Antecedents of Guest Booking Intention in the Home-Sharing Industry: Lessons Learned from Airbnb

ABSTRACT

This study aims to test the role of price consciousness, reputation, trust, and perceived value on tourists’ booking intentions on Airbnb. Additionally, this study investigates the mediating role of perceived value and consumer trust in Airbnb on the link between attributed causes and guest booking intention. A cross-sectional survey was designed to examine the theoretical framework, which yielded 311 responses. The data were analyzed using structural equation modeling. The study found that reputation and price consciousness influence tourists’ cognitive evaluation of Airbnb service and affect their intention to book accommodation on the Airbnb platform. The findings of this study may serve as the basis for designing effective marketing tools to understand better causes that may drive guests’ booking intentions.

KEYWORDS:

• Home-sharing industry
• guest-booking intention
• reputation
• price consciousness
• trust
• perceived value

Introduction

The sharing economy business model has revolutionized and reshaped the tourism industry’s business ecosystem (Barandiarán et al., 2019). It is believed that the rise of the sharing economy in tourism, especially in the accommodation market, can be ascribed to economic and societal factors (Botsman & Rogers, 2011). Tourists look for low-cost accommodations and direct connections with the local people (Guttentag, 2015). Airbnb is a prominent example of a sharing economy (also referred to as a collaborative economy; Ioannides et al., 2018) and peer-to-peer platforms (Prayag & Ozanne, 2018) that have penetrated the tourism industry since its inception. Airbnb is an online rental marketplace that offers short-term housing to visitors and is a notable hospitality firm in the sharing economy (Xie & Mao, 2017). Since its inception, it has created new business opportunities for incumbent businesses (Koh & King, 2017) and posed stiff competition in the market (Farmaki et al., 2020; Fernández et al., 2016). As a result, the accommodation service industry has been facing financial and non-financial challenges such as a lack of sufficient booking (Tiamiyu et al., 2020a). It has also been contended that the Airbnb service enables participants to earn extra income (Buhalis et al., 2020). A recent study conducted by Airbnb on 2000 guests and hosts found that 50% of the house owners or hosts managed to settle their housing mortgage and that 40% of hosts were able to earn extra income as a function of their participation in Airbnb (Mahalingam & Inn, 2019). This demonstrates the growth and pervasiveness of Airbnb in the hospitality industry. It also enables more consumers to book Airbnb and enhances the adoption of the Airbnb platform by many house owners (Merican, 2019).

According to the current literature, customers that patronize their business bring in considerable profit (Otim & Grover, 2006; Volz & Volgger, 2022), decrease transaction costs (Anderson et al., 1994; Sultana et al., 2022) and disseminate the positive word of mouth to their networks (family members, friends, relatives, virtual communities, etc.; Quoquab et al., 2021; Reid & Reid, 1993). Similarly, this study argues that consumer intention to book accommodation is essential for the sharing economy (i.e. Airbnb) as these guests can potentially revert to traditional service providers (i.e. hotels). Thus, it is crucial to identify and assess factors that are likely to increase consumers’ intentions to book accommodation through Airbnb (Agag & El-Masry, 2016; Chen & Chang, 2018; Hossain, 2020).

A growing number of researchers have investigated different aspects of Airbnb, such as Airbnb’s effect on the community (Smith et al., 2017), the platform’s brighter and darker sides (Buhalis et al., 2020), hotelier perspectives of Airbnb (Qiu et al., 2020), its wider geographical context (Sthapit et al., 2020), hosts’ motivation for listing their properties on it (Ikkala & Lampinen, 2015) and its branding strategies (Yannopoulou et al., 2013). Another stream of research focused on factors that motivate consumers to choose and adopt the Airbnb platform. For example, Ert et al. (2016) studied the effect of visual-based information, host attributes and product attributes. Another study conducted by Volz and Volgger (2022) tested the effect of affective and cognitive advertising on attitude and behavioral intentions to book with Airbnb. So et al. (2022) studied the role of customer values (e.g., price, quality, emotional, and social) in shaping guests’ attitudes and behavioral intentions toward Airbnb. Based on nationwide household tourism survey data from U.S. domestic tourists, Yang et al. (2019) examined the effect of past travel experience, socio-demographics, and destination characteristics on home-sharing stays. Wang et al. (2020) predicted the mediation effect of trust between social antecedents (e.g., social value orientation), technical antecedents (e.g., system quality), economic antecedents (e.g., monetary rewards) and privacy assurance antecedents (i.e., perceived effectiveness of privacy policy) and host intention to use Airbnb as a sharing platform. Also, Su and Mattila (2019) tested the mediating role of trust between gender congruity and booking intention with a sample of 200 U.S. consumers. In the Malaysian context, Tiamiyu et al. (2020b) tested the mediation effect of attachment to Airbnb in relation to guest booking intention. Nevertheless, there is a lack of research that has looked into factors that cause tourists to book accommodation through Airbnb such as Airbnb’s reputation and consumer trust in sharing economy platforms (Sarlay & Neuhofer, 2020), price consciousness and perceived value (Tiamiyu et al., 2020b) specifically in the Malaysian context. Most importantly, the mediating roles of “trust in Airbnb” and “value perception” underlying tourists’ intentions in booking Airbnb services are yet to be addressed. Given the fact that the popularity of Airbnb in Malaysia is growing, along with the rapidly growing availability of properties, this study aims to propose a research framework that draws on the attribution theory to fill these gaps in the literature. Particularly, this study aims to examine how attributed causes such as reputation, trust, price consciousness, and perceived value may affect guests’ intention to use Airbnb’s services. In addition, the study aims to examine the mediating role of trust and perceived value.

This study is expected to contribute significantly to consumer behavior studies in the context of Airbnb in a different aspect. Specifically, this is a pioneering effort to examine booking intention using attribution theory in the context of Airbnb. Understanding how Malaysia is dealing with platform accommodations may help other regions in their efforts to improve consumer accommodation service booking. Most importantly, this research is developing comparatively new relationships, such as the link between the attributed causes (price consciousness and reputation) and their outcome (i.e., guests’ booking intention). Additionally, the mediating effect of “trust in Airbnb” and “perceived value” between the attributed causes and the guest booking intentions are relatively new in the literature.

The rest of the article is organized as follows: the next section presents a concise understanding of the theoretical underpinning of the study, followed by a review of the literature and hypotheses development. The sections following on from there elaborate on the methodology adopted and the results of the study. In the concluding part, theoretical contributions, as well as practical implications, are addressed. Lastly, the limitations of the study and future research directions are discussed.

Theoretical Underpinning

Attribution Theory

This study uses the attribution theory to explain how individuals assign causes to their behaviors (Weiner, 1976). According to this theory, individuals’ behavior may be perceived as being caused by external factors outside the person’s control or perceived as being caused by internal factors within the person’s control. The perceived causes may influence their emotions/cognition, which may impact their intention/behavior. As such, they try to make sense of their behavior by embarking on an interpretation of the causes of their action by asking questions such as why they reserve or book rooms through a specific service provider. In general, two categories of attributions that may explain individual behavior, namely, dispositional (internal cause) and situational (external cause; Heider, 1958). In this study, internally deduced causes (dispositional), such as price consciousness, can be considered as an internal factor that may influence consumer cognition (perceived value) and lead to behavioral intention or action (Heider, 1958). On the other hand, reputation is identified as an external attribute (situational) that tourists may assign to the cause of an event or action. In other words, this cause may influence their internal emotion and cognition (trust) and lead to behavioral intention or action (Weiner, 1976). Booking intention is an example of a behavioral intention that consumers may attribute to both their internal and external causes in explaining their action, i.e., consumers’ tendency to reserve or book rooms on the Airbnb platform in the future.

Literature Review and Hypotheses Development

Factors that can motivate tourists toward the Airbnb accommodation service have been investigated by past studies from a different perspective (see, Table 1). At the micro-level and from a consumer view of point, Jang et al. (2021) examined the effect of COVID19 on peer-to-peer accommodation consumption. Another study by Julião et al. (2022) tested the effect of consumer socio-demographic factors on his/her intention to use Airbnb services. Jung et al. (2021) used the Technology Acceptance Model (TAM) factors to examine their effect on consumers’ intention to use the Airbnb service. Again, Mohsin and Lengler (2021) revealed that social, utilitarian and hedonic values can affect consumer repurchase intention of Airbnb through satisfaction. From a host perceptive, Boto-García (2022) evaluated the different price adjustments developed by professional and nonprofessional Airbnb hosts. A different study by Kirkos (2022) analyzed the performance of Airbnb listings in terms of occupancy rate, number of bookings and revenue. At the macro level, a growing number of scholars have started to look into how macro factors may drive consumer intentions toward Airbnb accommodation, for instance, social and cultural factors (Lee et al., 2021), and spatially heterogeneity (Jang & Kim, 2022). In addition, numerous methods and theories have been applied by previous researchers to understand drivers of Airbnb booking. For example, the Theory of Reasoned Action (Amaro et al., 2018), the Stimulus-Organism-Response model (Tiamiyu et al., 2022), Hofstede’s cultural dimensions theory and Ajzen’s theory of planned behavior (Lee et al., 2021).

Table 1. Synthesized literature on determinants of Airbnb booking.

Theme	Author and Year	Research Objective	Method/Theories	Context	Determinants
(Micro-level) Consumer perspective	Julião et al. (2022)	This study set out to explore the consumer profile and analyze its motivations to use Airbnb service.	Quantitative method, Sharing Economy Model	Portugal	Socio-demographic characteristics, user experience
	Tiamiyu et al. (2022)	This paper aims to investigate the Muslim tourists’ intention to book on Airbnb.	Quantitative method, Stimulus-Organism-Response Theory (S-O-R)	Malaysia	Reputation, Perceived value
	Jang et al. (2021)	To investigate the spatially varying relationship between destination attributes and COVID-19-disrupted Airbnb performance change.	Spatial analytics and experiment	Florida, USA	Trip purpose, Perceived threat
	Jung et al. (2021)	This study adopted the technology acceptance model (TAM) to examine tourist repurchase intention in Airbnb.	Quantitative method, Technology Acceptance Model (TAM)	South Korea	Network externalities, Trust, Interactivity, Ease of use, Usefulness
	Mohsin and Lengler (2021)	This study is set out to investigate millennial (Airbnb non‐consumers) repurchase intentions toward Airbnb service.	Quantitative method, MEC (means-end chain), Prospect theory	New Zealand	Value, Risk, Satisfaction
	Tajeddini et al. (2021)	This study examines the factors that motivate visitors’ decision-making concerning accommodation purchases in Airbnb service.	Quantitative method, Theory of planned behavior, Value-attitude-behavior model	Switzerland	Subjective norm, Attitude, Perception of quality
	Yang et al. (2021)	To examine the impact of social media posts on Airbnb booking in the south-eastern U.S.	Information model	USA	Social media information, Frequency of photo posts and video posts
	Kong et al. (2019)	This study set out to examine the effect of social and technical enablers on consumer trust and eventually lead to the continued use of Airbnb.	Quantitative method, Socio-technical theory	Asia/Pacific region	Social referrals, Information quality, Transaction Safety, Trust
	Amaro et al. (2018)	To investigate the factors that influence millennials’ intentions to book on Airbnb.	Quantitative method, Theory of Reasoned Action	Germany and China	Economic Benefits, Perceived Risk, Subjective Norm, Attitude, Unique Accommodation and Variety
(Micro-level) Airbnb Host perspective	Boto-García (2022)	This study seeks to evaluate the different price adjustments developed by professional and nonprofessional hosts to meet demand in Airbnb bookings.	Hedonic regressions techniques	Barcelona, Spain	Intertemporal price
	Kirkos (2022)	To investigate the determinants that influence customers’ purchase intentions and as well as listings’ performance of Airbnb.	Machine learning techniques	Thessaloniki, Greece	Super-host badge, Rich host presentation, Amenities, Overall rating, Quick response to customers’ requests
(Macro-level)	Jang and Kim (2022)	The research aims to empirically investigate the spatially heterogeneous COVID-19 disruptions in the Airbnb business.	Spatial Econometric Models	Florida, USA	Social, Community capital, environmental resources, Tourism clusters
	Lee et al. (2021)	To investigate the influence of cultural and social factors on a consumer’s participation in Airbnb accommodation.	Quantitative method, Hofstede’s cultural dimensions theory, Ajzen’s theory of planned behavior	South Korea	Social factors, Cultural factors
(Micro-level) Consumer behavior	Current study	This study is set to test the role of price consciousness, reputation, trust and perceived value on tourists’ booking intention in Airbnb.	Quantitative method, Attribution Theory	Malaysia	Price consciousness, Reputation, Trust, Perceived value

Source: Compiled by the researcher

These studies provide valuable insights into why guests select Airbnb, but this body of research also has some limitations. For example, these studies are limited in the breadth of possible stimuli they considered. In addition, the bulk of these studies are conducted from the western cultural perspectives (see, Jang & Kim, 2022; Lee et al., 2021; Yang et al., 2021), and less attention has been given to non-western contexts such as Malaysia. Thus, the present research will contribute to this area by considering a broad range of drivers (e.g., trust in Airbnb, perceived value, price-consciousness, and reputation). In addition, this study will predict consumer intention to book through Airbnb from a new cultural perspective.

Reputation and Trust in Airbnb

Trust refers to a “disposition to engage in social exchanges that involve uncertainty and vulnerability, but also potentially rewarding” (Bicchieri et al., 2004, p. 286). Similarly, Morgan and Hunt (1994) argued that trust represents customers’ confidence and tendency to engage in a relationship with the service provider based on their reliability. In online transactions, trust is a vital element between two strangers before engaging in a monetary transaction (Kim et al., 2008). Practically, a customer who trusts the quality of a service provider’s product or service is willing to accept the consequences of paying for it. Past studies found that consumers’ trust can be influenced by the reputation of a service provider (Agag & Eid, 2019; Shukor et al., 2019). Reputation is a public opinion representing a collective evaluation of a group concerning an entity or a person (Wang & Vassileva, 2007). Herbig and Milewicz (1993) argued that reputation refers to customers’ overall perception of the service provided. Casaló et al. (2015) revealed that a firm’s reputation is likely to enhance credibility infused in consumers’ minds. Similarly, Raithel et al. (2010) contend that reputation can increase customers’ trust in making purchasing decisions.

In the context of Airbnb, Yang et al. (2018) have conceptualized and tested the relationship between reputation and consumer trust in the context of the United States and South Korea. However, in their study, reputation was linked with affective-based trust, i.e., consumer trust in hosts. This is different from consumer trust in Airbnb, which is cognitive-based trust. Moreover, there is a distinctive difference in the consumer’s relationship with the host and with the Airbnb platform. This is consonant with prior studies, which refer to trust in hosts (disposition to trust) as consumers’ willingness to trust Airbnb hosts on the platform (Liang et al., 2018; Mao et al., 2020). In light of this gap, it is important to predict the relationship between reputation and consumer trust in Airbnb. This relationship can be supported by attribution theory (Weiner, 1976), which advocates those external factors such as reputation may influence individuals’ cognition and emotions (trust) and subsequently lead to their behavioral intention (Zhang et al., 2018). Based on this theoretical foundation and logical arguments, the following hypothesis is developed:

H1: Reputation positively affects tourists’ trust in Airbnb.

Trust in Airbnb and Guest Booking Intention

Guest booking intention refers to customers’ tendency to book accommodation in the future (Tsao et al., 2015). Prior studies have argued that trust is a crucial predictor of consumers’ behavioral intentions (Revilla-Camacho et al., 2017). This argument is in agreement with Revilla-Camacho et al.’s (2017) findings that consumers will consider patronizing a new or existing firm depending on the level of perceived trust. In other words, the perception of higher trust is likely to impact consumers’ intention to engage with a new service provider.

In the hotel industry context, Kühn and Petzer (2018) demonstrated that consumers might decide to purchase or book accommodation based on their trust, reflecting their confidence level in the service provider. However, Chiu et al. (2013) contend that the types and effects of trust can vary based on context. For example, in the sharing economy context, trusting Airbnb as a platform should be distinguished from trusting the hosts (Liang et al., 2018). Past studies, focused on trust in the host and its effect on guest intention (see, Mao et al., 2020; Yang et al., 2018), nonetheless less attention was given to cognitive trust (trust in the platform) and its effect on guest intention. Theoretically, the relationship between a guest’s trust in Airbnb and his/her intention to book through this platform can be explained by attribution theory (Weiner, 1976), which claims that consumer intention can be attributed to internal factors such as consumers’ positive emotions and expectation about trustee action and intention (the service provider). In the light of this theoretical background, this study proposed the following hypothesis:

H2: Trust in Airbnb positively affects guest booking intention.

Price Consciousness and Perceived Value

Perceived value refers to consumers’ assessment of benefits received in using the service in exchange for what they “give” (money, energy, etc.; Zeithaml, 1988). In the same manner, Parasuraman et al. (1988) defined it as consumers’ cost-benefit assessment of the purchased good/service. In other words, consumers tend to assess their benefits in exchange for their sacrifice when dealing with a service provider. In this study, it refers to consumers’ assessment of the tangible benefits of using the Airbnb accommodation service in exchange for what they give (money). Correia and Kozak (2016) found that consumers’ value perception of counterfeit products was affected by price consciousness. Price consciousness is defined as the extent to which customers are inclined to pay a low price for any service (Lichtenstein et al., 1993). Hence, the decision on whether to purchase a service tends to be based on the amount of money that can be saved. Thus, it can be assumed that price-conscious consumers are likely to find value in a service that is priced lower compared with what other service providers offer.

In the context of Airbnb, the more price-conscious a tourist is, the greater the interest he/she is likely to have in Airbnb accommodation service that offers lower prices in comparison with other accommodation services (e.g., hotel; Bresciani et al., 2021). This is constant with Lindblom et al.’s (2018) findings i.e., customers do engage in sharing economy in order to save money (Lindblom et al., 2018, which can eventually affect his /her perceived value, leading to boost his/her intention to book accommodation with Airbnb. This is in agreement with the assumption of attribution theory (Weiner, 1976), namely, price consciousness is an internal factor that may affect a consumer’s assessment of the benefits received from using a service (such as an Airbnb accommodation) and consequently lead to his or her intention to book with Airbnb. This is also consistent with Qiu et al. (2020) and Sthapit et al. (2019) findings that are among the reasons why consumers choose the Airbnb service is the benefit of getting an attractive price. From a consumer’s perspective, Airbnb is primarily a low-cost option of accommodation (Koh & King, 2017; Zervas et al., 2017). In other words, those consumers who focus on paying low prices will perceive Airbnb as a favorable choice of accommodation. Based on the foregoing discussion, the following hypothesis is developed:

H3: Price consciousness positively affects guest perceived value.

Perceived Value and Guest Booking Intention

Research has revealed that perceived value is a crucial predictor of consumers’ behavioral intentions (see, Chen & Chang, 2018; Chen & Xu, 2019). In the context of Airbnb, consumers are likely to book their accommodation through Airbnb if their perception of value in the service is high. Accordingly, if consumers perceive that the benefits of using the Airbnb service would outweigh the price being charged, it may influence their intention to book accommodation with Airbnb. This is in accord with attribution theory (Weiner, 1976), which assumes that a guest’s behavioral intentions can be driven by their internal characteristics, such as perceived values (emotional and cognitive values). However, less is known about the effect of perceived value on guest booking intention in the context of Airbnb. Thus, this study assumes that consumers’ value perception of the service may determine their intention to book accommodation with Airbnb. Based on this assumption, the following hypothesis is developed:

H4: Perceived value positively affects guest booking intention.

Reputation and Guest Booking Intention

Previous studies have found that a firm’s reputation is an external factor that may predict consumers’ behavioral intention in a different context. For example, Balakrishnan and Foroudi (2019) found a positive association between corporate reputation and consumer intention to purchase innovative food products. In the banking context, Maryam et al. (2019) tested the effect of the bank’s reputation on customer intention to use Islamic banking services and revealed a positive relationship concerning this link. Su et al. (2018) investigated the relationship between destination reputation and tourist intention to visit the hotel industry. Another study conducted by Gatti et al. (2012) in the food industry found a positive association between corporate reputation and consumer purchase intention of Panettone. In the telecommunication context, Quoquab et al. (2018) disclosed a negative connection between the reputation of service providers and the custom intention to switch. However, little attention has been paid to the effect of reputation on guest booking intention in the Airbnb context in Malaysia.

In the context of Airbnb, reputation is an essential factor in the sharing economy ecosystem because it can mitigate the feeling of uncertainty through the platform’s reputation (Chang, 2015). Therefore, the attributes of a firm that has a good reputation for providing a reasonable accommodation service may influence consumers’ behavioral intention positively. Attribution theory supports the notion that external factors such as reputation can explain consumers’ behavioral intentions (Weiner, 1976). Guided by empirical and theoretical evidence it is expected that Airbnb’s reputation will influence the booking intention of guests positively. Based on this expectation, the following hypothesis is developed:

H5: Reputation positively affects guest booking intention.

Price Consciousness and Guest Booking Intention

Price consciousness has been identified as a crucial predictor of consumers’ behavioral intention in various contexts (see, Correia & Kozak, 2016; Lindblom et al., 2018; Saleki et al., 2019). It refers to “the degree to which the consumer focuses exclusively on paying low prices” (Lichtenstein et al., 1993, p. 237). In the context of Airbnb, price consciousness is an important factor that drives tourists toward collaborative consumption (Lindblom et al., 2018). As such, consumers tend to attribute their inclination toward a low price as the cause of their booking intention toward Airbnb, which is in agreement with attribution theory (Weiner, 1976). This also agrees with Chen and Xie (2017) and Dogru and Pekin (2017) arguments that most travelers tend to stay with the brand for more functional requirements, such as price and space. Similarly, Mody and Gomez (2018) demonstrated that Airbnb’s attractiveness to family and group travelers can be due to price. In addition, Nowak et al. (2015) found the major motivators of the guest to choose Airbnb are “lower price,” “location,” and “authentic experience. Nonetheless, there remains a paucity of evidence on the relationship between price consciousness and guest booking intention in the Airbnb industry in the Malaysian context. Therefore, considering the gap in the literature, the following hypothesis is proposed:

H6: Price consciousness positively affects guest booking intention.

Mediating Role of Trust and Price Consciousness in Airbnb

The mediating role of trust and price consciousness can be explained by attribution theory (Weiner, 1976). This theory is concerned with how individuals interpret events and how this relates to their thinking and behavior. Likewise, Heider (1958) stated that there is a strong need for individuals to understand transient events by attributing them to the actor’s disposition (internal factors) or to stable characteristics of the environment (external factors).

This study considers price consciousness as an internal attribution that may influence consumer cognition (perceived value) and subsequently lead to his/her intention to book with Airbnb. In addition, reputation is considered an external attribution that may influence a guest’s cognitive and emotional status (trust) positively and subsequently lead him or her to develop a positive intention toward booking with Airbnb. Accordingly, this study postulates that reputation and price consciousness (situational and dispositional stimulus) positively influence customer-perceived trust and perceived value (i.e. psychological status), affecting, in turn, guests’ intention to book with Airbnb (i.e. booking intention). On the basis of these assumptions, the following hypotheses are developed:

H7: Trust in Airbnb mediates the relationship between reputation and guest booking intention.

The proposed conceptual framework for this study is outlined in Figure 1.

Figure 1. Conceptual framework.

H8: Perceived value mediates the relationship between price consciousness and guest booking intention.

Methodology

Measurement

Content validity was performed to ensure that the items appropriately and sufficiently measured the study constructs (Cavana et al., 2001; Quoquab et al., 2022). For this purpose, an exhaustive literature review was carried out to identify other researchers’ valid and reliable measures. Furthermore, three academicians and three experts in tourism marketing were consulted to scrutinize the questionnaire to validate the measurement items. Next, the questionnaire was face-validated to ensure clarity and readability of the questionnaire (Cavana et al., 2001; Jamil et al., 2019). After that, the scales were piloted using 100 questionnaires collected from the sample of UTMKL postgraduate students. Finally, SPSS statistic version 23 was used to check the reliability of the measurement items. The result of the SPSS confirmed that Cronbach alpha for all constructs crossed the threshold value of 0.70, establishing the reliability of all constructs of this study.

The study variables were measured on a seven-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). As presented in the Appendix, three items were used to measure reputation, borrowed from Kim et al. (2013). Price consciousness was accessed by three measurement items adapted from Campbell et al. (2014). A five-item scale was borrowed from Liang et al. (2018) to measure consumers’ trust in Airbnb. Adapting from Chen and Chang (2018), the perceived value was measured using three items, and guest booking intention was measured using four items borrowed from Xu and Schrier (2019).

Sample and Data Collection

The population of interest for this study comprised Malaysian consumers aged 18 years and over who had used the Airbnb service before or had heard about it. This group seemed an appropriate source of adequate information bearing on this subject. Hence, screening questions were clearly stated in the questionnaire to select the right respondents who met the population of interest criteria. Malaysia was selected as the research context of this study because in the South-East Asian region, it has been considered to be one of the fastest-growing countries for Airbnb’s accommodation service for the second consecutive year, having registered a growth of 73% – a total of 53,000 listings (Mahalingam & Inn, 2019). Non-probability judgment sampling was used to collect the data because of the lack of a readily available sampling frame (Cavana et al., 2001). Moreover, the main concern of the study was to generalize results to theory rather than population (Calder et al., 1981; Saleki et al., 2021). Data were collected by conducting a Web-based survey, and the questionnaire link was disseminated via e-mail, Facebook and WhatsApp. This process generated 326 responses.

In line with scholars’ suggestions (Hair et al., 2010; Kline, 2011), a 1:15 rule of thumb (i.e. one item requires fifteen observations) was considered to determine the optimum sample size. Following this rule, the minimum sample size for this study was required to be 270 (18 items x 15 cases). As such, the sample of 326 respondents was justified. A total of 15 cases were deleted owing to outlier and missing value issues. Eventually, 311 questionnaires were found usable for further analysis.

Analysis and Findings

Profile of the Respondents

With regard to the respondents’ demographic profile, 59.8% of respondents were male, and 40.2% were female. The ages of most of the respondents ranged between 26 and 35 years (52.2%). In the case of ethnicity, 51.8% of the respondents were Malay, followed by Chinese (27.3%), Indian (16.4%) and others (4.5%). In addition, most of the participants (48.6%) were married, and 47.6% were single. Most of the respondents held a bachelor’s degree (53.7%), followed by a master’s degree (35%). In terms of profession, respondents working in administrative and managerial positions (31.5%) constituted the largest group. Lastly, the monthly income of most of the respondents ranged between RM4001 and RM5000/per month (25.1%).

Assessment of Model Using PLS-SEM

To examine the hypothetical model of this research, Partial Least Square Structural Equation Modeling (PLS-SEM) using SmartPLS 3.2.8 software was utilized (Ringle et al., 2015). This statistical technique aligns with this study’s objective of predicting the endogenous constructs and maximizing their explained variance (Hair et al., 2017; Sivadasan et al., 2020). In addition, PLS-SEM can analyze the measurement model and structural model simultaneously, yielding more accurate results (Quoquab et al., 2020). Furthermore, it can handle complicated models featuring many relationships (Quoquab & Mohammad, 2020). Following Anderson and Anderson and Gerbing’s (1988) two-stage approach, the measurement model was assessed, and the structural model was evaluated. The measurement model reflects the relationship between the construct and its relevant items, whereas the structural model represents the relationships between the exogenous and endogenous constructs (Hair et al., 2017).

Measurement Model Assessment

The measurement items of the study constructs are all reflective. Hence, it is imperative to assess their composite reliability, convergent validity and discriminant validity (Mohammad et al., 2021a; Tan et al., 2017). The reliability of constructs can be assessed based on factor loading and composite reliability (Henseler et al., 2009; Quoquab et al., 2019). As shown in Table 1, the loadings of each item on its respective construct are all above the threshold value of 0.60 (Chin, 1998). Moreover, the composite reliability for all constructs is above 0.70. This confirmed the model’s reliability at the item and construct levels (Hair et al., 2017; Shahrin et al., 2020).

Convergent validity was assessed on the basis of AVE (Fornell & Larcker, 1981), which represents the shared variance between the construct and its relevant items. The AVE of each construct must be greater than 0.50 to establish the convergent validity (Henseler, 2017). The results show that AVE for trust in Airbnb (TA) is slightly lower than the cutoff value. Therefore, TA5, which has the lowest loading, was dropped to produce improvement in the AVE (Ping, 2009). As Table 2 shows, all constructs possessed AVE values greater than 0.50. Thus, the convergent validity of the study constructs was assured.

Table 2. Evaluation of the measurement model.

Construct	Item	Loadings	Composite Reliability	AVE
Reputation	RPT1	0.760	0.823	0.608
	RPT2	0.783
	RPT3	0.796
Price Consciousness	PC1	0.628	0.767	0.526
	PC2	0.724
	PC3	0.813
Trust in Airbnb	TA1	0.749	0.808	0.513
	TA2	0.652
	TA3	0.749
	TA4	0.711
Perceived Value	PV1	0.742	0.770	0.529
	PV2	0.670
	PV3	0.765
Guest Booking Intention	GBI1	0.813	0.853	0.595
	GBI2	0.824
	GBI3	0.810
	GBI4	0.620

Note. TA5 was deleted to increase AVE

Discriminant validity was assessed to demonstrate that each construct possesses distinctive characteristics that make it different from other constructs in the structural model. Statistically, discriminant validity is confirmed if the square root of each construct’s AVE is higher than the correlation with other latent variables (Fornell & Larcker, 1981). Table 3 shows that discriminant validity was established as all diagonal values (AVE) are higher than the corresponding values in column and row. In addition, the heterotrait–monotrait (HTMT) method was used to confirm the discriminant validity (Henseler et al., 2015). The cutoff value of HTMT needs to be less than 0.85 (Kline, 2011). As Table 4 shows, HTMT values were lower than 0.85, supporting the discriminant validity of all constructs.

Table 3. Discriminant validity (Fornel-Larcker’s method).

	GBI	PC	PV	RPT	TA
GBI	0.772
PC	0.314	0.725
PV	0.451	0.386	0.727
RPT	0.426	0.276	0.403	0.780
TA	0.479	0.267	0.458	0.476	0.716

Table 4. Discriminant validity (HTMT method).

	GBI	PC	PV	RPT	TA
GBI
PC	0.481
PV	0.695	0.626
RPT	0.565	0.448	0.647
TA	0.652	0.445	0.753	0.683

Note. GBI – Guest Booking Intention, PV – Perceived Value, TA – Trust in Airbnb, RPT – Reputation, PC- Price consciousness

Structural Model Assessment

The goodness of the structural model can be determined based on the significance level of path coefficient and coefficient of determination R2 (Hair et al., 2017; Mohammad et al., 2021b). Path coefficients and their corresponding t-values were generated via the PLS algorithm and bootstrapping procedures with 5000 resamples. As Table 5 shows, reputation (β = 0.480, t = 9.700, p < .000) positively and significantly affect consumers’ trust in Airbnb, thus supporting H1. Moreover, trust in Airbnb (β = 0.264, t = 4.328, p > .000) exerted a significant positive effect on guest booking intention, supporting H2. Also, price consciousness (β = 0.389, t = 7.542, p > .000) demonstrated a significant positive effect on consumer perceived value, thus supporting H3. In addition, perceived value (β = 0.214, t = 3.387, p > .001) positively and significantly affects guest booking intention, thus supporting H4. Furthermore, the direct link between reputation and guest booking intention (β = 0.184, t = 3.371, p > .001) was supported, confirming H5. Also, the relationship between price consciousness and guest booking intention was supported (β = 0.113, t = 2.017, p > .05), confirming H6.

Table 5. Results of structural model (Direct relationships).

Hypothesis	Relationship	Path Coefficient	STD	t-value	R^2	Q2	F^2	Supported
H1	RPT -> TA	0.480	0.049	9.700	0.227	0.108	0.293	Yes
H3	PC -> PV	0.389	0.051	7.542	0.147	0.072	0.172	Yes
H2	TA -> GBI	0.264	0.061	4.328	0.336	0.182	0.072	Yes
H4	PV -> GBI	0.214	0.063	3.387			0.047	Yes
H5	RPT -> GBI	0.184	0.054	3.371			0.036	Yes
H6	PC -> GBI	0.113	0.055	2.017			0.016	Yes

Next, the R2, which represents the model’s explanatory power, was evaluated (Henseler et al., 2009). R2 values of 0.26, 0.13 and 0.02 are considered substantial, moderate and weak, respectively (Cohen, 1988). As shown in Table 5 and Figure 2, the study found that the combined effect of the independent variables (price consciousness, reputation, trust in Airbnb and perceived value) explains substantial variance in the guest booking intention (R2 = 0.336). The R2 of trust in Airbnb was 0.227, i.e., reputation explained 22.7% of the variance in trust in Airbnb. In contrast, consumer perceived value had an R2 of 0.147, indicating that price consciousness explained 14.7% of the variance in perceived value. Furthermore, F2, which represents the individual effect of the exogenous construct in explaining variance in the endogenous construct in the structural model, was tested (Hair et al., 2017). According to Cohen (1992), 0.02, 0.15 and 0.35 depict small, medium and large effect sizes. The results generated and presented in Table 4 showed that price consciousness (0.016), reputation (0.036), trust in Airbnb (0.072) and perceived value (0.047) exert a weak effect on the guest booking intention. Reputation (0.172), on the other hand, was found to exert a moderate effect on trust in Airbnb, and the effect of price consciousness on perceived value too was found to be moderate (0.293). Additionally, Stone-Geisser’s Q2 value was calculated using the blindfolding procedure to evaluate the predictive power of the structural model (Geisser, 1974; Stone, 1974). The Q2 value, which is larger than zero, is considered to have predictive power (Henseler et al., 2009). As shown in Table 5, all Q2 values (GBI = 0.182, PV = 0.072, TA = 0.108) are greater than 0, confirming the predictive power of the structural model.

Figure 2. Structural model.

The mediating effect of trust in Airbnb and perceived value was tested via bootstrapping procedures with 5000 resamples (Preacher & Hayes, 2008). As Table 5 shows, the results of the indirect effect indicate that trust in Airbnb significantly mediates the relationship between reputation and guest booking intention (β = 0.083, t = 2.961, p <.01). Also, the relationship between price consciousness and guest booking intention is significantly mediated by consumer perceived value (β = 0.127, t = 3.849, p < .000). These results provide support for both H7 and H8, respectively (Table 6).

Table 6. Results of structural model (Indirect relationships).

Hypothesis	Path	Indirect Effect	STD	t-values	95% CI (LL – UL)	Supported
H7	RPT -> TA-> GBI	0.083	0.028	2.961	0.034–0.141	Yes
H8	PC-> PV-> GBI	0.127	0.033	3.849	0.068–0.194	Yes

Discussion

This study developed a framework to examine reputation, trust, price consciousness and perceived value as crucial antecedents of guest booking intention at Airbnb. Attribution theory was employed to develop this conceptual framework and justify all hypothesized direct and indirect relationships. PLS was used to examine this theoretical framework, and the result of the bootstrapping procedure in the SmartPLS software found support for all developed relationships. Specifically, reputation and trust were found to exert a positive and significant effect on guest booking intention on Airbnb, which is in agreement with previous research (see, Li et al., 2017; Shukor et al., 2019), that found positive relationships between reputation, trust and customer intention to book with the traditional service provider (i.e., the hotel). This indicates that consumers will probably patronize a service provider, whether new or existing, depending on the level of reputation and trust he/she perceives (Revilla-Camacho et al., 2017). In addition, this study confirmed the positive effect of price consciousness and perceived value on guest booking intention at Airbnb accommodation. These results are in line with the findings of past literature (see, Bhatiasevi & Yoopetch, 2015; Chen & Chang, 2018; Correia & Kozak, 2016) that have shown a significant association between price consciousness, perceived value and intention to book at a hotel. The results also agree with attribution theory, which argues that guest booking intention to stay at Airbnb can be attributed to internal (trust, perceived value, price consciousness) and external (reputation) causes.

Furthermore, the empirical results show that both “trust in Airbnb” and “perceived value” serve as important mediators between exogenous variables (reputation and price consciousness) and the outcome variable (guest booking intention). This means that if an individual perceives the service provider Airbnb as having a good reputation, his/her trust in Airbnb will increase, and eventually, his/her booking intention on the Airbnb platform will be enhanced. In addition, the results reveal that Airbnb consumers’ price-consciousness level enhances their perceived value and therefore induces their booking intention. These findings are in consonance with attribution theory, which argues that internal and external causes (reputation, price consciousness) are likely to motivate individual psychological (perceived value, trust) status, eventually causing him or her to book at Airbnb.

Theoretical Contribution and Practical Implications

Theoretical Contribution

The current study offers both practical and significant theoretical implications. Theoretically, this is a pioneer study in using attribution theory to develop a framework to predict factors affecting Airbnb guests’ intentions in developing countries like Malaysia. According to this theory, consumers attribute their booking intention to internal and external causes. In other words, it explains how guests assign causes to an event or behavior that is influenced by their cognitive and affective processes. The result of the study provided support for all developed relationships, which confirms the usability and applicability of this theory in the tourism industry. Additionally, this result supports the more pragmatic hybrid proposition that holds Airbnb’s peer-to-peer accommodation guests often have different internal and external motivators to adopt the Airbnb services (Volgger et al., 2019; Wang & Nicolau, 2017).

In addition, this research has developed relatively new linkages, which can advance consumer behavior studies in the hospitality industry in a non-western context like Malaysia. Specifically, the effect of price consciousness on perceived value and the impact of perceived value on guest booking intention are comparatively new in the Airbnb context. The results showed that a consumer for whom price is a concern would perceive high value in booking at Airbnb, which in turn would increase his/her intention to book at Airbnb. Similarly, the links between reputation and consumer trust and trust in Airbnb and his/her booking intention are relatively new in the hospitality industry. The results showed that the good reputation of Airbnb can affect guests’ thoughts and feelings positively, thereby increasing their trust in the service provider, leading them to book at Airbnb. In addition, the mediating role of trust in Airbnb in the relationship between the external cause and guest booking intention is comparatively new. Also, the inclusion of perceived value as a mediator between “price consciousness” and “guest booking intention” is relatively new in the literature. More clearly, the study highlights how consumers assign causes to an event, such as booking intention through their cognitive (perceived value) and affective states (trust) in Airbnb.

Furthermore, this study has confirmed the validity and reliability of all scales developed and used in a western context, confirming the applicability of these measures in developing contexts like Malaysia. Finally, this study provides a new avenue for future studies to further consider internal (dispositional) and external (situational) factors that can predict guest intention to book through Airbnb in a non-western context like Malaysia.

Practical Implications

The findings of this study provide significant insights to the tourism industry’s policymakers and practitioners in understanding the drivers of tourists’ booking intentions at Airbnb. Particularly, accommodation managers would do well to formulate strategies based on how tourists view and interpret causes that may deduce their booking intention. Furthermore, the study provides online accommodation managers with a better understanding of how attributed reputation can influence consumers’ trust in Airbnb and eventually lead to booking intention. Given this notion, accommodation managers must pay particular attention to creating and cultivating a firm’s reputation to retain their competitive advantage and stay relevant in the tourism market, particularly when reputation is a critical determinant of consumers’ booking intention. Therefore, when Airbnb’s reputation is considered good in terms of recognition formed from previous users, this tends to enhance consumers’ intention to book accommodation with Airbnb. For this reason, It is essential to promote the activities that can enhance Airbnb’s recognition and popularity to the consumer/tourist.

The results show that price consciousness is crucial in attracting tourists to book accommodation on Airbnb. On the positive front, the service provider can entice consumers by providing a more attractive price that seems more reasonable than that of competitors, thereby inducing booking intention. The findings of this study acknowledge that trustworthy relations between the consumer and Airbnb have a significant and positive effect on consumer booking intention. Consumers are likely to patronize a service provider once they trust the relationship. From the consumers’ perspective, their decision to purchase or use a service depends on their level of confidence in such a service provider, as it is vital for them to feel assured that service providers will deliver on what they promised. Airbnb must enhance the potential customers’ trust through their trustworthiness, commitments, and keeping the promise made to the consumers. By making efforts to understand consumers’ needs and put in place policies and strategies that will reduce their perception of risks and uncertainties. It is more likely for consumers to trust and feel that the service provider has their best interest in mind. Airbnb’s online accommodation service needs to provide a good value for money and make their service worth what the consumers paid.

The findings of this study are expected to help policymakers and practitioners associated with the hospitality industry, particularly in their marketing efforts, attract more tourists to engage in a long-lasting relationship with the service provider, especially by incorporating the identified internal and external causes. In addition, lessons learned from Malaysia could serve as an essential reference to other regions in understanding guest booking intention based on attributed causes.

Limitations and Future Research Direction

The study provides significant insights into the guest booking intention on Airbnb, yet is not beyond certain limitations that, in themselves, also provide ground for future researchers. This study used a cross-sectional survey design that may raise a question about causality; future research may adopt a longitudinal survey design to overcome this issue. Also, the current study investigated the tourist booking intention, whereas the scholars may potentially focus on booking behavior to understand the actual behavior of the tourists. Furthermore, future studies may consider gender as a moderator to understand the role of gender differences among consumers in their booking intention. It is possible for female consumers to perceive a higher risk in engaging or booking accommodation on Airbnb as compared with male consumers. Lastly, future studies may compare the findings of this study with the guest booking intention in the conventional hotel industry.

Acknowledgements

Open Access funding provided by the Qatar National Library.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Appendix

Table

Construct	Items	Source
Reputation	Airbnb is a large company that everyone recognizes.	Kim et al. (2013)
	Airbnb is well-known accommodation service.
	Airbnb has a good reputation for its accommodation service.
Price consciousness	When it comes to choosing accommodation service, I rely heavily on price.	Campbell et al. (2014)
	I am a price-conscious buyer when it comes to choose an accommodation.
	I purchase lowest priced accommodation that suits my needs.
Trust in Airbnb	Airbnb is trustworthy.	Liang et al. (2018)
	Airbnb gives impression that it keeps promises and commitments.
	I believe that Airbnb has my best interests in mind.
	I think Airbnb will keep promises it made to me.
	I think Airbnb wants to be known as one who keeps promises and commitments.
Perceived value	I think Airbnb provide good value for money (e.g., reasonably priced).	Chen and Chang (2018)
	I think Airbnb can help with special requirements (e.g., finding pet-friendly accommodations, arranging a small bed for baby etc.).
	I think booking for accommodations on Airbnb is worthwhile.
Guest booking intention	The probability that I would consider booking an accommodation from Airbnb is high.	Xu and Schrier (2019)
	If I were to book an accommodation, I would consider booking it from Airbnb.
	The likelihood of my booking an accommodation from Airbnb is high.
	My willingness to book an accommodation from Airbnb in the future is high.