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Article

Team Autonomy and Organizational Support, Well-Being, and Work Engagement in the Spain Computer Consultancy Industry: The Mediating Effect of Emotional Intelligence

by
Julio Suárez-Albanchez
1,*,
Pedro Jimenez-Estevez
1,
Juan Jose Blazquez-Resino
2 and
Santiago Gutierrez-Broncano
2
1
Deparment of Business Administration, Faculty of Law and Social Science, Castilla-La Mancha University, San Pedro Martir, 45001 Toledo, Spain
2
Deparment of Business Administration, Faculty of Social Science, Castilla-La Mancha University, Av. Real Fábrica de Seda, 45600 Talavera de la Reina, Spain
*
Author to whom correspondence should be addressed.
Adm. Sci. 2022, 12(3), 85; https://doi.org/10.3390/admsci12030085
Submission received: 1 June 2022 / Revised: 30 June 2022 / Accepted: 1 July 2022 / Published: 20 July 2022
(This article belongs to the Special Issue What Is in the Future of Business Research and Management? Emerging Issues after COVID-19 Time)
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Versions Notes

Abstract

:
The aim of this paper is to analyze the impact of autonomy at work and organizations’ support for their employees on employee well-being and, ultimately, their commitment to their work, and how employees’ emotional intelligence mediates between these constructs. We distributed a 5-point Likert scale questionnaire among professionals from different companies in the IT consultancy sector in Spain. The data collected were analyzed using the PLS-SEM (partial least squares structural equation modeling) technique in the SmartPLS software. The analysis of the data collected shows that there is indeed a positive relationship between job autonomy and the organizational support received by workers and their well-being, as well as between the well-being of workers and their commitment to work. Likewise, the mediation effect of emotional intelligence between job autonomy and organizational support and the well-being of workers is also evident. We can conclude that improving the autonomy and support given by organizations to their employees would have a beneficial effect on the well-being and work engagement of employees.

1. Introduction

One of the most important factors in making a difference in the business environment in current working conditions is to have committed, productive, and highly motivated employees who are willing to do their best for the company (Aydogdu and Asikgil 2011). The success and long-term survival of any organization depends on its ability to maximize the commitment of its key employees. To a large extent, aspects such as customer satisfaction, company performance, or the labor climate depend on the organization’s ability to retain and motivate the best employees (Das and Baruah 2013).
Different aspects such as work–life balance (Abeykoon and Perera 2019), stress (Lu 1999), or lack of compensation (Jonge et al. 2000) are capable of influencing employee well-being and have an impact on their professional performance and degree of organizational commitment. Being aware of these aspects and understanding how they are able to influence employees is key if we want to keep staff motivated and get the best out of them. While encouraging well-being and commitment to work is important in any company, it is especially important in a sector as competitive and dynamic as the IT consultancy sector; in addition to high turnover rates of over 30% according to the Spanish Association of Consultancy Firms (Asociación española de empresas de consultoría 2018), this sector requires a high level of qualifications and work experience, which are often difficult to achieve.
Previous studies have focused on the relationship between organizational support and workers’ well-being (Panaccio and Vandenberghe 2009), professional autonomy and employee well-being (Van Mierlo et al. 2007), or the impact of emotional intelligence on employee well-being (Bar-On 2005). Although different papers have been found that analyze the impact of these aspects separately in different ways, there are no studies that analyze how emotional intelligence mediates between perceived organizational support and team autonomy and employees’ well-being in the Spanish IT consulting industry and how the emotional intelligence of employees directly impacts their well-being. This paper aims to fill the existing gap in the literature and increase the current knowledge on how job autonomy and organizational support are related to employee well-being and work engagement within the IT consultancy sector in Spain. In addition, it aims to determine how emotional intelligence mediates between these aspects and analyze how companies should approach these aspects in order to improve the well-being and work engagement of professionals in this sector in Spain.
For this paper, the following research questions have been raised:
  • Research question 1: How does perceived organizational support and team autonomy impact the employees’ well-being?
  • Research question 2: How does the employees’ well-being impact their work engagement?
  • Research question 3: How does the employees’ emotional intelligence impact their well-being?
  • Research question 4: What is the impact of the mediation of emotional intelligence between perceived organizational support and work autonomy and employees’ well-being?
It is important to note that this study was conducted during the COVID-19 pandemic, which has had a major impact on both workers’ and companies’ concern for well-being at work in sectors in which, historically, concern for this aspect was secondary (Agius 2020; Godderis and Luyten 2020). It is also worth noting the importance of IT consultancy work during the COVID-19 crisis (Jeffery et al. 2020; Kaminski 2020; Ye 2020), which has led to increased workload and pressure on workers in the sector.
The remaining of this paper is structured as follows. In Section 2, the literature reviews are described, where mainly, we will address other paper about similar topics that can be found in literature. In Section 3, the followed research methodology and processes carried to perform our study will be shown. The results will be detailed in Section 4, and finally, Section 5 and Section 6 will include the discussion and the conclusions of this study, respectively.

2. Literature Review

2.1. Autonomy at Work, Organizational Support, and Employee Well-Being

Previous studies have analyzed the relationship between autonomy at work, organizational support, and employee well-being (Gunsel and Acikgoz 2013; Brough and Frame 2004; Timms et al. 2015), focusing on different activities.
The concept of autonomy at work refers to the ability of the practitioner(s) to make their own decisions relating to the project they are working on and to carry out the work in the way they see fit without external interference (Gerwin and Moffat 1997; Sethi 2000). Autonomy is one of the most important aspects of work flexibility (Lee and Xia 2007), and is of great importance when adapting to possible changes in the business environment. This is why team autonomy is often considered a key characteristic of work teams (Langfred 2000; Leach et al. 2005; Van Mierlo et al. 2006).
Several studies indicate that increased work autonomy is important for promoting both work effectiveness and employee well-being (Goodman et al. 1988; Sundstrom et al. 1990; Guzzo and Dickson 1996; Sonnentag 1996; Langfred 2000). Autonomy at work has been listed as a predictor of individual well-being in the workplace (Karasek 1998; Kompier 2003), while autonomy in the performance of individual professional tasks was found to be key to the psychological well-being of employees. Team autonomy was also found to contribute to individual work independence, which also contributes to the well-being of employees (Van Mierlo et al. 2007).
The concept of organizational support is closely related to employees’ perceptions or beliefs about the level at which the organization values their work and contribution to the company and cares for their well-being and professional development (Eisenberger et al. 1986).
Perceived organizational support has a positive influence on employees in inducing them to care for their company and help to achieve its objectives (Rhoades and Eisenberger 2002). This obliges management to strive to build a healthy work environment, where aspects such as organizational support become relevant, since they can contribute to the well-being of individuals (Berthelsen et al. 2008). Previous studies (Behson 2005; Timms et al. 2015) indicate that providing a supportive environment in the company has an impact on increasing employee satisfaction, reducing their stress levels, and therefore, improving their well-being.
This relationship has been previously studied in different sectors, such as flight crews or computer technicians (Guzzo and Dickson 1996), government organizations (Langfred 2000), sales and engineering (Sundstrom et al. 1990), post and manufacturing (Eisenberger et al. 1986), dental clinics (Berthelsen et al. 2008), or the IT sector (Hussain and Asif 2012). We believe that the implementation of adequate organizational support policies and job autonomy will have a positive impact on the well-being of workers in the IT consultancy sector in Spain.
Hypothesis 1 (H1).
There is a positive relationship between team autonomy and perceived organizational support and employee well-being.

2.2. The Mediation Role of Emotional Intelligence

The concept of emotional intelligence has become a common topic of research in recent years in areas such as strategic management, organizational behavior, and human resources (Sala 2005). The first definition of the term emotional intelligence is attributed to Mayer and Salovey (1995), who defined this construct as the ability to perceive emotions, integrate them to facilitate thinking, understand them, and be able to regulate them to promote personal growth. Emotional intelligence involves the ability to control and use feelings and emotions, both our own and others’, to guide our thinking and actions (Huy 1999).
Emotional intelligence is composed of a set of skills for perceiving and regulating emotions, both in oneself and in others, and being able to use these emotions to facilitate and improve one’s own and others’ performance (Mayer et al. 2000; Coté and Miners 2006).
Previous studies on emotional intelligence indicate that it is important for professional performance (Garcia-Prieto et al. 2007), helping to maintain appropriate relationships between team members, and contributing to improved communication and information exchange and decision making, thus improving team performance (Jordan and Troth 2004). Different studies have associated emotional intelligence with well-being (Huang et al. 2018; Salovey et al. 1999; Bar-On 2005), some of which consider emotional intelligence as a predictor of well-being (Lin et al. 2016; Koydemir and Schütz 2012; Gallagher and Vella-Brodrick 2008; Austin et al. 2005; Bar-On 2005; Ciarrochi and Scott 2006) and others consider that emotional intelligence will have a significantly positive effect on well-being (Huang et al. 2018; Di Fabio and Kenny 2016; Sánchez-Álvarez et al. 2016; Vergara et al. 2015; Zeidner and Olnick-Shemesh 2010).
Previous studies have examined the mediation between different human resource and organizational policies (Gunsel and Acikgoz 2013; Yamazakia and Petchdee 2015) and the mediation or moderation effect of emotional intelligence between different aspect and well-being (Zhang et al. 2020; Nauman et al. 2019; Chen et al. 2016; Kang and Bae 2015; Li and Zheng 2014; Schutte and Malouff 2011). This paper analyzes the mediation effect of workers’ emotional intelligence between job team autonomy and the organizational support received by workers and their well-being, a relationship not studied so far in the IT industry in Spain. In this paper, we want to demonstrate how emotional intelligence, in addition to having a direct positive relationship with employee well-being, also has a partial mediating relationship between job autonomy and the organizational support perceived by employees and their well-being.
Hypothesis 2 (H2).
There is a mediating relationship of emotional intelligence between team autonomy and perceived organizational support and employee well-being.
Hypothesis 3 (H3).
There is a relationship between employees’ emotional intelligence and their well-being.

2.3. Workers’ Well-Being and Commitment to Work

Interest in the study of organizational and work commitment emerged in the 1960s (Cohen 2003). As time went by, new approaches to commitment appeared that were not so centered on organizational aspects, but were more focused on the job and the work group (Randall and Cote 1991; Morrow 1993).
The concept of work engagement can be defined as a state of well-being, of an affective or motivational nature, related to work and characterized by high vigor, motivation, and dedication (Bakker et al. 2008); it can also be seen as the opposite of burnout, with most studies agreeing that engaged employees tend to have high levels of energy and a strong identification with their work activity (Bakker et al. 2008). Several studies have found a significant relationship between work engagement and well-being or personal fulfilment (Schaufeli et al. 2006; Kanste 2011; Shimazu et al. 2015) as well as with job satisfaction and a decrease in employees’ intention to change (Schaufeli and Bakker 2003).
Although this relationship has been previously studied in articles in sectors such as social work, health, teaching, policing, or management jobs in countries such as Australia, Belgium, Canada, Finland, France, Germany, the Netherlands, Norway, South Africa, Spain, Denmark, Sweden, or Japan (Schaufeli et al. 2006; Kanste 2011; Shimazu et al. 2015), we will focus on analyzing the magnitude of this relationship in the field of IT industry in Spain, a sector in which we have not found studies of this nature.
Hypothesis 4 (H4).
There is a positive relationship between workers’ well-being and their work engagement.
Figure 1 shows the research model.

3. Materials and Methods

In order to carry out this study, we used a questionnaire composed of 32 pre-questions with a 5-point Likert scale ranging from ‘totally disagree’ to ‘totally agree’. The questionnaire was distributed electronically among 1000 professionals from the main Spanish companies in the IT consultancy sector during November 2020. We obtained a total of 458 responses, of which we were finally left with 281 correctly completed questionnaires.
The questions used in this study were validated in previous studies (Holton 1996; Eisenberger et al. 1997; Wong and Law 2002; Lee and Xia 2007; Radic et al. 2020) and adapted to the particularities of our study. Of the 32 questions in the questionnaire, 5 correspond to the socio-demographic characteristics of the respondents, and the other 27 are grouped into 7 constructs and used for statistical analysis: team autonomy and organizational support (Holton 1996; Eisenberger et al. 1997; Lee and Xia 2007), regulation of emotion (Wong and Law 2002), self-emotion appraisal (Wong and Law 2002), others’ emotion appraisal (Wong and Law 2002), use of emotion (Wong and Law 2002), well-being (Radic et al. 2020), and work engagement (Radic et al. 2020).
The research questionnaire is available in the Table A1, Appendix A.
For the data analysis, a quantitative model was used due to the nature of the research questions and for the goal to generate greater understanding of the relations of the analyzed variables (Edmondson and McManus 2007), which was validated by using the PLS-SEM (partial least squares structural equation modeling) method in the SmartPLS 3 software (Sarstedt et al. 2017) as well as the IBM SPSS tool, which was used for the analysis of the socio-demographic data.
Structural equation modeling (SEM), including PLS-SEM, is a second-generation technique used for multivariate data analysis. It is able to provide a high level of reliability to investigations due to its statistical efficiency, largely achieved through the use of specialized software, such as SmartPLS, VisualPLS, or the various packages available for R. This software allows the simultaneous examination of the relationships between several variables, both dependent and independent, and is widely used in research in different fields such as the social sciences (Haenlein and Kaplan 2004). There has been a significant increase in the use of SEM in recent years and it is increasingly used in a wide variety of disciplines, including strategic management (Shook et al. 2003).
Statistically, SEM methods are an evolution of linear modeling procedures and are used to assess whether the analyzed model is consistent with the collected data in order to validate the theory (Lei and Wu 2007). Although the most commonly used approach in SEM is CB_SEM, increasingly more researchers are using partial least squares (PLS-SEM) to analyze structural equation models (Hair et al. 2012). SEM is a multivariate analytical method used to estimate complex relationships between several variables, even when these relationships are not directly observable (Williams et al. 2009) and is valid for use in both confirmatory and exploratory research (Hair et al. 2014).
We decided to use the PLS-SEM method because it has fewer restrictions regarding sample size, residual distributions, and data normality (Roy 2008; Gefen and Straub 2005; Chin et al. 2003; Cassel et al. 1999), in addition to being a powerful multivariate analysis technique suitable for evaluating the causal relationships between the different variables involved and with a high predictive value (Hair et al. 2019a; Sharma et al. 2019). This method is currently considered the most advanced among variance-based systems for structural equation modeling and is applied in a wide range of disciplines and provides high reliability and predictive strength (Fornell and Bookstein 1982; Urbach and Ahlemann 2010).
On the other hand, PLS facilitates the possibility of simultaneously generating and studying both formative and reflective measurement models, which constitutes an important advantage over covariance-based SEM modeling tools (Reinartz et al. 2009; Chin 1998), in addition to a high statistical power for the analysis of complex models such as those used in this paper.

Why the IT Industry in Spain?

According to the data compiled by the Spanish Association of Consultancy Firms (2020) in its 2019 report, the IT consultancy sector obtained revenues of 14,517 million euros in 2019, 3225 million of which came from abroad; this is an increase of 5.9% compared to 2018, when revenues stood at 13,714 million euros. In the field of employment, the sector experienced an 8.9% increase in its workforce compared to the previous year, thus exceeding 202,000 workers, and was also a national benchmark for youth employment. It is important to highlight that more than 66% of the workers in this sector have university-level qualifications, a much higher percentage than the national average, with 74% of the graduates having specializations in STEM areas (science, technology, engineering, and mathematics). This sector, therefore, has great importance for both the economy and national employment, which is why it is necessary to determine which aspects are likely to enhance the well-being and commitment of workers, and thus, improve their professional performance.

4. Results

Our working model was empirically analyzed through the use of the PLS-SEM technique, as it is the most suitable for this type of study due to its high predictive capacity (Hair et al. 2019b; Sharma et al. 2019). This method is currently considered the most evolved among variance-based systems for SEM and is applied in a wide range of disciplines. The analysis was conducted using a two-step approach: evaluation of the measurement model and evaluation of the structural model.
Regarding the socio-demographic characteristics of the participant sample, the largest group of the professionals surveyed (32.74%) are between 30 and 39 years old, with the second-largest group being 40 to 49 years old (28.83%), while 22.06% of the professionals are aged 18 to 29 years old, 16.01% are aged 50 to 59 years old, and only 0.36% are more than 60 years old. In terms of gender, there are more men than women (58.01% compared to 41.99%, respectively). Regarding the level of education, 75.08% are educated up to university studies, 32.74% have post-graduate studies, and 22.78% have secondary studies, while only 2.14% have only primary studies. We can also observe that the vast majority of those surveyed, 87.19%, have a permanent contract, compared to 12.81% who have a temporary contract. In terms of length of service in their current company, the majority of those surveyed (54.09%) have been at their current company for less than 5 years, 19.93% have been in their current company for between 5 and 10 years, 11.74% for between 11 and 15 years, and only 14.23% for more than 15 years, which confirms our premise of the high turnover in the sector, as can be seen in Table 1.

4.1. Measurement Model

The PLS-SEM method using the SmartPLS software was selected due to the complexity of the model to be analyzed and the reliability and predictive power of this method (Fornell and Bookstein 1982; Urbach and Ahlemann 2010). The model was evaluated by validating different factors of the model; thus, we can observe that the significance level of the model is 95%.
The internal consistency of the model was assessed through Cronbach’s alpha together with determining the composite reliability of the constructs to be analyzed. The convergent validity of the model was assessed through the indicator reliability and the average variance extracted (AVE), while the discriminant validity of the model was assessed via the Fornell–Larcker criterion. Cross-loadings between the indicators and latent variables were also assessed. Finally, to validate the internal consistency of the model, we checked that all the variables reached an adequate Cronbach’s alpha value (Hair et al. 2013), as can be seen in Table 2.
It was verified that the composite reliability (CR) values are adequate, reaching values ranging from 0.890 to 0.938 (Nunnally and Bernstein 1994), as can be seen in Table 2.
Convergent validity, which is validated through average variance extracted (AVE), indicates that a set of variables represents a single construct (Henseler et al. 2009). This value must be equal to or greater than 0.50; as can be seen in Table 2, this value is met for the model evaluated.
Discriminant validity is used to determine to what extent a construct differs from others and is measured mainly through the Fornell–Larcker criterion, which considers the amount of variance that a construct captures from its indicators (AVE): this must be greater than the variance that the construct shares with other constructs (Barclay et al. 1995). Table 3 shows how the observed values for our model meet this condition.
Table 2 also shows the loading factors of the different variables together with their mean values and standard deviation. The loading factors of all the variables have acceptable values, which serves to confirm the validity of our model.

4.2. Structural Model

The structural equation model used in this study is shown in Figure 2. This model is used to simultaneously evaluate the relationships between the different constructs in the model.
For the proposed model, a second-order construct was created using the hierarchical component method. Based on this analysis, we can see how autonomy and organizational support at work have a direct positive influence on employee well-being (0.196) and how employee well-being has a positive influence on employee work engagement (0.637). We also observe that emotional intelligence has a direct positive influence on employee well-being (0.573).

4.3. Mediation Analysis

We refer to the existence of mediation when a variable, Emotional Intelligence in our model, is able to enhance or reduce the influence of a preceding variable, Team Autonomy and Organizational Support in our model, on a dependent variable or construct, Well-being in our model, in such a way that the magnitude of the relationship between the two is modified (Mathieu and Taylor 2006).
Although mediation can be of different types, when we consider only the impact on the magnitude of the previous relationship between X and Y, two types of mediating effect can be distinguished: total mediation, when the initial relationship between X and Y is no longer significant, and partial mediation, when the initial relationship between X and Y is reduced but remains significant (Baron and Kenny 1986).
When the inclusion of the mediating variable reduces the strength of the relationship between the independent variable and the dependent variable, but it is still significant, this is partial mediation (Henseler et al. 2009); partial mediation is considered to be complementary when the direction of the two variables points in the same direction (Sarstedt et al. 2017). In this case, the inclusion of the variable emotional intelligence between the variables autonomy and support at work and well-being reduced the strength of the direct relationship between these two variables from 0.366 to 0.196 (Figure 2 and Figure 3), while maintaining the same direction.
To calculate the magnitude of the indirect effect, we must consider the value of the variance accounted for (VAF) (Hair et al. 2014), which is used to determine the magnitude of the total indirect effect. In our model, a calculated VAF of 0.462 was obtained; therefore, we found partial mediation with a magnitude of 0.462.
To evaluate and validate the mediation model, we used the bootstrap-ping method (Hayes et al. 2011), which is a non-parametric procedure used for the analysis of mediation, both simple and multiple; it is valid for small sample sizes and is, therefore, suitable for the PLS-SEM method (Hair et al. 2014). Aspects assessed for both direct and indirect effects include the magnitude of these effects, their confidence interval, t-value, and significance based on the p-value (Sarstedt et al. 2017). These values can be seen in Table 4.
Based on the values obtained for the data analyzed, we can consider as proven the existence of a significant partial and complementary mediation between these variables in our model.

5. Discussion

According to the results of the research, we are able to state that independence and support at work have a direct positive influence on the well-being of employees (Hypothesis 1). Well-being has a direct positive impact on employees’ work engagement (Hypothesis 4), while emotional intelligence significantly mediates between team autonomy and organizational support and employee well-being (Hypothesis 2). Finally, we found that emotional intelligence has a positive impact on employee well-being (Hypothesis 3).
These results show how, in the IT consultancy sector in Spain, independence and job support are factors of great importance for employee well-being, a result in line with that observed in previous studies in different professional sectors (Eisenberger et al. 1986; Goodman et al. 1988; Sundstrom et al. 1990; Guzzo and Dickson 1996; Sonnentag 1996; Langfred 2000; Rhoades and Eisenberger 2002; Berthelsen et al. 2008).
This finding is in line with our expectations: workers who have autonomy and feel supported in their professional work will enjoy greater well-being at work. These data also serve to verify Hypothesis 1 of our study: the existence of a relationship between team autonomy and organizational support and employee well-being.
We also observe that well-being has a positive impact on employees’ work engagement, which is also in line with previous studies (Schaufeli et al. 2006; Kanste 2011; Shimazu et al. 2015). These data confirm Hypothesis 4: the existence of a positive relationship between employee well-being and employee work engagement. As in the case of the previous hypothesis, this finding is also to be expected: if workers feel good at work, they will be more committed to their professional activity.
Finally, there is also a positive relationship between emotional intelligence and employee well-being, which is in agreement with previous studies (Salovey et al. 1999; Austin et al. 2005; Bar-On 2005; Ciarrochi and Scott 2006). This finding is in line with our expectations for Hypothesis 3: the existence of a relationship between workers’ emotional intelligence and their well-being.
Although this last aspect was also expected, the inclusion of the mediating effect of emotional intelligence between autonomy and job support and employee well-being proves how this variable positively affects the relationship between the other two variables, which is also in line with previous studies (Gunsel and Acikgoz 2013; Yamazakia and Petchdee 2015) and serves to test our Hypothesis 2: the existence of positive mediation between team autonomy and organizational support and employee well-being.

6. Conclusions, Limitations, and Future Lines of Research

According to the data analyzed in this study, we can confirm that workers who have sufficient autonomy and perceive adequate support at work tend to have a higher degree of well-being and, therefore, greater commitment to their work. We have also seen how emotional intelligence, in addition to having a direct positive effect on the well-being of workers, acts as a mediating variable between autonomy and support at work and the well-being of employees, increasing the positive effect of this autonomy and support on well-being at work.
Although these aspects have previously been addressed independently in other areas, in this study, we demonstrate how, in the case of IT consultancy in Spain, it is also important to consider autonomy and support at work as a priority in the well-being and, ultimately, work engagement of employees, without losing focus on the importance of emotional intelligence on their well-being.
Based on this study, and considering the high turnover in the sector, which is around 25.6% a year in the information and communication technologies sector (Taudien 2019), and its strategic importance, both from an economic and job creation point of view, both the management of these companies and the different public administrations need to strengthen and encourage autonomy and employment support for workers.
While it is clear that both autonomy and support at work improve the well-being and work engagement of workers, it is important not only to implement and improve policies that impact these aspects, but also to care for and improve the emotional intelligence of workers to the greatest extent possible.
Although previous studies have analyzed separately the impact of the aspects studied in this paper, no studies appear to have analyzed the combined impact of these factors in the national IT industry. Therefore, we contend that this study can fill this gap and help organizations to implement appropriate policies to improve the well-being and work engagement of their employees.
On the other hand, there are increasingly more papers in which the impact of corporate social responsibility in association with human resource management and leadership style on different aspects, such as innovation, improvement of intellectual capital, competitiveness, job satisfaction, or performance, are analyzed (Santos-Jaén et al. 2021; Suto and Takehara 2022; Gallardo-Vázquez et al. 2019; Gimeno-Arias et al. 2021; Hajiali et al. 2022).
While this work has succeeded in testing the selected working hypotheses, it is limited only to the impact of autonomy and support at work, and the mediating influence of emotional intelligence on them. From a practical point of view, this study demonstrates that providing workers with sufficient autonomy and improving employee support helps organizations to improve the well-being and work engagement of their employees. Further research will explore the impact of other factors, such as workplace health and safety (Suárez-Albanchez et al. 2021), organizational support (Suárez-Albanchez et al. 2022), organizational commitment, job satisfaction or stress on workers’ well-being, and their intention to leave the company (Parasuraman 1982; Shore and Martin 1989; Liu and Onwuegbuzie 2012; Lu et al. 2016), thus achieving a broader analysis that includes different work factors and helps in gaining a broader understanding of the relationships between them.
This paper is limited only to the IT industry in Spain and was prepared during the COVID-19 pandemic, which may limit its results, as it is focused on a specific sector and was carried out during this unusual period of time.

Author Contributions

This paper is the result of teamwork. The study was conceived of and designed by the authors. J.S.-A. wrote the paper and integrating and organizing the results. P.J.-E. wrote the paper and integrating and organizing the results. J.J.B.-R. wrote the paper and integrating and organizing the results. S.G.-B. wrote the paper and integrating and organizing the results. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Social Research Ethics Committee (SREC) of the University of Castilla-La Mancha on 19 July 2022.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Research questionnaire.
Table A1. Research questionnaire.
ConstructItemQuestion
Demographic characteristicsA1Age
A2Gender
A3Level of education
A4Number of years participants have worked for the company
A5Terms of employment
Work engagementI10(SQ001)At my work, I feel full of energy
I10(SQ002)I am enthusiastic about my job
I10(SQ003)Time flies when I am working
Team autonomy/Organizational Support B10(SQ001)My organization really cares about my well-being
B10(SQ002)My organization strongly considers my goals and values
B10(SQ003)Help is available from my organization when I have a problem
C10(SQ001)My supervisor will meet with me regularly to work on problems I may be having in trying to use this training
C10(SQ002)My supervisor will meet with me to discuss ways to apply this training on the job
C10(SQ003)My supervisor will help me set realistic goals for job performance based on my training
G10(SQ001)The project team was allowed to freely choose tools and technologies
G10(SQ002)The project team had control over what they were supposed to accomplish
Emotional Intelligence—Self-emotion appraisal D10(SQ001)I have a good sense of why I have certain feelings most of the time
D10(SQ002)I have good understanding of my own emotions
D10(SQ003)I really understand what I feel
D10(SQ004)I always know whether or not I am happy
Emotional Intelligence—Others’ emotion appraisal D11(SQ001)I always know my friends’ emotions from their behavior
D11(SQ002)I am a good observer of others’ emotions
D11(SQ003)I am sensitive to the feelings and emotions of others
D11(SQ004)I have good understanding of the emotions of people around me
Emotional Intelligence—Use of emotion D12(SQ001)I always set goals for myself and then try my best to achieve them
D12(SQ002)I always tell myself I am a competent person
D12(SQ003)I am a self-motivated person
D12(SQ004)I would always encourage myself to try my best
Emotional Intelligence—Regulation of emotion D13(SQ001)I am able to control my temper and handle difficulties rationally
D13(SQ002)I am quite capable of controlling my own emotions
D13(SQ003)I can always calm down quickly when I am very angry
D13(SQ004)I have good control of my own emotions
Well-beingG11(SQ001)I have felt cheerful and in good spirits
G11(SQ002)I have felt calm and relaxed
G11(SQ003)I have felt active and vigorous
G11(SQ004)I woke up feeling fresh and rested
G11(SQ005)My daily life has been filled with things that interest me

References

  1. Abeykoon, A. M. R. K., and A. Perera. 2019. Impact of work life balance on employee job satisfaction of executive level employees. Management Issues 4: 55–65. [Google Scholar]
  2. Agius, Raymond. 2020. COVID-19 and health at work. Occupational Medicine 70: 349–51. [Google Scholar] [CrossRef] [PubMed]
  3. Asociación Española de empresas de consultoría. 2018. 2017, la consultoría española. El sector en cifras. Madrid: AEEC. [Google Scholar]
  4. Austin, Elizabeth J., Donald H. Saklofske, and Vincent Egan. 2005. Personality, well-being and health correlates of trait emotional intelligence. Personality and Individual Differences 38: 547–58. [Google Scholar] [CrossRef]
  5. Aydogdu, Sinem, and Baris Asikgil. 2011. An empirical study of the relationship among job satisfaction, organisational commitment and turnover intention. International Review of Management and Marketing 1: 43–53. [Google Scholar]
  6. Bakker, Arnold, Wilmar B. Schaufeli, Michael P. Leiter, and Toon W. Taris. 2008. Work engagement: An emerging concept in occupational health psychology. Work Stress 22: 187–200. [Google Scholar] [CrossRef]
  7. Barclay, Donald W., C. Higgins, and Ron Thompson. 1995. The partial least squares (PLS) approach to causal modelling: Personal computer adoption and use as illustration. Technology Studies 2: 285–309. [Google Scholar]
  8. Bar-On, Reuven. 2005. The impact of emotional intelligence on subjective well-being. Perspectives in Education 23: 41–62. Available online: https://hdl.handle.net/10520/EJC87316 (accessed on 30 April 2021).
  9. Baron, Reuben M., and David A. Kenny. 1986. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology 51: 1173–82. [Google Scholar] [CrossRef]
  10. Behson, Scott J. 2005. The relative contribution of formal and informal organizational work–family support. Journal of Vocational Behavior 66: 487–500. [Google Scholar] [CrossRef]
  11. Berthelsen, Hanne, Karin Hjalmers, and Björn Söderfeldt. 2008. Perceived social support in relation to work among Danish general dental practitioners in private practices. European Journal of Oral Sciences 116: 157–63. [Google Scholar] [CrossRef]
  12. Brough, Paula, and Rachael Frame. 2004. Predicting police job satisfaction and turnover intentions: The role of social support and police organisational variables. New Zealand Journal of Psychology 33: 8–16. [Google Scholar]
  13. Cassel, Claes, Peter Hackl, and Anders H. Westlund. 1999. Robustness of partial least-squares method for estimating latent variable quality structures. Journal of Applied Statistics 26: 435–46. [Google Scholar] [CrossRef]
  14. Chen, Yiwei, Yisheng Peng, and Ping Fang. 2016. Emotional intelligence mediates the relationship between age and subjective well-being. The International Journal of Aging and Human Development 83: 91–107. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Chin, Wynne W. 1998. The partial least squares approach to structural equation modeling. Modern Methods for Business Research 295: 295–336. [Google Scholar]
  16. Chin, Wynne W., Barbara L. Marcolin, and Peter R. Newsted. 2003. A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research 14: 189–217. [Google Scholar] [CrossRef] [Green Version]
  17. Ciarrochi, Joseph, and Greg Scott. 2006. The link between emotional competence and well-being: A longitudinal study. British Journal of Guidance and Counselling 34: 231–43. [Google Scholar] [CrossRef]
  18. Cohen, Aaron. 2003. Multiple Commitments in the Workplace: An Integrative Approach. Mahwah: Lawrence Erlbaum Associates. [Google Scholar]
  19. Coté, Stéphane, and Christopher T. H. Miners. 2006. Emotional intelligence, cognitive intelligence, and job performance. Administrative Science Quarterly 51: 1–28. [Google Scholar] [CrossRef]
  20. Das, Bidisha Lahkar, and Mukulesh Baruah. 2013. Employee retention: A review of literature. Journal of Business and Management 14: 8–16. [Google Scholar] [CrossRef]
  21. Di Fabio, Annamaria, and Maureen E. Kenny. 2016. Promoting well-being: The contribution of emotional intelligence. Frontiers in Psychology 7: 1182. [Google Scholar] [CrossRef] [Green Version]
  22. Edmondson, Amy C., and Stacy E. McManus. 2007. Methodological fit in management field research. Academy of Management Review 32: 1246–64. [Google Scholar] [CrossRef] [Green Version]
  23. Eisenberger, Robert, Robin Huntington, and Debora Sava. 1986. Perceived organizational support. Journal of Applied Psychology 71: 500–7. [Google Scholar] [CrossRef]
  24. Eisenberger, Robert, Jim Cummings, Stephen Armeli, and Patrick Lynch. 1997. Perceived organizational support, discretionary treatment, and job satisfaction. Journal of Applied Psychology 82: 812–20. [Google Scholar] [CrossRef] [PubMed]
  25. Fornell, Claes, and Fred L. Bookstein. 1982. Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research 19: 440–52. [Google Scholar] [CrossRef] [Green Version]
  26. Gallagher, Emma N., and Dianne A. Vella-Brodrick. 2008. Social support and emotional intelligence as predictors of subjective well-being. Personality and Individual Differences 44: 1551–61. [Google Scholar] [CrossRef]
  27. Gallardo-Vázquez, Dolores, Luis Enrique Valdez-Juárez, and José Luis Lizcano-Álvarez. 2019. Corporate social responsibility and intellectual capital: Sources of competitiveness and legitimacy in organizations’ management practices. Sustainability 11: 5843. [Google Scholar] [CrossRef] [Green Version]
  28. Garcia-Prieto, Sol Patricia, Véronique Tran, and Susan Schneider. 2007. The Role of Emotion in Strategic Issue Interpretation: The Case of Diversity. Working Papers, Centre Emile Bernheim. Brussels: ULB-Universite Libre de Bruxelles. [Google Scholar]
  29. Gefen, David, and Detmar Straub. 2005. A practical guide to factorial validity using PLS-Graph: Tutorial and annotated example. Communications of the Association for Information Systems 16: 5. [Google Scholar] [CrossRef] [Green Version]
  30. Gerwin, Donald, and Linda Moffat. 1997. Withdrawal of team autonomy during concurrent engineering. Management Science 43: 1275–87. [Google Scholar] [CrossRef]
  31. Gimeno-Arias, Fernando, José Manuel Santos-Jaén, Mercedes Palacios-Manzano, and Héctor Horacio Garza-Sánchez. 2021. Using PLS-SEM to analyze the effect of CSR on corporate performance: The mediating role of human resources management and customer satisfaction. An empirical study in the Spanish food and beverage manufacturing sector. Mathematics 9: 2973. [Google Scholar] [CrossRef]
  32. Godderis, Lode, and Jeroen Luyten. 2020. Challenges and opportunities for occupational health and safety after the COVID-19 lockdowns. Occupational and Environmental Medicine 77: 511–12. [Google Scholar] [CrossRef]
  33. Goodman, Paul S., Rukmini Devadas, and Terri L. Griffith Hughson. 1988. Groups and productivity: Analyzing the effectiveness of self-managing teams. In Productivity in Organizations. Edited by John P. Campbell and Richard J. Campbell. San Francisco: Jossey-Bass, pp. 295–325. [Google Scholar]
  34. Gunsel, Ayşe, and Atif Açikgöz. 2013. The effects of team flexibility and emotional intelligence on software development performance. Group Decision and Negotiation 22: 359–77. [Google Scholar] [CrossRef]
  35. Guzzo, Richard A., and Marcus W. Dickson. 1996. Teams in organizations: Recent research on performance and effectiveness. Annual Review of Psychology 47: 307–38. [Google Scholar] [CrossRef] [PubMed]
  36. Haenlein, Michael, and Andreas M. Kaplan. 2004. A beginner’s guide to partial least squares analysis. Understanding Statistics 3: 283–97. [Google Scholar] [CrossRef]
  37. Hair, Joe F., Marko Sarstedt, Christian M. Ringle, and Jeannette A. Mena. 2012. An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science 40: 414–33. [Google Scholar] [CrossRef]
  38. Hair, Joseph F., Christian M. Ringle, and Marko Sarstedt. 2013. Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning 46: 1–12. Available online: https://ssrn.com/abstract=2233795 (accessed on 30 April 2021). [CrossRef]
  39. Hair, Joseph F., G. Tomas M. Hult, Christian M. Ringle, and Marko Sarstedt. 2014. A Primer on Partial Least Square Structural Equation Modeling (PLS-SEM). Thousand Oaks: Sage. [Google Scholar]
  40. Hair, Joseph F., G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt, Julen Castillo Apraiz, Gabriel Cepeda Carrión, and Jose Luis Roldán. 2019a. Manual de Partial Least Squares Structural Equation Modeling (PLS-SEM). Terrassa: OmniaScience Scholar. [Google Scholar]
  41. Hair, Joseph F., Jeffrey J. Risher, Marko Sarstedt, and Christian M. Ringle. 2019b. When to use and how to report the results of PLS-SEM. European Business Review 31: 2–24. [Google Scholar] [CrossRef]
  42. Hajiali, Ismail, Andi Muhammad Fara Kessi, B. Budiandriani, Etik Prihatin, and Muhammad Mukhlis Sufri. 2022. Determination of Work Motivation, Leadership Style, Employee Competence on Job Satisfaction and Employee Performance. Golden Ratio of Human Resource Management 2: 57–69. [Google Scholar] [CrossRef]
  43. Hayes, Andrew F., Kristopher J. Preacher, and Teresa A. Myers. 2011. Mediation and the estimation of indirect effects in political communication research. In The Sourcebook for Political Communication Research: Methods, Measures and Analytical Techniques. Edited by Erik P. Bucy and R. Lance Holbert. New York: Routledge, pp. 434–65. [Google Scholar]
  44. Henseler, Jörg, Christian M. Ringle, and Rudolf R. Sinkovics. 2009. The use of partial least squares path modeling in international marketing. Advances in International Marketing 20: 277–320. [Google Scholar] [CrossRef] [Green Version]
  45. Holton, Elwood F., III. 1996. The flawed four-level evaluation model. Human Resource Development Quarterly 7: 5–21. [Google Scholar] [CrossRef]
  46. Huang, Jiatao, Hongbo Shi, and Wei Liu. 2018. Emotional intelligence and subjective well-being: Altruistic behavior as a mediator. Social Behavior and Personality: An International Journal 46: 749–58. [Google Scholar] [CrossRef]
  47. Hussain, T., and S. Asif. 2012. Is employees’ turnover intention driven by organizational committment and perceived organizational support? Journal of Quality and Technology Management 8: 1–10. [Google Scholar]
  48. Huy, Quy Nguyen. 1999. Emotional capability, emotional intelligence, and radical change. Academy of Management 24: 325–45. [Google Scholar] [CrossRef]
  49. Jeffery, J. Jeffery, Hannah M. Hollandsworth, Francesca J. Torriani, Randy Taplitz, Shira Abeles, Ming Tai-Seale, Marlene Millen, Brian J. Clay, and Christopher A. Longhurst. 2020. Rapid response to COVID-19: Health informatics support for outbreak management in an academic health system. Journal of the American Medical Informatics Association 27: 853–59. [Google Scholar] [CrossRef] [Green Version]
  50. Jonge, Jan, Hans Bosma, Richard Peter, and Johannes Siegrist. 2000. Job strain, effort-reward imbalance and employee well-being: A large-scale cross-sectional study. Social Science and Medicine 50: 1317–27. [Google Scholar] [CrossRef]
  51. Jordan, Peter J., and Ashlea C. Troth. 2004. Managing emotions during team problem solving: Emotional intelligence and conflict resolution. Human Performance 17: 195–218. [Google Scholar] [CrossRef]
  52. Kaminski, June. 2020. Informatics in the time of COVID-19. Canadian Journal of Nursing Informatics 15: 1. [Google Scholar]
  53. Kang, Su-Mi, and Sun Hyoung Ba. 2015. The mediating effect of emotional intelligence on the relationship between emotional labor and psychological well-being of clinical nurses. Journal of Muscle and Joint Health 22: 185–94. [Google Scholar] [CrossRef] [Green Version]
  54. Kanste, Outi. 2011. Work engagement, work commitment and their association with well-being in health care. Scandinavian Journal of Caring Sciences 25: 754–61. [Google Scholar] [CrossRef]
  55. Karasek, Robert. 1998. Demand/Control model: A social, emotional, and physiological approach to stress risk and active behavior development. In Encyclopedia of Occupational Health and Safety. Edited by Jeanne Mager Stellman, Michael McCann, Leon Warshaw and Carole Dufresne. Geneva: International Labor Office, vol. 34, pp. 6–14. [Google Scholar]
  56. Kompier, Michiel. 2003. Job design and well-being. In The Handbook of Work and Health Psychology. Edited by Marc Schabracq, Jacques Winnubst and Cary Cooper. Chichester: Wiley, pp. 429–54. [Google Scholar]
  57. Koydemir, Selda, and Astrid Schütz. 2012. Emotional intelligence predicts components of subjective well-being beyond personality: A two-country study using self- and informant reports. The Journal of Positive Psychology 7: 107–18. [Google Scholar] [CrossRef]
  58. Langfred, Claus W. 2000. The paradox of self-management: Individual and group autonomy in work groups. Journal of Organizational Behavior 21: 563–85. [Google Scholar] [CrossRef]
  59. Leach, Desmond, Toby D.Wall, Steven G. Rogelberg, and Paul R. Jackson. 2005. Team autonomy, performance, and member job strain: Uncovering the teamwork KSA link. Applied Psychology: An International Review 54: 1–24. [Google Scholar] [CrossRef]
  60. Lee, Gwanhoo, and Weidong Xia. 2007. Relationships between software team flexibility, autonomy, diversity, and project performance. In The 67th Academy of Management Best 3 Paper Proceedings. Philadelphia: OCIS First Runner-up Best Paper Award. [Google Scholar] [CrossRef]
  61. Lei, Pui-Wa, and Qiong Wu. 2007. Introduction to structural equation modeling: Issues and practical considerations. Educational Measurement: Issues and Practices 26: 33–43. [Google Scholar] [CrossRef]
  62. Li, Xu, and Xue Zheng. 2014. Adult attachment orientations and subjective well-being: Emotional intelligence and self-esteem as moderators. Social Behavior and Personality: An International Journal 42: 1257–65. [Google Scholar] [CrossRef]
  63. Lin, Dana T., Cara A. Liebert, Jennifer Tran, James N. Lau, and Arghavan Salles. 2016. Emotional intelligence as a predictor of resident well-being. Journal of the American College of Surgeons 223: 352–58. [Google Scholar] [CrossRef]
  64. Liu, Shujie, and Anthony J. Onwuegbuzie. 2012. Chinese teachers’ work stress and their turnover intention. International Journal of Educational Research 53: 160–70. [Google Scholar] [CrossRef]
  65. Lu, Luo. 1999. Work motivation, job stress and employees’ well-being. Journal of Applied Management Studies 8: 61–72. [Google Scholar]
  66. Lu, Lu, Allan Cheng Chieh Lu, Dogan Gursoy, and Nathan Robert Neale. 2016. Work engagement, job satisfaction, and turnover intentions: A comparison between supervisors and line-level employees. International Journal of Contemporary Hospitality Management 28: 737–61. [Google Scholar] [CrossRef]
  67. Mathieu, John E., and Scott R. Taylor. 2006. Clarifying conditions and decision points for mediational type inferences in organisational behavior. Journal of Organisational Behavior 27: 1031–56. [Google Scholar] [CrossRef]
  68. Mayer, John D., and Peter Salovey. 1995. Emotional intelligence and the construction and regulation of feelings. Applied and Preventive Psychology 4: 197–208. [Google Scholar] [CrossRef]
  69. Mayer, John D., Donna M. Perkins, David R. Caruso, and Peter Salovey. 2000. Emotional intelligence and giftedness. Roeper Review 23: 131–37. [Google Scholar] [CrossRef]
  70. Morrow, Paula C. 1993. The Theory and Measurement of Work Commitment. Greenwich: Jai Press Inc. [Google Scholar]
  71. Nauman, Shazia, Usman Raja, Inam Ul Haq, and Waqas Bilal. 2019. Job demand and employee well-being: A moderated mediation model of emotional intelligence and surface acting. Personnel Review 48: 1150–68. [Google Scholar] [CrossRef]
  72. Nunnally, Jim C., and Ira H. Bernstein. 1994. Psychometric Theory, 3rd ed. New York: McGraw-Hill. [Google Scholar]
  73. Panaccio, Alexandra, and Christian Vandenberghe. 2009. Perceived organizational support, organizational commitment and psychological well-being: A longitudinal study. Journal of Vocational Behavior 75: 224–36. [Google Scholar] [CrossRef]
  74. Parasuraman, Saroj. 1982. Predicting turnover intentions and turnover behavior: A multivariate analisis. Journal of Vocational Behavior 21: 111–21. [Google Scholar] [CrossRef]
  75. Radic, Aleksandar, Juan M. Arjona-Fuentes, Antonio Ariza-Montes, Heesup Han, and Rob Law. 2020. Job demands–job resources (JD-R) model, work engagement, and well-being of cruise ship employees. International Journal of Hospitality Management 88: 102518. [Google Scholar] [CrossRef]
  76. Randall, Donna M., and Joseph A. Cote. 1991. Interrelationships of work commitment constructs. Work and Occupation 18: 194–211. [Google Scholar] [CrossRef]
  77. Reinartz, Werner, Michael Haenlein, and Jörg Henseler. 2009. An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing 26: 332–44. [Google Scholar] [CrossRef] [Green Version]
  78. Rhoades, Linda, and Robert Eisenberger. 2002. Perceived organizational support: A review of the literature. Journal of Applied Psychology 87: 698–714. [Google Scholar] [CrossRef]
  79. Roy, Subhadip. 2008. Measuring formative constructs in management research: Definitions, distinctions and measurement. The Icfaian Journal of Management Research 7: 18–34. [Google Scholar]
  80. Sala, Fabio. 2005. Making the business case: Emotional intelligence competencies and important business outcomes. In Linking Emotional Intelligence and Performance at Work: Current Research Evidence. Edited by V. Druskat, F. Sala and G. Mount. San Francisco: Jossey-Bass. [Google Scholar]
  81. Salovey, Peter, Brian T. Bedell, Jerusha B. Detweiler, and John D. Mayer. 1999. Coping intelligently: Emotional intelligence and the coping process. In Coping: The Psychology of What Works. Edited by C. Snyder. New York: Oxford Psychology Press, pp. 141–64. [Google Scholar]
  82. Sánchez-Álvarez, Nicolás, Natalio Extremera, and Pablo Fernández-Berroca. 2016. The relation between emotional intelligence and subjective well-being: A meta-analytic investigation. The Journal of Positive Psychology 11: 276–85. [Google Scholar] [CrossRef]
  83. Santos-Jaén, José Manuel, Antonia Madrid-Guijarro, and Domingo García-Pérez-de-Lema. 2021. The impact of corporate social responsibility on innovation in small and medium-sized enterprises: The mediating role of debt terms and human capital. Corporate Social Responsibility and Environmental Management 28: 1200–15. [Google Scholar] [CrossRef]
  84. Sarstedt, Marko, Christian M. Ringle, and Joseph F. Hair. 2017. Partial Least Squares Structural Equation Modeling. In Handbook of Market Research. Kuala Lumpur: Pearson, pp. 1–40. [Google Scholar]
  85. Schaufeli, W., and A. Bakker. 2003. UWES—Work Engagement Scale. In Preliminary Manual. Utrecht: Utrecht University: Occupational Health Psychology Unit. [Google Scholar]
  86. Schaufeli, Wilmar B., Arnold B. Bakker, and Marisa Salanova. 2006. The measurement of work engagement with a short questionnaire: A cross-national study. Educational and Psychology Measurement 66: 701–16. [Google Scholar] [CrossRef]
  87. Schutte, Nicola S., and John M. Malouff. 2011. Emotional intelligence mediates the relationship between mindfulness and subjective well-being. Personality and Individual Differences 50: 1116–19. [Google Scholar] [CrossRef]
  88. Sethi, Rajesh. 2000. New product quality and product development teams. Journal of Marketing 64: 1–14. [Google Scholar] [CrossRef]
  89. Sharma, Pratyush Nidhi, Galit Shmueli, Marko Sarstedt, Nicholas Danks, and Soumya Ray. 2019. Prediction-oriented model selection in partial least squares path modeling. Decision Sciences 52: 567–607. [Google Scholar] [CrossRef]
  90. Shimazu, Akihito, Wilmar B. Schaufeli, Kimika Kamiyama, and Norito Kawakami. 2015. Workaholism vs. work engagement: The two different predictors of future well-being and performance. International Journal of Behavioral Medicine 22: 18–23. [Google Scholar] [CrossRef]
  91. Shook, Christopher L., David J. Ketchen Jr., Cynthia S. Cycyota, and Dilene Crockett. 2003. Data analytic trends in strategic management research. Strategic Management Journal 24: 1231–37. [Google Scholar] [CrossRef]
  92. Shore, Lynn McFarlane, and Harry J. Martin. 1989. Job satisfaction and organisational commitment in relation to work, performance and turnover intentions. Human Relations 42: 625–38. [Google Scholar] [CrossRef]
  93. Sonnentag, Sabine. 1996. Work group factors and individual well-being. In Handbook of Work Group Psychology. Edited by Michael A. West. New York: Wiley, pp. 345–67. [Google Scholar]
  94. Suárez-Albanchez, Julio, Juan Jose Blazquez-Resino, Santiago Gutierrez-Broncano, and Pedro Jimenez-Estevez. 2021. Occupational health and safety, organisational commitment, and turnover intention in the Spanish IT consultancy sector. International Journal of Environmental Research and Public Health 18: 5658. [Google Scholar] [CrossRef]
  95. Suárez-Albanchez, Julio, Santiago Gutierrez-Broncano, Pedro Jimenez-Estevez, and Juan Jose Blazquez-Resino. 2022. Organizational support and turnover intention in the Spanish IT consultancy sector: Role of organizational commitment. Cogent Social Sciences 8: 2051790. [Google Scholar] [CrossRef]
  96. Sundstrom, Eric, Kenneth P. De Meuse, and David Futrell. 1990. Work teams: Applications and effectiveness. American Psychologist 45: 120–33. [Google Scholar] [CrossRef]
  97. Suto, Megumi, and Hitoshi Takehara. 2022. Employee-oriented corporate social responsibility, innovation, and firm value. Corporate Social Responsibility and Environmental Management 29: 765–78. [Google Scholar] [CrossRef]
  98. Taudien, S. 2019. La rotación laboral en España: Causas, datos e inconvenientes para las empresas. RRHHDigital. Available online: http://www.rrhhdigital.com/secciones/mercado-laboral/135288/La-rotacion-laboral-en-Espana-causas-datos-e-inconvenientes-para-las-empresas?target=_self (accessed on 30 April 2021).
  99. Timms, Carolyn, Paula Brough, Michael O’Driscoll, Thomas Kalliath, Oi Ling Siu, Cindy Sit, and Danny Lo. 2015. Flexible work arrangements, work engagement, turnover intentions and psychological health. Asia Pacific Journal of Human Resources 53: 83–103. [Google Scholar] [CrossRef]
  100. Urbach, Nils, and Frederik Ahlemann. 2010. Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application 11: 5–40. Available online: https://aisel.aisnet.org/jitta/vol11/iss2/2 (accessed on 30 April 2021).
  101. Van Mierlo, Heleen, C. Rutte, J. Vermunt, M. Kompier, and J. Doorewaard. 2006. Individual autonomy in work teams: The role of team autonomy, self-efficacy, and social support. European Journal of Work and Organizational Psychology 15: 281–99. [Google Scholar] [CrossRef]
  102. Van Mierlo, Heleen, C. Rutte, J. Vermunt, J. Kompier, and A. Doorewaard. 2007. A multi-level mediation model of the relationships between team autonomy, individual task design and psychological well-being. Journal of Occupational and Organizational Psychology 80: 647–64. [Google Scholar] [CrossRef]
  103. Vergara, Ana I., Natalia Alonso-Alberca, César San-Juan, Joaquín Aldás, and Laura Vozmediano. 2015. Be water: Direct and indirect relations between perceived emotional intelligence and subjective well-being. Australian Journal of Psychology 67: 47–54. [Google Scholar] [CrossRef]
  104. Williams, Larry J., Robert J. Vandenberg, and Jeffrey R. Edwards. 2009. Structural equation modeling in management research: A guide for improved analysis. Academy of Management Annals 3: 543–604. [Google Scholar] [CrossRef]
  105. Wong, Chi-Sum, and Kenneth S. Law. 2002. The effects of leader and follower emotional intelligence on performance and attitude: An exploratory study. The Leadership Quarterly 13: 243–74. [Google Scholar] [CrossRef] [Green Version]
  106. Yamazakia, Yoshitaka, and Sorasit Petchdee. 2015. Turnover intention, organizational commitment, and specific job satisfaction among production employees in Thailand. Journal of Business and Management 4: 22–38. [Google Scholar] [CrossRef] [Green Version]
  107. Ye, Jiancheng. 2020. The role of health technology and informatics in a global public health emergency: Practices and implications from the COVID-19 pandemic. JMIR Medical Informatics 8: e19866. [Google Scholar] [CrossRef]
  108. Zeidner, Moshe, and Dorit Olnick-Shemesh. 2010. Emotional intelligence and subjective well-being revisited. Personality and Individual Differences 48: 431–35. [Google Scholar] [CrossRef]
  109. Zhang, Hui-Hua, Rui Li, and Nicola S. Schutte. 2020. Friendship network mechanisms linking emotional intelligence and subjective well-being: Beyond a mediation model. The Journal of Social Psychology 160: 428–44. [Google Scholar] [CrossRef] [PubMed]
Admsci 12 00085 g001 550
Figure 1. Research model.
Figure 1. Research model.
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Figure 2. Structural equation model.
Figure 2. Structural equation model.
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Figure 3. Structural equation model without mediation.
Figure 3. Structural equation model without mediation.
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Table
Table 1. Demographic variables.
Table 1. Demographic variables.
Age
18–2922.06%
30–3932.74%
40–4928.83%
50–5916.01%
More than 600.36%
Gender
Men58.01%
Women41.99%
Education level
Primary studies2.14%
Secondary studies22.78%
University studies42.34%
Post-graduate studies32.74%
Terms of employment
Permanent contract87.19%
Temporary contract12.81%
Time of current company
Less than 5 years54.09%
Between 5 and 10 years19.93%
Between 11 and 15 years 11.74%
More than 15 years14.23%
Table
Table 2. Factor loadings, means, standard deviations, reliabilities, and average variance extracted.
Table 2. Factor loadings, means, standard deviations, reliabilities, and average variance extracted.
ConstructItemMeanSDFactor LoadingCronbach’s AlphaCRAVE
Team autonomy/Organizational SupportB10(SQ001)3.5341.1830.7740.9080.9260.612
B10(SQ002)3.3421.2210.801
B10(SQ003)3.5481.2210.766
C10(SQ001)3.2281.3380.827
C10(SQ002)3.0391.2940.867
C10(SQ003)3.0361.3220.866
G10(SQ001)3.1641.1960.631
G10(SQ002)3.4631.1050.696
Self-emotion appraisalD10(SQ001)4.1280.8690.8260.8570.9050.705
D10(SQ002)4.0960.8990.922
D10(SQ003)4.0890.9390.886
D10(SQ004)4.0960.9530.709
Others’ emotion appraisalD11(SQ001)3.6370.8640.8310.840.8940.679
D11(SQ002)3.9400.9140.876
D11(SQ003)4.0570.8930.714
D11(SQ004)3.7860.8180.864
Use of emotionD12(SQ001)4.0500.8050.7580.8350.890.669
D12(SQ002)3.9110.9200.790
D12(SQ003)4.0460.9570.870
D12(SQ004)4.1810.8010.849
Regulation of emotionD13(SQ001)3.8220.9470.8680.9110.9380.791
D13(SQ002)3.8080.9060.927
D13(SQ003)3.4521.0720.837
D13(SQ004)3.7580.8980.922
Well-beingG11(SQ001)4.0820.7910.8210.8250.8760.587
G11(SQ002)3.8010.9040.764
G11(SQ003)3.8330.8350.740
G11(SQ004)3.2631.0080.720
G11(SQ005)3.7121.0030.783
Work engagementI10(SQ001)3.6330.9050.9090.8680.9190.790
I10(SQ002)3.8330.9360.916
I10(SQ003)3.8901.0340.840
Table
Table 3. Discriminant validity—Fornell–Larcker criterion.
Table 3. Discriminant validity—Fornell–Larcker criterion.
Others’ Emotion AppraisalRegulation of EmotionSelf-Emotion AppraisalTeam Autonomy/Organizational Support Use of EmotionWell-BeingWork Engagement
Others’ emotion appraisal0.824
Regulation of emotion0.3590.889
Self-emotion appraisal0.3490.4180.840
Team autonomy/Organizational support 0.2190.2740.1960.782
Use of emotion0.3380.3800.3880.1560.818
Well-being0.3770.4920.4540.3650.5030.766
Work engagement0.3060.3700.3710.4930.3790.6370.889
Table
Table 4. Total, direct, and indirect effects.
Table 4. Total, direct, and indirect effects.
Direct Effect95% Confidence Interval for Direct Effectt ValueVAFf2Significance (p < 0.05)
Team autonomy and work support → Well-being0.196|0.093, 0.286|4.000 0.062Si
Indirect Effect95% Confidence Interval for Indirect Effectt Value Significance (p < 0.05)
Team autonomy and work support → Emotional Intelligence → Well-being0.168 |0.099, 0.234|4.9050.4620.528Si
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Suárez-Albanchez, J.; Jimenez-Estevez, P.; Blazquez-Resino, J.J.; Gutierrez-Broncano, S. Team Autonomy and Organizational Support, Well-Being, and Work Engagement in the Spain Computer Consultancy Industry: The Mediating Effect of Emotional Intelligence. Adm. Sci. 2022, 12, 85. https://doi.org/10.3390/admsci12030085

AMA Style

Suárez-Albanchez J, Jimenez-Estevez P, Blazquez-Resino JJ, Gutierrez-Broncano S. Team Autonomy and Organizational Support, Well-Being, and Work Engagement in the Spain Computer Consultancy Industry: The Mediating Effect of Emotional Intelligence. Administrative Sciences. 2022; 12(3):85. https://doi.org/10.3390/admsci12030085

Chicago/Turabian Style

Suárez-Albanchez, Julio, Pedro Jimenez-Estevez, Juan Jose Blazquez-Resino, and Santiago Gutierrez-Broncano. 2022. "Team Autonomy and Organizational Support, Well-Being, and Work Engagement in the Spain Computer Consultancy Industry: The Mediating Effect of Emotional Intelligence" Administrative Sciences 12, no. 3: 85. https://doi.org/10.3390/admsci12030085

APA Style

Suárez-Albanchez, J., Jimenez-Estevez, P., Blazquez-Resino, J. J., & Gutierrez-Broncano, S. (2022). Team Autonomy and Organizational Support, Well-Being, and Work Engagement in the Spain Computer Consultancy Industry: The Mediating Effect of Emotional Intelligence. Administrative Sciences, 12(3), 85. https://doi.org/10.3390/admsci12030085

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