Universidad del Zulia (LUZ)

Revista Venezolana de Gerencia (RVG)

Año 31 No. 114, 2026, e3111415

Abril-Junio

ISSN 1315-9984 / e-ISSN 2477-9423

Como citar: Yong, F. E., Fuchs, R. M., Jauregui, R. R., y Bonilla, J. D. (2026). Intention to join a union in peruvian professional workers: analysis of determining factors. Revista Venezolana De Gerencia, 31(114), e3111415. https://doi.org/10.52080/rvgluz.31.114.15

Intention to join a union in peruvian professional workers: analysis of determining factors

Yong Chung, Felipe Eduardo*

Fuchs Angeles, Rosa María**

Jauregui Arroyo, Ralphi Ricardo***

Bonilla Bermeo, Jeniffer Denisse****

Abstract

In a context where higher education coverage is expanding across Ibero-America, a greater proportion of professionally trained workers is projected. Hence, the aim of this study is to identify the main factors influencing the union affiliation intention among Peruvian professional workers. To this end, a cross-sectional, non-experimental research design was applied, involving a sample of 639 respondents. The collected data were analyzed using structural equation modeling via partial least squares (PLS). The study demonstrates that the primary determinant of union affiliation intention is the workers’ beliefs—whether accurate or not—about the unions’ benefits in terms of labor protection, followed by occupational stress and work-life balance. Furthermore, the results indicate that the main determinant of the psychological capital of Peruvian professional workers is their work-life balance.

Keywords: Work-life balance; union beliefs; occupational stress; union affiliation intention; professional workers.

Recibido: 03.06.25 Aceptado: 17.09.25

* Universidad Peruana de Ciencias Aplicadas. Email: felipe.yongchung@hotmail.com. ORCID: https://orcid.org/0000-0002-6495-6197

** Universidad del Pacífico. Email: fuchs_rm@up.edu.pe ORCID: https://orcid.org/0000-0002-7650-7508

*** Universidad Peruana de Ciencias Aplicadas. Email: ralphi.jauregui@upc.pe ORCID: https://orcid.org/0000-0002-0252-185X

**** Universidad Nacional Mayor de San Marcos. Email: jeniffer.bonilla@unmsm.edu.pe. ORCID: https://orcid.org/0000-0001-9765-1923

Intención de afiliarse a un sindicato entre los trabajadores profesionales peruanos: análisis de los factores determinantes

Resumen

En un contexto en donde la cobertura de la educación superior está creciendo en Iberoamérica, se proyecta una mayor proporción de trabajadores con formación profesional. Así el objetivo del estudio es determinar los principales factores de la intención de afiliación sindical de los trabajadores profesionales peruanos. Para ello se aplica una investigación transversal y no experimental en 639 personas encuestadas. Los datos obtenidos fueron sometidos al modelamiento de ecuaciones estructurales por mínimos cuadrados. La investigación logró demostrar que el principal determinante de la intención de afiliación sindical son las creencias de los sindicatos, fundadas o no, de los beneficios en cuanto a protección laboral, seguido del estrés ocupacional y el balance trabajo-vida. Asimismo, se comprueba que el principal determinante del capital psicológico de los trabajadores profesionales peruanos es el balance trabajo-vida.

Palabras clave: Balance trabajo-vida; creencia de los sindicatos; estrés ocupacional; intención de afiliación sindical; trabajadores profesionales.

1. Introduction

Union affiliation is a pivotal factor in shaping contemporary labor relations. It plays a key role in balancing the structural inequalities commonly observed in the interaction between employers and workers (Batista, 2011). While its original purpose is to ensure fundamental rights, unionization transcends this by exerting a transformative influence that significantly impacts key dimensions such as productivity, wage equity, and institutional strengthening (Freeman & Medoff, 1984; Ríos-Ávila, 2017). In Latin America, this impact takes on distinctive features. For example, in economies such as Peru’s, with fragmented labor markets, the effectiveness of unions is influenced by two interrelated phenomena.

On the one hand, the persistence of high levels of labor informality—a widely recognized issue in developing countries (OIT, 2021); and on the other, a reconfiguration of employment that aims to promote more specialized and diverse professional roles reflecting the demands of modern economies (Rodríguez Copé, 2022). This convergence of factors generates a highly specific context for analyzing union affiliation intention among professional workers, a group that faces complex challenges despite its academic background and predominant participation in the formal sector. These challenges include precarious labor practices—such as unregulated temporary contracts or flexible clauses without guarantees—as well as a gradual reduction in social benefits, trends that contribute to a growing sense of vulnerability in the world of work (OIT, 2021).

The literature has examined the factors influencing union affiliation from multiple perspectives. In this sense, classical approaches, such as the theory of expected utility (Pyman et al., 2017), suggest that workers make rational decisions by weighing the tangible benefits they could obtain through unionization, such as better wages, protection against arbitrary dismissals, or improved working conditions.

On the bright side, recent research has incorporated psychosocial variables into the analysis. Among these, occupational stress stands out, as it erodes individual security and acts as a determining factor that leads workers to seek support through collective structures (Dalessandro & Lovell, 2023; Sarkar, 2012). However, a notable geographic and sectoral bias can be seen in these studies: while most are focused on industrialized economies or manufacturing sectors, research on professionals in emerging contexts remains limited. This group is characterized by higher levels of autonomy and specialized training, and tends to perceive unions as less relevant to addressing their work-related concerns (Fiorito et al., 2018; Zhang et al., 2022).

This theoretical gap becomes especially significant in realities such as Peru, where nearly half of professionals (48.3%) work in microenterprises (INEI, 2023), organizations where union presence is marginal and labor decisions are typically concentrated in the hands of managers or owners, limiting opportunities for collective bargaining. In this regard, this research seeks to address the limitations found in the literature through an innovative theoretical framework that combines traditional and emerging dimensions of labor behavior.

To achieve this objective, the study incorporates classic factors commonly examined in union studies, such as collective bargaining strategies and economic incentives tied to tangible benefits. It also integrates contemporary constructs of growing relevance, notably psychological capital—understood as the combination of resilience, optimism, and self-efficacy that enables workers to overcome adversity—and work-life balance, a critical dimension in a global context increasingly marked by digitalization, flexible scheduling, and the demand for personal-life integration (Luthans et al., 2007; Sinniah et al., 2019).

Through seven interrelated hypotheses, this research systematically examines how these variables—both individual and organizational—interact to shape union affiliation intention among Peruvian professionals. This group is particularly relevant for analysis, since, despite its central role in key knowledge economy sectors—such as technology, education, and specialized services—its representation in global unionism research is marginal, revealing a significant gap in understanding its specific labor dynamics.

The contributions of this research are organized into three complementary dimensions, each with specific implications for academic, political, and organizational spheres. In terms of theoretical implications, the study challenges the external validity of classical union affiliation models—developed primarily in industrial contexts and advanced economies—by demonstrating their limited applicability to emerging realities such as Peru. The findings support the need for integrative analytical frameworks that combine insights from organizational psychology—focused on individual-level variables such as psychological capital—with those from labor sociology, which emphasize socio-economic structures and collective dynamics.

This theoretical hybridization not only enriches the specialized literature but also offers a more precise lens for studying labor phenomena in economies marked by high informality and occupational heterogeneity. Regarding empirical contributions, the research provides pioneering evidence on a scarcely studied group—Peruvian professionals—whose strategic role in key sectors (technology, education, specialized services) positions them as central actors in sustainable development policies. Finally, the practical implications highlight findings that present both challenges and opportunities for unions and employers alike.

2. Determinants factors in the Intention to Join a Union

According to Batista (2011), labor unions arise from workers’ perception of the need to defend their rights. In that sense, Sánchez (2018) states that workers join unions to protect their interests, with representation being the main bargaining tool of labor organizations. In his research in Latin America, Ríos-Ávila (2017) highlights that the impact of unions on company productivity varies across countries, as in some the impact is negligible or even negative, leading to persistent conflicts between workers and the company.

Likewise, Shin and Hwang (2021) and Yang and Tsou (2018) find that unions increase costs and reduce productivity. However, Noroño et al. (2020) and Newman et al. (2019) argue that proper labor relations management by the company and the ethical commitment of union leaders can improve workers’ perceptions and yield productivity benefits. Both companies and unions must acknowledge that today’s workforce is increasingly diverse and stratified due to individual differences and the growing specialization of the labor market (Rodríguez Copé, 2022).

One of the earliest significant scientific studies on union membership intention was conducted by Friedman et al. (2006). Their study found that when workers have a positive perception of a company’s financial performance, as well as favorable views on the sector’s growth, their intention to join a union increases. Later studies by Pyman et al. (2017) confirm these findings, concluding the intent of joining a union is driven by an expected sense of utility, meaning workers see unions as instrumental in obtaining benefits from their employers.

Another relevant study is that of Sinniah et al. (2019), who identified two primary determinants of union membership intention: economic rewards and incentives, and work-life balance policies. They found that effective management of these variables negatively impacts union membership intention. Similar findings were reported by Arissa et al. (2021), who concluded that job dissatisfaction is the most significant factor in increasing union membership.

The second most relevant factor is the perception that unions provide protection against unfair treatment by employers, and the third is the expectation that unions can help improve wages. Comparable results were found in studies by Tremblay (2016), Pungnirund (2020), and Paek (2025), which support the instrumental use of unions for securing better working conditions. Additionally, Kamphorst and Willer (2024) and Van Heuvelen et al. (2025) highlight the importance workers place on the benefits and protection that union membership can provide. Meanwhile, Sarkar (2012) and Dalessandro and Lovell (2023) conclude that workplace stress is the most influential determinant of union membership intention.

A more recent study by Zientara et al. (2023) found that the primary positive determinant of union membership intention is workers’ beliefs regarding the benefits of unions, such as job security and higher wages. Conversely, the study also found that workers with strong psychological and social capital have fewer reasons to join a union. Similarly, Fiorito et al. (2018) and Zhang et al. (2022) conclude that workers with high psychological capital perceive less need for union support. Regarding psychological capital, Shelton and Renard (2015) found that personalized rewards significantly enhance workers’ psychological capital. Likewise, Yong-Chung et al. (2024) and Cho and Lim (2020) identify the positive effects of rewards and incentives in strengthening workers’ confidence in themselves and in their company.

On the other hand, Moccia (2016) argues that work-life balance is a crucial factor in improving employees’ psychological capital. Similarly, Liossis et al. (2009) and Bouzari and Karatepe (2020) emphasize the positive effects of work-life balance on workers’ psychological capital, even increasing their levels of optimism. Finally, Samroodh et al. (2023) confirm in their studies that, in addition to the positive impact of work-life balance on psychological capital, these variables also mediate the relationship between job autonomy and employees’ intention to remain with the company.

Based on the above, the following hypotheses are proposed for Peruvian professional workers, and illustrated in diagram 1:

Diagram 1

Theorical model of the study

H1. Work-life balance negatively influences union membership intention.

H2. Psychological capital negatively influences union membership intention.

H3. Occupational stress positively influences union membership intention.

H4. Rewards and incentives negatively influence union membership intention.

H5. Perceptions of unions’ protective benefits positively influence union membership intention.

H6. Work-life balance positively influences psychological capital.

H7. Rewards and incentives positively influence psychological capita

3. Perspective methodology

In order to demonstrate the hypotheses proposed in the theoretical framework, a quantitative and causal research approach is developed. This study aims to verify the influence of occupational stress, work-life balance, rewards and incentives, psychological capital, and perceptions of protection offered by unions on the intention of union affiliation among Peruvian professional workers.

The inclusion of these variables allows for a deeper understanding of this complex social phenomenon, as well as the development of theories to explain it. Therefore, structural equation modeling using partial least squares (PLS-SEM) is applied. According to Hair et al. (2021), the structural equation modeling method can be applied to all studies involving multiple variables and their simultaneous relationships. However, the PLS-SEM variant is the most suitable for exploratory studies that seek to develop new theories.

According to the latest official census conducted in Peru by the National Institute of Statistics and Informatics (INEI, 2017), the population of employed professionals in Metropolitan Lima is 1,372,299 people (INEI, 2017). For data collection, a self-administered online questionnaire was applied from September 2023 to October 2024. The questionnaire was designed using the Google Forms platform, ensuring that responses were mandatory to prevent missing data. A sample of 639 professional workers from different sectors, age groups, and company sizes was obtained (Table 1).

Table 1

Sample characteristics

Geographical distribution	N	%	Organization Size (Nº of Employees)
North Lima	103	16.1%	1 - 10	96	15.0%
Center Lima	49	7.7%	11 – 50	153	23.9%
Modern Lima	231	36.1%	51 - 250	132	20.7%
East Lima	88	13.8%	Más de 250	258	40.4%
South Lima	111	17.4%	Total	639	100%
Callao	50	7.8%
Beaches	7	1.1%	Age distribution (years)
Total	639	100%	18 - 25	117	18.3%
Sex distribution			26 - 35	354	55.4%
Men	268	41,9%	36 - 45	138	21.6%
Women	371	58.1%	46 - 55	27	4.2%
Total	639	100%	56 - 65	3	0.5%
			Total	639	100%

Source: Own elaboration based on MINITAB

The obtained sample of 639 respondents is considered more than adequate, given that, according to Hair et al. (2011), the recommended sample size for the application of SEM should be above 200 subjects. Additionally, considering a confidence level of 95% and a margin of error of 5%, the probabilistic sample size is only 385 people. Furthermore, Table 1 presents the stratification technique by geographic sectors, in this case, up to six sectors. This aims to ensure the probabilistic nature of the sampling in order to generalize the study’s conclusions to the entire population.

In addition to the demographic variables described in Table 1, the measurement instrument includes 17 indicators: 4 indicators for the psychological capital variable adapted from Zientara et al. (2023), 3 indicators for occupational stress also from Zientara et al. (2023), 3 indicators for rewards and incentives from Sinniah et al. (2019) and Zientara et al. (2023), 4 indicators for work-life balance from Sinniah et al. (2019) and Zientara et al. (2023), 2 indicators for the perception of unions, and 2 indicators for the intention of union affiliation from Sinniah et al. (2019) and Zientara et al. (2023). To validate the reliability of the instrument, Cronbach’s alpha tests and exploratory factor analysis (EFA) were conducted, with the results shown in Tables 2 and 3. For the EFA, the maximum likelihood option and varimax rotation were used.

Table 2

Cronbach’s Alpha coefficients by construct

Construct	# Indicators	Cronbach’s Alpha
Work life balance (WLB)	4	0.888
Psychological Capital (PC)	4	0.891
Stress (ST)	3	0.736
Union Affiliation Intention (UAI 1 and 2)	2	0.923
Perception of Unions (UAI 3 and 4)	2	0.839
Rewards and Incentives (RI)	3	0.848

Source: Own elaboration based on SMART PLS 4.0.

Table 3

Results of the exploratory factor analysis

Variable	Factor1	Factor2	Factor3	Factor4	Factor5	Factor6	Communality
PC3	0.800	-0.256	-0.057	-0.063	-0.020	-0.021	0.714
PC2	0.785	-0.232	-0.075	-0.063	0.061	0.030	0.684
PC4	0.784	-0.288	-0.027	-0.029	0.013	0.009	0.700
PC1	0.690	-0.371	-0.060	-0.007	0.038	0.017	0.619
WLB2	0.284	-0.853	-0.017	-0.042	-0.010	-0.001	0.810
WLB1	0.367	-0.763	-0.005	-0.107	0.017	-0.002	0.729
WLB4	0.288	-0.738	-0.031	-0.008	-0.005	0.031	0.629
WLB3	0.179	-0.702	-0.091	-0.030	0.038	-0.034	0.536
RI2	0.070	-0.038	-0.905	-0.007	0.028	0.051	0.828
RI1	0.130	-0.025	-0.797	-0.018	0.053	0.048	0.659
RI3	-0.027	-0.055	-0.679	0.022	-0.014	-0.077	0.471
UAI1	-0.113	0.094	-0.009	0.931	-0.243	-0.229	1.000
UAI2	-0.043	0.080	-0.011	0.775	-0.192	-0.335	0.759
ST2	-0.160	0.037	0.035	0.193	-0.751	-0.054	0.633
ST3	0.008	-0.034	0.046	0.081	-0.689	-0.086	0.492
ST1	0.052	0.031	-0.018	0.076	-0.611	-0.082	0.389
UAI4	-0.017	-0.006	-0.037	0.303	-0.176	-0.936	1.000
UAI3	-0.003	-0.023	0.050	0.433	-0.145	-0.607	0.580
Variance	2.7428	2.7111	1.9455	1.8213	1.5713	1.4395	12.2314
% Var	0.152	0.151	0.108	0.101	0.087	0.080	0.680

Source: Own elaboration based on MINITAB, AFE and maximum likelihood estimation, and varimax rotation

The results of the Cronbach’s alpha coefficient for each of the constructs exceed the minimum value of 0.7, which is widely accepted in the literature (Bhattacherjee, 2012). Regarding the results of the EFA, a preliminary main component analysis with varimax rotation was applied, revealing that 73% of the sample variance is explained by five factors, which is considered considerably high. Additionally, a common bias test was conducted in SMART PLS 4.0 for the internal model, finding that all values of the variance inflation factor (VIF) were below 3.3, ruling out any bias issues in the instrument (Kock, 2015).

For the EFA using maximum likelihood and varimax rotation considering only five factors (Table 3), it was observed that the factor loadings per indicator confirm their grouping under the five studied constructs. According to Hair et al. (2011), the minimum factor loading for a sample size above 200 subjects should be at least 0.4. This, allow us to conclude that the data collection instrument is reliable and that its results can be used to infer conclusions for the entire population.

4. Results of the Multivariate Model (PLS-SEM)

Below is a summary of the statistical tests performed. The study model undergoes confirmatory factor analysis (CFA), for which convergent and discriminant validity tests are applied. Once the model’s validity is confirmed, an analysis of the normal distribution of the collected data is conducted. This step is applied to confirm the absence of a normal distribution and the need for a normality adjustment, for which a bootstrapping procedure with 5,000 samples is applied. Finally, the analysis of multivariate relationships is conducted (Dash & Paul, 2021). These tests are performed using SMART PLS 4.0. Diagram 2 presents the structural equation model.

Diagram 2

Theorical model of structural equations

For the convergent validity test, the composite reliability (CR) and the average variance extracted (AVE) are calculated for each of the constructs. According to Fornell and Larcker (1981), the minimum acceptable values for these coefficients are 0.7 and 0.5, respectively. Values below these thresholds indicate a lack of convergent validity. The obtained results are shown in Table 4.

Table 4

Results of the Convergent Validity Tests

	Composite reliability (rho_a)	Average variance extracted (AVE)
Work-life balance (WLB)	0.905	0.748
Psychological Capital (PC)	0.892	0.753
Stress (ST)	0.788	0.650
Union Affiliation Intention (UAI 1 and 2)	0.924	0.929
Perception of Unions (UAI 3 and 4)	0.839	0.861
Rewards and Incentives (RI)	0.906	0.866

The results shown in Table 4 confirm the convergent validity of the study model, as the obtained values exceed the minimum required thresholds. Once convergent validity is confirmed, the analysis of discriminant validity follows, which ensures that the constructs are distinct from each other and are measured appropriately. To confirm discriminant validity, the Fornell and Larcker test and the Heterotrait-Monotrait ratio (HTMT) test are applied. The results can be seen in Table 5.

Tabla 5

Results of Discriminant Validity Tests

Larcker and Fornell test
	BTV	CP	ST	UAI 1,2	UAI 3,4	PI
Work-life balance (WLB)	0.865
Psychological Capital (PC)	0.593	0.868
Stress (ST)	-0.055	-0.094	0.806
Union Affiliation Intention (UAI 1 and 2)	-0.158	-0.152	0.363	0.964
Perception of Unions (UAI 3 and 4)	-0.014	-0.038	0.281	0.637	0.928
Rewards and Incentives (RI)	0.099	0.168	-0.080	-0.041	-0.062	0.930
Heterotrait-Monotrait Ratio (HTMT) Test
	BTV	CP	ST	UAI 1,2	UAI 3,4	PI
Work-life balance (WLB)
Psychological Capital (PC)	0.655
Stress (ST)	0.075	0.120
Union Affiliation Intention (UAI 1 and 2)	0.169	0.168	0.426
Perception of Unions (UAI 3 and 4)	0.031	0.044	0.355	0.723
Rewards and Incentives (RI)	0.118	0.190	0.092	0.045	0.072

According to Fornell and Larcker (1981), the square root of the Average Variance Extracted (AVE) must always be greater than the correlation values between constructs. As observed in Table 5, this criterion is met. Additionally, according to Kline (2010, as cited in Sinniah et al., 2019), the Heterotrait-Monotrait (HTMT) ratio should remain below 0.85. From Table 5, it can be confirmed that this criterion is also met. Therefore, it is concluded that the study model has discriminant validity, meaning that the constructs are distinct from each other.

Diagram 3 presents the factor loadings of the indicators for the model’s constructs, all of which are above the established minimum value of 0.6 (Dash & Paul, 2021; Hair et al., 2011). This confirms that all displayed indicators are valid and representative for the model’s implementation (only the RI3 indicator was discarded as it did not reach the minimum required).

Diagram 3

Estimated model

As specified by Carrasco Gallego et al. (2015), once the model has been estimated, it must be verified whether it has sufficient reliability to reproduce the covariances of the collected sample. Hence, goodness-of-fit indices are used. According to Hu and Bentler (1998), the index that best measures the quality of structural equation models in partial least squares is the Standardized Root Mean Square Residual (SRMR). According to Henseler et al. (2016), the maximum allowable value for this coefficient is 0.08, with values below 0.06 considered highly acceptable. The results shown in Table 6 confirm that the study model fits well, as the SRMR values for both the saturated model and the estimated model are below 0.08.

Table 6

Goodness-of-Fit Indices

	Saturated model	Estimated model
SRMR	0.053	0.054
d_ULS	0.433	0.447
d_G	0.291	0.289
Chi-square	1209.608	1194.436

Before proceeding with the analysis of the relationships in the multivariate model, the normality test of the collected data is conducted to confirm the need for an adjustment. The results of the Kolmogorov-Smirnov (K-S) test, as well as the overall skewness and kurtosis values, indicate that the sample does not follow a normal distribution. The results observed confirm the absence of a normal distribution in the data and, therefore, the necessity of applying a normality adjustment through bootstrapping, in this case with 5000 samples. Below are the results of the regression tests for the multivariate relationships using bootstrapping with 5000 samples (Table 7).

Table 7

Hypothesis Testing

Hipótesis	Adjusted R2	Sample mean (M)	Standard deviation (STDEV)	P values
H1. Work Life Balance -> Union Affiliation Intention	0.459	-0.112	0.026	0.000
H2. Psychological Capital -> Union Affiliation Intention		-0.050	0.037	0.174
H3. Stress -> Union Affiliation Intention		0.191	0.035	0.000
H4. Rewards and Incentives -> Union Affiliation Intention		0.029	0.033	0.467
H5. Perception of union -> Union Affiliation Intention		0.581	0.030	0.000
H6. Work life balance -> Psychological Capital	0.362	0.582	0.044	0.000
H7. Rewards and Incentives -> Psychological Capital		0.110	0.031	0.000

Source: Created by author with SMART PLS 4.0.

The results obtained in Table 7 show R² values greater than 0.25, specifically 0.459 for the construct of union affiliation intention (UAI) and 0.362 for the construct of psychological capital (PC). These values can be considered moderate according to Hair et al. (2011).

Regarding Hypothesis 1, the p-value is less than 0.05, so the hypothesis is accepted. That is, work-life balance negatively influences the intention to join a union. This result is supported by Paek (2025), who states that unions contribute to improving working conditions, especially concerning work-life balance, which is a motive for union affiliation. However, if a worker perceives that their job allows them to maintain this balance, they are less likely to feel the need to join a union. Similarly, Tremblay (2016) highlights the growing interest in achieving this balance, suggesting that when employees believe they already have it, their intention to join a union decreases.

With respect to Hypothesis 2, the p-value is greater than 0.05, leading to the rejection of the hypothesis. That is, psychological capital does not significantly influence the intention to join a union. These results differ from those found in studies such as Fiorito et al. (2018), who argue that people with high psychological capital, such as self-efficacy, perceive a lower need for union support. Similarly, Zhang et al. (2022) assert that workers with these characteristics tend to rely less on unions. According to Jauregui-Arroyo et al. (2023), the lack of significance in the results could be explained by the peculiarities of labor policies in the Peruvian context, which differ from previous findings. However, it is also necessary to consider the greater autonomy that professional workers have, which could reduce the perceived usefulness of unions to insignificant levels.

For Hypothesis 3, the p-value is less than 0.05, so the hypothesis is accepted. That is, occupational stress positively influences the intention to join a union. Along these lines, Sarkar (2012) states that job stress positively influences union affiliation intention, as employees experiencing higher stress levels tend to consider it a favorable option. Similarly, Dalessandro and Lovell (2023) emphasize that high stress can impact workers’ decisions and behaviors in the workplace, potentially increasing their inclination toward union membership.

In relation to Hypothesis 4, the p-value is greater than 0.05, leading to the rejection of the hypothesis. That is, rewards and incentives do not significantly influence the intention to join a union. This result contrasts with studies such as Pungnirund (2020), who argues that workers turn to collective bargaining as a strategy to improve their benefits and working conditions. However, Sinniah et al. (2019) indicate that although employees may intend to join a union, organizational policy acts as a moderating factor that can influence or even prevent their decision to join. Additionally, it is important to consider the greater autonomy and rationality of professional workers, who, seeing fewer possibilities, might opt for other career alternatives rather than union membership.

Regarding Hypothesis 5, the p-value is less than 0.05, so the hypothesis is accepted. That is, the perception of the protective benefits offered by unions positively influences the intention to join a union. This result aligns with Kamphorst and Willer (2024), who state that clear communication about the benefits of union membership significantly increases workers’ interest in joining. Similarly, Van Heuvelen et al. (2025) argue that employees perceive unionization as a means to obtain greater protection, reinforcing its appeal as a labor alternative.

For Hypothesis 6, the p-value is less than 0.05, so the hypothesis is accepted. That is, work-life balance positively influences psychological capital. This result aligns with studies such as Bouzari and Karatepe (2020), who affirm that work-life balance is a key resource that strengthens employees’ psychological capital, including their level of optimism. This could be explained by the positive impact of better work-family balance and the reduction of negative effects arising from their interaction (Liossis et al., 2009).

Finally, for Hypothesis 7, the obtained p-value is less than 0.05, so the hypothesis is accepted. That is, rewards and incentives positively influence psychological capital. This result is consistent with Yong-Chung et al. (2024), who highlight a significant relationship between rewards and incentives and psychological capital, emphasizing social support and self-confidence as key elements. Likewise, the research by Cho and Lim (2020) reinforces this idea by stating that rewards not only strengthen psychological capital but also influence workers’ intention to stay in their jobs.

5. Conclusions

This study aimed to analyze how determining factors, such as work-life balance, psychological capital, occupational stress, rewards and incentives, and perceptions of unions, influence the intention of Peruvian professional workers to join a union. The results show that work-life balance, occupational stress, and union perceptions significantly impact union affiliation intention, aligning with international literature. Meanwhile, psychological capital and rewards and incentives did not show significant evidence of influencing union affiliation intention. This could be due to the specific characteristics of professional workers, where autonomy and self-confidence play an important role that counteracts the intention to depend on a union.

On the other hand, the study found that both work-life balance and rewards and incentives positively influence psychological capital, reinforcing previous findings. It is important to note the study’s limitations, particularly regarding the probabilistic randomness of the sample, which corresponds to an extremely broad and varied population. This may have implications for the generalization of conclusions. Finally, beyond its theoretical contribution to the literature, this research can serve as a reference for decision-makers in organizations, helping them identify the factors influencing union affiliation intention and develop strategies to reduce it.

Furthermore, future studies should address these variables in different regions of the country to expand and contrast the findings. Additionally, future research could incorporate new variables, such as leadership style, which might impact union affiliation intention.

References

Arissa, N., Yusoff, M., Othman, A., Mahazril ’, &, Yaacob, A. (2021). What Influence Employees’ Intention To Join a Union? 8th International Conference on Public Policy and Social Science (ICoPS) 2021, UiTM Cawangan N. Sembilan, Kampus Seremban. https://ir.uitm.edu.my/id/eprint/55216/1/55216.pdf.

Batista, M. (2011). Libre funcionamiento de las organizaciones sindicales: Garantías para su crecimiento y consolidación. Consejo Sindical Unitario de América Central y Caribe, 3, 1–12. https://www.ilo.org/sites/default/files/wcmsp5/groups/public/@ed_dialogue/@actrav/documents/publication/wcms_174972.pdf.

Bhattacherjee, A. (2012). Social Science Research: Principles, Methods, and Practices. Textbooks Collection. Book 3. http://scholarcommons.usf.edu/oa_textbooks/3?utm_source=scholarcom mons.usf.edu%2Foa_textbooks%2F3&utm_medium=PDF&utm_campai gn=PDFCoverPages.

Bouzari, M., & Karatepe, O. M. (2020). Does optimism mediate the influence of work-life balance on hotel salespeople’s life satisfaction and creative performance? Journal of Human Resources in Hospitality & Tourism, 19(1), 82-101. https://doi.org/ 10.1080/15332845.2020.1672250.

Carrasco Gallego, A., Donoso Anes, J. A., Duarte-Atoche, T., Hernández Borreguero, J. J., & López Gavira, R. (2015). Diseño y validación de un cuestionario que mide la percepción de efectividad del uso de metodologías de participación activa (CEMPA). El caso del Aprendizaje Basado en Proyectos (ABPrj) en la docencia de la 279 contabilidad. INNOVAR. Revista de Ciencias Administrativas y Sociales, 25(58),143-158. https://www.redalyc.org/articulo.oa?id=81841166011.

Cho, S. J., & Lim, S. J. (2020). Effects of positive psychological capital, reward on retention intention of hospital nurses. Journal of Korean Academy of Nursing Administration, 563-573. https://doi.org/10.11111/jkana.2020.26.5.563.

Dalessandro, C., & Lovell, A. (2023). Race, marginalization, and perceptions of stress among workers worldwide post-2020. Sociological Inquiry, 93(3), 571-591. https://doi.org/10.1111/soin.12505

Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173(June), 121092. https://doi.org/10.1016/j.techfore.2021.121092

Fiorito, J., Padavic, I., & Russell, Z. A. (2018). Pro-social and self-interest motivations for unionism and implications for unions as institutions. In Advances in Industrial and Labor Relations, 2017: Shifts in Workplace Voice, Justice, Negotiation and Conflict Resolution in Contemporary Workplaces (pp. 185-211). Emerald Publishing Limited. https://doi.upc.elogim.com/10.1108/S0742-618620180000024009

Fornell, C., & Larcker, D. F. (1981). SEM with Unobservable Variables and Measurement Error. In Algebra and Statistics, 47(3), 138-145. https://doi.org/10.2307/3150980

Freeman, R. B., & Medoff, J. L. (1984). What do unions do?. Basic Books. https://www.academia.edu/62364821/Review_of_What_Do_Unions_Do_by_Richard_B_Freeman_and_James_L_Medoff

Friedman, B. A., Abraham, S. E., & Thomas, R. K. (2006). Factors related to employees’ desire to join and leave unions. Industrial Relations, 45(1), 102–110. https://doi.org/10.1111/j.1468-232X.2006.00416.x

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202

Hair Jr., J., Hult, T., Ringle, C., Sarstedt, M., Danks, N., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook. In Structural Equation Modeling. Springer. Hair Jr., J., Hult, T., Ringle, C., Sarstedt, M., Danks, N., & Ray, S. (2021). Review of Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook.

Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382

Hu, L., & Bentler, P. M. (1998). Fit Indices in Covariance Structure Modeling: Sensitivity to Underparameterized Model Misspecification. Psychological Methods, 3, 424-453.
http://dx.doi.org/10.1037/1082-989X.3.4.424

INEI. (2023). Encuesta Nacional de Hogares (ENAHO) 2022. https://datosabiertos.gob.pe/dataset/encuesta-nacional-de-hogares-enaho-2022-instituto-nacional-de-estad%C3%ADstica-e-inform%C3%A1tica-%E2%80%93

Jauregui-Arroyo, R. R., Avila, N. M. G., & Rondon-Jara, E. (2023). Estilos de liderazgo de los millennials y su influencia en el desempeño de las pequeñas empresas del sector textil manufactura. Innovar: Revista de ciencias administrativas y sociales, 33(89), 147-160. https://doi.org/ 10.15446/innovar.v33n89.107047

Kamphorst, J., & Willer, R. (2024). Correcting misperceptions of the material benefits associated with union membership increases Americans’ interest in joining unions. Proceedings of the National Academy of Sciences, 121(19), e2321025121. https://doi.org/10.1073/pnas.2321025121

Kock, N. (2015). Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach. International Journal of e-Collaboration (IJeC), 11, 1-10.
https://doi.org/10.4018/ijec.2015100101

Liossis, PL, Shochet, IM, Millear, PM y Biggs, H. (2009). El programa Promoting Adult Resilience (PAR): la eficacia del segundo programa piloto más breve de prevención en el lugar de trabajo. Behaviour Change, 26(2), 97-112. https://doi.org/10.1375/bech.26.2.97

Luthans, F., Youssef, C. M., & Avolio, B. J. (2007). Psychological capital: Developing the human competitive edge. Oxford University Press. https://academic.oup.com/book/26255?login=false

Moccia, S. (2016). Felicidad en el trabajo. Papeles del Psicólogo, 37(2).

Newman, A., Cooper, B., Holland, P., Miao, Q., & Teicher, J. (2019). How do industrial relations climate and union instrumentality enhance employee performance? The mediating effects of perceived job security and trust in management. Human Resource Management, 58(1), 35–44. https://doi.org/10.1002/hrm.21921

Noroño, J., Nuñez, M., & Gonzalez, R. (2020). Ética Sindical Como Mecanismo Impulsor De Competitividad En Las Pequeñas Y Medianas Empresas. Utopía y Praxis Latinoamericana, 25(1), 154–173. https://produccioncientificaluz.org/index.php/utopia/article/view/32648

OIT. (2021). Perspectivas Sociales y del Empleo en el Mundo: Tendencias 2021. https://www.ilo.org/sites/default/files/wcmsp5/groups/public/@dgreports/@dcomm/@publ/documents/publication/wcms_825200.pdf

Paek, E. (2025). Sindicatos, contextos laborales y acceso de los trabajadores a políticas de conciliación trabajo-familia. Social Forces, 103(3), 865-885. https://doi.org/10.1093/sf/soae103

Pungnirund, B. (2020). Impact of Effective Implementation of HR Practices on Employee Performance in Pharmacy Business in Thailand. Systematic Reviews in Pharmacy, 11(2). https://doi.org/10.5530/srp.2020.2.90

Pyman, A., Teicher, J., Cooper, B., & Holland, P. (2017). The Predictors of Unmet Demand for Unions in Non-Union Workplaces: Lessons from Australia TT - Les prédicteurs de la demande non comblée pour la syndicalisation dans les milieux de travail non syndiqués : une perspective australienne Los predictores de l. Relations Industrielles, 72(2), 270–293. https://www.erudit.org/fr/revues/ri/2017-v72-n2-ri03115/1040401ar.pdf

Ríos-Avila, F. (2017). Unions and Economic Performance in developing countries: case studies from latin america. Ecos de Economía, 21(44), 4–36. https://doi.org/10.17230/ecos.2017.44.1

Rodríguez Copé, M. L. (2022). El difícil papel del sindicato de clase en la GIG-ECONOMY: Viejas y nuevas fórmulas para crear: Sindicalismo y capitalismo digital:los límites del conflicto. (pp.227-272). Bomarzo. https://www.researchgate.net/publication/358462219%0AEL

Samroodh, M., Anwar, I., Ahmad, A., Akhtar, S., Bino, E., & Ali, M. A. (2023). The Indirect Effect of Job Resources on Employees’ Intention to Stay: A Serial Mediation Model with Psychological Capital and Work–Life Balance as the Mediators. Sustainability, 15(1), 301 https://doi.org/10.3390/su15010551

Sánchez de Diego Fernández de la Riva, M. (2018). Apuntes sobre la Transparencia. Universdidad Complutense de Madrid. https://digitum.um.es/digitum/bitstream/10201/102103/1/LIBRO2018A..A.pdf

Sarkar, S. (2012). Determinants of employees’ attitudes toward union membership in India. Journal of World Business, 47(2), 240-250. https://doi.upc.elogim.com/10.1016/j.jwb.2011.04.011

Shelton, S. A., & Renard, M. (2015). Correlating nurses’ levels of psychological capital with their reward preferences and reward satisfaction. SA Journal of Industrial Psychology, 41(1), 1–14. https://doi.org/10.4102/sajip.v41i1.1271

Sinniah, S., Mohamed, R. K. M. H., Alam, S. S., Nadarajan, R., Shukor, S. F. A., & Aziz, C. N. B. S. A. (2019). Employee intention to join a union in private university. International Journal of Engineering and Advanced Technology, 8(6 Special Issue 3), 962–972. https://doi.org/10.35940/ijeat.F1078.0986S319

Tremblay, DG (2016). Sindicatos y derechos laborales y familiares: el desafío de la interrelación entre el trabajo y la vida familiar en un entorno sindical. Employee Responsibilities and Rights Journal, 28, 171-187. https://doi.org/10.1007/s10672-016-9279-8

Van Heuvelen, T., Han, X., & VanHeuvelen, J. (2025). The mortality implications of a unionized career. Social Science & Medicine, 365, 117620. https://doi.org/ 10.1016/j.socscimed.2024.117620

Yang, C. H., & Tsou, M. W. (2018). Labour Unions and Firm Productivity: Evidence from China. Manchester School, 86(6), 699–721. https://doi.org/10.1111/manc.12225

Yong-Chung, F., García-Salirrosas, E. E., Bonilla-Bermeo, J. D., & Medina de la Cruz, R. M. (2024). Capital psicológico en trabajadores profesionales peruanos: análisis de factores determinantes. Revista Venezolana de Gerencia, 29(108), 1615-1629. https://doi.org/10.52080/rvgluz.29.108.9

Zhang, J. A., Bai, T., Tang, R. W., Edgar, F., Grover, S., & Chen, G. (2022). The development of individual ambidexterity across institutional environments: Symmetric and configurational analyses. Management international review, 62(4), 517-540. https://doi.org/10.1007/s11575-022-00477-y

Zientara, P., Adamska-Mieruszewska, J., & Bąk, M. (2023). Unpicking the mechanism underlying hospitality workers’ intention to join a union and intention to quit a job. Evidence from the UK. International Journal of Hospitality Management, 108(May 2022), 103355. https://doi.org/10.1016/j.ijhm.2022.103355