European family business owners: what factors affect their job satisfaction?

Abstract

Purpose

This research aims to better understand the factors and determinants that shape the job satisfaction of European family business owners.

Design/methodology/approach

The study is based on a unique sample of 11,362 European family business owners surveyed within the European Union Labour Force Survey (EU LFS) framework, and the main findings were obtained by estimating ordered logistic regression models.

Findings

The authors show that only 26.8% of European family business owners are women, which underlines the gender imbalance in family business ownership, and the authors' results also report that their job satisfaction is significantly lower compared to males. The authors also find the highest job satisfaction amongst family business owners with master-level degrees and point out several interesting statistically significant differences across the industry focus of the family business.

Originality/value

This research contributes to the body of knowledge on the job satisfaction of family business owners by conducting a large-scale study based on a statistically representative sample of European respondents.

Citation

Dvouletý, O., Orel, M. and Procházka, D.A. (2024), "European family business owners: what factors affect their job satisfaction?", Journal of Family Business Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JFBM-12-2023-0303

1. Introduction

Gender differences found between men and women family business owners are more a function of subjective perceptions and in economic and social structural aspects than of biological specialisations of the sexes.

Lerner and Malach-Pines (2011, p. 128)

The study behind Lerner and Malach-Pines's (2011) work suggests that the differences observed between male and female family business owners are influenced mainly by subjective perceptions and the economic and social structures they operate within rather than any inherent biological differences between the sexes. Family businesses are integral to the European economy, characterised by their unique blend of personal and professional dynamics (Fletcher, 2002; Chaudhary et al., 2021; Zapata-Cantu et al., 2023). Often passed down through generations, these enterprises represent a delicate balance between familial loyalty and business acumen (Davis, 1983; Allio, 2004; Ratten et al., 2023). Fusing personal relationships and professional roles often leads to distinctive management styles and company cultures that differ significantly from nonfamily corporations (Hall and Nordqvist, 2008). This distinctive environment can foster a deep sense of belonging and purpose amongst family members (Simarasl et al., 2020), yet it also brings challenges such as succession planning and conflict resolution. It is vital to note that the impact of family-to-work conflict (FWC) on job satisfaction and social networks differs between family and nonfamily businesses. Specifically, in nonfamily businesses, FWC negatively impacts job satisfaction and positively impacts social networks, whilst in family businesses, FWC does not significantly affect job satisfaction and social networks (Kwan et al., 2012). Integrating family and business values and objectives, termed as “family–business embeddedness”, enhances job satisfaction amongst family firm employees and reduces their turnover intentions (Khanin et al., 2012). Nevertheless, the impact of these dynamics on business operations and sustainability is profound, making family businesses not just economic entities but also social units with complex interpersonal networks (Romano et al., 2001; Von Schlippe and Frank, 2013; De Massis and Foss, 2018). Given our paper's opening and the surrounding finding that perceptions and social structures more influence gender differences in family business ownership than biological factors, plus the assumption that the entrepreneurship research has been limited by a persistent gender bias, which marginalises and misrepresents women entrepreneurs (Ahl and Marlow, 2012), there is a clear need for further research to understand how these subjective and societal elements specifically impact job satisfaction in family businesses, an area not yet fully explored.

The set of rather unique characteristics of family businesses necessitates focussed research to understand how these dynamics influence owner satisfaction. Whilst extensive literature exists on general business management and entrepreneurship (e.g. Cooper and Artz, 1995; Block and Koellinger, 2009; Cruz et al., 2009; Kautonen and Palmroos, 2010; Truant et al., 2019), the intersection of family and business presents a distinct set of challenges and opportunities that can significantly affect job satisfaction. Investigating these aspects is crucial for developing tailored management strategies and support mechanisms (e.g. succession planning, conflict resolution tools/approaches, etc.) that cater specifically to the needs of family business owners. That being said, this paper seeks to explore the multifaceted realm of job satisfaction amongst family business owners in Europe, a topic that, whilst critical, has not been extensively probed in academic research. The core objective of our study is to comprehensively understand and articulate the various factors that influence the job satisfaction of family business owners. As noted in the previous paragraph, we recognise that family businesses are not just economic units but are enmeshed in complex family dynamics, significantly influencing owners' professional contentment and motivations. Our exploration extends beyond the surface level, diving into the nuances of how these businesses function and the personal satisfaction derived from running them. The latter encompasses an analysis of demographic data, such as age, gender and education level, along with business-related elements (i.e. industry type, company size and geographical location). Given the importance of family firms in the European economy, we examine the attributes that differentiate them from nonfamily businesses. At the core of our investigation is the recognition that combining family and business domains presents a unique array of difficulties and incentives that impact job satisfaction.

That said, this study seeks to unravel these complex dynamics, examining how personal factors like gender and education level, alongside business-specific aspects such as industry sector and geographical location, shape the job satisfaction of family business owners. The gender perspectives are a central interest in this paper to reveal gender-specific findings of family business owners. Utilising a robust European Union Labour Force Survey (EU LFS) dataset, our research employs a methodological approach designed to capture a broad and representative picture of the European family business landscape, thus further contributing to the earlier evidence's replicability. This data-driven approach underpins the credibility of our findings, offering insights grounded in real-world evidence. The paper does not merely investigate the job satisfaction of European family business owners in isolation. Instead, it positions this inquiry within the broader context of economic and social dynamics, offering a nuanced understanding of a segment that is crucial to the European economy yet often overlooked.

2. Literature review

2.1 Gender dynamics in family business

The literature review on gender dynamics in family businesses reveals a nuanced landscape where gender roles deeply influence leadership, succession and overall business performance (Sonfield and Lussier, 2009; Lerner and Malach-Pines, 2011; Ferrari, 2019; Kubíček and Machek, 2019; Franco and Piceti, 2020; Ratten et al., 2023). For the opening before the dive into the literature overview on gender dynamics in the family business, it is vital to note that the concept of “gender” significantly informs the representations and practices in family businesses, reflecting social attitudes towards the division of labour between men and women in entrepreneurship (Lingas, 2013). The latter is vital to comprehend as family dynamics significantly influence business performance both directly and indirectly through effective leadership, with larger family firms exhibiting better leadership and consequently better business performance (Maharajh et al., 2023). The traditional gender roles that have long influenced leadership styles have been gradually evolving, especially as newer generations enter the business world (Goktepe and Schneier, 1989; Rhee and Sigler, 2015). Traditionally underrepresented in family business leadership, women often face significant challenges due to entrenched stereotypes and biases (Vadnjal and Zupan, 2011). A recent study on family dynamics and female entrepreneurship in China by Dewitt et al. (2023) revealed that, although there have been changes in family dynamics encouraging female entrepreneurship, women continue to encounter challenges and face unfair expectations when opting for entrepreneurship as a career, adversely affecting their professional responsibilities.

These challenges include a lack of recognition and difficulties in asserting authority. However, when women ascend to leadership roles, they bring diverse perspectives and management styles, which can catalyse innovation and positive change within the business. That being said, whilst women's formal roles in family businesses do not generally correlate with their participation in strategic decision-making, the socio-cultural context and sector-specific factors, particularly in agriculture and farming, significantly influence their involvement and highlight the importance of considering both explicit and implicit power in strategic decision-making (Ratten and Tajeddini, 2017; Dettori and Floris, 2023). Given the fact that decision-making processes in a family business can be split between the couple, the successful family business commonly relies on professionalisation, task division between the couple and relational-based factors like trust, communication, flexibility and common goals for effective business management and stable personal relationships (Franco and Piceti, 2020).

Looking at the discussed from another perspective, the cultural context plays a crucial role in shaping these gender dynamics. In some cultures, rigid gender norms significantly hinder women's participation and success in family businesses. In contrast, other cultures exhibit a growing acceptance and support for female leadership, reflecting broader societal shifts towards gender equality. For example, Wang (2010) concluded that daughter succession in family businesses is often hindered by a combination of societal attitudes and family dynamics, although daughters can ascend to leadership under special circumstances and offer unique advantages compared to sons. However, Aldamiz-Echevarría et al. (2017) note that although gender is not seen as an obstacle to becoming a successor in family businesses in many cultures, there is still more male than female successors, influenced by birth order and personal choices of women regarding joining the family business. This cultural diversity affects how female leaders are perceived and how effectively they can implement changes within their businesses (Lerner and Malach-Pines, 2011). That said, the formation and management of plural identities are distinctively shaped by the key influences of gender and ethnicity, which are crucial in organising and performing business practices (Aygören and Nordqvist, 2015). Different types of paternalistic leadership styles can significantly influence the success of family business succession, with this impact varying according to the gender of the successor (Cicellin et al., 2015).

The research indicates that gender plays a crucial role in family business succession, with the literature revealing fragmentation and a lack of comprehensive analysis. Key areas impacted by gender-related factors include environment and context, people and processes (Kubíček and Machek, 2019; Ratten et al., 2023). Furthermore, there is an ongoing evolution in gender roles within family businesses, with younger generations challenging traditional norms (Konopaski et al., 2015). With that in mind, fostering family business entrepreneurial processes requires a rather highly explicit and open organisational culture that promotes questioning and change of old cultural patterns (Hall et al., 2001; Hadri et al., 2023). The shift around the latter is transforming leadership dynamics, succession planning and business governance towards greater inclusivity and diversity. Nonetheless, the study by Samara et al. (2019) shows that women's (pro)active presence in family business corporate boards likely leads to economic and socioemotional benefits, enhancing prosperity, family cohesion and reputation, whilst their exclusion can have negative ramifications. That being said, acknowledging the significant impact of diverse leadership in the realm of gender dynamics, which enhances decision-making and fosters positive business outcomes, underscores the necessity for more in-depth research in these crucial areas.

Moreover, the gender dynamics inside family businesses are intricate and constantly changing. Numerous factors, such as cultural standards, societal developments and generational shifts, shape these dynamics. As these changes occur, they bring about distinct difficulties and possibilities for family businesses, emphasising the necessity for flexible and inclusive management strategies that acknowledge the importance of diversity in leadership. However, examining gender dynamics in family enterprises uncovers significant insights into the functioning and development of these institutions. As we shift our focus from analysing the influence of gender to investigating educational and age-related factors, it becomes clear that these components are also essential in creating the structure and dynamics of family enterprises. Gender roles and cultural contexts play a defining role in some dynamics, whilst education and age have a considerable impact on leadership styles, succession planning and the overall strategic direction of family firms.

2.2 Educational and age-related factors

Educational and age-related factors in family businesses are pivotal in shaping their evolution and approach to business challenges. The interplay between the successor's personal characteristics, the family business's organisational characteristics and the context leads to various patterns that determine different outcomes in the succession processes of family businesses (Porfírio et al., 2020). Small family firms express their entrepreneurial orientation differently from nonfamily firms, influenced by a blend of firm- and family-level factors, leading to unique configurations of entrepreneurial behaviour (Dessì et al., 2023). Higher education levels amongst family business owners often correlate with a more progressive stance towards business management, leading to innovative practices and a greater openness to change (Rondi et al., 2019). Duran et al. (2016) note that family firms, due to family control, wealth concentration and nonfinancial goals, invest less in innovation but have a higher conversion rate of innovation input to output and a greater innovation output compared to nonfamily firms. Despite this being said, whilst family firms have the capacity and distinctiveness to innovate, they often face a paradox between ability and willingness to do so. This paradox stems from their conservative nature and emotional attachment to traditional business models, leading to a reluctance to embrace change and invest in innovation activities. However, some family firms can overcome these challenges and become beacons of innovation in their industries, demonstrating the complexity and diversity within family firm innovation practices (Eddleston et al., 2019).

In a similar manner, education also plays a role in influencing the approach to traditional business models, driving modernisation (Hwang, 1990; Entwisle et al., 1995; Mariussen et al., 1997) and adaptation to new market demands (Zapata-Cantu et al., 2023). Higher education levels often encourage a more analytical and innovative approach to business, allowing family business owners to integrate new technologies and strategies effectively, with Stewart and Hitt (2012) confirming that higher levels of education amongst family business members indeed often correlate with more innovative and progressive management practices, leading to a great openness to change and – as noted – modernisation of the business. Over and above that, educational exposure can broaden the perspectives of family business members, fostering a culture of continual learning and adaptability that is crucial in today's rapidly changing business environment (Beech et al., 2020).

On the other hand, age-related factors create a diverse spectrum of perspectives within the business (Stamm and Lubinski, 2011). Younger family members, often more attuned to current trends and technologies, are likely to push for modern practices and innovative strategies (Laforet, 2013). They bring a fresh perspective that can be crucial for the business's adaptability in a fast-changing environment. Jaskiewicz et al. (2015) note that the use of modern technology and adaptability in family businesses is that families with an entrepreneurial legacy engage in strategic activities, including adopting modern technologies, to nurture transgenerational entrepreneurship. This practice leads to enhanced adaptability and innovative growth in family firms across generations. In contrast, older generations typically hold a wealth of experience and may favour time-tested approaches, emphasising stability and tradition (Barnes and Hershon, 1976; Mazzola et al., 2008). Vesperi et al. (2023) identified the crucial role of the “latent generation” in the family business, which consists of members coeval with the first generation who support the entrepreneur without being formally engaged in the business, a concept not yet considered in the existing literature. Understanding the dynamics introduced by the latent generation is essential, as it adds complexity to the interactions between generations in family businesses, potentially influencing how new ideas and changes are perceived and adopted within the family structure.

This generational gap can lead to significant challenges in succession planning and business strategy as younger members seek to implement new ideas that may be met with resistance from older family members (Mokhber et al., 2017). With that being said, Miller et al. (2003) indicated that the educational and professional experiences of successors significantly influence their approach to running the business, with those lacking broad business education and diverse experience more prone to extreme behaviours in managing family businesses. This tendency can impact the business's adaptability and success across generations. Therefore, navigating these educational and generational dynamics is vital for the long-term success and sustainability of family businesses. Balancing the innovative drive of the younger generation with the experience and wisdom of the older generation can lead to a harmonious and effective business strategy.

Understanding and addressing these dynamics is thus vital for family businesses as they strive to remain competitive and relevant in an ever-evolving business landscape. As we delve into the educational and generational dynamics in family businesses, understanding their implications becomes as crucial as recognising the diverse influences of industry and geographic location. Just as education and age shape internal business practices and perspectives, the industry sector and geographical setting play a pivotal role in defining the external environment and operational challenges family businesses face.

2.3 Industry and geographical influences

Industry and geographic influences significantly affect the operation and success of family businesses as they operate in the so-called entrepreneurial ecosystem (Bichler et al., 2022). Basco (2015) underlines that the embeddedness of family businesses in regional productive structures significantly affects regional development. This influence is manifested through the interaction of family businesses with regional factors, processes and proximity dimensions, thereby impacting the economic and social development of the region. The industry sector determines market dynamics (Matthews et al., 2011), competition levels (Morris et al., 1996) and opportunities for growth (Marques et al., 2022), which can shape the strategic direction of the business. In the dynamic and swiftly evolving realm of technology, family-run businesses encounter a distinct set of challenges and opportunities, markedly different from those faced in more conventional sectors such as agriculture. This distinction becomes particularly significant in the context of globalisation. Wu et al. (2022) highlight that family businesses are not merely reacting to these changes; they are proactively adapting and innovating. Their adaptations encompass the adoption of novel strategies, cutting-edge technologies and modern management practices. This proactive stance is essential for maintaining competitiveness in the increasingly globalised economic landscape, where staying ahead means constantly evolving with the times.

With that in mind, the role of geographic location is pivotal in the context of family businesses, as it significantly influences their access to resources, markets and talent (Royer et al., 2008; Hauswald et al., 2016; Zhang, 2019). Urban family businesses, with their access to larger, more diverse markets and a skilled workforce, are often better positioned to innovate and expand. This urban setting facilitates networking and collaborations, leading to potential growth opportunities and greater exposure to new business trends and technologies (Backman and Palmberg, 2015). On the flip side of the coin, rural family businesses, capitalising on strong community ties and lower operational costs, have the advantage of deep-rooted local support and trust. This unique position often leads to enhanced customer loyalty and a stable, familiar market, which can be crucial for long-term sustainability and resilience in challenging economic times (Entwisle et al., 1995; Seaman, 2015; Backman and Palmberg, 2015). Brewton et al. (2010) have determined that their social capital and disruption variables negatively impact rural firms' resilience, whilst urban firms' resilience is positively influenced by viewing the business as a way of life. Rural businesses face unique challenges due to their community involvement and disruption handling, whereas urban businesses benefit from a strong identification with their business. This geographic variation affects business strategies and outcomes, highlighting the importance of location in shaping the unique challenges and opportunities family businesses face (Baù et al., 2019).

Bridging the literature insights on industry and geographic influences with the forthcoming data analysis, it becomes rather vital to see how these broader trends manifest in the specific demographic, educational and geographical characteristics of our sampled European family business owners. The following section's analysis will delve into these details, offering a nuanced understanding of how these factors interplay in shaping the realities of family businesses across different European contexts.

3. Data, variables and sample summary statistics

Our research is based on a unique sample of 11,362 European family business owners surveyed within the framework of the EU LFS and the sample is unique for its statistical representativeness across participating countries [1] and its extensive coverage and large sample size. The national statistical offices are responsible for the survey amongst economically active individuals via Computer-Assisted Personal Interview (CAPI), Pen-and-Paper Personal Interview (PAPI) or Computer Assisted Telephone Interview (CATI) techniques (for details, see Eurostat, 2018, Table 1.1 on p. 11), and then Eurostat harmonises the data and creates the cross-country-level dataset, which researchers can request in an anonymised form to study economic behaviour of the European population. As already noted before, the size of the sample ensuring robust statistical evidence together with a large variety of variables and information belong to advantages of EU LFS, whilst the lack of control over the data collection process and limits resulting from disability to specify own variables are its weaknesses.

In particular, we extract data from the 2017 edition, published online in 2018 (details available via Eurostat, 2019, 2020), which had a unique Ad-Hoc Module dedicated explicitly to self-employment and job satisfaction, which allowed us to determine family business owners, i.e. those who answered that the main reason for becoming self-employed (i.e. REASSE) was the “continuation of the family business” (Eurostat, 2018, p. 36). The research team acknowledges that using data from 2017 might be a time limitation of this approach (especially within the recent global events of the COVID-19 pandemic or the Ukraine war conflict, Eckey and Memmel, 2023), but at the same time, we remember that since then, there was no other effort to collect such a large dataset, harmonised across the European countries, meeting the objectives of this study. The unique combination of job satisfaction variables, individual-level characteristics and identification of family business owners makes the dataset still relevant for research, even though it is slightly older.

The respondents self-reported the occupational choice, i.e. it does not allow us to double-check the individual-level status with the widely used definition of family business owners (Hadri et al., 2023, p. 1). Also, it means that we work with second and later generations of family business owners and not primarily with those who initially founded the family business (Rondi et al., 2019; Hadri et al., 2023; Rosecká and Machek, 2023), and within these boundaries, one needs to interpret the presented findings. Although we do not know the family system position and the particular generation of the owners within the business, we at least work with the age of business, deduced from the year of the first official registration, which enables us to control this aspect in our analysis at least partially. From the summary statistics reported in Table 1, we know that the average age of the represented business organisation in the studied countries was 20.3 years, with the eldest being 55 years.

We study the role of personal characteristics and individual-level variables, which were documented to be influential determinants of job satisfaction, in earlier research, such as in studies by Boles (1996) or Lauto et al. (2020). We use a standardised measure of job satisfaction, ranging from 1 = not satisfied at all to 4 = satisfied to a large extent. This is also a dependent variable in our analysis, representing a subjective perception of the individual's work-related life (Kristensen and Westergaard-Nielsen, 2007; Dvouletý, 2023).

The data allow us to control family business owners' age structure and gender, coded into zero-one dummy variables in this analysis. As we have already implied in the previous section, Danes et al. (2007) and Sonfield and Lussier (2009) note the significant underrepresentation of females in family business ownership and differences in management practices of running a business and their performance. In Table 2, we show that only 26.8% of European family business owners are women, which underlines the gender imbalance in family business ownership, also documented by other researchers (Kubíček and Machek, 2019). From the perspective of age structure, our data reveal that the 50–54 age cohort is the most frequent in our sample. The highest level of education data, coded into the International Standard Classification of Education (2011) levels, shows that almost half of the family business owners possess upper secondary education, and their average years of experience are 20. We also extracted several family-related variables (Hadri et al., 2023). We see that the sample represents families including, on average, three persons in the household, more likely without children (average 0.5) and most of the owners (68.2%) are married. Geographical and local context was found to be important by family business researchers (Gomez-Mejia et al., 2020) and general entrepreneurship scholars within the concept of entrepreneurial ecosystem (Muñoz et al., 2022). Thus, we use information from our data that allows us to divide family business owners into those who live in cities, towns and suburbs and those living in rural areas. Our analysis also considers the family enterprise's industry focus, which was documented to be influential by prior researchers (Romano et al., 2001; De Groote et al., 2021).

Lastly, we also point out the need to consider cross-country diversity in institutions and macroeconomic development (Dvouletý, 2017; Chowdhury et al., 2019), which is why we include in the forthcoming analysis a) a set of country-related dummy variables and b) a set of country-level control variables, namely gross domestic product (GDP) per capita (constant US dollars), Business Freedom Index (index, where 100 = freest business environment), Taxes on income, profits and capital gains (in percentage) and proportion of individuals using the Internet to capture level of country digitalisation (in percentage). We obtained these variables from the World Bank database (2023) and the Business Freedom index from the Heritage Foundation (2023).

4. Empirical analysis and discussion

This section presents the findings of the estimated multivariate regression models that we used to understand better the factors and determinants that shape the job satisfaction of European family business owners. The presented results were obtained from the two ordered logistic regression models chosen due to the scale-based dependent variable, i.e. the job satisfaction levels. Both models were found to be statistically significant. The first estimated Model 1, presented in Table 3, is controlled for the cross-country heterogeneity by the set of country-based zero-one variables, and the Model 2 reflects the attempt to move from the black box of country institutional and socio-economic environment and to understand better the role of specific variables. Nevertheless, as we have only one year of survey and one year of these country-level control variables, we provide this instead as empirical support of cross-country diversity and the possible role of the country-specific variables. Notably, the second model documents the importance of business freedom and the tax rates as shapers of job satisfaction at the country level.

Our findings on individual-level variables were consistent across both models. However, a notable discovery was the gender-based disparity in job satisfaction, with female family business owners exhibiting lower satisfaction levels compared to males. Despite attempts to identify unique drivers for this trend through gender-specific models, we did not find any novel factors that could explain the gender difference in job satisfaction. This finding aligns with existing literature indicating gender disparities in various aspects of business ownership and management (e.g. Vadnjal and Zupan, 2011; Lingas, 2013; Dewitt et al., 2023), yet – similarly to the study by Lerner and Malach-Pines (2011) where we took our opening quote from - it also points to a gap in understanding the rather unique challenges faced by female entrepreneurs in family businesses. Educational attainment emerged as a significant predictor of job satisfaction, with the highest levels noted amongst those with a master's degree. Interestingly, this trend did not extend to doctoral-level graduates, where the educational variable lost its statistical significance. This observation could reflect the different expectations and career paths associated with various educational levels, particularly in the context of family businesses, which goes in line with the indications from Vadnjal and Zupan (2011)'s study on family business as a career opportunity for women. In contrast, other variables such as years of experience, working hours and family background factors like household size, marital status, or number of children did not demonstrate a significant impact on job satisfaction. This finding suggests that job satisfaction in family businesses may be more closely tied to personal achievements and the business environment than to traditional metrics of career progression or work-life balance. Industry-specific analysis revealed interesting patterns of job satisfaction. Owners in sectors like public administration, compulsory social security, human health and social work activities and information and communication reported higher levels of satisfaction. This suggests that the nature of the industry, possibly reflecting varying degrees of stability, social impact and technological advancement, can significantly influence the job satisfaction of family business owners.

That being said, our study offers a comprehensive view of the factors influencing job satisfaction amongst European family business owners. It highlights the intricate interplay between personal characteristics, family roles, business context and broader socio-economic factors. This nuanced understanding is crucial for developing targeted policies and support mechanisms for family businesses, particularly in a landscape that is continually shaped by global events and changing market dynamics.

5. Conclusion, limitations and implications for further research

To conclude the paper, our study integrates the rich insights from the reviewed literature on family business dynamics, emphasising the complex interplay of personal characteristics, family roles and business context in determining job satisfaction amongst European family business owners. The findings echo and expand upon previous research, highlighting unique challenges and opportunities within this domain. The study's exploration of gender, education and industry variables concerning job satisfaction reveals nuanced facets of family business operations that are crucial for informed policy-making and targeted support strategies. This reflection on the diverse, context-specific nature of family business experiences sets the stage for our comprehensive analysis and conclusions.

There are three final aspects to outline before halting our debate. First, the limitations of this study also include potential biases inherent in self-reported data, which may affect the accuracy of the findings. For example, the authors could not determine the owners' position in the family enterprise; ownership share and to verify their particular role in managing day-to-day business operations or strategic management. Knowing these details would provide additional insights into job satisfaction. The focus on European family businesses specifically limits the study's applicability to other geographical contexts, potentially overlooking distinctive challenges and dynamics present in family businesses in other parts of the world. Also, due to the cross-sectional character of the data, it is not entirely possible to draw inferences on the existence of causal linkages or – looking from another perspective – to monitor changes over the course of time. This is needed, especially in the current dynamic times, where the world experiences adverse events, such as the global COVID-19 pandemic or the Ukraine war conflict and subsequent energy costs increase, which might possibly affect the established patterns and reshape the behaviour of individuals and family business owners (Bouncken et al., 2022; Eckey and Memmel, 2023). The ideal empirical scenario would be to have longitudinal data, tracking job satisfaction before the global COVID-19 pandemic and comparing them in subsequent years. Unfortunately, this is impossible with our dataset.

Second, we need to note that the COVID-19 pandemic has significantly altered the landscape of job satisfaction and management within family businesses. Recognising this, our implications for practice emphasise the need for adaptive strategies that cater to the evolving challenges posed by the pandemic. We propose targeted support programs that address the unique impacts of COVID-19 on family business owners, especially focussing on resilience-building and crisis management skills, plus different leadership styles suitable to the needs of male and female family business owners (Fries et al., 2021). The importance of apprenticeship programs, as highlighted by Hidayati et al. (2021), is re-emphasised with a renewed focus on equipping the next generation of family business leaders with skills pertinent to a rapidly changing business environment. This dovetails with the findings of Maharajh et al. (2023), who stressed the significant influence of family dynamics on business performance, underscoring the need for leadership development that is sensitive to these dynamics. Policymakers and support organisations are encouraged to consider these aspects in their efforts to foster a supportive environment for family businesses. Initiatives should be directed towards creating a robust ecosystem that not only supports the survival and growth of family businesses post-pandemic but also encourages innovation and sustainability, keeping in mind the unique challenges and strengths of these businesses. These ideated initiatives could be vital in fostering a supportive ecosystem that nurtures growth and sustainability in family businesses (Bichler et al., 2022; Nikolakis et al., 2022).

Third, this study opens several avenues for future research. One potential topic could be an in-depth analysis of generational differences in job satisfaction within family businesses, exploring how values and expectations evolve across generations. A recent work by Vesperi et al. (2023) identified “latent generation” in family businesses, a supportive but informally engaged generation, crucial in implicit generational transitions, but the exploration of transforming values and changing expectations shows to be vital. The latter could bring (more) clarity to the evolution of gender roles in the context of family businesses, especially as the media portrayals often take the mother role for granted and depict the business role of women in family businesses as problematic, potentially influencing the perception and enactment of these roles (Bjursell and Bäckvall, 2011). Another area could be investigating the impact of regional economic conditions on the dynamics of family businesses, particularly in different European contexts. Some work on that has been done – for instance, recent studies by Sreih et al. (2019) and Parada et al. (2020) – but there is much left to be explored to paint a more comprehensive picture of the said subject. To set another example, a comparative study of job satisfaction and employee engagement between family-owned and nonfamily businesses could offer valuable insights. Finally, research focussing on the role of technological adoption in family businesses and its influence on job satisfaction and business sustainability could be highly beneficial, both to further the scholarly debate in the field of family business research as well as to contribute to the growing body of policy and industry research.

Table 1

Descriptive statistics for continuous variables (family business owners only, 15–64 years)

Variable	Mean	Standard deviation	Minimum	Maximum	Observations
Job satisfaction	3.3	0.7	1	4	11,362
Number of hours per week usually worked	45.5	18.8	0	80	11,362
Business age since first registration	20.3	11.8	0	55	11,362
Years of experience	20.3	11.8	0	55	11,362
Number of persons in the household	3.3	1.4	1	11	11,362
Number of children in the household aged less than 15 years	0.5	0.9	0	6	11,362
GDP per capita	28,423.8	12,809.3	7,599.1	67,424.2	11,362
Business freedom index	72.7	6.5	58.2	89.9	11,362
Taxes on income, profits and capital gains	24.0	7.8	7.3	40.0	11,362
Individuals using the Internet	75.5	9.6	63.1	93.2	11,362

Note(s): Post-stratification weights applied. For parsimonious reasons, summary statistics per country and industry are not reported

Source(s): Authors' own calculations based on the Labour Force Survey (LFS) 2017 data (Eurostat, 2018)

Table 2

Summary statistics for categorical variables (family business owners only, 15–64 years)

Variable	Percentage share	Observations
15–19 years of age (=1)	0.1	11,362
20–24 years of age (=1)	1.5	11,362
25–29 years of age (=1)	4.1	11,362
30–34 years of age (=1)	7.5	11,362
35–39 years of age (=1)	10.8	11,362
40–44 years of age (=1)	14.4	11,362
45–49 years of age (=1)	17.0	11,362
50–54 years of age (=1)	19.7	11,362
55–59 years of age (=1)	14.8	11,362
60–64 years of age (=1)	10.1	11,362
Female (=1)	26.8	11,362
Nationality non-native (=1)	2.0	11,362
Less than primary education (=1)	0.4	11,362
Primary education (=1)	4.4	11,362
Lower secondary education (=1)	25.8	11,362
Upper secondary education (=1)	49.8	11,362
Post-secondary non-tertiary education (=1)	1.5	11,362
Short-cycle tertiary education (=1)	5.3	11,362
Bachelor's or equivalent level (=1)	6.6	11,362
Master's or equivalent level (=1)	6.1	11,362
Doctoral or equivalent level (=1)	0.1	11,362
Married (=1)	68.2	11,362
Widowed, divorced or legally separated (=1)	7.7	11,362
Partner/spouse living in the same household (=1)	73.9	11,362
Cities (densely populated area) (=1)	18.0	11,362
Towns and suburbs (intermediate density area) (=1)	32.2	11,362
Rural area (thinly populated area) (=1)	49.8	11,362

Note(s): Post-stratification weights applied. For parsimonious reasons, summary statistics per country and industry are not reported

Source(s): Authors' own calculations based on the Labour Force Survey (LFS) 2017 data (Eurostat, 2018)

Table 3

Job satisfaction drivers: a multivariate analysis results

Model number	(1)		(2)
Dependent variable	Job Satisfaction (1 = not satisfied at all, 4 = satisfied to a large extent)
Independent variables	Coefficient	Standard error	Coefficient	Standard error
20–24 years of age	1.292	(0.799)	1.405+	(0.821)
25–29 years of age	1.578*	(0.789)	1.691*	(0.811)
30–34 years of age	1.263	(0.786)	1.361+	(0.808)
35–39 years of age	1.267	(0.784)	1.354+	(0.806)
40–44 years of age	1.104	(0.785)	1.189	(0.807)
45–49 years of age	0.987	(0.784)	1.044	(0.806)
50–54 years of age	0.934	(0.786)	0.974	(0.808)
55–59 years of age	0.928	(0.787)	0.950	(0.810)
60–64 years of age	0.899	(0.788)	0.930	(0.811)
Female	−0.218***	(0.0591)	−0.199***	(0.0583)
Nationality non-native	−0.506	(0.357)	−0.465	(0.357)
Primary education	0.614*	(0.267)	0.625*	(0.257)
Lower secondary education	0.658*	(0.264)	0.616*	(0.256)
Upper secondary education	0.726**	(0.258)	0.650**	(0.248)
Post-secondary non-tertiary education	1.147***	(0.327)	1.256***	(0.309)
Short-cycle tertiary education	0.675*	(0.279)	0.582*	(0.270)
Bachelor's or equivalent level	0.946***	(0.277)	0.928***	(0.268)
Master's or equivalent level	1.136***	(0.281)	1.057***	(0.272)
Doctoral or equivalent level	1.196	(1.069)	1.200	(1.090)
Years of experience	0.213	(0.291)	0.211	(0.292)
Number of hours per week usually worked	0.000904	(0.00163)	0.000847	(0.00151)
Business age since first registration	−0.214	(0.291)	−0.210	(0.292)
Number of persons in the household	0.0405+	(0.0239)	0.0342	(0.0236)
Widowed, divorced or legally separated	−0.0638	(0.114)	−0.0760	(0.114)
Married	0.108	(0.102)	0.152	(0.101)
Partner/spouse living in the same household	0.103	(0.102)	0.0573	(0.101)
Number of children in the household aged less than 15 years	−0.0106	(0.0432)	−0.0168	(0.0427)
Cities (Densely populated area)	−0.101	(0.0807)	−0.0817	(0.0788)
Towns and suburbs (Intermediate density area)	0.0884	(0.0665)	0.0890	(0.0645)
Mining and quarrying	−0.0868	(0.792)	0.136	(0.808)
Manufacturing	0.497***	(0.0995)	0.557***	(0.0944)
Electricity, gas, steam and air conditioning supply	1.256	(1.006)	1.286	(1.152)
Water supply, sewerage, waste management and remediation activities	0.716	(0.728)	0.842	(0.723)
Construction	0.327**	(0.107)	0.374***	(0.103)
Wholesale and retail trade; repair of motor vehicles and motorcycles	0.296***	(0.0799)	0.337***	(0.0749)
Transportation and storage	0.122	(0.184)	0.197	(0.180)
Accommodation and food service activities	0.333*	(0.137)	0.378**	(0.135)
Information and communication	1.466**	(0.563)	1.527**	(0.543)
Financial and insurance activities	0.926**	(0.282)	0.909**	(0.283)
Real estate activities	0.763**	(0.278)	0.778**	(0.270)
Professional, scientific and technical activities	0.599***	(0.165)	0.683***	(0.164)
Administrative and support service activities	0.516*	(0.237)	0.571*	(0.233)
Public administration and defence, compulsory social security	10.79***	(1.029)	11.16***	(1.013)
Education	0.0828	(0.483)	0.194	(0.479)
Human health and social work activities	1.148**	(0.386)	1.215**	(0.371)
Arts, entertainment and recreation	0.950**	(0.344)	1.098**	(0.335)
Other service activities	0.973***	(0.250)	1.037***	(0.243)
Activities of households as employers, undifferentiated goods- and services-producing activities of households for own use	1.214	(0.986)	1.018	(1.128)
Belgium	−0.00385	(0.160)
Bulgaria	−0.892**	(0.342)
Cyprus	−1.002**	(0.318)
Czech Republic	0.441*	(0.217)
Germany	−0.519*	(0.221)
Estonia	0.794	(0.730)
Spain	−0.0141	(0.125)
France	−0.452**	(0.144)
Greece	−0.0234	(0.121)
Croatia	−0.605**	(0.213)
Hungary	0.612**	(0.218)
Ireland	0.984***	(0.170)
Italy	0.254*	(0.121)
Lithuania	−0.378	(0.337)
Latvia	0.444	(0.492)
Malta	1.448***	(0.267)
The Netherlands	−0.296*	(0.137)
Poland	−0.479***	(0.124)
Portugal	−0.898***	(0.165)
Romania	−0.631***	(0.154)
Slovenia	−0.372*	(0.177)
The United Kingdom	0.300+	(0.162)
GDP per capita			0.00000437	(0.00000685)
Business Freedom Index			−0.0234***	(0.00495)
Taxes on income, profits and capital gains			0.0352***	(0.0104)
Individuals using the Internet			0.00565	(0.00550)
Cut 1: Constant	−1.482+	(0.837)	−1.628+	(0.962)
Cut 2: Constant	−0.120	(0.836)	−0.269	(0.964)
Cut 3: Constant	2.483**	(0.838)	2.308*	(0.966)
Observations	11,362		11,362
Prob > χ2	0.00		0.00
Pseudo R2	0.042		0.036
Akaike information criterion (AIC)	8638.3		8661.0
Bayesian information criterion (BIC)	9174.0		9064.6

Note(s): Ordered logistic regression analysis results. A pooled sample of the EU LFS participating countries. Post-stratification weights were applied

Statistical significance is reported as follows: + p < 0.10, *p < 0.05, **p < 0.01 and ***p < 0.001

Reference groups for dummy variables: Age (15–19 years); Male; Native of own Country; Less than Primary Education; Single; Rural area (thinly populated area); Agriculture, forestry and fishing; Austria

Source(s): Authors' own calculations in STATA 14 software based on the Labour Force Survey (LFS) 2017 data (Eurostat, 2018)

Note

1. Specifically included in this research are country samples from Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Germany, Estonia, Spain, France, Greece, Croatia, Hungary, Ireland, Italy, Lithuania, Latvia, Malta, the Netherlands, Poland, Portugal, Romania, Slovenia and the United Kingdom.