Autonomy and Performance: Proactive Vitality Management and Work Engagement as Sequential Mediators of the Relationship

Abstract

A current trend in organizational job design is to provide employees increased autonomy to enhance their performance. Using the Job Demands-Resources (JD-R) theory as a working framework, the present study proposes proactive vitality management and work engagement as sequential mediators of this relationship. Thus, we test a parsimonious model that encompasses both individual strategies (i.e., proactive vitality management) and affective-cognitive (i.e., work engagement) factors as explanatory mechanisms in the link between autonomy and performance. Data from 256 Romanian employees were gathered and analyzed via structural equation modeling. The results provided support to our model. Specifically, we found proactive vitality management and work engagement to fully mediate the relationship between autonomy and performance. As such, our model validates the theoretical assumption of the JD-R theory that employees who engage in individual strategies (i.e., proactive vitality management) can capitalize on existing job resources (i.e., autonomy) to increase their well-being (i.e., work engagement) and performance. Furthermore, by identifying proactive vitality management as a mediator in the relationship between autonomy and performance, we provide practitioners with a set of proactive behaviors that can complement resource-replenishing activities (e.g., coffee break) in securing and sustaining high energy levels at work.

Keywords

Autonomy proactive vitality management work engagement performance

Autonomy and Performance: Proactive Vitality Management and Work Engagement as Sequential Mediators of the Relationship

Organizations nowadays tend to reduce direct control over employees and encourage more autonomous planning and decision-making (Kubicek et al., 2017). Existing evidence suggests that such changes in organizational job design are beneficial because they enhance employees’ work-related well-being and performance (Kubicek et al., 2017; Wegman et al., 2018). This approach is warranted by existing empirical evidence stemming from two distinct theoretical perspectives (Muecke & Iseke, 2019). The motivational perspective states that job autonomy is associated with an increase in employee work motivation because it satisfies a psychological need (Ryan & Deci, 2017) and generates a sense of ownership over one’s work (Dawkins et al., 2017). Thus, individuals who experience job autonomy become more invested in their tasks (Bakker & Demerouti, 2017; Christian et al., 2011), leading to increased efficiency (Tisu et al., 2021), and creativity (Volmer et al., 2012) at work. The work-strain perspective argues that autonomy is linked to a decrease in work-related strain via recovery activities. Workers who have the liberty to decide when, where, and how to carry out their tasks can employ various behavioral strategies to restore their physical and mental energy (e.g., micro-breaks; Kim et al., 2018; Sonnentag et al., 2017), resulting in increased work-related well-being (Zacher et al., 2014) and performance (Kim et al., 2018). Although both perspectives have undeniable merits in explaining the link between job autonomy and employee performance, they also yield specific gaps in knowledge.

The motivational perspective focuses almost exclusively on affective-cognitive factors as explanatory mechanisms in the relationship between autonomy and performance (e.g., work engagement; Borst et al., 2019; intrinsic motivation; Li et al., 2018). Although such findings provide valuable insights as to how autonomy impacts performance through changes in employees’ work-related states and perceptions, they fail to include actual behaviors into existing models. This is problematic because practitioners must also foster specific behaviors among employees, which can allow the latter to translate their autonomy into positive work-related outcomes. The work-strain perspective proposes such behaviors (e.g., micro-breaks; Fritz et al., 2011; Kim et al., 2018). However, these behaviors (e.g., stretching, napping, and browsing the Internet) reflect actions that employees take when their resources-reservoir is almost depleted and thus unable to promote their work (Op den Kamp et al., 2018).

Furthermore, existing findings report mixed results regarding the efficiency of utilizing micro-breaks to enhance employees’ energy levels and thus performance (Fritz et al., 2011; Sonnentag et al., 2017). It is, therefore, unclear what type of behaviors specialists ought to foster to ensure employees are able to capitalize on their autonomy to attain increased performance levels at work. The current study addresses this gap in knowledge by proposing proactive vitality management (behavioral component) and work engagement (affective-cognitive component) as explanatory mechanisms in the relationship between autonomy and job performance.

Proactive vitality management represents “individual, goal-oriented behaviors aimed at managing physical and mental energy to promote optimal functioning at work” (Op den Kamp et al., 2018, p. 493). Imagine two employees who know they have an upcoming busy day at work. In preparation, one of the employees eats a nutritious breakfast prior to going to work and listens to lively music while on the way to the job. In contrast, the other does none of these things but goes for a coffee break right after arriving at the office. Which of these employees is better charged and energized to face the busy workday ahead? Who will be more productive? According to existing research, the first employee, who engages in actions encompassed by proactive vitality management, will exhibit high levels of work engagement (Bakker et al., 2020; Op den Kamp et al., 2018) and performance (Op den Kamp et al., 2018; Op den Kamp et al., 2020). The second employee, who engages in a micro-break, will probably report no changes or even a decrease in his or her motivational state (Fritz et al., 2011; Sonnentag et al., 2017). Thus, proactive vitality management appears to be a promising behavioral alternative to resource-replenishing activities (e.g., micro-breaks) in promoting performance because it allows employees to proactively manage and sustain their energy at work rather than seeking to replenish it.

Yet, what are the premises that enable employees to engage in proactive vitality management? Op den Kamp et al. (2018) suggest autonomy as a potential precursor. Perhaps, to accommodate the nourishing breakfast, the employee had to push the start of the workday half an hour later, which is only possible if he or she had autonomy in deciding when to start the workday. Once energized by it, the employee will have the necessary physical and mental energy to focus on his or her work for longer periods (absorption), will be more energized to carry out various tasks (vigor), and probably also prouder of the conducted activities (dedication) (Bakker et al., 2020; Op den Kamp et al., 2018). Exhibiting the three components defining work engagement (i.e., vigor, dedication, and absorption; Schaufeli et al., 2006) will reflect enhanced employee performance (Christian et al., 2011; Op den Kamp et al., 2018; 2020).

The above process is best explained through the lens of the Job Demands-Resources (JD-R; Bakker & Demerouti, 2017) theory. The JD-R theory proposes a dual pathway related to employee performance—a health-impairment process and a motivational process. On one hand, individuals are bound to experience job strain via inherent job demands (e.g., high workload). This can trigger a health-impairment process (e.g., exhaustion), resulting in higher burnout rates thus negatively impacting employee performance (for meta-analysis see Taris, 2006). On the other hand, organizations can provide job resources (e.g., autonomy) that allow employees to attain their goals and reduce strain, leading to increased motivation at work. Once this positive affective-cognitive state (i.e., work engagement) is triggered, this allows employees to be more goal-oriented and focused (Bakker & Demerouti, 2017), resulting in augmented performance (for meta-analysis see Halbesleben, 2010).

The present study focuses on the motivational process proposed by the JD-R theory because, as previous research demonstrates, job resources can buffer the negative effect of job demands (for synthesis see Bakker & Demerouti, 2017). As such, organizations can implement and control various resources to reduce strain and ensure an increase in employee performance. Furthermore, employees also seek to capitalize on existing resources by engaging in various individual strategies—developable methods that employees proactively use to accomplish tasks (Bakker, 2017; Bakker & Demerouti, 2018; Demerouti et al., 2019), such as proactive vitality management. According to Bakker and Demerouti (2018), these proactive, goal-directed behaviors enable employees to benefit from existing resources and translate them into positive work-related outcomes. However, these theoretical assumptions are yet to be validated.

Therefore, in the present investigation, we propose and test a model where autonomy is a precursor of performance, employing proactive vitality management (individual strategy), and work engagement (affective-cognitive component) as sequential mediators of the relationship. The model aims to (1) identify a developable individual strategy (i.e., proactive vitality management) that can be fostered, allowing employees to capitalize on their job autonomy to promote performance; and (2) integrate behavioral and affective-cognitive components into a unitary model, by investigating the joint mediating role of proactive vitality management and work engagement in the linkage between autonomy and performance. As such, from a theoretical perspective, our study aims to empirically test the assumption of the JD-R theory that individual strategies (e.g., proactive vitality management) link job resources to workplace outcomes. From a practical perspective, we intend to provide practitioners with a novel individual strategy that employees can enact to boost their energy at work. By cultivating proactive vitality management, employees could translate their autonomy into increased work-related well-being and performance. This will ensure that organizations and employees alike benefit from the current organizational job design trend, which allows employees increased autonomy at work.

Autonomy and performance

As a job resource, autonomy reflects the degree of freedom employees have in deciding when, where, and how to carry out their tasks (Bakker & Demerouti, 2014). According to the JD-R theory, when employees perceive they possess autonomy in their job, they experience heightened levels of work engagement, resulting in higher performance (Bakker & Demerouti, 2017). This link occurs due to job resources, such as autonomy, that allow employees to experience reduced strain while also facilitating goal attainment (Bakker & Demerouti, 2017). For instance, employees who have the liberty to schedule their work assignments will be able to choose the task they are more interested in carrying out at a certain time point. Similarly, employees who have independence in carrying out their tasks could elaborate novel and more efficient ways of completing their work. Existing evidence supports this claim. Borst et al. (2019) demonstrate on a large sample of Dutch public workers that autonomy is associated with performance. Tisu et al. (2021) also found autonomy to be an antecedent of proficiency, as a form of performance, on a sample of Romanian employees working in the private sector, while Berdicchia and Masino (2019) establish a link between autonomy and performance on a sample of Italian workers. Considering these arguments, we state:

H1: Autonomy will be positively associated with performance.

Autonomy and performance: Proactive vitality management as mediator

Although autonomy is a precursor of performance, the link between the two concepts is mediated by various affective-cognitive (e.g., work engagement; Borst et al., 2019) and behavioral (e.g., job crafting; Petrou et al., 2012) mechanisms. Relying on their autonomy allows employees to engage in individual strategies that are beneficial in achieving work-related objectives. According to a recent expansion in the JD-R theory (Bakker & Demerouti, 2018), employees are not merely passive recipients influenced by external resources but active actors who seek to capitalize on existing resources by engaging in goal-directed behaviors, known as individual strategies (Bakker, 2017; Demerouti et al., 2019). Op den Kamp et al. (2018) indicate proactive vitality management as an essential individual strategy that can occur when employees perceive they have the liberty to decide when, where, and how to carry out their tasks.

Proactive vitality management encompasses idiosyncratic, self-initiated behaviors directed at sustaining, rather than replenishing (e.g., coffee break), one’s energy levels to promote performance (Bakker et al., 2020; Op den Kamp et al., 2018; 2020). These behaviors can occur both in and outside of work, yet their primary goal is to promote optimal functioning at work (Op den Kamp et al., 2018). Therefore, employees decide when, where, and how to employ proactive vitality management based on their personal needs and preferences. Whereas some may listen to music using headphones to not get distracted by discussions among colleagues, others may decide to take a moment and meditate about the meaning of their work. Nevertheless, to be able to engage in any of the above behaviors, employees must have the liberty to do so. If employees are not allowed to use headphones or briefly detach from their tasks (i.e., autonomy), they will be unable to engage in such beneficial behaviors that can sustain their energy levels. As such, autonomy appears to be a prerequisite for proactive vitality management to emerge. Considering these arguments, we state:

H2a: Autonomy will be positively associated with proactive vitality management.

Existing studies on proactivity and energy management also indicate a clear relationship between behaviors directed at managing one’s energy levels at work and employee performance (Op den Kamp et al., 2018; Ye et al., 2020). This link occurs because employees who ensure they are energetic, vital, and vibrant when carrying out their tasks also tend to possess an abundance of resources to invest in their work (Op den Kamp et al., 2018). As such, individuals who engage in proactive vitality management can boost their performance through tailored actions that enable them to focus for longer periods on their work (Op den Kamp et al., 2018) or think creatively and find novel solutions to existing problems (Bakker et al., 2020; Op den Kamp et al., 2020). For instance, employees can decide to ignore e-mails or phone calls for a period and invest their existing energies to finish a critical presentation they are focused on. As such, due to taking specific actions that ensure they can work without distractions, employees will be able to focus and channel their resources (e.g., working memory capacity; De Dreu et al., 2012) to achieve better results. Others will have the energy to mobilize their colleagues to initiate a brainstorming session to find alternative approaches for streamlining existing working procedures. Also, employees with high autonomy at work report feeling more vital (Tummers et al., 2018), enabling them to be more productive. Therefore, to engage in proactive vitality management, employees must first have the liberty to act in such a fashion (i.e., autonomy). This will then reflect on their performance. Based on these considerations, we argue that:

H2b: Proactive vitality management will be positively associated with performance.

H2c: Proactive vitality management will mediate the relationship between autonomy and performance.

Autonomy and performance: Work engagement as mediator

Next to individual strategies (e.g., proactive vitality management) that link autonomy to performance, an essential role is also played by affective-cognitive factors (Borst et al., 2019; Li et al., 2018). The JD-R theory (Bakker & Demerouti, 2017) is well-known for explaining the association between job resources and performance employing work engagement as a mediator. Work engagement represents “a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and absorption” (Schaufeli et al., 2002, p. 74). As such, it represents a work-related state of mind in which employees are energized and willing to invest effort in their work (vigor), take pride in their job and feel it is meaningful (dedication), and are captivated by what they are doing (absorption) (Schaufeli et al., 2006).

According to the JD-R theory, work engagement emerges when employees perceive they have various job resources at their disposal that enable them to reach their objectives (Bakker & Demerouti, 2017, 2018). Autonomy is one such resource. Empowering employees to make decisions on their own and reducing control over them will enable employees to focus on tasks that best suit their needs, allowing them to become engaged in their work. For example, having the liberty to decide which task to focus on and how long will create the premises for employees to become engrossed in the said task. Existing findings support this claim. De Spiegerle et al. (2014) link autonomy to the three components of work engagement on a sample of Flemish workers, while Virgă et al. (2015) find an association between job resources (including autonomy) and work engagement on a sample of Romanian employees. Furthermore, an existing meta-analysis demonstrates that autonomy, as a job resource, is linked to work engagement (Christian et al., 2011). Considering these arguments, we posit:

H3a: Autonomy will be positively associated with work engagement.

Next, the positive state elicited by work engagement, where employees are enthusiastic and proud about their job and willing to go the extra mile, is also a strong predictor of performance (for a review, see Christian et al., 2011). Indeed, being able to pursue work goals with persistence and devotion is likely to result in better results. Past research clearly demonstrates that engaged employees excel not only in attaining role-prescribed objectives (Borst et al., 2019; Tisu et al., 2020) but also in generating innovative solutions to work-related problems (De Spiegelaere et al., 2014; Op den Kamp et al., 2020). These results align with the propositions of the JD-R theory, which links work engagement to performance, employing various job resources (e.g., autonomy) as antecedents (Bakker & Demerouti, 2017). Additionally, existing meta-analytical findings that investigate the link between job resources, employee well-being, and performance reach a similar conclusion (Nielsen et al., 2017). Based on the above arguments, we state:

H3b: Work engagement will be positively associated with performance.

H3c: Work engagement will mediate the relationship between autonomy and performance.

Autonomy and performance: Proactive vitality management and work engagement as sequential mediators

Up to this point, we have presented how a top-down approach in job design (i.e., autonomy) is linked to performance through a behavioral (i.e., proactive vitality management) and affective–cognitive (i.e., work engagement) explanatory mechanism, separately. However, Bakker (2017) suggests that encouraging employees to take a complementary bottom-up approach by engaging in individual strategies to capitalize on existing resources is necessary to attain high-performance levels. Following Bakker’s (2017) proposition, we argue that proactive vitality management also mediates the relationship between job autonomy and employees’ work engagement. Past research indicates a strong connection between proactive vitality management and work engagement (Bakker et al., 2020; Bălăceanu et al., 2021; Op den Kamp et al., 2018; Ye et al., 2020). Employees who engage in beneficial behaviors to sustain their energy levels (e.g., listening to lively music while working) will secure the relevant resources to become absorbed, enthusiastic, and proud by their tasks. Therefore, we argue that:

H4a: Proactive vitality management will be positively associated with work engagement.

Finally, as presented, we expect both top-down (i.e., autonomy -> proactive vitality management; autonomy -> work engagement) and bottom-up (i.e., proactive vitality management -> work engagement) approaches to be beneficial in enhancing employee well-being and performance. In the proposed model, we expect autonomy to yield an association to performance through both individual strategies and affective-cognitive mechanisms. Furthermore, following the new JD-R theory extension (Bakker, 2017; Bakker & Demerouti, 2018), we argue that the individual strategy component also precedes and reinforces the affective-cognitive component. Therefore, having the liberty to organize their work tasks and schedule as seen fit (i.e., autonomy) should create the premises for employees to engage in proactive vitality management (e.g., meditating; Op den Kamp et al., 2018), which will then boost their cognitive-motivational energies (i.e., work engagement; Bakker et al., 2020; Op den Kamp et al., 2018). In turn, when employees experience work engagement, a state where they are energized by their activities (vigor), proud of their work (dedication), and fully immersed in their tasks (absorption) (Schaufeli et al., 2006), this will also reflect in their performance (Christian et al., 2011). We thus reach our final hypothesis:

H4b: Proactive vitality management and work engagement will act as sequential mediators in the relationship between autonomy and performance.

Method

Participants and procedure

Three-hundred-and-fifty questionnaires were distributed to employees from a large multinational organization located in Western Romania. The local hub, where data were collected, provides shared services, such as worldwide support services for the automotive industry, or financing services and the administration of financing portfolios. It also comprises various support departments (e.g., HR department). Employees are white-collar workers who mostly perform desk duties (e.g., fielding phone calls, use of specialized software, etc.) in an open-space office building. Out of the 350 employees who received the invitation to complete the questionnaire, 278 opened the survey, and 256 provided complete answers (73.14% response rate). Thus, the final sample consists of 256 employees. Participants’ age ranges between 19–61 years (M = 30.69; SD = 7.44), with 78.9% being women. Most respondents are not married (67.6%) and have a bachelor’s degree (89.5%). Regarding participants’ work tenure, 25.6% have less than 2 years of experience, 14.6% report 3–5 years of experience, 28.3% have 6–10 years of experience, 24.8% report 10–20 years of experience, while 6.7% have a total work experience exceeding 21 years.

The data were collected using online survey software. With the help of the organization’s HR Department, the researchers contacted participants via email, inviting them to complete the questionnaire. The first section of the questionnaire contained a consent form, informing participants about the study’s scope, data confidentiality, and their right to retreat from the study at any point. Responses were offered voluntarily and anonymously, and no incentives for presented for participation. Considering this investigation’s aim, the study was exempted from obtaining approval from the University’s Ethics Committee.

Instruments

All variables were measured using Romanian versions of established instruments that have been previously used (autonomy – α = .79; Tisu et al., 2021, performance – α = .75; Tisu et al., 2020) or adapted (proactive vitality management – α = .96; Bălăceanu et al., 2021, work engagement – α = .89; Vîrgă et al., 2009) on Romanian samples. Based on the internal consistency, the instruments yield good psychometric properties and conducted confirmatory factor analyses on Romanian samples.

Autonomy was measured with a set of three items from the Job Demands-Resources (JD-R) Questionnaire (Bakker & Demerouti, 2014). The items were measured on a 1 (strongly disagree) to 5 (strongly agree) Likert scale. An example item is: "Do you have control over how your work is carried out?"

Proactive vitality management was assessed using the scale developed by Op den Kamp et al. (2018). The instrument contains eight items, with response options ranging from 1 (totally disagree) to 7 (totally agree) Likert scale. A sample item is "I make sure that I feel energetic during my work."

Work engagement was measured with the short version of the Utrecht Work Engagement Scale (UWES-9; Schaufeli et al., 2006). The scale consists of 9 items measured on a 7-point scale, ranging from 0 (never) to 6 (always). A sample item is "Time flies when I am working."

Performance was assessed with a 7-items scale developed by Williams and Anderson (1991). Response options ranged on a Likert scale from 1 (strongly disagree) to 5 (strongly agree). A sample item is "I adequately complete my assigned duties."

In this study, all employed instruments yield good internal consistency values, with alpha Cronbach’s values ranging between .83 (autonomy) and .90 (proactive vitality management) (see Table 1).

Table 1.

Descriptive statistics, correlation coefficients, and reliability coefficients.

Observed Variable	M	SD	1	2	3	4	5	6	7
Age	30.69	7.44	—
Gender	1.21	.41	−.07	—
Work tenure	8.78	7.12	.95**	−.07	—
Autonomy	10.56	2.83	−.07	.01	−.03	(.83)
Proactive vitality management	46.26	6.65	.14*	−.09	.16*	.16**	(.90)
Work engagement	44.36	10.81	.16**	−.20**	.18**	.33**	.46**	(.89)
Performance	30.27	3.58	.06	−.03	.06	.21**	.32**	.30**	(.85)

Note. N = 256, * p < .05, ** p < .01.

Statistical analyses

The data were analyzed based on the structural equation modeling framework in R software (R Core Team, 2013) using the lavaan package (Rossell, 2012). We tested two measurement models and three structural models (Schreiber et al., 2006) using maximum likelihood estimation for model fit assessment. Three absolute fit indices (chi-square statistic; RMSEA – root mean square error of approximation; and SRMR – standardized root mean square residual) and two relative fit indices (CFI – Comparative fit index; and TLI – Tucker-Lewis index) were computed. According to Marsh et al. (2005), the cut-off point for an acceptable fit is .90 for CFI and TLI and .08 for RMSEA and SRMR, while CFI and TLI indices are higher than .95 and RMSEA and SRMR values lower than .06 indicate an excellent fit. Direct, indirect, and total effects were calculated using a 5000-bootstrapping procedure.

To assess the two measurement models, we conducted a confirmatory factor analysis (CFA). Three modification indices that had theoretical coverage, namely residuals correlations between items of the same scale with similar wording, were employed. The first model (M1) is the four factors model, consisting of 4 latent variables—autonomy, proactive vitality management, work engagement, and performance. The second model (M2) is the single-factor model (Podsakoff et al., 2012).

Before testing the structural models, we used item parceling to assure a good parameters-to-sample size ratio (Schreiber et al., 2006). Hence, we created factor scores for the latent variables: proactive vitality management, work engagement, and performance. The parceling process followed the factorial algorithm procedure developed by Rogers and Schmitt (2004). Specifically, we ranked and then computed the observed variables into factor scores. Three factors were generated for each latent variable, consisting of 2 or 3 combinations of items. This also led to the elimination of the modification indices from the model.

Next, we tested three structural models where autonomy predicts performance through several mediation mechanisms. First, we tested the hypothesized model (M3), where autonomy predicts performance directly and through a serial mediation mechanism (Hayes, 2017) enabled by proactive vitality management and then by work engagement (see Figure 1). The second structural model (M4) is like M3 yet proposes a full mediation mechanism. Hence, in M4, all relationships remain identical to M3, except for the direct link between autonomy and performance, which is eliminated. The third structural model (M5) proposes a parallel mediation mechanism (Hayes, 2017). In M5, the proposed mediators are no longer positioned sequentially but are treated as two separate mediators, with no relationship between them. Therefore, in M5, we propose a direct link between autonomy and performance, with the relationship being partially and distinctly mediated by proactive vitality management and work engagement.

Figure 1.

The hypothesized model. Note. * p < .05; ** p < .01; *** p < .001; i1 – i3 = item 1 through item 3; p1–p3 = parcel 1 through parcel 3.

Results

The means, standard deviations, Cronbach’s alpha values, and the correlation matrix of the used variables are presented in Table 1. All variables are normally distributed and correlate with each other. To strongest correlation was observed between proactive vitality management and work engagement (r = .46, p < .001), suggesting these constructs are closely tied. However, the correlation is not strong enough to raise questions regarding a conceptual overlap between them.

Model comparison: Measurement models

The four factors model (M1) has acceptable fit indices, χ2(315) = 584.54, p < .001, CFI = .93; TLI = .92; RMSEA = .06, 90% CI [.05, .07], SRMR = .06, while the single-factor model (M2) displays poor fit indices: χ2(321) = 2087.30, p < .001; CFI = .55; TLI = .51; RMSEA = .15, 90% CI [.14, .15], SRMR = .14. The chi-square difference test confirms that M1 shows a better fit to the data than M2 (Δχ2(6) = 1502.8, p < .001). Following Podsakoff et al. (2012), we argue that these results indicate a slim chance for the occurrence of common method bias.

Model comparison: Structural models

Table 2 shows the fit indices for the three structural models. The hypothesized model (M3) and the full mediation model (M4) yield excellent fit indices, while the parallel mediation model (M5) fails to reach acceptable fit indices. Therefore, the parallel mediation model (M5) was discarded, and only the two remaining models (M3 & M4) were analyzed further. The chi-square difference test indicates no statistically significant differences between M3 and M4 (Δχ²(1) = 2.04, p = .15). Hence, we inspected the relationships between the study’s variables in both models. In the hypothesized model (M3), all relationships are statistically significant, except for the direct link between autonomy and performance (β = .13, p = .11). In the alternative full mediation model (M4), all relationships included in the model are statistically significant. Therefore, we consider the full mediation model (M4; χ2(48) = 54.80, p = .20, CFI = 1.00; TLI = .99; RMSEA = .03, 90% CI [.01 - .05], SRMR = .04) as the single acceptable model.

Table 2.

Fit indices and model comparisons for measurement and structural models.

Model	χ2	df	χ2/df	CFI	TLI	RMSEA [90% CI]	SRMR	∆χ2	∆df
Measurement model
M1 – four factors model	584.54**	315	1.84	.93	.92	.06 [.05–.07]	.06
M2 – single-factor model	2087.30**	321	6.44	.55	.51	.15 [.14–.15]	.14	1502.8**	6
Structural model
M3 – hypothesized model	52.76	47	1.12	1.00	1.00	.02 [.01–.05]	.03
M4 – full mediation model	54.80	48	1.14	1.00	.99	.03 [.01–.05]	.04	2.04	1
M5 – parallel mediation model	107.12**	49	2.19	.97	.96	.07 [.05–.09]	.12	54.35**	2

Note. N = 256; For M2 the comparison is versus M1, while M3 is compared to M4 and M5, * p < .05, **p < .01.

Hypothesis testing

Figure 2 shows the regression coefficients for the full mediation model (M4). As mentioned, in the hypothesized model (M3) the direct link between autonomy and performance was non-significant; Hypothesis 1 was therefore discarded. Concordant with Hypothesis 2a and Hypothesis 3a, autonomy is positively related to proactive vitality management (β = .18, p < .01) and work engagement (β = .32, p < .001). Proactive vitality management is positively associated with work engagement (β = .45, p < .001) and performance (β = .23, p < .05), favoring Hypothesis 4a and Hypothesis 2b. Consistent with Hypothesis 3b, work engagement also shows a positive link to performance (β = .26, p < .01). Hypotheses 2c, 3c, and 4b were also supported, with proactive vitality management and work engagement fully mediating the relationship between autonomy and performance. Specifically, autonomy predicts performance through proactive vitality management (H2c; indirect effect = .04, 95% CI [.01 - .10]) and work engagement (H3c; indirect effect = .08, 95% CI [.02 - .13]) separately, but also jointly, through a serial mediation mechanism (H4b; indirect effect = .02, 95% CI [.01 - .04]).

Figure 2.

The full mediation model.

Thus, the full mediation model was supported by the data and explained 3% of the variance in proactive vitality management, 35% of the work engagement variance, and 18% of the performance variance (Table 3).

Table 3.

Standardized indirect effects with bootstrapped 95% confidence intervals.

Independent variable	Mediator 1	Mediator 2	Dependent variable	Estimate	95% CI
Autonomy →	Proactive vitality management →	Work engagement →	Performance	.02	[.01 - .05]
Autonomy →	Proactive vitality management →		Performance	.04	[.01 - .09]
Autonomy →	Work engagement →		Performance	.08	[.02 - .13]

Note. N = 256.

Discussion

A recent expansion of the JD-R (Bakker, 2017; Bakker & Demerouti, 2018) theory specifies that the link between job resources (e.g., autonomy) and work-related outcomes is also explained by employees engaging in proactive, goal-directed behaviors, aimed at capitalizing on existing resources. This study aimed to validate this assumption empirically. As such, we proposed and tested a model where autonomy (job resource) is a precursor of performance, employing proactive vitality management (behavioral component) and work engagement (affective-cognitive component) as sequential mediators of the relationship. The data provided partial support to our model. Specifically, we found proactive vitality management and work engagement to fully mediate the link between job autonomy and employee performance. Thus, we identified a relevant individual strategy (i.e., proactive vitality management) that allows employees to benefit from existing resources and translate them into increased work engagement and performance.

Detailing our main findings, employees who have the liberty to decide when, where, and how to carry out their work-related duties report higher levels of proactive vitality management. This finding is in line with the assumption of Op den Kamp et al. (2018) that autonomy may be a precursor of proactive vitality management. Based on our results, for employees to engage in behaviors that keep their batteries charged (e.g., listening to lively music while working or meditating about the meaning of their jobs), they must first perceive they have the liberty to do so. This will allow them to sustain their physical and mental energy to promote their work. We also found autonomy to be a direct predictor of employees becoming more enthusiastic, proud, and engrossed in their work (i.e., work engagement). Indeed, allowing employees to experience more freedom in decisions regarding their work enables them to tackle role-prescribed duties with an energetic motivational state, probably because it satisfies a psychological need (Ryan & Deci, 2017) and allows them to experience a sense of ownership over their tasks (Dawson et al., 2018). This result is also in line with the theoretical assumptions of the JD-R theory (Bakker & Demerouti, 2017) and existing research (Borst et al., 2019).

An important and novel finding is that proactive vitality management acts as a mediator between autonomy and work engagement. As such, for employees to experience a state of vigor, dedication, and absorption, they must also proactively engage in behaviors that allow them to capitalize on their autonomy. For instance, employees can use their autonomy to start their workday after accommodating a nutritious breakfast in the morning, which will then boost their physical and mental energies, resulting in increased work engagement. The strong association between proactive vitality management and work engagement is similar to results obtained in other recent studies (Bakker et al., 2020; Bălăceanu et al., 2021; Op den Kamp et al., 2020). We argue that this strong association can be explained by the fact that proactive vitality management encompasses idiosyncratic behaviors directed at boosting one’s energy levels, a prerequisite for employees exhibiting work engagement.

We also found proactive vitality management and work engagement as precursors of performance. The two employed mediators fully explain the link between autonomy and performance. Employees who have autonomy appear to (1) voluntarily initiate behaviors directed at boosting their physical and mental energy (e.g., meditating about the meaning of one’s work), and thus (2) become enthusiastic and spirited about their job. Together, proactive vitality management and work engagement allow employees to exert extra effort into their tasks, be more focused and creative at work (Bakker et al., 2020; Christian et al., 2011; Op den Kamp et al., 2020; Ye et al., 2020), reflecting in increased performance. Our model, therefore, validates the current trend in organizational job design that allows employees increased autonomy, uncovering both individual strategies and affective-cognitive factors that translate employees’ job autonomy into performance.

Contrary to previous findings (Tisu et al., 2021), we were unable to establish a direct relationship between autonomy and performance. Nevertheless, this result is in line with the assumptions of the JD-R theory (Bakker & Demerouti, 2017), which argues that various explanatory mechanisms mediate the link between job resources and performance. Indeed, Borst et al. (2019) also find work engagement to partially mediate the relationship between autonomy and performance. In this study, next to the affective-cognitive component, we also included one individual strategy as a potential mediator. It appears that the proposed model, which includes both proactive behaviors and positive affective-cognitive states, manages to capture the essential aspects that explain the link between autonomy and performance.

In other words, autonomy appears to be only a distal precursor of performance. Having the liberty to decide when, where, and how to carry out their tasks is insufficient to directly trigger an increase in performance. However, it creates the premises for employees to engage in resource-gaining activities encompassed by proactive vitality management (e.g., ignoring e-mails for a period to fully concentrate on preparing an important presentation). Those who profit from this resource and engage in proactive vitality management will then display enhanced performance levels (e.g., streamlined presentation that facilitates the efficient delivery of the information to stakeholders). Similarly, encouraging more autonomous decision-making among employees can create a sense of control over their tasks, which will increase their psychological well-being (i.e., work engagement). This positive motivational state will then allow them to be more focused and goal-oriented in their job, leading to higher performance (Bakker & Demerouti, 2017).

Lastly, based on our results, these two avenues are manifested not only separately but also jointly in a sequential manner. For instance, after dealing with a problematic customer, employees can utilize their autonomy to briefly detach from work and meditate on how their job helps various individuals (proactive vitality management). This self-initiated perspective-changing activity will be associated with the emergence of a more energetic and vigorous affective-cognitive state (i.e., work engagement) (Arnold & Walsh, 2015). Such a process should then create the premises for better interactions with the next client, resulting in an enhanced performance at work.

Limitations

As with any study, our investigation has a series of limitations. First, we employed a correlational design to test our model. While the proposed model yields excellent fit indices, suggesting that it can accurately model how autonomy is associated with performance through the proposed predictors, it does not allow any causal inferences. Hence, these results should be interpreted in a rather exploratory manner. Future studies should aim to test the model through randomized controlled trials to assess whether changes in autonomy, proactive vitality management, and work engagement lead to increased performance levels. Based on existing interventions that demonstrate that an increase in autonomy (Cordery et al., 2010) and work engagement (Van Wingerden et al., 2017) elicit an increase in performance, we expect our model to both inspire and pass the scrutiny of an experimental design.

Second, we relied upon self-report data collection, making the results susceptible to common method bias (Podsakoff et al., 2012). The conducted CFA suggests that there are small chances for such an event to have happened. However, we encourage future studies seeking to replicate our findings to implement more diverse and objective measures to assess this study’s variables (e.g., objective performance indicators, such as a number of sales). Lastly, the sample included in this study comprises workers from a specific industry (i.e., shared support services), with all respondents being white-collar Romanian employees working in a large organization, from the private sector, with already clear established work procedures. Therefore, further studies are needed to validate the proposed model on other industries/sectors, company sizes (e.g., SME’s), and cultures.

Theoretical and practical implications

Our investigation has several important theoretical and practical implications. Regarding the theoretical contribution of our paper, we managed to (1) empirically validate the assumption of the JD-R theory that individual strategies (e.g., proactive vitality management) act as explanatory mechanisms in the link between job resources and workplace outcomes, and (2) identify a set of proactive behaviors that can complement, if not substitute, resource-replenishing activities, such as those proposed by the work-strain perspective (e.g., micro-breaks; Fritz et al., 2011; Kim et al., 2018; Sonnentag et al., 2017). Thus, the new JD-R extension appears to provide a more clear-cut understanding of how employees capitalize on resources because it expands beyond affective-cognitive factors to also including moldable, self-initiated behaviors. In line with the assumption of Bakker (2017), cultivating an organizational climate that encourages autonomy appears to facilitate the emergence of bottom-up approaches that allow employees to stay engaged at work, reflecting on their performance.

Importantly, the sample used in this study consists of white-collar employees working in the private sector. Do the same propositions of the JD-R theory, and implicitly the findings of this study, apply to other sectors (e.g., public sector) or types of workers (e.g., blue-collar workers)? As Borst et al. (2020) demonstrate in a recent comparative meta-analysis, work engagement is a vital indicator linked to attitudinal and behavioral outcomes (e.g., performance) for employees working in the public and semipublic sectors. However, depending on the type of sector, differential patterns in the relationship between job demands and workplace outcomes were identified (i.e., health-impairment avenue). As such, Borst et al. (2020) conclude that further contextualization of the JD-R theory is warranted. When investigating the motivational process in the case of public servants, however, Borst et al. (2019) find autonomy as an essential precursor of work engagement.

Furthermore, Luu (2020) establishes that individual strategies (e.g., job crafting) mediate the relationship between job resources (e.g., leadership style) and work-related well-being. Similarly, for blue-collar workers’ job resources (including autonomy) represent antecedents of engagement (Yoon et al., 2021). We, therefore, argue that the current model, stemming from the motivational perspective of the JD-R theory, would also find support if tested in other sectors or on different types of employees. We encourage researchers to empirically validate this assumption and thus provide additional evidence on whether the JD-R theory should be contextualized according to the employment sector or types of employees.

A somewhat surprising result in this study was the modest amount of variance in proactive vitality management explained by autonomy. We interpret this finding in three ways. First, this result may be attributed to the nature of the work our respondents engage in. Acting as shared support services may reduce their autonomy in deciding when, where, and how to carry out their tasks. Therefore, it would be necessary to test whether this tendency remains when the model is tested in other types of jobs that allow more autonomy (e.g., sales, research).

Second, it is also possible that external factors, such as job resources, are not paramount or not sufficient to elicit proactive vitality management. As previous research indicates, employees must also possess personal resources, such as PsyCap (Luthans et al., 2007), that can fuel employees’ engagement in various individual strategies (e.g., job crafting; Van Wingerden et al., 2017). Maybe that is also the case for proactive vitality management; employees must first believe they are capable of orchestrating resource gains (i.e., personal resources) to engage in individual strategies (Van Wingerden et al., 2017). Therefore, to fully capture the motivational process of the JD-R theory, future studies could include personal resources as moderators in the link between individual strategies and workplace outcomes to test this proposition.

Third, proactive vitality management represents actions that employees take both in and outside of work to manage their physical and mental energies towards work (Op den Kamp et al., 2018). Therefore, it is possible that employees mostly engage in proactive vitality management prior to getting to their job (e.g., listening to their favorite music while driving to work) and then resort to resource-replenishing activities (e.g., coffee break) once at the office. Considering that recovery activities lead to inconsistent results regarding their efficiency (Fritz et al., 2011; Sonnentag et al., 2017), practitioners should aim to help employees transition from engaging in micro-breaks to proactive vitality management.

From a practical perspective, HR specialists and leaders ought to help employees cultivate proactive vitality management. Although this construct implies self-initiated behaviors that employees engage in at specific times, this does not imply that individuals are also constantly aware of the benefits of such actions. Hence, practitioners could help employees recognize when they engage in proactive vitality management and thus foster such proactive behaviors in the future. While there are currently no interventions directed at enhancing employees’ proactive vitality management, we feel some suggestions could be made based on existing interventions for developing other individual strategies (Niemiec, 2018; Van Wingerden et al., 2017). First, practitioners could help employees identify their self-initiated behaviors, which are directed at boosting their physical and mental energies to promote their work. This could be done in conjuncture with noticing the context in which they employ proactive vitality management. Once employees become aware of these behaviors and their context, they could be coached to employ them regularly, replacing resource-replenishing activities. Furthermore, employees could be encouraged to share these behaviors with their colleagues so that other individuals test them to verify whether they facilitate higher levels of engagement and performance. Kim et al. (2020) have already demonstrated that such sharing practices lead to increased creative performance at work.

Conclusion

Organizations nowadays provide increased levels of autonomy to enhance employees’ well-being and performance. In this study, we uncover specific mechanisms that explain the relationship between autonomy and performance. Specifically, next to a well-known affective-cognitive component (i.e., work engagement), we also identify proactive vitality management as an individual strategy component that links autonomy to work performance. This is particularly important because proactive vitality management encompasses energy-sustaining behaviors rather than energy-replenishing ones. It is encouraging employees who experience increased levels of autonomy to transition from micro-breaks to proactive vitality management may prove to be an essential tool for sustaining employees’ motivation at work. This should reflect increased performance levels due to employees ensuring they maintain high energy levels at work, thus being more focused, creative, and efficient.

Footnotes

Acknowledgments

The work of Delia Vîrgă and Luca Tisu was supported by a grant from the Ministry of Research, Innovation and Digitization, CNCS/CCCDI – UEFISCDI Romania, project number PN-III-P4-ID-PCE-2020–1880, within PNCDI III ().

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Luca Tisu

Delia Vîrgă

Author Biographies

Luca Tisu, Ph.D. in Psychology, is a research assistant at the Department of Psychology from the West University of Timisoara. His main research interests revolve around job/personal resources and employee/entrepreneurial performance. https://orcid.org/0000-0002-3181-1629

Delia Vîrgă, Ph.D. in Psychology, is a Full professor in Organizational and Occupational Psychology at the Department of Psychology from the West University of Timisoara, Romania. Her research includes studying personal resources (e.g., PsyCap) and individual proactive strategies (e.g., strengths use, proactive vitality management) about well-being and performance at work. https://orcid.org/0000-0002-9321-4520

Ioana Mermeze holds a Master’s Degree in Organisational and Occupational Health Psychology, obtained at the West University of Timisoara. She works as an HR Professional, focusing on employee training and development.

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