The finding of this research indicated that while economic benefit, usefulness website, home benefit, hostguest relationship and authenticity have an impact on customer perceived value, Covid19 increase risk perception of tourist, therefore, decreasing Airbnb repurchase intention. HostGuest relationship contributes a considerable impact on perceived value and overall perceived value largely decide customer repurchase intention. In contrast, perceived risk indicates the little effect on repurchase intention even in this unstable period. At the end of the research, suggestions for developers are mentioned for future improvement.
Introduction
Background to the topic
The sharing economy (SE) is a disruptive business model that emerged with the evolution of Internet technologies, particularly Web 2.0 While the concept of sharing is not new, the recent decades have seen the rise of sharing products and services for profit between strangers, driven by digital technologies Terms such as collaborative economy and peer-to-peer economy are often used interchangeably with SE In 2015, the Oxford Dictionaries defined the sharing economy as “an economic system in which assets/services are shared between private individuals, either for free or for a fee, typically by means of the Internet.”
The sharing economy is transforming various industries, particularly tourism, by altering resource sharing, business models, and customer behavior (Pusshmann) In tourism, the sharing economy can be categorized into four main areas: transportation services like Uber and Grab, dining experiences through platforms such as Eatwith, tour guide services offered by Vayable, and accommodation options including Airbnb, Homeaway, and Couchsurfing (Ert et al., 2016) Companies like Airbnb actively identify themselves as key players in the sharing economy.
Since its introduction in Vietnam in 2015, Airbnb has attracted millions of customers and hosts, with over 50,000 listings established within just four years This platform offers a unique living experience through homestays, reflecting a significant rise in demand for alternative accommodations among tourists.
Since its establishment in 2007, numerous studies have focused on Airbnb, exploring various aspects such as customer satisfaction (Bai et al 2008; Fang et al 2014; Mửhlmann 2015), motivations for using the platform (Johnson 2013; Guttentag et al 2017; Tran and Filimonau 2020), and comparisons with the hotel industry (Tussyadiah and Zach 2015; McGowan and Mahon 2018; Li et al 2020) Recently, there has been a growing interest among researchers in identifying factors that influence repurchase intention (RI) (Matute et al 2016; Liang et al 2017; Amaro et al 2018).
Research by Liang et al (2017) indicates that the relationship intention (RI) in Airbnb bookings can be evaluated by comparing perceived value (PV) and perceived risk (PR) The study highlights the importance of factors such as pricing, unique experiences, and positive e-word of mouth in influencing RI This finding aligns with Matute et al (2016), which emphasizes the impact of e-word of mouth on purchase intentions Additionally, further studies by Amaro et al (2017) reinforce these insights.
(2018) try to build an integrated model in order to examine the interactions between multiple factors and determine how customer RI is affected.
Reasons for choosing the topic
Maintaining repeat customers is crucial for the sharing economy, particularly for Airbnb, as these customers can easily switch to traditional accommodations like hotels Understanding the factors that influence Airbnb's repeat intention (RI) can significantly contribute to sustainable business development Therefore, it is essential to identify the antecedents that drive customer loyalty in this sector.
The significance of Risk Intelligence (RI) has been established through various studies, yet there is limited literature on RI within the context of Airbnb, particularly in developing countries like Vietnam This research aims to explore RI during the recent pandemic, an unstable period for the tourism sector Previous risk assessment studies by An et al (2010), Chiu et al (2014), and Jun (2020) provide a foundation that can be adapted to create a new model for this investigation Ultimately, this study seeks to address existing gaps in the literature, enhance understanding of RI in the Airbnb context, and inspire further research in this area.
Industry rationale
Despite Airbnb's strong global reputation in the tourism and hospitality sectors, research on its return on investment (ROI) remains limited and primarily focuses on a few major urban centers (Bong and Seo, 2018) In today's highly competitive digital economy, the intention of consumers to repurchase has become a vital factor for online vendors and businesses (Jun, 2020) From a marketing perspective, retaining existing customers can cost five times more than acquiring new ones, shifting the focus of eCommerce businesses from attracting new customers to fostering loyalty to encourage repeat purchases Additionally, studies indicate that a mere 5% increase in customer loyalty can lead to profit increases ranging from 25% to 75% (Amanda).
2014), thus it is important to find the factors that affect the RI of online shopping consumers.
Research aim and objectives
The main purpose of this study is to “explore and identify the factors that influence Vietnamese travellers’ Airbnb repurchase intention in the post-pandemic (COVID-19) context.”
Hence, the following objectives need to be achieved:
1 To investigate the relationship between past experience, current situation and future repurchase intention of Airbnb customers
2 To examine the impact of economic benefit, usefulness website, home benefit, host- guest relationship and authenticity on perceived value
3 To test the impacts of the Covid-19 pandemic in Vietnamese travellers’ Airbnb perceived risk, hence, leading to repurchase intention
4 To give possible recommendations for further research
In order to make a valid and comprehensive research of the topic, appropriate research techniques are required to be employed in this study.
Structure of the dissertation
This study is divided into five chapters Each chapter is summarily described as below:
The first chapter outlines the background of the topic, highlighting both academic and practical motivations for selecting this dissertation subject It also specifies the aims and objectives of the research Chapter 2 provides a comprehensive literature review, analyzing existing studies and theories related to the topic.
In the upcoming chapter, the researcher will analyze existing literature on Airbnb's establishment and the factors influencing its rise, which will be further explored in Chapter 3 The goal is to comprehensively review all available academic resources pertinent to the topic, including books, journals, reports, and various online materials sourced from libraries and the internet.
To achieve the stated aims and objectives, this chapter outlines the specific research techniques employed, including the determination of sampling methods, sample size, questionnaire design, data collection processes, and the data analysis methods that will be discussed in the subsequent chapter.
This chapter presents the main findings, after the data collection process The SPSS system is used to produce the result, findings are also mentioned and discuss
The final chapter reiterates the aims and objectives of the discussion, summarizing the results obtained It also highlights the academic contributions made and offers recommendations for the industry, while addressing the limitations encountered and suggesting implications for future research.
Literature review
Introduction
This chapter reviews the existing literature on the Airbnb phenomenon, introducing its rise and examining prior studies within this context It defines the dependent factor, referred to as RI, alongside independent factors, supported by relevant literature to establish hypothetical models Additionally, this study aims to develop a new model to analyze how purchase decisions are influenced by these variables.
Airbnb
Positioning the company with its business model is the sharing economy (Oskam & Boswijk,
Airbnb, a tech-unicorn company established in 2016, revolutionized the rental market by utilizing spare resources and technology to create an innovative business model This online marketplace enables users to list, search, and book short-term accommodations, capturing approximately 20% of the global short-term rental market by 2019 With the market valued at around $87 billion in 2020, Airbnb was projected to generate revenues of up to $20 billion However, the COVID-19 pandemic significantly impacted these growth forecasts, leading to a decline in travel and forcing Airbnb to lay off 25% of its workforce, with 2020 revenues expected to drop by half.
Airbnb has become the largest lodging provider globally, boasting over 7 million property listings worldwide Acting as a broker, the company does not own any properties but earns booking commissions from hosts and guests Users can easily find suitable accommodations by applying filters such as location, room type, dates, and price, and can search for unique homes, bed and breakfasts, or vacation houses Similar to traditional booking sites, guests must provide personal information and make advance payments according to the host's policy Hosts share detailed property information, including prices, guest capacity, location, home type, amenities, and house rules Additionally, the platform features a chatbox system for communication between hosts and guests, allowing both parties to leave reviews about their experiences.
Airbnb has emerged as a disruptor in the lodging industry, sparking diverse opinions regarding its impact Proponents argue that this model offers significant socio-economic benefits, including expanded destination choices, increased travel frequency, longer stays, and a wider range of activities for tourists (Tussyadiah and Péonen 2015; Heo 2016) Unlike traditional hotels, which cater to a more corporate experience, Airbnb appeals to travelers seeking authentic local interactions at affordable prices (Guttentag, 2015; Dogru et al 2020) Staying in a shared home allows guests to feel at home, engage with host families, and immerse themselves in local culture, all while enjoying cost-effective accommodation (Byers et al 2013; Kulshreshtha & Kulshrestha, 2019).
Numerous studies indicate that Airbnb significantly impacts traditional hotels, particularly in the low-end market (Roma et al 2019; Guttentag and Smith 2017; Kuo 2020) Additionally, research suggests that Airbnb contributes to rising long-term rental prices in various regions, as landlords shift to short-term rentals for better financial returns (Thompson 2018) Attitudes toward peer-to-peer (P2P) accommodation are polarized; one faction views it as a threat to tourism, with the accommodation sector and governments criticizing it for job insecurity, unfair competition, safety concerns, and tax evasion (Juul 2015) Conversely, startups and travelers generally hold a positive view of P2P platforms, highlighting their role in creating job opportunities, offering affordable lodging, and extending travelers' stays (Dolnicar, 2018).
Airbnb research has a limited history, spanning from 2008 to 2020, and covers various topics Studies have primarily focused on hosts' motivations for listing properties and generating income (Stern 2014; Gibbs et al 2018; Xie and Chen 2019), host behavior towards customers (Li et al 2015), regulatory concerns (Lee 2016; Edelman and Geradin 2015; Uzunca and Borlenghi 2019), the platform's system (Fradkin et al 2014; Ert et al 2015), and branding strategies (Yannopoulou et al 2013; Roma et al 2019) Additionally, research has explored Airbnb's impact on the traditional hotel industry (Neeser et al 2015; Zervas et al 2014), local communities (Guttentag 2013), and work environments (Fang et al 2015) Consumer experiences have also been studied, with Guttentag (2015) identifying Airbnb as a disruptive innovation in the market, driven mainly by economic factors However, Lane and Woodworth (2016) challenged the notion of Airbnb as the cheaper option, noting that in some urban U.S areas, Airbnb accommodations can actually be pricier than small hotels.
Tran and Filimonau (2020) explored the factors influencing consumer participation in home-sharing services like Airbnb, identifying key drivers such as local community involvement, past experiences, sustainability, and economic benefits Conversely, they noted that perceived value and trust issues regarding technology hinder user motivation These insights align with Guttentag et al (2017), highlighting a gap in existing literature on the factors affecting Airbnb consumers' repurchase intentions (RI) While previous studies have predominantly utilized qualitative methods, this research will adopt a quantitative approach to further investigate these dynamics.
Repuchase intention in Airbnb
Repurchase intention refers to an individual's assessment of the likelihood of purchasing a service again from the same company, influenced by their current situation and future circumstances (Hellier et al 2003) Numerous studies have investigated repurchase intention in online settings, examining various antecedents and research models (Wu and Chang 2007; An et al 2010; Chiu et al 2012; Fang et al 2014; Matute et al 2016; Talón-Ballestero et al 2019) The findings indicate that internal factors such as customer satisfaction, trust, and technology awareness, along with external influences like social media, information quality, electronic word-of-mouth, and brand credibility, significantly impact the intention to repurchase.
Research on the relevance of Return Intention (RI) in the context of Airbnb has gained traction among scholars from 2014 to 2020, with key studies highlighting the influence of perceived value factors—such as positive attitudes, subjective norms, economic benefits, and unique accommodations—on RI, while perceived risks present a contradictory effect (Matute et al 2016; Tussyadiah and Pesonen 2016; Liang et al 2017; Chen and Chang 2018; Kim 2019; Lee et al 2020; Amaro et al 2018) Additionally, Jun (2020) found that brand credibility and past experience positively correlate with RI, although this relationship is negatively influenced by perceived risks Overall, the literature indicates that both perceived value and perceived risk are crucial in predicting Return Intention in the Airbnb market.
Online booking is inherently risky due to its intangible nature, making risk perception a crucial factor in customers' pre-purchase decisions (Park et al 2016, pp 467–480) The significant growth of Airbnb over the past 12 years, coupled with the profound effects of the COVID-19 pandemic, highlights the relationship between customer-perceived value and the associated risks for the company Thus, understanding the interplay between value and risk is vital for predicting repurchase intentions in this volatile environment This research aims to enhance existing literature by examining the perceived value of Airbnb customers and the risks they associate with the COVID-19 pandemic.
Perceived value
Perceived value plays a crucial role in understanding customer repurchase intentions (Wang and Jeong, 2018) Zeithaml (1988) defines perceived value as the consumer's overall assessment of a product's utility, based on the balance of what is received versus what is given Kashyap and Bojanic (2000) emphasize that this concept typically involves a trade-off between the benefits received and the costs incurred, including price, time, and effort This study aligns with Sweeney and Soutar (2001), defining perceived value as the consumer's overall assessment of the net benefits associated with booking accommodations through Airbnb.
Research has established a significant relationship between perceived value (PV) and repurchase intention (RI) in consumer behavior (Grewal et al 1998; Kuo et al 2009) Consumer value serves as a critical foundation for exchange activities (Holbrook, 1994) and influences purchasing decisions (Neal 1999) Studies indicate that higher perceived value correlates with an increased willingness to pay (Dodds et al 1991) Furthermore, in the context of repurchase behavior, PV has been shown to positively impact consumers' repurchase intentions (Chiu et al 2012; Wu et al 2014) For instance, Liang et al (2017) identified antecedents that further support this connection.
Price sensitivity, perceived authenticity, and positive electronic word-of-mouth significantly enhance perceived value (PV) in the context of Airbnb Additionally, research by Stollery and Jun (2017) indicates that factors such as monetary savings, hedonic benefits, and novelty contribute to increased PV, ultimately leading to a greater intention for users to continue their stay with Airbnb.
This study explores how perceived value in the context of Airbnb is influenced by various factors, drawing from existing literature Key antecedents include the usability and effectiveness of the website, as well as home benefits such as amenities, host-guest interactions, personal assistance, and novelty.
According to (Davis et al 1989), perceived usefulness refers to consumers’ assessment in regard to the experience's outcome
Perceived usefulness, defined by Mathwick et al (2002) as an individual's belief that adopting a system will enhance performance, is a key indicator of customer value perception for new technologies, particularly in the context of Airbnb (Bruner and Kumar 2005; Taylor and Todd 1995) Wang and Jeong (2018) found that the repurchase intention of Airbnb users in the United States is significantly influenced by their attitude, which is rooted in the perceived usefulness of the Airbnb website Additionally, Amaro et al (2017) emphasize that perceived usefulness is vital for assessing users' intentions towards Airbnb, highlighting its importance within the sharing economy.
Economic factors play a crucial role in influencing consumer behavior, as highlighted by various studies (Roma et al 2019; Dogru et al 2020) One of the primary attractions of the sharing economy, exemplified by platforms like Airbnb, is the economic benefits it offers to participants (Tussyadiah).
The Airbnb business model provides financial advantages for both hosts and guests It is regarded as a cost-effective alternative to traditional hotels, as it offers lower prices for travelers Additionally, hosts can earn substantial income by renting out their available spaces to guests.
According to Guttentag (2016), the perceived value of Airbnb is significantly influenced by household amenities and location Essential amenities, such as kitchenware, in-home laundry facilities, and refrigerators, are key offerings that guests expect from an Airbnb property Unlike traditional hotels, which provide services like 24/7 staff, check-in/out processes, on-site restaurants, fitness centers, and business facilities, Airbnb customers prioritize home-like amenities Research by Wang and Jeong indicates that over 85% of guests consider these amenities when selecting their Airbnb accommodations.
Research by Tussyadiah and Zach (2015) highlights the importance of location for both hotels and peer-to-peer short-term rentals, noting that hotel customers prioritize convenience, while sharing guests seek desirable locations Furthermore, Yang and Mao (2020) reveal that location advantages—such as accessibility to points of interest, transport convenience, and favorable market conditions—significantly enhance property performance, resulting in increased property value (PV).
While existing research has highlighted the advantages of household amenities and location benefits (Guttentag 2015; Quinby and Gasdia 2014), there is limited empirical investigation into how these functional factors influence customer attitudes and perceived value when staying at an Airbnb Bellotti et al (2015) and Yu et al (2020) emphasize that amenities are crucial elements that significantly impact guests' value perception and overall stay experience in Airbnb accommodations.
Airbnb operates as a sharing service, making the interaction between hosts and guests crucial throughout the entire process, from initial information search to transaction completion and departure Establishing a Host-Guest Relationship (HGR) significantly influences consumer behavior (Lampinen and Cheshire, 2016).
Airbnb exemplifies value co-creation, where hosts and guests collaboratively generate mutually beneficial outcomes (Vargo et al., 2008) The quality of the host-guest relationship is crucial, as customers seek not only material value but also enriching experiences (Dimmock, 2012) With advancements in technology, such as online reviews, travelers are less concerned about unpleasant surprises, leading to a growing demand for unique and authentic experiences, which Airbnb prominently offers (Maney, 2014; Yannopoulou et al., 2013) Hosts play a vital role by sharing local insights and travel advice, fostering a deeper connection with guests (Tussyadiah and Pesonen, 2016) This co-creation process enhances consumer satisfaction, ultimately influencing their intention to make repeat purchases (Finster and Kuppel, 2011).
In the Airbnb context, both hosts and guests act as consumers, with Host-Guest Relationship (HGR) significantly influencing their willingness to enhance each other's experience (Guttentag, 2016) Among the five categories of short-term accommodation seekers—Money Savers, Home Seekers, Collaborative Consumers, Pragmatic Novelty Seekers, and Interactive Novelty Seekers—three types of guests, namely Collaborative Consumers, Pragmatic Novelty Seekers, and Interactive Novelty Seekers, are particularly driven by the opportunity to engage with hosts and local residents (Guttentag, 2015) Furthermore, Kim et al (2015) highlight that guests who establish strong relationships with their hosts report higher satisfaction levels compared to those who lack such interactions, underscoring the importance of connection in the Airbnb experience.
Since its introduction by MacCannell in 1973, the concept of perceived authenticity has been extensively studied in tourism research Studies suggest that tourists seeking a local lifestyle are more inclined to choose Airbnb over traditional hotels Furthermore, perceived authenticity has been shown to significantly influence the cultural behavioral intentions of tourists, particularly on the island of Mauritius Both Couchsurfing and Airbnb emphasize authenticity as a key brand characteristic Additionally, research indicates that perceived authenticity affects the purchasing behaviors of millennials, especially in wine consumption Lastly, findings highlight that tourists' desires for interactions with locals and authentic experiences positively impact their travel behaviors.
Perceived risk
Travel risk refers to the potential dangers and insecurities that travelers may face during their journeys, encompassing both the awareness of possible harm and the likelihood of adverse events occurring According to various studies, it is characterized as a consumer's perception of the uncertainties linked to negative outcomes in travel-related purchases.
Risk perception is a critical factor influencing customer purchase behavior, particularly in online contexts like Airbnb In this setting, perceived risk refers to consumers' beliefs about potential negative outcomes when making reservations This perceived risk acts as a barrier between consumers and sellers, especially for intangible products such as Airbnb accommodations Customers often rely on available information and past experiences to assess risk, as they must pay before experiencing the service Consequently, perceived risk significantly impacts Airbnb consumers' repurchasing decisions Since its introduction in marketing literature, various types of risk have been identified, highlighting its importance in consumer behavior.
In the realm of online shopping, consumers face various risks that can impact their purchasing decisions Jacoby and Kaplan (1972) categorized risks into seven types, including financial, performance, physical, psychological, social, time, and opportunity cost risk However, Bhatnagar et al (2000) highlighted that financial risk, product risk, and information risk—pertaining to security and privacy—are particularly significant in the context of web shopping.
Numerous studies indicate a strong inverse relationship between perceived risk and the intention to repurchase, as highlighted by Vijayasarathy and Jones (2000), Wu and Chang (2007), and An et al (2010) Wu and Chang identified that risk attitudes significantly influence online purchasing intentions, particularly emphasizing four critical types of risk: natural disaster risk, physical risk, political risk, and operational risk Among these, natural disaster risk is deemed to have the most substantial effect on travel intentions, with An et al (2010) supporting the notion that tourists view disaster risks as direct threats to their travel plans Furthermore, Chiu et al (2014) established a negative correlation between perceived risk (PR) and repurchase intention (RI) In consumer behavior studies, PR is often recognized as a precursor to perceived value (PV).
Previous research has established a positive relationship between perceived value (PV) and repurchase intention (RI), while perceived risk (PR) negatively impacts RI This study suggests that risk acts as a moderating factor, indicating that consumers evaluate both the benefits and risks when determining the value of a product or service, ultimately influencing their purchasing intentions.
2.5.1 Perceived risk in a crisis (Covid-19 pandemic)
The COVID-19 pandemic has profoundly impacted the tourism industry, primarily due to widespread travel restrictions and a dramatic decline in traveler demand (Gửssling et al 2020; Hall et al 2020) This sector, known for its labor intensity, faces significant challenges with millions of jobs at risk (UNWTO 2020) The UNWTO forecasts a staggering 60% to 80% decrease in international tourist arrivals in 2020, potentially jeopardizing 100–120 million direct tourism jobs (UNWTO 2020) Additionally, the crisis has disrupted the broader economy, including the sharing economy, with peer-to-peer accommodations like Airbnb experiencing a sharp decline in demand after a period of consistent growth (Watson 2020) Consequently, Airbnb's revenue is projected to fall to less than half of its 2019 levels (Khan 2020).
Airbnb's flexibility in amenities, such as pet-friendly options and home-based comforts, has challenged traditional hotel practices (Yu et al 2020) However, the COVID-19 pandemic has altered travel consumption patterns, leading to a decline in the use of peer-to-peer (P2P) accommodation services (Wen et al 2020; Wood 2020) Despite this, Dolnicar and Zare (2020) suggest that demand for P2P accommodations could rebound with effective pandemic management In response to ongoing health concerns, P2P rentals have implemented enhanced cleaning protocols, such as 24-hour vacancies between bookings, to mitigate virus transmission risks (Wood 2020) Cleanliness and hygiene have become crucial factors influencing traveler choices in the current climate (Chadwick 2020; Naumov et al 2020; Wen et al 2020) The unpredictable nature of COVID-19 complicates forecasts for the global short-term rental market (Gửssling et al 2020) A recent McKinsey & Company survey indicates that consumer sentiment is shifting towards a stronger demand for enhanced sanitation measures during the crisis (McKinsey and Company 2020) Furthermore, research by Naumov et al (2020) reveals that Bulgarian tourists often distrust P2P cleanliness protocols, favoring family-owned accommodations for better hygiene As lockdowns ease, the demand for higher cleaning standards in the P2P rental sector is essential for rebuilding customer trust (Chadwick 2020; Gửssling et al 2020; Naumov et al 2020).
Hypothesis
Based on the exiting literature, the hypotheses relating to PV, PR, and RI were proposed as follows:
H1: Economic benefit positively related to Vietnamese customer PV
H2: Perceived usefulness of Airbnb website positively related to Vietnamese customers’ PV H3: Home benefit positively related to Vietnamese customers’ PV
H4: Host-Guest relationship positively related to Vietnamese customers’ PV
H5: Perceived authenticity positively related to Vietnamese customers’ PV
H6: COVID-19 is positively related to Vietnamese customers’ PR
H7: PV positively related to RI
H8: PR negatively related to RI
Research gaps
The literature review serves as a vital tool for researchers to gather secondary data for further analysis; however, significant gaps exist in the resources, particularly within Vietnam's sharing economy market The availability of secondary data regarding the Airbnb market in Vietnam is notably scarce, with only a handful of published articles specifically addressing this sector Furthermore, there is a lack of research focused on repurchase intentions in Vietnam Additionally, studies on the impact of the Covid-19 pandemic are limited, with an even smaller number examining its effects on the sharing economy.
Methodology
Introduction
This chapter builds on previous assumptions regarding the connections between resource integration (RI), perceived value (PV), perceived risk (PR), past experience, and COVID-19 resources To validate these associations, further research is essential It outlines appropriate research methods utilized in the study, with a focus on primary data collection to achieve the research aims and objectives Additionally, this chapter will address the limitations encountered during the analysis of the chosen research method and provide a concluding overview Finally, it emphasizes a review of the research aims and objectives.
The main purpose of this study is to “explore and identify the factors that influence Vietnamese travellers’ Airbnb repurchase intention in the post-pandemic (COVID-19) context.”
Thus, there is a need to achieve aim and objectives which are listed as below:
1 To investigate the relationship between past experience, current situation and future repurchase intention of Airbnb customers
2 To examine the impact of economic benefit, usefulness website, home benefit, host- guest relationship and authenticity on perceived value
3 To test the impacts of the Covid-19 pandemic in Vietnamese travellers’ Airbnb perceived risk, hence, turn into repurchase intention.
Research Philosophies
Research methods are systematically designed approaches to address research problems, as defined by Kothari (2006) The primary aim of conducting research is to expand knowledge, enabling researchers to test proposed hypotheses in a structured manner (Saunders et al.).
Research philosophy is essential for guiding the approach to a research topic and enhancing knowledge development (Saunders et al., 2000) It is primarily categorized into two types: positivistic philosophy, which focuses on observing social reality in its true form and analyzing data to support theories, and phenomenological philosophy, which views social reality as complex and multifaceted The distinction between these two philosophies is further illustrated in the accompanying table.
Figure 3.1: The Comparison between Positivistic Philosophy and Phenomenology
(Amended from Collis and Hussey (2003)
As displayed in Figure 3.1, the research philosophy is adapted for this research is the positivistic approach because this research requiring a quantifiable analyses method to gather primary data.
Research Approach
Understanding research approaches is crucial for researchers to effectively design their projects There are two main methods: inductive and deductive Inductive research utilizes collected data to form general theories, while deductive research tests existing theories to validate specific claims This study employs a deductive approach, leveraging existing literature from chapter 2 alongside primary data gathered from questionnaires to address the original research objectives.
Secondary research
Secondary research involves the collection of existing data, which can be raw or published, including sources such as books, scientific papers, and journal articles (Zikmund et al 2010; Blaxter et al 2010; Blumberg et al 2005) While it typically serves as a summary or synthesis rather than a complete research paper, secondary research is crucial for aggregating data to support primary research (Crouch and Housden 2003) According to Jennings (2001), reviewing literature helps researchers avoid missing key concepts, and it is generally a quicker and more cost-effective method Hart (2000) notes various channels for collecting secondary data, including textbooks and online resources This research paper heavily relies on secondary data gathered from online sources, such as e-resources, Google Scholar, and the Bournemouth University Library (Sharp et al 2002) The convenience of the internet has significantly aided researchers in accessing information, especially during the pandemic However, it is vital to assess the reliability and validity of sources, as many unauthenticated resources are available online (Jennings 2001).
Primary research
Primary research involves the direct collection of new data through methods such as interviews, surveys, observations, or experiments, as highlighted by Veal (2018) It is essential when secondary data is insufficient to address research questions (Clark et al 1997) While primary research can be time-consuming and costly (Baggio and Klobas 2011), it offers significant advantages It enables researchers to target specific sample audiences (Ghauri and Gronhaug 2010) and allows for the tailored analysis of data relevant to the research objectives (Jennings 2001) Additionally, primary research provides updated and closely related data, minimizing the risk of errors (ibid) and is crucial for understanding customer behavior, intentions, and attitudes (Ghauri and Kjell).
In primary data collection, researchers typically utilize three methods: qualitative, quantitative, and a mixed-method approach that combines both qualitative and quantitative techniques (Jennings, 2001) The most widely used methods are qualitative and quantitative, each offering unique insights and data analysis capabilities.
In this study, the Veal method is utilized for objective analysis, employing a questionnaire to gather response data Despite Veal's (1997) criticism that questionnaires restrict participants' ability to respond freely compared to interviews, this method remains effective for efficiently collecting data in a timely manner.
Quantitative research, unlike qualitative research that relies on interviews, primarily utilizes questionnaires to collect numerical data, making it more prevalent in empirical studies (Punch, 2004) According to Jones et al (2012), this method excels in hypothesis testing, while Walliman (2006) and Veal (2010) emphasize its effectiveness in exploring relationships between various factors.
To ensure reliable results, it is crucial to collect a sufficiently large sample size (Veal 1997) This study employs a quantitative research approach through a survey questionnaire, which is effective for understanding the factors influencing customers' intentions to continue using Airbnb in the post-pandemic era Participants complete the survey online using electronic devices, with the self-administered questionnaire taking approximately 3 to 5 minutes to finish The results are then automatically sent to the researcher's data collection system.
Questionnaire design
Questionnaire surveys are a widely used method in quantitative research, defined by De Vaus (2002) as encompassing all data collection techniques In these surveys, participants receive the same questionnaire and respond in a predetermined order, making it an efficient way to gather a large volume of data in a relatively short time compared to interviews (Finn et al.).
Effective questionnaire design is crucial in the research process, as it directly influences the quality of data collected and its alignment with the original research objectives According to Brace (2013), careful design is essential to minimize bias A well-constructed questionnaire not only saves costs, time, and resources but also enhances the overall efficiency of research (Wallen and Fraenkel, 2011) Additionally, improving the presentation, maintaining an appropriate length, and ensuring clarity in questions can significantly boost response rates (Wallen and Fraenkel, 2011).
There are two types of questions in the questionnaire, closed questions and open questions
Closed questions require participants to select from provided answers, typically yielding concise responses like "yes" or "no." For instance, in Appendix 1, question 2 exemplifies a closed question with binary options Another variant is the multiple-choice question, which allows respondents to select more than one answer, as seen in questions 4 and 5 of Appendix 1 (Veal, 1997) When crafting these questions, researchers should rely on literature reviews and current facts; however, this approach may limit the spontaneity of responses (Oppenheim, 2009) To enhance response diversity, designers can include an "other" option in the answer choices.
Open questions enable respondents to freely express their opinions and ideas (Oppenheim, 2009) However, research suggests that these types of questions should be minimized in questionnaires, as they may lead to unanswered items Additionally, the absence of a structured response can complicate data analysis, according to Veal.
In 1997, it was noted that open questions pose challenges in examining and comparing responses across different groups, suggesting their limited use in surveys For this study, a self-completed questionnaire was developed using the JISC system, a widely utilized, free, and easily accessible data collection platform The researcher first created the questionnaire in English, which was later translated into Vietnamese to better serve the needs of Vietnamese participants.
The questionnaire was divided into three sections which included 18 main questions In Section
The initial section of the survey focuses on gathering general data about respondents' past experiences with Airbnb It includes both closed questions with single answers and multiple-choice questions aimed at understanding customers' experiences while staying at Airbnb properties.
The main section of the questionnaire, covering questions 6 to 11, focuses on factors influencing Airbnb's repurchase intention (AirbnbRI) This part includes 27 sub-questions utilizing a five-point Likert scale (1=Strongly Disagree to 5=Strongly Agree), based on a modified structure from previous research Additionally, Covid-19 related questions also employ a five-point Likert scale to assess attendees' future repurchase intentions for Airbnb, considering their original intentions prior to the pandemic and their perceived risks associated with it For further reference, the academic sources for the questionnaire are detailed in Table 3.2.
E1 I could save money when I book accommodations from local hosts on Airbnb
(2008) E2 I stay at Airbnb for a better place with less money
7 Usefulness U1 Airbnb website is understandable (Wang and Jeong
U2 Information about the property is fully provided) U3 Convenient to find a suitable accommodation
8 Home benefit H1 Airbnb allows me to use amenities and facilities which hotel does not have
2018) H2 The Airbnb provides me with what I need as expected
HG1 The host is nice and friendly (Wang and Jeong
HG2 The host is helpful HG3 I like to interact with the host as way to connect with local cuture
10 Authenticity A1 Living in an Airbnb place allows me to experience an authentic local lifestyle
(2017) A2 Living in an Airbnb place allows me to live as a resident A3 Living in an Airbnb place allows me to interacted with the local community
11 Covid-19 C1 Covid-19 affects my budget for my future accommodation
C2 Covid-19 affects my intention to stay with Airbnb
C3 Covid-19 affects the frequency of my stay with
C4 I will stay in an apartment / private room instead of sharing an apartment / room with the owner for health reasons
PV1 I find using the Airbnb website is a good idea (Liang et al
PV2 I find staying at Airbnb is worth the money PV3 Living in an Airbnb place would help me make more local culture experience PV4 Overall, I enjoy living in Airbnb
PR1 I think it is risky to book a room on Airbnb after pandemic
2017) PR2 I am concerned that it may not be safe to stay at an
Airbnb place/listing PR3 I am concerned about the cleanliness and sanitation protocols at Airbnb PR4 I am concern that I may not financial stable to stay at
RI1 I will purchase rooms via Airbnb after the pandemic (Liang et al
2017) RI2 I would recommend Airbnb to others
The final section, comprising questions 12-15, gathers respondents' demographic information, including age, gender, education level, and income This data aims to analyze potential differences in purchasing intentions based on these demographic factors.
Personal characteristics such as age, values, occupation, and economic circumstances significantly influence customer buying behavior (Kotler et al., 2010) Additionally, gender differences play a crucial role in shaping product preferences and interests (Richards & Wilson, 2003).
Sampling and sample size
Effective sampling plays a crucial role in the quality of research analysis, as it involves selecting a representative group of individuals to answer research questions (Walliman, 2006) This sample serves as a reflection of the entire population from which data cannot be gathered According to Clark et al (1998), sampling techniques are categorized into two main types: probabilistic sampling, which involves random selection of respondents, and non-probabilistic sampling, where selection is based on the researcher’s preferences and the number of samples.
This study employed a probability sampling technique to conduct an online questionnaire survey, effectively gathering data through random selection of Vietnamese travelers aged 18 and older who have made at least one reservation on the Airbnb website Figure 3.2 outlines the key sampling measures associated with each sampling technique utilized in the research.
This study targets Vietnamese participants over the age of 18 who have previously stayed in Airbnb accommodations Selecting an appropriate sample is crucial, as a sample that is too large can incur excessive costs and time, while a small sample may yield inaccurate results Given the extensive population and the unknown variability in the proportion of Vietnamese participants, the Cochran formula, as suggested by Israel, is utilized for sample size determination.
Figure 3.3: Sample size equation (Israel 1992)
This formula calculates the required sample size using a Z score, which represents the abscissa of the normal curve that defines an area α in the tails, along with the desired precision level (e) and the estimated proportion (p) of a specific attribute within the population.
At a confidence level of 85%, the Z-score is 1.44, with p set at 0.5 and q calculated as 1-p, resulting in a sample size of 208 To account for potential unsatisfactory responses, the number of survey samples was increased to 220.
Pilot test
A pilot test is a crucial step in assessing the reliability of a questionnaire before conducting a larger survey (Finn et al 2000; Saunders et al 2009) This empirical evaluation aims to identify and mitigate potential issues that may arise during data collection, as well as to uncover any challenges respondents may face while answering the survey.
In 2010, Bell (2005) highlighted the importance of conducting a pilot test to evaluate the clarity of instructions and questions in a questionnaire, as well as the overall usefulness of the data collected For this pilot test, it is recommended to involve 10 participants, specifically students who have previously used Airbnb Their feedback will guide necessary adjustments to the presentation, language translations, and the number of questions before the survey is distributed to a wider audience.
In statistical analysis, Z represents the abscissa of the normal curve that delineates an area α at the tails, such as 1.44 for an 85% confidence level The variable e indicates the desired level of precision, while p denotes the estimated proportion of a specific attribute present within the population Additionally, q is calculated as 1 minus p, representing the complement of the estimated proportion.
Data analysis
After reaching the target sample size, the data collected from the JISC survey was exported to SPSS 26, a widely used tool for sorting and analyzing quantitative research data (Moore 2006) Given the complex nature of the conceptual model, the study also utilized Analysis of Moment Structures (AMOS) to enhance accuracy and outcomes (Bryman and Cramer 2005; Brown et al 2009) The researcher employed various techniques for data analysis, including Descriptive Analysis, Factor Analysis, Correlation Analysis, and Linear Regression.
This research employed descriptive statistics to analyze personal and demographic information from survey respondents, revealing key data characteristics and the frequency distribution of Airbnb usage (Brotherton, 2008) Additionally, factor analysis, including reliability and validity assessments, was conducted to ensure the results were valid for further analysis, utilizing Confirmatory Factor Analysis (CFA) for measurement Correlation analysis was performed to evaluate the significance of relationships among elements in the conceptual model Finally, linear regression analyses were applied to explore the connections between past experiences, the post-pandemic context, and repurchase intentions, aiming to identify the key factors in the Airbnb experience that influence customer repurchase intentions.
Conclusion
This chapter outlined the various research methodologies utilized in this study, including the research approach, questionnaire design, sampling methods, sample size determination, pilot testing, and data analysis processes It also addressed the limitations of the study to provide a comprehensive understanding of the research process The subsequent chapter will present the findings and discussions derived from this research.
Finding and Analysis
Introduction
Chapter 4 presents the findings from the quantitative research, utilizing SPSS software to analyze the factors within the conceptual model Initially, descriptive statistics are employed to provide an overview of the sample's general data and characteristics Following this, reliability and validity analyses are conducted to ensure the research's credibility, allowing for further exploration The analysis then focuses on the descriptive evaluation of research variables, with key results emerging from correlation and regression analyses that examine the relationships between perceived value, perceived risk, and repurchase intention Lastly, an Analysis of Variance (ANOVA) is performed to highlight differences in attitudes towards the research variables based on participants' backgrounds.
Descriptive Analysis
This study analyzed a sample of 238 respondents, revealing that 45.4% are male and 52.2% are female Additionally, 47.1% of participants fall within the 18 to 24 age range, while 40.3% are aged 25 and older.
The survey results reveal that the majority of respondents are aged 35 years old, with 9.2% falling within the 36 to 45 age range and 3.4% being over 46 years old In terms of education, an overwhelming 89.5% of participants hold a bachelor's degree or higher, while 8.8% have completed high school, and 1.7% have education levels at middle school or below Regarding income, half of the respondents (50%) earn an average national salary, ranging from 6 to 15 million VND per month, as reported by Statista (2020) Additionally, 39.1% of participants earn more than 15 million VND, whereas 10.9% earn below 6 million VND monthly.
Middle school or below 4 1.7 Income From 6 to 15 million 119 50.0
The data indicates that most respondents have utilized Airbnb services only once, with 42.1% (101 individuals) falling into this category Additionally, 36.1% of the sample has stayed at least twice, while 21.4% have experienced more than four stays on the platform.
Table 4.2: The number of Airbnb stay
According to Table 4.3, the primary reason customers choose to book through Airbnb is for leisure, with 74.4% of respondents indicating this motivation Additionally, 17.2% of users book accommodations to visit friends or relatives, while approximately 8.4% utilize Airbnb for business trips.
During their stay, guests predominantly opted for Entire houses and Private rooms, making up 52.9% and 43.3% of accommodations, respectively In contrast, only 3.8% chose to stay in Shared rooms with the Host, as detailed in Table 4.4.
Table 4.5 presents the responses to a survey question regarding tourist accommodations in Vietnam The data reveals that a significant majority of tourists prefer the North, home to the capital city, Hanoi, with 54% of respondents, and the South, where the largest city, Ho Chi Minh City, is located, attracting 58% The Central region, featuring popular destinations like Da Nang and Hoi An, is gaining traction, with 31% of tourists choosing this area Additionally, 11.9% of respondents opted for Airbnb accommodations in the Highlands, particularly in Da Lat, a city renowned for its beautiful flowers.
Multi answer: Percentage of respondents who selected each answer option (e.g 100% would represent that all this question's respondents chose that option)
Reliability Analysis
Reliability analysis assesses the consistency and stability of measurements within research variables (Tolmie et al 2011) It is primarily evaluated through three methods: equivalent form procedure, test-retest procedure, and split-half reliability, also known as internal consistency reliability (Tolmie et al 2011) This study employs the widely used internal consistency reliability method to determine the correlation among internal items The Cronbach’s alpha index is utilized to measure the internal consistency and reliability of each dimension (Hinton et al 2014), with benchmark values provided for reference.
Figure 4.1: Cronbach’s apha value (Tolmie et al 2011)
Cronbach’s Alpha values are crucial for assessing internal consistency in research A value below 0.6 indicates unacceptable internal consistency, while values ranging from 0.6 to 0.7 suggest a lack of consistency Conversely, a Cronbach’s Alpha above 0.7 reflects good internal consistency, and values between 0.8 and 0.9 indicate great reliability Values exceeding 0.9 may be considered excessively high, as noted by George and Mallery (2002).
The study demonstrates that the average Cronbach’s alpha exceeds 0.7 across all dimensions, indicating a high level of internal consistency reliability for the questionnaire The detailed results are summarized in the table below.
Research Variables Items Cronbach’s Alpha
Table 4.6 presents nine research variables: Economic Benefit, Usefulness, Home Benefit, Host-Guest Relationship, Authenticity, Covid-19, Perceived Value (PV), Perceived Risk (PR), and Relational Intimacy (RI) The Cronbach α values for these variables range from 0.797 to 0.888, indicating high reliability, with Perceived Risk achieving the highest value of 0.879 and Economic Benefit the lowest at 0.797 These findings confirm that the data collected for this survey sample is adequate for further analysis.
Validity Analysis
Exploratory Factor Analysis (EFA) is employed to assess the internal structural consistency of factors by examining the correlation between independent and dependent variables, which indicates the validity of the research structure (Tolmie et al., 2011) Key components of EFA include the KMO value and Bartlett's test, which evaluate the integration of variables and scales (Davis, 2013) Additionally, factor loading serves as the correlation coefficient between variables and factors, while the cumulative variance explains the effectiveness of the research factors.
The KMO and Bartlett’s test are essential for assessing construct validity, with KMO values ideally ranging from 0.5 to 1, as noted by Davis (2013) A KMO value below 0.5 indicates poor structural validity, necessitating a revision of the questionnaire due to low correlation among research variables Acceptable KMO values fall between 0.6 and 0.8, while values above 0.8 suggest strong construct validity suitable for further analysis This study's validity results are detailed in the analysis tables (Table 4.7 for KMO and Table 4.8 for Rotated Component Matrix), where KMO and loading factors will be thoroughly analyzed Each variable will undergo separate factor analysis to yield more consistent results, ensuring an appropriate measurement for the study.
Table 4.7: KMO and Bartlett's Test value
Table 9 presents 10 research variables, with Economic benefit, Home benefit, and Repurchase intention each represented by only two items, resulting in KMO values of 0.500 The remaining five variables—Usefulness, Host-Guest relationship, Authenticity, Covid-19, Perceived value, and Perceived risk—offer a broader scope for analysis.
N of Items KMO Approx Chi-
Total 9 909 5264.629 000 three or four items; their KMO values are 0.719, 0.694, 0.733, 0.774, and 0.802 respectively, all of which are greater than 0.7 with the largest value of 0.802 Additionally, Bartlett's Test of all variable indicates at 0.000, which can understand that the measurement value is acceptable and each item can clearly explain the concept of research variables which in turn is valid for further examination
Extraction Method: Principal Component Analysis
Rotation Method: Varimax with Kaiser Normalization a Rotation converged in 9 iterations
Table 4.8 shows that all factor loading values for the relevant questions exceed 0.6, indicating that these questions effectively capture the necessary information about the research variables Consequently, this study demonstrates strong construct validity.
Research Variable Descriptive Analysis
N Minimum Maximum Mean Std Deviation
Table 4.9: Research Variable Descriptive Analysis value
The survey results reveal a strong overall positive attitude towards Airbnb, with a mean rating of 4.0210, indicating that most respondents are likely to choose Airbnb for future stays and recommend it to others Satisfaction with past experiences is also high, reflected in a mean value of 4.1271 for perceived value Key aspects such as Economy, Home Benefit, Usefulness, Host-Guest Relationship, and Authenticity all received mean scores between 3.9 and 4.2, showcasing a favorable evaluation of Airbnb's value However, the lowest mean score of 3.5315 for pandemic-related concerns suggests varied opinions and anxiety levels among customers Despite this, the overall index confirms that customers maintain a positive outlook on their Airbnb experiences amid current circumstances.
Correlation Analysis
To assess the significance of relationships among various dimensions and their impact on the intention to repurchase, the Pearson correlation coefficient, commonly referred to as the r-value, is utilized (Muijs 2011).
According to Greasley (2008, p.77), Correlation “describes the direction and strength of a relationship between two interval variables and that the direction can be positive or negative”
A positive correlation indicates that an increase in one variable corresponds to an increase in another, while a negative correlation occurs when an increase in one variable results in a decrease in another (Statistics 2020) Pearson’s correlation coefficient ranges from -1 to +1; values between 0 and 1 signify a positive correlation, whereas values below 0 indicate a negative correlation.
1 shows a negative correlation between two variables (Davis 2013)
This section analyzes the correlation between perceived value (PV) and various factors such as economic benefits, usefulness, home advantages, host-guest relationships, and authenticity It also examines the relationship between public relations (PR) and COVID-19 Finally, the connections between relational intimacy (RI) with both perceived value (PV) and public relations (PR) are clarified.
4.6.1 Correlation Analysis between PV and other Research Variables
** Correlation is significant at the 0.01 level (2-tailed)
Table 4.10: Correlation Analysis between PV and other research variables
Table 4.10 illustrates the correlations between perceived value (PV) and various independent variables, including economic factors, usefulness, home benefit, host-guest relationship, and authenticity, using correlation analysis The r-values, ranging from 0.692 to 0.768, indicate moderately high and positive correlations, all significant at the 0.01 level Notably, the strongest correlation of 0.768 is observed between the host-guest relationship and PV, highlighting a particularly close relationship.
4.6.2 Correlation Analysis between PR and Covid-19
Table 4.11: Correlation Analysis between PR and Covid-19
Table 4.11 reveals a significant positive correlation of 0.570 between public relations (PR) and the independent variable, Covid-19, at the 0.01 level This indicates that the pandemic has a positive impact on PR.
4.6.3 Correlation Analysis between RI, PV and PR
Table 4.12: Correlation Analysis between RI, PV and PR
The correlation analysis reveals a positive r-value of 0.682 between RI and PV, indicating a significant relationship at the 0.01 level In contrast, the r-value between RI and PR is -0.104, suggesting a negative correlation Further insights into the impact of these variables on RI will be provided in the regression analysis section.
In summary, the study reveals significant positive correlations between perceived value (PV) and various factors, including economic benefits, usefulness, home advantages, host-guest relationships, and authenticity Furthermore, both perceived risk (PR) and Covid-19 demonstrate clear positive correlations Notably, all correlation coefficients (r-values) exceed zero, indicating a consistent positive relationship among the variables analyzed.
Regression Analysis
While correlation indicates the strength and potential connections between two variables, it does not clarify the effect of each independent variable on the dependent variable (Finn et al 2006) Consequently, it is essential to investigate the relationship further.
RI and independent variables, regression analysis is chosen This research performs 2 types of regression analysis, multiple regression and simple regression
In terms of simple regression, the analysis focuses on measuring the relationship between two variables and representing the correlation in the form of a linear equation: Y = a + bX + e
In this study, Y denotes the dependent variable, such as RI, while a and b represent the Beta values derived from SPSS analysis X signifies the predictive variable, like PV, and e accounts for measurement error, which assesses the influence of additional unexplored factors (Tolmie et al., 2011).
Multiple regression analysis enables researchers to predict a dependent variable by examining the relationships with several independent variables simultaneously The general formula for multiple regression is expressed as Y = b0 + b1X1 + b2X2 + b3X3 + , where Y represents the predicted outcome and X1, X2, X3, etc., denote the relevant predictors.
This research employs multiple linear regression analysis to explore the impact of five predictor variables, including economic factors and usefulness, on the dependent variable, Return on Investment (RI) The formula incorporates various predictor variables (X1, X2, etc.), with b0 representing a constant value and b1, b2, and b3 as coefficients that indicate the relationship between these variables (Tolmie et al., 2011) Additionally, Table 4.12 presents the correlations among RI, perceived value (PV), and purchase intention (PR), highlighting how PV and PR influence RI Furthermore, simple regression analysis is utilized to assess the effect of Covid-19 on PR.
4.7.1 Regression Analysis for PV and Other Research Variables
Std Error of the Estimate
1 855 a 731 725 34390 2.178 a Predictors: (Constant), A, E, H, U, HG b Dependent Variable: PV
Table 4.13: Model Summary for PV and Other Research Variables
Squares df Mean Square F Sig
Total 102.093 237 a Dependent Variable: PV b Predictors: (Constant), A, E, H, U, HG
Table 4 14: ANOVA of PV and PE
The study examines the impact of five independent variables—economic factors, usefulness, home benefit, host-guest relationships, and authenticity—on customer satisfaction, represented by perceived value (PV) The multiple regression analysis reveals an R square of 0.731 and an Adjusted R Square of 0.725, indicating that these variables account for approximately 72.5% of the variance in PV The F value of 126.246, with a significance level of 0.000, confirms a significant relationship between the independent variables and Airbnb customers' perceived value, aligning with findings from Guttentag et al (2017) Correlation analysis shows positive relationships between PV and the independent variables, and the VIF values, all below 10, indicate no multicollinearity, confirming the model's suitability for further analysis.
Table 4 15: Coefficients of PV and PE
According to the B values shown in Table 4.15, the multiple regression equation is:
PV= 0.764 + 0.152*Economic + 0 103 *usefulness + 0 193 * home benefit + 0.252*host- guest relationship + 0.114* authenticity
The sig values are all less than 0.01, which 0.00, 0.00, 0.009, 0.00 and 0.005 respectively In term of the coefficient values, five variables have positive value at 0.186, 0.215, 0.137, 0.303 and 0.157, conserquenly, all positively impact PV
Within five independent variables, Host-guest relationship has the highest Beta index, so it can be treated as a key factor that affects PV when choosing Airbnb in Viet Nam
In summary, Airbnb's past experiences indicate that various factors such as economic benefits, usefulness, home advantages, host-guest relationships, and authenticity significantly enhance perceived value (PV) The hierarchy of these factors, ranked from greatest to least impact, underscores their importance in shaping user experiences.
Host guest relationship < Home benefit < Economic < Authenticity < Usefulness
The hypothesis H1 is validated, aligning with findings from several prior studies (Carlson Wagonlit Travel 2015; Folger 2016; Guttentag 2017; Tussyadiah and Pesonen 2016; Stollery and Jun 2017) Furthermore, the host-guest relationship emerges as a crucial element influencing customer perceived value (PV) Consequently, Airbnb hosts should prioritize fostering strong connections with their guests, as this significantly enhances customer PV.
4.7.2 Regression Analysis for COVID 19 and PR
The Covid-19 pandemic is a significant factor influencing customer perceptions and reviews when booking Airbnb accommodations To understand the effects of Covid-19 on these perceptions, it is essential to analyze its impact on customer behavior and preferences.
The analysis of the independent variable 19 reveals a significant relationship with public relations (PR) through simple regression, as indicated by an R square value of 0.325, which demonstrates that satisfaction accounts for 32% of the variance in PR changes The F test results, shown in Table 4.18, confirm the model's validity with a significance value of 0.03, which is below the 0.05 threshold While other influencing factors on PR were not explored in this study, it is assumed that Covid-19 has a notable impact Consequently, the hypothesis H2, positing a positive correlation between Covid-19 and PR, is supported.
Std Error of the Estimate
Table 4.16: Model Summary of Covid-19 and PR
Squares df Mean Square F Sig
Total 263.389 237 a Dependent Variable: PR b Predictors: (Constant), C
Table 4.17: ANOVA of Covid-19 and PR
B Std Error Beta Tolerance VIF
Table 4.18: Coefficientsanalysis of Covid-19 and PR
4.7.3 Regression Analysis for RI, PV and PR
Std Error of the Estimate
1 850 a 723 720 38939 1.878 a Predictors: (Constant), PR, PV b Dependent Variable: RI Table 4.19: Model Summary of RI, PV, and PR
To investigate the impact of perceived value (PV) and perceived risk (PR) on relational intention (RI), a multiple regression analysis was conducted using PV and PR as independent variables The results of this analysis are presented in Table 4.19.
The R square value is 0.723, and the Adjusted R Square is 0.720, indicating that the variables PV and PR account for 72% of the variance in RI Additionally, the F value is 306.126, with a significance value as shown in Table 4.20.
The analysis indicates a significant relationship between the independent variables PV and PR and revisit intention, as evidenced by a p-value of 0.000, which is below the 0.01 threshold Table 4.21 will provide further insights into how these variables influence revisit intention Additionally, the Variance Inflation Factor (VIF) values for both independent variables are favorable at 1.132, well below the threshold of 10, confirming the absence of multicollinearity and validating the model for subsequent analysis.
Squares df Mean Square F Sig
Total 128.466 237 a Dependent Variable: RI b Predictors: (Constant), PR, PV
Table 4.20: ANOVA of RI, PV, PR
Table 4.21: Coefficients of RI, PV, PR
Table 4.21 shows that the B values of 0.558 and 0.377 for PV and PR, the multiple regression equation can be expressed as:
The significance values of both variables are below 0.01 (Sig=0.00), indicating a definitive impact on RI Additionally, as shown in Table 4.21, the B value for PV is relatively high, suggesting a positive influence, while the PR exhibits a negative value, indicating a negative effect.
In conclusion, hypotheses H4 and H5 are both supported in this research, which is agree with literature review, such as Fang et al (2014), Matute et al (2016), Tussyadiah and Pesonen
(2016), Liang et al (2017), Chen and Chang (2018), Talón-Ballestero et al (2019), and Lee et al (2020)
This section demonstrates that factors such as economic value, usefulness, home benefits, host-guest relationships, and authenticity positively influence perceived value (PV), with the host-guest relationship being the most significant Additionally, the regression analysis indicates that COVID-19 has a positive effect on perceived risk (PR), while both PV and PR positively impact revisit intention (RI) Consequently, hypotheses H1, H5, H6, H7, H8, H9, H10, H11, and H12 are supported, while H2, H3, and H4 are rejected Furthermore, environmental quality emerges as a crucial factor affecting attendee satisfaction, which in turn positively influences revisit intention, highlighting its importance as a determinant of RI.
Hypothesis test
The findings have tested all the posited relationships with the hypothesis The SPSS analysis result indicates that every hypothesis is agreed and support previous research, the discussion shows below:
H1,2,3,4,5: Economic, Usefulness, Home Benefit, Host-Guest Relationship, and Authenticity positively related and significantly impact on PV
H6: Covid-19 has positive impact on PR
H7: PV and PR have significantly impact on RV PV has positive influence while PR has negative influence
Beta=0.558 (PV) and -0.169 (PR), Sig = 0.000 and 0.007 (