Job satisfaction is a key organizational construct that significantly influences employee performance, as well as talent attraction and retention. This study examines generational differences in job satisfaction across Europe, analyzing whether Baby Boomers and Generations X, Y, and Z differ both in their levels of job satisfaction and in their main determinants. In doing so, it contributes to the ongoing debate on the relevance of generational distinctions in the workplace. The analysis is based on data from the European Social Survey (2020–2022, Round 10), comprising a sample of 21,349 employees from 31 European countries and multiple sectors. Using an adaptation of the TWISA model as a theoretical framework, ANOVA and regression analyses are conducted to assess differences across generational cohorts in job satisfaction and its explanatory factors. The results reveal small generational differences in several working conditions, such as autonomy, work schedule flexibility, influence on policy decisions, and supervisor work-life support, while no differences are observed in job satisfaction or team identification. However, these factors appear to operate similarly across generations, as no statistically significant differences are detected in their effects on job satisfaction. Team identification and supervisor work-life support consistently emerge as the most relevant predictors of job satisfaction across all generational groups, providing additional support for the relational mechanisms proposed in the original TWISA framework despite the conceptual adaptations introduced in this study. These findings provide empirical evidence for an ongoing debate in the literature and offer practical guidance for designing human resource management strategies aimed at improving job satisfaction.
Resumen
La satisfacción laboral es un constructo organizacional clave que influye significativamente en el desempeño de los empleados, así como en la atracción y retención del talento. Este estudio analiza las diferencias generacionales en la satisfacción laboral en Europa, examinando si los Baby Boomers y las generaciones X, Y y Z difieren tanto en sus niveles de satisfacción como en sus principales antecedentes. Con ello, contribuye al debate sobre la relevancia de las distinciones generacionales en el ámbito laboral. El análisis se basa en datos de la European Social Survey (2020-2022, ronda 10), con una muestra de 21.349 empleados de 31 países europeos y múltiples sectores. A partir de la adaptación del modelo TWISA como marco teórico, se aplican análisis ANOVA y regresiones lineales para evaluar las diferencias entre cohortes generacionales en la satisfacción laboral y en sus factores explicativos. Los resultados muestran pequeñas diferencias generacionales en varias condiciones laborales como la autonomía, la flexibilidad horaria, la influencia en decisiones organizativas y el apoyo del supervisor para la conciliación, mientras que no se observan diferencias en la satisfacción laboral ni en la identificación con el equipo. No obstante, estos factores operan de manera similar en todas las generaciones, ya que no se encuentran diferencias significativas en sus efectos sobre la satisfacción laboral. La identificación con el equipo y el apoyo del supervisor para la conciliación emergen de forma consistente como los predictores más relevantes de la satisfacción laboral en todos los grupos generacionales, aportando evidencia adicional sobre la relevancia de los mecanismos relacionales propuestos en el modelo TWISA original. Estos hallazgos aportan evidencia empírica a un debate vigente en la literatura y ofrecen orientaciones prácticas para el diseño de estrategias de gestión de recursos humanos orientadas a mejorar la satisfacción laboral.
Keywords:job satisfactiondeterminants of job satisfactionTWISAworkforce segmentationgenerational differencesPalabras clave:satisfacción laboraldeterminantes de la satisfacción laboralTWISAsegmentación de la fuerza laboraldiferencias generacionales.JEL codes:J28M12J24J14Códigos JEL:J28 M12J24J14This research has not received external funding.Introduction
The increasingly competitive business environment in which companies operate today highlights the critical importance of differentiation and value creation. Job satisfaction, commonly defined as a positive emotional state resulting from the evaluation of one’s job or job-related experiences (Locke et al.,1976), is a key organizational factor influencing value creation. Satisfied employees tend to demonstrate higher productivity, produce higher-quality work, exhibit greater creativity and engagement, and are more inclined to offer suggestions that enhance customer value. Conversely, dissatisfied employees are more prone to tardiness, increased absenteeism, and reduced motivation to remain in the organization (Andrade et al., 2019). A deeper understanding of employee needs can enhance job satisfaction, which in turn positively influences organizational climate, customer satisfaction and loyalty, and overall financial performance (Schulte et al., 2009).
Job satisfaction has received considerable attention in both academic and professional literature, particularly as it has emerged as a critical concern within the broader context of mental health. This concern has become especially salient with the entry of Generation Z into the workforce, a cohort characterized by elevated turnover intentions when specific organizational conditions are unmet. The COVID-19 pandemic further tested the resilience of Generation Z, highlighting contrasts with earlier generations such as Baby Boomers and Generation X. Compounding these challenges, Generation Z faces growing awareness of constrained economic opportunities, contributing to increased experiences of depression and loneliness. These issues are further intensified by the pervasive use of social media, which, while enabling expansive online social networks, often diminishes the frequency and quality of face-to-face interactions.
The literature presents divergent perspectives on generational differences in job satisfaction. While some scholars argue that substantial differences exist across generational cohorts, others downplay these presumed distinctions, emphasizing that employees, regardless of generation, are subject to similar organizational environments. Accordingly, it is not solely the preferences or expectations of generational groups that determine job satisfaction, but rather how individuals across generations respond to the conditions and demands of their work environment.
Given these inconsistent findings, it is essential to undertake studies that examine whether, and in what ways, employees across generational cohorts experience job satisfaction. Such insights can inform the development and implementation of human resource policies, including those related to job design, compensation, and career advancement. Ultimately, these efforts can contribute to creating a supportive and inclusive workplace environment that accommodates the diverse needs and expectations of a multigenerational workforce.
In today’s labour market, understanding the diverse work values, needs, and motivations that influence job satisfaction is essential, particularly as these factors may vary across generational cohorts. Such generational differences are often shaped by the distinct social, technological, political, and cultural contexts individuals experience during their formative years. In other words, what underlying factors contribute to the achievement of job satisfaction across different generations?
This research builds on generational cohort theory, using widely recognized generational cohorts as a framework for analysis. The study focuses on four cohorts: Baby Boomers (born between 1946 and 1964), Generation X (born between 1965 and 1980), Generation Y or Millennials (born between 1981 and 1996), and Generation Z (born between 1997 and 2012).
The aim of this study is to deepen the understanding of the antecedents of job satisfaction by identifying key elements of job design and management practices that contribute to employee satisfaction, using generational differences as a segmentation variable. Drawing inspiration from the Three-Way Interaction of Satisfaction Antecedents (TWISA) model (Alegre et al., 2016) this research uses data from the European Social Survey (ESS) to analytically distinguish between job satisfaction as an outcome variable and its underlying determinants. The study focuses on five core antecedents: Team Identification (T), Work Schedule Flexibility (W), Influence on Policy Decisions (I), Supervisor Work-Life Support (S), and Autonomy (A), which together constitute the adapted TWISA framework used in this study. This approach aims to generate actionable insights for managers and human resource professionals seeking to enhance job satisfaction across diverse generational cohorts.
Theoretical frameworkJob satisfaction
Job satisfaction has been conceptualized from multiple perspectives. Some scholars emphasize its emotional dimension. For instance, Locke et al. (1976) define it as a pleasurable or positive emotional state resulting from the appraisal of one’s job or job experiences. Others, such as Weiss (2002), highlight the cognitive dimension, viewing job satisfaction as the outcome of an evaluative judgment regarding the favourability or unfavourability of one’s job.
Another perspective, represented by authors such as Spector (1997), adopts a facet-based approach, suggesting that job satisfaction can arise from positive evaluations of specific aspects or components of the job. Recent studies further extend this view by linking job satisfaction to broader subjective well-being and happiness (Cuesta-Valiño et al., 2024), as well as to the interaction between individuals and their work environment, including organisational and relational conditions (Salazar-Altamirano et al., 2024). In this context, job satisfaction refers to how employees subjectively perceive their work, along with the personal, social, and organizational factors that shape that perception.
In this context, it refers to how employees subjectively perceive their work, along with the personal, social, and organizational factors that shape that perception (Locke et al., 1976; Spector, 1997).
Antecedents of job satisfaction
While some scholars argue that job satisfaction is influenced by individual traits, for example, Arvey et al. (1989) suggest that approximately 30% of the variance in general job satisfaction can be attributed to genetic factors, other researchers emphasize the role of stable personality characteristics. These include positive affectivity (Watson and Slack, 1993) and core self-evaluations, a higher-order construct encompassing self-esteem, self-efficacy, locus of control, and emotional stability (Judge et al., 1998; Contreras-Pacheco et al., 2024).
Rather than focusing on individual traits, this study emphasizes modifiable organizational factors that influence job satisfaction. A review of the job satisfaction literature reveals that numerous studies link employee well-being to a variety of organizational determinants, underscoring the importance of factors such as family circumstances, work–family balance, and broader psychological, physical, social, and familial conditions (Zheng et al., 2015). Seminal frameworks in this area include Herzberg’s Two-Factor Theory, which distinguishes between hygiene factors (e. g., salary and working conditions) and motivational factors (e. g., recognition and self-actualization), and Hackman and Oldham’s Job Characteristics Model, which identifies core job dimensions such as autonomy, task variety, task identity, and feedback. Together, these models conceptualize job satisfaction as a multidimensional construct shaped by a complex interplay of personal, social, and organizational variables.
In this research project, we build on the TWISA job satisfaction model, as it integrates key antecedents of job satisfaction identified in the literature. Specifically, the model distinguishes five core components: Team Identification (T), Work Schedule Flexibility (W), Influence on Policy Decisions (I), Supervisor Work-Life Support (S), and Autonomy (A) (Alegre et al., 2016). A graphical representation of the model is provided in Figure 1.
Adapted Twisa Job Satisfaction Model
Adapted Twisa Job Satisfaction ModelSource: Adapted from Alegre et al. (2016).
The model is particularly valuable as it enables a concise examination of generational differences in job satisfaction and offers insights to guide managers in designing targeted interventions by identifying the key factors that enhance satisfaction across diverse employee groups. However, our approach departs from the original TWISA model, originally conceptualized as the Three-Way Interaction of Satisfaction Antecedents, which viewed the antecedents of job satisfaction as interrelated configurations. In contrast, we analyse these antecedents independently. The following sections provide a detailed description of each of the five TWISA job satisfaction components.
TWISA job satisfaction modelT: Team identification
Social factors as antecedents of job satisfaction have been widely examined, with particular emphasis on the role of organizational climate as a key determinant of employee satisfaction (Padilla Vergara et al., 2025). Research on the social dimensions of individual well-being suggests that a strong sense of team belonging is fundamental to fostering job satisfaction (Govea Andrade and Zuñiga Briones, 2020). This sense of belonging strengthens organizational attachment, which, in turn, positively influences overall job satisfaction.
While teamwork has traditionally been associated with collaboration and organizational functioning (Hughes et al., 2016; Phulpoto, 2023) its importance also lies in the social relationships and group connections that emerge through team interactions. According to social identity theory, individuals tend to define themselves through their membership in relevant social groups, including work teams. Through these dynamics, employees may develop a stronger sense of belonging and identification with their work teams.
Accordingly, this study focuses specifically on team identification, understood as the individual perception of feeling part of the work team. Previous research has shown that work group identification is positively associated with favourable work attitudes and organizational outcomes, including job satisfaction and organizational commitment (Rudolph et al., 2021).
Previous studies across different sectors have shown that positive team environments and supportive workplace relationships are associated with employee well-being, organizational commitment, and job satisfaction. For example, teamwork has been associated with job satisfaction among healthcare professionals in hospital settings (Bragadóttir et al., 2023). Similarly, positive team dynamics have been linked to improved staff well-being and retention, particularly in nursing environments (Al Sabei et al., 2022). These findings suggest that supportive and cohesive team environments may reinforce employees’ identification with their work teams, ultimately contributing to more positive workplace experiences and higher levels of job satisfaction.
This growing body of research underscores the critical role of team identification and supportive team environments as relevant social antecedents of job satisfaction.
W: Work schedule flexibility
Research on work schedule flexibility has gained increasing attention in management and organizational studies due to its implications for employee well-being and job satisfaction (Andrade et al., 2019; Boccoli et al., 2024). In organizational settings, schedule flexibility refers to employees’ perceived ability to exercise control over their working time, particularly regarding starting and finishing times. This form of temporal control may help employees better adjust their work schedules to personal and professional demands, contributing to more positive work experiences (Allen et al., 2013).
Previous research on alternative work schedules shows that flexible schedules are generally associated with more favourable work-related outcomes, particularly job satisfaction and satisfaction with work schedules (Baltes et al., 1999). More recent studies further suggest that the benefits of flexibility depend less on the formal existence of flexible arrangements than on employees’ subjective experience of control. Boccoli et al. (2024) show that employees who experience both psychological job control and boundary control report higher levels of job satisfaction, work engagement, and work-life balance.
The role of worker scheduling flexibility in explaining differences in job satisfaction across national contexts has been highlighted using data across 37 countries (Andrade et al., 2019). Their findings are particularly relevant for this study, as they show that employees’ ability to manage their work schedules is not merely a formal organizational arrangement, but a meaningful feature of the work experience. Accordingly, this study conceptualizes work schedule flexibility as an organizational antecedent of job satisfaction, capturing employees’ perceived control over their starting and finishing times at work.
I: Influence on policy decisions
Employee participation in organizational decision-making has long been recognized as an important antecedent of job satisfaction (Pacheco and Webber, 2016). Previous research suggests that when employees perceive that they have opportunities to influence organizational decisions, they tend to report higher levels of involvement, engagement, and satisfaction at work (Schuler, 1980). In this regard, participation in decision-making may strengthen employees’ sense of influence within the organization and reinforce the perception that their opinions and contributions are valued.
Research has consistently shown that participative decision-making is positively associated with job satisfaction (Pacheco and Webber, 2016; Woznyj et al., 2022). This is partly because participative decision-making enhances employees’ sense of feedback, task meaningfulness, and opportunities for career development (Wright and Kim, 2004). Moreover, participation in decision-making is often correlated with improved interpersonal relationships among colleagues and increased employee engagement, both of which are key sources of job satisfaction (Kirkman and Shapiro, 2001; Sageer et al., 2012).
Accordingly, this study focuses on employees’ perceived influence on policy decisions, understood as the extent to which employees feel able to influence decisions affecting their work and organizational environment. From this perspective, perceived influence on organizational decision-making constitutes a relevant antecedent of job satisfaction.
S: Supervisor work-life support
The relationship between employees and their supervisors has long been recognized as a relevant antecedent of job satisfaction (Buick et al., 2024; Edgar and Geare, 2005; Fila et al., 2014). However, more recent research has emphasized that support specifically oriented toward employees’ work-life needs may be particularly important for employee well-being and work attitudes. In this regard, the literature on family-supportive supervisor behaviours highlights the role of supervisors in helping employees manage the interface between work and nonwork demands through behaviours such as emotional support, flexibility, understanding, and practical assistance.
Importantly, this work-life-specific form of support appears to have stronger effects on employee outcomes than general supervisor support. Furthermore, work-family-specific supervisor support is more strongly associated with reduced work-family conflict than general forms of workplace support (Kossek et al., 2011).
Supportive supervisor behaviours may also contribute to higher job satisfaction by reducing work-family strain and fostering a more supportive work environment. Important examples include showing respect toward employees, demonstrating understanding of employees’ nonwork responsibilities, encouraging open communication, and facilitating flexible solutions when work and personal demands conflict (Hammer et al., 2009).
Accordingly, this study conceptualizes supervisor work-life support as an employee-supervisor relationship antecedent of job satisfaction, referring to employees’ perceptions that their supervisors understand and support their work-life needs and personal responsibilities.
A: Autonomy
One of the most prominent factors in promoting job satisfaction is autonomy, specifically the perceived control employees have over their work. Previous research indicates that jobs offering greater freedom, independence, and autonomy in planning work tend to result in higher job satisfaction (Federici, 2013; Yang et al., 2023). However, job control can also increase stress, negatively affecting workers' well-being and health (Meier et al., 2008), which might contribute to job dissatisfaction within the company.
On the positive side, autonomy refers to workers' ability to make decisions related to their tasks and schedules, while control involves the perception of influence over their work environment. The positive relationship between autonomy, control, and job satisfaction has been documented in various academic studies. This perspective is also consistent with recent research on workplace happiness, which conceptualises happiness at work as emerging when working conditions allow employees to experience autonomy, a sense of belonging, and opportunities to develop creativity (Martínez-Arvizu et al., 2025). Furthermore, various academic studies (Khoshnaw and Alavi, 2020; Seeber and Erhardt, 2023) provide a critical review of the literature on the relationship between job autonomy and job performance, while recent longitudinal studies identify four distinct autonomy profiles where high psychological job control combined with boundary management maximizes satisfaction and engagement.
Work autonomy is associated with higher levels of satisfaction because it allows employees to experience a sense of ownership over their tasks and outcomes. Autonomy is a central element of self-determination theory, which posits that individuals are more motivated and satisfied when they feel they have control over their actions (Deci and Ryan, 1985). Furthermore, when workers have greater control over their tasks, schedules, and decisions related to their roles, they experience an increase in job satisfaction, particularly when autonomy aligns with clear organizational boundaries (Boccoli et al., 2024; Rizwan et al., 2014).
In a study on self-managed organizations, it was found that high levels of autonomy in decision-making and methods used correlate positively with job satisfaction, particularly when there is a good match between employees' expectations and their perceptions of autonomy (Doblinger and Class, 2023).
All five factors discussed are modifiable through managerial action and should, therefore, be carefully considered by human resource management when seeking to attract and retain employees, as well as to enhance organizational performance. However, it is important to recognize that not all variables exert uniformly positive effects. For example, although job autonomy is often regarded as beneficial, it does not necessarily mean that all employees prefer autonomous work or are equally capable of managing such independence effectively. Similarly, while team cohesion can create a productive and supportive work environment for many, it may pose challenges for individuals whose personal preferences or traits make them less suited to highly collaborative settings.
Building on the literature review, which highlights the key role of relational and organizational factors in shaping job satisfaction, a relevant question emerges regarding the extent to which job satisfaction and its antecedents may vary across generational cohorts.
Generational differences
Before discussing the generational differences commonly referenced in the literature, it is important to rephrase our key research question: this study encompasses a large sample of employees working in different types of organizations. In other words, these employees are selected by their organization, currently remain with their organizations, and have not left. This observation is crucial because much of the existing literature on generational differences tends to focus on how people’s values and preferences outside of the organizational context, rather than examining how employees respond to organizational variables such as supervisor support or influence on policy decisions in relation to job satisfaction. Examples of such general studies include how individuals experience culture, how they perceive organizational climate, or what their political views are. But first we present a summary of the generational differences as they relate to how they experience their work environment.
Baby Boomers (born between 1946 and 1964) typically place a high value on job stability and long-term security. They often prioritize steady employment and are motivated by opportunities for career advancement and the attainment of professional status. Consequently, this generation tends to demonstrate higher levels of organizational loyalty compared to subsequent generations (Lyons and Kuron, 2014).
Generation X (born between 1965 and 1980) places greater emphasis on work-life balance and seeks increased autonomy and involvement in decision-making processes. This generation also values continuous skill development, recognizing that younger cohorts tend to possess higher levels of formal education and advanced technological competencies. By leveraging their own professional experience alongside these evolving skill sets, members of Generation X are well positioned to assume leadership and management roles as Baby Boomers transition into retirement (Twenge, 2010).
Generation Y or Millennials (born between 1981 and 1996), tend to seek work that aligns with their personal values and contributes positively to society. They place strong emphasis on social connections in the workplace and often thrive in collaborative, team-oriented environments. Additionally, Millennials prioritize recognition and rapid career progression, frequently seeking feedback and opportunities for accelerated professional development (Deloitte, 2025; Twenge, 2010).
Generation Z (born between 1997 and 2012) is characterized by a strong preference for workplace flexibility, including options for remote work and adaptable scheduling, preferences likely shaped by growing up in a digitally connected and technologically advanced environment. This generation also demonstrates greater openness to diversity, influenced by increased global mobility, migration, and exposure to different cultures. Additionally, Generation Z places a heightened emphasis on mental health, which some scholars attribute to their extensive use of social media and a relative decline in face-to-face social interaction. A notable characteristic of this cohort is their comparatively lower level of organizational loyalty, as they are more inclined to leave their employer if their expectations around flexibility, values alignment, or well-being are not met (Ozkan and Solmaz, 2015; Wiedmer, 2015).
While these broad descriptions are commonly found in the literature, it is important to recognise that they are often regarded as stereotypes, and not all individuals within a generation adhere to these generalizations. Furthermore, employees operate within organizations designed to promote certain similarities across generations. As previously suggested, these generational distinctions may reinforce stereotypes and could potentially be divisive, leading to a greater emphasis on differences rather than on commonalities.
Regarding generational differences, two primary schools of thought exist. The first, most prominently represented in the literature posits that generational differences are significant due to changes in technology, economic and educational opportunities, as well as broader societal shifts (Twenge, 2010). The second school argues that these differences are less pronounced as individuals across all generations under consideration have adapted to the technological changes mentioned; for example, Baby Boomers also use mobile phones and computers at work. Additionally, they may seek work-life balance as well, particularly as organizations and technology increasingly enable such flexibility (Costanza et al., 2012; Parry and Urwin, 2017; Rudolph et al., 2018).
Based on the literature review the following research questions are proposed:
RQ1: To what extent do Job Satisfaction and the adapted TWISA dimensions differ across generational cohorts?
RQ2: To what extent do the relationships between the adapted TWISA dimensions and Job Satisfaction vary across generational cohorts?
Methodology
To address the research questions posed, secondary data were utilized. This study adopts a quantitative, cross-sectional research design based on secondary data obtained from the European Social Survey (ESS, Round 10-2020, 2023) incorporating editions 3.1 and 3.2. The ESS is a scientific survey conducted biennially across Europe. In its most recent iteration, it encompasses data from 31 European countries and 21,349 employees who reported having engaged in paid work within the past seven days. For the statistical analysis, the program used was IBM SPSS Statistic 22.
The ESS rotating module includes items ranging from G1 to G61. However, only a subset of these items was selected for this study based on their theoretical alignment with the adapted TWISA framework. Specifically, items related to Team Identification, Work Schedule Flexibility, Influence on Policy Decisions, Supervisor Work-Life Support, and Autonomy were retained. Furthermore, our study also makes use of Module F, which gathers information on sociodemographic aspects.
Table 1 provides a detailed mapping between the survey items and the corresponding adapted TWISA constructs. Given the limitations of secondary data, each construct is operationalized through a single survey item selected for its theoretical alignment with the corresponding variable.
Survey Questions and Variable Descriptions From the ESS Dataset
Variable type
Variables
Survey questions
Responses
Dependent variable J
Job Satisfaction (G34)
How satisfied are you in your main job?
Scored from 0 to 10, where 0 means extremely dissatisfied and 10 means extremely satisfied
Independent variable T
Team Identification (G51)
If you work in a team, how much do you feel like part of your team?
Scored from 0 to 10, where 0 means not at all and 10 means completely
Independent variable W
Work Schedule Flexibility(G38)
To what extent are you allowed to decide your own starting and finishing times at work?
“Not at all” =1; “To some extent” =2; “Completely” =3
Independent variable I
Influence on Policy Decisions (F28)
How much the management at your work allows you to influence policy decisions about the activities of the organization?
Scored from 0 to 10, where 0 means no influence and 10 means complete control
Independent variable S
Supervisor Work-life Support (G44)
If you have a line manager, how much does he or she support employees in balancing work and personal commitments?
Scored from 0 to 10, where 0 means not at all and 10 means completely
Independent variable A
Autonomy (F27)
How much the management at your work allows you to decide how your own daily work is organized
Scored from 0 to 10, where 0 means no influence and 10 means complete control
Source: European Social Survey (“European Social Survey (ESS), Round 10-2020”, 2023).
A descriptive analysis of the sample reveals that, among the 21,349 employees surveyed, 48.3% identified as male, while 51.7% identified as female. The generational distribution indicates that 7% are classified as Generation Z, 35% as Generation Y, 41.5% as Generation X, and 16.6% as Baby Boomers. With respect to organizational size, 20.5% of respondents work in micro-enterprises employing fewer than 10 individuals, 19.2% are employed in small enterprises with 10 to 24 employees, 25.1% in medium-sized firms with 25 to 99 employees, 18.3% in organizations employing 100 to 499 employees, and 16.8% in large corporations with a workforce exceeding 500 employees. Contractual arrangements reveal that 85.4% of employees hold permanent contracts, 12.4% are engaged under fixed-term agreements, and 2.2% report no formal contractual relationship. Regarding organizational type, 60% of the workforce is employed in private-sector entities, 10.9% in central or local government positions, and 16.2% in other public-sector roles, including education and healthcare services.
To investigate levels of job satisfaction, its determinants, and potential generational differences, the adapted TWISA Job Satisfaction Model was employed. First, an analysis of variance (ANOVA) was conducted to examine differences in mean values across generational cohorts. Second, a series of nested hierarchical regression models were estimated to assess whether the effects of the explanatory variables vary across generations.
To facilitate interpretation, the estimated regression model can be expressed in functional form. The dependent variable is job satisfaction, while the independent variables correspond to the adapted TWISA dimensions, generational cohort, and control variables. To improve clarity, the regression models are formally specified below:
where JSᵢ denotes Job Satisfaction for individual i; Tᵢ, Wᵢ, Iᵢ, Sᵢ, and Aᵢ denote Team Identification, Work Schedule Flexibility, Influence on Policy Decisions, Supervisor Work-Life Support, and Autonomy, respectively; Generationᵢ represents generational cohort, with Generation Z as the reference category; Controlsᵢ includes gender and household income; CountryFEᵢ denotes country fixed effects; and εᵢ is the error term.
Furthermore, four separate linear regression models were estimated, each corresponding to a specific generational cohort, in order to provide a more detailed, complementary robustness analysis of the relationships with each group.
In the linear regression analyses, regarding the independent variables, since their numerical ranges varied significantly, normalization was applied to facilitate the comparability of coefficients across predictors.
Results
The results are presented in two stages. First, descriptive and one-way ANOVA tests are used to examine differences across generational cohorts. Second, hierarchical regression models are estimated to analyse the determinants of job satisfaction and their stability across generations.
Table 2 reports the mean scores of the adapted TWISA framework for the overall sample as well as for each generational subgroup. To identify significant differences across generations in the associated factors, an analysis of variance (ANOVA) was conducted, supplemented by Games-Howell post-hoc comparisons. Given the large sample size, effect sizes (partial η²) were considered alongside statistical significance in order to assess the substantive magnitude of the observed differences. No statistically significant differences were observed in Job Satisfaction and Team Identification. Although statistically significant differences emerged for Work Schedule Flexibility, Influence on Policy Decisions, Supervisor Work-Life Support, and Autonomy, effect sizes remained consistently small (partial η² ranging from .001 to .004), suggesting limited substantive differences between generations. Specifically, Generation Z reports the lowest scores in Work Schedule Flexibility (1.64), Influence on Policy Decisions (3.82), and Autonomy (5.63), indicating a diminished perception of control in the workplace. Conversely, Generation Z, along with Generation Y, reports higher levels of Supervisor Work-Life Support (7.13) compared to the other generational cohorts. Nevertheless, these differences should be interpreted cautiously given the very small effect sizes observed. Additional ANCOVA models controlling for gender, household income, and country fixed effects yielded substantively similar results. Although some adjusted generational differences remained statistically significant, effect sizes continued to be very small (partial η² ranging from .001 to .004), reinforcing the conclusion that generational differences are limited in practical terms. Household income consistently exhibited larger effect sizes than generational cohort across all models.
Mean Scores, Anova Results and Games-Howell Comparisons
Variables
Total
Mean
(SD)
Generation Z
Mean
(SD)
Generation Y
Mean
(SD)
Generation X
Mean
(SD)
Baby Boomers
Mean
(SD)
F-value (df 3, 21345)
P-value
Partial η²
J: Job Satisfaction
7.35
(2.02)
7.30a
(2.00)
7.35a
(2.02)
7.34a
(2.02)
7.42a
(2.02)
1.807
(p= .14)
0.000
T: Team Identification
8.28
(1.94)
8.21a
(1.96)
8.29a
(1.92)
8.29a
(1.95)
8.29a
(1.97)
0.723
(p= .54)
0.000
W: Work Schedule Flexibility
1.73
(0.69)
1.64a
(0.65)
1.75b
(0.68)
1.72b
(0.70)
1.73b
(0.72)
9.800***
(p< .001)
0.001
I: Influence on Policy Decisions
4.34
(3.19)
3.82a
(3.08)
4.46c
(3.11)
4.39c
(3.21)
4.16b
(3.34)
21.046***
(p< .001)
0.003
S: Supervisor Work-Life Support
6.94
(2.55)
7.13a
(2.44)
7.02a,b
(2.50)
6.89b,c
(2.58)
6.83c
(2.63)
8.189***
(p< .001)
0.001
A: Autonomy
6.32
(3.17)
5.63a
(3.15)
6.41b
(3.10)
6.38b
(3.17)
6.25b
(3.29)
27.206***
(p< .001)
0.004
N=21349
n=1487
n=7466
n=8861
n=3535
a,b,c Subsets labelled as a, b, and c represent homogeneous groups derived from Games-Howell post-hoc comparisons.
Table 3 presents the results of the hierarchical regression analysis.
Notes: Unstandardized coefficients (B) are reported. Reference category for Generation is Generation Z. Household income is measured in deciles and treated as a continuous variable. Country Fised Effects are included (coefficients not reported). N refers to the number of observations used in each model.
Model 1 includes the main effects of the TWISA variables and generational cohort and shows a substantial level of explained variance (R² = 0.249). All TWISA dimensions are positively associated with job satisfaction, with Team Identification (B = 0.573, p < .001) and Supervisor Work-Life Support (B = 0.542, p < .001) emerging as the strongest predictors.
In Model 2, the inclusion of control variables leads to an increase in explanatory power (R² = 0.274). The main effects remain largely stable, with team identification (B = 0.565, p < .001) and Supervisor Work-Life Support (B = 0.536, p < .001) continuing to show the strongest associations. Among the control variables, household income (B = 0.037, p < .001) is statistically significant, whereas gender is not (p = .348). Country fixed effects are included.
Model 3 adds the interaction terms between TWISA variables and generational cohort. The increase in explained variance is negligible (R² = 0.274), and none of the interaction terms reach statistical significance (all p > .05). The main effects remain consistent, although Work Schedule Flexibility is no longer significant (B = 0.037, p = .567).
Using Generation Z as the reference category, Baby Boomers exhibit a positive and statistically significant association with job satisfaction across all three models (B ≈ 0.22-0.25, p < .001), indicating that older cohorts report slightly higher levels of job satisfaction than Generation Z. However, the magnitude of this difference remains modest. In contrast, Generation X does not show a stable effect, as statistical significance disappears once control variables are included, while Generation Y is not significantly different from Generation Z in any of the models.
Overall, these results indicate that while TWISA dimensions are significant predictors of job satisfaction, generational differences are limited and not consistently observed across cohorts, and the inclusion of interaction terms does not meaningfully improve model fit, suggesting that the effects of TWISA dimensions appear to be relatively stable across generational groups.
As a complementary exploratory analysis, separate regression models were estimated for each cohort (see Table 4). Since these cohort-specific models are not intended to provide a fully adjusted test of between-cohort differences, the results should be interpreted with caution. All models are statistically significant (p < .001) and exhibit comparable levels of explanatory power, with R² values ranging from 0.230 to 0.259.
Linear Regression Results by Generational Cohort
Variables
Generation Z
B (SE)
Generation Y
B (SE)
Generation X
B (SE)
Baby Boomers
B (SE)
T: Team Identification
0.578*** (0.049)
0.587*** (0.021)
0.566*** (0.020)
0.557*** (0.031)
W: Work Schedule Flexibility
0.134** (0.052)
0.057*
(0.024)
0.063** (0.023)
0.111** (0.036)
I: Influence on Policy Decisions
0.077
(0.058)
0.158*** (0.027)
0.154*** (0.026)
0.231*** (0.041)
S: Supervisor Work-life Support
0.510*** (0.050)
0.576 *** (0.022)
0.534*** (0.021)
0.509*** (0.032)
A: Autonomy
0.130*
(0.060)
0.116*** (0.027)
0.140*** (0.025)
0.167*** (0.039)
R²
0.230
0.259
0.242
0.259
Adj. R²
0.227
0.258
0.241
0.258
F
88.507***
620.818***
564.140***
247.255***
N
1487
7466
8861
3535
VIF range
1.126-1.635
1.120-1.757
1.149-1.762
1.150-1.735
Notes: Unstandardized coefficients (B) are reported; standard errors in parentheses.
p < .001;
p < .01;
p < .05. VIF values indicate no multicollinearity concerns.
Across all generations, a highly consistent pattern emerged. Team Identification and Supervisor Work-life Support were the strongest predictors of job satisfaction in every cohort, showing large and highly significant coefficients throughout (Team Identification: B ranging from 0.557 to 0.587, p < .001; Supervisor Work-Life Support: B ranging from 0.509 to 0.576, p < .001). The remaining dimensions displayed comparatively smaller effects. Influence on Policy Decisions was statistically significant for Generation Y (B = 0.158, p < .001), Generation X (B = 0.154, p < .001), and Baby Boomers (B = 0.231, p < .001), but not for Generation Z (B = 0.077, p = .181), while Autonomy (B ranging from 0.116 to 0.167, p < .05) and Work Schedule Flexibility (B ranging from 0.057 to 0.134, p < .05) remained positively associated with Job Satisfaction across all cohorts, albeit with modest variation in coefficient magnitude.
Despite these minor differences, the overall pattern of relationships remained remarkably stable across generations. These findings reinforce the conclusions from the hierarchical regression models, suggesting that the determinants of Job Satisfaction are broadly similar across generational cohorts and providing additional support for the absence of meaningful interaction effects.
Discussion
A recent question that has emerged is whether employees from different generations, specifically Baby Boomers, Generation X, Generation Y, and Generation Z, experience varying levels of job satisfaction, commitment to the firm, or perceptions of their work environment, given their upbringing in distinct technological, political, and cultural contexts. Recently, this interest in generations has become more salient because Generation Z employees are often perceived as different from previous cohorts in terms of values, expectations, and resilience when facing workplace challenges. Nevertheless, there is still a lack of a comprehensive understanding of job satisfaction among Millennials and especially Generation Z employees (Waworuntu et al., 2022), therefore, further research is needed (Jayathilake et al., 2021).
We focused on job satisfaction as it is a critical variable influencing employee productivity, both in terms of their willingness to commit to organizational goals and client needs, as well as its impact on employee creativity. In addition, this study adopts an adapted version of the TWISA Job Satisfaction Model proposed by Alegre et al. (2016), as its dimensions capture organizational factors that can be influenced through managerial and human resource management practices.
This study is guided by two main research questions based on the adapted TWISA framework. RQ1 examined the extent to which Job Satisfaction and the adapted TWISA dimensions differ across generational cohorts. RQ2 investigated whether the relationships between the adapted TWISA factors and Job Satisfaction vary across generations.
Regarding RQ1, the results indicate that generational differences in Job Satisfaction and the adapted TWISA dimensions are generally limited. While no significant differences were observed in overall Job Satisfaction or Team Identification across generations, some differences emerged in specific workplace dimensions, particularly among Generation Z, which reported lower scores in several workplace conditions. However, effect sizes remained consistently small, suggesting that these differences are limited in substantive terms.
Most importantly, the findings associated with RQ2 reveal that the key drivers of job satisfaction are remarkably consistent across generational cohorts. The main contribution of this study lies in demonstrating that, despite adapting several dimensions of the original TWISA framework and analysing a large cross-national European sample, the fundamental determinants of Job Satisfaction remain largely stable across generations. In particular, Team Identification and Supervisor Work-life Support emerge as the most robust and influential predictors across all generations, highlighting the central role of social and relational factors in shaping employee Job Satisfaction. These findings are broadly consistent with the original TWISA model developed by Alegre et al. (2016), which highlighted the importance of relational and organizational factors (including teamwork, supervisor support, and identification with organizational strategy) in explaining job satisfaction. Although the present study adapted several TWISA dimensions to fit the ESS data structure and employs a different analytical approach, both studies converge in emphasizing the central role of workplace relationships in shaping employee job satisfaction.
Conclusion
The question, of course, is what do these findings imply? We offer the following conclusions.
First, Generation Z employees appear to experience their organizations differently. They report lower levels of Work Schedule Flexibility, perceive less Influence on Policy Decisions, and experience reduced Autonomy. However, these differences should be interpreted cautiously, as their magnitude remains relatively small. Moreover, such differences may partly reflect career stage and lower organizational tenure rather than fundamentally distinct generational values or attitudes. This interpretation is consistent with research suggesting that younger employees are often in more unstable or transitional employment situations.
Second, Team Identification and Supervisor Work-Life Support show the strongest associations with Job Satisfaction, which constitutes the central finding of this study. Other factors, such as Influence on Policy Decisions, Autonomy, and Work Schedule Flexibility, exhibit comparatively smaller effects. In terms of general employee needs, Team Identification and Supervisor Work-Life Support are universal factors that reflect fundamental human needs: individuals thrive in social environments, rely on social support, and require feedback, particularly from managers and teammates (Griffin et al., 2001; Kirkman and Rosen, 1999; Rowold et al., 2014; Tang et al., 2014).
It is important to note, however, that Generation Z employees are also highly responsive to these two adapted TWISA dimensions in achieving Job Satisfaction. Therefore, we argue that human resource practices may benefit from prioritizing these drivers when aiming to enhance job satisfaction, especially for this generation. This is in line with Waworuntu et al. (2022) who found that Millennials and Generation Z employees value supportive and friendly work environments. This leads to another observation: while Autonomy and Work Schedule Flexibility have often been regarded as critical antecedents of job satisfaction, they appear to exert comparatively weaker effects in the present study. Although we cannot identify a definitive explanation for this pattern, it nevertheless constitutes an informative finding for managers. Finally, the consistency of results across both hierarchical and cohort-specific regression models provides additional support for these findings and indicates that, despite some differences in how generations experience the workplace, the underlying drivers of job satisfaction remain largely similar across generational groups. Overall, these results contribute to the ongoing debate on generational differences by supporting recent research questioning the magnitude and practical relevance of strong generational distinctions in organizational settings (Costanza et al., 2012; Parry and Urwin, 2017; Rudolph et al., 2018). Although some cohort-related differences emerge in how employees perceive specific workplace conditions, the present study suggests that the core relational drivers of Job Satisfaction remain remarkably stable across generations. In this regard, the findings call into question the idea that different generations are driven by fundamentally different workplace motivations. Instead, the results suggest that the main factors shaping employee job satisfaction are more similar across generations than current generational narratives often assume.
Limitations and future lines of research
Despite its contributions, this study is not without its limitations. The cross-sectional nature of the data precludes the possibility of examining temporal trends. Future studies could benefit from comparing data across successive waves of the survey to track changes and identify evolving patterns in job satisfaction over time.
Another promising avenue for future research involves exploring the relationship between job satisfaction and broader indicators of worker well-being. Understanding these connections could provide valuable insights into how employee satisfaction contributes to holistic health and productivity outcomes. Furthermore, investigating the correlation between employee satisfaction and firm competitiveness would help substantiate the hypothesis that higher job satisfaction positively impacts organizational performance. Most scholars and managers would agree that employee performance is of utmost importance for organizations’ effectiveness. (Bakker et al., 2004).
Lastly, emerging research should consider incorporating new models and identifying additional factors that may influence job satisfaction, including technological advancements, remote work trends, and changing workforce demographics. Moreover, some control variables, such as country and income, were found to be significant, suggesting that contextual factors may also play an important role in shaping job satisfaction. Future research could explore these dimensions in greater depth to better understand how institutional and organizational contexts influence employee satisfaction. Future research could incorporate additional variables such as contractual conditions and sector, as these factors may significantly influence job satisfaction and interact with generational differences.
This would enable the development of more robust and inclusive frameworks for analysing and enhancing job satisfaction in diverse organizational environments. This is consistent with Judge and Klinger (2008) who argue that continued attention to the determinants of job satisfaction remains critical for both scholars and practitioners.
Managerial implications
From a theoretical standpoint, these findings contribute to the growing body of literature on generational differences in job satisfaction, particularly in the context of highly changing work environments. This research highlights valuable insights for both scholars and companies aiming to boost internal employee satisfaction while enhancing external customer experience and strengthening their competitive edge. Developing strategies that address core drivers of job satisfaction across the workforce, while remaining sensitive to generational differences, can significantly improve engagement and satisfaction. By addressing these internal dynamics, organizations can indirectly enhance the quality of service delivered to their customers, leading to stronger competitive positioning in the marketplace, helping them increase productivity, attract talent, and improve customer satisfaction (Núñez-Sánchez et al., 2024).
In an era marked by frequent employee turnover, fostering job satisfaction is especially critical. Job satisfaction continues to be one of the most studied job attitudes (Judge et al., 2017). This is particularly important because employees who feel valued and fulfilled are more likely to remain loyal to their organizations and act as vocal advocates, helping to elevate the company’s public image and attract new talent (Judge et al., 2017). From a practical perspective, these insights offer actionable strategies for organizations to enhance job satisfaction and employee retention. This managerial relevance is reinforced by recent evidence showing that workplace happiness significantly reduces turnover intention, although this protective effect may be weakened when employees experience high levels of hopelessness (Salazar-Altamirano et al., 2025).
Additionally, growing evidence in the literature points to a connection between internal and external marketing strategies, with an increasing acknowledgment of employees as a vital internal market. Despite this progress, further research and practical advancements are needed to fully integrate and capitalize on internal marketing practices. Recent evidence on internal market orientation suggests that treating employees as key internal stakeholders can help align human resource management strategies with employee satisfaction, social purpose fulfilment, and business purpose fulfilment (Cuesta-Valiño et al., 2025).
We propose that studies like this can help organizations enhance their competitiveness by focusing on job satisfaction improvements. The results confirm the relevance of interpersonal relationships across all generational cohorts while highlighting the varying degrees of importance attributed to structural and flexible work arrangements. Segmenting the internal client (workforce) to better understand and fulfil their specific needs could foster a more committed and productive team, ultimately driving long-term organizational success in this challenging post-pandemic era.
Funding
This research has not received external funding
Author’s contribution
Conceptualisation, M. V. A., W. B. and J. M. N.S.; Methodology, M. V. A. and W. B.; Data collection, M.V. A., and J. M.N. S.; Data analysis, M. V.A., and W. B.; Writing - Preparation of original draft, M. V. A., W. B., E. C. R., J. M. G., M. V. A. and J. M. N. S.; Writing - Review and editing, M. V. A., W. B., E. C. R., M. V. A. and J. M. N. S., Supervision, J. M. N. S. and. M. V. A.
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