Index

• Introduction
• Literature Review
• Methodology
• Results
• Discussion and Conclusions
• References

Abstract

Daily life is inextricably linked to the economy, especially when it comes to eating well and saving money, which can be seen as creating favourable economic conditions for both the individual and the household. The social economy is a critical engine for economic restructuring in the digital economy, as well as for increasing societal and economic resilience, fairness and sustainability. This study employed a quantitative approach to investigate the factors that influence the social economy in the community enterprise sector in developing countries. An online questionnaire was used to collect data from 619 participants in Thailand recruited through accidental sampling. Multiple regression analysis was used to analyse the data. The findings revealed that the social economy can be influenced by demographic factors such as age, residence and technological readiness, as well as online communication platforms such as Instagram, Pinterest, Facebook and blogs. Career opportunities should be encouraged by helping groups of people, gathered based on common interests, to establish businesses that endorse community and social activities, thereby strengthening the community and beyond. People of diverse ages should be included to broaden the participation in activities.

Keywords: Community and social activity, Community development, Community enterprise, Online communication, Social economy, Technological readiness.

Received: 17 November 2022 / Revised: 25 January 2023 / Accepted: 15 February 2023/ Published: 10 March 2023

JEL Classification: H00; M14; P49.

Contribution/ Originality

The factors affecting Thailand’s community enterprise sector’s influence on the social economy have not been extensively studied, and this study fills the research gap. This study contributes to the existing body of literature on the factors influencing the social economy through the activities of the community enterprise sector.

1. INTRODUCTION

Globalisation is entering a qualitatively new stage of development in the digital age, as evidenced by the advances in information and communication technology (ICT) and the spread of the Internet and mobile communications. The computer and novel ICTs are important technological attributes of the current stage of globalisation, uniting the world in a single communication system and creating an integrated financial and information space (Limna, Kraiwanit, & Siripipatthanakul, 2022). The use of current technologies such as artificial intelligence (AI) and big data demonstrates that the real world and the virtual world coexist. This naturally affects people, who must act as citizens of both society and community enterprises (Helbing et al., 2019; Limna, 2022). Being a citizen of a digital society entails more than merely going about one’s regular activities; it is also critical to maintain one’s digital skills, not only to understand how to safely use ICT and carry out transactions but also to have the knowledge and skills to use technology appropriately. Furthermore, for society to develop in a better way, leading to good quality of life, there must be social responsibility in aspects such as charitable donations, volunteering in the community, preserving the environment and so on.

A community enterprise generates economic benefits in terms of participation in the public sector and digital citizenship, and this has given rise to the field of social economics, which studies private consumption and investment by both households and businesses, both of which are important components of the national income (Sharma, 2019; Suwanik, Chantarat, Rittinon, & Samphantharak, 2017). The economic development of the individual and the household is the basis for the economic life of the community: it is a fundamental community enterprise, and as it develops, the exchange process becomes more complex. However, in practice, community enterprises in different areas have different contexts, including the community’s basic infrastructure, its culture, its traditions, its ways of life and the social landscape. The importance of community enterprises is that they provide a foundation of resilience for the basic economy. This can be seen in the changing economic conditions across the world, which have resulted in many previously wealthy countries falling into terrible economic crises as a result of domestic policy, political instability and intervention from superpowers. An economy that hopes to rely solely on the export of domestic resources does not value the fundamental economy that forms a foundation for the country’s people (Nuanchuen, 2018). The social economy plays a critical role here (Barr, 2020). Additionally, as a result of its distinct, people-oriented principles, there is a growing interest in the effects that the social economy may have on the achievement of sustainable development goals (Núñez, Bandeira, & Santero-Sánchez, 2020).

Community enterprises are vital when a country’s economy is poor, and the creation of community enterprises in every community of the country requires the development of all sectors and results in a large number of community enterprises. It is critical to consider more than the income generated by a community enterprise. One way to create sustainability for community enterprises is to consider local wisdom and to create opportunities for community enterprises to grow stably and sustainably using local wisdom. This study investigates the factors that influence the social economy through the community enterprise sector in developing countries and seeks to understand digital citizenship as it relates to the social economy. This study focuses on community enterprises as they affect household income, production, the use of local resources, participation and the sustainability of the community involved in the community enterprise, all of which lead to a strong social economy.

2. LITERATURE REVIEW

Human daily life is inextricably linked to the economy, particularly in terms of eating well and having savings, which can create good economic conditions not only for the individual but also for the household. At the macro level, whether a country’s economy is good or bad can be determined by the population’s well-being and standard of living, the unemployment level, whether there is a trade deficit and the international balance of payments (Sijtsema, Snoek, Van Haaster-de Winter, & Dagevos, 2019; Suttisomboon, Na-Nakorn, & Angkavanich, 2001). Economic, financial and development research is increasingly focusing on the economics of household finances because, despite being the smallest unit of economic activity, households are economic producers, consumers and investors. The economic development and stability of households inevitably have a direct impact on a country’s economic development (Chitiga-Mabugu, Henseler, Mabugu, & Maisonnave, 2021; Mishra, 2020).

A community enterprise is a group of people in a community who work together to achieve a common goal. Such enterprises develop as a result of a local community’s entrepreneurial activities, which make use of its social resources, structures and networks. Community enterprises are managed and governed by local citizens who pursue a common goal that produces long-term individual and group benefits. Local knowledge, culture, resources and capacity to run community enterprises are dependent on a community’s people and resources. Locals use community enterprises to improve their livelihood by creating new sources of income, gaining better access to resources and consolidating land claims (Petcho, Szabo, Kusakabe, & Yukongdi, 2019). Community enterprises that play an important role in rural development can thus bridge the gap between the state and the market; they provide an opportunity for community development by allowing members to understand and discover their own potential, as well as their local capital. Community enterprises also help build capacity in the management of existing resources and capital, build a stronger community economy and increase community members’ confidence in their own roots, starting with their local ancestors and the wisdom that has been passed down over time. Community enterprises are a truly innovative development strategy that helps the community and the country – as well as the economy – to grow steadily.

The social economy also plays an important role (Bouchard, 2012; Subiyakto, Jumriani, Abbas, Muhaimin, & Rusmaniah, 2022). It is a vital engine for economic restructuring, as well as for achieving greater resilience, fairness and sustainability in society and the economy (Milotay, 2020). Yet, despite its importance, the social economy remains a hazy concept with no clear definition. According to Liger, Stefan, and Britton (2016), it refers to private, formally organised enterprises and networks that operate on the basis of democratic and participatory decision-making processes and that produce market and non-market goods and services. The distribution of profits or surpluses among members in social economy initiatives is not directly linked to the capital or fee contributed by each member but is directed toward meeting the members’ needs through the production of goods and the provision of services, insurance and finance. Social economy organisations, also known as non-profit or third sector organisations, have grown in number and importance, contributing to employment, social inclusion, democratic participation and community building (Noya & Clarence, 2007). Traditionally, the social economy has been viewed as an ever-expanding set of private, formally organised enterprises and networks that rely on a variety of resources and collaboration, with local anchorage and democratic and participatory decision-making processes. The primary goal of such organisations is not to make a profit, but to meet the needs of its members and society as a whole. The social economy is active in an increasing number of sectors, and its actors range from small non-profits to large organisations with international reach. It accounts for 6 to 8 per cent of the gross domestic product (GDP) of the European Union. However, in addition to economic activity, it is a driver of normative values such as solidarity and inclusion. Since its inception in the nineteenth century, the social economy has incorporated social relations, societal and community spheres, human development goals and socio-political empowerment (Milotay, 2020).

According to the County Health Rankings (2022) by the University of Wisconsin, income, education, employment, community safety and social support are social and economic factors that can have a significant impact on how well and how long we live. These factors influence our ability to make healthy choices, afford medical care and housing and manage stress, among many other things. Social and economic opportunities, such as good schools, stable jobs and strong social networks, are essential for long and healthy lives. Employment, for example, provides income, which influences decisions about housing, education, childcare, food and medical care. Unemployment, on the other hand, restricts these options and the ability to accumulate savings and assets that can help provide a cushion in times of economic distress. Núñez et al. (2020) investigated the specific contribution of social economy entities to the reduction of labour-market gender inequalities. Their findings show that social economy entities contribute significantly to the achievement of sustainable development goals through higher female participation, more stable jobs and a reduction in the glass-ceiling phenomenon. Cardoso, Meireles, and Peralta (2012) introduced and empirically validated a theoretical model for social economy organisations that included organisational commitment, a knowledge-centred culture and training as critical variables for formal and informal knowledge management practices. Organisations in the social economy that are developing knowledge management practices should focus on the most prevalent type of organisational commitment and base their training policies on a knowledge-centred culture. In addition, Felice (2018) evaluated the civil economy in the social market economy from a theoretical standpoint and found that the social market economy may be a resonant theory for the concept of the civil society economy. With the participation of civil society, the link between the government and the market is established, creating a setting in which individuals can express their freedom and empathy, as well as respond to potential actions without the intervention of the state, thus emphasising their freedom. As a result, civil society should encourage political, economic and cultural ties that benefit humanity. Civil society is also a mechanism upon which the functioning of the state and the market depend. If civil society becomes more involved, the market mechanism and government operations will improve.

The terms ‘technological readiness’ and ‘digital readiness’ are used interchangeably in the literature. These terms, do, however, have a number of connotations. The primary definition is the willingness of individuals, organisations or segments of the economy to introduce and use novel digital technologies to maximise the benefits of these innovations. Several factors have been thoroughly investigated to identify the critical success factors for technology readiness and adoption, including user age, knowledge, experience and attitudes, which may each have a positive or a negative influence (Terdpaopong & Kraiwanit, 2021). Furthermore, age and other characteristics of digital technology users may have an inverse effect on digital technological readiness. Yang and Shih (2020) used the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) to investigate the impact of cognitive age on technology acceptance behaviour in two groups, including digital natives (those under the age of 34) and non-digital natives (those over the age of 34). They discovered that younger people (digital natives) and digital immigrants (those who perceive themselves to be younger than their chronological age) experienced significantly higher perceived usefulness, perceived ease of use and flow.

3. METHODOLOGY

This study employed a quantitative research methodology. The data were obtained using closed-ended questionnaires. The items in the questionnaire were developed based on reliable and valid research data. The questionnaire was tested on 30 respondents (pre-testing) to obtain a dedicated questionnaire, following the recommendation of Limsangpetch, Siripipatthanakul, Phayaphrom, and Limna (2022). The validity of the measurement instruments was also evaluated. Testing was used to determine the dependability and accuracy of the measuring instruments. According to Siripipatthanakul, Limna, Sriboonruang, and Kaewpuang (2022), it is critical to recognise that the validity of an instrument refers to how well it measures the researcher’s conceptual framework. A typical survey has a 95% confidence level. Thus, a minimum of 385 cases at p = 0.5 must be collected using convenience sampling with a sample error of 5% and a precision level of 95% (Jandawapee, Siripipatthanakul, Phayaphrom, & Limna, 2022). The sample in this study contained 619 participants, which was above this minimum. Accidental sampling, a form of non-probability sampling, was employed.

Multiple regression is a statistical technique for examining the relationship between a single dependent variable and a number of independent variables. The goal of multiple regression analysis is to use known independent variables to predict the value of a single dependent variable (Moore, Anderson, Das, & Wong, 2006). Multiple regression analysis was therefore used for the data analysis. The variables in the study were technological readiness score, social economy score, demographic variables such as education, age, income, residence, technological readiness and gender (using a dummy variable with male (1) and female (0)), and online communication platforms: YouTube, Line, Facebook, Pinterest, Podcast, Blog, WhatsApp and Instagram (using a dummy variable with can use (1) and cannot use (0)). The dependent variables were the average monthly household income and participation in social activities in the previous year, measured as whether the individual worked in a community enterprise (using a dummy variable with joining (1) and not joining (0)).

4. RESULTS

Table 1 shows the means and standard deviations for the technological readiness score and the social economy score. The social economy average was 44,250 baht, and the average technological readiness score was 6.28 out of 10.

Category	N	Mean	Standard deviation
Technological readiness	619	6.28	1.635
Social economy	619	44,250.00	20,355.994

Table 2 shows the frequency, percentage, proportion and weight scores for age. Most respondents (69.3 per cent) were under the age of 25; 18.1 per cent of respondents were between the ages of 25 and 34, 6 per cent were between the ages of 35 and 44, 3.9 per cent were between the ages of 45 and 54, and 2.7 per cent were older than 55.

Age	Frequency	Percent	Proportion	Weight score
< 25 Years old	429	69.3	429/429	1.000
25 - 34 Years old	112	18.1	112/429	0.261
35 - 44 Years old	37	6.0	37/429	0.086
45 - 54 Years old	24	3.9	24/429	0.056
> 55 Years old	17	2.7	17/429	0.040
Total	619	100.0

Table 3 shows the frequency, percentage, proportion and weight scores for education. Most respondents (80.1 per cent) held a bachelor’s degree or higher. In addition, 16.2 per cent of the respondents had a high school diploma or degree, 1.9 per cent had a master’s degree or higher, and 1.8 per cent had less than a high school diploma.

Education	Frequency	Percent	Proportion	Weight score
Lower than high school	11	1.8	11/496	0.022
High school diploma	100	16.2	100/496	0.233
Bachelor’s degree	496	80.1	496/496	1.000
Master’s degree or higher	12	1.9	12/496	0.024
Total	619	100.0

Table 4 shows the collinearity statistics. As can be seen, the tolerance does not approach 0, and the variance inflation factor (VIF) does not exceed 10; therefore, this group of independent variables can be analysed without concerns about multicollinearity (Sulaiman et al., 2021).

Variable	Collinearity statistics
	Tolerance	VIF
Technological readiness	0.962	1.040
Gender	0.953	1.070
Age	0.838	1.193
Monthly save	0.559	1.788
Residence	0.808	1.238

Table 5 shows the collinearity statistics for online communication platforms. As can be seen, the tolerance does not approach 0 and the VIF does not exceed 10, so this group of independent variables can also be analysed without concerns about multicollinearity.

Variable	Collinearity statistics
	Tolerance	VIF
Instagram	0.482	2.075
Line	0.864	1.158
Pinterest	0.837	1.195
WhatsApp	0.637	1.569
Podcasts	0.881	1.135
YouTube	0.418	2.394
Facebook	0.57	1.753
Blogs	0.735	1.36

Table 6 shows the coefficients of determination. As can be seen, R-squared equals 0.588, meaning that about 58.8 per cent of the variation in the dependent variable can be explained by the examined independent variables. The adjusted R-squared equals 0.583, meaning that about 58.3 per cent of the variation can be explained.

Model	R	R-squared	Adjusted R-squared	Model
1	0.767a	0.588	0.583	1

Note:

a. Dependent variable: Social economy.

Table 7 shows the coefficient values for the social economy forecast. All the independent variables were able to predict the social economy dependent variable at a significance of 0.000.

Model		Sum of squares	df	Mean square	F	Sig.
1	Regression	49.434	7	7.062	124.336	0.000b
	Residual	34.703	611	0.057
	Total	84.137	618

Note:

Dependent variable: Social economy; b: statistically significant. If the p-value is under 0.01, results are considered statistically significant; below 0.005, they are considered highly statistically significant.

Table 8 shows the coefficients of social economy (online communication platforms).

Model		Unstandardised coefficients		Standardised coefficients	t	Sig.
		B	Std. error	Beta
1	(Constant)	9.238	0.110		84.191	0.000
	Instagram (X8)	0.449	0.084	0.289	5.313	0.000
	Line (X9)	-0.023	0.038	-0.024	-0.588	0.557
	Pinterest (X10)	0.121	0.031	0.164	3.974	0.000
	WhatsApp (X11)	-0.008	0.050	-0.008	-0.169	0.865
	Podcasts (X12)	-0.063	0.048	-0.053	-1.320	0.187
	YouTube (X13)	0.037	0.052	0.042	0.714	0.475
	Facebook (X14)	0.176	0.061	0.143	2.864	0.004
	Blogs (X15)	0.176	0.034	-0.124	-2.828	0.005

Note:

Dependent variable: Social economy.

Table 8 shows that the variables that have a positive relationship with social economy at the 0.00 level are (X8), (X10), (X13), (X14) and (X15). The variables with a negative or inverse relationship are (X9), (X11) and (X12), which suggests that as Line, WhatsApp and Podcast use increase, so too does social economy. The following equation can be written:

Unstandardised Equation: Social Economy (Y) = 9.238 + .449(X8) − 0.023(X9) + 0.121(X10) − 0.008(X11) − 0.063(X12) + 0.037(X13) + 0.176(X14) + 0.176(X15).

Standardised Equation: Social Economy (Y) = 0.289(X8) − 0.024(X9) + 0.164(X10) − 0.008(X11) − 0.053(X12) + 0.042(X13) +0.143(X14) − 0.124(X15).

Table 9 shows the coefficients for the social economy variable.

Model		Unstandardised coefficients		Standardised coefficients	t	Sig.
		B	Std. error	Beta
1	(Constant)	8.218	0.071		115.34	0.000
	Instagram (X8)	0.42	0.044	0.27	9.531	0.000
	Age (X3)	0.009	0.001	0.22	7.153	0.000
	Residence (X6)	0.102	0.021	0.132	4.776	0.000
	Technological readiness (X1)	0.031	0.006	0.134	5.124	0.000
	Pinterest (X10)	0.106	0.022	0.143	4.747	0.000
	Facebook (X14)	0.066	0.033	0.054	2.019	0.000

Note:

Dependent variable: Social economy; Independent variable: Instagram, age, residence, technological readiness, Pinterest, Facebook

According to Table 9, the variables with a positive inverse relationship to social economy include (X8), (X3), (X6), (X1), (X10) and (X14). This means that as Instagram use, age, housing, technological readiness, Pinterest use and Facebook use rise, so too does social economy. The equations can be written as follows:

Unstandardised Equation: Social Economy (Y) = 8.218 + 0.420(X8) + 0.009(X3) + 0.102(X6) + 0.031(X1) + 0.106(X10) + 0.066(X14).

Standardised Equation: Social Economy (Y) = 0.270(X8) + 0.220(X3) + 0.132(X6) + 0.134(X1) + 0.143(X10) + 0.054(X14).

5. DISCUSSION AND CONCLUSIONS

This study investigated the factors influencing the social economy through the work of the community enterprise sector in developing countries. The multiple regression model indicated that the variables with a positive inverse relationship to the social economy include Instagram (X8), age (X3), residence (X6), technological readiness (X1), Pinterest (X10) and Facebook (X14). This means that as Instagram use, age, housing, technological readiness, Pinterest use and Facebook use rise, so does the social economy. Moreover, the online communication platform variables that have a positive effect on the social economy at the .00 level are Instagram (X8), Pinterest (X10), YouTube (X13), Facebook (X14) and Blogs (X15). The variables with a negative effect are Line (X9), WhatsApp (X11) and Podcasts (X12), implying that as the use of Line, WhatsApp and podcasts increases, so does the social economy. Radivojević, Krstić, and Stanišić (2018) suggested that technological readiness has a significant impact on the Serbian economy’s global competitiveness in today’s business environment. According to the County Health Rankings (2022) by the University of Wisconsin, income, education, employment, community safety and social support are social and economic factors that can have a significant impact on how well and how long people live. Moreover, Núñez et al. (2020) indicated that there is an increasing interest in the effects that the social economy might have on the achievement of sustainable development goals because of how the social economy applies people-oriented principles. Di Maggio and Notarstefano (2019) concluded that the social economy has an impact on the process of generating national income and increasing population participation in production. The social economy is also a laboratory for the development of new hybrid forms of social innovation, particularly social policies, the management of common goods, and activities that promote employment, entrepreneurship, social cohesion and community enterprise.  There are several ways to improve the social economy; for example, community enterprises can cooperate on fundraising as well as various types of volunteering. As a result, it is critical that governments, administrations and organisations support any community enterprise that seeks to improve the social economy, as these do not only have desirable qualities that drive the community but also benefit society as a whole. This research adds to the existing body of literature on the factors influencing the social economy in developing countries through the work of the community enterprise sector. The findings of the study may assist academics in expanding their research by incorporating additional potential factors. The findings of community enterprise research in developing countries in the digital era could provide metrics to guide the social economy in the future.

With regard to policy recommendations, the community development of careers should be encouraged by gathering groups of people based on their common interests to establish businesses that support community and social activities, thereby strengthening the community and beyond. To broaden participation in such activities, a diverse range of age groups should be included. In the academic field, as this study focused on community enterprises that affect the social economy, future research should investigate other factors, such as the length of stay in the community, as well as the impact of community enterprises on the social economy and civil society participation, which may lead to a better understanding of these issues. This study was based on a self-administered questionnaire, and qualitative methods, such as interviews or focus group discussions, could thus provide additional insight for future research.

Funding: This study received no specific financial support.

Competing Interests: The authors declare that they have no competing interests.

Authors’ Contributions: All authors contributed equally to the conception and design of the study.

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