The Impact of the Covid-19 Pandemic on Older Workers: The Role of Self-Regulation and Organizations

Abstract

We live in an unusual time, which effects all of us in different ways. Due to the Covid-19 pandemic, some people are working harder than ever, some people have lost their job, some people can only work from home, and some people have to reinvent how they work (Kniffin et al., 2020). Older 50+ workers might even be more affected by the pandemic compared to younger workers because they are labeled as vulnerable and as being at risk in terms of Covid-19 (Ayalon et al., 2020). However, emerging studies on the impact of Covid-19 suggest that older workers respond more effectively to measures that counter Covid-19 (Losada-Balter et al., 2020). This is in line with the lifespan developmental perspective, which theorizes and demonstrates that older adults generally are very capable of adapting and very effective in dealing with the aging process (Baltes and Baltes, 1990; Freund, 2008). Multiple studies show that older adults engage in various self-regulation strategies aimed at continuously maintaining or restoring person-environment fit (e.g., Kooij et al., 2020; Taneva and Arnold, 2018; Zacher, Kooij, & Beier, 2018a) thus helping them to age successfully at work. In this commentary, I will take a more positive perspective on older workers and discuss the self-regulation strategies that older workers engage in and how organizations can stimulate this. I will end my commentary with some suggestions for future research.

A LIFESPAN DEVELOPMENT PERSPECTIVE: ENGAGING IN SELF-REGULATION STRATEGIES

In this commentary, I build on Rudolph and colleagues (2020); Rudolph and Zacher (2020), who suggested taking a lifespan development perspective on the impact of Covid-19 on older workers. The lifespan development perspective investigates “constancy and change in behavior throughout the life course” (Baltes, 1987, p. 611). The aim of this perspective is to identify general principles of lifespan development as well as differences and similarities between individuals in development (Baltes et al., 1980). An important assumption of the lifespan development perspective is that this development depends on three types of influences and older adults’ responses (Baltes, 1987): normative age-graded influences (e.g., general age-related improvement in emotion regulation, general loss in physical abilities), nonnormative or idiosyncratic influences (e.g., accidents at work), and normative history-graded influences (e.g., pandemics; Rudolph et al., 2020). In this line of reasoning, the Covid-19 pandemic is a normative history-graded life event influencing older adults’ development. Such a life event can be disruptive (Akkermans, Seibert, & Mol, 2018) and can lead to the depletion of one’s personal resources (Ten Brummelhuis and Bakker, 2012) and to person-environment misfit (Kooij, Zacher, Wang, & Heckhausen, 2020), thus harming work outcomes (Bakker, Du, & Derks 2018). Similarly, Akkermans, Richardson, and Kraimer (2020) suggest that Covid-19 is a career shock, which might disrupt career outcomes by causing resource loss cycles.

The literature on lifespan development proposes that older workers will self-regulate to deal with these types of life events. Similarly, Guan and colleagues (2020) propose to examine self-regulation strategies to understand individual responses to emerging stressors associated with the Covid-19 pandemic. More particularly, Guan and colleagues (2020) propose that individuals may engage in primary control (e.g., directly solving the associated problems) or secondary control (e.g., accommodating and reappraising existing problems) strategies.

Kooij and colleagues (2020) also argue that unforeseen changes in the work environment (e.g., due to the Covid-19 pandemic) trigger a self-regulation process. More particularly, they argue that self-regulation processes involve goal engagement and goal disengagement that influence and are influenced by person–environment (P–E) fit. Because the work environment changes continuously over time (e.g., due to technological developments) and because the individual changes continuously as well (e.g., with respect to abilities and work motives), employees are continuously motivated to engage in self-regulation behaviors to counter the effects of changes in the individual and work environment that can lead to P–E misfit. In addition, unforeseen changes in the work environment or self can result in experienced misfit, also motivating self-regulation behaviors.

Kooij and colleagues (2020) build on the distinction between primary and secondary control to identify four types of self-regulation strategies used by older workers to maintain or restore P–E fit (Grant & Ashford, 2008; Heckhausen et al., 2010; Pulakos, Arad, Donovan, & Plamondon, 2000; Parker et al., 2006). These would also apply to responding to Covid-19 measures. The first strategy is proactive goal engagement, which refers to self-initiated, anticipatory action aimed at goal pursuit, such as when older workers upgrade their skills and competencies. Many older workers have to work from home due to Covid-19 measures, which often means using more technology. Older workers engaging in this strategy would take the opportunity to upgrade their technological skills beyond what is currently necessary to anticipate future technological advancements in their work (see also Akkermans, Richardson, & Kraimer, 2020). The second strategy is proactive goal disengagement, which refers to self-initiated, anticipatory action aimed at protecting motivational resources, such as when older workers use the current situation to reflect on past career experiences. The third is adaptive goal engagement, which refers to coping or dealing with or effectively responding to already experienced changes in personal resources or the work environment by engaging in goal pursuit, such as when older workers ask colleagues for support in dealing with technological issues. Finally, the fourth is adaptive goal disengagement, which refers to coping, dealing with, or effectively responding to already experienced changes in personal resources or the work environment to protect motivational resources, such as when older workers downgrade the importance of using all technological gadgets or features of systems that are available.

The literature on aging at work demonstrates that older workers indeed effectively engage in these types of self-regulation behaviors (e.g., Shane et al., 2019; Zacher & Frese, 2011). In addition, this literature demonstrates that older workers are better at emotion regulation and thus at recovering from work demands (Scheibe et al., 2016). According to Restubog and colleagues (2020), emotion regulation is needed to effectively adjust and respond to career challenges and events such as the current pandemic. They argue that employees who can successfully regulate their emotions may be more effective and efficient in completing their work goals despite having to suddenly transition their work to a home environment including many distractions and home demands. In line with these findings, Losada-Baltar and colleagues (2020) found that age was negatively associated with distress in the first week of the lock-down period linked to the COVID-19 outbreak in Spain.

HETEROGENEITY IN OLDER WORKERS’ ENGAGEMENT IN SELF-REGULATION STRATEGIES

Although older workers seem to respond more effectively to the Covid-19 pandemic than their younger counterparts, this might differ depending on the individual. Inter-individual differences increase with age (e.g., Light, Grigsby, & Bligh, 1996), and there is much variability in how older adults respond to the aging process and, thus, the extent to which they age successfully (e.g., Baltes & Baltes, 1990; Kooij, 2015; Morack et al., 2013). Hence, older workers are a very heterogeneous group (e.g., Ayalon et al., 2020; Nelson & Dannefer, 1992). For example, the higher rates of death of Covid-19 among the elderly trigger some older workers to start retirement planning, whereas other older workers, particularly those (previously) working in health care, work more or even come out of retirement to help with the high demands in healthcare (Akkermans, Richardson, & Kraimer, 2020).

Older workers are also likely to differ in the type and extent to which they engage in self-regulation strategies. For example, Thrasher and colleagues (2018) found different profiles of self-regulation strategy use among older workers. These different profiles of self-regulation strategies might be explained by older workers’ personal resources (Freund, 2008). Personal resources refer to aspects of individuals’ capacity to control and influence their environment successfully and thus achieve their goals (Featherman, Smith, & Peterson, 1990; Hobfoll et al. 2003). Personal resources thus partly explain the extent to which older workers engage in self-regulation strategies. Similarly, Akkermans, Richardson, and Kraimer (2020) argue that certain personal career resources (e.g., career competencies and resilience) could make the Covid-19 career shock more manageable. In this line of reasoning, Losada-Baltar and colleagues (2020) also found that personal resources (e.g., positive emotions, self-efficacy) were negatively associated with loneliness and psychological distress in the first week of the lock-down period linked to the Covid-19 outbreak in Spain. Similar to self-regulation strategies, relevant personal resources differ among older workers. For example, Morack Ram, Fauth, and Gerstorf (2013) found subgroups of older workers based on their psychological and physical resources. Similarly, Hirschi and Valero (2015) found different profiles of career adaptability, referring to individuals’ resources for coping with current and anticipated occupational tasks, transitions, and traumas, which are differently related to adaptive behavior.

IMPROVING SELF-REGULATION STRATEGIES AND PERSONAL RESOURCES: THE ROLE OF ORGANIZATIONS

To support older workers with profiles of low self-regulation strategies and personal resources, research suggests a number of factors that might improve self-regulation strategies and personal resources. Kooij and colleagues (2020) propose factors at multiple levels that might enable self-regulation strategies at work by increasing the ability or motivation to engage in these strategies. For example, Kooij and colleagues (2020) point to job crafting interventions which trigger the proactive goal engagement strategy of strengths and interests crafting. Kooij and colleagues (2017) tested the effects of a job crafting intervention and found that it helped increase job crafting behavior among middle-aged workers, who in turn perceived a higher person-job fit. Kooij and colleagues (2020) also highlight the role of High Involvement Management (HIM; Parker, Wall, & Cordery, 2001), which refers to HR practices that encourage greater proactivity, flexibility, and involvement among workers (Wood, Van Veldhoven, Croon, & de Menezes, 2012). HIM includes HR practices such as extensive training, teamwork, decentralized decision making, information sharing, flexible job descriptions, career development, feedback, and job rotation (Vandenberg, Richardson, & Eastman, 1999; Wood et al., 2012). Earlier studies indeed demonstrated that these practices result in psychological empowerment (e.g., self-efficacy and self-determination; Messersmith, Patel, Lepak, & Gould-Williams, 2011), including among older workers (Kooij & De Lange, 2017) and are beneficial for workforce aging at the organizational level (von Bonsdorff et al., 2018). In addition, high involvement HR practices are likely to increase the zone of acceptance (i.e., the array of decisions or actions accepted as part of a job; Simon, 1997), thus enabling employees to engage in different types of self-regulation strategies. In some sectors, the current pandemic involves a natural shift to high involvement HR practices by forcing employees to work from home thus granting them more autonomy. Similarly, research indicates that virtual work settings lack traditional physical cues of dominance and status and can thus foster more participatory relationships (Kniffin et al., 2020). Previous research also proposes (positive psychology) interventions that might improve personal resources of older workers (Meyers, van Woerkom, & Bakker, 2013; Truxillo, Cadiz, & Hammer, 2015). For example, Luthans, Avey, Avolio, Norman, and Combs, (2006) introduce a micro-intervention to develop the personal resources hope, optimism, efficacy, and resiliency. Furthermore, gratitude interventions are likely to trigger positive emotions (Wood, Froh, & Geraghty, 2010). Finally, researchers found that vitality or health promotion interventions increase older worker health indicators (e.g., Strijk, Proper, van der Beek, & Van Mechelen, 2012).

SUGGESTIONS FOR FUTURE RESEARCH

I would like to end this commentary with some suggestions for future research. Research on self-regulation strategies of older workers is still limited (Kooij, 2015). Since the Covid-19 pandemic is perceived as a career shock (Akkermans, Richardson, & Kraimer, 2020), it provides an excellent opportunity to conduct research on the effects of such a shock among older worker, and particularly the strategies they use to self-regulate and restore or maintain their person-environment fit (see also Kniffin et al., 2020; Restubog, Ocampo, & Wang, 2020). One way of studying this is by conducting qualitative interview studies to explore the type of self-regulation strategies that older workers engage in to respond to the effects of the Covid-19 pandemic on their work. An example could be an interview study asking older workers to reflect on issues they experienced due to Covid-19 measures at work and their responses to these issues. Another way of examining this is by conducting experience sampling methods (ESM) studies to capture real-time responses to real-time changes at work due to Covid-19. For example, measuring technological demands, self-regulation strategies, and person-job fit perceptions, as well as other work outcomes in the moment, will provide insight in the effectiveness of self-regulation strategies as a response to increased technological demands due to working from home. Finally, a longitudinal design would also help to provide more insight into how work outcomes develop over time after experiencing such a career shock (Zacher, Kooij, & Beier, 2018b), explaining mechanisms such as changes in personal resources and self-regulation strategies.

Another fruitful area for future research is to test the effectiveness of some of the interventions proposed here to improve older workers’ personal resources and self-regulation strategies. For example, future studies could test the effects of health promotion interventions on older workers’ personal resources and the use of self-regulation strategies. Similarly, future studies could test job crafting interventions triggering strengths and interests crafting, which are particularly likely to increase the use of self-regulation strategies among older workers (compared to their younger counterparts; Kooij et al., 2017). In addition, it would be beneficial to examine wise interventions specifically targeting particular personal resources or self-regulation strategies (Walton, 2014). These interventions could be tested in a more common waitlist control experimental field study, but could also be tested with a within-person field experiment using ESM studies mentioned earlier (Song et al., 2018).

A final suggestion for future research is to examine older workers as a heterogeneous group and to identify distinct subgroups of older workers based on their unique profiles of personal resources and self-regulation strategies (e.g., Thrasher, Zabel, Bramble, & Baltes, 2018). For example, future research can use ESM studies to examine heterogeneity in self-regulation processes by distinguishing different subgroups of older workers according to their real-time dynamics in the use of self-regulation strategies or apply person-centered approaches to identify subgroups of successful, average, and unsuccessful older workers and examine which individual and organizational factors predict subgroup membership. This will help us understand why older workers respond effectively to the Covid-19 pandemic, identify vulnerable older workers, and which interventions are necessary to help the vulnerable older workers to respond more effectively to the Covid-19 and future pandemics.