The Human Capital of the Freelancers and Their Satisfaction with the Quality of Life

Abstract

Digital technologies are underpinning a wide range of products, services, processes, and business model innovations that are significantly transforming industries, organizations, and society. They are increasingly permeating every aspect of our daily lives. In this paper, we looked at the challenges of digitalization, leading to a new way of working in the market and new business models, such as the gig economy. Individuals are becoming more mobile and self-directed in their careers, as opposed to traditional careers. We investigated the demographic characteristics of the new, growing group of self-employed, so-called freelancers, at the national level in Slovenia and investigated the relationships between human capital and their subjective (job and career satisfaction, life satisfaction) and objective success (income satisfaction). The survey was conducted among 200 freelancers (respondents). The empirical results show that most Slovenian freelancers working from home work in professional, scientific, and technical occupations; are between 35 and 44 years old; have completed the second level of tertiary education; work more than 40 hours per week; and have 16 years or more of work experience. To explore the quality of life of freelancers, empirical analysis showed that human capital factors are important for the success of a new career as a freelancer. The results show that freelancers’ human capital factors (education and experience, skills, and training) positively influence subjective and objective success. Using structural equation modeling, we also confirmed that human capital has a greater impact on freelancers’ subjective success (life satisfaction, career satisfaction) than on objective success (income satisfaction), suggesting that job and life satisfaction should be considered an important resource that freelancers rely on to determine their quality of life.

1. Introduction

In the last two decades, new and contemporary occupational concepts have emerged in relation to general developments in information technology and the reorganization of work arrangements in the wake of digitalization. Digital technologies have made it possible for many things to be done online or remotely, and for work to be broken down into smaller components called projects. Individuals are expected to become increasingly mobile and self-determined in terms of their careers, in contrast to traditional careers characterized by hierarchical advancement, organizational career management, and low mobility [1]. These flexible work schedules are often referred to as nonstandard, and are performed by independent contractors called freelancers. In this paper, we use the term freelancers to refer to independent contractors of businesses without employees who do not want to grow [2]. We aim to examine the role of this particular type of entrepreneurial category of self-employment and their quality of life in terms of subjective (job and career satisfaction and life satisfaction) and objective (income satisfaction) success. Quality of life refers to the nonfinancial dimension associated with job satisfaction and life. In terms of work, quality of life refers to time and the opportunity to do what we enjoy [3]. Different jobs and occupations (careers) and working conditions are also important aspects of quality of life. Different types of jobs may require workers having to work under extreme stress or repetitive work. On the other hand, flexible work arrangements can provide professionals with more autonomy to perform their jobs [4]. In the past, career success was generally measured objectively (as wages, promotions). Today, however, new technologies and the changing nature of business are leading to a shift in the way success is viewed, and more importance is being placed on the more subjective component. According to [5], one of the most important resources affecting quality of life is satisfaction with work–life balance (i.e., the extent to which people feel they have control over their lives). In workplaces where people have a high degree of control over their working hours and other aspects of work, they tend to be more satisfied with the balance between their personal and professional lives [6,7]. This relationship is usually explained by the fact that these individuals are better able to balance the spheres of work and nonwork because of their greater control over work. This raises the question of the quality of life of nonstandard workers—the freelancers.

Since there is no scientific work and research on this topic in Slovenia yet, we first examined the selected demographic characteristics of freelancers in Slovenia, such as educational level, age, profession, work experience, and gender. Second, we examined the relationships between human capital and subjective (job and career satisfaction, life satisfaction) and objective success (income satisfaction). The survey was conducted among 200 freelancers (respondents). With this study, we make contributions to the research literature on human capital, freelance career, and entrepreneurship. Previous studies also support a positive relationship between human capital and success. Human capital enhances an individual’s ability and potential to discover and exploit business opportunities. It helps individuals acquire resources, such as financial capital, as well as new knowledge and skills. Even though a positive relationship between human capital variables and success has already been established, it remains to be seen how large this relationship is, what circumstances determine its strength, and whether the relationship differs in terms of subjective or objective success. The contribution of this study is also to provide insight into the demographic characteristics of the new, growing workforce of freelancers in Slovenia, focusing on their human capital and career satisfaction based on empirical data. As Slovenia implements the National Digital Strategy 2030 [8], the paper provides guidance for future directions and background information for policy proposals and decisions. Government recognition of freelancers through programs could be considered to promote and make this new career sustainable. Moreover, our findings can influence national policies and institutions addressing the needs of this category of workers in terms of legal support, education, social support, and professional development, but also provide insights into the benefits of freelance work (freelancers as important inputs enabling the performance of an innovation-driven and entrepreneurial economy).

In terms of methodology, we conducted exploratory and confirmatory factor analysis and structural equation modeling to test the conceptual model of the study.

Regarding the structure of the paper, Section 2 presents the literature review and hypotheses development. Section 3 describes the data, methods, and analyses. In Section 4, the results are presented, while the concluding Section 5 introduces the discussion with final considerations.

2. Literature Background and Hypotheses Development

As mentioned in the Introduction, quality of life is a subjective measure that consequently influences other decisions. While the factors that contribute to quality of life may vary according to individual preferences, they generally relate to satisfaction with one’s job, life, family life, health, and safety [9].

The increasing career mobility of individuals (freelancers) and the changing nature of work, which alters the way success is viewed and adds a more subjective component, can be explained by the following acknowledged theoretical concepts of contemporary careers: the protean career concept [10], the boundaryless career concept [11], and the intelligent career concept [12]. The common focus is on career self-management in a dynamic labor market. Authors [13,14] argue that these are the dominant theoretical approaches as fruitful ways to study careers in the coming decades, tailored to the individual and the environment. The protean career concept is focused on the individual, where the individual oversees his or her career (as the one in control) and where his or her unique human resource qualities (including human capital and skills) determine his or her success in a multiemployer environment [9]. Also [15] suggest that protean career orientation is a significant antecedent to subjective career success and is consistent with the findings of [16,17,18], who also found that individuals with a protean attitude toward career had higher levels of subjective and objective career success. The concept of boundaryless careers [11] refers to temporary jobs and embraces a new form of employment in which the individual, not a company, takes an active role in shaping his or her career, assuming that the individual works for several companies and on several different projects.

In this regard, boundaryless career theory adopts a behaviorist/competency-based approach where human capital in the form of transferable skills, continuous learning, and acquisition of experience is a crucial aspect. The concept of an intelligent career, presented by [12], refers to the behaviorist view where self-knowledge or self-awareness is central.

The intelligent career model consists of three interrelated variables: knowing why, knowing how, and knowing whom. The knowing why variable reflects our values, motivation, interests, and the issues that emanate from work. The knowing how variable reflects the skills and expertise (human capital) we have to offer, and the knowing whom variable reflects personal relationships inside and outside the workplace [19].

According to these career concepts, freelancers are primarily guided by psychological factors, feelings of success, and preferences rather than traditional career paths based on occupational and career management systems [20]. According to human capital theory [21], one of the benefits of career mobility is that by working in different jobs, new and different professional experiences are gained, leading to new acquisition of knowledge, skills, and different perspectives, which in turn improves human capital for the employer. Social capital theory [22,23] also states that greater job mobility leads to promotions, better-paying jobs, and greater self-esteem through more contacts and relationships [24]. To the extent that individuals with greater career mobility pursue protean or boundaryless careers, research supports that they have greater career skills in managing change, a greater sense of identity, and a greater sense of career success [25].

In this paper, we investigate the quality of life through subjective and objective career success. Career or professional success is defined as the accumulated positive work results and the psychological outcomes that result from work experience [24]. The success of individuals is doubly important because it ultimately contributes to the success of the individuals as well as the organization for which they work [26]. Career success usually consists of two components: an objective one and a subjective one. It is important to evaluate both aspects because there is evidence that predictors of objective success differ from predictors of subjective success [27]. For example, [28] argue that individual differences correspond to subjective rather than objective success.

2.1. Objective Success

Objective success refers to the external categories that society defines as actual achievement and that reveal the typical steps to success [29]. The measures of objective career success, therefore, are the following indicators of career advancement or accumulation of rewards: compensation, promotions, incentives, salary increases, and so forth [30].

2.2. Subjective Success

Subjective career success expresses a person’s assessment of his or her career experiences as influenced by his or her developmental inclinations, needs, desires, and standards [29]. The measures of subjective career success refer to the individual’s attitude, emotions, and perception of performance. The career satisfaction dimension is most commonly measured by the commonly used Career/Job Satisfaction Scale. The variables studied include job satisfaction, life satisfaction, organizational commitment, and career identification [31,32,33]. According to the authors [34], it also relates to other outcomes, such as a sense of identity [35], purpose [36], and work–life balance [37].

2.2.1. Job and Career Satisfaction

Authors [38] argue that job satisfaction is an inadequate measure of subjective career success because it relates to the current job rather than the prior cumulative sequence. Consequently, people should tend to view their workplace experiences in one of three ways: as job, career, or call [39]. For these reasons, job and career satisfaction may be an appropriate construct to assess subjective career success.

2.2.2. Life Satisfaction

Recent studies show an increase in remote working, with growth rates estimated at 11–30% in many regions of the world [40]. In addition, studies show that people’s subjective assessments of their career and well-being are becoming increasingly important. When asked about their subjective assessment of career success, 10 of 10 respondents described various aspects of their careers, such as time for family, more time for themselves, and continuing education, in a way that suggests that they think in multiple dimensions. Since freelance careers represent a flexible form of working in which work is done remotely, we focused on the dimension of overall life satisfaction to examine how such a work arrangement affects quality of life.

2.3. Human Capital

The author [41] notes that according to a project funded by the Professional Contractors Group (PCG) and the European Forum of Independent Professionals (EFIP), highly skilled freelancers are the fastest-growing group in the EU labor market. Over a 10-year period (between 2004 and 2013), their number increased by 45%. As mentioned at the beginning, freelancing is part of the gig economy as a nonstandard and flexible working arrangement. The authors [42] describe freelancers as workers who perform remotely, have higher levels of entrepreneurial spirit and human capital, and provide creative and knowledge-intensive services by using the global age of the Internet to get their work done. Companies are eager to hire freelance human capital to do contract work for them because it goes for specialized people in the labor market. Therefore, they want to use their expertise, skills, and abilities to improve the company’s performance [43]. According to a study by [44], the main motive for workers to choose a freelance career is flexibility, followed by autonomy and additional earnings. Successful freelancers have strong human and social capital, which enables them to get more assignments [45]. To maintain their independence and work on multiple projects (clients), freelancers have greater flexibility in balancing work and family life [44]. As freelancers, individuals have several opportunities to find companies to work for, starting with their former employer as well as their former employer’s clients, vendors, and competitors. Freelancers are not tied to specific companies, as is the case with permanent employees. Moreover, they do not need to invest in additional firm-specific human capital, but can instead focus on developing their particular aspect of human capital for a project in their area of interest [20]. In this regard, freelance human capital is seen as an enabler and facilitator in the startup market and contributes to an innovation-driven economy [43,46]. In some cases, it is better for startups to hire freelancers for a short period of time to start a business than hire and invest in full-time employees for the long term. In other cases, startups may also rely on freelance human capital if they cannot find permanent human capital [47]. The study [48] highlights that involving freelancers from the beginning can contribute to the development of innovative value propositions as they bring different perspectives and ideas due to their expertise and interactions with customers as members of the startup team. Study [49] also shows that startups can successfully innovate their value propositions to gain competitive advantage by including freelancers as a source of creative ideas and as skilled resources to implement the ideas. In a recent research [50], the authors conducted an international survey by analyzing 75 countries over a 10-year period using the Global Entrepreneurship Monitor (GEM) database. They found that freelance independent contractors boost entrepreneurial activity, with a 10% increase in freelance workforce leading to about a 1% increase in entrepreneurial activity. The significance of this positive effect is stronger in innovation-driven economies. This suggests that a flexible workforce is important for an entrepreneurial economy. It is interesting to note that, according to report [51], freelancers generated USD 1 trillion in the US in 2019, which is comparable to the revenue generated by a large industry, such as information, which accounts for nearly 5% of the US GDP.

In addition to the financial aspects of such employment, there are also nonfinancial dimensions to freelancers’ well-being, such as the potentially greater ability of freelancers to balance family and leisure and the issue of job and career satisfaction [52] through a more flexible, results-oriented leisure career as opposed to the more rigid employee-based working hours. There is also the question of job satisfaction and whether freelancing allows for more fulfillment through project work with clear mission goals, or whether paid workers with long-standing and family ties to the company lead to more fulfilled workers.

Since a freelancer is not only a boundaryless worker but also a self-employed person, self-employment is seen as a result of entrepreneurial performance and success. There is a considerable amount of research in the entrepreneurship literature to identify personal characteristics and other attributes that are associated with entrepreneurial performance and entrepreneurial success. The findings suggest that personal characteristics, motivation, and social and human capital, as a general rule, relate to higher performance and success in entrepreneurship [53]. This approach is consistent with the intellectual career model, where personal characteristics are the “know why” variable, human capital is the “know how” variable, and social capital is the “know who” variable.

In this paper, we focus primarily on the knowing how dimension of the protean approach (i.e., the human capital dimension of freelance success) since human capital is strongly supported in both the career and entrepreneurship literature.

Based on human capital theory [54], a person’s successful career depends on the quantity and quality of his or her human capital (investments in time, effort, money in education and experience) [55], contributed to the labor market. The theory states that the labor market rewards individuals’ investments, which often leads to higher rates of advancement and wages [21]. More personal qualities allow workers to do their jobs better, and their pay should be increased in proportion to compensate them for the extra human capital their jobs require. As noted in [56], the theory has also been taken up by entrepreneurship researchers and has sparked a considerable amount of directly related research [57,58,59,60,61]. This has led to numerous studies on the inclusion of human capital in predictive models of success in entrepreneurship. Several variables representing human capital are used in these research studies: formal education, training, work experience, initial experience, ownership experience, parental background, skills, knowledge, and others.

Among the many factors of human capital, education and experience have been proven to be the most representative determinants of career success [62,63,64,65,66,67]. As pointed out in [68], through formal education, individuals acquire basic skills to learn about the market and technology and to better identify opportunities in their environment [69]. Formal education thus allows individuals to develop learning and organizational skills to better adapt to the dynamic market and organize themselves to take advantage of these new opportunities [70].

Consequently, a higher level of education can give an individual a better ability to solve problems and make decisions. In addition, due to their long stay in the educational system, better-educated individuals may also have better social networks that can be useful later on for the development of their business [71]. In an extensive meta-analysis of studies on entrepreneurial education, it is concluded in [72] that entrepreneurial performance is significantly and positively influenced by formal education, regardless of how success is measured.

Then experience usually gives individuals different levels of knowledge and so-called tacit knowledge [73], making them desirable to future employers. Nowadays, experience is often referred to as a transferable skill. Consequently, employers often select and promote individuals based in part on their experience [74], and experience is associated with productivity and reward [54,75,76,77].

We also believe that a completed training is another important investment factor that can increase a person’s human capital. Authors [74,78] found that training and development opportunities are positively related to promotion and salary for both men and women. According to [54], knowledge and skills are the results of education and work experience as investments in human capital [57].

According to the above literature review, we propose the following hypothesis and subhypotheses:

Hypothesis 1 (H1):

Human capital positively influences freelancers’ objective success.

Hypothesis 1.1 (H1.1):

Freelancers’ education and work experience positively influence perceived objective success.

Hypothesis 1.2 (H1.2):

Freelancers’ skills have a positive impact on perceived objective success.

Hypothesis 1.3 (H1.3):

Freelancers’ training has a positive impact on perceived objective success.

Moreover, subjective career success expresses individuals’ assessments of their career success as the value of their human capital [79,80], their assessments of their self-worth and abilities [81], and the satisfaction they experience in their careers [32,45]. On the other hand, the results show that human capital has a significant influence on objective career success, but also an indirect influence on subjective career success [32,82].

To the extent that individuals with greater career mobility pursue protean or indefinite careers, research supports the retention of greater professional experience, skills, and knowledge in managing change; a greater sense of identity; and a greater sense of subjective career success [83,84].

Accordingly, we propose the second hypothesis and subhypotheses:

Hypothesis 2 (H2):

Human capital positively influences freelancers’ subjective success.

Hypothesis 2.1 (H2.1):

Freelancers’ education and work experience have a positive impact on perceived job and career satisfaction.

Hypothesis 2.2 (H2.2):

Freelancers’ skills have a positive impact on perceived job and career satisfaction.

Hypothesis 2.3 (H2.3):

Freelancers’ training has a positive impact on perceived job and career satisfaction.

Hypothesis 2.4 (H2.4):

Freelancers’ education and work experience have a positive impact on perceived life satisfaction.

Hypothesis 2.5 (H2.5):

Freelancers’ skills have a positive impact on perceived life satisfaction.

Hypothesis 2.6 (H2.6):

Freelancers’ training has a positive impact on perceived life satisfaction.

3. Methodology

Given the background of the literature, we present a conceptual model below (Figure 1), based on which we will present the data, use the most appropriate methods, and conduct analyses.

3.1. Conceptual Model

The dimensions included in the model indicate the following:

• Education—the level of perceived education/knowledge required to obtain quality freelance work;
• Experience—the level of perceived work experience that facilitates work and builds confidence to perform quality freelance work;
• Skills—the level of perceived confidence in the ability to use digital technologies and the ease of use of new software packages or online applications to perform quality freelance work;
• Training—the level of perceived attendance at training in the last 2 years (basic skills courses, new skills development courses, related or support skills);
• Job and career satisfaction—the perceived extent to which a pleasant emotional state results from the evaluation of one’s career achievements and evaluation of one’s job values;
• Life satisfaction—the extent of perceived positive effect or feelings toward one’s life outside of work;
• Income satisfaction—the extent of perceived satisfaction with earnings from freelance work.

3.2. Sample Data

We conducted an empirical study with a sample of freelancers in Slovenia from July to September 2020. Respondents were randomly selected from a list of self-employed people from the database Bisnode Gwin [85], online labor platforms, and online self-employment groups. In order to obtain a representative sample, we restricted our survey as follows:

• Respondents belonging to the entrepreneurial category of self-employed with zero employees;
• Who work from home or in coworking spaces or elsewhere where they have access to the necessary resources; and
• Who have worked with at least 2 clients in the last 12 months.

After some restrictions for our study, we arrived at 200 valid responses collected using Computer-Assisted Telephone Interviewing (CATI) and Computer-Assisted Web Interviewing (CAWI) technology. The following table (

Table 1. Sample Characteristics. Source: authors.

Items	Attribute Levels	In Total (N = 200)
Place of work	From home Coworking spaces Completely remote	57.5% 40.0% 2.5%
Gender	Male Female	59.0% 41.0%
Age	18–25 25–34 35–44 45–54 55–64 65 years and more	1.5% 17.5% 30.0% 27.9% 18.5% 5.5%
Educational attainment	Completed secondary education Completed first stage of tertiary education Completed second stage of tertiary education Completed specialization or master of science cycle	27.6% 20.6% 42.2% 9.5%
Weekly working hours	20–30 hours 31–40 hours >40 hours	21.6% 29.1% 49.2%
Years of experience in freelancing	less than 1 year 1–5 years 6–10 years 11–15 years 16 years and more	1.0% 15.1% 17.1% 21.6% 45.2%
Business sector	Manufacturing Trade; maintenance and repair of motor vehicles Information and communication activities Financial and insurance activities Real estate activities Professional, scientific, and technical activities Other various business activities Education Cultural, entertainment, and recreational activities	5.0% 2.0% 19.6% 0.5% 2.5% 62.3% 1.0% 4.5% 2.5%

) contains the selected demographic characteristics of the sample.

According to survey results, 40.9% of the respondents mainly use an online labor platform to look for a job. Moreover, more than half of them (57.5%) work from home (regardless of the situation during the pandemic COVID-19), followed by 40% who work in coworking spaces (rented offices), while only 2.5% work anywhere if they have access to the necessary resources.

Regarding gender, there are more male freelancers (over 50%) than female freelancers. In the matter of age, most Slovenian freelancers belong to the 35–44 age group (30.0%), followed by the 45–54 age group (27.9%), while the fewest are found in the 65 and over age group (5.5%) and the 18–24 age group (1.5%).

Regarding the level of education, most Slovenian freelancers have completed the second stage of tertiary education (42.2%), while the smallest percentage of them have completed a specialization (9.5%). Looking at the weekly working hours, almost 50% or half of the respondents work more than 40 hours per week, and over 45% have 16 years or more experience working as freelancers.

In terms of the type of business sector, according to standard classification, more than 80% of freelancers in Slovenia work in the professional, scientific, and technical and information and communication sectors. These types of activities usually include software development, design and multimedia, website development, network and information systems development, writing, and translation.

3.3. Measures and Questionnaire Development

Measurement scales verified in previous studies, together with the refinement and upgrading of measurement scales for the needs of our research, were used to develop the measurement instrument. Each statement (variable) was measured on a 7-point Likert scale [86] (1 = strongly disagree; 7 = strongly agree) on which respondents expressed their agreement or disagreement. To measure job and career satisfaction (dependent factor in the model), an adapted 7-point Likert scale from [87] was used, based on the studies of [88,89,90,91,92], and contains 6 items. To measure another dependent variable, life satisfaction, the 4-item measurement scale of [93] was used. Objective success is measured using a 7-point Likert scale (1 = strongly disagree; 7 = strongly agree) with 1 item (statement): “I am satisfied with the progress I have made toward meeting my income goals”. The measurement scales of the GEM National Expert Survey were used to measure education and experience and skills [94]. The items were adapted to freelance work. To measure training participation, we adopted and modified the items on a 7-point Likert scale from [44]. Accordingly, participation in training was divided into three different types: core skills training, new skills development training, and adjacent or supporting skills training. The measurement scales are listed at Section 3.6.1.

3.4. Methods

In this paper, structural equation modeling (SEM) was conducted to examine the influences of human capital on objective and subjective success. Since the study contained multidimensional variables, Cronbach’s alpha and exploratory factor analysis (EFA) were first used to examine the internal consistency reliability of each latent variable in the model and to reduce many of the included items to the new set of variables (factors) [95], in order to extract the smallest number of linear combinations of the measured variables that explained the total variance of the data as much as possible [96].

In addition, confirmatory factor analysis (CFA) was used [97]. It was used to test the observed set of variables (factors) (i.e., the extent to which the observed variables that EFA assumes to measure a factor fit the observed data set) [98]. Moreover, SEM is conducted in two stages to validate the proposed model and test the research hypotheses and subhypotheses. Structural equation modeling first uses CFA to create an acceptable measurement model before estimating, validating, and interpreting the structural relationships (path) between the factors [99].

In conducting the analysis, EFA was performed using the statistical program IBM Statistical Package for the Social Sciences (SPSS) 22, while the statistical program WarpPLS 6.0 was used for CFA and SEM.

3.5. Sample Adequacy

At the very beginning of the analysis, we checked the adequacy of the sample. The analysis of the interdependence between the variables of human capital and subjective success shows the appropriateness of the use of EFA, which is confirmed by Bartlett’s test of sphericity (p < 0.05) [100] and Kaiser–Meyer–Olkin measure of sampling (>0.5) [101] in

Table 2. BTS and KMO of HC and SS. Source: authors.

Construct	Kaiser–Meyer–Olkin Measure of Sampling Adequacy	Bartlett’s Test of Sphericity; Chi-Square; p-Value
Human capital (HC)	0.691	0.000
Subjective success (SS)	0.888	0.000

.

3.6. Data Analysis

Table 3. EFA results for the HC construct. Source: authors.

Variable Label	Variable	Communalities	Factor Loadings Factor 1 Training	Factor Loadings Factor 2 Skills	Factor Loadings Factor 3 Education and Experience
q1_HC	I have required education to obtain a freelance activity of high quality.	0.640			0.798
q2_HC	I am very confident in my abilities to use digital technologies.	0.877		0.912
q3_HC	It is easy for me to learn how to use new software packages or online applications.	0.888		0.931
q4_HC	The more experience I have as a freelancer, the more self-confidence I have in doing a freelance activity.	0.576			0.626
q5_HC	All my work experience has helped me to perform a freelance activity of high quality.	0.754			0.853
q6_HC	In the last 2 years, I have participated in training courses for my core skills.	0.781	0.869
q7_HC	In the last 2 years, I have participated in training courses for the development of new skills.	0.854	0.916
q8_HC	In the last 2 years, I have participated in training courses for related or supporting skills (e.g., administration or personal effectiveness).	0.772	0.877
Number of items			3	2	3
Variance explained			30.250	23.55	22.96
Cronbach’s alpha			0.878	0.855	0.699

Note: Principal component analysis with varimax rotation and Kaiser normalization.

and

Table 4. EFA results for the SC construct. Source: authors.

Variable Label	Variable	Communalities	Factor Loadings Factor 1 Job and Career Satisfaction	Factor Loadings Factor 2 Life Satisfaction
q1_SC	I am satisfied with the success I have achieved in my career.	0.734	0.825
q2_SC	I am satisfied with the way I feel about my job as a whole.	0.777	0.811
q3_SC	I am satisfied with the opportunities to use my abilities on the job.	0.600	0.754
q5_SC	I am satisfied with the progress I have made toward meeting my goals for the development of new skills.	0.654	0.765
q6_SC	I am satisfied with the support I receive from my clients.	0.563	0.738
q7_SC	I am happy with my private life.	0.736		0.757
q8_SC	I enjoy my nonwork activities.	0.717		0.844
q9_SC	I am satisfied with my life overall.	0.812		0.810
q10_SC	I am dedicated to my work.	0.526	0.658
Number of items			6	3
Variance explained			42.27	25.84
Cronbach’s alpha			0.885	0.814

Note: Principal component analysis with varimax rotation and Kaiser normalization.

show the EFA results for the human capital factor and the subjective success factor on the basis of measured variables. The tables show the variables and their labels, the communalities, the factor loadings, the explanation of the variance, and Cronbach’s alpha of the constructs (factors).

3.6.1. Exploratory Factor Analysis (EFA)

As indicated in Table 3, the values of the communalities for all items are within the criteria (>0.5) [102], as are the factor loadings (>0.5) [97]. Based on the results, the three factors that define the construct human capital (training, skills, education and experience) are formed. The Cronbach’s alpha coefficient for the training factor is 0.878, for the skills factor is 0.855, and for the education and experience factor is 0.699, which are above the minimum required value of 0.60 [97]. Thus, it can be said that the dimensions of human capital were measured with sufficient reliability using the three factors formed. Moreover, the total variance explained is above the required criterion (>60%) [103,104]. Below in Table 4 are the EFA results for the construct subjective success (SC).

Consistent with the EFA results in Table 4, the communalities values for all items of the SS construct as well as the factor loadings meet the criteria (>0.5) [83,88]. In addition, two factors were formed: job and career satisfaction factor and life satisfaction factor. In

Table 5. CFA results. Source: authors.

Construct	Variable	Mean	SD	Indicator Loading	Indicator Weight	p-Value	VIF	Effect Size
Education and experience	q1_HC	6.365	1.028	0.745	0.394	<0.001	1.319	0.293
	q4_HC	6.251	1.078	0.772	0.408	<0.001	1.390	0.315
	q5_HC	6.452	0.939	0.861	0.455	<0.001	1.638	0.500
Skills	q2_HC	6.010	1.152	0.926	0.540	<0.001	2.035	0.500
	q3_HC	6.030	1.107	0.926	0.540	<0.001	2.035	0.354
Training	q6_HC	4.834	2.281	0.885	0.368	<0.001	2.335	0.326
	q7_HC	4.835	2.305	0.923	0.384	<0.001	2.962	0.354
	q8_HC	3.964	2.348	0.878	0.365	<0.001	2.219	0.320
Job and career satisfaction	q1_SC	5.754	1.100	0.856	0.225	<0.001	2.956	0.193
	q2_SC	5.920	1.113	0.876	0.231	<0.001	3.298	0.202
	q3_SC	5.850	1.267	0.815	0.215	<0.001	2.156	0.175
	q5_SC	5.677	1.144	0.727	0.191	0.003	1.668	0.139
	q6_SC	5.880	1.068	0.755	0.199	0.002	1.822	0.150
	q10_SC	6.035	1.123	0.732	0.193	0.003	1.633	0.141
Life satisfaction	q7_SC	5.880	1.068	0.876	0.395	<0.001	2.285	0.346
	q8_SC	5.883	1.228	0.787	0.355	<0.001	1.522	0.279
	q9_SC	6.121	1.347	0.911	0.411	<0.001	2.607	0.375
Objective success	q1_OS	5.293	1.305	1.000	1.000	<0.001	0.000	1.000

, the variable q4_SC is missing. The variable q4_SC had a high weight (above 0.40) on 2 factors; it was eliminated, and the clarity of the factor structure was checked after this step. As a result, a suitable solution of the formed factors with 9 variables was obtained. The factors explained a sufficient proportion of the total variance (42.27% and 25.84%), and the Cronbach’s alpha coefficients were 0.885 and 0.814, which means that the factors were measured with sufficient reliability.

3.6.2. Confirmatory Factor Analysis (CFA)

As mentioned in Section 3.4, the first part of SEM is the implementation of CFA. For the implementation of CFA, we followed the criteria according to [97,102]. The results of CFA are shown in Table 5. The table shows the constructs, variables, mean, standard deviation, indicator loadings, indicator weights, statistical significance of indicator loading and indicator weight, variance inflation factor, and effect size.

In line with the CFA results in Table 5, all variables satisfy the criteria stated in [105,106]. The indicator loadings satisfy the required condition (>0.5), and their values range from 0.727 to 1.000, suggesting adequacy. Moreover, the indicator weights of the observed variables prove to be positive. Moreover, the statistical significance of the indicator loadings and indicator weights is given (p < 0.05). The variance inflation factor (VIF) used to test for multicollinearity among the independent variables indicates that the model does not exhibit multicollinearity as the standard rule is that the VIF is 3.3 or less. Moreover, the threshold for the effect size is 0.02, and if the value is less than 0.02, the exclusion of this observed variable from further analysis should be considered [106]. As for the results, the effect size for all variables is greater than 0.02, which means that the variables meet the specified criteria. In continuation,

Table 6. Reliability and validity indicators of the formed factors. Source: authors.

Construct	R-Square	Adjusted R-Square	Composite Reliability	Cronbach’s Alpha	AVE	VIF	Q2
Education and experience			0.836	0.704	0.630	1.555
Training			0.924	0.876	0.802	1.083
Skills			0.923	0.833	0.857	1.194
Job and career satisfaction	0.361	0.351	0.911	0.882	0.633	2.736	0.229
Life satisfaction	0.217	0.205	0.894	0.821	0.739	1.604	0.372
Objective success (satisfaction with earnings)	0.188	0.175	-	-	-	1.867	0.195

shows the reliability and validity indicators of the formed factors.

Consistent with Table 6, the adjusted R-square (coefficient of determination) shows that 17.5% of the variance in objective success is explained by human capital factors. In the case of job and career satisfaction, over 35.1% of the variance is explained by the human capital factors, while 20.5% of the variance of the life satisfaction factor is explained by the human capital factors. Furthermore, all the factors formed are in accordance with the standards for reliability and validity of the measurement, the composite reliability (>0.7) [106] and Cronbach’s alpha coefficient (>0.6) [97], and the average variance extracted (AVE > 0.5), which ranges from 63.0% to 85.7%, indicating the validity of the measurement tool. Moreover, all factors of the model satisfy the condition of non-multicollinearity (VIF indicators range from 1.083 to 2.736, >3.3). Finally, Q2 indicates the predictive relevance of the model (>0) [97].

Table 7. Correlation matrix between constructs of the SEM model.

Factor	Education and Experience	Training	Skills	Job and Career Satisfaction	Life Satisfaction	Objective Success (Income Satisfaction)
Education and experience	(0.794)
Training	0.241 ***	(0.895)
Skills	0.364 ***	0.189 ***	(0.926)
Job and career satisfaction	0.542 ***	0.203 **	0.271 ***	(0.796)
Life satisfaction	0.380 ***	0.157 **	0.254 ***	0.605 ***	(0.860)
Objective success (income satisfaction)	0.392 ***	0.158 ***	0.242 ***	0.679 ***	0.413 **	(1.000)

Note: correlations are significant at *** p ≤ 0.01; ** ≤ 0.05. Source: authors.

shows the correlation between the factors included in the model. It can be seen that the strongest relationship is between objective success and job and career satisfaction (r = 0.679, p < 0.01). However, the weakest relationship is between life satisfaction and training (r = 0.157, p < 0.05). On the diagonal of the correlation matrix are the values of the square root of the AVE coefficient [107]. The values indicate discriminant validity [108] (each observed construct has a weak relationship with constructs other than the one to which it belongs), and the statistical significance of the correlation coefficients corresponds to the nomological validity of the model [109]. In addition, the quality and validity of the model presented in

Table 8. Model fit and quality indices—included latent variables (factors).

Criterion	General Rule for Acceptable Fit	Value
Average path coefficient (APC)	acceptable if p < 0.05	APC = 0.209, p < 0.001
Average R-squared (ARS)	acceptable if p < 0.05	ARS = 0.255, p < 0.001
Average adjusted R-squared (AARS)	acceptable if p < 0.05	AARS = 0.244, p < 0.001
Average block VIF (AVIF)	acceptable if ≤5, ideally ≤ 3.3	AVIF = 1.139
Average full collinearity VIF (AFVIF)	acceptable if ≤5, ideally ≤ 3.3	AFVIF = 1.673
Tenenhaus GoF (GoF)	small ≥ 0.1, medium ≥ 0.25, large ≥ 0.36	GoF = 0.445
Sympson’s paradox ratio (SPR)	acceptable if ≥0.7, ideally = 1	SPR = 1.000
R-squared contribution ratio (RSCR)	acceptable if ≥0.9, ideally = 1	RSCR = 1.000
Statistical suppression ratio (SSR)	acceptable if ≥0.7	SSR = 1.000
Nonlinear bivariate causality direction ratio (NLBCDR)	acceptable if ≥0.7	NLBCDR = 1.000

were also examined.

Results from Table 8 point to the suitability of the model and prompt us to continue using SEM.

The previously presented model is mainly used to test the formed subhypotheses. In addition, we formed a model according to the latent variables, formed on the basis of the factors, and single observed variables in order to test the main hypotheses. The generated latent variables and the observed variables were formed into second-level constructs [110] accordingly:

• Human capital (this second-level construct comprises the latent variables education and experience, skills, and training);
• Subjective success (this second-level construct comprises the latent variables of job and career satisfaction and life satisfaction).

To obtain the final results from SEM for testing the main hypotheses, CFA was also first performed. The results of CFA are shown in

Table 9. CFA results. Source: authors.

Second-Level Constructs		Indicator Loading	Indicator Weight	p-Value	VIF	Effect Size
Human capital	Education and experience	0.779	0.507	<0.001	1.195	0.395
	Training	0.613	0.399	<0.001	1.075	0.245
	Skills	0.744	0.484	<0.001	1.167	0.360
Subjective success	Job and career satisfaction	0.896	0.558	<0.001	1.577	0.500
	Life satisfaction	0.896	0.558	<0.001	1.577	0.500

.

As for the CFA implementation criteria, the indicator loading (>0.5), indicator weight (positive), their significance (<0.05), VIF (<3.3), and effect size (>0.02) of the human capital and subjective success constructs meet the criteria according to the results. In addition,

Table 10. Correlation matrix between constructs of the SEM model.

Construct	Human Capital	Subjective Success	Objective Success
Human capital	(0.716)
Subjective success	0.483 ***	(0.896)
Objective success	0.379 ***	0.609 ***	(1.000)

Note: correlations are significant at *** p ≤ 0.01. Source: authors.

shows the relationships between the constructs. The relationships are statistically significant and positive. The strongest relationship is between subjective success and objective success (r = 0.609, p < 0.01).

The following

Table 11. Reliability and validity indicators of the formed second-level factors. Source: authors.

Second-Level Construct	R-Square	Adjusted R-Square	Composite Reliability	Cronbach’s Alpha	AVE	VIF	Q2	MSV	ASV
Human capital	-	-	0.757	0.519	0.512	1.324	-	0.233	0.164
Subjective success	0.282	0.279	0.890	0.754	0.802	1.803	0.301	0.371	0.302
Objective success	0.151	0.147	-	-	-	1.615	0.159	0.371	0.257

shows the reliability and validity indicators of the factors formed.

The results from Table 11 show that 27.9% of the variance (adjusted R-square) of the subjective success factor is explained by the human capital construct, while 15.1% of the variance of the objective success is explained by the human capital construct. In addition, the constructs of the model satisfy the condition of average variance extraction (AVE > 0.5), the VIF indicator does not indicate multicollinearity (the VIF indicators range from 1.324 to 1.803, <3.3), and Q² indicates the predictive relevance of the model (>0). The constructs of the second level also satisfy the conditions for composite reliability (>0.7) and Cronbach’s alpha coefficient (>60%), except for the construct human capital, which has a lower value (51.9%). However, the correlation matrix between the constructs of the second level confirms the discriminant [109] and nomological validity [110] for the constructs. Discriminatory validity is also confirmed by the maximum total variance (MSV) and average total variance (ASV). The condition is that AVE > MSV and AVE > ASV [111], where MSV is the square of the largest correlation coefficient between latent variables, and ASV is the mean of the square of the correlation coefficients between variables. Convergent validity is also confirmed. It refers to the extent to which the scale is related to other variables and other measures of the same construct. Not only should it correlate with related variables, but it should not correlate with other unrelated variables [112]. To satisfy this, AVE should be higher than 0.5, and composite validity higher than AVE [113].

The analysis results from SEM show that the model achieves high goodness of fit. The detailed results can be found in

Table 12. Model fit and quality indices—included constructs. Source: authors.

Criterion	General Rule for Acceptable Fit	Value
Average path coefficient (APC)	acceptable if p < 0.05	APC = 0.460, p < 0.001
Average R-squared (ARS)	acceptable if p < 0.05	ARS = 0.217, p < 0.001
Average adjusted R-squared (AARS)	acceptable if p < 0.05	AARS = 0.2134, p < 0.001
Average block VIF (AVIF)	acceptable if ≤5, ideally ≤ 3.3	Not available
Average full collinearity VIF (AFVIF)	acceptable if ≤5, ideally ≤ 3.3	AFVIF = 1.580
Tenenhaus GoF (GoF)	small ≥ 0.1, medium ≥ 0.25, large ≥ 0.36	GoF = 0.409
Simpson’s paradox ratio (SPR)	acceptable if ≥0.7, ideally = 1	SPR = 1.000
R-squared contribution ratio (RSCR)	acceptable if ≥0.9, ideally = 1	RSCR = 1.000
Statistical suppression ratio (SSR)	acceptable if ≥0.7	SSR = 1.000
Nonlinear bivariate causality direction ratio (NLBCDR)	acceptable if ≥0.7	NLBCDR = 1.000

, showing that the model provides significant insights into the predictors of objective and subjective success. In Table 12, it can be seen that the VIF measure is not available for the average block. This can be explained by the fact that VIF is a measure of the degree of vertical collinearity or redundancy between latent variables that are assumed to affect another latent variable [114]. Block VIFs cannot be computed for latent variables with only one predictor or no predictor [106], as is the case in our research. The proposed hypotheses and subhypotheses were tested as follows. The test results of the hypotheses are summarized in

Table 13. Hypothesis testing.

Hypothesis	Impact	Effect Size
H1.1	0.342 ***	0.323
H1.2	0.123 **	0.033
H1.3	0.086 n.s.	0.016
H2.1	0.549 ***	0.323
H2.2	0.037 n.s.	0.010
H2.3	0.120 **	0.028
H2.4	0.377 ***	0.139
H2.5	0.103 n.s.	0.027
H2.6	0.139 **	0.027

Note: *** p ≤ 0.01; ** p ≤ 0.05; n.s.—nonsignificant. Source: authors.

and

Table 14. Hypothesis testing.

Hypothesis	Impact	Effect Size
H1	0.531 ***	0.282
H2	0.389 **	0.151

Note: *** p ≤ 0.01; ** p ≤ 0.05. Source: authors.

in the next section.

4. Results

Having achieved high goodness of fit in the measurement models for the included latent variables (factors) and constructs, we add the structural paths (see Figure 2 and Figure 3).

The results of the structural path analysis support six of the nine subhypotheses. The results show (Table 13) that the education and experience factor has a positive and significant (p < 0.01) impact on objective success, job and career satisfaction, and life satisfaction, confirming hypotheses H1.1, H2.1, and H2.4. Besides, the skills factor has a positive and significant (p < 0.01) influence on objective success, confirming hypothesis H1.2, while its influence on job and career satisfaction and life satisfaction is positive but not significant, which rejects hypotheses H2.2 and H2.5. In addition, the training factor is positively related and has a significant influence on job and career satisfaction and life satisfaction, but no significant influence on objective success, which confirms hypotheses H2.6 and H2.3, but rejects hypothesis H1.3.

Along with the impacts of the factors and statistical significance, the results in Table 11 also show the effect size, which is within the bounds (>0.02, according to [110]) for all relationships, except for hypotheses H1.3, H2.2, and H2.5, where the effect size is too small and not statistically significant. Therefore, we reject hypotheses H1.3, H2.2, and H2.5.

Accordingly, Figure 3 shows a conceptual model designed according to second-level constructs, and the impacts associated in Table 14 to test the main hypotheses.

The results in Table 14 indicate that human capital has a statistically significant and positive impact on subjective success (b = 0.531; p ≤ 0.01). The effect size of the predictor variable (construct) human capital is 0.282 (>0.02). Based on the above, hypothesis H1 can be confirmed. Moreover, human capital has a statistically significant and positive impact on objective success (b = 0.151; p ≤ 0.01). The effect size of the construct human capital is 0.151. Therefore, we confirm hypothesis H2.

5. Discussion

As a result of economic, technological, and social changes influenced by digitalization, traditional full-time employment is increasingly shifting to nonstandard forms of employment, such as freelancing.

In this paper, we examine the demographic characteristics of this growing group of entrepreneurs in Slovenia and find that more than half of the freelancers surveyed (57.5%) work from home, most freelancers are found in the 35–44 age group, almost 50% have completed the second level of tertiary education, half of the respondents work more than 40 hours per week, and more than 45% have 16 years or more of experience working as freelancers. In regard to gender, 59% are male freelancers and 41% are female freelancers. In terms of the type of business sector, more than 80% of freelancers in Slovenia work in the professional, scientific, and technical and information and communication sectors.

Human capital theory also suggests that investment in education, skills, and training should lead to higher value in the labor market [21]. This is because income and promotions are indicators of how much a person is worth in a free market. From the results of the study, education and experience and training positively and significantly affect freelancers’ objective success (satisfaction with income) and subjective success (perceived job and career satisfaction and perceived life satisfaction). We see that freelancers still rely on their knowledge and work experience. This does not mean that work tasks have become less difficult—quite the opposite. Freelancers are constantly confronted with new challenges and acquire new knowledge and skills [115,116,117] (i.e., experience) during their work. As freelancers change jobs frequently, they increasingly find themselves in situations where they try to obtain challenging assignments. Therefore, in order to keep their job and remain competitive in the market, they often maintain their employability by increasing their expertise in several related fields and broadening their skills. This is supported by our findings that skills have a positive and significant impact on job and career satisfaction. In addition, we found that the dimension of training also affects objective success, but not significantly. This can be explained by the fact that successful freelancers work for themselves, usually more than 40 hours per week, and in the competitive market do not have enough time for additional training or even do not know enough about opportunities for their personal development in their countries. Moreover, education and experience have been found to be the most representative predictors of both objective and subjective success, as reported in the literature [62,63,64,65,66,67,68,69,70,71,72,73,74]. It has been shown that successful freelancers have strong human capital and experience, which help them to perform a freelance work. This is also consistent with human capital theory [21] and the benefits of career mobility, where working on different projects provides new and different professional experiences, which lead to new knowledge acquisition and different perspectives, which in turn improves human capital and leads to higher satisfaction. Moreover, we find support for our initial assumptions that freelancers’ human capital is an important predictor of subjective success rather than objective success, as it is an important resource that freelancers rely on to determine their quality of life. This is due to the flexibility and independence, which are the main motivations for many freelancers in their working lives [118]. Apparently, acquiring adequate education and experience in terms of being qualified and independence in choosing the projects they work on have a positive effect on job and career satisfaction and ultimately on life satisfaction. In addition, the skills dimension showed the expected positive relationship with subjective success (job and career satisfaction), but the level of perceived confidence in skills (ability) to use digital technologies and the ease of use of new software packages or online applications for the quality of work were not found to be too significant or decisive for freelancers’ subjective success. On the other hand, training was found to have a significant impact on freelancers’ subjective success. As freelancers try to manage their work–life balance and be their own boss, they are expected to attend training and education that interests them. Unsurprisingly, there is a stronger link between human capital and subjective success (i.e., life satisfaction and job and career satisfaction). Given the global expansion of new forms of employment, such as freelancing, it is important to understand the quality of life of these nonstandard workers (freelancers).

In 2016, Slovenia introduced the Digital Slovenia Strategy [8] for the Information Society. The strategic goals were to increase the share of the ICT sector in Slovenia’s GDP, provide computer infrastructure and applications in the digital service economy, unleash knowledge and innovation to create digital jobs and wealth and use the achievements for effective marketing, accelerate the adoption of e-commerce standards and their implementation in practice, and create conditions for easier and faster penetration of global markets. In the course of preparing the upcoming strategy, an overview of the implementation of Digital Slovenia 2020 was first prepared. The worst implementation is in the area of digital entrepreneurship, where the implementation is only 22%. Additionally, in the area of recognizing the benefits of the use and knowledge of new technologies, approaches, and business processes/models, activities need to be strengthened and adapted in the future, as the implementation is only 42%. In this segment, activities are planned to promote and raise awareness of the entire Slovenian society, which is important in terms of both future, new competences and the involvement of all groups in the modern opportunities offered by technology [119].

The [120] showed that more than 3 million people work as freelancers in professional services in France, Germany, and Spain. In the Croatian neighborhood, a nonprofit association, Croatian Society of Independent Professionals (CSIP), was founded in 2013 with the aim of creating, maintaining, and promoting a healthy professional ecosystem for all freelancers working in Croatia. Today, CSIP has over 2300 members. Three important priority programs of the association are the provision of legal, accounting, and business advice to freelancers; lobbying in Croatia and the EU; and an Internet portal that centralizes information and services that can help members [121]. As such an association does not exist yet in Slovenia, the results of our study provide an evidence-based approach that can be useful for evaluating future digital policy proposals. Policymakers need to recognize the diversity of the self-employed market and try to divide it into needs and challenges. They need to create a policy environment that promotes a variety of contractual models, advice, business and legal support, education and professional development, and accessible office infrastructure (coworking spaces) and advocate for common interests at the national and international levels to improve labor market participation and inclusion.

The year 2019 had the highest number of self-employed in the EU between 2011 and 2020, and there were an estimated 19.4 million self-employed [122]. This trend has also continued in Slovenia. In 2018, the number of self-employed people increased by 2.5% (4731 people). It is worth highlighting that the number of self-employed persons increased the most in the hospitality sector (by 8.1%), followed by the information, communication, finance, insurance, and real estate sectors (by 7.6%). According to the available data, it is important to note that the self-employed generated an operating surplus of EUR 3.723 million in 2018, which is 8.9% more than in 2017 [123]. It is interesting to note that at the end of January 2020, for the period of the COVID-19 pandemic, there were 0.7% fewer employed persons in Slovenia than at the end of 2019, of which slightly more than 10% were self-employed, 1.4% more than in December 2019 [124].

According to report [51], freelance job openings increased by 25% in 2020 compared with the previous year. Like the US, Europe is also experiencing a major shift in employment trends. The pandemic has pushed people to use technology that allows them to collaborate, communicate, and conduct business virtually through digital platforms. Although digital freelancers earn an income, many view them as insecure because they do not have permanent jobs. This misconception leads to freelancers struggling to find housing, even though many of them are solvent enough. There are many problems, but the main one is that freelancers have no one to turn to. Institutions have to advise them or reassure them that they are doing the right thing and meeting their legal obligations [120]. In France and Italy, governments are not shying away from the status of the self-employed and are trying to provide additional social protection to these workers. These are hybrid solutions that consider the needs of different stakeholders [125]. In addition, digital nomadism has never been greater, and the COVID-19 pandemic has accelerated this shift in the economy. Today, the digital nomad program in Europe is gaining momentum with a unique e-residency program, a digital identity, and a government-issued status that allows access to a transparent digital business environment. Such a program allows foreigners to live in the country while running a location-independent business or working for a company based outside their country. The main purpose of introducing such a program and regulating the status of digital nomads is to attract as many people as possible, who will indirectly contribute to the state budget and local entrepreneurs through their spending and stimulate additional economic activity [126]. It is therefore important that Slovenia recognizes the growth of freelancers and knows how to respond to their needs, but also how to benefit from them.

The research also has some limitations that may serve as a basis or recommendation for further research. First, the consideration of the success of Slovenian freelancers is limited to analysis from the perspective of human capital. To gain a more comprehensive insight into these constructs, the study should be extended to other behavioral dimensions of freelancers, such as personality traits, motivation, and social capital. Second, new scales specifically tailored to freelancers could be developed in the future to provide a more comprehensive and detailed measurement of their characteristics (i.e., their unique traits), as the analysis in this paper is based on the questionnaires used and adapted. In addition, interviews with freelancers could be conducted in the future to gain a deeper understanding of their needs, career drivers, and challenges. Third, the analysis is limited and applies only to freelancers. Comparisons with other types of entrepreneurs or employees, or gender differences, would provide better insight into the level of quality of life. In Slovenia, freelancers still do not receive enough attention in the media and in academic circles. There is still no literature on freelancers at the national level, and there is no register on them or other official data.

Finally, the paper presents the aspect of career, human capital, and success of freelancers. The paper also provides suggestions for national policy. Like any entrepreneurial venture, freelancing is also affected by government decisions (i.e., programs). The study shows that government recognition through education and programs could be considered to promote and make sustainable this new career of entrepreneurship category. For further research, we suggest that our findings can influence national policies and institutions related to self-employment. According to [127], a sustainable career proposes to promote human and career development with an emphasis on balance. Statements about hard work, long hours, and steady employment promoting career well-being are not consistent with contemporary career trends [128,129]. As the new digital economy expands the freelancing career demand for it, the issue of quality of life (i.e., work–life balance) will continue to play a crucial role in workers’ lives. Given the link between freelancers’ human capital and their satisfaction with work, life, and earnings, including their ability to reconcile their work and nonwork lives, these issues might be considered by policymakers as they think about how to promote workers’ productivity and quality of life in the new economy. This focus on quality of life, which includes work and leisure, is becoming increasingly important today and has a major impact on workers’ preferences and also on society.