Academy of Strategic Management Journal (Print ISSN: 1544-1458; Online ISSN: 1939-6104)

Research Article: 2020 Vol: 19 Issue: 4

Predicting Work Performance: A Paradigm Shift from Organizations Empowerment to Employees Autonomous Strategy

Fouzia Hadi Ali, University of the Punjab

Ahmed Muneeb Mehta, University of the Punjab

Farah Naz Naqvi, University of the Punjab

Sayeda Zeenat Maryam, University of the Punjab


Organizations are pluralistic in terms of size with larger organizations that possess considerable human resources and financial capital than the smaller organizations that have lesser access to such resources. Therefore, employees should try to stimulate their physical and mental energy to enhance their work performance proactively.

The purpose of this study is to examine the role of job demands and discretion with the mediation of proactive vitality management towards the work performance of employees. A well-operationalized questionnaire was distributed among the operations managers working in small and medium enterprises in Pakistan. Besides this, to test the model, 350 responses were analyzed; the results reveal that proactive vitality management plays a vital role as a mediator among all three independent variables and work performance.

The findings of the current study unveil that in smaller organizations, the employees hold high skill discretion, which helps them to learn new ways, involve in creative activities, and have an opportunity to broaden their level of skill that, in turn, affects their performance positively. This research is novel as it sheds an empirical light to unearth its nuances in small and medium enterprises in the case of developing countries like Pakistan.

Since this avenue is underexplored so this research can help the policymakers of and suggests a shift in the focus from the organization “provide it all” to employees “manage it all” perspective to create a synergy that may help improving work performance.


Job Characteristics, Job Demand-Control Model, Proactive Vitality Management, Work Performance, Small-Medium Enterprises.


Despite rapid advancement in technologies, human capital plays a vital role in attaining organizational success (Op den Kamp et al., 2018). However, as the nature of work and environment varies from organization to organization, employees may face differences in the requirements to accomplish their tasks. Organizations are pluralistic in terms of size with larger organizations that possess considerable human resources and financial capital than the smaller organizations that have lesser access to such resources (Cooper et al., 1994; Forbes & Milliken, 1999). Such a paucity of resources in small and medium organizations burdens fewer employees to perform overlapping activities that may result in exhaustion of mental and physical energy. The past literature suggests that measures taken by the organizations for making employees vigorous can ultimately improve their well-being and performance at work (Ahmed et al., 2018). However, recent literature argues that it is unwise to hold the organizations responsible for the provision of all the needs that the employees demand or expect (Op den Kamp et al., 2018). They further assert that the employees should try to stimulate their physical and mental energy to enhance their work performance proactively (Grant & Ashford, 2008; Grant & Parker, 2009). This ongoing debate signifies the need for employees to take the first step to improve their work outcomes rather than merely waiting for organizations to provide an improved environment.

Moreover, it is essential to examine the proactive behavior of employees. This proactive behavior is termed as Proactive Vitality Management (henceforth, PVM). PVM is defined as “an individual’s outcome-oriented behavior that aims to manage the physical and psychological energy to promote maximal operating activities at work” (Op den Kamp et al., 2018). The definition suggests that proactive employees who possess greater insight are more likely to manage vitality at work. Calling to the above claim, we can infer that when employees divulge PVM, they do not tend to alter their work environment. Instead, they adapt their behaviors to achieve a better outcome at work (Parker et al., 2010). The recent literature provides evidence of how PVM enhances in-role performance, cognitive performance (Op den Kamp et al., 2018) and creative work performance (Op den Kamp et al., 2018 & 2020) but the same studies propose future directions to examine PVM scale in different contexts and with other relevant work outcomes.

While examining the degree of employees’ proactive behavior, it is essential to observe the nature of job demands and the discretion available to them to accomplish their assigned tasks in different contexts. Such job demands and discretions in decision making may vary from organization to organization. Unlike the larger organizations, smaller organizations are resource-constrained, restricted in managerial expertise and short term goal-oriented with limited financial capital (Brouthers et al., 1998; Cooper et al., 1994; Forbes & Milliken, 1999; Pissarides, 1999). Moreover, owing to lesser available human capital and financial resources in smaller organizations, the job demands may significantly vary in comparison to larger organizations. As smaller organizations possess relatively lesser human resources, so the employees are required to perform multiple tasks simultaneously (Marija et al., 2014). Besides this, large organizations are characterized to be more formalized, and the job descriptions are widely communicated and followed. However, such characteristics are lesser prevalent in small and medium enterprises. So in smaller organizations, the staff is the biggest asset for the organizations (Penning, 2016). In light of the above discussion, it seems essential to assess the nature of the job characteristics of such employees and its effect on their overall work performance. Besides this, it will be interesting to investigate how employees use the nature of job characteristics to their advantage and consider it as a driving force to manage their proactive behavior.

Theoretical Background

This study is based on the job demand-control (JDC) model developed by Karasek (1979). The JDC model envisages that adverse psychological and physiological reactions are affected by the joint effect of two structural conditions at the workplace: demand (role overload) and controls (skill discretion plus decision authority). Besides this, Karasaek (1989) asserts that learning occurs when the job demands and controls are relatively higher. This situation motivates employees to learn new skills and tackle challenges differently (Karsaek & Theorell, 1990). This means that even though while facing structural limitations within the organizations, the intervention of innate goal-oriented behavior can help employees to manage physical and mental energy that can promote their optimal functioning at work. Such an innate goal-oriented behavior can be derived through the self-determination theory (henceforth, SDT) (Ryan & Deci, 2008). SDT suggests that individuals need to feel energetic so that they can function at the optimum level. This argument can be further strengthened up by the broaden-and-build theory (henceforth, BBT) (Fredrickson, 2001). This theory argues that the employees possess an ability to create their resources, and can achieve their work-related goals.

SDT advocates that for optimal functioning, human motivation and wellness play a pivotal role in shaping behaviors. So SDT proposes an organismic integration process that assumes a growth tendency that the individuals inherit. Such a process, in turn, triggers intrinsic motivation and develops interests with a combination of internal practices and external world values. Besides this, such an ongoing functioning of interaction among the internal practices and external world values tend to develop capacities such as subjective human competence, sense of autonomy and relatedness that are named as the individual’s psychological needs under SDT (Kessler, 2013)

Similarly, BBT is a description of the positive emotions, i.e., joy, contentment, and interest held by individuals. According to BBT, the individuals' momentary thought-action is broadened through positive emotions. Broadened thought-action promotes an individual’s skill of creativity, the sharpness of ideas, novel discovery, and social bonding. Resultantly, such thought-actions help to build an individual’s personal, intellectual and psychological resources (Fredrickson, 2004).

Additionally, BBT also suggests that positive effect works as a psychological resource that induces individuals to broaden their beliefs and actions. This psychological resource also enables employees to think out of the box and try to find better ways to undertake the same tasks (Tarris & Kompier, 2005). Therefore, both SDT and BBT contribute to explain the construct of proactive vitality management. Furthermore, the JDC model (role overload, skill discretion, and decision authority) can be treated as a factor that helps to trigger positive emotions presented by SDT and BBT. Besides this, such positive emotions induce individuals to manage their mental and physical energy that may enhance work performance.

Hypotheses Development

Influence of job characteristics on work performance

There is a dearth of literature that empirically sheds light on the linkage between the job characteristics and work performance in small and medium organizations. So this study fills this gap by investigating the role of skill discretion, decision authority, and role overload of employees working in SMEs and their consequent effect on employees’ work performance. The past studies primarily focus job characteristics proposed by the scale developed by Hackman & Oldham (1980) in large organizations and are linked mostly with behavioral outcomes of employee behavior such as job satisfaction (Glisson & Durick, 1988; Putra et al., 2018), organizational commitment (Elanain, 2009), turnover intention (Nguyen, 2015), and job performance (Johari et al., 2019), etc. Therefore, this study intends to develop a link of the JDC model with work performed in the context of SMEs.

Proactive vitality management as a mediator

The construct of proactive vitality management (henceforth, PVM) is an evolving concept conceived by Op den Kamp et al. (2018). Some studies advocate that if employees engage in “preferred activities,” then they will require to exert fewer efforts and may prove beneficial in terms of their psychological and physical energies (Hunter & Wu, 2016; Trougakos & Hideg, 2009). However, before linking PVM to different factors, it is essential to highlight the literature that displays the relationship of vitality with performance. The study of Carmeli (2009) argues that vitality is not only associated with higher job performance but also with better adaptation to change and mental wellness (Van den Heuvel et al., 2010; Ryan & Frederick, 1997). Similarly, Ryan & Deci (2008) also claim that vitality is directly related to higher performance and better physical and mental health.

Moreover, Tummers et al. (2018) report that job autonomy positively impacts vitality, higher the job autonomy (freedom to use your approach, having control over your work) higher will be the employee’s proactive vitality at work. Therefore, this study intends to focus on job autonomy through skill discretion and decision authority presented by Karasek (1979) in the JDC model. However, another dimension of the JDC model, i.e., role overload, calls for a link with proactive employee behavior that may be prevalent in the context of SMEs. Based on the above discussion, the current study proposes to investigate the relationship of job characteristics (i.e., skill discretion, decision authority, and role overload) with work performance through the intervention of PVM in the context of SMEs (Figure 1). So to test the relationships, the study proposes the following hypotheses:

Figure 1 Conceptual Model

H1 Proactive vitality management mediates the relationship between skill discretion and Work Performance.

H2 Proactive vitality management mediates the relationship between decision authority and Work Performance.

H3 Proactive vitality management mediates the relationship between role overload and Work Performance.


This study uses a survey technique to collect the data. The participants of the survey consist of employees working in SMEs. Small and Medium Enterprises refer to organizations with human capital, not more than 250 employees (SMEDA, 2011). Among the Organization for Economic Cooperation and Development (OECD) countries, SMEs are the dominant form of enterprises that are estimated to constitute 99% of all businesses. Moreover, SMEs are essential for the economic well-being of all economies and play a central role in achieving economic sustainability and growth.

In emerging economies, SMEs add up to 45% of the total employment and 33% of their GDP (Organisation for Economic Cooperation and Development, 2017). Similarly, in 2017, MSCI (Morgan Stanley Capital International) Pakistan was upgraded from a frontier economy status to an emerging one. In Pakistan, the Small and Medium Enterprise Development Authority (SMEDA) indicates that 90% of all enterprises are SMEs.

Past research indicates that those small and medium enterprises that pay more attention to proactively managing their affairs to pace up with the industry trends are more able to enhance work performance (Matchaba-Hove et al., 2015). However, due to lesser human capital, it is essential to examine how the job characteristics in such organizations may be more demanding and how the employees manage their physical and mental energies proactively. Most importantly, it is also argued that the assigned tasks to the employees are directly linked to the overall work performance of small and medium enterprises (Matchaba-Hove et al., 2015; Muda & Rahman, 2016).

So in light of the above discussion, this study provides evidence that aims to achieve the following objectives: investigating the relationship of job characteristics with work performance through the intervention of PVM. Furthermore, this study is novel as it sheds an empirical light to unearth its nuances in small and medium enterprises.

The population consists of 4494 SMEs in the service sector operating in the city of Lahore. The sample size was determined through random sampling at a 95% confidence interval and a 5% margin of error. The sample of firms was calculated to be a total of 354. All the firms selected randomly were sent a letter to seek permission for gathering information from the operations manager of each firm. Three hundred fifty firms showed their willingness to participate. So in total, 350 duly filled questionnaires were used for the analysis.


The variables of the study were operationalized with well-established measures. To evaluate the Proactive Vitality Management (PVM) eight-item scale of Op den Kamp et al. (2018) was adopted. PVM was measured on a 7-point scale ranging from 1 (very strongly disagree) to 5 (very strongly agree). A four-item scale of Tan & Liu (2014) was used to measure work performance (WP). The 5-point Likert scale ranged from 1 (strongly disagree) to 5 (strongly agree). To evaluate job characteristics, i.e., skill discretion and decision authority, the scale of Karasek (1979) was adopted. Skill discretion and decision authority were measured on a 5-point scale ranging from 1 (never) to 5 (extremely often). Moreover, to evaluate the third dimension of job characteristics, i.e., role overload, the scale of Ivancevich & Matteson (1980) was adapted. Originally the survey required to assigned a score of 1 to 7 (1=Never to 7=Always). However, the current study developed a scale ranging from 1(Never) to 7 (Always) instead of scoring each item.


In order to assess the common method bias, we used Harman's single factor test (Podsakoff et al., 2003), in which all scale items were loaded into a single latent variable and found 20.71% of the total variance. This variance is below the 50% threshold, which indicates that there is no issue of common method bias (Mattila & Enz, 2002).

Table 1 above depicts the values of Outer Loadings, AVE, and CR to test for convergent validity. Hair et al. (2013 & 2014) suggest that convergent validity is established when the values of AVE, Outer Loadings, and CR are higher than 0.50, 0.60, and 0.70. Moreover, Diamantopoulos & Siguaw (2000) recommend that the value of AVE should not be less than 0.40. The values of Outer Loadings and AVE are within the prescribed range. However, the values of CR in case of decision authority, role overload, and skill discretion are less than the prescribed limit (higher than 0.7). Therefore the convergent validity is established as the calculated values of AVE, CR, and Outer loadings are in the range of the acceptable region.

Table 1 Reliability and Convergent Validity of the Instrument
Construct Sources Outer Loadings CR AVE
JC(Skill Discretion) Karasek, 1979   0.537 0.514
JC1   0.637    
JC2   0.738    
JC4   0.77    
JC(Decision Authority) Karasek, 1979   0.64 0.482
JC5   0.738    
JC6   0.648    
JC7   0.729    
JC8   0.656    
JC(Role overload) Ivancevich & Matteson, 1980   0.535 0.469
JC11   0.706    
JC12   0.876    
JC13   0.378    
PVM Op den Kamp et al., 2018   0.847 0.489
PVM1   0.51    
PVM2   0.726    
PVM3   0.781    
PVM4   0.73    
PVM5   0.699    
PVM6   0.678    
PVM7   0.75    
PVM8   0.685    
BP Tan & Liu, 2014   0.728 0.551
BP1   0.729    
BP2   0.778    
BP3   0.758    
BP4   0.702    

After assessing convergent validity, discriminant validity was tested to confirm that each construct in the model measures a different concept. Rather than using Fornell & Larcker (1981) technique to test for discriminant validity, researchers suggest using a heterotrait-monotrait ratio of correlations (HTMT) (Hair et al., 2017; Henseler et al., 2015). So Table 2 below depicts discriminant validity.

Table 2 Discriminant Validity
Constructs HTMT Correlation
Decision Authority-> Work Performance 0.451
Proactive Vitality Management-> Work Performance 0.801
Role Overload Q-> Work Performance 0.294
Skill Discretion-> Work Performance 0.346
Proactive Vitality Management-> Decision Authority 0.424
Role Overload Q-> Decision Authority 0.288
Skill Discretion-> Decision Authority 0.267
Role Overload Q-> Proactive Vitality Management 0.279
Skill Discretion-> Proactive Vitality Management 0.401
Skill Discretion-> Role Overload Q 0.155

According to Henseler et al. (2015) and Hair et al. (2017), the HTMT ratio should be less than 0.90 to establish discriminant validity. Table 2 above indicates that discriminant validity is established as the HTMT ratio is less than 0.90.

Descriptive Results

The results of the job characteristics (i.e., skill discretion, decision authority, and role overload) prevalent in SMEs are shown in Table 3 below.

Table 3 Descriptive Results
Variables Percentages
Low Medium High
Skill Discretion 5.40% 30.60% 64%
Decision Authority 8.30% 39.70% 52%
Role Overload 8.60% 50.60% 40.90%

Table 3 shows the level of skill discretion, decision authority, and role overload from low to high. The levels for two variables, i.e., skill discretion and decision authority are categorized based on responses from the instrument, for Low Level: from 1 (Never) to 2 (Rarely); Medium 3 (Sometimes); and High from 4 (Often) to 5 (Extremely Often). Whereas, the level of role overload is categorized based on responses from the instrument, for Low-Level l: from 1 (Never) to 2 (Rarely); Medium 3 (Occasionally) to 4 (Sometimes); and High from 5(Often) to 7 (Always). The results reveal that in the context of SMEs the employees perceive that they have a high level of skill discretion (64%), medium (39.7%) to high (52%) decision authority and medium (50.6%) to high (40.9%) level of role overload.

Structural Model Assessment

After establishing and verifying the reliability and validity of the model, the next step is to test the proposed hypotheses with the help of the bootstrapping procedure using Smart PLS-SEM. Bootstrapping is a procedure that tests the statistical significance of path modeling. Moreover, the bootstrapping procedure assesses the model fitness by calculating the value of Standardized Root Mean Square (SRMR). The value of SRMR ranges from 0 to 1, and less than 0.08 is considered a perfect fit (Hooper et al., 2008). The calculated value of SRMR for the structural model is 0.072, which shows that the proposed model is perfectly fit. Figure 2 below indicates the model extracted through the bootstrapping procedure.

Figure 2 PLS-SEM Model

Figure 2 above shows the direct and indirect effect of job characteristics (i.e., skill discretion, decision authority, and role overload) on Work Performance (WP) through the mediation of proactive vitality management (PVM). The model extracted through bootstrapping procedure shows that only decision authority has a direct positive and significant impact on BP (p=0.025<0.05). However, skill discretion and role overload do not reveal any direct impact on BP (p=0.228, 0.150>0.05).

Moreover, job characteristics (i.e., skill discretion, decision authority, and role overload) have a positive and highly significant impact on the mediating variable, i.e., PVM (p=0.000, 0.000, 0.002 respectively which are <0.05). Furthermore PVM have a positive and highly significant impact on BP (p=0.000 <0.05). Additionally, the results in Figure 2 above also shows that PVM partially mediates the relationship of decision authority with Work Performance. While PVM fully mediates the relationship of skill discretion and role overload with Work Performance. Therefore as PVM mediates the relationship between job characteristics (i.e., skill discretion, decision authority, and role overload) and Work Performance, so all the proposed hypotheses (i.e., H1, H2, and H3) are accepted. Though such relationships were not examined in the past, these results support the theoretical premise of the study based on the JDC model, SDT, and BBT. This premise strengthens the argument that when job demand and controls are high, such characteristics will induce employees to broaden their beliefs and actions that will enable them to try to find better ways to accomplish the same tasks. Consequently, both SDT and BBT will help to enhance the Work Performance of SMEs.

Predictive Accuracy

PLS-algorithm procedure calculates the value of R2 to assess the predictive accuracy of the proposed hypotheses. The value of R2 refers to the coefficient of determination that represents the amount of combined variance, as explained by exogenous variables on an endogenous variable, i.e., BP and PVM have the value of R2 0.420 and 0.173. Additionally, for cross-validating, the calculated values of R2 of endogenous variables, PLS-SEM also computes the value of Stone-Geisser Q2 value (Geisser, 1974; Stone, 1974). Table 3 below displays the values of R2 (predictive accuracy) and Q2 (cross-validated predictive relevance) for the model. The table also discusses the effect size through the values of Q2.

Table 4 above discusses the values of R2 and Q2. The predictive relevance of the structural model is established when the values of Q2 are > Zero. The value of Q2 for PVM and BP are 0.212 and 0.072. The values of Q2 are > Zero, so the results establish the predictive relevance of the structural model. Moreover, the effect size of Q2 varies from small to medium.

Table 4 Results of R2 and Q2
Constructs R2 Adjusted R2 Q2 Effect Size
Work Performance 0.42 0.414 0.212 Medium
Proactive Vitality Management 0.173 0.166 0.076 Small

Discussion and Implications

Although the concept of PVM is conceived recently by Op den Kamp et al. in 2018 and tries to link it with different constructs such as work engagement; creativity (Bakker et al., 2020); in-role performance; job crafting; vigor; fatigue; psychological attachment; relaxation; cognitive performance; cognitive liveliness; self-insight; proactive personality (Op den Kamp et al., 2018) and creative work performance (Op den Kamp et al., 2020). However, the proposed link of assessing PVM with work performed in the context of SMEs through the lens of the JDC model was absent. The context of SMEs significantly differs as compared to larger organizations (Cooper et al., 1994; Forbes & Milliken, 1999). The reason can be attributed to the nature of the job assigned to the employees that vary in scope. The findings of this study reveal that the employees in SMEs tend to possess more skill discretion and role overload and are given a higher level of decision authority. Most importantly, the findings of the present study are consistent with a previous study that reveals that the assigned tasks to the employees are not directly linked to their work performance (Johari et al., 2019) but are indirectly linked in the case of small and medium enterprises (Muda & Rahman, 2016).

Besides this, due to the scarcity of human capital, the employees who tend to indulge in multiple tasks (Avlijaš, 2008), require to manage their physical and mental energies effectively. For this reason, this study focuses on PVM that mediate the relationships among the job demand (role overload) and controls (decision authority and skill discretion) with work performance in SMEs.

The findings of the present study reveal that in SMEs, the employees hold high skill discretion through which they can learn new ways and skills, involve in creative activities, and broaden their level of skill. Besides this, a high skill discretion induces employees to assess their abilities. This behavior can be attributed to the self-determination theory. Moreover, when employees recognize their abilities, they broaden their skills by learning to accomplish tasks differently, which supports the broaden and build theory. Moreover, they have a high level of decision authority, so they give themselves the autonomy to decide how to organize their work, control over what happens on their job, and assist in making their own decisions.

Moreover, due to the lack of human capital in SMEs, the employees are responsible for an unmanageable number of tasks. They tend to have more tasks to accomplish that can be done in a working day, and feel that they do not have time to take frequent breaks. Such discretion in decision-making and application of skills helps employees to focus well on their work, motivate and inspire themselves, feel energetic at work, approach their work with a positive attitude, be able to complete tasks innovatively and enthusiastically (Johari et al., 2019). Moreover, the role overload that requires multitasking will induce the employees to find ways to manage their jobs creatively and innovatively.

Most importantly, the results reveal that when employees possess skill discretion and role overload at work, they try to stimulate their mental and physical energies that help in enhancing work performance (Tarris & Kompier, 2005). We can infer that individuals have an innate motivation that can be triggered while confronting the situational factors prevalent in the work environment and bring some positive outcomes of the challenging situations that may help in improving work performance. Additionally, the decision authority given to employees assists them in becoming adaptive and accomplishes tasks in a way that keeps them energized both mentally and physically, which in turn, enhances work performance.

Such findings implicate that instead of burdening the organization to provide facilities to fulfill all the needs required by the employees (Op den Kamp et al., 2018), the employees themselves need to try to stimulate their physical and mental energy to enhance their work performance proactively (Grant & Ashford, 2008; Grant & Parker, 2009). This link fits well in the context of SMEs due to the scarcity of financial and human resources.

Additionally, although the present study shows that employees are involved in proactive vitality management to accomplish tasks, their behavior tends to be more an unconscious attempt rather than a conscious effort to work as a stimulus to help improve work performance. This behavior should be considered as a useful tool and can be nourished only if the organizations realize their importance. Therefore, human resource managers and policymakers should realize its importance and arrange ways to boost proactivity at work among the employees.

Limitations and Future Directions

Although the present study provides new avenues to discuss the importance of proactive vitality management that can intervene in different environments and its link with work Performance still, it is subject to a few limitations. This study is limited to the services sector of small and medium enterprises that may affect the generalizability of the study. So future studies can examine the same relationships in different sectors and sizes of organizations. A self-reporting measure is used to gather data that may possess self-reporting bias. The present study is the first to focus PVM as a mediator so future studies can link other organizational and behavioral antecedents such as, supervisory support, organizational support, organizational virtuousness and personality traits with PVM that may affect other outcomes such as, quality of life, work-life conflict, work engagement, employee performance and many more apart from merely work performance.


Small and medium firms face resource constraints, restrictions in managerial expertise, and short term goal orientations with limited financial capital. So considering the structural limitations of SMEs, the present study suggests a shift in the focus from the organization “provide it all” to employees somewhat “manage it all” perspective to create a synergy that may help to improve the work performance of employees. So this study provides a platform to consider proactive vitality management as an essential factor that can help in enhancing work performance of employees working in challenging organizational environments.


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