Multinationalization stimulated the growth of world tourism, and, with technological advances, the era of innovations was born. This led to a process of changes and adaptations concerning the services offered by organizations such as Airbnb and hotels. According to Guttentag (2015), Airbnb is a platform where common people rent their spaces as accommodation for tourists. The rental model is carried out in a very simple way, where the owners, called “hosts”, advertise their property for free on the platform's website, and interested parties (guests) can effectively book accommodation available anywhere in the world.

Regarding hotels, the services are linked to the needs of tourists and travelers, consisting of hotels, inns, and pensions that provide services, mainly to tourists (Mello & Goldenstein, 2016). According to Duduche et al. (2011), hotels can be considered as “service providers whose main activity is accommodation, differing from other economic activities, depending on their customers for their operation” (p.106). Hotels can be classified as a service industry, with peculiar characteristics, with the main objective of providing accommodation, food, leisure, safety, and comfort to guests (Ferrando, Prusaczyk, & Tejera, 2012; Maurício & Ramos, 2011).

It is indisputable that services such as Airbnb and hotels, both of which provide accommodation, have tourists and travelers as their main audience. However, customer needs are different to those who decide for a more cautious experience, and others who choose a more singular experience (André, 2018).

The growing demand for economy-sharing providers in several consumer markets tends to increase competition in the hotel sector. Considered its main adversary, Airbnb has been identified as the largest global provider of peer-to-peer accommodation services, after a sequence of acquisitions (Almeida, Costa, & Simões, 2014).

André (2018) portrays that the hotel industry at a global level has endeavored to promote new brands and concepts, to better compete with Airbnb. The author notes the need for hotels to create new models of lodging services, offering a more authentic experience. On the other hand, Elliott (2016) mentions that themes such as equitable conditions, occupancy rates, and non-compliance with legal health and safety requirements, formed several criticisms by the hotel sector about the models of the sharing economy.

Guttentag et al. (2017) show that users of the Airbnb platform can be divided into market segments, according to the motivations for which they prioritize the use of this platform. Some motivations comprise the most practical aspects (price, space, and location), while others have more experimental characteristics (novelty, interaction, and experience factor). The price factor has repeatedly stood out as one of the essential motivators. Still, factors such as sustainability, authenticity, space, and interaction are also reported by consumers in this service segment (Guttentag et al., 2017).

Ariffin (2013) and Hamari et al. (2015) suggest that the dimensions of ethical consumption and hospitality play an important role in creating memorable experiences. It is up to hotels to further expand the hosting experience, incorporating additional dimensions that can be critical to their guest experiences (Teng, Wu & Liu, 2015). Castillo and Quintero (2013) announce that the development of the hotel industry must accompany the growth of the tourism sector, being attentive to the demands that manifest themselves with technological evolution and new generations of customers. Environmental factors such as economy, consumer behavior, and social networks, directly affect the hotel sector (Becerra, Santaló, & Silva, 2013).

Generation Y, also known as Millennials, are part of a considerable number of consumers, but not just the only ones in Airbnb's strategic focus (Airbnb Citizen, 2021). In this sense, hotels seek, through a differentiated strategy, to offer a more homely experience and interaction with the people who use them (Oskam & Boswijk, 2016).

Due to the competition with Airbnb, in 2016 the Accor group integrated its structure in the startup “Onefinestay”. This model is characterized by the short-term rental of private homes. Although Accor belongs more to the luxury segment, the principle of this initiative is to offer stays in private homes, providing a more familiar and unique experience for guests (Onefinestay, 2017). Known as Rent by Season, the model is defined through the establishment of partnerships between independent organizations of local tourist accommodation. Created by the Choice Hotels group, owner of Clarion, Comfort Inn, Quality Inn brands, among others, this business model is carried out only in the United States of America. The client does not have a direct relationship with the group, only with partner entities for renting beach houses, tents, condominiums, among others (Vacation rental, 2021).

Regardless of the strategy used, according to André (2018), the hotels' service still presents an imprecise scenario regarding the tendency to resemble the concept of services established by Airbnb, essentially about the effective results and the re-acquisition of its market share. According to Soares (2017), the expansion of brands in virtual domains and the creation of competitive solutions for consumer loyalty as the main challenges emerging from the hotel sector, with greater reflex in the commercialization and strategic development.

According to Oliver (1999), loyalty consists of “a profound behavior in repurchasing or favoring a product/service in the future, causing a repetition of the brand [...], notwithstanding situational influences and marketing efforts have the potential to cause an exchange behavior” (p.34). According to Li and Petrick (2008), behavior can be understood as a predecessor of loyalty, and exchange behavior must be seen as a profitability driver (Anderson & Mittal, 2000).

We can understand behavior as a precursor of loyalty, especially when the customer purchases the service again, expresses favorable comments about the hotel, its products, and services, becomes less susceptible to prices, and contributes with suggestions regarding products and services (Kotler & Keller, 2006).

For users of the Airbnb platform, the way the customer behaves, acts, or reacts to a given situation, in the face of inconvenience when experimenting with the services, can be considered as the most common manifestation of loyalty in the hospitality and tourism literature. It is defined as "a deep psychological commitment to repurchase a product or reproduce a service in the future" (Oliver, 2010, p.23). Thus, service providers must invest in the creation of facilities, providing users with unique and unforgettable experiences, with the purpose of customers to reuse the offers (Kim, Brent, & McCormick, 2012).

The quality of the services provided by segments considered traditional, as the hotels' sector, must be offered in a way that the clients perceive the quality, and their expectations about it are realized. Services must be provided within detailed parameters of the sector, enabling changes to take place, and quality to evolve, to win the loyalty of current and potential customers (Souza, Meira, & Maske, 2012). To increase the level of customer loyalty it is necessary to add factors that substantially instigate customer behavior (Kim, Brent, & McCormick, 2012).

According to Ribeiro (2011), diversifying and increasing the quality of services provided are essential for developing customer loyalty. The author explains that investments in improving the quality of accommodation, facilities, and agility in service imply a better balance of costs and increased revenue. For satisfaction to be measured, it is necessary to understand the customer's perception of the product or service. In this sense, Albrecht (1992) mentions that identifying the characteristics of the product or service becomes crucial to create a competitive differential to competitors.

Oliver (2010) defines customer satisfaction as "the contentment of the consumer when judging the products or services offered, which may be greater or lesser according to their assessment" (p.13). This definition was summarized in Solomon's (1999) statement, defining satisfaction as a reaction, or feeling, about an expectation.

According to Longenecker, Petty, and Moore (1997), service excellence is nothing new for service providers looking to expand their competitive advantage. According to the authors, there are three factors responsible for the increase in the level of dissatisfaction: a) the behavior or performance contributes to 20% of the dissatisfaction; b) unnecessary rules and procedures contribute 40% of dissatisfaction; c) and the remaining 40% is due to improper use of products by customers. Excellent service and the intensity of pleasant memories of guest experiences influence word-of-mouth communication and overall satisfaction (Vanhamme, 2000).

According to Hoffman (2001), measuring the level of customer satisfaction significantly contributes to hosting service providers to increase their customers' satisfaction and loyalty. It allows the company to recognize possible problems, identify the degree of service provided by employees, compare the company's performance with the competition and, mainly, disseminate the concept of caring for the well-being of customers. The customer recognizes the quality of service through the people who perform them (Lovelock & Wright, 2006).

Santos, Vassallo, and Rabahy (2009) show that satisfaction with the products and services offered is based on the customer's perception of structural and human components. Renovating the facilities and having modern equipment is essential to increase the customer's perception of quality. In addition, the perception of human components can be considered a characteristic of the reliability of the service provided, putting into practice the promises made to customers, being solicitous, solving impasses, keeping the information registered by the customers updated. Lu and Shiu (2011) define the training of employees as essential, raising their level of competence to improve communication with customers and their perception of the services offered.

Constantinides (2014) mentions that social media act as a marketing strategy, providing greater brand exposure, reducing advertising costs, bringing greater satisfaction and recommendation of products and services by customers, and, consequently, increasing revenue. Customers seek to enhance the relationship between the cost of purchasing the product or service and the benefit received, increasing the level of satisfaction with the services provided.

Among the perspectives of guests using Airbnb is having different experiences in each place visited. Thus, the main aspects analyzed by customers can be considered the price paid to obtain the service, and the costs of time, research, convenience, feelings, and people's behavior (Cabral, 2015).

Given the reflections on loyalty and satisfaction, two hypotheses were made (Table 1), as follows:

Table 1
Research Hypotheses

Source: research data (2021).

The three hypotheses based on the theoretical framework were tested empirically, and how this occurred is presented below.

The instrument for data collection, containing three sections, was appropriated from previous works (Table 2). The first section, containing 22 statements in five dimensions (attendance; installation; reliability; service; value), measures the services’ performance; and the six variables of the second section assess customers’ loyalty and satisfaction. All the statements in both sections use a seven-point interval scale, ranging from (1) very dissatisfied to (7) very satisfied. And the third section of the instrument gathers the respondents’ demographics and general characteristics.

Table 2
Dimensions and statements that make up the research instrument.

Source: Prepared by the authors (2021).

The first version of the data collection instrument was submitted for content validation through the analysis of fifteen specialists (researchers and managers) with knowledge acquired from experiences lived by users of Airbnb and hotels services. Then, data was collected by Internet, using google forms. The survey data were collected between October 2020 and January 2021 throughout Brazil.

The sample is non-probabilistic and obtained by convenience, composed by respondents who were willing to participate in the survey by answering the data collection instrument, separated into two groups (Airbnb; Hotels) based on the last stay (Hair Jr. et al., 2009).

The total collected sample is 665 respondents. Of these, 645 answers were validated, with complete data, without filling errors, low variance, or incompleteness, this being the final sample size of the research. The data were tabulated in an Excel® spreadsheet, imported, and treated with the help of statistical software SPSS® (Statistical Package for the Social Sciences) version 22. The sample characteristics are shown in Table 3.

Table 3
Distribution of respondents

Source: research data (2021).

To assess the influence of the quality of Airbnb and Hotel services on customer satisfaction and loyalty, the Structural Equation Modeling [SEM] procedure was used using the software Amos Graphics® version 21, as this is the appropriate analysis system for the sample size.

Based on the proposed research instrument, we proceeded with descriptive statistics to better understand the perception of users of Airbnb and Hotels, observing the level of performance and the impact of the investigated dimensions on loyalty and satisfaction before and during the pandemic.

Specifically, in this study, we model five first-order dimensions to assess the level of satisfaction of the services offered, which, together, form a second-order construct (Hosting services) – combining the list of services provided to Airbnb and or hotels users. For Astrachan, Patel, and Wanzenreid (2014), methods based on Structural Equation Modeling are essential in the development and expansion of theories, in particular, when second and even third-order factors provide a better understanding of the relationships that may not be initially apparent.

Finally, a comparison was made between the level of service satisfaction observed by the researched hosting service and the average loyalty and satisfaction, using t-test. Differences between groups are characterized by the criterion: level of service satisfaction. The Student’s t parametric test is used to test the hypothesis that two means of a quantitative variable are equal, considering two independent groups. (Gaddis & Gaddis, 1990).

To test whether the data fit the model based on the theoretical framework, unidimensionality and convergent validity were verified through Confirmatory Factor Analysis [CFA]. To verify if the reliability levels of the model are acceptable and, thus, to conclude if the dimensions that compose it are significant, the following criteria were applied: factor loading > 0.700; T-statistic> 1.8; p-value <0.050; and R²> 0.500 (Hair Jr. et al., 2009). Only three statements did not meet the defined criteria, being excluded from the model. After this deletion, all claims were confirmed.

The results (Table 4) exceeded the recommended values: the Cronbach's Alpha [CA] and the Composite Reliability [CR] were above 0.700 and the Average Variance Extracted [AVE] was greater than 0.500 (Hair Jr. et al., 2009).

Table 4
Values of reliability and validity of the structural model

Source: research data (2021).

Convergent validity was attested by the values of Average Variance Extracted (AVE), that consider adequate values greater than 0.50 (Fornell & Larcker, 1981). The AVE was greater than 0.5, indicating that there is convergent validity. The Composite Reliability exceeded 0.8 in all the factors, surpassing the recommended minimum of 0.7 (Henseler, Ringle, & Sinkovics, 2009; Tenenhaus et al., 2005).

The discriminant validity was assessed by the Fornell-Larcker (1981) criteria (Table 5), what establishes that the square root of each factor’s AVE must be greater than the correlation between the factor itself and all the other factors in study (Brambilla, 2011). As can be seen at Table 5, all the AVEs square roots (in the main diagonal) exceed the corresponding correlations, what attests the presence of discriminant validity.

Table 5
Discriminant validity using the Fornell and Larcker method

** . The correlation is significant at the 0.01 level (2 ends). Source: research data (2021).

To evaluate the structural model, we examined the values of the path coefficient, its statistical significance, and the determination of the coefficient (R²). Figure 1 shows the structural model with the path coefficient estimated in the model itself.

The indices (Table 6) indicated a good fit of the model to the data, obtaining the CMIN/DF of 3.796 (Chi-square (χ2) of 1013.580 divided by the degree of freedom (df) of 267), which compares the matrix of covariance with the observed matrix. A value less than 5.0 is recommended, is statistically significant at the p-value level of 0.000 (Byrne, 2010; Hair Jr. et al., 2009). The Goodness of Fit Index [GFI] was 0.880, with a GFI index > 0.900 being recommended. However, the index obtained is at the level of peripheral acceptance (Hair Jr. et al., 2009). The Comparative Fit Index [CFI] of 0.953 and the Tucker-Lewis Index [TLI] of 0.947 reached the value proposed in the literature (CFI > 0.900 and TLI > 0.900). The Root Mean Square Error of Approximation [RMSEA] presented the index of 0.066, respecting the suggested index of 0.04 to 0.08. The results obtained are summarized in Table 6.

Table 6
Model Fit Indices

Source: research data (2021).

It should be noted that the second-order construct, using together with the dimensions of hosting services, contributed to a more parsimonious model, presenting a significant improvement in the Goodness-of-fit parameters (CMIN/DF; GFI; CFI; TLI; RMSEA). For Hair Jr., Gabriel, and Patel (2014), modeling with a second-order construct presents more theoretical parsimony and reduces the complexity of the model.

E-mail: trentinluciano@yahoo.com.brE-mail: aline.espig@gmail.comE-mail: gersontontini@gmail.comE-mail: profjuliosilva72@gmail.com