Original Research

The influence of nonwork resources, nonwork demands and external support on work engagement and productivity: A moderated mediation model

Hamfrey Sanhokwe

Received: 05 Oct. 2021; Accepted: 19 July 2022; Published: 15 Nov. 2022

Copyright: © 2022. The Author Licensee: AOSIS.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Orientation: Working from home eliminated the work–nonwork divide. The lives of employees at home were instantaneously connected to their engagement and productivity at work. The mechanisms and pathways through which an individual’s access to and management of nonwork resources and demands influence behaviours and outcomes at work have been scantily investigated.

Research purpose: Hinged primarily on the conservation of resources theory, the study examined the influence of the external support, nonwork demands and resources on work engagement and employee productivity.

Motivation for the study: Understanding how work–nonwork resources and demands interact(ed) to shape behaviour and outcomes in the work domain could shape cross-domain resource conservation and enhancement efforts.

Research approach/design and method: Data were collected from a convenient sample of 185 nongovernmental organisation employees using a standard questionnaire. Structural models, with bootstrapping, were used to evaluate the hypothesised moderating and mediating effects.

Main findings: Nonwork resources were positively associated with work engagement. External support moderated the negative relationship between nonwork demands and work engagement. Work engagement mediated the effects of nonwork resources and nonwork demands on employee productivity.

Practical/managerial implications: Organizational leaders should appreciate the ecological conditions within which work and nonwork resources are generated and expended. This has implications on desirable, value creating workplace behaviours and related outcomes.

Contribution/value-add: The study further exposed the interdependence of the work and non-work domains. Workplaces that enrich both domains will likely enjoy sustained value generation.

Keywords: workplace behaviour and outcomes; resource conservation; moderated mediation analysis; cross-domain effects; ecological conditions.

Introduction

Working from home eliminated the traditional work–nonwork divide, more so in developing countries. The lives of employees at home were instantaneously connected to their engagement with and productivity at work (Chen & Fellenz, 2020). Furthermore, the pandemic created new challenges that went ‘beyond previous findings in the area of demands and resources’ (Meyer et al., 2021, p. 532). The mechanisms and pathways through which an individual’s access to and management of nonwork resources, nonwork demands and external support influence behaviours and outcomes at work have been scantily addressed (Chen & Fellenz, 2020; Meyer et al., 2021).

Nonwork resources, nonwork demands and external support are all situated within the home environment. The home environment not only serves as a place for daily recovery (i.e. an energy reservoir of sorts) from evolving job demands (Sonnentag et al., 2017) but can also be a source of energy-sapping demands that affect the employee at work (Chen & Fellenz, 2020). The elastic nonwork demands created by the pandemic, coupled with finite nonwork resources and external support, could have an effect on how engaged employees are, and by extension, their productivity.

Using the term ‘home’, the research acutely recognises not only the living arrangements but the influence of family and external support (Chen & Fellenz, 2020). Both Zimbabwe and Eswatini embrace strong family culture and ubuntu (Natukunda, 2021). Thus, the home environment could significantly influence employees’ work–life and related outcomes, especially considering that working from home has traditionally not been a common phenomenon in both countries.

Appreciating how the work and nonwork domains interacted to shape critical workplace outcomes could influence leadership decision-making and related organisational actions as the workplace continues to evolve (Caldas et al., 2021; Chadee et al., 2021).

Literature review

Appreciating the constructs-in-use

Nonwork resources, nonwork demands and external support

Hinged on the conservation of resources (COR) theory, nonwork resources are conceived as valuable, tangible, psychosocial, symbolic or economic assets that are available, accessible and could be utilised by the employee to satisfy the existing or emerging needs at work (Chen & Fellenz, 2020; see also Hobfoll, 1989, 2001). Contextual nonwork resources included spousal emotional support, informational support, availability of domestic appliances, information and communication technologies (ICTs) as well as the physical space to telework. This definition gives the employee the latitude to manipulate resource use between the work and nonwork domains to offset emerging stressful imbalances.

Based on the definition by Chen and Fellenz (2020), nonwork demands were conceived as:

[T]angible, social, psychological or symbolic factors that attract individual attention and require physical, cognitive or emotional effort to prevent them from interfering with valued activities or with the personal resources required to pursue such activities. (p. 2)

These included home-schooling needs, daily child and other care needs, spousal demands and conflict or ambiguity about the role at home.

According to the second principle of the COR theory, employees invest resources to recover from losses. Drawing from the crossover concept (see Hobfoll et al., 2018), external support was viewed as a mechanism of resource exchange; that is, resources are transferred between the home and social contexts to satisfy emerging needs. Included in these compensatory actions were home-schooling support through paid tutors or teachers, child and other care support for family (through paid childcare and other support minders), psychosocial support (professional psychologists, paraprofessionals or other accessible people who offer such support in times of need) and socio-economic support (this includes those who lend financial resources, deliver food purchases or offer electronic or cash transactions).

Building on the work of Westman (2001), external support is conceived as a specific moderating mechanism that intervenes in the transmission of experiences and states between nonwork demands and work engagement. The term ‘external support’ was selected instead of social support to acknowledge that some of the support was drawn from nontraditional sources and included contextual contract-type arrangements, for example, private tutors for home-schooling support.

Work engagement

Acknowledging the work of Goffman (1961), Kahn (1990) and Saks (2006), this study hinges on the conceptualisation of work engagement by Schaufeli et al. (2002). Work engagement is a multidimensional phenomenon comprising behavioural and attitudinal characteristics that lead to a positive experience in the workplace. Schaufeli et al. (2002, p. 74) defined work engagement as a ‘positive, fulfilling, work-related state of mind characterized by vigour, dedication, and absorption’. This conceptualisation is operationalised using the Utrecht Work Engagement Scale (UWES) (Schaufeli et al., 2002, 2017). Positively engaged employees allocate personal (nonwork) resources to satisfy their mandates and goals (Decuypere & Schaufeli, 2020). Further, positively engaged employees bring their gifts of imagination, energy, and dedication to work everyday. Hence, the need to appreciate the mechanisms and pathways that generate or debilitate work engagement-related resources.

Employee productivity

Employee productivity is a common domain of leadership in contemporary research. Classical management theories identify four main groups of factors influencing the employee productivity construct, namely environmental, organisational, group dynamics and individual-level aspects (Ailabouni et al., 2010). Employee productivity can be defined as the value generated by an individual employee over a defined period. In the context study, perceived employee productivity included elements such as efficiency, level of collaboration with peers and satisfaction of deliverables viz. organisational objectives.

The social exchange theory (SET; Blau, 1968) and self-determination theory (SDT Ryan & Deci, 2000, 2017) suggest the central role of the employee in activating specific behaviours in the workplace. Social exchange theory emphasises that employee relations and behaviour are rooted in an exchange process. Self-determination theory suggests that the employee exudes the internal motivation necessary to be productive. In this way, the employee can complement and, by extension, optimise work resources by utilising personal resources to meet predetermined work objectives (Bawa, 2017). Thus, the construct could be influenced directly or indirectly by specific actions in the nonwork domain. In the same vein, the existing literature suggests the direct and mediating roles of work engagement on desirable, value generating workplace outcomes such as productivity (Ferreira et al., 2019).

Model and hypotheses

Working from home implies that employees consciously or subconsciously transition across the work and nonwork domains in a seamless manner (Sanhokwe et al., 2022). Employees are cognisant of the resources available in both the work and nonwork domains, including the demands inherent therein (Halbesleben et al., 2014). In this way, resources are strategically harvested to satisfy identified demands across domains (cross-domain effect; see Hobfoll et al., 2018).

Given the sudden implementation of the lockdown measures, most organisations were ill-prepared to fully satisfy the employees’ teleworking needs (Carillo et al., 2021). It is therefore plausible that available and accessible nonwork resources at home could be utilised to satisfy the emerging needs in the work domain. There is empirical support for this claim. For instance, Chen and Fellenz (2020) reported that nonwork resources motivated employees to be engaged at work; so too did Meyer et al. (2021). The findings by Contreras et al. (2020) suggested that personal resources had a protective effect on burnout.

Earlier, Xanthopoulou et al. (2009) reported both the causal relationship between personal resources and work engagement and the mutual relationship between personal and job resources (see resource caravans, COR theory, Hobfoll et al., 2018). Previous studies have also suggested that psychological and physical resources, as well as material resources at home, could increase employees’ persistence and resilience at work, as well as attributes of being engaged (Greenhaus & Powell, 2006). Studies by Bakker and Demerouti (2009) and Friedman and Greenhaus (2000) reported the influence of emotional spousal support and information or knowledge regarding an employee’s ability to handle needs, demands and expectations in the workplace. Anecdotal evidence from Zimbabwe suggests that employees tapped into their resources to secure generators to avoid frequent power outages. Based on these perspectives, the study advances the argument that:

Hypothesis 1: There is a positive and significant relationship between nonwork resources and work engagement.

The COR theory suggests that employees may become less engaged at work in the presence of high demands at home (Hobfoll, 2001; Hobfoll et al., 2018). The pandemic created sudden and unplanned demands on the employees. According to COR theory, these demands can create swift imbalances that, if not carefully and proactively managed, exhaust available personal and job resources. According to the first principle of the COR theory, resource loss ‘not only is more powerful than resource gain in magnitude but also tends to affect people (employees) more rapidly and at increasing speed over time’ (Hobfoll et al., 2018, p. 105).

Thus, sudden demands on resources in both domains threaten the survival of the employee and his or her family. Conceived this way, nonwork demands could create cross-domain vicious effects as employees seek to conserve the remaining resources at their disposal. Negative cross-domain effects may include reduced work engagement. Such a posture is a proactive (viz. reactive) ‘perceiving, sense-making, meaning-giving and choreography’ type of response to mitigate the effects of a sudden imbalance in resources and demands. There is empirical support for these claims (see Chen & Fellenz, 2020). Based on these submissions, this study hypothesises that:

Hypothesis 2: There is a negative and significant relationship between nonwork demands and work engagement.

Employees are more sensitive to resource losses than resource gains (Hobfoll et al., 2018). Under such circumstances, they immediately seek options to survive (Hobfoll et al., 2018). Obtaining external support creates a sense in employees that they can contain sudden demands. The ensuing resource investments through external support can have a dual effect, that is, could be beneficial in offsetting the losses or could create a vicious cycle (inducing accelerating resource loss cycles) (Meyer et al., 2021). Existing research (see Meyer et al., 2021) suggests the protective effect of external support. However, demands for child schooling and care for sick relatives, among others, come at a cost, typically unplanned. Based on these assertions, this study hypothesises that external support could affect the strength and direction of the relationship between nonwork demands and work engagement.

Hypothesis 3a: External support moderates the relationship between nonwork demands and work engagement.

Child care responsibilities rose because of school closures across the world. Existing literature suggests that childcare responsibilities reduced working mothers’ labour supply (Croda & Grossbard, 2021). Single parents were at high risk. Various studies also suggest that women, in general, bear the brunt of child and family care (Cagliesi & Hawkes, 2021). These studies reported that mothers reduced their working hours compared to their male counterparts. The existing literature also shows that employed mothers spent more time on home-schooling activities than fathers (Petts et al., 2021). The latest studies seem to suggest shifts towards more egalitarian distributions of household or family labour (Parry et al., 2021). It may be too early to conclude.

Research, more so in corporate settings, also suggests that the increasing demands in the nonwork domain affected some of the eudaimonic drivers of workplace happiness. Whereas workplace happiness was associated with achievement, purpose and learning at work, working from home recreated values and perceptions on what matters the most in the work and nonwork domains. The following argument is made:

Hypothesis 3b: Marital status, gender, parenting, and level of work reliably differentiate the moderated relationship between nonwork demands and work engagement.

From a COR perspective, the relationship between work engagement and employee productivity could be explained using the ‘crossover of engagement’ concept (Hobfoll et al., 2018). Engaged employees exude energetic and affective connections with their daily work activities. The self-expansion theory (see Aron et al., 2001) emphasises the fundamental motivation for employees to self-expand to increase the ‘physical and social resources, perspectives, and identities that facilitate achievement of any goal that might arise’ (Aron et al., 2001, p. 478). Engaged employees mobilise these energetic resources in pursuit of increased productivity. The self-determination theory also suggests that the desire for growth and achievement drives employee productivity. Positive affective and motivational states push employees to go beyond what is expected in satisfying work objectives. The study submits that:

Hypothesis 4: There is a positive and significant relationship between work engagement and employee productivity.

The literature on work engagement suggests that the accretion of resources may directly or indirectly create the impetus for engaged employees to participate in other roles such as increased productivity (Hobfoll et al., 2018). In the other vein, nonwork demands may cause employees to engage less at work – a defensive mode of sorts – to preserve their compromised personal resource reservoirs, thus compromising employee productivity. Alternatively, employees could experience a stalemate in resource investment. This deprives the employee of adequate resources to engage at work, culminating in suboptimal productivity.

These anticipated relationships recognise the evaluative and affective responses to the employee’s acquisition and loss of resources and how that could shape work outcomes such as employee productivity. Work engagement is viewed as an underlying mechanism through which nonwork demands and nonwork resources influence employee productivity. The study argues that:

Hypothesis 5: Work engagement mediates the effects of nonwork demands and nonwork resources on employee productivity.

Figure 1 delineates the hypothesised relationships.

FIGURE 1: Expected relationships among the constructs in use.

Methods

This quantitative study utilised a cross-sectional survey design to satisfy its objectives. A convenient sample, drawn using the snowball technique, was derived from employees serving in nongovernmental organisations (NGOs) in Zimbabwe and Eswatini. Although most nonessential service providers were instructed to halt their operations as part of the efforts to mitigate the spread of the coronavirus disease 2019 (COVID-19) services, NGOs continued to provide (Sanhokwe et al., 2022; Wilke et al., 2020). Hence, the choice to situate the study in the NGO sector.

Data were collected online using the SurveyMonkey platform (www.surveymonkey.com). The survey was closed when 185 employees had completed the online questionnaire. The sample size was guided by previous studies. A study by Zulkifli et al. (2022) on the performance of maximum likelihood in structural equation modelling suggests that a minimum sample size of 100 suffices.

Forty-nine per cent of the respondents were women. Ninety-seven per cent attained tertiary education. Sixty-five per cent of the respondents were married. Of those who were married, 95% had children. The average age of the respondents was 35.6 years (standard deviation [SD] = 7.13 years). Junior-level employees constituted 48% of the sample, and middle- and senior-level employees made up 32% and 20% of the total sample, respectively. Average tenure of the employment was 6 years (SD = 5 years).

Data collection instrument

An integrated questionnaire was used to collect the data. Extant research by scholars such as Cagliesi and Hawkes (2021), Cheche et al. (2019) and Das et al. (2021) suggests the influence of specific demographic variables on the constructs in use. Based on their submissions, the following demographic data were collected: marital status, gender, level of work, parenting and age.

Data on the five constructs in use were collected using the following measures.

Dependent variable

Perceived employee productivity

Perceptions of employee productivity were measured using three items, namely, ‘I believe I have been efficient in my work’, ‘I believe I have collaborated well with my peers at all the levels of work’ and ‘I believe I have been very productive towards the agreed organisational goals’. Employees self-reported on these three items using a five-point Likert-type scale (1 = strongly disagree; 5 = strongly agree). The scale had an omega coefficient of 0.79, thus confirming its reliability.

Explanatory variables

The following explanatory variables were utilised in the investigation:

Work engagement

This study used the ultra-short version of the UWES; that is, the UWES-3 (Schaufeli et al., 2017). The three elements are: (1) ‘At my work, I feel bursting with energy’ (vigour); (2) ‘I am enthusiastic about my job’ (dedication); and (3) ‘I am immersed in my work’ (absorption). The employees rated their experiences using a seven-point rating frequency scale (1 = never; 2 = rarely; 3 = occasionally; 4 = sometimes; 5 = often; 6 = very often; and 7 = always). The scale had an omega coefficient of 0.75, thus confirming its internal consistency.

Nonwork resources

Employees self-reported on nonwork resources available and accessible to them using a five-point Likert-type scale (1 = strongly disagree; 5 = strongly agree). Sample items include the following: ‘My partner makes me feel loved and cared for’; ‘My partner provides useful informational resources or advice that I use in my work’; ‘At home, I have adequate domestic appliances, including information, technology and communication (ICTs) to effectively telework’; and ‘at home, I have adequate space for teleworking’. The scale had an omega coefficient of 0.80.

Nonwork demands

Employees self-rated the presence or absence of the following demands: ‘I have home-schooling needs’; ‘I have daily child and other care needs’; ‘I have spousal demands to satisfy’; ‘I experience role conflict at home’. The cumulative scores indicated the burden of the demands. The scale had an omega coefficient of 0.78, thus confirming the reliability and convergent validity of the measure.

External support

Four elements were used to measure access to external support contacts using a five-point Likert type scale (1 = Strongly disagree; 5 = Strongly agree). The elements included: ‘I access reasonable childcare, including teaching services for my child(ren)’; ‘I access other family care support as needed’; ‘I access psychosocial support as needed’; and ‘I access socioeconomic support as needed’. The scale had an omega coefficient of 0.76.

Analytical approach

The study used the Kaiser–Meyer–Olkin (KMO) test of sampling adequacy and Bartlett’s test of sphericity to determine the suitability of the data for structure detection. A KMO of 0.89 (Cerny & Kaiser, 1977) and a significant Bartlett’s test, that is, p = 0.000 (Bartlett, 1954) suggested the utility of factor analysis. Based on the results of the bifactor analysis, item-total scores for each of the five measures were used for structural equation modelling. Two structural equation models were developed to test the relationships among the study constructs using IBM® SPSS® Amos (Analysis of Moment Structures). The bootstrapping method (Borst et al., 2019) was applied to test the mediating effects of work engagement on the hypothesised relationship between nonwork demands and nonwork resources with employee productivity.

Assessing common method bias

The study used Harman’s single factor test to assess common method bias. The first factor accounted for 39.98%. The result suggests that the risk of the common method bias was not significant (Podsakoff et al., 2003).

Ethical considerations

This article followed all ethical standards for a research without direct contact with human subjects. The purpose of the study was clearly explained on the survey landing page. All participants gave their informed consent. Participation in the study was strictly voluntary. No personally identifiable or organisational information was collected, and the study adhered to strict rules of confidentiality and anonymity.

Results

The study results are presented in Table 1 and Table 2, as well as in Figure 2. Model A (see Table 1) shows that nonwork resources and nonwork demands explained 0.482 of the variances in work engagement. Nonwork resources were positively associated with work engagement (β = 0.503, p < 0.01). The result provides support for Hypothesis 1. As expected (see Hypothesis 2), nonwork demands had a negative association with work engagement (β = −0.417, p < 0.01). Work engagement was positively associated with productivity (β = 0.56, p < 0.01), thus supporting Hypothesis 4.

FIGURE 2: Interactive effects of higher (vs. lower) external support on the relationship between nonwork demands and work engagement.

As claimed in Hypothesis 3a, external support had a moderating effect on the relationship between nonwork demands and work engagement. External support significantly reduced the negative effect of nonwork demands on work engagement. The explained variance of work engagement increased by 11.2% (R² = 0.112) when the interaction term nonwork demands × external support was added to the model, supporting Hypothesis 3a. The results in Models A and B also partially support Hypothesis 3b. The relationship between gender and work engagement was insignificant when the external support variable was introduced in the model. The relationship between parenting and work engagement remained negative and significant but closely approached zero in the presence of external support (see models A and B). Level of work was significantly associated with work engagement. The relationship was greater when external support was added to the model. There was no significant correlation between marital status and work engagement.

Figure 2 depicts the estimated effects of high (viz. low) external support in the relationship between nonwork demands and work engagement. The output shows the negative relationship between nonwork demands and work engagement. Further, it shows the protective effect of higher (viz. lower) external support on nonwork demands; that is, higher external support reduces the strength of the negative effect of nonwork demands on work engagement.

These findings emphasise three issues. Firstly, nonwork resources serve as an important conduit for resources in the work domain. Secondly, external support has a protective effect on nonwork demands. Thirdly, despite the moderating effect of external support, the negative relationship between nonwork demands and work engagement subsist. This relationship is resource depleting and thus warrants the attention of the employee and the organisation.

Table 2 presents the mediating effects of work engagement on the relationship between nonwork demands and nonwork resources with perceived employee productivity.

The direct effects of nonwork resources on perceived employee productivity were positive but not significant. Similarly, the direct effects of nonwork demands on perceived employee productivity were negative but not significant. However, the indirect effects of nonwork resources and nonwork demands on employee productivity through work engagement were significant, that is, β = 0.216, SE = 0.009, p < 0.05 for nonwork resources and β = -0.174, SE = 0.01, p < 0.05 for nonwork demands. The results suggest that work engagement mediates the relationship between nonwork resources and nonwork demands on employee productivity, thus supporting Hypothesis 5.

Discussion

Nonwork resources were positively associated with work engagement. External support moderated the negative relationship between nonwork demands and work engagement. Work engagement was positively associated with perceived employee productivity. Work engagement mediated the effects of nonwork resources and nonwork demands on work productivity.

By confirming Hypothesis 1, the study suggests that employees invested personal resources to protect against resource losses and/or recover from losses experienced in the work domain. The resource interactions between the nonwork and work domains indicate that employees strategically shifted resources in a compensatory fashion to offset the sudden imbalances created by the pandemic (see resource caravans, Hobfoll et al., 2018). This result provides further support to the notion that employee resources exist in ecological conditions – situated in the work and nonwork domains – that promote or curtail resource creation and sustenance. The nonwork resources expand possibilities in the work domain. Alternatively, the results suggest that exhausting the non-work resources could be detrimental to the work domain.

By confirming the expected negative relationship between nonwork demands and employee engagement, the results highlight the role strain that employees were exposed to during the pandemic. From a COR perspective, the result suggests that employees may become less engaged at work as a proactive response to the emerging, if not sudden, demands at home. Activation of this behaviour suggests that the employee is exploring opportunities to acquire additional resources to guarantee survival and adaptation.

Support for Hypothesis 3a further affirms the protective role of external support against emerging nonwork demands. As shown in Figure 2, external support significantly reduced the negative effect of nonwork demands on work engagement. As posited by COR theory, when faced with resource-depleting demands, employees find creative ways to reduce the impact. For example, employees had to recruit external tutors to assist with schooling support for children. In addition, they had to tap into external psychosocial and socio-economic support to mitigate stressors at home.

The variation by level of work highlights the differential access to resources. Senior level employees are usually better resourced (salary, benefits, and other organisational support). Thus, they can tap into their non-work resources and external support with relative ease viz. junior employees, ceteris paribus. By confirming Hypothesis 4, the study provides further evidence that highly engaged employees contribute positively to the organisational goals (Schaufeli & Bakker, 2010). By having a strong mental and emotional connection, such employees are efficient and effective, collaborate effectively with their peers, and their energy can penetrate and radiate widely, thus influencing organisational productivity. Engaged employees are self-motivated and care about their productivity and overall organisational goals. The results suggest the need to develop ecological conditions that nurture and propagate work engagement (Sanhokwe, 2022). As posited earlier, the thrust is to ensure that the resource streams that oxygenate work engagement are healthy. This places the onus on both the employee and the organisation (see SET and SDT).

Support for H5 suggests that the net effect (±) between nonwork demands and nonwork resources can have a virtuous or vicious impact on employee productivity via work engagement. Healthy resource reservoirs have a catalytic effect; that is, work engagement optimally transmits or converts the available energy into organisational value by being productive. On the other hand, suboptimal or compromised nonwork resources push employees into their defensive or protective shells. Employees seek to protect the limited resources they have, thus creating a vicious cycle. This compromises value generation at both the individual and organisational levels. These results have a bearing on workplace actions such as job (re)design, improvement of conditions of work (decent work) and promotion of employee rights at work (e.g. the right to disconnect and adequate compensation), and employee well-being initiatives.

Clark (2000) suggested that the work and nonwork domains were culturally unique in terms of process, person, context, and time characteristics. The results highlight how mutually dependent the work and nonwork domains have become. The two domains seamlessly exchanged resources. The results suggest the need for theoretical dynamism when interrogating and explaining the employee–organisation nexus, including behaviours and related outcomes.

Conclusion

The study further exposed the increasing interdependence between the work and nonwork domains, and by extension, the employee and the organisation. Organisational leaders should appreciate the ecological conditions within which work and nonwork resources are generated and expended. This has implications on workplace behaviour. Fostering resource enriching relationships between the two domains is vital for developing and sustaining desirable, value creating workplace behaviours and outcomes such as work engagement and productivity.

Acknowledgements

The author acknowledges the contribution and support of survey participants. Their willingness to share information led to the success of this research.

The author declares that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.

I declare that I am the sole author of this research article.

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Data used in this study will be available upon reasonable request to the author.

The views and opinions expressed in this article are those of the author and do not necessarily reflect the official policy or position of any affiliated agency of the author.

References