THE SHARING ECONOMY IN A DEVELOPING ECONOMY: THE PERSPECTIVE OF THE LEISURE BUSINESS

How to cite this paper: Singharat, W


INTRODUCTION
The widespread adoption of digital technologies has given rise to a new phenomenon called -the sharing economy‖, which encompasses novel ways of resource sharing (Sanasi et al., 2020). This transformation has occurred in two aspects, namely the technical aspect, which enables us to share a diverse range of resources, and the social aspect, which transcends traditional personal networks to facilitate resource allocation among larger groups. Such a twofold shift in digital sharing has enabled unparalleled efficiency in coordinating access to resources and has fostered innovative sharing patterns and practices, thus giving birth to a new class of resource allocation systems known as the digital sharing economy (Pouri & Hilty, 2021). In addition, the sharing economy has altered how people conduct transactions in digital spaces (Sutherland & Jarrahi, 2018). The sharing of underutilised assets to enhance efficiency and sustainability has been the primary focus of scholars, practitioners, policymakers, and individuals who closely monitor this emerging trend.
The sharing economy, also known as the collaborative economy, collaborative consumption, access economy, platform economy, and communitybased economy, represents a novel business model with significant overlap across these concepts (Govindan et al., 2020;Klarin & Suseno, 2021). The emergence of the sharing economy has created new business opportunities, with Airbnb and Uber being household names for travellers worldwide (Lee et al., 2021;Tseng & Chan, 2021). While sharing is not a new practice, the sharing economy phenomenon, powered by the internet and supporting tools, is a recent development (Hossain, 2020). Digital technologies play a crucial role in the growth of the sharing economy, which relies on scalable technology to connect individuals across vast networks and facilitate access to goods and services (Markman et al., 2021;Yeganeh, 2021). The success of sharing economy businesses and the social futures of collaborative networks is deeply intertwined with the technologies that drive them. Moreover, the sharing economy fosters new contexts for using technology and novel forms of social interactions through digital channels (Sutherland & Jarrahi, 2018).
The travel and leisure industry has become one of the world's most significant economic sectors, encompassing several sub-sectors operating across various business segments (Sevil et al., 2018). Business and leisure travellers have distinct travel habits, preferences, and needs. The leisure industry caters to recreational activities, entertainment, sports, and tourism, while business travel is typically more constrained. Business travellers usually engage in work-related activities while travelling but in a different location from their office or home (Camilleri, 2018; Tribe, 2020). Most people travel for leisure experiences that they actively seek, and most tourism around the world is based on leisure activity. Tourism is a highly valued leisure activity, and both leisure and tourist experiences can provide similar benefits, i.e., relaxation (Lubowiecki-Vikuk et al., 2021). Because tourists engage in numerous leisure activities while away from home, researchers have expressed an interest in the types and variety of leisure activities that people engage in while on vacation. Tourists' behaviour is frequently more liberated and less restrained than their leisure behaviour at home. Tourists are also more likely to engage in social interaction-promoting conversations while on vacation, which is one of the most powerful factors influencing overall satisfaction with the tourist or leisure experience (Patterson & Pegg, 2013).
It is thus critical to study the sharing economy for leisure businesses. This study investigated the factors influencing the sharing economy for leisure businesses in Thailand: online music (Spotify), online accommodation (Airbnb), and online taxi service (Grab). The sharing economy for leisure enterprises, a dependent variable, is defined as awareness and participation in any platform economy (Govindan et al., 2020;Klarin & Suseno, 2021) pertaining to Spotify, Airbnb, and Grab. Based on the study by Iedjaroen and Kraiwanit (2019), independent variables were selected, including score, gender, occupation, student, income, saving, Facebook, Twitter, YouTube, Line, and Instagram. There have been numerous studies on the sharing economy (Anwar, 2018; Quang, 2021; Pandey, 2019), but only a few have examined the factors that influence the sharing economy in leisure enterprises. This research may fill a gap. A binary regression analysis was conducted to determine whether or not demographic factors and social media platform uses influence leisure businesses' awareness and usage of the sharing economy. The results indicate that awareness and usage of Spotify are associated with Twitter and Line, while those of Airbnb are associated with Facebook, Twitter, and YouTube. Student status and Line usage appeared to correlate with Grab awareness and usage. This research may be useful for platform economies in leisure businesses, particularly Thai businesses, seeking to understand the characteristics of their consumers in order to enhance the effectiveness of marketing initiatives designed to retain current customers and attract new users. Therefore, the Thai sharing economy will grow substantially and sustainably, and it will be able to compete with global businesses.
To outline the structure of the paper, the study is divided into six major sections. Section 1 is the introduction to the study. Section 2 provides a review of the literature. Section 3 then presents the research methodology, and Section 4 contains the results. Section 5 presents the study's discussions, and Section 6 presents the conclusions, as well as the limitations and research recommendations.

LITERATURE REVIEW
Sharing has a long history, with bartering systems and communal lifestyles being commonplace, as highlighted by many sharing economy scholars. However, it is only in recent years that the sharing and collaborative economy has gained attention, with the advent of the -sharing economy‖ term (Minami et al., 2021 Pouri & Hilty, 2021). The prevailing definitions of the sharing economy conceptualise it as a system in which customers play an expanded role as both resource providers and users (Lang et al., 2022). These customers, known as -prosumers‖ among sharing economy scholars, argue that this system can optimise the use of excess capacity. Prior research indicates that the sharing economy may crowdsource tangible and intangible resources accessed through sharing platforms, thereby blurring the boundaries between personal and professional life and between a full-time workforce and casual labour (Eckhardt et al., 2019).
Sharing economy business models currently play a crucial role in the economy, enhancing the quality of life by reducing travel costs, improving transportation service quality, and mitigating unemployment, especially in the aftermath of the COVID-19 pandemic. Among the various sharing economy business models, ridesharing holds the most potential for expansion, having already expanded to numerous countries and contributed significantly to economic growth (Quang, 2021). Another popular model focuses on lodging, with Airbnb emerging as a sharing economy concept that transformed traditional walk-in hotel accommodation into an online platform where individuals can list their homes for short-term rentals. However, using Airbnb in Thailand offers a business advantage despite its illegality under Thai laws, including the Hotel Act which lacks a definition of -hotel‖ covering Airbnb accommodations. To safeguard the interests of citizens, visitors, and the government, Thailand needs to legalise Airbnb by amending the Hotel Act and other relevant laws and regulations. Such measures would promote tourism while also ensuring adequate lodging for visitors (Pandey, 2019).
According to Anwar (2018), a study of Uber and Airbnb's global expansion strategies offers valuable insights for business leaders dealing with the challenges and opportunities of the sharing economy. Despite operational and managerial setbacks, as well as logistical and regulatory hurdles, both companies have achieved substantial international growth. Their success is attributed to entrepreneurial initiatives, effective use of digital models, strong branding, and customer-centric focus, making Uber and Airbnb highly competitive in their respective industries. Their experiences illustrate the significance of niche strategies, effective business models, and networking. In a study on the level of acceptance of the sharing economy in Thailand and the factors influencing the intention to use sharing economy apps in daily life, Iedjaroen and Kraiwanit (2019) found that, except for average monthly earnings and social networking channels with an R-squared of 0.531, the variance in almost all demographic factors indicated that the sharing economy achieved a significance level of 0.05. Encouraging the participation of new consumer groups, such as the elderly, and promoting prosumers among them, could be vital in reducing the risk of offering new services and designing offerings that appeal to a broader audience of potential entrepreneurs or startups. Boateng

RESEARCH METHODOLOGY
A quantitative approach was used in this study. The data was gathered using closed-ended questionnaires. The questionnaire items were developed using reliable and valid research data, and the questionnaire was pre-tested on 30 participants to obtain a dedicated questionnaire, as suggested by Limna et al. (2023). The validity of the measurement instruments was also evaluated, and the dependability and accuracy of the measurement instruments were also assessed. According to Kaewnaknaew et al. (2022), it is important to recognise that the validity of an instrument refers to how well it measures the researcher's conceptual framework. The respondents were Thai people over 18 years of age who lived in Thailand. It was necessary to collect a minimum of 385 cases at p = 0.05 through convenience sampling, with a sample error at the 5% confidence level of 95% (Napawut et al., 2022; Jangjarat et al., 2023). The sample in this study contained 620 participants identified through convenience sampling. The dependent variables included online music (Spotify), online accommodation (Airbnb), and online taxi service (Grab). The independent variables included score, gender, occupation, student, income, saving, Facebook, Twitter, Line, YouTube, and Instagram. According to Chicco et al. (2021) and Gomila (2021), binary regression is a model in which the target can only take one of two values, 0 or 1. Binary regression was therefore used to analyse the data.
For a deeper examination of the sharing economy, an in-depth interview or focus group interview can be conducted. This may explain why individuals are aware of and employ platform economies in the leisure industry. By conducting interviews with professionals in the sharing economy or related fields, it is possible to obtain a comprehensive understanding of the sharing economy in Thailand and its potential to contribute to the country's sustainable economy.

RESULTS
According to Table 1, which shows the sharing economy in the leisure business, most respondents (82.9%) are familiar with and use the online music service Spotify. Nearly two-thirds of respondents (65.8%) know and use the online accommodation service Airbnb. A slightly smaller percentage of respondents (62.9%) are familiar with and use the online taxi service, Grab.

Spotify
According to Table 2, which contains the model summary using all the independent variables, the model can explain approximately 18.5% of the variation in the results with a significance value of 0.5.  Table 3 shows a classification table for backtesting (including all of the independent variables). The classification indicates that the model with all of the independent variables can predict the prevalence of Spotify in Thailand with an accuracy rate of 74.0% of cases when there is a cut-off value of 0.500 or 50%.  Table 4 shows the variables in the model using all of the independent variables, along with their significance level. It shows that awareness and use of the dependent variable Spotify could be described by using Twitter. Conversely, score, gender, occupation, student, income, saving, Facebook, YouTube, Line, and Instagram are not significant. When using Twitter, the likelihood of being involved in Spotify will increase by 1.343.  Table 5 shows the model summary using all the independent variables. The model can explain approximately 16.0% of the variation in the results for Airbnb with a significance value of 0.5. Table 6 shows a classification table for backtesting (including all the independent variables). The classification indicates that the model with all of the independent variables can predict the prevalence of Airbnb in Thailand with an accuracy rate of 63.1% of cases when there is a cut-off value of 0.500 or 50%.  The significance level of each independent variable is presented in Table 7. It shows that the dependent variable Airbnb could be described based on the use of Facebook, Twitter, and YouTube. Conversely, score, gender, occupation, student, income, saving, Line, and Instagram are not significant. When using Facebook, the prevalence of Airbnb in Thailand will increase by 0.209 to 0.791. When using Twitter, the prevalence of Airbnb in Thailand will increase by 1.242; finally, for YouTube use, the prevalence of Airbnb in Thailand will increase by 1.253.  Table 8 shows the model summary using all the independent variables and shows that the model can explain approximately 17.8% of the variation in the result with a significance value of 0.5. Table 9 shows a classification table for back-testing (including all the independent variables) and indicates that this model can predict the prevalence of Grab in Thailand with an accuracy rate of 63.4% of cases when there is a cut-off value of 0.500 or 50%.   Table 10 shows the variables in the model using all the independent variables and their significance level. The dependent variable Grab could be described by student and Line. Conversely, score, gender, occupation, income, saving, Facebook, Twitter, YouTube, and Instagram are not significant. When being a student, the prevalence of Grab in Thailand will increase by 1.417. Finally, when using Line, the prevalence of Grab in Thailand will increase by 1.227.

DISCUSSION
This study examined the factors influencing the adoption of sharing economy services in the leisure business in Thailand. The findings suggest that knowledge and use of Spotify may be associated with the use of Twitter or Line, while knowledge and use of Airbnb may be associated with the use of Facebook, Twitter, or YouTube. Being a student and using Line appeared to be related to knowledge and use of Grab. Information perception variables emerged as a key influence on the adoption of sharing economy services, with digital platforms serving as a means of connecting buyers and sellers and facilitating payment and service usage through mobile devices such as smartphones and tablets, which enable people to stay connected anytime and anywhere. According to Botsman and Rogers (2010), internet technology has reduced the cost of business operations and improved communication effectiveness and affordability. The sharing economy has been around for over a decade, and information technology plays a crucial role in enabling communication and facilitating effective collaboration. Shared consumption or the sharing economy makes it easier, more convenient, and faster for individuals to access common resources, and its growth is closely linked to the internet and social media, as highlighted by Tussyadiah (2015). This business model enables consumers to connect with each other and distribute access to resources, fostering communication and collaboration across different groups. Yousaf et al. (2014) observed that Uber, a smartphone app connecting drivers with passengers, provides a convenient and efficient way to share rides and enables people to propose and book rides whenever and wherever they want.

CONCLUSION
Sharing economy business models have become increasingly essential in the present era, enhancing the quality of life by reducing travel costs, improving transportation services, mitigating unemployment, and more, especially in the wake of the COVID-19 crisis. With the emergence of new service providers, businesses must adapt significantly, and social media platforms provide a simple and easy means for anyone to participate in the sharing economy. The development of comprehensive and consistent laws is necessary for the next phase of resource sharing to strengthen community cooperation, instil a sense of security and peace of mind, and narrow the consumption gap for goods and services in the sharing economy.
To facilitate the growth of sharing economy services, businesses must adapt to the evolving market as more companies enter the industry. Simple and accessible social media platforms offer equal opportunities for everyone to participate in the sharing economy. To foster community cooperation, instil a sense of security and peace of mind, and bridge the consumption gap for goods and services, the development of comprehensive and consistent laws and regulations is crucial for the next phase of resource sharing. This study's limitation is that it relies on a self-administered questionnaire that cannot explain why people participate or do not participate in a sharing economy; therefore, future research might gain from qualitative studies. Moreover, further research should include case studies of sharing economy successes and failures to help new entrepreneurs or startups reduce the risk of offering services and devise innovative ways to introduce new groups of consumers and providers, such as the elderly.