Job Resources and Strengths Use in Relation to Employee Performance: A Contextualized View

Abstract

Job resources play a prominent role in employee performance literature, yet a fine-grained understanding of how resources are relevant for several performance types is still needed. Relying on the Job Demands-Resources and Conservation of Resources theories, the present study addresses this call in two ways. First, it examines the predictive effect of four job resources (i.e., role clarity, feedback, autonomy, and opportunities for development) on nine types of performance (i.e., proficiency, adaptivity, and proactivity as an individual, team, and organization member). Second, it tests the moderator role of strengths use in these relationships. Data was gathered from a sample of Romanian employees (N = 332) and analyzed via hierarchical multiple linear regression. The results indicate that the selected job resources are, indeed, predictors of different types of employee performance and not in a unitary manner. Role clarity and feedback appear to be the most relevant predictors for various performance types, while autonomy seems to be the least important. Also, strengths use moderates these relationships, but in a reinforcing manner only regarding opportunities for development. The interaction of strengths use with role clarity and feedback renders the latter two obsolete, indicating that individual strategies may act as substitutes for job resources. These findings add to the Job Demands-Resources theory's versatile nature and provide more clarity to practitioners who plan interventions to enhance specific performance types, taking individual strategies into account.

Keywords

Job resources employee performance strengths use moderation

Introduction

Organizations strive to maintain a competitive advantage in today's rapid-paced economy (Carpini et al., 2017). Hence, a vital point of research has been focused on means of increasing employees' performance. It is well known that individual aspects, such as cognitive abilities (Van Iddekinge et al., 2018) or conscientiousness (Wilmot & Ones, 2019), are relevant to individual employee task performance. However, nowadays, it is widely acknowledged that performance expands beyond being task proficient, to helping other team members or speaking highly of the organization (Carpini et al., 2017). The recognition of the broad spectrum of performance types pushed organizations towards widening their human resources approaches. Hence, next to looking for an optimal fit between profiles based on individual differences and job contexts, companies also invest in generating and customizing job resources to stimulate various job performance behaviors.

Job resources, such as autonomy or feedback, are those components of the workplace that facilitate goal attainment, reduce work-related strain, and allow personal growth and development (Bakker & Demerouti, 2017). According to the Job-Demands Resources theory (JD-R; Bakker & Demerouti, 2017), job resources relate to performance through a motivational process. When employees encounter such resources, they become more dedicated and more energized in their tasks (motivated) and more willing to invest in their work. In turn, the relationship is associated with an increase in performance (Christian et al., 2011). The development of such Human Resources Management (HRM) practices has uncovered dozens of job resources that may be fostered to increase various types of performance. These range from aspects pertaining to job design (e.g., autonomy, role clarity) to management and leadership practices (e.g., feedback, opportunities for development) (Nielsen et al., 2017; Schaufeli, 2017).

Nevertheless, scholars argue that job resources fail to exert an all-encompassing positive link to performance, especially when considering the levels of activity within the company (Van Veldhoven et al., 2020) and types of performance (Carpini et al., 2017). For instance, researchers determined that providing autonomy is beneficial to individual performance (Park & Choi, 2020) while also finding that it detracts from team-level performance (Mikalsen et al., 2019). As such, designing jobs that allow individuals to decide when, where, and how they carry out their tasks does enable employees to be more proficient in their job. However, it encumbers how well they correlate as group members. Similarly, employing job resources that stimulate innovative behaviors has been linked to a rise in task and contextual performance (Harari et al., 2016), but also to a decline in firm performance, especially in larger companies with well-established rules and procedures ( Gong et al., 2013 ). These conflicting results may disconcert practitioners who seek to implement HRM practices for the development of job resources. Are there specific job resources that, once fostered, enable employees to show an increase in performance as individuals and team members? Is a set of job resources necessary to stimulate multiple performance types, or does one resource suffice? Should such resources be developed, is there a trade-off to be expected, with a decline in another type of performance or at another level of activity? Unfortunately, there is little to no empirical evidence that may provide answers to these questions in a unitary manner, limiting the current literature.

The present investigation addresses this gap in the literature. To answer the questions above, we focus on four core job resources and investigate their concomitant relationships to distinct types of performance across several levels of activity within a company. The four selected job resources are role clarity, feedback, autonomy, and opportunities for development. These variables were included due to three criteria: (1) they are implemented through direct HRM practices (Breevaart et al., 2014; Park & Choi, 2020), allowing organizations to intervene and adjust their levels directly; (2) are generalizable and thus employed across industries (Schaufeli, 2017); (3) have yielded conflicting results in terms of association with performance outcomes. To achieve the stated objective, we employ the performance appraisal framework proposed by Griffin et al. (2007), which integrates three currently relevant and highly sought-after (Carpini et al., 2017; Wegman et al., 2018) performance types (proficiency – performing role-prescribed behaviors well; adaptivity – responding and adapting to change; proactivity – engaging in extra-role behaviors that are beneficial for the organization). Each type of performance is analyzed across three levels of activity within the organization (as an individual member, team member, and organization member), resulting in nine types of employee performance. This approach can thus provide specialists with a more fine-grained understanding of the relationships between job resources and performance.

Importantly, employees are not merely static agents influenced by job resources, but actors who actively seek to modify their work environment. Such proactive behaviors are known as individual strategies—developable methods that employees rely upon to accomplish tasks, “which generally involve some planning or marshaling of resources for their most efficient use” (Bakker & Demerouti, 2018, p. 9). Bakker and Demerouti (2018) include individual strategies into the JD-R theory as reinforcers of the relationship between job resources and workplace outcomes. The authors argue that investigating the interaction between job resources and individual strategies will enable an enhanced understanding of what helps individuals perform efficiently and effectively in a specific work context (Bakker & Demerouti, 2018; Schaufeli & Taris, 2014). A relevant individual strategy is strengths use—capitalizing on one's strong points, such as humor or zest (Miglianico et al., 2020) because it can directly affect how employees perform at work. As previously demonstrated, employees who rely upon their best personal characteristics at work tend to thrive and flourish (Bakker & van Woerkom, 2018). Employees who flourish at work report higher levels in various performance types (Kong & Ho, 2016; Meyers et al., 2020; Redelinghuys et al., 2019). Nevertheless, the interactive effect of strengths use—its potential to boost the relationship between job resources and workplace outcomes as hypothesized by the JD-R theory—has yet to be investigated. The present study also addresses this gap by testing the moderator role of strengths use on the relationship between the four types of job resources and the nine types of performance outcomes.

Summarizing, this study's purpose is two-fold: (1) to investigate which organization contingent resources are relevant for various types of performance, and (2) to understand the role of an individual strategy—strengths use—in these relationships. Therefore, our approach aims at developing the theoretical perspective of the JD-R theory by refining the connections between job resources and performance and understanding the role of proactive individual strategies in this process. Moreover, our results might provide practitioners and organizations with a contextualized model that argues more precisely which type of resources organizations should generate to stimulate certain kinds of performance, according to strategic goals and accounting for individual strategies.

Job resources and performance

The JD-R theory states that job resources are linked to performance through a mediation mechanism enabled by work engagement; conversely, job demands negatively impact performance through a health impairment process, specifically burnout. Nevertheless, as past research demonstrates, a wide array of developable job resources, such as role clarity (Griffin et al., 2007; Ma et al., 2020; Park & Choi, 2020), feedback (Park & Choi, 2020; Tagliabue, 2020), autonomy (El Baroudi et al., 2019; Ogbonnaya & Messersmith, 2019; Park & Choi, 2020), or opportunities for development (Aguinis & Kraiger, 2009; Tabiu et al., 2020) also show direct associations with performance. As such, organizations that provide job resources will facilitate an increase in employees' performance both directly, and through the enhancement of employees' work engagement (Chung & Angeline, 2010).

Despite their positive impact, scholars have recently started to question the universality of job resources. Van Veldhoven et al. (2020) argue that job resources' positive role is conditioned by a series of factors, such as levels of activity within the company. Also, Carpini et al. (2017) highlight the importance of considering performance types when investigating workplace resources. Therefore, the surprising meta-analytical results obtained by Nielsen et al. (2017)—a shared variance of 4% between job resources and performance—may be explained considering the arguments above. Nielsen et al. (2017) investigated the overall impact of different workplace resources at various organizational levels over different types of performance. The positive effect of a resource at a specific organizational level was probably countered by the lack of impact of said resource at a different company level or on a distinct type of performance.

Furthermore, Ogbonnaya and Messersmith (2019) demonstrate that certain job resources (i.e., performance feedback) alleviate stress, while others (i.e., opportunities for development, autonomy) may enhance it and detract from performance. The authors argue that this phenomenon occurs mainly in highly competitive economies, with employees perceiving they must exert extra effort to compensate for the resources the company had offered (i.e., autonomy), in a health-impairing trade-off. Therefore, it is vital to have a more nuanced approach when analyzing the specific links between job resources and types of performance to discern their role, relevance, and relationships more accurately.

The performance framework proposed by Griffin et al. (2007) allows such a fine-grained analysis. Integrating two well-established avenues of performance research, namely in- and extra-role performance (i.e., proficiency and proactivity) (for a review, see Carpini et al., 2017), the authors also include a third dimension to their model: adaptivity, that is adapting to change. This capacity has become increasingly important in the past decades due to the changing nature of work caused by technological improvements, globalization, flattened organizational hierarchies (Wegman et al., 2018), as well as participation in multiple teams at work (Crawford et al., 2019). Nowadays, people need to shift from individual work to team-based, to be able to refocus rapidly and efficiently from specific work tasks to a more creative or proactive approach to work and interpersonal relationships. A one scale view of performance would prove inefficient in discerning these transitions between various types of performance central to the ways people work today. Hence, we employ Griffin et al.'s (2007) framework and definitions and focus on employee proficiency, adaptivity, and proactivity as distinct types of performance. Specifically, proficiency refers to in-role performance, with employees meeting role-prescribed objectives, such as sales targets. Adaptivity indicates how well an employee responds and adapts to changes at work, such as new software. Proactivity, the correspondent of extra-role performance, describes behaviors that are not role-prescribed yet beneficial for the organization, such as helping colleagues with their tasks.

Each of the three dimensions is particularized for the three types of positions an individual occupies within a company. An employee activates at an individual level (micro perspective) while being part of a team (meso perspective) and the organization (macro perspective). This results in nine subdimensions of employee performance, from proficiency at an individual level to the proactivity individuals exhibit towards the organization. Significantly, this approach distinguishes between positions of activity within the company for the same individual, as opposed to the multi-level perspective, which investigates the individual as an aggregate at the team and organizational level (Bakker & Demerouti, 2018). Thus, the proposed framework enables the identification of “Why?” and “When?” resources are beneficial (Van Veldhoven et al., 2020) by discriminating between performance types, considering the micro (individual), meso (team), and macro (organization) position an employee holds within an organization.

Following Bakker and Demerouti (2017), we analyze four job resources as antecedents of each performance type that are contingent on the organization's strategy through aspects of job design (i.e., role clarity and autonomy) and leader-follower interactions (i.e., feedback and opportunities for development). Including job resources over which the organization has an increased control might facilitate a more successful implementation of interventions aimed at fostering said resources, through approaches such as the Energy Compass proposed by Schaufeli (2017), or a swifter adjustment of their levels. We will now briefly introduce each of the selected resources.

Role clarity describes the degree of knowledge employees' have regarding what is expected of them in their position (Kauppila, 2014). Feedback fosters learning, enabling employees to do their work more effectively (Evans & Dobrosielska, 2019). Autonomy refers to the degree of freedom employees have in deciding when, where, and how they perform their job (Tabiu et al., 2020). Opportunities for development represent “activities leading to the acquisition of new knowledge or skills for purposes of personal growth” (Aguinis & Kraiger, 2009, p. 451). As mentioned, the selected job resources have all been previously linked to performance. However, each of these variables yields conflicting results when the type of performance and activity level are considered. In what follows, we will briefly elaborate on each job resource, highlighting and discussing the empirical findings concerning various types of performance.

Role clarity has been linked to employee role-prescribed behavior (Griffin et al., 2007; Park & Choi, 2020) and proactivity (Ma et al., 2020), but its relationship to adaptivity performance is yet to be investigated. Providing employees with a clear understanding of their job description and ensuring they possess the necessary task completion guidelines contributes to increased efficiency in their job-related tasks and allows them to engage in proactive behaviors. However, organizing unambiguous guidelines may be perceived as rather restrictive and affect employees' adaptive reactions to change. This may prove problematic since many employees (95%) are required to activate in multiple teams (Crawford et al., 2019) and continuously adapt to changes within each of those groups. Therefore, employees with clear objectives for their role (e.g., number of sales) will be more prone to achieve these (Park & Choi, 2020) and may even find novel ways of attaining their targets (e.g., innovative advertisement campaign) (Ma et al., 2020). Nevertheless, activating in multiple teams will increase the number of work-related tasks and, subsequently, guidelines that employees must pursue. Having to consider and pay attention to many instructions could prove cumbersome; if accompanied by numerous guidelines, quickly adapting to new tasks or leadership styles could be more difficult for employees. Considering these arguments, we posit that:

H1a: Role clarity is positively related to proficiency and proactivity for all the three roles within the organization (i.e., individual, team, organizational), but not to adaptivity.

Feedback shows the strongest association with performance when presented in the context of the meso-position—how the individual acts within the team (Dufour et al., 2019). When employees receive information about their team's performance, they are more likely to respond positively and adjust. For example, individuals who are informed that their input helped their colleagues are bound to reinforce that behavior. This, in turn, will be associated with an increase in proficiency and proactivity. However, at the individual level, feedback may prove to be a double-edged sword. On the one hand, Park and Choi (2020) link feedback to individual task proficiency, while Ogbonnaya and Messersmith (2019) link it to proactivity performance. Nevertheless, according to Tagliabue et al. (2020), feedback may be problematic because it may elicit feelings of insecurity or inadequacy thoughts, thus leading to a decline in extra-role performance. Therefore, using feedback can prove useful for adjusting performance when presented at the team level, probably because it does not single out individuals; by being addressed to as a group, employees can integrate the information better. Should the feedback process occur during a one-on-one discussion, this can lead to both positive and negative performance outcomes, depending on how employees interpret the information. Thus, we expect:

H1b: Feedback is positively related to proficiency, adaptivity, and proactivity, but not for all three roles within the organization.

Also, autonomy has been linked to increased adaptivity, proactivity, and proficiency at the micro-position (El Baroudi et al., 2019; Park & Choi, 2020), as well as an increase in perceived demands (Ogbonnaya & Messersmith, 2019). Mikalsen et al. (2019) argue that it may also negatively interact with the meso-position individuals hold within the team and the organization. While autonomy allows an efficient individual organization of one's job, it might also interfere with how teams achieve their objectives while accommodating personal preferences. Hence, employees who perceive they have the freedom to decide on their work-related behaviors are, indeed, more likely to organize their work in a beneficial way achieving role-prescribed objectives. However, should each employee schedule without a responsible consideration to the team can lead to detrimental outcomes in highly interdependent teams. Therefore, we state that:

H1c: Autonomy is positively related to proficiency, adaptivity, and proactivity, but not for all three roles within the organization.

Next, opportunities for development have been shown to impact role-prescribed behavior across the micro-, and meso-positions individuals hold within the company (Tabiu et al., 2020), but proactivity only at the macro-position (Aguinis & Kraiger, 2009). Thus, an employee who develops new skills through work will increase proficiency and may even praise the company for this to others. Perceiving that the company has allowed them to develop themselves, gain new knowledge and expertise will be associated with employees utilizing said information to optimize the way they carry out their tasks. However, during the training sessions, they may also perceive that they are presented with the required way to carry out tasks. Considering that they are taught the most efficient way of carrying out their tasks during the training could inhibit employees from proactively devising new ways of achieving their objectives. Therefore, while development opportunities will allow employees to be more proficient, they may also detract from their proactive performance, at least at an individual or team level. Hence, we argue that:

H1d: Opportunities for development is positively related to proficiency, adaptivity, and proactivity, but not for all three roles within the organization.

Strengths use as a moderator in the relationships between job resources and performance

As presented in the previous section, existing studies provide evidence that job resources have different relevance for performance depending on the employee's investigated position within the organization (e.g., individual vs. team) or performance type (e.g., proficiency vs. adaptivity). However, personal strategies, such as strengths use, may surmount this situation because they encompass proactive behaviors that can potentiate job resources' role in enhancing positive work outcomes. We explain this perspective using the JD-R and Conservation of Resources (COR; Hobfoll, 2011) theories.

Bakker and Demerouti (2017) expand the JD-R theory by including individual proactive behaviors, such as job crafting or strengths use, as reinforcers of the relationship between job resources and workplace outcomes. They argue that employees experience an increase in well-being in the presence of job resources, enabling them to engage in behaviors directed at maintaining or increasing their engagement levels, thus mobilizing further resources. This reinforcement process is bound to increase employee performance (Christian et al., 2011). Hence, employees who rely upon their strong points at work also use other resources to enhance their performance.

While the JD-R theory proposes an interaction between job resources and individual strategies concerning well-being (Bakker & Demerouti, 2017; Bakker & Demerouti, 2018), we argue that individual resources may also facilitate the relationship between job resources and performance outcomes. Our assumption is rooted in the Conservation of Resources (COR; Hobfoll, 2011) theory. COR theory stipulates that individuals act in such a way as to attract, potentiate, and maintain their resources, which are defined as anything that allows them to achieve their objectives. Based on COR theory, an abundance of resources at the individual's disposal tend to attract others because “those with greater resources are less vulnerable to resource loss and more capable of orchestrating resource gain” (Hobfoll, 2011, p. 117). This process is known as a resource-gaining cycle, based on creating a passageway for resource caravans. To illustrate, when employees experience autonomy in their work (a job resource), they are more likely to develop a sense of self-efficacy at their job (a personal resource) because they can organize their work in a way that is best suited to their needs (van Mierlo et al., 2006). In turn, this will enable them further to generate other resources, such as appreciative feedback, thus engaging in new exciting work-related projects with colleagues. Consequently, both proficiency and proactivity—types of work performance—would increase. Through the lenses of the COR theory, it can be argued that individual resources enhance the relationship between contextual resources and performance outcomes as well.

The current study proposes strengths use as a potential reinforcer in the relationship between the four different job resources and the nine types of employee performance. Strengths have been defined as “the characteristics of a person that allows them to perform well or at their personal best” (Wood et al., 2011, p. 15). This broad definition implies that strengths are individual-dependent; while one individual possesses humor or perseverance, the other may rely upon leadership or temperance as strengths. Therefore, strengths use interventions are not directed at developing a singular aspect, such as resiliency or optimism, but rather allow individuals to consolidate a strategy that helps them identify their strengths and capitalize on them (Miglianico et al., 2020). When individuals use their strengths at work, they become energized and flourish (Bakker & van Woerkom, 2018), yielding an increase in different types of performance (Kong & Ho, 2016). Furthermore, it has been noted that strengths use has a more substantial impact on performance enhancement than deficit correction (Van Woerkom et al., 2016).

Therefore, we argue that employees who capitalize on their strong points at work will be able to make better use of existing workplace resources to increase their performance. For instance, providing role clarity to employees who rely upon their persistence to succeed is bound to be associated with an increase in proficiency. Similarly, suppose employees use their empathy and self-confidence while receiving feedback from their colleagues. This should capacitate them to be more receptive to the information and make use of it to adjust their performance. As another example, suppose employees rely upon their critical thinking or creative skills when making use of their autonomy. This may enable them to provide solutions to recurring problems, thus strengthening the link between autonomy and distinct performance types. Furthermore, engaging in opportunities for development could be associated with an increase in proficiency and adaptivity if employees use their analytical abilities in the process. Considering all these arguments, we reach our second and final hypothesis:

H2: Strengths use will interact with job resources in predicting all nine types of performance.

A graphical depiction of the research models can be found in Figure 1.

Method

Participants and procedure

After discarding one case of random answers (i.e., a participant who had almost 0% variance in answers, scoring minimum or maximum on all scales), the collected sample consisted of 332 Romanian employees (75.3% female) with ages ranging between 19 and 62 years (M = 35.04, SD = 9.60). Out of the total, 71.7% of the participants worked in the private sector, 24.4% in the public sector, while 3.9% were freelancers; 88% come from an urban environment (versus rural), 32.8% held a supervisory role within their organization, and 94.9% were white-collar workers (lower level white-collar such as secretary or typist - 13%; intermediate level white collar such as administrator or engineer - 27%; high-level white-collar such as programmer or instructor - 34%; superior white-collar such as senior manager or school headmaster - 21%), with the rest being either unqualified (2.1%) or qualified blue-collar workers (3%). In terms of education, 87.7% of the respondents obtained at least a bachelor's degree. Moreover, 78% of the surveyed employees had a total working experience of 4 years and over, with 50.9% of the total respondents having work experience ranging between 8 and 25 years of experience. The domains and positions of the respondents were also diverse; the sample comprises employees from the following fields: information technology (13.4%), automotive and heavy industries (12%), commerce and sales (11.1%), consultancy services (9.3%), education and research (8.2%), health care (6.1%), banking and financial services (5.5%), human resources services (5.5%), transportation services (5.2%), public administration (5.2%), marketing (4.4%), food services (3.8%), entertainment services (2.3%), construction (2%), and others with less than 2% (e.g., agriculture, tourism).

To reach this diverse sample of employees, we based our data collection on snowball sampling. After creating an online survey containing all instruments used in this study, we emailed it to our database of collaborators from the private sector with the request to fill it in and share it (where possible) within their companies. The first page of the survey contained an informed consent form, presenting the study's aim and scope to the participants and informing them about data confidentiality and their right to retreat from the study. Participation was entirely voluntary, anonymous, and no incentives were offered. Our email reached 566 people, out of whom 333 filled in the questionnaire (59.8% success rate). Considering the study's design and employed instruments, this study was exempted from obtaining approval from the university's Ethics Committee where the research was conducted.

Measures

Performance. We measured employee performance with the scale developed by Griffin et al. (2007). It comprises a set of 27 items, covering the nine subdimensions of performance. The items have been previously used on a Romanian sample by Fischmann et al. (2015). Each subscale measured one of three types of performance: proficiency, adaptability, and proactivity, at one of the three levels of an individual's activity within the organization: individual, team, and organizational level. Therefore, each subscale comprises a set of three items reported on a 1 (strongly disagree) to 5 (strongly agree) Likert scale. Sample items for each subdimension are detailed below:

At the individual level (micro-position), for proficiency performance, a sample item is “You have made sure that your tasks have been performed as required”; for adaptability performance, a sample item is “You have adapted well to changes which occurred regarding your main tasks”; for proactivity performance, a sample item is “You have come up with improvement ideas regarding the way your main tasks should be performed”.

For the individual within the team (meso-position), regarding proficiency performance a sample item is “You have communicated efficiently with your colleagues”; for adaptability performance, a sample item is “You have efficiently managed changes which affected your department (e.g., the arrival of a new colleague)”; for proactivity performance, a sample item is “You have made suggestions on how you could make your team more efficient”.

For the individual within the organization (macro-position), for proficiency performance, a sample item is “You have discussed in a positive manner about your organization”; for adaptability performance, a sample item is “You have reacted with flexibility to changes regarding your organization as a whole (e.g., change in management)”; finally, for proactivity performance, a sample item is “You have made suggestions on how you could enhance the general efficiency of your organization (e.g., suggested changes within the administrative procedures)”.

The Romanian version of all other instruments was generated employing the standard back-translation technique (Brislin, 1970).

Role clarity. We measured role clarity with a set of five items from the Questionnaire on the Experience and Evaluation of Work (QEEW; Van Veldhoven & Meijman, 1994). A sample item is “Do you know exactly what your superior thinks of your performance?”. All items were measured on a 1 (strongly disagree) to 5 (strongly agree) Likert scale.

Feedback, autonomy, and opportunities for development. All other predictor variables were measured based on the Job-Demands Resources Questionnaire developed by Bakker and Demerouti (2014). The items were all measured on a 1 (strongly disagree) to 5 (strongly agree) Likert scale. Feedback was assessed with a set of three items. A sample item is: “I receive sufficient information about the results of my work”. Autonomy was assessed with a set of three items. A sample item is: “Do you have control over how your work is carried out?”. Opportunities for development was assessed with a set of three items. A sample item is “In my work, I have the opportunity to develop my strong points”.

Strengths use. The presumed moderator was measured with a set of nine items, from the scale proposed by Van Woerkom et al. (2016), rated on a 0 (almost never) to 6 (almost always) Likert scale. A sample item is “I seek opportunities to do my work in a manner that best suits my strong points”.

Statistical analyses

To assess the trustworthiness of our measures and verify the possible occurrence of common method bias (Podsakoff et al., 2012), we first conducted a confirmatory factor analysis (CFA) using the lavaan package (Rosseel, 2012) in R software (R Core Team, 2013). Thus, we tested two measurement models: M1 – a model with 14 factors (role clarity, feedback, autonomy, opportunities for development, strengths use, proficiency at individual, team, and organization level, adaptivity at individual, team, and organization level, and proactivity at individual, team, and organization level), and M2 – a single factor model (Podsakoff et al., 2012). Model fit was evaluated using maximum likelihood estimation; we calculated three absolute fit indices (the chi-square statistic; RMSEA - the root mean square error of approximation; and SRMR - the standardized root mean square residual) and two relative fit indices (CFI - Comparative fit index; and TLI - Tucker-Lewis index). The cut-off values for the fit indices that indicate acceptable fit are: RMSEA < .08; CFI and TLI > .90, and SRMR < .08 (Marsh et al., 2005).

We tested the effect of the selected predictors (i.e., role clarity, feedback, autonomy, and development opportunities) and the moderator role of strengths use in relation to each performance type using hierarchical multiple linear regressions in SPSS v20 software (IBM Corp. Released, 2011). Hence, we tested nine separate regression models. After mean-centering all the independent variables, we first tested a series of regression models regarding the relationships between the selected predictor variables and each dimension of performance (Step 1). Afterward, the presumed moderator was introduced (i.e., strengths use; Step 2), and finally, the interaction terms between each predictor and the moderator (Step 3). To test the nature of the moderated relationships, all significant interactions were further analyzed employing simple slopes analyses (Aiken & West, 1991) and plotted at ±1 SD from the mean of the moderator (Dawson, 2014).

Results

Table 1 indicates the correlations among the study variables, descriptive statistics, and their reliability estimates. All operationalizations had at least acceptable to good internal consistencies, hence offering overall optimal reliability. Except for sex and age, which are only correlated with each other, all the other variables are completely intercorrelated. This implies that all the selected job resources are, to some degree, associated with all the performance facets. Furthermore, all performance outcomes had small to high correlations with each other, suggesting that they all measure the same general construct, yet are different enough to assess different types of results (see Fischmann et al., 2015; for details on the factor structure of the performance measure on Romanian workers).

Table 1.

Correlations among study variables, descriptive statistics, and reliability estimates.

Variable	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
1. Sex	–
2. Age	−.11*	–
3. Role clarity	−.13*	.07	(.79)
4. Feedback	−.04	−.03	.46**	(.88)
5. Autonomy	−.11	.06	.47**	.36**	(.79)
6. Op. Dev.	−.05	−.01	.24**	.49**	.37**	(.87)
7. Strengths use	.00	.01	.32**	.28**	.33**	.33**	(.92)
Individual performance
8. Proficiency	.09	.07	.27**	.26**	.17**	.24**	.36**	(.76)
9. Adaptivity	.05	.06	.33**	.30**	.24**	.26**	.28**	.58**	(.82)
10. Proactivity	.00	.10	.28**	.26**	.35**	.22**	.42**	.33**	.48**	(.86)
Team member performance
11. Proficiency	.08	.03	.30**	.30**	.15**	.20**	.20**	.47**	.46**	.31**	(.82)
12. Adaptivity	.09	.10	.25**	.29**	.12*	.20**	.23**	.48**	.56**	.50**	.58**	(.77)
13. Proactivity	−.10	.14*	.30**	.27**	.25**	.17**	.34**	.24**	.40**	.68**	.36**	.60**	(.91)
Organization member performance
14. Proficiency	−.03	.17**	.35**	.40**	.28**	.32**	.26**	.25**	.30**	.32**	.41**	.43**	.48**	(.90)
15. Adaptivity	.01	.13*	.36**	.38**	.24**	.27**	.33**	.38**	.52**	.49**	.45**	.65**	.59**	.57**	(.80)
16. Proactivity	−.07	.16**	.26**	.27**	.30**	.12*	.31**	.17**	.32**	.57**	.24**	.46**	.66**	.43**	.57**	(.80)
M	–	35.04	3.82	3.59	3.68	3.95	4.65	4.38	4.29	3.85	4.32	4.12	3.77	4.14	3.92	3.51
SD	–	9.60	0.68	0.87	0.91	0.76	1.02	0.48	0.54	0.80	0.58	0.63	0.88	0.81	0.70	1.07

Note. N = 332; Op. Dev. = Opportunities for development; Reliabilities (Cronbach’s α) are presented on the main diagonal; M = mean, SD = standard deviation. *p < .05, **p < .01 (two-tailed).

Measurement models

The first model (M1) had acceptable fit indices, χ² (1084) = 2014.18, p < .001, CFI = .91; TLI = .90; RMSEA = .05, 90% CI [.05, .06], SRMR = .05. Conversely, the common method factor model (M2) displayed poor fit: χ² (1175) = 6900.84, p < .001; CFI = .42; TLI = .39; RMSEA = .13, 90% CI [.13, .14], SRMR = .12. The chi-square difference test indicated that M1 fitted the data better than M2, Δχ² (91) = 4886.7, p < .001. Therefore, common method bias does not seem to be sufficient to account for the associations among the study variables.

Main effects

Tables 2, 3, and 4 present the results of the hierarchical multiple regression analyses on each of the nine types of performance. To facilitate the legibility of the results, we grouped them based on the three superordinate categories (i.e., individual, team, and organization member performance). Overall, data from nine separate regression analyses are shown.

Table 2.

Hierarchical multiple regression analyses predicting individual task performance.

Variable	Individual task
	Proficiency							Adaptivity							Proactivity
	B	95% CI for B		SE B	Β	R ²	ΔR ²	B	95% CI for B		SE B	Β	R ²	ΔR ²	B	95% CI for B		SE B	β	R ²	ΔR ²
	B	LL	UL	SE B	Β	R ²	ΔR ²	B	LL	UL	SE B	Β	R ²	ΔR ²	B	LL	UL	SE B	β	R ²	ΔR ²
Step 1						.11	.11***						.15	.15***						.15	.15***
Constant	4.37***	4.32	4.42	.03				4.28***	4.23	4.34	.03				3.84***	3.76	3.92	.04
Role clarity	.14**	.05	.23	.05	.20**			.18***	.08	.27	.05	.22***			.13	−.01	.27	.07	.11
Feedback	.06	−.02	.13	.04	.10			.07	−.01	.15	.04	.17			.09	−.03	.20	.06	.09
Autonomy	−.01	−.08	.05	.03	−.02			.03	−.04	.10	.04	.05			.21***	.11	.32	.05	.24***
Op. Dev.	.10*	.02	.18	.04	.15*			.10*	.01	.18	.04	.13*			.06	−.06	.19	.06	.06
Step 2						.17	.06***						.17	.02**						.23	.08***
Constant	4.37***	4.33	4.42	.02				4.28***	4.23	4.34	.03				3.84***	3.77	3.92	.04
Role clarity	.10*	.02	.19	.04	.15*			.16**	.06	.25	.05	.20**			.06	−.08	.20	.07	.05
Feedback	.05	−.02	.12	.04	.09			.07	−.01	.15	.04	.11			.07	−.04	.18	.06	.08
Autonomy	−.03	−.10	.03	.03	−.06			.01	−.06	.08	.04	.02			.17**	.07	.27	.05	.19**
Op. Dev.	.06	−.01	.14	.04	.10			.07	−.01	.16	.04	.10			−.01	−.13	.11	.06	−.01
Strengths use	.13***	.08	.18	.03	.27***			.08**	.02	.14	.03	.15**			.25***	.17	.33	.04	.32***
Step 3						.21	.04**						.22	.05**						.24	.01
Constant	4.36***	4.31	4.41	.03				4.25***	4.19	4.30	.03				3.82***	3.74	3.90	.04
Role clarity	.09*	.01	.18	.04	.13*			.15**	.06	.25	.05	.19**			.06	−.08	.20	.07	.05
Feedback	.05	−.02	.11	.04	.08			.05	−.02	.13	.04	.08			.08	−.03	.19	.06	.09
Autonomy	−.03	−.09	.03	.03	−.06			.01	−.06	.08	.04	.02			.17**	.07	.27	.05	.19**
Op. Dev.	.07	−.01	.14	.04	.11			.09*	.01	.17	.04	.13*			−.01	−.13	.12	.06	−.01
Strengths use	.15***	.09	.20	.03	.31***			.13***	.07	.19	.03	.24***			.26***	.17	.34	.05	.33***
RC × SU	−.10**	−.17	−.03	.04	−.15**			.03	−.05	.11	.04	.04			.02	−.10	.14	.06	.02
Fb × SU	−.16	−.08	.05	.03	−.03			−.09*	−.16	−02	.04	−.16*			.06	−.05	.17	.06	.07
A × SU	.08**	.03	.14	.03	.17**			.06	−.01	.12	.03	.11			.02	−.07	.11	.05	.02
OD × SU	.05	−.02	.12	.04	.09			.14***	.06	.21	.04	.24***			−.02	−.13	.09	.06	−.02

Note. N = 332; CI = confidence interval; LL = lower limit; UL = upper limit; Op. Dev./OD = opportunities for development; RC = role clarity; Fb = feedback; A = autonomy; SU = strengths use. *p < .05, **p < .01, ***p < .001.

Table 3

. Hierarchical multiple regression analyses predicting team member performance.

Variable	Team member
	Proficiency							Adaptivity							Proactivity
	B	95% CI for B		SE B	Β	R ²	ΔR ²	B	95% CI for B		SE B	Β	R ²	ΔR ²	B	95% CI for B		SE B	β	R ²	ΔR ²
	B	LL	UL	SE B	Β	R ²	ΔR ²	B	LL	UL	SE B	Β	R ²	ΔR ²	B	LL	UL	SE B	β	R ²	ΔR ²
Step 1						.13	.13***						.11	.11***						.12	.12***
Constant	4.31***	4.23	4.37	.03				4.12***	4.05	4.18	.03				3.77***	3.68	3.86	.05
Role clarity	0.19***	.09	.30	.05	.23***			.17**	.05	.28	.06	.18**			.23**	.07	.39	.08	.18**
Feedback	0.12**	.03	.20	.04	.18**			.14**	.05	.23	.05	.19**			.14*	.01	.27	.07	.14*
Autonomy	−.04	−.11	.04	.04	−.06			−.05	−.13	.04	.04	−.07			.10	−.01	.22	.06	.11
Op. Dev.	.07	−.03	.16	.05	.08			.07	−.03	.17	.05	.08			.02	−.12	.16	.07	.02
Step 2						.14	.01						.13	.02*						.17	.05***
Constant	4.31***	4.26	4.37	.03				4.12***	4.05	4.18	.03				3.76***	3.68	3.85	.04
Role clarity	.18***	.07	.29	.05	.21***			.14*	.03	.26	.06	.15*			.18*	.02	.34	.08	.14*
Feedback	.12**	.03	.20	.04	.17**			.13**	.04	.23	.05	.19**			.13*	.01	.26	.06	.13*
Autonomy	−.05	−.12	.03	.04	−.07			−.06	−.15	.02	.04	−.09			.07	−.05	.18	.06	.07
Op. Dev.	.05	−.04	.15	.05	.07			.05	−.06	.15	.05	.06			−.03	−.17	.11	.07	−.03
Strengths use	.05	−.02	.11	.03	.08			.09*	.02	.16	.04	.14*			.21***	.11	.30	.05	.24***
Step 3						.16	.02*						.16	.03*						.19	.02
Constant	4.31***	4.24	4.37	.03				4.08***	4.01	4.15	.04				3.72***	3.62	3.81	.05
Role clarity	.17**	.06	.27	.05	.19**			.13*	.02	.25	.06	.14*			.17*	.02	.33	.08	.13*
Feedback	.11**	.03	.20	.04	.17**			.12**	.03	.21	.05	.17**			.12	−.01	.24	.06	.12
Autonomy	−.04	−.12	.03	.04	−.07			−.07	−.15	.02	.04	−.10			.06	−.05	.18	.06	.06
Op. Dev.	.06	−.03	.16	.05	.08			.06	−.04	.16	.05	.08			−.02	−.16	.12	.07	−.01
Strengths use	.07	−.01	.13	.03	.12			.13**	.06	.20	.04	.21**			.26***	.16	.36	.05	.30***
RC × SU	−.12*	−.21	−.02	.05	−.15*			−.02	−.12	.08	.05	−.02			.07	−.07	.20	.07	.06
Fb × SU	−.01	−.09	.07	.04	−.01			−.05	−.14	.04	.05	−.07			−.09	−.21	.04	.06	−.09
A × SU	.05	−.02	.12	.04	.09			.06	−.01	.14	.04	.10			.06	−.04	.17	.05	.07
OD × SU	.08	−.01	.16	.04	.12			.12**	.03	.22	.05	.18**			.14*	.01	.26	.06	.14*

Table 4.

Hierarchical multiple regression analyses predicting organization member performance.

Variable	Organization member
	Proficiency							Adaptivity							Proactivity
	B	95% CI for B		SE B	Β	R²	ΔR²	B	95% CI for B		SE B	Β	R²	ΔR²	B	95% CI for B		SE B	β	R²	ΔR²
	B	LL	UL	SE B	Β	R²	ΔR²	B	LL	UL	SE B	Β	R²	ΔR²	B	LL	UL	SE B	β	R²	ΔR²
Step 1						.21	.21***						.20	.20***						.13	.13***
Constant	4.14***	4.06	4.22	.04				3.92***	3.85	3.99	.04				3.50***	3.40	3.61	.06
Role clarity	.22**	.08	.35	.07	.18**			.24***	.11	.36	.06	.23***			.13	−.07	.32	.10	.08
Feedback	.22***	.10	.33	.06	.23***			.18***	.08	.28	.05	.22***			.24**	.08	.39	.08	.19**
Autonomy	.05	−.05	.15	.05	.05			.01	−.08	.10	.05	.02			.26***	.12	.40	.07	.22***
Op. Dev.	.15*	.03	.27	.06	.14*			.09	−.02	.20	.05	.10			−.11	−.28	.06	.09	−.08
Step 2						.22	.01						.23	.03***						.17	.04***
Constant	4.14***	4.06	4.22	.04				3.92***	3.85	3.99	.03				3.50***	3.40	3.61	.05
Role clarity	.20**	.06	.34	.07	.17**			.20***	.08	.32	.06	.20***			.07	−.13	.26	.10	.04
Feedback	.21***	.10	.32	.06	.23***			.17***	.08	.27	.05	.22***			.22**	.07	.38	.08	.18**
Autonomy	.04	−.07	.14	.05	.04			−.01	−.10	.08	.05	−.01			.22**	.08	.36	.07	.19**
Op. Dev.	.13*	.01	.25	.06	.12*			.06	−.05	.16	.06	.06			−.17	−.34	.00	.09	−.12
Strengths use	.07	−.02	.15	.04	.09			.13***	.05	.20	.04	.18***			.23***	.11	.34	.06	.22***
Step 3						.23	.01						.25	.02*						.19	.02
Constant	4.14***	4.06	4.23	.04				3.89***	3.82	3.97	.04				3.44***	3.33	3.56	.06
Role clarity	.20**	.05	.34	.07	.16**			.19**	.07	.31	.06	.19**			.06	−.13	.25	.10	.04
Feedback	.21***	.10	.32	.06	.23***			.16***	.07	.26	.05	.20***			.21**	.05	.36	.08	.17**
Autonomy	.04	−.06	.14	.05	.05			−.01	−.10	.08	.05	−.02			.22**	.08	.36	.07	.19**
Op. Dev.	.13*	.01	.25	.06	.12*			.07	−.03	.18	.05	.08			−.15.	−.32	.02	.09	−.11
Strengths use	.07	−.02	.16	.05	.08			.16***	.09	.24	.04	.24***			.29***	.17	.41	.06	.28***
RC × SU	−.01	−.14	.11	.06	−.01			−.04	−.15	.06	.05	−.04			.04	−.13	.20	.08	.03
Fb × SU	−.04	−.15	.07	.06	−.05			−.06	−.16	.03	.05	−.08			−.08	−.23	.07	.08	−.07
A × SU	.03	−.06	.12	.05	.04			.04	−.05	.12	.04	.05			.10	−.02	.23	.06	.09
OD × SU	−.01	−.12	.11	.06	−.01			.14**	.04	.24	.05	.19**			.15	−.201	.31	.08	.13

Starting with individual task performance, proficiency was significantly predicted only by role clarity (β = .20, p < .01) and opportunities for development (β = .15, p = .01). The overall model explained 11% of variance (R² = .11; Step 1). By adding the main effect of strengths use (β = .27, p < .001) the model significantly gained another 6% of explanatory power (ΔR² = .06; Step 2), while the effect of opportunities for development ceased to be significant (β = .10, p = .11). When it came to individual task adaptivity, also role clarity (β = .22, p < .001) and opportunities for development (β = .13, p < .03) were the significant predictors (R² = .15). Similarly, strengths use had a significant contribution (β = .15, p < .01; ΔR² = .02), case in which opportunities for development became non-significant (β = .10, p = .09). Moving forward, individual task proactivity was significantly predicted only by autonomy (β = .24, p < .001; model R² = .15) and strengths use (β = .32, p < .001; ΔR² = .08).

Team member proficiency was significantly predicted by role clarity (β = .23, p < .001) and feedback (β = .18, p < .01), with the job resources model explaining 13% of variance (Step 1). On this outcome, strengths use had a non-significant main effect. Team member adaptivity was also predicted by role clarity (β = .18, p < .01) and feedback (β = .19, p < .01), the entire model explaining 11% of variance. In this case, strengths use had a significant main effect (β = .14, p = .01; ΔR² = .02). Moreover, team member proactivity had the same two predictors: role clarity (β = .18, p < .01) and feedback (β = .14, p = .03) (model R² = .12). Also, strengths use added a significant increment in the prediction (β = .24, p < .001; ΔR² = .05).

Organization member proficiency was significantly predicted by role clarity (β = .18, p < .01), feedback (β = .23, p < .001), and opportunities for development (β = .14, p = .02). The overall model explained 21% of variance. On this outcome the main effect of strengths use was non-significant. Organization member adaptivity relied only on role clarity (β = .23, p < .001) and feedback (β = .22, p < .001), but the model had a similar predictive power (R² = .20). This time, strengths use significantly contributed with another 3% in predicting the criterion (β = .18, p < .001). Finally, organization member proactivity had a more different configuration of predictors, namely feedback (β = .19, p < .01) and autonomy (β = .22; p < .001). The model explained 13% of variance and was significantly improved by strengths use (β = .22, p < .001; ΔR² = .04).

Overall, role clarity significantly predicted seven out of the nine subdimensions of performance (with an emphasis on proficiency and adaptivity across all three positions in the organization), feedback predicted only the team member and organization member facets of performance (all six of them), autonomy predicted only two criteria (i.e., individual and organization proactivity). In comparison, opportunities for development significantly predicted three performance subdimensions (i.e., individual proficiency and adaptivity, and organization member proficiency). Hence, Hypotheses 1a to 1d were partially supported by the results, aspect which will be further detailed in the Discussion section. Even though the main effects of strengths use were not our hypotheses' focus, it is worth highlighting that this individual strategy predicted seven out of the nine performance types.

Moderation effects

The interactions between all four job resources and strengths use in relationship with the nine facets of performance are reported in each Step 3 from Tables 2, 3, and 4. Overall, there were seven¹ significant interaction effects concerning five of the nine types of performance. Specifically, strengths use significantly moderated the effect of role clarity on individual task proficiency (β = −.15, p < .01) and on team member proficiency (β = −.15, p = .01). As can be seen in Figure 2(a), role clarity was a stronger positive predictor of individual task proficiency at low levels of strength use (b = .20, t = 4.33, p < .001), while at high levels of the moderator the effect ceased to be significant (b = −.01, t = −0.30, p = .77). Similarly, as depicted in Figure 2(b), role clarity was also a significant predictor of team member proficiency at low (b = .29, t = 4.09, p < .001) rather than high levels of strengths use (b = .05, t = 0.63, p = .53). Hence, for these two facets of performance, role clarity seems to be an important job resource, but only for those employees who have a reduced capacity to capitalize on their strong points.

Figure 1.

The research models.

Moreover, strengths use significantly moderated the relationship between feedback and individual task adaptivity (β = −.16, p = .02; Figure 3(a)). The prediction was positive and significant at low (b =.14, t = 2.78, p < .01), rather than high levels of strengths use (b = −.04, t = 0.69, p = .49). Another significant interaction was between autonomy and individual task proficiency (β = .17, p < .01; Figure 3(b)). In this case, the relationship between the job resource and performance was also significant at low strengths use, but negatively (b = −.12, t = −4.25, p < .001). At the same time, at high levels of the moderator, it was not statistically significant (b =.06, t = 1.84, p = .07). Hence, feedback has a role in predicting performance when employees are not aware of their strengths, while providing high autonomy levels to employees who do not use their strengths at work appears to be detrimental for performance.

Figure 2.

Interaction effect of role clarity and strengths use in predicting individual task proficiency (a) and proficiency as a team member (b).

The last three significant interaction effects are depicted in Figure 4. Strengths use interacted with opportunities for development in predicting individual task adaptivity (β = .24, p < .001). As one can see from Figure 4(a), the prediction was positive and significant at high (b = .23, t = 3.75, p < .001), rather than low levels of strengths use (b = −.05, t = −0.89, p = .37). Strengths use also boosts the relationship between opportunities for development and team member adaptivity (β = .18, p < .01; Figure 4(b)). In a similar way, the relationship was positive and significant for employees with high strengths use (b = .19, t = 2.53, p = .01), but not for those with low levels (b = −.06, t = −0.97, p = .33). The last significant moderation effect followed the previous two patterns. Opportunities for development interacted with strengths use in predicting organization member adaptivity (β = .19, p < .01; Figure 4(c)), and the relationship was positive and stronger at high levels of the moderator (b = .22, t = 2.76, p < .01), as compared to lower ones (b = −.07, t = −0.98, p = .33). Hence, these last three results suggest that to transpose opportunities for development into adaptive performance (regardless the organization level), employees need to be highly capable of using their strong points.

Figure 3.

Interaction effect of feedback and strengths use in predicting individual task adaptivity (a) and interaction of autonomy and strengths use in predicting individual task proficiency (b).

Overall, Hypothesis 2 was also partially supported by the data, finding which will be further detailed in the next section.

Figure 4.

Interaction effect of opportunities for development and strengths use in predicting individual task proficiency (a), adaptivity as a team member (b), and adaptivity as an organization member (c).

Discussion

Scholars have recently launched the call for a more fine-grained analysis of the relationship between job resources and performance (Van Veldhoven et al., 2020). The current study answers this call by investigating the potential predictive role of four job resources (role clarity, feedback, autonomy, and opportunities for development) for nine different types of performance that are based on a combination between three perspectives—proficiency, proactivity, and adaptivity—and three organizational levels (individual, team, organization). Moreover, employing the COR perspective and building upon the JD-R theory, our research investigates the moderator role of strengths use on the relationships mentioned above to understand whether this particular individual strategy plays a relevant role in this equation.

The main findings of this study indicate that the relationship between job resources and job performance should, indeed, be approached in a more nuanced manner. Consistent with our first four hypotheses, we identified the selected predictors to be related to employee performance, yet not for all types of performance. Moreover, strengths use appears to have a differential moderating effect on the job resources – performance types relationships, partially supporting the second hypothesis. For a more comfortable understanding of the findings, Table 5 offers a bird's-eye view of the significant results.

Table 5.

Summary of findings from the nine hierarchical multiple regression analyses.

Predictor	Performance subdimensions
	Individual task			Team member			Organization member
	Proficiency	Adaptivity	Proactivity	Proficiency	Adaptivity	Proactivity	Proficiency	Adaptivity	Proactivity
Role clarity	P + I	P		P + I	P	P	P	P
Feedback		I		P	P	P	P	P	P
Autonomy	I		P						P
Op. Dev.	P	P + I			I		P	I
Strengths use	P	P	P		P	P		P	P

Note. P = significant prediction (main effect); I = significant moderation (interaction effect); Op. Dev. = opportunities for development.

Based on the present results, when employees comprehend what they need to do at work and how (role clarity), they tend to complete their tasks well (be proficient) and cope effectively with changes (be adaptive) in every organizational role they have (as an individual, as a team member, or as an organization member). Role clarity also enables employees to be more proactive as team members, suggesting novel approaches to carrying out tasks at the meso-level. These findings expand Griffin et al.’s (2007) work, who argued that role clarity is productive only regarding proficiency while obscuring the necessity for engaging in extra-role behaviors.

Receiving feedback is related to all performance types affecting how the employee carry out their task as a team member (such as good coordination with team colleagues, positive response to changes within the team) or as an organization member (such as supporting the organization's positive image, engaging in flexible responses to organizational changes, and in an active involvement for improving organizational effectiveness). Considering employees are nowadays asked to work interdependently in an increasingly diverse and multicultural workforce, feedback appears to be a useful approach in prompting performance in such conditions (Whitaker & Levy, 2012). However, at an individual level, a series of conditions must be met for feedback to be useful (e.g., feedback must be precise and informative; Whitaker & Levy, 2012), conditions that are not easily satisfied. This is a potential explanation of why we were unable to identify a link between feedback and performance at an individual level. Moreover, autonomy enables employees to be more proactive as individuals and organizational members. These findings are in line with El Baroudi et al. (2019), who identify autonomy as a precursor of proactivity. Having the liberty to organize one's work as seen fit stimulates employees to find novel ways of carrying out their own tasks and generate ideas on improving organizational effectiveness. The lack of association with other types of performance is somewhat surprising, considering past studies link autonomy with adaptivity and proficiency (Park & Choi, 2020; Tabiu et al., 2020). However, as Dysvik and Kuvaas (2011) have shown, the link between autonomy and performance is influenced by moderators, such as intrinsic motivation, thus providing a possible explanation for our findings. Employees also seem to carry out their core parts of their role well and positively present the organization to others when experiencing an environment that fosters opportunities for development at work. In such a context, individuals also react flexibly to changes in their work-related tasks. Still, they do not engage in proactive behaviors as compensation for the growth opportunities, results which are in line with the findings of Aguinis and Kraiger(2009). In short, our results support the direct relationships between resources and performance and indicate a refined perspective on how different resources are, more or less, relevant for various types of performance.

Our study also aimed to test the moderator role of strengths use in the previously mentioned relationships. Beforehand, we deem it necessary to also highlight its direct relationships with performance. Our data revealed that this individual strategy is essential for several types of performance, except for team and organization member proficiency. When employees actively look for work tasks where they can employ their talents and focus on the things they do well, they tend to be proficient in individual tasks and adaptive and proactive in positions as individual, team, and organization members. These findings are in line with those of Kong and Ho (2016), who found strengths use to be linked to both in- and extra-role performance at the individual and organizational level. When employees feel that they apply their talents in their job and focus on the things they do well does not relate to how they engage in positively talking about their organization or an effective collaboration with their team colleagues; perhaps focusing on personal strong points might distract employees from considering the team and organizational needs. When it comes to the moderator role of strengths use, the data offered several significant interaction effects. Based on COR and JD-R theories, employees who use individual strategies while at work should also make better use of job resources, as they tend to reciprocally stimulate each other in so-called positive gain spirals. This study indicates that both high and low levels of strength use are relevant but in different contexts.

On the one hand, it appears that when resources such as role clarity and feedback exist, employees best perform or adapt at individual level when they use their strengths less. Mainly, strengths use interacts with role clarity concerning individual task proficiency and team member proficiency. The relationship between the given resource and types of performance is significant at lower levels of the moderator. Hence, it is essential to provide clear guidelines and expectations regarding their role (as an individual and team member) to employees who might be unaware of their strong points or are unable to use them in engaging their role-prescribed duties. This approach will encourage them to reach an optimum level of performance. Strengths use moderates the relationship between feedback and individual adaptivity in a similar fashion. Feedback appears to be a useful resource in enabling employees to adapt better to new tasks, especially when employees do not capitalize on their strong points at work. Encouraging employees to discover and use their talents at work could enable them to adapt better to changes in their work routine without risking the emergence of thoughts of ineptitude due to feedback (Tagliabue et al., 2020). The moderator role of strengths use in the relationship between autonomy, and individual proficiency indicates a different moderation pattern. At low levels of strengths use, autonomy exhibits a negative relationship with individual proficiency. As such, allowing employees with a decreased awareness of their talents to decide when and how to carry out their core tasks appears to have detrimental effects on their core performance. A potential explanation is that they might feel overwhelmed by their responsibility to organize their work. Not relying on their strong points will elicit a sense of helplessness, in which high autonomy might translate into uncertainty, thus decreasing their in-role performance.

On the other hand, the relationship between opportunities for development and individual, team, and organizational adaptivity is enabled by high levels of strengths use. It appears that to translate the opportunities provided by the environment (job resources) into adaptive performance behaviors, employees need to acknowledge their strong points and even focus on matching the favorable circumstances with their need for development and learning.

Summarizing, job resources represent the direct antecedents of different types of performance. They play a vital role in enhancing said outcomes, especially when it comes to tasks that individuals do not feel confident about or do not like. The relevance of job resources in the presence of strengths use, however, appears to fade. When employees are focused on their strong points and actively looking for tasks they are good at, they no longer need an abundance of job resources to attain high-performance levels. These results indicate that, rather than customizing and balancing many different job resources, companies may invest in interventions aimed at employing signature-strengths at work, doubled by fostering only a handful of job resources, such as opportunities for development.

Limitations

As with any study, our study has certain limitations. First, considering the correlational design, the results do not allow any causal conclusions and should be interpreted in a somewhat exploratory manner. Therefore, future studies should aim to validate these findings through experimental, randomly controlled trials to test whether manipulating specific resources will culminate with changes in particular types of performance.

Second, we relied upon self-report instruments for data collection and a single time point, making the data susceptible to common-method bias (Podsakoff et al., 2012). Nevertheless, the conducted CFA indicates low chances for the occurrence of common method bias. Next, although self-reported data collection is a common practice in this research field, existing literature indicates that it is not necessarily interchangeable with objective measures, which are considered to more accurately reflect the relationship among variables (Andrews et al., 2006). However, Andrews et al. (2006) argue that both approaches have specific flaws. While subjective measures are exposed to common method bias, self-presentation biases, or introspective flows, objective measures are difficult to correlate across industries, as was the case in this study. Also, although the role clarity scale employed in this study yields good psychometric properties (see the Method section), newer instruments have been developed to assess this specific construct from a reversed perspective (i.e., role ambiguity; see Bowling et al., 2017). Thus, we encourage researchers aiming to replicate our results to employ such newer versions of role clarity scales to further enhance our findings' trustworthiness.

Third, the sample included in this study consists of Eastern European, educated white-collar workers, limiting the generalization of our research to specific occupational categories and cultural profiles. As previously demonstrated (Schuler et al., 2019), individualistic and collectivistic cultures perceive job resources and engage in individual strategies differently. Hence, future studies should try and replicate these results in other specific cultures and different categories of employees (e.g., blue-collar workers).

Fourth, the sample used for data collection is heterogeneous. As Calder et al. (1981) argue, relying on heterogenous samples increases variability, which may affect the predictions' accuracy. However, in this study, the (1) major industries represented in the sample are evenly distributed, (2) selected variables are generalizable across industries (Schaufeli, 2017), and (3) data were evenly distributed and contained no outliers. Exponents of the heterogeneous samples approach argue that, once these conditions are met, variability enables a more realistic modeling of the world (Allen & Seaman, 2017; Hochwarter, 2014).

Theoretical and practical implications

From a theoretical perspective, our results provide additional support to job resources' prominent role concerning performance, as stated by the JD-R theory. The employed predictors account for a substantial amount of variance in each of the nine performance types measured in this study. Furthermore, the results indicate that job resources have a differential relation with performance when considering both its types and the micro, meso, and macro positions individuals hold within a company. While specific resources (i.e., opportunities for development, role clarity) are relevant predictors for employee proficiency across positions, others appear to be more strongly related to employee proactivity (i.e., autonomy). Three employed predictors yield a cascading effect across positions as an individual, team, and organization member. Thus, our study answers Van Veldhoven et al.'s (2020) call by providing more clarity regarding the context in which job resources produce the most satisfactory results.

Specifically, considering that role clarity predicts most performance types suggests that organizations should, indeed, provide clear guidelines to their employees and aim to correlate these instructions among teams. This will ensure that individuals who are members of multiple teams will be provided with similar approaches across teams. Consequently, it will enable them to quickly transition between roles and also attain high-performance in each role. Moreover, our results indicate that feedback can enhance performance, but that it should be used in relation to team and organizational outcomes. Furthermore, employees who participate in development opportunities do gain the necessary expertise to increase their task-performance. However, they should also be reminded that while trainings equip them with efficient tools for task completion, they can also independently search for novel ways of carrying out their tasks.

By including strengths use as a moderator of these relationships, we heed the call of Bakker and Demerouti (2017). The authors encourage researchers to build upon the JD-R theory by integrating individual strategies as possible reinforcers of the link between job resources and workplace outcomes (Bakker & Demerouti, 2017). The current results seem to partially contradict rather than support this assumption. Strengths use does potentiate the relationship between opportunities for development and different types of employee performance. This may be particularly important for organizations transitioning to New Ways of Working, in which employees perceive fewer opportunities for development (Van Steenbergen et al., 2018). However, low strengths use hinders the positive effect of autonomy regarding individual proficiency. Therefore, managers who aim to provide autonomy to their employees ought to ensure that they first help their employees recognize their strengths and encourage them to engage their talents in their work. Thus, they will ensure that autonomy does not turn into a stressor (Ogbonnaya & Messersmith, 2019).

In contrast, high levels of strengths use renders the effect of role clarity concerning proficiency (individual and team member) and feedback concerning adaptivity (individual) obsolete. As such, especially in the case of members of multiple teams, encouraging employees to develop and employ their strengths in the workplace might prove to be a more time-efficient strategy for enhancing performance. For instance, coupled with an increase in autonomy, fostering strengths use may allow managers to invest their one-on-one time with their employees in motivating techniques, rather than engaging in discussions to clarify specific job-related tasks. These findings add to the JD-R theory's somewhat flexible nature (Schaufeli & Taris, 2014). It appears that, depending on the context, strengths use may assume multiple functions, from reinforcer (i.e., opportunities for development) to substitute of job resources (i.e., role clarity, feedback), or even act as a buffer of their positive effect (i.e., autonomy). If strengths use acts as a reinforcer, which are the specific resources this individual strategy potentiates, other than opportunities for development? Conversely, what other job resources does it substitute or buffer? Does the same effect occur concerning employee well-being or regarding job demands? We encourage researchers to further investigate this unique phenomenon, considering that strengths use is a flourishing research point in the IOP domain (Miglianico et al., 2020).

From a practical perspective, this study provides HR specialists with more clarity regarding which contextual resources to foster based on the performance type they intend to enhance. As such, practitioners could rely, for example, on the Energy Compass intervention proposed by Schaufeli (2017) to increase specific types of performance by cultivating certain job resources. Based on this study's results, to elicit an increase in multiple types of performance, practitioners should foster a climate that assures role clarity and strengths use. Existing interventions, based on job resources' development, have already been successfully tested in various industries, such as healthcare (van Wingerden et al., 2017) or banking (Seppälä et al., 2018). The common denominator of these interventions is represented by employees learning to craft their jobs through self-initiated individual strategies, altering work conditions to match their abilities (Oprea et al., 2019). The results of this study suggest an alternative individual strategy, strengths use, as a potential instrument that may complement or even replace specific job resources. Talent-based interventions enable employees first to recognize their signature strengths and then encourage their usage in the workplace (e.g., keeping a diary of how they engage their strong points in their work). If employees notice that they use their strengths rarely, they can be coached to utilize their strong points in a manner that suits both them and the organization (Niemiec, 2017).

Conclusion

Job resources are, indeed, related to performance types in a different manner. Strengths use also plays a moderator role in the link between job resources and performance. While in some cases, the presence of one's strong points use is enough to overcome the necessity of job resources such as role clarity or feedback, in others, it strengthens the relationship (i.e., opportunities for development and performance). Overall, these findings support the claim that the link between job resources and performance should be carefully contextualized, depending on performance type and considering the micro, meso, or macro position of an individual within the company. Moreover, training employees on how to capitalize on their strong points may compensate for the lack of some job resources, while being indispensable for translating other resources into performance. Finally, the present study also provides practitioners with a more accurate view of which resources to foster to enhance distinct types of performance while taking individual strategies into account.

Footnotes

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The work of Andrei Rusu was supported by a grant of the Ministry of Research and Innovation, CNCS - UEFISCDI, project number PN-III-P1-1.1-PD-2016-1912, within PNCDI III.

ORCID iDs

Luca Tisu

Delia Vîrgă

Note

Author Biographies

Luca Tisu, PhD student, 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.

Andrei Rusu, PhD, is an Assistant Professor at the Department of Psychology from West University of Timisoara, Romania. His main research interests revolve around social and personality psychology applied in the broad area of I/O Psychology.

Coralia Sulea, PhD, is assistant professor of Organizational and Occupational Health Psychology at the West University of Timisoara. Her research interests include employees’ wellbeing and interpersonal mistreatment at the workplace.

Delia Vîrgă, PhD, is a full professor of Organizational and Occupational Health Psychology at the West University of Timisoara. Her research interests include work-family relation and employees’ well-being.

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