Which Appeal is Important to Buy Airbnb?
Purpose: This study aims to understand guest’s purchase decision in Airbnb from the perspective of Aristotle’s appeals. In host-created information, which information appeals are significantly considered by guests is investigated.
Design/methodology/approach: It is hypothesized that guest’s purchase would be affected by host-created information’s ethos (super host badge, ID verification, host review), pathos (use of emotional and social word), and logos (price, occupancy, safety feature, place picture, star-rating).
Findings: For ethos, super host badge and host review have positive impacts on the purchase; for pathos, a positive impact of use of social word is significant. For logos, it is examined that price, place picture, and star-rating have positive impacts but occupancy has a negative impact on the purchase.
Research limitations/implications: The dependent variable, the number of place reviews, could not represent the exact number of purchases. Other possible influential factors, such as direct communication between hosts and guests, are not regarded.
Practical implications: The findings suggest specific guidelines for Airbnb and its host users. Specifically, the management of normal host users is indicated as a necessary process for Airbnb’s development. For host users, several guidelines, how to attract more guests effectively, are provided.
Originality/value: Different from other studies about Airbnb, various information appeals are considered in holistic perspectives and each information appeal’s impact on sharing behavior is understood based on unique theoretical model, Aristotle’s appeals.
Keywords: Sharing economy, Aristotle’s appeals, Peer-to- peer accommodation, Airbnb, Smart tourism
Paper type: Research paper
Sharing economy has been continuously emerging and created substantial impacts in various industries (Tussyadiah and Park, 2018). Especially, the novel business model has generated a higher economic and social impact in the hospitality and tourism industry (Tussyadiah and Pesonen, 2016). A leading sharing economy business, Airbnb provides millions of accommodations in 65,000 cities in about 200 countries at a global scale and its value is estimated at 10 billion US dollars (Gunter, 2018). As Airbnb has become a major player in the hospitality and tourism area, it has been one of top priority research topics in the field (Liang et al., 2018).
Different from products of conventional accommodation service, products of Airbnb are individuals’ private places, thus it is much more difficult to get prior knowledge for potential travelers (Fagerstrøm et al., 2017). Although hotels’ experiential products are also hard to be pre-tasted, people have general knowledge about hotel products based on their past experiences, hotel brands, or hotel images. However, Airbnb’s idiosyncratic places are almost impossible to be anticipated in terms of their qualities even potential travelers have past experiences using Airbnb (Wang et al., 2016). This situation makes the communication between hosts (individuals lending their places) and guests (individuals borrowing hosts’ places) highly important in Airbnb, because the sharing transaction is processed primarily based on the communication. By considering the information about both places and hosts created by hosts themselves (host-created information), guests search and decide places to stay, indicating that host-created information is one of the core factors in Airbnb system (Ert et al., 2016). Thus, many studies have attempted to investigate regarding the communication process in Airbnb with host-created information, but most cases have focused on specific parts of the information, such as price (Wang and Nicolau, 2017) or host profile (Fagerstrøm et al., 2017). However, how the whole host-created information is delivered and perceived has been scarcely investigated.
Therefore, this study tries to examine how various information appeals in host-created information are communicated in Airbnb to further understand communication process in sharing economy. Specifically, it is examined that which appeals in host-created information are significantly influential on guest’s decision making. Based on Aristotle’s appeals, general aspects of host-created information in Airbnb are empirically analyzed.
Sharing economy and Airbnb
Sharing economy is defined as “peer-to-peer (P2P) based activity of obtaining, giving, or sharing the access to goods and services, coordinated through community-based online services” (Hamari et al., 2016). Social media have enabled the emergence of P2P online networks as well as social sharing (Priporas et al., 2017). Airbnb is a successful example of the sharing economy business. The sharing economy website for short-term rentals was founded in 2008 (Libert et al., 2014). By enabling people to easily lease and rent short-term lodging with their own living spaces, Airbnb has brought an impact of sharing economy on the hospitality and tourism business field (Morgan, 2011). With affordable prices and highly various locations and types of lodgings, Airbnb has become a major player in the industry and been expanding its business area to relevant fields, such as airline (Rizzo, 2018). Currently, Airbnb accommodates about 5 million lodging listings in all over the world and facilitates over 260 million check-ins in average (Airbnb, 2018). Since sharing economy is a P2P based network, its value is created, distributed, and consumed by users and this makes the interactive communication between users indispensable to sharing economy (Xie and Mao, 2017). Lampinen et al. (2013) found communication flow between users is important for successful online sharing service. Thus, communication in sharing economy website is performed with the provider’s informational message and the recipient’s response (Poon and Huang, 2017). In Airbnb, hosts introduce their places and themselves through information messages (i.e. host-created information) and potential guests make decisions by evaluating the places and hosts based on the host-created information (Ert et al., 2016). Given the importance of communication process in Airbnb, it has been great interests of researchers (Xie and Mao, 2017). However, existing studies have usually focused on specific appeals, such as price (Gibbs et al., 2017; Wang and Nicolau, 2017) and host profile (Gunter, 2018; Tussyadiah and Park, 2018; Wu et al., 2017). Up to now, the role of host-created information has been partially understood. To address this research gap, this study examines the influences of information appeals in host-created information in Airbnb to suggest the basis of information.
Aristotle’s appeals in online information messages
As social media has been prevalent in most activities of daily life, the wide adoption of social media and its online information have generated substantial impacts on individuals and businesses (Colicev et al., 2018). Thus, the influence of social media information has been an important research topic of great interest and recognized in various contexts (Thakur and Hale, 2018). By examining the information’s significant impact on individual’s perception and behavior in pre-consumption (Swani et al., 2017), consumption (Chen et al., 2017) , and post-consumption stage (Nadeem et al., 2015; VanMeter et al., 2015), it has been acknowledged that the information communicated in online platforms has a large impact and, thus, it is important to understand how the online information could be influential in stimulating, persuading, and inspiring people (Yang et al., 2018).
Aristotle’s appeals is a proper framework to analyze the persuasive influence of information (Otterbacher, 2011). According to Aristotle’s appeals (Ramage et al., 2015), interpersonal messages can be persuasive and powerful through the three components: ethos, pathos, and logos (Xun and Reynolds, 2010). First, ethos is an ethical appeal standing for all the proofs about message senders’ authority and credibility. Second, pathos is an emotional appeal to the recipient. Finally, logos is a rational appeal to recipients. Usually facts, figures, and examples are used to influence the recipients’ perceptions of the messages as reasonable. Xun and Reynolds (2010) showed to persuade the readers by mixing ethos, logos, and pathos at online forum as if they spoke in offline. Otterbacher (2011) compared with logos, ethos, and pathos in the online review communities, demonstrating ethos more in the reviews required for experiences and references, pathos more in the reviews for daily necessaries, and logos in the most prominent reviews. Bronstein (2013) revealed that the candidates for President had exposed their identities to the voters with emotional and synchronous approach using Aristotelian language of persuasion in SNS. All of the studies confirmed that the propositions of Aristotle’s appeals were supported in online communication by identifying the significance of online information messages to the users’ reactions (Bronstein, 2013). However, it has not been extensively applied to the communication process in sharing economy despite of the importance of understanding the persuasive impacts of information messages.
RESEARCH MODEL AND HYPOTHESES
In case of Airbnb, the main platform is its online website and sharing transaction is processed primarily based on communication between users (Ert et al., 2016). Individuals who want to share their places join Airbnb and become hosts by registering their places. During the registration, potential hosts input various information about their places and about themselves. Then, guests search, evaluate, and select places they want to stay based on the host-created information. Since there are no information sources for getting to know places other than the host-created information, such information would be highly important in guest’s decision making (Chen and Xie, 2017). Based on the highly expected importance of host-created information, a general proposition is suggested which assumes the significant influence of host-created information on guest’s purchase decision in Airbnb. The various information appeals’ influences are empirically examined based on Aristotle’s appeals which explain which information appeal is effective in persuading people (Scott, 1967).
In this research, host-created information is defined as information available in Airbnb which is written by hosts to convince potential guests to choose their places. In Airbnb, various information appeals are available in each host-created information. Based on the Aristotle’s appeal, we categorize these as three dimensions: (1) ethos, (2) pathos, and (3) logos.
/* Insert Fig. 1 here */
In case of dependent variable, this research uses the number of times places were shared. In Airbnb, only the guests who shared places can post reviews, so the number of reviews about places indicates the number of times the places are shared. The research model is depicted in Fig.2.
/* Insert Fig. 2 here */
Ethos in host-created information
Ethos symbolizes message sender’s credibility. If messages are from trustworthy sources, the messages tend to be more influential (O’keefe, 1987). In online communications, source’s credibility is related to user’s reputation perceived by the other users as a signal of trustworthiness (Slee, 2013). Many researchers verified the impact of website user’s reputation on other user’s reactions (Liu and Park, 2015) and online behaviors (Jin and Phua, 2014). In addition, several studies proved the persuasive impact of message sender’s credibility in online information based on Aristotle’s appeals (Yang et al., 2018). In Airbnb, users can establish their reputation with three types of proof: super host badge, ID verification, and host review.
Since 2014, a super host badge system has been existed in Airbnb. According to Airbnb, a super host badge is awarded to the “experienced hosts who are passionate about making your trip memorable” (Airbnb, 2017). There are four requirements to get a super host badge:
(1) Complete at least ten stays in a year,
(2) Achieve an over than 4.8 ratings out of five ratings,
(3) Respond within 24 hours at least 90% of the time
(4) No cancellations of confirmed reservations without extenuating circumstance
Once these requirements are met, the badge is awarded automatically to focal hosts. Also, the requirements are re-confirmed every year, so if there any changes, the status is updated (Gunter, 2018). To guests, a super host badge could be perceived as a reliable indication of host experience and commitment, and also represent quality of staying or place. It has been found that guests are willing to pay higher price for sharing to super hosts (Liang et al., 2017) and that the prices of places of super hosts tend to be higher (Wang and Nicolau, 2017). Based on these studies, it could be expected a super host badge is a reliable signal showing host’s credibility and place’s quality.
H1-1: Super host badge will have a positive impact on guest’s decision making.
ID verification indicates whether the host submits his or her own personal information to Airbnb. If users 1) submit government-issued ID, 2) connect their Airbnb accounts to other online accounts such as Google, Facebook, and 3) upload a profile photo, phone number, and email address, Airbnb gives an ID verification badge. Although the ID disclosure is kept private, the signal that this host verified his or her ID can improve guest’s perception about the host (Racherla and Friske, 2012). Previous studies demonstrated the positive reaction of user aroused by ID disclosure of message provider. Since revealing personal information in online environments can reduce uncertainty (Tidwell and Walther, 2002) and enhance source credibility (Sussman and Siegal, 2003), information representing the ID disclosure of message provider is a crucial factor in the recipient’s perception. Hence, ID verification can be an evidence of host’s credibility.
H1-2: ID verification will have a positive impact on guest’s decision making.
As mentioned above, the number of reviews indicates how many times the host has accommodated guests, because only the guests who actually stayed in the places could post reviews. In other words, host review can indicate host’s hosting experience (Weiss et al., 2008). Guests would prefer stay in the places guided by more experienced hosts, and Tussyadiah and Park (2018) found that more qualified, skilled, and experienced hosts are more positively perceived.
H1-3: Host review will have a positive impact on guest’s decision making.
Pathos in host-created information
Pathos indicates the elements conceiving message recipients emotionally and this emotional appeal is powerful in terms of its ability to persuade (Xun and Reynolds, 2010). Airbnb hosts have to write summaries about places or themselves in their own words, so guests are able to perceive emotional or social stimulus through hosts language uses in the descriptions. According to Tussyadiah and Pesonen (2016), one of the main reasons for using Airbnb is that guests want to get opportunities to explore local life by communicating with hosts. Hence, they would prefer the places whose hosts are emotional, social, and friendly and such characteristics could be reflected by their writing styles (Ludwig et al., 2013). Tussyadiah and Park (2018) found that Airbnb user’s intention to book is higher when hosts describe themselves as people willing to meet new people in host-created information.
H2-1: Use of emotional word will have a positive impact on guest’s decision making.
H2-2: Use of social word will have a positive impact on guest’s decision making.
Logos in host-created information
Logos persuades people with reasoned discourse. In case of product choice situation, consumer’s rational thought is mostly involved with the product awareness (Xun and Reynolds, 2010). In host-created information of Airbnb, various objective information about place is provided: price, occupancy, safety feature, place picture, and star-rating and these accommodation characteristic information is important for guest’s decision making (Yang et al., 2018). In Airbnb, price means the price per night. It is decided by hosts. As examined that generally lower prices of places compared to conventional hotel rooms are the major competitiveness of Airbnb (Guttentag, 2015), Airbnb users would expect lower costs for their staying and select the places of affordable prices (So et al., 2018).
H3-1: Price will have a negative impact on guest’s decision making.
In Airbnb, occupancy refers to the maximum number of spaces available, so higher occupancy signifies the more space. Lu and Zhu (2006) indicated that guests usually consider room size as a crucial factor in the quality of accommodation facility.
H3-2: Occupancy will have a positive impact on guest’s decision making.
Airbnb promotes hosts to equip basic safety features in their places by providing the information about the number of safety features in places. Specifically, smoke detectors, carbon monoxide detectors, first aid kit, safety card, fire extinguisher, and lock on bedroom door are listed as desirable safety features in Airbnb. Hosts can list the features they installed in their places and the equipped features are shown in host-created information. Safety could be more important in Airbnb than in hotel, because each place of Airbnb is highly different in terms of its quality and safety accidents are usually outside the pale of platforms in the market (Richard and Cleveland, 2016).
H3-3: Safety feature will have a positive impact on guest’s decision making.
Place picture indicates the picture of place uploaded. Hosts in Airbnb are able to upload photographs of their places as many as they want. When people search intangible products to purchase, pictorial information can be more effective means to show the products’ qualities (Jin and Phua, 2014).
H3-4: Place picture will have a positive impact on guest’s decision making.
Airbnb guests can evaluate the places they stayed with star-rating. In Airbnb, the guests stayed specific places are encouraged to appraise the whole stay experience by giving star point from one-star to five-star. In online communities, peer evaluation has been widely used to give helpful information for other users’ decision making (Tsao, 2018). Lee et al. (2015) examined that star-rating of places is an important factor to sales of place in Airbnb.
H3-5: Star-rating will have a positive impact on guest’s decision making.
Table 1 and Figure 1 indicate each variable’s description. Super host badge and ID verification are measured by checking whether each host has the badges. Host review is measured in the number of reviews which each host has received.
Use of emotional and social word are measured by linguistic inquiry word count (LIWC). LIWC is an automated word analysis software providing content analysis results based on 70 preset linguistic categories (Tausczik and Pennebaker, 2010). In case of emotional words, the words describing individual’s emotion are considered, indicating how much they are emotionally oriented. In case of social words, the words explaining social relations are included, showing how much they are socially oriented (Tausczik and Pennebaker, 2010). By calculating the proportion of the number of focal categories of words appeared in the whole text, LIWC could be indicate what kinds of tendencies, inclinations, or personalities the writers have (Mehl et al., 2006). Over 100 studies have applied the approach in various context (Cohn et al., 2004; Humphreys, 2010; Ludwig et al., 2013) and confirmed its reliability and validity. Thus, this research measures use of emotional and social word by adopting the resulting values of LIWC’s analyses of the textual descriptions in host-created information (Lee and van Dolen, 2015).
Price, occupancy, and star-rating are measured in numbers as shown on host-created information. Similarly, safety feature and place picture are counted in numbers as shown on host-created information. Finally the dependent variable, actual purchase, is measured in the number of place reviews. The number of place reviews can represent the number of guests who actually stayed the place because only the guests who actually stayed in the place can write reviews in Airbnb. Thus, the number of place reviews could indicate the number of times the place has been shared.
Airbnb is selected as a data source. From December 12, 2015 to December 24, 2015, 854 host-created information pertained to places in Bangkok, London, and New York, which are ranked as top global destination cities in Asia Pacific, Europe, and the US, was collected (Hedrick-Wong and Choong, 2015). At the Airbnb website, places are searched by location, without date or other filters. The 306 places were searched for each city; 64 rooms are excluded because some are duplicated results and others’ host-created information is not written in English. Finally, 291 cases about places in Bangkok, 288 in London, and 275 in New York are submitted to data analysis.
DATA ANALYSIS AND RESULTS
The aim of this study is to examine the influences of various appeals in host-created information on user’s purchase decision in Airbnb, so empirical impacts of different appeals in host-created information on guests’ decision making were investigated in Airbnb context. For this goal, a tobit regression model was used because of the censored nature of dependent variable (Qazi et al., 2016). The distribution of dependent variable was skewed to the left and its observed value was within a certain range and censored. When the dependent variables have these features, Ordinary Least Square (OLS) analysis results in the bias and inconsistent estimates. Tobit model has been known as the way to overcome these problem, because it is a regression model with a censored variable and with a non-negative dependent variable. The second reason was to resolve selection biases. The dependent variable in this research is a proxy variable for an actual purchase, which is not the exact number of sharing transactions. The number of actual purchases for the place is possible to be much more than the number of place reviews. As such, our sample has inherent selection biases and tobit model has been recognized as an effective solution for such select biases (Qazi et al., 2016).
Table 2 presents the descriptive statistics for variables. Table 3 shows the correlation coefficient between variables. The highest coefficient (coef.) was 0.32 indicating that singularity and multicollinearity were not problems with our data set (Tabachnick and Fidell, 2007). In addition, tolerance and the variance inflation factor (VIF) were also checked to assess multicollinearity. The tolerance ranged from 0.81 to 0.98 and VIF ranged from 1.02 to 1.23. Thus, it was confirmed that there is no evidence of multicollinearity.
/* Insert Table 1 here */
/* Insert Table 2 here */
Tobit model was proposed with 12 hypothetical relationships as follows.
Tobit Model: Actual purchase (Number of place reviews) = dummySH + dummyID + β1*HR + β2*Emotional + β3*Social + β4*Price + β5* Occupancy + β6*Safety + β7*Picture + β8*Rating + ε
dummySH = whether the host is a super host
dummyID = whether the host performed ID verification
HR = the total number of reviews the host received
Emotional = LIWC Index of use of emotional word in a summary describing the places or hosts
Social = LIWC Index of use of social word in a summary describing the places or hosts
Price = price of the place per night
Occupancy = the maximum number of people can be accommodated in the place
Safety = the number of safety features equipped in the place
Picture = the number of pictures about the place
Rating = the average evaluation about the place made by the experience guests
ε = Random error
Table 4 summarizes the result of our model (log likelihood = -3708.71). All variables explained about 3.2% of the variance in the dependent variable (Pseude R2 = 0.032).
Ethos: Except ID verification (coef. = 0.32), the positive impacts of super host badge (coef. = 9.32, p-value < 0.01) and host review (coef. = 0.02, p-value < 0.001) were significant. Thus, while H1-1 and H1-3 were accepted, H1-2 was rejected.
Pathos: Although the positive impact of use of social word was significant (coef. = 0.34, p-value < 0.05), use of emotional word was not significant (coef. = -0.18). Hence, in case of hypotheses about pathos appeals, only H2-2 was accepted.
Logos: Out of five variables, only safety feature (coef. = -0.44) was not significant. Among the significant variables, while price (coef. = 0.07, p-value < 0.01), place picture (coef. = 0.32, p-value < 0.001), and star-rating (coef. = 6.28, p-value < 0.001) were positively significant, occupancy (coef. = -4.12, p-value < 0.01) was negatively significant. As a result, H3-3 was rejected because of insignificant impact, but H3-1 and H3-2 were rejected because the observed significant impacts have opposite directions to hypothesized directions. Other than the three hypotheses, H3-4 and H3-5 were accepted.
/* Insert Table 3 here */
/* Insert Table 4 here */
This study hypothesizes that various information appeals of host-created information have significant effects on sharing behavior in Airbnb. Specifically, three categories of appeals would be expected to relate to guest’s actual purchase because guest’s decision making of Airbnb would be affected by host-created information.
On the one hand, Airbnb hosts who had super host badges and more reviews sold more products. The significant impacts of super host badges and host reviews have been also examined in previous studies (Wang and Nicolau, 2017). A super host is an appellation given to a very small number of hosts. Therefore, the places of super hosts tend to be more purchased because they looked more credible. In addition, since having more host reviews implies more experience, guests are more likely to choose the hosts with more reviews. Furthermore, Airbnb where people purchase unknown accommodation products from unfamiliar suppliers is based on a review mechanism for the sake of trust (Guttentag, 2015). The results confirmed the importance of a role of host review in Airbnb. On the other hand, unexpectedly, verifying ID had no significant effect on the dependent variable. The insignificance of ID verification is along with previous studies of Airbnb (Teubner et al., 2016) and online communities (Racherla and Friske, 2012). This could be attributed to a large number of users who verified their ID. ID verification of a host was common among Airbnb hosts. This may make ID verification have no discrimination in deciding trust of a host and have no impact on the purchase decision (Teubner et al., 2016).
In case of Pathos, the more social words were used to describe hosts and places, the more likely to be selected. Emotional words however, had no impact on the purchase. To Airbnb guests, sociable hosts introducing themselves and their places with more social words were attractive, but emotional hosts were not highly preferable. In Airbnb, unlike hotels, guests demand local experiences (Guttentag, 2015). As such, social appeal is a crucial factor in guest’s choice. Tussyadiah and Park (2018) found the significant influences of social appeals of hosts in the Airbnb context. The following sentences are the actual examples which hosts described their places or themselves socially: “Hello! Happy to hear from you! I and my husband delight to offer this lovely SINGLE ROOM on your consideration,” “We’re excited to provide a warm welcome, and a clean, secure, and comfortable stay while you are visiting New York.,” “We are creating this space for like minded people, Travellers, expats, explorers, students… people who enjoy a company of local and intertiol hosts who know bangkok well.” In case of emotional words, as hosts try to show themselves and their places attractively to guests, most words would be related to positive terms. According to relevant studies investigating impacts of emotional online contents on individual’s perception, negative information tends to be perceived as more persuasive than positive information (Lee et al., 2017). This negativity bias has been also found in Airbnb context, indicating that negative reviews have significant effects on decreases in host’s reputation (Abramova et al., 2015). In these regards, the emotional word-laden information tend to be positive emotional word-laden one in Airbnb and, hence, the host-created information trying to present the places as attractive would be difficult to be trustworthy or authentic.
Lastly, logos is appeal to recipient’s logic. This logos appeal is assumed to be an objective figure or obvious characteristic (Bronstein, 2013). Especially, because places in Airbnb have no precise criteria unlike existing products, it is particularly important to describe what features the products have (Tussyadiah and Zach, 2017). Accordingly, this study took into account price, occupancy, safety feature, place picture, and star-rating as logos appeals. According to the study findings, the higher price, the more place pictures, the better star-rating, the lower occupancy increase the attractiveness of place. The impact of price, place picture, star-rating and occupancy on actual purchase in this study is consistent with the literature arguing that objective characteristics of a product influence the theoretical judgment of recipients (Garvin, 1984). Also, the impact of price, place pictures, and star-rating have been demonstrated in some previous Airbnb studies (So et al., 2018; Jin and Phua, 2014). One of the most important factors in purchasing an accommodation is price. When compared to hotels, Airbnb was competitive in its prices (Tussyadiah and Pesonen, 2016). However, this study found that the higher the price, the more likely the product was to be purchased. A higher price may imply higher quality even though some people prefer a less expensive product (Lichtenstein et al., 1993). Furthermore, Airbnb users have a difficulty in identifying the quality of shared economic goods. Therefore, the guests would have tried to determine place’s quality according to the price because they were unable to know the real quality. Such situation also increases the importance of visual evidences about products in Airbnb. Therefore, it is supposed that higher prices and more pictures have significant effects on the purchase because the characteristics of products in sharing economy are understood differently from those of products in the traditional economy. The impact of star-rating can be understood in relation to the mechanism of sharing economy platform. In order for the sharing economy to earn trust, it has a review mechanism (Guttentag, 2015). An online review has a close relation with consumer attitudes, evaluation, and ratings (Liu and Park, 2015; Hlee et al., 2018). In light of this, the star-rating has a significant impact on the purchase in Airbnb. Interestingly, Chen and Rothschild (2010) found the positive impact of place size information, the current results showed that the lower occupancy tends to be more selected by Airbnb guests. The reason for the negative impact of occupancy on the actual purchase is as follows. According to Lu and Zhu (2006), the size of the accommodation was assumed to represent the standard of accommodation facility. The results showed that the smaller the accommodation, the more reservations it had. That is, users of peer-to-peer accommodation prefer smaller rooms to larger ones. In comparison with the reservation for one or two persons, it is not easy for guests to decide to purchase when the number of persons for staying is to increase because there will be more to consider accordingly. This may be the reason for the result showing that the fewer the number of occupants, the higher the chance of purchase. Lastly, safety feature was found to have no influence on the purchase. This result is different from the finding of previous research of Airbnb, safety and security issues are significant for guest satisfaction (Birinci et al., 2018). However, the previous case adopted a survey approach and sampled through Mechanical Turk without screening questions such as ‘have you ever used Airbnb?’ (Birinci et al., 2018). Thus, current research result could be more reliable in that actual behavioral data in Airbnb have been used. In Airbnb, a host can list a maximum of six basic safety features. Therefore, the number of safety features is seen to have no meaningful impact on the purchase because it can only show basic details for safety.
CONCLUSIONS AND IMPLICATIONS
This research has several theoretical and practical implications. As for theoretical implications, this research could contribute to the literature on Airbnb and sharing economy, because it tries to address existing studies’ limitations. Although many previous cases have attempt to study information communication between users in Airbnb by recognizing its higher importance, only fragmentary investigation has been performed and a partial understanding has been provided, such as the impact of price information or host profile (Ert et al., 2016; Fagerstrøm et al., 2017; Tussyadiah and Park, 2018; Wang and Nicolau, 2017). In the sharing economy context, since the information messages available in online platforms are usually the only sources for checking products, interacting with others, and making decisions, individuals tend to consider various components and aspects of information messages (Chen and Xie, 2017; Gibbs et al., 2017). Thus, to fully understand the communicative role of information in sharing economy, a holistic perspective is required rather than a partial focus. In these regards, this research focused on the various information appeals in host-created information by considering different categories of appeals and examined how they are delivered and perceived by individuals. By addressing the limitation of previous literature, this study furthers sharing economy research.
As most activities in everyday life have been possible in online circumstances, the importance of online information in individual’s decision making has been continuously appreciated (Li et al., 2017). Although a number of studies have investigated which information is more helpful for individual’s decision making in online environments or effective in stimulating individual’s choice, few theoretical frameworks have been adopted, such as dual-coding theory or heuristic-systematic model (Hong et al., 2017). Hence, most previous results and implications have been explained in limited perspectives (Park et al., 2007). By adopting an untapped theoretical background, this research articulates the persuasive impacts of information components of message appeals. Considering that research topics could be differently explored with an original background, applying a new but proper theoretical framework would be meaningful for development of research field (Haugh, 2012).
On the other hand, major practical implications could be proposed to Airbnb management and host users of Airbnb. First, it would be better for Airbnb to work on measures for a number of normal hosts. In our results, super host badges are examined as the most influential factor making guests choose places. Although increasing general qualities of places through attractive incentives is important, it could make it hard for beginner hosts to be selected by guests. Furthermore, in our data set, the ratio of super host is quite low, so super host badges could bring the situation which the rich get richer, the poor get poorer. The significant impact of the number of host reviews could make it worse. As the higher number of host reviewers is more attractive, the hosts registering more than one places would be advantageous to get more reviews. This might create more difficult situation for normal hosts. In conclusion, guests tend to choose places owned by experienced hosts having super host badges and several places. Airbnb has tried to promote users to be new hosts for broad coverage in various locations. To attract new hosts, Airbnb needs to ensure potential hosts that they would be selected by guests. However, the current system is mostly favorable to a small number of super hosts and commercial hosts. To achieve its effort accommodating more hosts and its original goal providing guests real local experiences by connecting them to normal local hosts, Airbnb needs to work out some measures for new hosts and normal hosts. Another practical implication is about star-rating system. According to the results, star-rating had a significant positive impact of guest’s decision making. Although it is reasonable that the places evaluated positively are more selected by guests, if the evaluation is not reflecting quality of place effectively, it would be problematic. If guests choose specific places because they get higher ratings, they would have higher expectation about the places. In this situation, if the higher ratings are not correct, the guests would be highly disappointed due to the incorrect evaluation, decreasing the reliability of Airbnb system. Fradkin et al. (2015) found that star-rating in Airbnb generally tends to be inflated because Airbnb system enable not only guests to review hosts and places, but also hosts to review guests. Namely, the reciprocal evaluation system creates the biased trend in star-rating and this could lead guests to distrust its system. Since star-rating is examined as significant, Airbnb deserves to consider such possibility.
Other than Airbnb, host users could be provided few practical implications, especially for how to attract guests effectively. The results shows the positive impacts of use of social word, price, and place picture. If hosts can make appeals to pathos by using social words and introduce places as if guests are staying in a friend’s house, it will increase the rates of purchases. Thus, hosts are encouraged to describe themselves and their places with more social words and provide visual evidences as many as possible. Also, in Airbnb, price could be an indication of place quality, so too low price could bring opposite effects, disregarding low qualities of places rather than selecting cheap priced places. Hence, hosts need to carefully consider prices of their places. Lastly, the fewer the number of guests the higher the purchase rate, a negative influence of occupancy. This indicates that guests are more willing to stay places which are not too big and not too expensive. Also, if places are crowded with guests, hosts could be difficult to pay attention to every guest who might want to communicate with hosts to get local information or experience. Rather than accommodating guests as many as possible, providing better experience to guests by treating a specific number of people or by considering room-design for adequate occupancy.
Thus, hosts should focus on few guests for quality rather than many guests for profits.
Despite these findings, there are some limitations to the study. This study used the number of room reviews as a proxy variable; only a customer who actually bought a product can write it. Accordingly, the current measured value cannot explain all the actual purchases. Therefore, interpreting the results of this study needs to be done carefully. As this study looked at the persuasive power of host-created information in Airbnb, it is possible to overlook the other factors which can be crucial for the actual purchase. Therefore, considerations of other factors, such as direct communication with host users, will make it easier to understand an actual purchase. Finally, although this study adopted several different cities as data samples for generalizability of results, the three cities are difficult to be regarded as good representative cases to generalize the results properly. Also, the results are inconsistent depending on cities (Appendix A). Thus, future research can develop the current study by examining the results with more representative samples and comparing the results with specified explanations.
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2016S1A3A2925146).
Abramova, O., Shavanova, T., Fuhrer, A., Krasnova, H., and Buxmann, P. (2015). “Understanding the Sharing Economy: The Role of Response to Negative Reviews in the Peer-to-peer Accommodation Sharing Network”, Paper presented at the European Conference on Information Systems (ECIS), Münster, Germany.
Airbnb. (2017). “Superhost terms and conditions”, available at: https://www.airbnb.com/superhost/terms (accessed 10 April 2018).
Airbnb. (2018). “Fast Facts”, available at: https://press.atairbnb.com/fast-facts/ (accessed 13 June, 2018).
Birinci, H., Berezina, K., and Cobanoglu, C. (2018). “Comparing customer perceptions of hotel and peer-to-peer accommodation advantages and disadvantages”, International Journal of Contemporary Hospitality Management (just-accepted).
Bronstein, J. (2013). “Like me! Analyzing the 2012 presidential candidates’ Facebook pages”, Online Information Review, Vol. 37 No. 2, pp. 173-192.
Chen, C.-F., and Rothschild, R. (2010). “An application of hedonic pricing analysis to the case of hotel rooms in Taipei”, Tourism Economics, Vol. 16 No. 3, pp. 685-694.
Chen, P.-Y., Hong, Y., and Liu, Y. (2017). “The value of multidimensional rating systems: Evidence from a natural experiment and randomized experiments”, Management Science.
Chen, Y., and Xie, K. (2017). “Consumer valuation of Airbnb listings: a hedonic pricing approach”, International Journal of Contemporary Hospitality Management, Vol. 29 No. 9, pp. 2405-2424.
Cohn, M. A., Mehl, M. R., and Pennebaker, J. W. (2004). “Linguistic markers of psychological change surrounding September 11, 2001”, Psychological science, Vol. 15 No. 10, pp. 687-693.
Colicev, A., Malshe, A., Pauwels, K., and O’Connor, P. (2018). “Improving Consumer Mindset Metrics and Shareholder Value Through Social Media: The Different Roles of Owned and Earned Media”, Journal of Marketing, Vol. 82 No. 1, pp. 37-56.
Ert, E., Fleischer, A., and Magen, N. (2016). “Trust and reputation in the sharing economy: The role of personal photos in Airbnb”, Tourism Management, Vol. 55, pp. 62-73.
Fagerstrøm, A., Pawar, S., Sigurdsson, V., Foxall, G. R., and Yani-de-Soriano, M. (2017). “That personal profile image might jeopardize your rental opportunity! On the relative impact of the seller’s facial expressions upon buying behavior on Airbnb™”, Computers in Human Behavior, Vol. 72, pp. 123-131.
Fradkin, A., Grewal, E., Holtz, D., and Pearson, M. (2015). “Bias and reciprocity in online reviews: Evidence from field experiments on Airbnb”. paper presented at the Proceedings of the Sixteenth ACM Conference on Economics and Computation, 15 June – 19 June, Oregon, USA.
Garvin, D. A. (1984). “What Does “hltoduct Quality” Really Mean?”, Sloan management review, Vol. 25.
Gibbs, C., Guttentag, D., Gretzel, U., Yao, L., and Morton, J. (2017). “Use of dynamic pricing strategies by Airbnb hosts”, International Journal of Contemporary Hospitality Management (just-accepted).
Gunter, U. (2018). “What makes an Airbnb host a superhost? Empirical evidence from San Francisco and the Bay Area”, Tourism Management, Vol. 66, pp. 26-37.
Guttentag, D. (2015). “Airbnb: disruptive innovation and the rise of an informal tourism accommodation sector”, Current issues in Tourism, Vol. 18 No. 12, pp. 1192-1217.
Hamari, J., Sjöklint, M., and Ukkonen, A. (2016). “The sharing economy: Why people participate in collaborative consumption”, Journal of the Association for Information Science and Technology, Vol. 67 No. 9, pp. 2047-2059.
Haugh, H. (2012). “The importance of theory in social enterprise research”, Social Enterprise Journal, Vol. 8 No. 1, pp. 7-15.
Hedrick-Wong, Y., and Choong, D. (2015). “MASTERCARD, 2015 global destination cities index”, MasterCard Worldwide Insights” available at: https://newsroom.mastercard.com/wp-content/uploads/2015/06/MasterCard-GDCI-2015-Final-Report1.pdf (accessed 11 April 2014).
Hlee, Sunyoung, Hanna, Lee, and Chulmo, Koo. (2018). “Hospitality and Tourism Online Review Research: A Systematic Analysis and Heuristic-Systematic Model”, Sustainability, Vol. 10 No. 4, pp. 1141.
Hong, H., Xu, D., Wang, G. A., and Fan, W. (2017). “Understanding the determinants of online review helpfulness: A meta-analytic investigation”, Decision Support Systems, Vol. 12, pp. 1-11.
Humphreys, A. (2010). “Megamarketing: The creation of markets as a social process”, Journal of Marketing, Vo. 74 No. 2, pp. 1-19.
Jin, S.-A. A., and Phua, J. (2014). “Following celebrities’ tweets about brands: The impact of twitter-based electronic word-of-mouth on consumers’ source credibility perception, buying intention, and social identification with celebrities”, Journal of Advertising, Vol. 43 No. 2, pp. 181-195.
Lampinen, A., Lehtinen, V., Cheshire, C., and Suhonen, E. (2013). “Indebtedness and reciprocity in local online exchange”. In Proceedings of the 2013 conference on Computer supported cooperative work. ACM, pp. 661-672.
Lee, D., Hyun, W., Ryu, J., Lee, W. J., Rhee, W., and Suh, B. (2015). “An analysis of social features associated with room sales of Airbnb”, in Proceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work and Social Computing, pp. 219-222.
Lee, H.-H. M., and van Dolen, W. (2015). “Creative participation: Collective sentiment in online co-creation communities”, Information and Management, Vol. 52 No.8, pp. 951-964.
Lee, M., Jeong, M., and Lee, J. (2017). “Roles of negative emotions in customers’ perceived helpfulness of hotel reviews on a user-generated review website: a text mining approach”, International Journal of Contemporary Hospitality Management, Vol. 29 No. 2, pp. 762-783.
Li, H., Zhang, Z., Meng, F., and Janakiraman, R. (2017). “Is peer evaluation of consumer online reviews socially embedded?–An examination combining reviewer’s social network and social identity”, International Journal of Hospitality Management, Vol. 67, pp. 143-153.
Liang, L. J., Choi, H. C., and Joppe, M. (2018). “Understanding repurchase intention of Airbnb consumers: Perceived authenticity, electronic word-of-mouth, and price sensitivity”, Journal of Travel and Tourism Marketing, Vol. 35 No. 1, pp. 73-89.
Liang, S., Schuckert, M., Law, R., and Chen, C.-C. (2017). “Be a “Superhost”: The importance of badge systems for peer-to-peer rental accommodations” Tourism Management, Vol. 60, pp. 454-465.
Lichtenstein, D. R., Ridgway, N. M., and Netemeyer, R. G. (1993). “Price perceptions and consumer shopping behavior: a field study”, Journal of Marketing Research, pp. 234-245.
Liu, Z., and Park, S. (2015). “What makes a useful online review? Implication for travel product websites”, Tourism Management, Vol. 47, pp. 140-151.
Lu, Z., and Zhu, F. (2006). “The post-purchase evaluation of the China’s star rated hotel service quality: a content analysis of guest comments on third party hotel booking websites”, The Journal of China Tourism Research, Vol. 2 No. 1/2, pp. 93-103.
Ludwig, S., De Ruyter, K., Friedman, M., Brüggen, E. C., Wetzels, M., and Pfann, G. (2013). “More than words: The influence of affective content and linguistic style matches in online reviews on conversion rates”, Journal of Marketing, Vol. 77 No. 1, pp. 87-103.
Mehl, M. R., Gosling, S. D., and Pennebaker, J. W. (2006). “Personality in its natural habitat: Manifestations and implicit folk theories of personality in daily life”, Journal of personality and social psychology, Vol. 90 No. 5, pp.862.
Morgan, B. (2011). “The Most Amazing And Absurd Places For Rent”, Forbes, available at: https://www.forbes.com/sites/morganbrennan/2011/09/16/the-most-amazing-and-absurd-places-for-rent/
Nadeem, W., Andreini, D., Salo, J., and Laukkanen, T. (2015). “Engaging consumers online through websites and social media: A gender study of Italian Generation Y clothing consumers”, International Journal of Information Management, Vol. 35 No. 4, pp. 432-442.
O’keefe, D. J. (1987). “The persuasive effects of delaying identification of high‐and low‐credibility communicators: A meta‐analytic review”, Communication Studies, Vol. 38 No. 2, pp. 63-72.
Otterbacher, J. (2011). “Being heard in review communities: Communication tactics and review prominence”, Journal of Computer-Mediated Communication, Vol. 16 No. 3, pp. 424-444.
Park, H. S., Levine, T. R., Kingsley Westerman, C. Y., Orfgen, T., and Foregger, S. (2007). “The effects of argument quality and involvement type on attitude formation and attitude change: A test of dual‐process and social judgment predictions”, Human communication research, Vol. 33 No. 1, pp. 81-102.
Poon, K. Y., and Huang, W.-J. (2017). “Past experience, traveler personality and tripographics on intention to use Airbnb”, International Journal of Contemporary Hospitality Management, Vol. 29 No. 9, pp. 2425-2443.
Qazi, A., Syed, K. B. S., Raj, R. G., Cambria, E., Tahir, M., and Alghazzawi, D. (2016). “A concept-level approach to the analysis of online review helpfulness”, Computers in Human Behavior, Vol. 58, pp. 75-81.
Racherla, P., and Friske, W. (2012). “Perceived ‘usefulness’ of online consumer reviews: An exploratory investigation across three services categories”, Electronic Commerce Research and Applications, Vol. 11 No. 6, pp. 548-559.
Ramage, J. D., Bean, J. C., and Johnson, J. (2015). Writing arguments: A rhetoric with readings, Pearson.
Richard, B., and Cleveland, S. (2016). “The future of hotel chains: Branded marketplaces driven by the sharing economy”, Journal of Vacation Marketing, Vol. 22 No. 3, pp. 239-248.
Rizzo, C. (2018). “Airbnb Founder Reveals Secret Plans to Launch an Airline”, Travel + Leisure, available at: https://www.travelandleisure.com/travel-news/airbnb-wants-to-start-an-airline
Scott, R. L. (1967). “On viewing rhetoric as epistemic”, Communication Studies, Vol. 18 No. 1, pp. 9-17.
Slee, T. (2013). “Some obvious things about internet reputation systems”, available at: http://tomslee.net/wordpress/wp-content/uploads/2013/09/2013-09-23_reputation_systems.pdf(accessed 11 April 2018).
So, K. K. F., Oh, H., and Min, S. (2018). “Motivations and constraints of Airbnb consumers: Findings from a mixed-methods approach”, Tourism Management, Vol. 67, pp. 224-236.
Sussman, S. W., and Siegal, W. S. (2003). “Informational influence in organizations: An integrated approach to knowledge adoption”, Information Systems Research, Vol. 14 No. 1, pp. 47-65.
Swani, K., Milne, G. R., Brown, B. P., Assaf, A. G., and Donthu, N. (2017). “What messages to post? Evaluating the popularity of social media communications in business versus consumer markets”, Industrial Marketing Management, Vol. 62, pp. 77-87.
Tabachnick, B. G., and Fidell, L. S. (2007). Using multivariate statistics, Allyn and Bacon/Pearson Education, Boston.
Tausczik, Y. R., and Pennebaker, J. W. (2010). “The psychological meaning of words: LIWC and computerized text analysis methods”, Journal of Language and Social Psychology, Vol. 29 No. 1, pp. 24-54.
Teubner, T., Saade, N., Kawlitschek, F., and Weinhardt, C. (2016). “It’s only pixels, badges, and stars: On the economic value of reputation on Airbnb”. paper presented at the Australasian Conference on Information Systems 2016, Wollongong, New South Wales, Australia.
Thakur, R., and Hale, D. (2018). “Special Issue Editors’ Introduction: Impact of Social Media on Businesses”, The Journal of Business Inquiry, Vol. 17 No. 1, pp. 3-5.
Tidwell, L. C., and Walther, J. B. (2002). “Computer‐mediated communication effects on disclosure, impressions, and interpersonal evaluations: Getting to know one another a bit at a time”, Human communication research, Vol. 28 No. 3, pp. 317-348.
Tsao, W.-C. (2018). “Star power: the effect of star rating on service recovery in the hotel industry”, International Journal of Contemporary Hospitality Management (just-accepted).
Tussyadiah, I., and Park, S. (2018). “When Guests Trust Hosts for Their Words: Host Description and Trust in Sharing Economy”, Tourism Management, Vol. 67, pp: 261-272.
Tussyadiah, I. P., and Pesonen, J. (2016). “Impacts of peer-to-peer accommodation use on travel patterns”, Journal of Travel Research, Vol. 55 No. 8, pp. 1022-1040.
Tussyadiah, I. P., and Zach, F. (2017). “Identifying salient attributes of peer-to-peer accommodation experience”, Journal of Travel and Tourism Marketing, Vol. 34 No. 5, pp. 636-652.
VanMeter, R. A., Grisaffe, D. B., and Chonko, L. B. (2015). “Of “Likes” and “Pins”: The effects of consumers’ attachment to social media”, Journal of Interactive Marketing, Vol. 32, pp. 70-88.
Wang, D., Li, M., Guo, P., and Xu, W. (2016). “The impact of sharing economy on the diversification of tourism products: implications for tourist experience”. in Information and Communication Technologies in Tourism 2016, Springer. pp. 683-694
Wang, D., and Nicolau, J. L. (2017). “Price determinants of sharing economy based accommodation rental: A study of listings from 33 cities on Airbnb.com”, International Journal of Hospitality Management, Vol. 62, pp. 120-131.
Weiss, A. M., Lurie, N. H., and MacInnis, D. J. (2008). “Listening to strangers: whose responses are valuable, how valuable are they, and why?”, Journal of Marketing Research, Vol. 45 No. 4), pp. 425-436.
Wu, J., Ma, P., and Xie, K. L. (2017). “In sharing economy we trust: the effects of host attributes on short-term rental purchases”, International Journal of Contemporary Hospitality Management, Vol. 29 No. 11, pp. 2962-2976.
Xie, K., and Mao, Z. (2017). “The impacts of quality and quantity attributes of Airbnb hosts on listing performance”, International Journal of Contemporary Hospitality Management, Vol. 29 No. 9, pp. 2240-2260.
Xun, J., and Reynolds, J. (2010). “Applying netnography to market research: The case of the online forum”, Journal of Targeting, Measurement and Analysis for Marketing, Vol. 18 No. 1, pp. 17-31.
Yang, SB, Lee, H, Lee, K, Koo, C. (2018). “The Application of Aristotles’ Rhetorical Theory to the Sharing Economy: An Empirical Study of Airbnb”, Journal of Travel and Tourism Marketing, pp. 1-20.
Cite This Work
To export a reference to this article please select a referencing stye below:
Related ServicesView all
Related ContentAll Tags
Content relating to: "Consumer Decisions"
The consumer decision making process involves how consumers identify their needs and gather and process information prior to a purchase. Consumer decisions involve how the emotions and preferences of consumers can impact their buying decisions.
Consumer Behaviour in the UK Automotive Industry
Consumer behaviour is dependent on few factors that need to be considered in any industry. In the Automotive industry, there are several intertwining factors known. The Internet as one knows well has ...
Impact of FMCG food Packaging on Consumer Buying Behaviour
Chapter 1 Introduction & Preliminary Data Gathering 1.1: Introduction to the Report: During the last two decades many important developments in the position of food in daily life of consumers a...
DMCA / Removal Request
If you are the original writer of this dissertation and no longer wish to have your work published on the UKDiss.com website then please: