Autonomy at work as a predictor of hardy personality and motivation in sport facilitators

Autonomía laboral como predictor de personalidad resistente y motivación en dinamizadores deportivos

Introduction

The importance of autonomy at work

One of the most frequently studied topics in the field of organisations is organisational behaviour (Sarkar, 2013). In fact, Iglesias and Torres (2018) point out that organisational climate is one of the most significant factors for intervening in innovation processes or those related to change management.

Authors such as Castillo and Lengua (2011) define organisational climate as the way in which workers perceive the surrounding environment of the organisations where they work. In more detail, García and Duque (2013) indicate that it is "the perception and appreciation of workers in relation to structural aspects (process and procedures), relationships between people and the physical environment (infrastructure and work elements)" (p.13)

One of the reasons of its study is its tangible effect on employee motivation, leading to increased productivity (Segredo & Castello, 2019) or sense of belonging (Permarupan et al., 2013) or on employee satisfaction and motivation (Dinu, 2013).

With regard to the measurement instruments identified in the literature for assessing organisational climate, it should be pointed out that they are very varied and are characterised by multiple and not always structured factors.

In the absence of a specific instrument, this paper is based on the organisational climate model proposed by Patlán & Flores (2013), which is composed of three levels: individual (satisfaction and autonomy), interpersonal (social relations and social support) and organisational (managerial consideration, incentives, motivation-effort and managerial leadership). The reason for its choice is that it is a scale that has been validated using natural semantic networks, reduced and that in its validation process was applied to the health sector with a broad group of professional profiles; which, in the absence of a specific instrument, is suitable for the purposes of this work.

Of the factors that make up the instrument, the autonomy factor is selected at the individual level, and at the organisational level, motivation and effort.

These same authors (Patlán & Flores, 2013) define the autonomy variable as the "degree to which workers perceive that they feel independent in their work and have the freedom to choose and decide the most convenient way to carry out their activities at work" (p.134) Authors such as Faya et al. (2018) demonstrated that when autonomy is granted at work, the level of employee satisfaction is high. In this regard, the study conducted by the European Foundation for the Improvement of Living and Working Conditions (2007) found that perceived personal autonomy is often the most significant positive predictor of job satisfaction (concretised in four aspects: engagement, participation, performance and motivation in the workplace). Similarly, work such as that of Puigarnau et al. (2016) found in participants of a physical activity programme based on autonomy that the motivating and autonomous style of the professional was a powerful facilitator for the achievement of objectives and greater commitment in the supervised group and not so much in the free practice group. Similarly, Moreno et al. (2020), in a study with adolescents in Physical Education classes, verified the importance of teaching styles in predicting the autonomy and key competences of students. More specifically, authors such as Leo et al. (2020) found that only autonomous motivation is associated with student motivation and Manzano and Jiménez (2021) found that students with a high autonomy profile also had higher scores on adaptive psychological variables such as: competence, social relationship, teaching climate and enjoyment.

The fact is that the approach to autonomy as well as other decisions in the field of sport is related to the previous planning process. Thus, in a study with football coaches, a relationship has been found between democratic styles - flexible planning and authoritarian styles - rigid planning (Feu et al., 2023).

Motivation and effort in workers

As noted above, the present work incorporates a second variable which is the motivation and effort factor defined by Patlán & Flores (2013) as the "degree to which workers are encouraged by the organisation and the conditions that make workers work hard. These aspects are reflected in workers feeling responsible for doing their work, feeling committed to their work and caring about the quality of their activities, so they seek to make an effort in their work" (p.134). In fact, this last variable would be very close to the concept of engagement (Bakker & Leiter, 2010), whose meaning would be that of a positive motivational state related to work and characterised by a sense of personal fulfilment projected in its three components: vigour, dedication and absorption.

Rodríguez and Rosquete (2018) point out that motivation is one of the factors that directly influences students' academic performance. In this sense, satisfaction, boredom or fun can be predictors of abandonment of the practice of physical activity or of the educational centre. As León (2017) points out human talent management has a positive, significant and moderate relationship with work motivation.

Some authors point out that increased autonomy is related to higher motivation (Batista et al., 2022; Pérez et al., 2019). A positive correlation of students' perceived autonomy support with their engagement in the classroom has also been found (De Meyer et al., 2016; Yew & Wang, 2016).

As can be seen, most of the works consulted that deal with the autonomy-motivation binomial do so mainly from the point of view of the students, but not so much from the point of view of the teaching professionals. Nor is it often asked whether the autonomy-motivation binomial, which is key to the management of the work climate, can be influenced by the hardy personality of the employees. In other words, whether the autonomy-motivation-effort relationship develops equally in the different factors that make up the hardy personality of employees.

The hardy personality at work

Therefore, the third variable under study, the hardy personality, is presented below. Occupational resilience or hardy personality is defined as an individual resource that regulates the effects of stressful events affecting health (Kobasa, 1979). Its importance lies, among other reasons, in the fact that it mediates the perceived threat of stressful events (Eschleman et al., 2010). In this sense, Garrosa and Carmona (2010), point out that a high hardy personality is related to a greater optimistic perception of events and disposition to coping strategies. In fact, according to Oliver (1993), hardy personality explains 33% of the variance of burnout and also significantly predicts burnout with a negative sign, but engagement with a positive sign (Arias & Vizoso, 2018). Moreover, it also contributes to predicting life satisfaction (Gutiérrez et al., 2019).

Other authors such as Peñacoba & Moreno (1998) point out that the hardy personality should be understood from a globalising approach where, although it presents three partially independent factors (commitment, control and defiance), it is necessary to understand them in relation to each other. Among these factors, they point to the control dimension, the most analytical, characterised by the conviction of being able to influence the course of events through thinking and acting on events. Thus, the interpretation of events would make it possible to perceive possible predictable consequences and therefore to be able to manage stimuli from the reference of one's own actions.

On the other hand, there is the commitment factor, a quality related to personal competence, self-esteem or community feeling, for coping with stressful situations from this component of commitment.

In this sense, it is worth noting the importance of social support in the work environment, where authors such as Ganellen and Blaney (1984) found relationships between the factors commitment and challenge with social support but not with the control dimension.

Maury et al. (2014) found a positive and moderate relationship between occupational endurance and engagement. In this sense, studies such as Moreno Jiménez et al. (2012) with a sample of nurses showed that engagement is the only variable that has significant effects on burnout and vigour.

And finally, the challenge factor, shaped by the belief that change is the usual feature of life. From this perspective, coping with the stressful situation would be done through a tolerance of ambiguity and as an opportunity for personal growth. Therefore, the positive perception of change and the search for resources for the effective coping of events could be highlighted in this dimension; emotional self-control is not so necessary.

Authors such as Florian et al. (1995) pointed out that the correlations between the control and commitment dimensions are higher than those between control and challenge, although the three components are related. Other studies such as Moreno et al. (2006), with a sample of firefighters, found that the challenge dimension acts on organisational stressors and burnout, while the commitment factor is the one that modulates the associated symptomatology. Other studies consider that the control and commitment components, or only the control component, are the ones that really make up the concept (Florian et al., 1995).

The aim of the present study is to explore the autonomy-motivation binomial in greater depth, including a third variable that could mediate between the two, the hardy personality. Thus, the aim is to find out the predictive capacity of the variables autonomy and hardy personality components on motivation-effort in a sample of sports facilitators, who are professionals who carry out their work in sports education in most of the towns with less tan 20,000 inhabitants in the Extremadura region and for the population in general.

Method

Design

This research followed an associative strategy with a predictive, cross-sectional study (Ato et al., 2013).

Structural equation modelling was used to test several hypotheses at the same time (Ruiz et al., 2010).

Participantes

The sample consisted of 112 sports promoters, 24.1% of whom were women and 75.9% men; the majority of sports promoters were aged between 41-50 years (52.7%), followed by those aged between 31 and 40 years (32.3%) and those aged between 20 and 30 years (12.5%). 42.3% have more than 15 years of professional experience, and 22.3% have between one and five years of experience.

These workers have a contractual relationship with associations of municipalities and are part of a regional programme subsidised by the regional government of Extremadura and provincial councils.

The sample was accessed through an anonymous questionnaire using Google Forms that was distributed by the Directorate General of Sports of the Regional Government of Extremadura.

Instruments

The Occupational Resilience Questionnaire (Moreno et al., 2014) was used to measure hardy personality in its three dimensions: Commitment, Control and Challenge; through a series of statements about various situations on a four-option Likert-type scale (1 = Completely disagree to 4 = Completely agree).

To measure autonomy and motivation, two factors from the Multidimensional Organisational Climate Scale (Patlán & Flores, 2013) were used: Autonomy at work (three items) and Motivation and effort (three items). These items were answered on a five-choice Likert-type scale (1 = never, 2 = almost never, 3 = sometimes, 4 = almost always and 5 = always).

Statistical analysis

Descriptive data were calculated for the variables used, and a correlational analysis was carried out using Spearman's correlation coefficient for the factors that did not have a normal distribution. The reliability of the questionnaires was also calculated using Crombach's alpha, with factors >.70 being considered adequate (Nunnally & Bernstein, 1994).

Inferential analysis was carried out through nonparametric tests of the variables on personal resilience, autonomy and motivation at work according to the variables gender, age and professional experience.

A structural equation model (SEM) was then applied where the initial hypotheses outlined in Figure 1 were tested. The maximum likelihood method was used, as it is suitable for a sample size between 100 and 150 participants, as is the case, (Hair et al. 2004). Several goodness-of-fit indices were studied to accept or reject a model (Hu & Bentler, 1999). These goodness-of-fit statistics are: the Chi-square Ratio / degrees of freedom (X²/gl), where values below 5 are acceptable and below 2 are excellent indicators (Hu & Bentler, 1999). The fit indices CFI (Comparative fit index), IFI (Incremental fit index); TLI (TuckerLewis coefficient) IFI; where values ≥.90 are adequate and values ≥.95 are excellent. The Square Root of the Residual (RMR) and Standardised Square Root of the Residual (SRM) were also analysed, where values <.05 are adequate and between .05 and .08 are considered reasonable (Ruiz et al., 2010). IBM SPSS Statistics 25 and AMOS 21 software applications were used.

Results

Table 1 provides a descriptive and correlational analysis of the study factors. The mean values of the variables on hardy personality or work resilience indicate that the sports facilitators present optimal values. Before analysing the hypothesised model, a CFA of the five-factor scale (control, commitment, challenge, autonomy and, motivation and effort), with three items from each factor, was performed to analyse its factor structure. The CFA showed an adequate fit CMIN/DF = 1.31; CFI = 0.96; IFI = 0.96; TLI = 0.95; SRMR = 0.065; RMSEA = 0.053; PClose = 0.42. The scales showed adequate reliability (Nunnally & Bernstein, 1994), Table 1. Similarly, the mean values indicate a high hardy personality, with the variable commitment scoring the highest (M = 3.66±.33). As for the organisational climate variables, the variable that scored highest was motivation and effort (M = 4.33±.54). Sports facilitators indicated high autonomy (M = 3,40±,57).

Spearman correlations showed that commitment is correlated with challenge (rs = ,501; p<,01) and control (rs = ,557; p<,01). Commitment is the variable most highly correlated with effort motivation (rs = ,345; p<.01). No significant differences were found in the factors according to gender, age and experience (p>.05).

A hypothetical model was established, Figure 1, in which autonomy predicted resilience at work (with different strength in the dimensions: control, commitment and challenge). On the other hand, it is commitment that predicts motivation and work effort. The approach has been tested through a structural equation model. This model consisted of 39 variables: 15 observed variables or indicators, and 24 unobserved variables. Three items per factor were used; Challenge (items 13, 5 and 11 of the OHQ scale), Control (items 6, 9 and 15), Commitment (items 1, 7 and 4). Regarding the Autonomy of the EMCO scale, items 5, 6 and 7 (5. Workers are free to choose how to do their activities, 6. Each worker is free regarding how to solve the problems related to their activities and 7.) Finally, regarding the motivation and effort factor of the EMCO scale, items 24, 25 and 26 (24. Each worker performs his activities with enthusiasm, 25).

The skewness and kurtosis data indicate that one item showed values greater than |1.96| and that the multivariate kurtosis was high (km = 23.43; C.R.= 5.49) (Byrne, 2010). Under these conditions, the bootstrapping technique was implemented which allowed us to check that the estimation results were consistent and not affected by non-normality (Byrne, 2010). The model fit values indicate that the model is adequate (CMIN/DF = 1.488; CFI = 0.93; IF I = 0.94; TLI = 0.92; SRMR = 0.099; RMSEA = 0.066; PClose = 0.139). All the saturations of the latent indicators obtained in the model are between ,54 and .91.

Autonomy correlates significantly with control (ß = .49) and challenge (ß = .32), but not directly with commitment (ß = .04), with a high coefficient of determination (R2 = .54), predicting 54% commitment. Commitment is highly predictive of motivation and effort (ß = .69). The predictive capacity of the model jointly for the motivation variable is expressed by the coefficient of determination, (R2 = .24), where autonomy and the variables challenge, control and commitment explained 24% of motivation and effort.

Discussion

The aim of this study was to determine the predictive capacity of the variables autonomy and hardy personality in the motivation-effort of sports facilitators.

Firstly, it should be noted that the simple consulted showed a very high level of motivation and effort in their jobs.

The autonomy shown by the workers in the Sports Dynamisation Programme was high, with a high hardy personality, where the highest score was observed in the commitment variable.

It is not surprising to find that commitment is the variable most highly correlated with motivation and effort, in line with the work of Ganellen and Blaney (1984) who showed relationships between the factors commitment and challenge and social support.

In the same way that these results would be in line with Manzano and Jiménez (2021) that students with a high autonomy profile also had higher scores on adaptive psychological variables including social relatedness; a factor more closely related to the hardy personality commitment variable.

The above model indicates that autonomy at work has a relevant influence on the hardy personality and, through it, also on the motivation and effort of the workers consulted.

A more precise reading of the values presented shows, firstly, that the predictive power of autonomy on the three dimensions of the hardy personality is unequal. On the one hand, and directly, autonomy is a strong predictor of the dimensions of control, followed by defiance, but not of commitment, which is very low.

Authors such as Peñacoba and Moreno (1998) pointed out that autonomy and intentionality are qualities of the control dimension, within the resilient personality, as this emphasises understanding the why of things and therefore the analytical function.

It is striking that autonomy exerts its predictive power directly on the dimensions of control and challenge, and indirectly predicts the dimension of commitment, explaining 54% of the variance.

Therefore, it could be indicated that autonomy exerts its most direct predictive action on the analytical and executive functions of the hardy personality. In fact, the very definition of autonomy by Patlán and Flores (2013) already emphasised this executive aspect, pointing out choice and decision as the qualities highlighted in it.

On the other hand, it is also relevant that it is commitment that exerts the highest predictive power (ß = .69) on the motivation and effort of the workers consulted. In this sense, a possible explanation could be found in the proximity of motivation to the community and the social bond, areas where the commitment dimension seems to be the most relevant dimension of the hardy personality. In this sense, we should recall the work of Ganellen and Blaney (1984) where no relationships were found with social support and the control dimension, but they did find relationships with the commitment and challenge factors. Similarly, in the study by Moreno et al. (2006), the commitment factor is the one that most modulates the symptoms associated with burnout. In addition, the work of Moreno et al. (2012) showed that the engagement factor is the only one with significant effects on vigour and burnout.

Similarly, it is not negligible that the model composed of autonomy, control and commitment explains 24% of the variance of motivation and effort. In this respect, we should recall the study by Florian et al. (1995), which found that the correlations between the control and commitment dimensions are greater than those between control and challenge, although the three components are related. We need only recall the results of the work carried out by the European Foundation for the Improvement of Living and Working Conditions (2007), where perceived personal autonomy is often the most significant positive predictor of job satisfaction, including performance, motivation, participation and commitment.

However, it is commitment, as a highly interpersonal quality, which seeks to give meaning to what is done, and perhaps this more social effect is what makes it the most predictive dimension of motivation and effort. This would be in line with the work of Pérez et al. (2019), whose review indicates that increased autonomy is related to greater student motivation, as well as various studies (De Meyer et al., 2016; Yew & Wang, 2016), which indicate a positive correlation between students' perception of support for autonomy and their engagement in the classroom.

Conclusions

The main conclusion drawn from the present work is that autonomy is an important factor in the organisational climate for the hardy personality and the motivation and effort of workers.

Thus, the results indicate that autonomy does not influence the dimensions of hardy personality in the same way, with a significant direct influence on the dimensions control and challenge (as analytical and executive dimensions) and a non-significant influence on commitment (more social component).

On the other hand, the relevant predictive power of the model composed of the autonomy, commitment through control and challenge dimensions of the hardy personality is noteworthy.

Likewise the important predictive influence on motivation and effort that autonomy through resilient personality control and engagement has on motivation and effort.

And finally, that the most social component of the hardy personality (commitment) is the strongest predictor of motivation and effort.

Practical applications

Taking into account the important role of people management in the field of sport, this study shows whether autonomy in the workplace, used as an incentive or professional growth, would be appropriate for all hardy personality profiles in a professional profile such as sports promoters.

The results indicate that autonomy does not have the same relationship with the different components of the hardy personality of the sports facilitators surveyed. This indicates that the development of autonomy predicts more directly and more strongly the hardy personality profiles with higher scores in the control and challenge components, i.e. with those workers with predominance in the perception of the work context who present more analytical approaches and with confidence to influence events; as well as those who perceive stressful situations with a higher level of tolerance to ambiguity and as personal growth.

However, if the focus is on motivation or effort, autonomy has the highest predictive capacity with the commitment factor (key in the motivational bond).

In sum, this work highlights that autonomy seems to connect and would be more appropriate for hardy personality profiles with more rational and empowered qualities in terms of their confidence to influence and manage environmental stress; whereas it would be less appropriate to connect with motivation, which connects more with interpersonal qualities.

And finally, that the most social component of the hardy personality (commitment) is the strongest predictor of motivation and effort.

References

Author notes

* Correspondence: Sebastián Feu Molina, sfeu@unex.es

Additional information

How to cite this article: Blázquez-Manzano, A., Feu Molina, S., & Gutiérrez-Caballero, J. M. (2023). Autonomy at work as a predictor of hardy personality and motivation in sport facilitators. Cultura, Ciencia y Deporte, 18(56), 157-172. https://doi.org/10.12800/ccd.v18i56.1975

Cultura, Ciencia y Deporte
ISSN: 1696-5043
Vol. 18
Num. 56
Año. 2023

Autonomy at work as a predictor of hardy personality and motivation in sport facilitators

AlbertoSebastiánJuana María Blázquez ManzanoFeu MolinaGutiérrez Caballero
Junta de ExtremaduraUniversidad de ExtremaduraUniversidad Isabel I,EspañaEspañaEspaña