Do Formal and Informal Volunteering Compete for Engaged Individuals?

Abstract

In Central Europe, people trust each other less than in Western European countries. According to the common approach to volunteering, this should also mean fewer volunteers available. Our research examines whether two forms of volunteering—formal and informal—compete for engaged individuals in Central Europe. On a sample of 2,034 respondents from Czechia, Hungary, Poland, and Slovakia, we analyzed by structural equation model on how social capital and human capital influence wealth and time availability in relation to formal and informal volunteering. Our findings show that the two forms of volunteering do not compete but rather complement each other. Additionally, income has a stronger influence on both formal and informal volunteering than time availability. Furthermore, formal volunteering appears to be more sensitive to fluctuations in the availability of financial resources and time among potential volunteers, confirming resource theory.

Introduction

Engaged people can help others, especially in moments of crisis, and they can start volunteering. Non-profit organizations (NPOs), which already have the necessary structures to coordinate volunteers, also become active. Volunteers can thus contribute to their efforts in formal structures. In addition to formal volunteering at NPOs, people also participate in informal volunteering to assist wider networks of family, friends, and neighborhoods. Both formal and informal volunteering provide a chance to deliver specific qualities and quantities of services to satisfy particular needs.

Volunteering contributes to social and economic development, as well as improving individual and community well-being (Kim et al., 2018). Volunteers are often the backbone of community organizations and nonprofit entities, providing essential services and support that may not be feasible otherwise. These people bring diverse skills, experiences, and perspectives that can help address social and economic issues, foster social cohesion, and strengthen community networks.

Moreover, volunteers themselves benefit from volunteering, which offers opportunities to develop new skills, build social connections, and gain valuable work experience that can increase employability (Grönlund, 2011). The availability, capacity, and willingness to volunteer are strongly connected to human and social capital (Wilson, 2000).

Despite the evidence of positive effects of volunteering, few people are engaged. Moreover, there are also differences in involvement in formal and informal volunteering. Some studies confirm competition between the two types of volunteering (Pettigrew et al., 2017), while others confirm the supportive character of both (Simonson, 2017). Thus, this inconclusiveness in research findings, underlined by the scarcity of volunteering in Central Europe, leads us to aim to fill this research gap by answering the research questions:

(1) Do volunteers dedicate their time to both formal and informal volunteering, or do these two types of volunteering compete? (2) Are different influences of human capital and social capital on both formal and informal volunteering through resources needed to be invested in volunteering?

By answering these questions, we contribute to theoretical knowledge about whether the various forms of volunteering require different types of resources.

For our study, we selected Visegrad countries in Central Europe (Czechia, Hungary, Poland, and Slovakia). These countries provide an excellent opportunity to study the relationship between formal and informal volunteering because of the developing nature of their young civil societies after the communist era. These countries faced social and economic transitions and began to rebuild civil society three decades ago. This process was not straightforward and was influenced by national and international political events, trust in societies, and economic development (see, Potuček, 1999, for a political discussion about competing streams in shaping civil society in Czechia in the 1990s; or Schreier, 2015, for a comprehensive description of the development of civil society, including volunteering, in all four Visegrad countries).

The current statistics show that among EU countries, the Visegrad countries are below the average level of volunteering in Europe (CAF, 2012, 2024). The low numbers in volunteering contrast with the need for engagement in volunteering and raise the question of why people do not volunteer on a greater scale. It is possible that people contribute more through informal volunteering than formal volunteering, although they do not report it, as they consider it to be normal behavior. For example, Shandra (2017) noted that people with disabilities have the capacity to volunteer, but they do not obtain information from NPOs to join their volunteering programs. Moreover, informal volunteering is easy to begin (Pettigrew et al., 2019). The relative scarcity of volunteering in Visegrad countries enables us to easily compare whether the two types of volunteering compete for engaged people.

The remainder of the article is organized as follows. After the introduction, we define formal and informal volunteering in Sect. 2. This section also covers the specifics of volunteering and the role of human and social capital in volunteering. In Sect. 3, we present the method of data collection and processing via structural equation modeling (SEM). Section 4 includes the results and discussion, highlighting the importance of trust for social capital and volunteering. Moreover, this section describes the strong positive relationship between the two types of volunteering. The final section concludes.

Literature Review

Formal and Informal Volunteering

Volunteering is an emotional and value-based activity (Haski-Leventhal & Bargal, 2008, p. 97), and activities performed by volunteers are quite spontaneous, helping to make the world a better place (Musick & Wilson, 2003). Volunteering represents “any activity when time is given freely to benefit another person, group or cause” (Wilson, 2000, p. 215) and takes two forms: formal and informal. While formal volunteering involves “…serving in a formal nonprofit group or volunteer program of a larger parent organization that sponsors or directs the individual’s volunteer action” (Smith et al., 2016, p. 1396), informal volunteering is performed independently of any formal organization, often within community, family, or social networks (Smith et al., 2016, p. 1396).

The literature has revealed similarities and differences between formal and informal volunteering. Regarding similarities, both forms not only enrich civil society and the lives of the volunteers themselves but are also important complements to those activities the state cannot cover with its resources. Additionally, volunteering can be considered an investment in human capital (Meier & Stutzer, 2008) and is connected with social capital in society (Putnam, 2000; Wilson & Musick, 1999).

Both forms of volunteering lead to community development and provide opportunities for personal growth and development for volunteers, who often choose their form of volunteering according to their life goals, relationships and community spirit, life values, and life satisfaction and needs (Meier & Stutzer, 2008). Thus, some of the motives for both informal and formal volunteering are similar (Einolf et al., 2016, p. 223).

On the other hand, there are also differences between formal and informal volunteering. People differ in motivation to volunteer, in the levels of volunteer commitment and involvement. Moreover, other social factors play a role. Compared with informal volunteering, which is more spontaneous and usually involves close persons, formal volunteering is associated with higher costs and can involve individuals outside one’s immediate circle (Mitani, 2014).

Formal volunteering may be associated with greater professionalization and specialization of volunteering activities and the potential for a greater impact on society, whereas informal volunteering is associated with a greater degree of personal satisfaction and the creation of strong social relationships because of strong emotional ties (Haski-Leventhal & Bargal, 2008, p. 97) and usually does not require any special skills or training. Formal volunteering is organized through various organizations and is mainly a “long-term planned, prosocial behavior that benefits strangers and occurs within an organizational setting” (Penner, 2002, p. 448), and volunteers are involved in organizations. Volunteers’ social roles or identities play a crucial role in informal volunteering rather than the type of activities they carry out (Nencini et al., 2016, p. 223).

A wave of informal, spontaneous volunteering often occurs in response to crises, such as natural disasters, pandemics (Trautwein et al., 2020) or other emergencies (Whittaker et al., 2015).

To sum up the differences between formal and informal volunteering, while formal volunteering has clearly defined goals and objectives and a measurable impact on the development of an organization and volunteers, it benefits both volunteers and the organization (Wu et al., 2016). Informal volunteering is spontaneous and focused on helping the community at a specific time of need (Einolf et al., 2016, pp. 224–225). Does this mean that formal volunteering is less flexible in meeting community needs than informal volunteering, and that both types of volunteering compete with each other?

The comparison of the similarities and differences between formal and informal volunteering poses an interesting question. If two forms are similar, then they should compete—i.e., persons who volunteer formally are not involved in informal volunteering and vice versa. However, if this is not the case, there are more important aspects of the motivation to volunteer, and volunteers should engage in both types of volunteering simultaneously. Thus, we investigated the following research questions: (1) Do volunteers dedicate their time to both formal and informal volunteering, or do these two types of volunteering compete? (2) Are different influences of human capital and social capital on both formal and informal volunteering through resources needed to be invested in volunteering?

Specifics of Volunteering in Central Europe

Volunteering was primarily spontaneous, mainly informal, at the beginning of the 1990s in Central Europe, usually involving neighborhood assistance, but it was growing (Soltes & Gavurova, 2016; Voicu & Voicu, 2009). It was associated with activism (often political or environmental). The greatest development in informal volunteering occurred during the first wave of the COVID-19 pandemic in the spring of 2020 (sewing face masks, delivering medicines and food to elderly individuals (e.g., in Poland by Nowakowska & Pozzi, 2024) and recently during the Russian occupation of Ukraine (providing spontaneous aid to Ukrainian refugees and to people in Ukraine) (Mishchuk & Vlasenko, 2023; Sengupta et al., 2023).

The development of philanthropic activities and volunteering significantly changed during the last two decades too. There are three characteristic features of the development of volunteering in Central Europe. The first is that in Poland, there has been a decline in the rate of volunteering in the adult population over the last decade (with respect to any philanthropic activities; see Table 1). The second feature relates to the differences between countries. Czechia has long held a high position in this region compared with its neighbors with respect to philanthropic activities.

Table 1 Development of philanthropic activities in Central Europe

% of population	Czechia	Hungary	Poland	Slovakia
Charitable giving in 2012 (CAF, 2012)	29	20	39	25
Charitable giving in 2024 (CAF, 2024)	49	37	18	25
Volunteering (Gesthuizen & Scheepers, 2012)	48.3	37.2	19.3	N/A
Volunteering in 2012 (CAF, 2012)	18	8	13	16
Volunteering in 2024 (CAF, 2024)	22	12	4	17

Source: Charities Aid Foundation (CAF, 2012, 2024), Gesthuizen and Scheepers (2012)

Third, the data show that a fraction of the Visegrad countries' population volunteers compared with the U.S. or Western European population; for example, in the U.S., 28% of the population volunteered in 2001 (Sundeen et al., 2007), and in Switzerland, 38.9% of the population volunteered in 2019 (Lamprecht et al., 2020).

Human and Social Capital in (In)formal Volunteering

The literature review clearly revealed that human capital and social capital influence volunteering.

Both human capital and social capital are studied in the fields of philanthropy and volunteering, and human capital impacts volunteering rates (Day & Devlin, 1998; Forbes & Zampelli, 2014; Wilson, 2000). The basic human capital characteristics that influence the willingness to volunteer include education, religion, health, and financial status. Similarly, social capital is connected with networks and social contacts, trust, and the identification of solutions for social problems (Putnam, 2000). Thus, in our research, we tested whether there are different influences of human capital and social capital on both formal and informal volunteering through resources needed to be invested in volunteering.

Human Capital and Volunteering

Human capital is an important condition for volunteers to volunteer effectively. The availability of resources such as knowledge, skills, and jobs (Brown & Ferris, 2007; Hager & Brudney, 2011; Haski-Leventhal et al., 2018; Perpék, 2012) increases the likelihood of becoming a volunteer (Chambré, 2020; Einolf & Yung, 2018). This occurs not only because of the supply of resources from volunteers, including education (Forbes & Zampelli, 2014), but also because of training from organizations utilizing volunteers (Studer & von Schnurbein, 2013). Moreover, people with greater human capital are also more often asked to volunteer by NPOs (Helms & McKenzie, 2013; Musick & Wilson, 2008, p. 293), as they are accessible through various channels (Piatak et al., 2019). Nevertheless, as Shandra (2017) noted, people with disabilities volunteer with the same intensity as people without disabilities do. The only difference is that organizations do not ask people with disabilities to volunteer.

Among the components of human capital, educational attainment is among the most important (Helms & McKenzie, 2013). Education is a source of social ties and civic values, which are among the determinants of volunteering (Oesterle et al., 2004). People who are more educated have greater self-confidence in their abilities; low self-confidence is one of the barriers to volunteering (Lasby, 2004; Son & Wilson, 2012). Research in 17 OECD countries revealed that more educated groups perform a larger share of volunteer work; however, cognitive competence and job status amplify the importance of education for volunteering (Gesthuizen & Scheepers, 2012).

The other components of human capital, such as employment status or ethnicity, influence the likelihood of volunteering (Sundeen et al., 2007) through social contacts. Unemployed individuals or those with part-time jobs have a lower level of volunteering because of their social status. For these individuals, it is unacceptable to engage in unpaid work if one does not have a paid job (Taniguchi, 2006). Moreover, communities without social infrastructure and relationships are more threatened by crises and experience decreased volunteering rates (Lim & Laurence, 2015). The resource theory (Wilson, 2000) confirms inequality in volunteering (Hustinx et al., 2022; Seabe & Burger, 2022). The resource theory is based on social interactions, when people satisfy their needs in social interactions and relationships provide the means by which individuals can obtain needed resources from others (Foa et. al, 1993).

Social Capital and Volunteering

Social capital relates to trust and norms within a society. Trust in people and institutions is a cornerstone of social capital (Putnam, 1993, 2000), correlates with generosity (Chambré, 2020; Kneidinger, 2010, p. 25), and enhances the willingness to volunteer (Schnurbein, 2013). According to Potluka et al. (2018) trust is lacking in the countries in question. It is especially visible in crises like COVID-19 pandemics when trust in other people, even the relatives and close friends about information about the vaccination was non-existent (see, Muthová and Svidroňová, 2023, p. 71).

Moreover, social capital is often understood as networks, trust, and norms. Thus, a distinction is needed in the structural and cultural parts of social capital (Westle & Gabriel, 2008, p. 22). Through socialization, people engage with other people, including by volunteering and participating in other prosocial activities (Brown & Ferris, 2007; Dang et al., 2022; Lim & Laurence, 2015; Putnam, 2000; Ramaekers et al., 2022; Perpék, 2012). From this perspective, socialization is related to identity creation, following the values of social groups and social networks. Together, these factors create a sense of belonging to a community and support internal motivation to volunteer (Grönlund, 2011) and external moderators through demand from NPOs and other organizations (Rotolo & Wilson, 2012). Moreover, people who are socialized and have prosocial attitudes and norms are more likely to engage in volunteering (Dang et al., 2022; Shantz et al., 2014).

Communities with more social capital and trust are more resilient and have greater ability to cope with challenging situations (Hollenstein, 2013, p. 46; Putnam, 1993, 2000). For example, people are more engaged if they feel attached to a place and identify themselves with the place (Dang et al., 2022), as well as participate in other social activities such as religious activities. Nevertheless, research on places and volunteering is inconclusive. On the one hand, people living in suburban and rural areas are slightly more likely to volunteer than those living in urban areas are, regardless of age (Musick & Wilson, 2008). On the other hand, dependence between place of living in rural in comparison to urban areas has not been reported (Dury et al., 2016; Hooghe & Botterman, 2012), or even the opposite has been noted (Musick & Wilson, 2008). Moreover, the social composition of the population plays a role: volunteering rates are lower in low-income rural areas (Hussein & Manthorpe, 2014) and increase as the affluence of the municipality increases (Dury et al., 2016).

The family represents a specific way of socialization and social capital creation. Although it could be expected that the presence of children reduces the willingness to volunteer, while socialization with other parents leads to the opposite result. Owing to the activities of schoolchildren, the level of volunteerism of parents is relatively high. For individuals caring for children under six years of age, the impact of the presence of children on their willingness to volunteer is neutral; higher demands on time are eliminated with the positive effects of institutional involvement (Einolf, 2011). However, these conclusions are not universally valid, and it is necessary to consider the influence of society and religion; for example, the participation rate of women is lower, and childcare reduces this rate more significantly (Wang & Han, 2022). On average, women spend more time volunteering than men do (Einolf, 2011; Taniguchi, 2006), and women are more inclined toward informal volunteering (Helms & McKenzie, 2013). However, the amount of time spent working and caring for family members has a significant influence on voluntary engagement. Taking care of elderly parents or working full-time jobs has a strong negative effect on women’s volunteer activities (Taniguchi, 2006). The gender-specific division of labor in society determines this role as women take the role of those who care for family members (von Rosenbladt, 2000). However, numerous studies have already shown that the influence of gender cannot be observed separately but rather in connection with other factors (Einolf, 2011).

Data and Methods

Data

We used the data from a survey carried out in October and November 2023, which was conducted on a representative sample of 2,034 respondents from the Visegrad countries. The survey was conducted as an online questionnaire. The number of respondents was distributed according to the size of each country (region) to maintain the possibility of comparing results across countries (regions). In total, 2,034 responses were obtained (see descriptive statistics in Table 2).

The data collection process employed a combination of computer-assisted web interviewing (CAWI) and computer-assisted telephone interviewing (CATI) methods. It enabled collecting data also from people with low access to internet. The data were collected from a statistically representative sample of the population above 18 years of age in each participating country. Representativeness was ensured by applying sociodemographic quotas for gender, age, education level, and type of housing.

The analysis was performed with 1,971 responses, with 69 out of 2,034 responses excluded due to listwise deletion of missing values. This constituted approximately 3.4% of the dataset, so the impact on the results was minimal. When other methods were used to handle missing values ("pairwise" and "imputed means"), the result of the performed model was slightly worse than that with listwise deletion of missing values, so we decided on a listwise deletion. The full version of the questionnaire and basic statistics are available at https://business.vscht.cz/1133-motivation-for-volunteering.

Methods

The SEM method best suited the needs and characteristics of this research. It estimates relationships between latent and observed variables in a model when the relationships among them are indirect (Table 2).

Table 2 Descriptive statistics of both types of volunteering

			Informal volunteering
			No				Yes
N			SK	HU	PL	CZ	SK	HU	PL	CZ	Total
Formal volunteering	No	SK	148				98				246
		HU		126				136			262
		PL			275				109		384
		CZ				129				165	294
	Yes	SK	41				115				156
		HU		42				148			190
		PL			32				184		216
		CZ				50				236	286
Total			189	168	307	179	213	284	293	401	2,034
%			SK	HU	PL	CZ	SK	HU	PL	CZ	Total
Formal volunteering	No	SK	36.8				24.4				61.2
		HU		27.9				30.1			58.0
		PL			45.8				18.2		64.0
		CZ				22.2				28.4	50.7
	Yes	SK	10.2				28.6				38.8
		HU		9.29				32.7			42.0
		PL			5.33				30.7		36.0
		CZ				8.62				40.7	49.3
Total			47.0	37.2	51.2	30.9	53.0	62.8	48.8	69.1

Source: Authors’ calculations based on the survey, in the whole sample of 2,034 respondents, there were these categories of responses: no formal and no informal volunteering: 678 (33.3%), yes–no: 165 (8.1%), no-yes: 508 (24.9%), yes-yes: 683 (33.6%)

We created reflective latent variables for human capital, social capital, time availability and income level. The descriptive statistics are displayed in Annex 1, and the initial configuration of the constructs is detailed in Annex 2, while the composition following model optimization is outlined in Table 3. Additionally, we integrated two single-item variables, represented as rectangles, into the model: engagement in formal volunteering and engagement in informal volunteering.

Table 3 Composition of the model

Latent variables	Model fit Cronbach α	AVE	Variable	Factor loadings
Social capital	0.888	0.422	Trust in government	1.9433
			Trust in parliament	1.7032
			Trust in courts of justice	2.1177
			Trust in region	2.3729
			Trust in municipality	2.2656
			Trust in media	1.5921
			Trust in science	1.6013
			Trust in strangers	1.1402
			Trust in neighbors	1.2738
			Trust in friends	1.1362
			Trust in relatives	1.0343
Time	0.748	0.238	Being pensioned	0.3795
			Working at 50% max	0.2044
Human capital	0.355	0.144	University degree	0.0332
			Employed	0.2993
			Size of the household	0.3616
			Age	0.5757

Confirmatory Factor Analysis

We performed the analysis as described below. The survey dataset was provided in Microsoft (MS) Excel tables. We prepared the data set and exported it to Jamovi statistical software (version 2.4.11). We selected the variables related to the research questions and the models tested, cleaned them and computed/transformed some of them (for more details, see Table 3). As several people were involved in data collection, it was necessary to check and align the scale for each variable (Table 4).

Table 4 Estimates of the measurement model

Latent variable	Observed	Estimate	SE	95% Confidence Intervals		β	z	p
				Lower	Upper
Human capital	Age	1.000	0.0000	1.0000	1.000	0.521
	Size of the household	-1.122	0.0636	−1.2468	−0.998	−0.405	−17.65	< .001
	Employed	−1.318	0.0727	−1.4605	−1.176	−0.687	−18.14	< .001
	University degree	−0.276	0.0674	−0.4077	−0.144	−0.144	−4.09	< .001
Social capital	Trust in relatives	1.000	0.0000	1.0000	1.000	0.434
	Trust in friends	1.116	0.0534	1.0111	1.220	0.527	20.88	< .001
	Trust in neighbors	1.271	0.0710	1.1317	1.410	0.560	17.90	< .001
	Trust in strangers	1.140	0.0765	0.9899	1.290	0.531	14.90	< .001
	Trust in government	1.679	0.1201	1.433	1.914	0.640	13.98	< .001
	Trust in parliament	1.749	0.1179	1.5175	1.980	0.720	14.83	< .001
	Trust in courts of justice	1.819	0.1213	1.5809	2.056	0.720	15.00	< .001
	Trust in region	1.984	0.1278	1.7334	2.235	0.814	15.52	< .001
	Trust in municipality	1.942	0.1245	1.6984	2.186	0.780	15.60	< .001
	Trust in media	1.417	0.1000	1.2210	1.613	0.571	14.17	< .001
	Trust in science	1.462	0.0965	1.2726	1.651	0.610	15.15	< .001
Time	Being pensioned	1.000	0.0000	1.0000	1.000	1.277
	Working at 50% max	0.107	0.0473	0.0143	0.200	0.137	2.26	0.024

We conducted a normality test (Shapiro–Wilk), which indicated that the data deviated from normality. This justified our decision to use a modeling method designed for non-normal distributions, ensuring the robustness and validity of our findings.

Afterward, we performed confirmatory factor analysis (CFA) on the constructed latent variables, namely, income level, social capital, time and human capital, via the factor module from the Jamovi library. Based on the results of the CFA and related reliability analysis, we excluded variables from each construct to optimize Cronbach’s alpha (see Table 3). However, for human capital, we kept the variable ‘university education’, although Cronbach’s alpha was lower. We did so because education is an important component of human capital. We transformed the original variable ‘education level’, which had several categories, into the variable ‘university degree’, reflecting whether the respondent had a university degree.

Finally, we performed diagonally weighted least squares-structural equation modeling (DLWS-SEM) via the SEM module with Lavaan syntax. DWLS is suitable for this type of data due to the high prevalence of categorical/ordinal variables and that it is robust to violations of normality. For optimization, we correlated (as suggested by modification indices in SEM library of Jamovi) formal volunteering and informal volunteering for the last 24 months (see the description of the relationships in Fig. 1).

Fig. 1 Tested model

We constructed reflective latent variables for human capital, social capital, time availability, and income. Human capital is comprised of household size, age, university degree, and employment status as theoretically discussed in Sect. 2.3. Social capital is composed of eleven indicators of trust (e.g., trust in government, trust in friends, trust in science, again, theoretically discussed in Sect. 2.3). For the latent variable of time availability, we use pension status and part-time employment. The last latent variable, income, is a single-item construct based on the income of the household. The composition of these latent constructs, following optimization, can be seen in Table 3 (we also provide the descriptive statistics about the variables in our sample in Annex 1, and the initial version of the model in Annex 2). Additionally, we include two single variables in the models, which concern whether individuals engage in formal or informal volunteering (Fig. 2).

Fig. 2 Results of the SEM model. Notice: See the information about variables used in Annex 1

Results and Discussion

The basic statistics of the data sample show that two-thirds of the population in the Visegrad countries were currently engaged in or had previously participated as volunteers (see Table 2). From this perspective, it is also evident that it is easier for people to engage as informal volunteers, as this form of volunteering requires less preparation and can be more spontaneous (Trautwein et al., 2020; Whittaker et al., 2015), either in normal life or in emergencies.

We also see differences in engagement across countries in our sample. Czechia is at the forefront in terms of the share of people who engage in both types of volunteering (40.7%), while in other countries this proportion is around 30%. Central European countries are more stratified in terms of people who do not engage at all. Czechia is again at the forefront with only 22.2% of respondents not involved anywhere, followed by Hungary (27.9%), Slovakia (36.8%), and Poland (45.8%).

Moreover, the direct likelihood of volunteering for an organization and informal volunteering was positively correlated and statistically significant (0.573, p-value: 0.000). Thus, preconditions for volunteering affect both types of volunteering.

Results of the SEM

The whole model fit was good; the comparative fit index (CFI) (= 0.872) was still less than 0.9 even after the latent variables were optimized, and the root mean square error of approximation (RMSEA) value (= 0.079) was quite high. The ratio of χ2 and df was > 3 (specifically > 13, p-value < 0.001), indicating a poor model fit.

Discussion

Human and Social Capital in Forming Preconditions for Volunteering

There were two main aspects contributing to social capital and human capital in our model. When constructing the latent variables (see Table 3 for details), trust in social capital emerged as an important factor. However, it involves not only trust in people but also in institutions (similarly to Kneidinger, 2010) in terms of the overall variety of understanding of the term “trust”. This finding is noteworthy because trust is generally low in Visegrad countries compared to other countries in Western Europe (Potluka et al., 2018). Nevertheless, this finding confirms the role of trust in the composition of social capital, as discussed by Putnam (1993, 2000), regardless of the overall level of trust in a given society. However, the results also indicate that social capital is shaped more by trust in various types of institutions than by trust in individual people or groups. It concerns trust in local and regional governments, which was the most influential in our sample. When comparing the cumulative values of trust in people versus trust in institutions and analyzing their relationship to the age of respondents, we found that trust in people correlates more strongly with age (0.110, p-value 0.000) than trust in institutions to age (0.055, p-value 0.003). This finding aligns with the notion that older individuals tend to be more skeptical of institutions. This skepticism may stem from the legacy of the communist regime (Potluka et al., 2018), during which there was widespread distrust of institutions due to the significant power distance (see, for example, the discussion about trust and power distance in Kaasa & Andriani, 2022), or it may result from disappointment with developments over the past 30 years.

In the case of human capital, experience appeared to be a strong factor, as increased age (used as a proxy for work experience) increases people’s human capital and employability (Grönlund, 2011), because older individuals had more time to collect experience or learn. The size of household follows as the second highest factor loading and reflects the importance of social contacts for volunteering (Brown & Ferris, 2007; Dang et al., 2022; Lim & Laurence, 2015; Putnam, 2000; Ramaekers et al., 2022), while university education is the least related to the construct of human capital in our model (similarly to Potluka & von Schnurbein, 2025).

Availability of Time and Income

Although human capital is taken as an important precondition for volunteering, it also has limiting aspects. Busy people have less time to volunteer. Thus, in our models, it appeared that those having part-time jobs or already retired have time to volunteer. It is in compliance with findings of (Haski-Leventhal et al., 2018; Sundeen et al., 2007).

In case of income, both individual and household income play a positive role, though the income of the households is more important. It only underlines the importance of economies of scale in larger families or households. Again, our findings are in compliance with the previous research (e.g., Potluka & von Schnurbein, 2025).

Effects on Volunteering

When the impacts of the availability of financial resources and time on both types of volunteering were compared, resources were a more significant factor than the availability of time in influencing engagement. With fewer financial resources, the likelihood of volunteering declines faster than with less available time. This finding is in accordance with previous research, as unemployed people do not accept unpaid work (Taniguchi, 2006), and in less wealthy communities, there is a lack of infrastructure for volunteering (Lim & Laurence, 2015). These results also confirm the resource theory application in volunteering (Hustinx et al., 2022; Seabe & Burger, 2022; Wilson, 2000).

Moreover, socioeconomic resources were more strongly related to formal volunteering than to informal volunteering (Mitani, 2014). This confirms that it is easier to start informal volunteering and therefore even people with fewer resources can do it (Pettigrew et al., 2019). On the other hand, it underlines the usual approach of organizations to recruiting volunteers, where these organizations focus on better-off people with the assumption that they have more resources available and therefore are more likely to provide some (Shandra, 2017).

Although we identified strong ties between formal and informal volunteering, some of the volunteer population invests time in only one type of engagement. Pettigrew et al. (2019) explain this difference with respect to the concern, especially among informal volunteers, that if they were formally involved in an organization, they would have less time to devote to their informal volunteering for people whom they know better.

A similar effect to that observed for financial resources is also seen with time as a resource available for volunteering. As the availability of time increases, participation in formal volunteering rises more quickly than in informal volunteering. Conversely, when time is limited, people tend to leave formal volunteering more rapidly than informal volunteering. This underscores the importance of time as a crucial resource for volunteering. When people are occupied with job duties or family care, their availability to volunteer becomes limited (Taniguchi, 2006). Indeed, time constraints are among the most frequently mentioned obstacles to volunteering (Haski-Leventhal et al., 2018; Sundeen et al., 2007).

As our model indicates, individuals with more available time are more likely to be engaged in volunteering, particularly retired people (Einolf & Yung, 2018). Additionally, these individuals often have more work experience. Furthermore, those working up to 50% of their time also have time available for volunteering (Seabe & Burger, 2022). However, for unemployed individuals, the model does not show increased participation, suggesting that factors other than time availability play a role.

The two forms of volunteering (formal and informal) are complementary rather than substitutive. Our study confirms that individuals who engage in one type of volunteering are likely to participate in the other form as well (similarly as conclusion by Simonson, 2017). Differences in engagement may be influenced by who people spend their time with (Taniguchi, 2011) and the other responsibilities they perceive (Pettigrew et al., 2019). This relationship could be an important factor in addressing societal challenges where volunteering is valuable support to the services provided by the public sector, such as care for older people. Individuals can leverage the knowledge and skills gained in one type of volunteering to enhance their contributions to the other. For example, volunteers involved in care for older people may receive formal training from organizations, which they can then apply in informal settings, thereby enriching both types of volunteering experiences.

Conclusions

In our research, we aimed to explore whether informal and formal volunteering compete for the same pool of engaged individuals and to identify the sociodemographic characteristics that influence simultaneous involvement in both forms of volunteering through the lenses of human and social capital, and availability of resources as wealth and time.

The study contributes to our theoretical knowledge of volunteering in three main aspects. First, we confirmed the positive link between formal and informal volunteering. If people engage in one form of volunteering, the likelihood of their engagement in the other form increases. Thus, the two forms of volunteering do not compete but rather complement each other. Our survey shows that informal and formal volunteering complement each other, which is of great importance for both volunteers and voluntary organizations. Volunteers, if they acquire greater competencies for volunteering during formally organized volunteering (especially through training), then use these competencies in informal volunteering after leaving the volunteer organization. The costs spent on formal volunteering are not lost, but on the contrary, they help to develop the informal civic sector. At the same time, often informal, spontaneous volunteering leads to a greater interest in citizens' volunteering, and often these volunteers continue in organizations as part of formal volunteering.

This conclusion has implications in relation to resource theory. It allows volunteers to be engaged where barriers to engagement are lower and then to be used where there are inequalities in volunteering and barriers to engagement.

Second, our findings indicate that income level has a stronger influence on both formal and informal volunteering than the availability of time. This suggests that financial resources play a critical role in enabling or constraining access to volunteering opportunities, highlighting a potential inequality in volunteer participation based on economic status, confirming resource theory in volunteering. The disparity in wealth and income could therefore be a significant barrier, limiting the ability of lower-income individuals to engage in volunteering activities.

Third, formal volunteering appears to be more sensitive to fluctuations in the availability of both financial resources and time among potential volunteers. This observation aligns with existing theoretical frameworks, which suggest that formal volunteering requires a higher level of commitment, including the need for specific knowledge, skills, and resources, as well as a psychological commitment to the organization. This explains why individuals often begin with informal volunteering before transitioning to formal roles within organizations. The perceived demands and responsibilities associated with formal volunteering can be a deterrent for those with limited resources or time, making informal volunteering a more accessible entry point.

Overall, our research highlights the complementary nature of formal and informal volunteering, the significant role of financial resources in volunteer engagement, and the varying demands associated with different types of volunteering. These insights contribute to a deeper understanding of how to better support and encourage volunteerism across different segments of the population, particularly in contexts, where demand for services is high and where volunteer contributions are increasingly vital.

We are aware of the limitations of our study. First, the analysis was based on a survey. Thus, people report their perceptions, not actual actions. This can especially influence reporting about informal volunteering, as people connect volunteering with formal engagement in NPOs, whereas informal volunteering is not recognized as volunteering, especially in wider families. Second, our dataset concerns Czechia, Hungary, Poland, and Slovakia, countries with a specific historical communist legacy and transition development that began approximately thirty years ago. In all included countries, the level of trust in other people in society is lower than that in Western European countries and also community life is not as developed as in Western Europe. Thus, future research should explore this avenue and test whether formal and informal volunteering fulfill the characteristics of complements also in other countries.