.3 Hypotheses Development .................................................................................................... 16 3.3.1 Perceived Value (PV) & Perceived Risk (PR) ................................................................. 16 3.3.2 Perceived Authenticity (PA) .......................................................................................... 19 3.3.3 Electronic Word of Mouth (eWoM) .............................................................................. 21 3.3.4 Price Sensitivity (PS) ...................................................................................................... 23 CHAPTER 4: METHODOLOGY ...................................................................................................26 4.1 Research Design & Sampling .................................................................................................... 26 4.2 Measurement of the Constructs .......................................................................................... 27 4.3 Data Analysis & Findings ...................................................................................................... 28 4.3.1 Demographics of the Respondents ............................................................................... 29 4.3.2 Scale Validity & Reliability............................................................................................. 29 4.3.3 Structural Model Analysis ............................................................................................. 34 CHAPTER 5: CONCLUSION ......................................................................................................37 5.1 Implications .......................................................................................................................... 38 5.2 Limitations & Future Study Direction .................................................................................. 40 CHAPTER 6: BIBLIOGRAPHY ........................................................................................................... 42 iv List of Tables, Figures and Appendices Table 1: Overview of reviewed online repurchase studies………………………..6 Table 2: Validity test………………………………………………………………30 Table 3: Confirmatory factor analysis for measurement model…………………..32 Table 4: Results of hypothesis tests…………………………………………….…36 Figure 1: Model of repurchase intention for Airbnb………..………………….…16 Figure 2: Structural path coefficients……………………………………………...35 Appendix: All items used in survey………………………………………………52 v CHAPTER 1: INTRODUCTION Web 2.0 has reshaped the way consumers buy products and services (Cheung, Chan,& Limayem, 2005), not only in terms of how transactions are conducted, but also the nature of the buyers and sellers. On the supply side, individuals can rent out whatever they have, whether a tangible or intangible good or service, to someone they might never have met before via various web 2.0 sites. On the demand side, buyers can obtain what they need, whether a service or product, at a lower price. These ways of doing business have attracted increasing attention around the world since 2010, and are labeled as the ‗sharing economy‘, ‗collaborative consumption‘, or ‗peer-to-peer consumption‘. The platforms that offer the matching services for these buyers and sellers, have quickly strengthened their positions in different markets, including the hospitality industry. One of the most representative platforms occupying the hospitality market is Airbnb,a peer-to-peer transaction-based online marketplace that matches hosts who wish to share their spare space with travelers who are looking for accommodations (The Economist, 2013). Millions of individuals participate in sharing their unused places and roomsthrough fee-based transactions where travelers can rent private rooms or entire places at lower rates for a short-term period, increasing the opportunity for travelers to mingle with local people and experience locals‘ lives (Sacks, 2011). However, to date, little research has been conducted on these fee-based online communities in the hospitality context. Ferocious debates have occurred as a result of the growing popularity of Airbnb (e.g.Dickerson, 2015; Folger, 2014). Extreme opinions, either strongly supportive or strongly opposed, were found in various media reports (e.g. New York Times; The Economist), economists‘ blogs (e.g. Tom Slee), etc. Nevertheless, Airbnb continues to 1 gain popularity at an astonishing rate at the global level, with the total nights booked increasing from 180 in 2008 to 10 million in 2012 (Airbnb, 2014). Unlike Bookswap1 or Lending Club2, Airbnb does not involve the ownershipexchange of a product. Unlike Zipcar3 and Uber4, the transaction in Airbnb involves human gathering and the sharing of the private sphere. Unlike Couchsurfing5 and HomeExchange6, it involves a direct money transaction. Unlike hostels, hotels or bed and breakfast, the trading in Airbnb involves more potential risks becauselisting a property on Airbnb does not require government approval or inspections (Airbnb, 2014). Thus, Airbnbshows a unique characteristic as it offers a transaction that contains human-to-human gathering and the sharing of the private sphere, making this study of the repurchase intention in the Airbnb context complicated but unique and important. Furthermore, from a marketing perspective, understanding why tourists would choose Airbnb again provides valuable information for the traditional hospitality industry managers. Hotel managers can learn about the changes in tourists‘ demand with respect to accommodation. For Airbnb and other similar network hospitality exchange platforms, understanding what the consumers care about when using those platforms is important for their marketing strategy as the cost of retaining a customer is much less compared to the cost of obtaining a new one. In addition, little attention has been paid to date to the tourism related factors associated with online repurchasing behaviors such as perceived authenticity. Therefore, this study proposes a research framework based on prospect theory and means-end chain (MEC) theory on consumer repurchase intention (RI), perceived 1 Buys and sells books, www.bookswap.ca Peer-to-peer lending platform, www.lendingclub.com 3 An alternative to car rental, www.zipcar.com 4 A mobile app that allows consumers to submit a trip request, which is routed to crowd-sourced taxi drivers 5 A hospitality exchange website, www.couchsurfing.com 6 A home exchange website, www.homeexchange.com 2 2 value (PV) and perceived risk (PR) in the realm of the peer-to-peer economy, specifically in the context of Airbnb. Three antecedents —perceived authenticity (PA); electronic word-of-mouth (eWoM) and price sensitivity (PS) — were identified based on a content analysis (see Proposed Model page 14 for full details). Three main objectives of the study are to 1) explore the effects of the three antecedents on PV, PR and on consumers‘ RI; 2) examine the mediating roles of PV and PR on the relationships between the extrinsic product cues and RI; and 3) investigate the relationships between PV, PR and RI. 3 CHAPTER 2: LITERATURE REVIEW 2.1 Online Repurchase Studies Wu, Chen, Chen and Cheng (2014) argue that empirical studies on examining the value-RI or risk-RI linkage in the online context are scarce. Therefore, this study explores online RI and defines it as Airbnb consumers‘ self-reported likelihood of repeat purchasing accommodations on www.Airbnb.com. Several prior studies have exploredRI in the online context, while various antecedents as well as research models were examined (See Table 1 for a brief summary of theliterature). Among the reviewed articles, satisfaction seems to dominate the online RI studies. However, in the realm of the peer-to-peer economy, satisfaction alone may not necessarily predict RI because there may be a need to differentiate between the satisfaction with the website/platform and the satisfaction with the peer seller. Moreover, satisfaction may be reflected by consumers in different forms. For example, Chiu, Wang, Fang and Huang (2014) explored the relationships between utilitarian value, hedonic value, PR and RI, finding that there are significant influences of utilitarian value, hedonic value and PR on RI as well as powerful effects from PR on those two values. This shows that value and risk may be effective in predicting RI. Wu et al. (2014) also showed that satisfaction is not the only way to predict RI. They examined the interactions between PV, transaction costs and RI. Positive influences of PV on RI as well as the relative importance of the three types of costs on PV and RI were found. Nevertheless, they designed the model only with customers buying tangible products online in mind, whereas Airbnb products are intangible; therefore their model is not suitable to be adopted for this study. In other words, evidence from the literature suggests that satisfaction is not the only way to predict intentions to repurchase in the online context. To better 4 understand the RI of Airbnb consumers, a model with PV, PR and RI can be built. Moreover, researchers have to look at tourism related constructs when exploring this model because Airbnb is a platform that offers accommodation searching services for tourists. As shown in Table 1, no known tourism related constructs have been investigated in the context of online RI. In an effort to enrich the extant literature, this study incorporates effective antecedents in the field of tourism and consumer behavior, to build a theoretical framework based on the initial model of the relationship between PV, PR and RI. 5 Table 1 Study Overview of reviewed online repurchase studies Constructs Theoretical framework Risk attitude, online shopping experience, Adaptation-level Wu & Chang evaluation-based theory; attribution (2007) SAT, emotion-based theory SAT, RI Methodology Pilot tested with 45 undergraduate students, 7-point scale Sampling Findings Analytic techniques 303/486 questionnaires (Taiwan) Risk attitude directly influence online shopping experience, SAT and RI; SAT would enhance RI when consumers are characterized as having higher risk preference SEM /AMOS 5.0 Cluster analysis /SPSS12.0 Perceived risk, travel On sight Performance risk most significantly 253/270 SAT, RI, 4 types of questionnaire influence SAT while political risk has no Expectation-performa international air An, Lee, & risks: natural disaster collection at a model effects on SAT; Natural disaster risk affects T test, ANOVA, nce inconsistency routes Noh, (2010) risk; physical risk; global airline RI most significantly while physical risk Regression theory passengers political risk; company; 5-point has no significant influence.Travel SAT (South Korea) performance risk scale positively influence RI. SAT, trust, adjusted Ha, Janda, & expectation, positive Muthaly (2010) attitude, RI SAT model; attribution theory In-depth discussion with 42 online 514/1500paper shoppers;a focus questionnaires via group with 23 online marketing shoppers; research firm 5-point scale The mediating effects of adjusted expectations, trust and positive attitude were found between SAT and RI. Armstrong & Overton, 1977‘s method of non-response bias, SEM /PLS 2 scholars verify No direct relationship between trust and RI. PEU, Confirmation, TAM; Expectation- items; 6 PhD students Direct relationship between SAT/ PU/ Wen, Prybutok, Trust, PU, SAT, 214/230 college CFA, SEM / Confirmation Model pretest; paper-based Enjoyment and RI. Post-purchase stage, & Xu (2011) Perceived students (U.S.) LISREL 8.72 (ECM); Flow theory questionnaire, 5 point utilitarian factors are more valuable in enjoyment,RI scale predicting RI. 6 Psychological contract violation is Psychological Pilot tested with 20 Chiu, Fang, Expectancy-disconfir 162 PChome negativelyassociated with SAT and trust. contract violation, graduate students, SEM, PLS Cheng, & mation model; equity online customers SAT is positively associated with buyers‘ switching cost, SAT, online surveyposted /SmartPLS 2.0 Yen(2013) theory (Taiwan) RI. A higher levelof switching cost trust, RI on BBS,7-point scale diminished SAT‘s effect on RI. Chiu,Wang, utilitarian value, Fang, & Huang hedonic value, (2014) perceived risk, RI Perceived effectiveness of Fang, Qureshi, e-commerce Sun, McCole, institutional Ramsey, & Lim mechanisms (2014) (PEEIM), trust, SAT, RI Lin & Lekhawipat (2014) Online shopping experience&habit, customer SAT, adjusted expectations, RI Prospect theory; Means-end chain (MEC) theory Prospect theory; Sociological and organizational theories of trust Self-perception theory, psychology, attitude-behavior model, contingency theory Pretest with 20 graduate students; Pretest with 168 online customers, 7-point scale Pilot test with 12 staff and 10 students; two parts of the survey: general perceptions &recall method; Mailed survey, 7-point scale 782 Yahoo!Kimo online shopping customers. 40 random $10 incentive (Taiwan) No significant difference in terms of gender, age or education; Utilitarian and Z-test, PLS, hedonic value have direct effects on RI. SEM /SmartPLS Perceived risk is a negative determinant of 2.0 RI. Perceived risk is a moderator between hedonic as well as utilitarian value and RI. 362/865 University personnel/ students PEEIM negatively moderates the relationship between trust in an online Armstrong & vendor and RI, as it decreases the Overton, 1977‘s importance of trust to promoting method of repurchase behavior; PEEIM positively non-response moderates the relationship between SAT bias, EFA, PLS, and trust as it enhances thecustomer‘s SEM /AMOS7.0 reliance on past transaction experience with the vendor to reevaluate trust in the vendor. Online shopping habit acts as a moderator of bothcustomers SAT and adjusted 204/1000 Harman‘s one expectations, whereas online shopping questionnaires(Tai factor test, SEM experience can be considereda key driver wan) /SmartPLS 2.0 for customer SAT. Direct effect from SAT towards RI. 7 Purposive sampling Perceived value; 3 Consumer behavior on two websites; types of transaction theory; elaboration questionnaire The relative importance of the three types Wu, Chen, costs: information likelihood model; 887/1016 online developed in English of costs on PV and RI is found and the Chen, & Cheng searching cost; cognitive dissonance consumer then translate into information searching cost has the greatest (2014) moral hazard cost; theory; theory of (Taiwan) Chinese for effect; PV positively influences online RI. specific asset psychological distribution; 5-point investment; RI reactance scale CFA; SEM /LISREL Initial trust building Survey developed in Trust in Trust in intermediary has significant Jia, Cegielski, model; expectationEnglish then 242/255 intermediary, trust in influence on trust in online sellers; Trust in CFA, SEM & Zhang confirmation theory; translated into university online sellers, SAT, intermediary, trust in online sellers and /SmartPLS 2.0 (2014) D&M IS success Chinese, 7-point students (China) RI, etc. SAT significantly affect RI. model scale 8 2.2 Peer-to-peer Economy Studies on the peer-to-peer economy can be found in many different disciplines and are labeled by various names. Most typical are ‗collaborative consumption‘ and ‗sharing economy‘(Botsman & Rogers, 2010), but so far there is no clear distinction between these terms. Many researchers discuss collaborative consumption together with the sharing economy. However, these two concepts should be distinguishedbecause their actors and transaction types are not necessarily the same. In other words, actors in collaborative consumption can be an individual, a group of people or a company with fee-based transactions, whereas the sharing economy refers to individual peers and the emphasis is on sharing behaviors, originally with no fees. Airbnb combines these two approaches and therefore the term, ‗peer-to-peer economy‘ was chosen, defined as the trading between individuals (normally strangers) via an online matching platform that offers private room/apartment online match booking service for a fee by a company that also charges a service fee, www.airbnb.com. Felson and Speath (1978) defined the act of collaborative consumption as individuals or groups interacting in consuming goods or services activities. However, Belk (2013) critiques their definition as too broad and not reflecting the acquisition and distribution of the resource. He offers his own as the acquisition and distribution process of a resource by people for a fee or other compensation. Botsman and Rogers (2010), on the other hand, define collaborative consumption as an activitythat includes traditional sharing, bartering, lending, trading, renting, gifting, and swapping. Still, these definitionsare also too vague and confound the concept of marketplace exchange, gift giving, and sharing.Moreover, neither Belk nor Botsman and Rogers clarified what the actors would be in this type of consumption. Researchers had used other terms to define similar types of business models, 9 too. For example, Bardhi and Eckhardt (2012) used the term ‗access based consumption‘, defined asmarket mediated transactions without any transfer of ownership taking place.Nevertheless, this definition is too broad to be applied for Airbnb as many transactions such as regular hotels and car rentals also do not transfer ownership. Ikkala (2014) labeled Airbnb as ‗monetary network hospitality‘. While this concept speaks directly to the characteristics of Airbnb, it is limited to hospitality platforms only and therefore very narrow. 2.3 Studies on Airbnb Research on the Airbnb concept is very limited and recent, addressing a variety of issues. For example, Miller (2014) focused on the regulation alternatives of Airbnb, proposing the concept of Transferable Sharing Rights (TSR) to set up the laws for Airbnb‘s operation. Stern (2014) tried to interpret the phenomenon of people listing their properties on Airbnb via social proof theory. In contrast, Ikkala (2014) used social exchange theory to explore the reasons people rent out their properties, which he labeled as monetary network hospitality in his Master‘s thesis. Using in-depth interviews with Helsinki hosts, he found that two major reasons for doing so are for the social interaction with guests from different places plus the financial gains. Zervas, Proserpio and Byers (2014) explored the impact of Airbnb on the hotel industry. They compared the data collected on Airbnb.com on the Texas listings with quarterly hotel revenue tax data on over 4,000 Texas hotels from Smith Travel Research (STR) in a 10 year period and found that the rise of Airbnb had a negative impact on hotel revenue, especially at the lower end hotels without conference facilities. Zekanovic-Korona and Grzunov (2014) highlighted the impact of information 10 and communication technology (ICT) on the tourism business. They used descriptive statistics based on an online survey to determine the user structure of Airbnb (sceptics, 36.44%; pioneers, 61.86% and researchers, 1.69%) and discussed its merits and shortcomings. Fradkin, Grewal, Holtz and Pearson (2014) conducted a field experiment on Airbnb.com to determine an efficient way to decrease the bias of the online review system. They suggested the establishment of a reputation system and the perceived social distance between reviewer and reviewee should be not be neglected throughout the operation. Guttentag (2013) felt that Airbnb is a disruptive innovation and researched on the legality issue and tax concerns of Airbnb. He found that low cost is the main draw of people participating in Airbnb. Different from hotel services, the tourists may obtain a feeling of home in their trip and useful local advice may also be offered. His article provides general insights intoAirbnb as well as a review of some legal issues in North America.However, there is no empirical data. He refers to Airbnb as an internet-based marketplace for peer-to-peer accommodation. Yannopoulou, Moufahim and Bian (2013) highlighted Couchsurfing and Airbnb utilizing authenticity to build up their brand identities. Based on qualitative research, they found that both companies attract customers by providing ―access to the private sphere, human dimension and meaningful inter-personal discourses and authenticity‖ (p. 85). To summarize, the studies on Airbnb have broadly touched on different areas, but none so far have addressed what factors influence Airbnb consumers‘ repurchasing behavior. Furthermore, much of the work to date has been qualitative in nature, whereas this study will take a quantitative approach. 11 CHAPTER 3: THEORETICAL FRAMEWORK & MODEL DEVELOPMENT 3.1 Theoretical Background When researchers discourse about the factors that influence RI, ‗satisfaction‘ seems to be their first choice as it has been recognized as an important antecedent of consumers‘ loyalty and their repeat buying behaviors (Qureshi, Fang, Ramsy, McCole, & Compeau, 2009). However, some studies argue that customer satisfaction alone does not necessarily bring higher intention to repurchase (Pavlou, 2003; Guttentag, 2013). There may be other forms of interpretations of the satisfaction construct that affect RI, or perhaps the so called satisfaction is not that important when consumers are dealing with peer-to-peer arrangements. For example, when making a decision, people are consciously or unconsciously comparing the statistical properties of the perceived value and risk (Christopoulos, Tobler, Bossaerts, Dolan, &Schultz, 2009). Generally, alternatives with higher value are preferred when all other things are equal. Nevertheless, the introduction of risk will influence the expected value, modulating the subjective evaluation of the decision, no matter whether it is satisfactory or not. Pires, Stanton and Eckford (2004) suggested that the behavior intention of consumers can be explored as a consequence of a decision-making process with evaluation of value and risk. Since Airbnb is a third-party platform that offers online matching services for accommodations between sellers and buyers, risk might be a very important factor that influences their behavior intention. However, the Airbnb consumers must also see value in this kind of peer-to-peer economy given Airbnb‘s exponential growth. Therefore, the interaction between value and risk seems to be important in terms of predicting RI in this context. The most famous theory on risk would be the prospect theory, introduced by 12 Kahneman and Tversky (1979). It suggests thatattitudes towards PR will be different because consumers are setting their reference points differently. In other words, consumers‘ PV would be higher when the expected consequence is stated to be reducing loss rather than keeping gains because most people are loss averse. For example, when consumers are confronted with the possibility of high risk, their perception of value diminishes more compared to the increase of risk perception when they are facing a high value proposition. This interaction between risk and value leading to behavior can be applied when Airbnbconsumers consider repurchasing Airbnb accommodation. In this case, their PR would more likely affect the PV of this transaction and therefore directly or indirectly result in different repurchasing behaviors. Thus, PR was investigated in this study due to its relativelystrong risk-value/intention mechanism suggested in the prospect theory. According to the Means-End Chain (MEC) theory, consumers link and form their cognition towards something through a combination of the attributes of the object and their goals, which has been widely applied to explore consumer behaviors in literature (Walker & Olson, 1991; Olson & Reynolds, 2001). It demonstrates that there are hierarchical relationships from the means (extrinsic factors), the ends (PV) to the outcomes (RI) (Gutman, 1982). Gutman also referred to the means as the attributes, representing the perceptions of consumers of a product or service‘s characteristics. The ends are related to how consumers choose to react, i.e. the modes of conduct, being values consumers perceived in the transaction. The outcomes are also termed consequences or benefits, referring to the physiological or psychological response of a consumer. Values are the result of the cognition process that is completed by a mental transformation with a functional consequence and a psychosocial consequence, generating the estimated value of a product or service as 13 aguideto how consumers are expected to behave (Gutman, 1982; Parks & Guay, 2009). In other words, values are the final goals that trigger behavior (Chiu et al., 2014). Therefore, behavior intention can be revealed by certain attributes or values (Reynolds & Gutman, 1988). That is to say, there is an attribute-value-intention linkage according to this theory, on which the proposed model of this study is built. The applications of MEC theory in the tourism field are popular (McIntosh & Thyne, 2005; McDonald,Thyne, & McMorland, 2008). McIntosh and Thyneaverredthat more applications of MEC theory to explore the tourist behavior in tourism research are needed and that MEC theory can be very effective in terms of understanding personal values. This is supported by previous studies such as Klenosky, Gengler and Mulvey‘s (1993)research into the factors that affect consumers‘ ski destination choices. Therefore, this study incorporatesGutman‘s (1982) MEC theory and Kahneman and Tversky‘s (1979) prospect theory to build the theoretical model, proposing the mediating effects of PV and PR between extrinsic factors and RI. The adoption of these two theories to build a consumer behavior model has been shown to be reliable in previous studies. For example, Chiuet al. (2014) bridged prospect theory and MEC theory to explore the relationships between utilitarian value, hedonic value, PR and RI and found that there were significant influences of utilitarian value, hedonic value and PR on RI as well as powerful effects from PR on those two values. In this study, RI was examined rather than repurchase behavior because based on the Theory of Reasoned Action (TRA, Ajzen & Fishbein, 1977), intention is considered as the best immediate antecedent in the relationship between attitude and behavior. Therefore, it is appropriate to predict a consumer‘s behavior by measuring his or her intention. Perceived behaviors or perceptions are widely accepted to have 14 significant influence on consumers‘ online repurchase behaviors. 3.2 Proposed Model As illustrated in Figure 1, the proposed model consists of three dependent variables, which are PV, PR and RI, with PR influencing PV and both resulting in RI. Zeithaml (1988) suggested that various external cues would be used by consumers to form the perceptions of the product‘s value, thereby affecting their intentions to rebuy. Similarly, according to Baur (1960) and MEC theory, consumers would also evaluate the risks of this transaction via external cues, which would affect their behavior intentions as well as their perceptions of the product‘s value. Therefore, there should be external cues that influence Airbnb consumers‘ PV and PR when they consider to repurchase accommodations on the Airbnb website. In order to identify those external cues, a content analysis was conducted on Facebook, Twitter and Fodors7, using key words ―why you used Airbnb‖ or ―why you don‘t/won‘t use Airbnb‖. The Leximancer8 results indicated that there were three main themes among the collected discussions, identified as perceived authenticity (PA), electronic word-of-mouth (eWoM) and price sensitivity (PS). Based on Zeithaml (1988) and Baur‘s (1960) suggestions, external cues are expected to have influence on PV,PR and RI, resulting in the proposed model shown in Figure 1. 7 www.fodors.com, a forum that offers tourism products/travel review discussions among its members. Leximancer is an analyzing software that allows presenting the linkage between words and reveals themes of the data. 15 8 H4a Perceived Authenticity H4b Perceived Value H4c H1 H1 H5a H1 EWoM H1 H5c H1 H3 H6a Price Sensitivity H1H1 H6b H1 H5b H1 H2 H1 H1 H1 Perceived Risk H1H1H3 H1 Repurchase Intention H3 H1 H1 H6c Figure 1 H1 H1 Intention for Airbnb Model of Repurchase H1 H1 3.3 Hypotheses Development 3.3.1 Perceived Value (PV) & Perceived Risk (PR) Zeithaml (1988) definedPV as the overall assessment of consumersofthe utility of service or product based on their perceptionsof gain and given. PV is also defined as the overall evaluation of the net value (benefits) of a product or service based on consumer perception(Bolton & Drew, 1991; Sweeney & Soutar, 2001). Kashyap and Bojanic (2000) summarized that all definitions of PV refer to some form of tradeoff between what the consumer gives up (price, sacrifice) and what the consumer receives (utility, quality, benefits).PV is understood as a critical construct in consumer behavior studies, not only because it is a key determinant influencing behavior intention (Zeithaml, 1988; Zhuang, Cumiskey, Xiao, & Alford, 2010), but also because it is a dependent variable that is affected by many other antecedents (Sheth, Newman, & Gross, 1991a; 1991b). This study adopts the views of Sweeney and Soutar as well as Sheth et al., defining PV as the consumers‘ overall assessment of the net values of 16 booking accommodations via Airbnb and their perceptions of value which are affected by PR, PS, eWoM and PA. The relationship between PV and RI has been studied and confirmed in various consumer behavior studies (Grewal, Monroe, & Krishnan, 1998; Kuo, Wu, & Deng, 2009). Moreover, it was found that higher PV would lead to willingness to pay (Dodds & Monroe, 1985). Within the repurchasing behavior studies, PV was found to positively influence consumers‘RI in studies by Chiuet al. (2014) and Wuet al. (2014). PR is defined in terms of uncertainty and consequence, in that it increases with higher levels of uncertainty and/or the chance of greater associated negative consequences (Oglethorpe & Monroe, 1987). Similarly, Kim, Ferrin and Rao (2008) viewed a consumer‘s PR as one‘s belief in possible negative results that would happen from this transaction. Forsythe, Liu, Shannon and Gardner (2006), however, highlighted that financial and product performance risks are two types of risks that are highly associated with internet shopping. Two components of PR that have been identified are uncertainty and consequences (Baur, 1960). Even though researchers had defined PR in slightly different ways, its components have been consistently described. In fact, Airbnb consumers have no choice but to estimate the risk of this transaction from the available information and communications because they cannot experience the actual service before arriving at the property. In this sense, the PR of Airbnb consumers plays a crucial role in their repurchasing decision-making. Therefore, Kim et al. (2008) and Forsythe et al.‘s definition on PR were adopted because Airbnb includes the sharing of private sphere. PR in the Airbnb context is referred as Airbnb consumers‘ beliefs in all possible negative results that may happen after they book rooms via Airbnb. 17 PR occurs where there is uncertainty, information asymmetry and fear of opportunism, as is the case in online shopping.However, there has been little effort to empirically examine the impact of PR relative to that of PV in motivating repurchases in the online environment. Higher risks have been shown to lead to lower intention to repurchase (Wu & Chang, 2007; Vijayasarathy & Jones, 2000; An, Lee,& Noh, 2010). Wu and Chang found that risk attitude directly influences online RI, identifying four types of risks (natural disaster risk, physical risk, political risk and performance risk). An, Lee and Noh exploredtourist‘s RI for travelling and found that natural disaster risk affectedthis RI most. Vijayasarathy and Jones affirmed that the PRby consumers would significantly affect their online behavior intention. Chiu et al. (2014) found that PR is a negative determinant of RI. Regarding the relationship between PR and PV, PR is perceived as an antecedent of PV by most of the previous consumer behavior studies (Sweeney, Soutar,& Johnson, 1999; Agarwal & Teas, 2001;Chen & Dubinsky, 2003; Chang & Tseng, 2013). Sweeney et al.determined that PR affects consumers‘ PV of a product.Agarwal and Teas used two experimental designs to authenticate the mediating relationship of PR on consumers‘ perceptions of the product value.Chiu et al. confirmed that PR was a moderator between value and RI. To summarize, PV is a positive determinant of RI, while PR is an antecedent that influences PV and RI. As per the previous studies, the hypotheses between PV, PR and RI were proposed as follows: H1: There is a negative relationship between perceived risk and perceived value. H2: There is a positive relationship between perceived value and repurchase intention toward Airbnb. H3: There is a negative relationship between perceived risk and repurchase intention toward Airbnb. 18 3.3.2 Perceived Authenticity (PA) Since MacCannell‘s (1973) seminal work on authenticity, the concept has been widely investigated in tourism research (Chhabra, Healy, & Sills, 2003; Ramkissoon & Uysal, 2011).Wang (1999) argued that the concept of authenticity is problematic. He further clarified that in tourism studies, there are two main types of authenticity: the objective-related authenticity (objective authenticity and constructive authenticity) and activity-related authenticity (existential authenticity). In other words,authenticityusually can be conveyed in two distinct concepts: objective authenticity (genuineness or realness of things) and existential authenticity (human nature)(Steiner &Reisinger, 2006). It is also regarded as a multidimensional construct associated with perceptions of honesty, truth and sincerity (Blackshaw, 2008). Grayson and Martinec (2004) elaborated that when something or someone is perceived to be real, they are authentic. They further explained that authenticity should not be viewed as an attribute, but as an assessment of a specific situation. That is to say, authenticity is the perception of an individual‘s cognitive recognition of the reality of something, someone or someplace. As previous studies on Airbnb suggested that seeking local living experiences maybe a main attractive to Airbnb consumers (Guttentag, 2013; Yannopoulouet al., 2013), this study focused on existential authenticity, which emphasize on the human nature. Thus, this study adopts Grayson and Martinec‘sdefinition, referring to PA as the perceptions of Airbnb consumers‘ cognitive recognition of ‗real‘ experiencesof staying in an Airbnb place. Tourism studies, on the other hand, either define authenticity as a determinant of customer satisfaction (Chhabra et al., 2003) or as a decisive factor of destination choices (Ramkissoon & Uysal, 2011). Kolar and Zabkar (2009) focus on the concept of PA, exploring its relationship with cultural tourism, motivation and customer 19 loyalty. Specifically in the Airbnb context, some Airbnb consumers highlighted Airbnb as ‗real people with a real home‘ and they can ‗make real life friends‘ (Khan, 2011; Paul, 2013; Garrett-Price, 2014). PA seems to play an important role in the process of consumers repurchasing Airbnb accommodation. Ramkissoon and Uysal (2011) found that PA positively and significantly influenced the cultural behavioral intentions of tourists in the island of Mauritius. Authenticity is also used as a brand characteristic by Couchsurfing and Airbnb (Yannopoulouet al.,2013). They found that consumers of both platforms are attracted by the authentic experience they perceived to have by staying in Couchsurfing and Airbnb accommodation. In a related study, Lunardo and Guerinet (2007) found that the perceptions of authenticity influence the purchasing behaviors of young consumers in wine consumption. Kovács, Carroll and Lehman (2013) empirically tested the relationship between consumers‘ PA of a restaurant and the corresponding value ratings. They found that the more authentic consumers perceived it to be, the higher the value ratingassigned even when they controlled for a lower quality of restaurant. Therefore, it is believed that authenticity increases a consumer‘s value ratings. Chen (2009) made it clear that ―people seek out… authenticity… to validate worth‖ (p.65). Cova and Cova (2002) mentioned that lack of product quality cues (e.g. origins, materials, producing procedures) increases consumers‘ physical risks towards a product. They summarized that consumers perceived those products to be not authentic and thus buying those products is risky. Lunardo and Guerinet (2007) used originality and projection as two dimensions of authenticity to measure its effects on the PR, perceived price and purchase intentions of young consumers. Using a combination of qualitative and quantitative methods, they found that authenticity 20 decreasesPR. Therefore, based on the above discussion, the following hypotheses are proposed: H4a: Perceived authenticity increases consumers’ repurchasing intention toward Airbnb. H4b: Perceived authenticity increases the consumers’ perceived value toward Airbnb. H4c: Perceived authenticity decreases the consumers’ perceived risk toward Airbnb. 3.3.3 Electronic Word of Mouth (EWoM) EWoM is defined as any statements made by future, present or former customers about a product or enterprise, either positive or negative, and is accessible by anyone online (Hennig-Thurau, Gwinner, Walsh, & Gremler, 2004). Litvin, Goldsmith and Pan (2008) defined eWoM in the tourism context as every internet-based communication about the usage or characteristics of something (products, services or a company). In this study, the Litvin et al.’s (2008) definition is adopted, referring to eWoM as all informal communications for Airbnb consumers through the Internet related to the usage or characteristics of booking and living in Airbnb accommodations. EWoM is a construct that has been popularly examined regarding its relationship with branding (Sandes & Urdan, 2013), purchase intention (Mauri &Minazzi, 2013; See-To & Ho, 2014; Sandes & Urdan, 2013), perceived credibility/trust (Mauri & Minazzi, 2013; See-To & Ho, 2014); value (See-To & Ho, 2014); and PR (Hung & Li, 2007; Schau, Muñiz, &Arnould, 2009; Wu, 2014). EWoM is especially important in this context because the product/service researched is intangible, i.e., its quality is hard to evaluate before consumption. Therefore, consumers will try to seek references from eWoM before making decisions. 21 Several studies support that eWoM is positively related to PV. For example, Gruen, Osmonbekov and Czaplewski (2006) explored the relationship between eWoM, customer PV and customer loyalty intentions based on an online forum with 616 participants. They found a direct positive influence from eWoM on PV. Cheung, Luo, Sia and Chen (2009) suggest that eWoM has informational and normative influences on consumers‘ believes and conformity. In other words, eWoM can affect consumers‘ PV of products by strengthening consumers‘ believes and conformity. Keaveney and Parthasarathy (2001) empirically supported the positive effect of eWoM on PV. Evidence was also found to support the positive relationship between eWoM and RI. Boulding,Kalra, Staelin and Zeithmal (1993) argued that positive WOM predicts future behavior intentions. Mauri and Minazzi (2013) confirmed that there is a positive correlation between the hotel purchasing intention and eWoM through an online survey. An exploratory study conducted by Keaveney and Parthasarathy (2001) indicated eWoM positively increases RI. After selecting to repurchase accommodations on Airbnb, (or revisit the Airbnb website to choose a property), certain PRs are likely to occur because they are probably dealing with different properties and sellers. The advertisements and words from the sellers alone will not be enough to convince the consumers of the actual product value. As previous studies suggested (Hung & Li, 2007; Cheunget al., 2009; Schauet al., 2009), eWoM is one of the most influential ways to decrease consumers‘ PR by providing advice from the online community. Hung and Li suggest that eWOM can effectively strengthen brand knowledge, leading to a lower customer PR of the product by decreasing the incident of being deceived. Schauet al.agree that eWoM has significant effects on decreasing the perceived possibility to be deceived. Recently, Wu (2014) found evidence on the relationship between eWoM and PR in her Master 22 thesis within the context of hospitality. She further explored how consumers use eWoM when they try to book accommodations online to decrease the potential risk of the transaction. Therefore, based on the findings of the previous studies, the hypotheses of eWoM were proposed as follows: H5a: EWoM increases consumers’ perceived value toward Airbnb. H5b: EWoM increases consumers’ repurchase intention toward Airbnb. H5c: EWoM decreases consumers’ perceived risk toward Airbnb. 3.3.4 Price Sensitivity (PS) Price has been widely recognized as a determining factor that influences consumers‘ behavior intentions (Chang & Wildt, 1994; Kim & Kim, 2004; Moon, Chadee, & Tikoo, 2008; Yoon, 2002). However, the price differences between similar products leads to various reconsiderations by consumers. For example, product A is price X, which is relatively lower than its comparable market price. In this scenario, the RI of consumers will be increased based on the normal price theories. However, there is a product B at price Y, and price Y is much cheaper than price X. In this scenario, there are many more factors to consider but consumers‘ RI is increased if consumers are sensitive to price. This means, consumers will react differently to different price levels,regardless of the other factors. This is supported by Masiero and Nicolau‘s (2012) study in which they found that PS plays a complicated role in affecting tourists to choose tourism products. To define PS, Goldsmith and Newell (1997)argued it as a variable that measures how consumers react differently to the price levels and the alteration of the price. Erdem, Swait and Louviere (2002) explored the relationship between brand credibility and consumer PS and found that brand credibility decreases consumer PS. They refer to PS as a consumer‘s consideration of the price when evaluating something‘s value or 23 utility. Casado and Ferrer (2013) found that consumers have latitude of acceptance, within which they are less sensitive to the change in price while beyond which they are more sensitive. Since Airbnb stressesa ‗home‘ much more than a low price in their marketing strategy, it would be interesting to see how consumers react to the price differences compared to other types of accommodations. To achieve this goal, this study adopts Erdem et al.‘s definition of PS, that is, it is viewed as the weight attached to price in an Airbnb consumer‘s valuation of Airbnb accommodation‘s overall attractiveness or utility. Prior studies indicated that higher PS negatively influences the consumers‘ PV, while lower PSis positively related to PV (Kashyap & Bojanic, 2000; Zeithaml, 1988). In other words, when consumers are very sensitive to the price of their accommodations, they tend to perceive more value in choosing Airbnb. PS was found to influence consumers‘PRof a product as well. Bearden and Shimp (1982) conducted two field experiments to examine the influence of different price levels, reputation and warranty of a product on the PR associated with it. Two aspects of risks, namely financial risk and performance risk, were found to have direct effects on PR. Shimp & Bearden (1982) corroborated a significant relationship between PS and PR. There is little doubt that being sensitive to different prices will influence the intention to repurchase. For example, Chen, Monroe and Lou (1998) suggested that buyers would have stronger intentions to purchase something when the product is cheaper than one with the same function at a higher price. These findings were supported by Grewal et al.’s (1998) conceptual model on the effects of the (reference) prices on PV and behavior intentions. 24 In summary, the following hypotheses proposed are all supported by previous studies: H6a: Consumers’ price sensitivity increases their perceived value toward Airbnb. H6b: Consumers’ price sensitivity decreases their perceived risk toward Airbnb. H6c: Consumers’ price sensitivity increases their repurchase intention toward Airbnb. 25 CHAPTER 4: METHODOLOGY& RESULTS 4.1 Research Designand Sampling Based on the initial model, a content analysis was conducted to identify the antecedents that influence Airbnb consumers‘ PV, PR and RI. The key words ‗why use Airbnb‘ and ‗why don‘t use Airbnb‘ were used to explore the websites like Facebook, Twitter, and Fodors. A total of 50,495 words were captured and the software Leximancer was used to analyze the result. Three major themes emerged as a result, where frequently occurring words such as‗people‘, ‗real‘, ‗experience‘, and ‗host‘created a cluster that was categorized as PA; the‗money‘, ‗booking‘, ‗rent‘, ‗cheap/cheaper‘, ‗save‘ cluster was identified PS; and the cluster of ‗place‘, ‗review/reviews‘, ‗report‘ was called eWoM. Airbnb consumers who had booked and stayed in Airbnb accommodation at least once were selected for this study. A panel member database in North America was chosen in cooperation with a research company as reaching Airbnb consumers directly is very difficult and costly. Residing in Canada and the United States, these panel members were mainly enrolled through the Internet. People are coming from all walks of life. They would normally indicate the types of surveys they are interested in,according to which the research company will send them corresponding invitations when there are related projects. Panel members are rewarded with points when they completed a survey. These can be convertedinto gift cards, donated to charity, or used in other ways depending on the company‘s policy. The main aim of this project is to explore the relationships between the identified antecedents and dependent variables. To achieve this, a survey with items measuring all of the proposed constructs and demographic questions was developed. As the chosen constructs are relatively well examined in the prior studies, existing 26 scales of each construct are adopted with minor changes to suit the context for this study. Since convenience sampling was used for this study, potential systemic error was taken into consideration. To increase the content validity of the study and the reliability of the questionnaire, a pretest was carried out with 10 graduate students that had used Airbnb prior to the distribution of the final survey link. Minor changes including wording and questions sequencing were made as result of the pretest. Invitation letters were sent to the panel members of the specified database to obtain their agreement to participate in the study. Only participants who had experiences with Airbnb were qualified to take part. Since participants are rewarded by the research company, potential malice respondents were taken into consideration. To reduce the possibility of disingenuous data, two identical but opposite questions (Q12-1 ―I cannot trust Airbnb‖ and Q12-7 ―Airbnb is trustworthy‖) were integrated into the survey. A total of3,262 surveys were collected over a period of one month (mainly in January, 2015). 2,678 were screened out because they indicated they had not use Airbnb and a further 189 were eliminated because they either showed contradictions in answering Q12-1 and Q12-7, answered all questions the same, or skipped too many questions. Therefore, only 395 surveys were retained for the analysis of this study, yielding a 12.11% response rate. 4.2 Measurement of the Constructs The survey has a total of 24 questions, excluding demographic questions. All of the items used a five-point Likert scale from 1 to 5, rating from strongly disagree to strongly agree. Literature had supported that the reliability of the five-point scale 27 isstatistically significantly higher than that of the four-point scale. For instance, some studies found that construct validity may not be influenced by the midpoints (Adelson & McCoach, 2010; Kulas, Stachowski, & Haynes, 2008), whileothers suggest the omission of the midpoints may impair the validity (Johns, 2005). Moreover, the setting of midpoints allows participants to indicate that they do not care about this question rather than forcing them to choose to agree or disagree. In the social sciences, participants might not think the questions asked are important to them because researchers develop surveys mainly based on their personal knowledge. Therefore, based on the literature (Adelson & McCoach; Kulaset al.) and to reduce researcher bias, a five-point scale was chosen. The items were all adopted from the literature so as to be operationalized as this study was undertaken within a specific context (Airbnb). RI is a construct that has been frequently measured. Thus the measurement of RI was similar to the previous studies. Four items from Ramkissoon and Uysal (2011) were chosen to measure PA whilefive items to measure EWoM were adapted from Jalilvand and Samiei (2012). The items from Irani and Hanzaee (2011) to measure PS were adapted in the context of Airbnb. PV was measured using the items employed from Sweeney and Soutar (2001) while the items to measure PR were adapted from Forsythe et al. (2006).To increase the validity of the scales, both measurements of PV and PR only focused on one dimension. All validated measuring items are available in Table 3 (See Appendix for all items). 4.3 Data Analysis and Findings Various statistical methods were used to examine the relationships among the mentioned constructs. First, frequency analysis was conducted to summarize the demographic information of the sample via SPSS 22.0. Gerbing and Anderson (1988) 28 proposed a two-step procedure to analyze a proposed model. To follow Gerbing and Anderson‘s method, a confirmatory factor analysis (CFA) was employed to identify the validity of the measuring items via Amos 21.0. Third, structural equation modeling (SEM) was performed using Amos 21.0 to examine the model fit since it was a theoretical model.CFA was performed for this study instead of exploratory factor analysis (EFA) because all the latent constructs and the measurement items were employed from prior studies where they were empirically shown to be acceptable, reliable and valid. 4.3.1 Demographics of the Respondents Among the respondents, 52.2% are female and 46.8% are male. The age of respondents ranges from 18 to 75. More specifically, 31.9% are 46 years old and over, 17.7% are 35 to 45, 25.6% are 25 to 34, and 17.7% are under 25 years old. Most of them have university or higher education, accounting for 58.9% of respondents. 26.6% graduated from college/technical school, while 13.9% have high school or less. 42.8% participants chose a private room on their most recent trip with a comparable 40% choosing the whole house or apartment. 53.9% of Airbnb consumers stayed short term (2-4 nights). The main purpose of trips is leisure (66.6%) and they were travelling alone (21.8%) or with their partner (41%). 4.3.2 Scale Validity and Reliability The reliability of constructs was examined using composite reliability (CR). 29 According to Nunnally (1978), to achieve reliability of a construct, the Cronbach‘s Alpha should be greater than 0.7 for an existing scale(Nunnally & Berstein, 1994).However, Cronbach‘s Alphawas critiqued by Peterson and Kim (2013) as beingexplored as ‗a lower bound‘ to reliabilityand hence may not be efficient todemonstrate true reliability when this is a multi-factor model. They suggested CRas a popular alternative coefficient alpha, which is usually calculated as part of SEM. A CR value of 0.7 or higher suggests good reliability (Churchill, 1979; Hair, Anderson, Tatham, & Black, 1998) (Table 3). Among 28 scale items, 6 were found to have low loadings on their corresponding construct and therefore were discarded to obtain a better model fit. The CR values range from 0.664 to 0.836. Discriminant validity and convergent validity were also tested (Table 2). According to Fornell and Larcker (1981), when the square root of the AVE from a construct is larger than the correlations shared between the construct and other constructs in the model, then they are discriminant from each other. Convergent validity was achieved because all values of AVE are above 0.5 (Hairet al., 1998). Table 2 Validity test CR 0.836 PR 0.820 PA EWOM 0.826 0.664 PS 0.813 PV AVE 0.719 0.543 0.544 0.502 0.594 MSV 0.099 0.398 0.338 0.305 0.398 ASV 0.031 0.262 0.202 0.184 0.225 PR 0.848 -0.090 0.067 0.105 -0.315 PA EWOM PS PV 0.737 0.581 0.552 0.631 0.737 0.493 0.472 0.770 0.709 0.422 30 The CFA result indicated the research model is of adequate fit. According to Bentler (1995), the rule that the chi square/degrees of freedom (2/df) ratio being less than 5 (2/df = 1.911; 2=149.079; df = 78 were achieved for this model) is used to justify the sensitivity of chi-square to a large sample size. The Root Mean Square Error of Approximation (RMSEA) is 0.048, below the cutoff point of 0.08, indicating a good model fit (Hairet al., 1998). The Normed Fit Index (NFI) and Comparative Fit Index (CFI) revealed a goodness of model fit when they achieve higher than 0.90 where NFI = 0.941 and CFI = 0.971 were achieved in this model. Based on these indices, the model is concluded to be adequate fit. 31 Table 3 Confirmatory factor analysis for measurement model Items Perceived Authenticity Living in an Airbnb place represents local ways of life. Living in an Airbnb place represents the local community. An Airbnb place offers a feeling of real home for my trip. Living in an Airbnb place allows for interaction with the local community. Electronic Word-of-Mouth I often read other tourists‘ online reviews to know whether Airbnb makes a good impression on others. To make sure I choose the right Airbnb place, I often read other tourists‘ online reviews I often consult other tourists‘ online reviews to help choose a good Airbnb place. I frequently gather information from tourists‘ online reviews before I choose to book an Airbnb place. Price Sensitivity I am more willing to purchase the Airbnb place if I think it is cheaper than a hotel room. In general, the price or cost of purchasing an Airbnb place is important to me. Perceived Value Airbnb places are reasonably priced. Factor Loadings Composite Reliability 0.543 0.820 AVE 0.871 0.846 0.801 0.707 0.544 0.826 0.842 0.818 0.810 0.769 0.502 0.6649 0.861 0.861 0.594 0.813 0.883 9 Unpredictable factors may had affected the results of PS, however, evidence show that PS is a reliable construct and have significant effects on PV and RI. The overall CR is 0.664, just below the standard cut point of 0.7, but considering all the other factors, e.g. the item such as “ In general, the price of purchasing Airbnb accommodations is important to me”, shows an average score over 4 in the dataset, we consider this construct is acceptable and should not be deleted. 32 Airbnb places offer value for money. Airbnb places are good products for the price. Perceived Risk I may not successfully get into the house. I cannot examine the quality of the Airbnb place. Repurchase Intention I will purchase rooms via Airbnb again 0.842 0.829 0.719 0.836 0.91 0.79 0.70 33 4.3.3 Structural Model Analysis This study set out to explore the relationships between the extrinsic factors, PV, PR and RI. Therefore, the proposed model was examined using the SEM method after a curve estimation was done for all the relationships. All were statisticallylineal to be tested in the variances used in SEM. Common method bias was examined through a common latent factor. No significant change of the loadings was found when a common latent factor was added to the model, indicating that no obvious common method bias existed in this study. The result of the SEM analysis is shown in Figure 2. The RMSEA is 0.071, below the cutoff point of 0.08, indicating a good model fit (Hairet al., 1998). The 2/df ratio of 2.976 (2=147.193; df=81), which is between 1 and 3, indicates a good adjustment of the sensitivity of chi-square to a large sample size (Bentler, 1995). GFI is 0.925, which is close to the suggested point of 0.95, and AGFI is 0.883.Therefore, the measurement model showed satisfactory goodness-of-fit indices. 34 Notes: ***p<0.001; **p<0.01; *p<0.05; ns = not significant. Figure 2 Structural path coefficients In terms of the hypotheses tested, only H4a and H6b were not supported but all of the remaining ten hypotheses were statistically significant. The hypothesis test results are shown in Table 4. This indicates that the influence of PA on RI was fully mediated by PV and PR. However, the mediating effect of PR between PS and RI was not supported. This may because a lower price alone cannot alleviate the risks Airbnb consumers perceived but can enhance their value perceptions towards the next Airbnb transaction (Kwun & Oh, 2004). The standardized estimates for the following mediating effects ranged from 0.019 to 0.129, and all p-values are significant (p<0.005), suggesting that the links of 35 extrinsic factorsPV/PRRI are true as theorized. Based on the results, it is confirmed that there were mediating effects between the extrinsic factors and RI except for PS through PR: 1) 2) 3) 4) 5) PAPVRI; EWOMPVRI; PSPVRI; PAPRRI; and EWOMPRRI. Table 4 Results of hypothesis tests Standard Regression weight H1: PRPV -0.318*** H2: PVRI 0.172* H3: PR RI -0.416*** H4a: PA RI 0.069 H4b: PA PV 0.406*** H4c: PAPR -0.712*** H5a: EWOMPV 0.166*** H5b: EWOMRI 0.144** H5c: EWOMPR -0.148** H6a: PSPV 0.153** H6b: PSPR 0.023 H6c: PS RI 0.186** Notes: ***p<0.001; **p<0.01; *p<0.05 Hypotheses Support Yes Yes Yes No Yes Yes Yes Yes Yes Yes No Yes 36 CHAPTER 5: CONCLUSION The main purposes of this study were to examine the proposed model as well as the factors that influence Airbnb consumers‘ RI. In particular, this research focused on identifying the effects from the extrinsic factors on PV, PR and RI. The results of this study indicate that Airbnb consumers‘ sensitivity level to price does not reduce their PR, but their PA and peers‘ comments do. PS was found to not have significant effects on PR but on PV and RI. This makes sense because consumers‘ sensitivity level to price may enhance PV therefore increase RI, but would not necessarily significantly reduce their PR of repurchasing the Airbnb products.Many other factors like the credibility of the sources would have influence on the effects from PS on PR. As Grewal, Gotlieb, & Marmorstein (1994) suggested, the significance level of the relationship between price and PR depends on how the advertised information is communicated. In other words, a cheaper price alone may not necessarily help to relieve Airbnb consumers‘ PR regarding the next transaction with Airbnb. Moreover, according to the evidence provided by Aqueveque (2006), although the abatement effect from price on PR is generally expected, there is exception when the product was regarded as a private consumption, and was not creditably recognized by the public. That is to say, it is possible to presume that PS is to some extent irrelevant in terms of its effect on PR estimation as Airbnb is a relatively new platform and transactions with Airbnb normally are private consumptions. However, consumers‘ sensitivity level to price was found to significantly improve their PV of Airbnb products. In accord with prior studies like 37 Guttentag (2013) who found low cost is the main draw for people participating in Airbnb, this finding empirically proves that consumers‘ sensitivity of price is a critical factor that enhance consumers‘ PV. Another interesting finding is that PA seems to be a powerful way to enhance PV as well as reduce PR of Airbnb consumers. One possible explanation for this strong effect from PA is that, Airbnb consumers that repeatedly stay with Airbnb are not just concerned about the price, but actually seek the authentic local experience more. This result is in line with previous studies (Lunardo & Guerinet, 2007; Ramkissoon & Uysal, 2011; Yannopoulouet al.,2013) and therefore, PA can be considered the most important factor that affects the PV and PR of Airbnb consumers. Finally, the initial model exploring the relationships between PV, PR and RI was found to be statistically supported. The negative influences of PR were found not only on PV, but also on RI. Therefore, finding a way to reduce customers‘ PR would be effective because it would increase PV and RI at the same time. In conclusion, the findings of this study can be valuable for tourism researchers as well as the industry professionals in terms of understanding the Airbnb consumers‘ repurchasing behavior as well as changes in tourist demand regarding seeking ‗authentic‘ accommodations for their travels. 5.1 Implications Airbnb is currently a hot topic in the hospitality industry and the peer-to-peer economy because it has shown rapid growth since 2012. A major strength of this study is that it researched current Airbnb consumers, offering a reliable explanation for the 38 main factors that influence their intention to keep choosing Airbnb. Both academic implications and managerial implications are revealed. Academically, this study extends the application of tourism related factors (PA)to the analysis of online consumer behavior studies. The results indicate that PA plays a critical role in enhancing Airbnb consumers‘RI by reducing their PR and increasing their PV. Applying these constructs in a new setting also helps to enrich the literature. Specifically, the exploration of the concept authenticity provides significant insights for the tourism literature. Distinguished from objective authenticity, which refers to the ‗genuineness or realness‘ of things (Steiner &Reisinger, 2006), this study reveals the effects of existential authenticity on consumer behavior, which emphasize the human nature. Therefore, it is shown that the essence of human individuality should not be neglected in academic and market research. Second, the findings show that PR negatively influences PV and RI but PV positively influences RI. This proves that the relationships between PV, PR and RI are as suggested in prospect theory and MEC theory, confirming the effectiveness of this framework. This study proves the validity of the initial model and the relationships between PV, PR and RI. Therefore, it may be utilized as an initial model when applied into different context. Investigating the mediating role of PV and PR may provide a relative comprehensive understanding of the factors that influence RI. Several useful implications for practitioners who are interested in enhancing the value of their accommodations by marketing the authentic experience are indicated through the results of this study. First, tourists tend to seek an authentic 39 accommodation experience, and desire to connect to the locals by living in Airbnb properties. This would be important for Airbnbas well as hotel managers asit shows that tourists tend to seek local experiences by living in the local community, which can significantly influence their PV and PR for the forthcoming rebuying behavior. Industry managers can try to reconcile the elements of authenticity in their future marketing strategy. At the same time, while offering great service to the customers, hotel managers should also consider how to fulfill the customers‘ PA needs. Second, consumers‘ sensitivity to price may not significantly reduce their PR according to our findings but can improve their PV and intentions to repurchase. This can be valuable to the industry professionals when dealing with price strategy. Last but not least, eWoM plays a significant role in terms of its effects on all three constructs of the initial model (PV, PR and RI). Therefore, keeping a good response to the online reviews is recommended for industry managers. 5.2 Limitations and Future Research Direction The sample is limited to consumers that used Airbnb before and who reside in Canada or the United States. Individuals who have not stayed with Airbnb may have different perceptions about the platform and they may have different experiences with Airbnb, either with the hosts, or with the Airbnb company. Therefore, the results should be interpreted as only explaining the majority of Airbnb consumers rather than all individuals. Second, the results may have been influenced by common method bias. Although tests were conducted to examine for this bias, potential bias from the 40 researcher in developing the survey still exists. However, several methods like content analysis and pretest were done to reduce it as much as possible. Third, this study only focuses on one dimension of the construct PV and PR, whereas they are regarded as multidimensional constructs. Future studies should try to measure different dimensions and compare the differences with this model, as well as other geographic areas to extend the generalizability of the model. Finally, future studies should also consider whether there is a comparatively higher rate of sharing accommodation experiences on social network sites for Airbnb consumers. This can be compared to other consumer groups, e.g. five-diamond hotel consumers within the same area. 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Journal of Global Business Management, 6(2), 1-7. 51 Appendix All items used in survey Items Perceived Authenticity Living in an Airbnb place represents local ways of life. Living in an Airbnb place represents the local community. An Airbnb place offers a feeling of real home for my trip. Living in an Airbnb place allows for interaction with the local community. Electronic Word-of-Mouth I often read other tourists‘ online reviews to know whether Airbnb makes a good impression on others. To make sure I choose the right Airbnb place, I often read other tourists‘ online reviews I often consult other tourists‘ online reviews to help choose a good Airbnb place. I frequently gather information from tourists‘ online reviews before I choose to book an Airbnb place. If I don‘t read tourists‘ online reviews when purchasing an Airbnb place, I worry about my decision. Price Sensitivity I don‘t mind paying more to try and stay in an Airbnb place. I am less willing to purchase the Airbnb place if I think that it will be expensive. I am more willing to purchase the Airbnb place if I think it is cheaper than a hotel room. Factor Loadings Standard deviation Mean 0.871 0.846 0.801 0.835 0.798 0.785 4.02 4.06 4.10 0.707 0.770 4.02 0.842 0.883 4.17 0.818 0.806 4.34 0.810 0.99 3.99 0.769 0.816 4.08 0.532 1.224 3.61 0.540 0.941 3.36 0.746 0.949 3.67 0.861 0.849 4.05 52 A good lodging experience with Airbnb is worth paying a lot of money for. In general, the price or cost of purchasing an Airbnb place is important to me. Perceived Value Airbnb places are reasonably priced. Airbnb places offer value for money. Airbnb places are good products for the price. Airbnb places are economical. I enjoy living in Airbnb places. Airbnb places have a consistent quality. Living in an Airbnb place would help me make more friends. Perceived Risk I cannot trust Airbnb. I may not successfully get into the house. I cannot examine the quality of the Airbnb place. I may have problems when living in a stranger‘s house. It‘s too complicated to use Airbnb. Repurchase Intention I will purchase rooms via Airbnb again 0.440 0.948 3.35 0.861 0.769 4.10 0.883 0.842 0.829 0.736 0.730 0.497 0.607 0.688 0.666 0.676 0.874 0.773 0.934 1.057 3.93 4.01 4.01 3.95 4.11 3.55 3.65 0.690 0.910 0.790 0.708 0.613 1.081 0.833 0.960 1.038 1.214 1.85 2.58 2.88 2.75 1.99 0.70 0.909 4.09 53
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