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. Author manuscript; available in PMC: 2026 Feb 24.
Published in final edited form as: J Manag Psychol. 2024 Jun 19;39(7):930–946. doi: 10.1108/jmp-10-2023-0582

Transfer of a leadership training intervention prior to COVID-19 on leadership support during the pandemic

Rebecca M Brossoit 1,2, Leslie B Hammer 3, Todd E Bodner 4, Cynthia D Mohr 5, Shalene J Allen 6,7, Tori L Crain 8, Krista J Brockwood 9, Amy B Adler 10
PMCID: PMC12928495  NIHMSID: NIHMS2147027  PMID: 41737586

Abstract

Purpose –

We examined the impact of a leadership support training intervention implemented prior to the coronavirus (COVID-19) pandemic on support behaviors specific to COVID-19 during the pandemic. Primary intervention targets (i.e. family-supportive supervisor behaviors and sleep leadership behaviors) were explored as mediators between the intervention and supportive COVID-19 leadership behaviors.

Design/methodology/approach –

A cluster randomized controlled trial intervention was implemented with service members and their supervisors in the Army and Air National Guard throughout 2017–2019. Follow-up survey data were collected after the intervention, including during the COVID-19 pandemic in 2020. Direct and indirect intervention effects were tested.

Findings –

A pre-COVID intervention targeting leader support for family and sleep health had a direct effect on leader support specific to the COVID-19 pandemic. Additionally, sleep leadership, but not family-supportive supervisor behaviors, mediated the intervention effects on supportive COVID-19 leadership. These findings suggest that certain leadership training interventions can transfer across knowledge domains and time.

Practical implications –

Findings from this study demonstrate that training leaders on support behaviors improves their ability to support employees during the COVID-19 pandemic and may translate to crisis leadership in other contexts.

Originality/value –

We examined the long-term effects of an intervention that was implemented approximately 1–2 years prior to the COVID-19 pandemic on leadership support behaviors specific to the pandemic. Our findings contribute to the leadership, training, and organizational intervention literatures, and have implications for how leaders can support employees during crises.

Keywords: Leadership, Training, Intervention, Social support, Work-life issues, Sleep

The COVID-19 pandemic introduced and exacerbated a host of stressors for working adults, including concerns about health and safety, heightened job and income insecurity, modified work arrangements, increased family demands, intensified racial discrimination, and isolation (Sinclair et al., 2020), which had a profound impact on individuals’ mental health and well-being (Jimenez et al., 2023; Wu et al., 2021a). The American Psychological Association (APA) Stress in America poll has called attention to a national mental health crisis and highlighted the impact of the pandemic on disrupted mental health (APA, 2020). Identifying effective ways to protect individuals’ psychological health and well-being remains at the forefront of national priorities. The present study evaluates one promising option: workplace interventions that enhance supportive leader behaviors (e.g. Hammer et al., 2023).

Organizational leaders play a critical role during times of crisis (DuBrin, 2013). A crisis is defined as a rare, high-stakes, and ambiguous (in terms of cause, impact, and resolution) situation that threatens organizations and their members, and warrants an urgent response (Collins et al., 2023; James et al., 2011; Pearson and Clair, 1998). Crises can be categorized by their source (i.e. internal or external to the organization) and intentionality (i.e. controllability or degree to which they result from deliberate decisions); the COVID-19 pandemic reflects an external and unintentional crisis (Collins et al., 2023). During times of crisis, leaders can offer guidance, clarity, and support to their employees (Collins et al., 2023; James et al., 2011), which is imperative to protect employee well-being during public health crises like pandemics (e.g. Giorgi et al., 2020; Sinclair et al., 2020). We evaluate whether an organizational intervention that focused on training leaders in providing support related to family and sleep influenced their engagement in supportive leadership behaviors 1–2 years after the intervention was administered and during the COVID-19 pandemic.

Throughout the pandemic, leaders played an important role in sharing up-to-date health and safety information, helping to protect employee health and safety at work, conveying that employee well-being was prioritized, and being a source of stability in an otherwise uncertain and ambiguous time (e.g. Adler et al., 2022; Gray et al., 2023). Yet, most leaders received little guidance on how to best support their employees during this time of crisis. In the present study, we examine whether a leadership training intervention focused on the domains of family support and sleep support – implemented prior to the onset of the pandemic – transferred to supportive leadership behaviors during the pandemic. There is strong evidence for the effectiveness of leadership training (Lacerenza et al., 2017), and growing evidence for the potential benefits of leadership interventions targeting different areas of support on employee health and well-being (e.g. Hammer et al., 2023; Sonnentag et al., 2023). However, the generalizability of these targeted leadership training interventions to other domains of support, and the maintenance of training outcomes over time, is less clear.

In contrast to general leadership theories that identify the broad traits, skills, behaviors, and/or tactics used by effective leaders, domain-specific leadership models are targeted to a specific context or goal (Adler et al., 2014), such as workplace safety (e.g. Clarke et al., 2013; Kelloway et al., 2006), employee health (e.g. Gurt et al., 2011), stress and mental health (e.g. Adler et al., 2014; Dimoff et al., 2016), family demands (e.g. Hammer et al., 2009), and sleep (Gunia et al., 2015). Domain-specific leadership behaviors have been found to be conceptually and empirically distinguishable from general leadership behaviors and demonstrate incremental validity beyond measures of general leadership (Gunia et al., 2015; Gurt et al., 2011; Hammer et al., 2009; Kelloway et al., 2006). That is, generally being a good leader is important but engaging in these targeted, domain-specific leadership behaviors is associated with better outcomes for targets that align with those behaviors.

In response to the emergence of COVID-19, the concept of COVID-specific leadership support was introduced in the military (Adler et al., 2022). COVID-19 leadership captures the extent to which military supervisors engage in open discussions about COVID-19, emphasize self-care and mental health, share information and updates regarding the pandemic, personally follow public health guidelines, and modify work unit tasks to reduce soldiers’ risk of contracting COVID-19 (Adler et al., 2022). Adler and colleagues found that soldiers with supervisors who enacted COVID-19 leadership behaviors reported better mental health outcomes (i.e. fewer symptoms of depression and anxiety) and greater adherence to public health guidelines. Other researchers have similarly demonstrated the importance of leadership support, communal behaviors (e.g. compassion, sensitivity, understanding), and regular communication during the COVID-19 pandemic in civilian populations (Charoensukmongkol and Phungsoonthorn, 2021; Eichenauer et al., 2022; Gray et al., 2023; Men et al., 2022; Sawhney et al., 2023). In the present study, we examined the potential transfer of training across different forms of domain-specific leadership, with a focus on pre-pandemic leader training to support family and sleep, and the ability of this training content to transfer to supportive leadership behaviors in the context of an external and unintentional crisis (see Figure 1).

Figure 1.

Figure 1.

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Conceptual model of hypothesized effects

Study contributions

There has been a surge of interest in crisis leadership research since the COVID-19 pandemic (Collins et al., 2023). However, it is challenging to conduct rigorous studies of crises, given the rarity of their occurrence and necessity of time-sensitive responses from organizations. Consequently, the crisis leadership literature is characterized by methodological shortcomings. For example, of the few experimental studies of crisis leadership, all relied on hypothetical scenarios and behaviors (i.e. vignettes), leading Collins et al. (2023) to urge scholars to assess more real-world crises, their process (i.e. what occurs before, during, and after), and behavioral outcomes. We had a rare opportunity to evaluate leadership behaviors before and during the COVID-19 pandemic while also evaluating leadership behaviors following a randomized controlled trial leadership intervention that was implemented prior to the onset of the crisis. This study therefore offers a unique examination of an intervention’s effect on real-world crisis management and has broad implications regarding the value of leadership training for effective leadership during crises.

We also evaluate training transfer (i.e. the application of content learned in training on-the-job) in a new way by exploring the effects of a rigorous pre-COVID intervention designed to improve domain-specific leadership support (i.e. family-supportive supervisor behaviors; FSSB and sleep leadership), on leadership support during a crisis. There is meta-analytic evidence that training leaders on “soft skills” is more influential for employee and organizational outcomes compared to “hard skills” (despite soft skills being harder to learn and transfer), emphasizing the importance of interpersonal skill development in leaders (Lacerenza et al., 2017). Illustratively, at the onset of the pandemic, organizational leaders and supervisors struggled with interpersonal skills, like communication (e.g. keeping employees informed about response plans; Harter, 2020). Therefore, there are both theoretical and practical implications of the present study, in which we assess whether an intervention targeting family and sleep support from leaders transferred to a new domain of leader support in the context of the pandemic.

There is also a need to examine the sustained long-term effects of interventions, as this information is essential for refining intervention and training approaches and ultimately improving their utility (e.g. Ford et al., 2018). We take a novel approach by examining an intervention implemented in one context and evaluate the impact it had during a considerably different context 1–2 years later (i.e. prior to and during the COVID-19 pandemic). One relevant study found that a managerial intervention designed to increase psychosocial safety climate was effective in “steady-state” conditions (i.e. prior to the pandemic), but also sustained during the during the COVID-19 pandemic (Dollard and Bailey, 2021). Although we expect to find similar intervention effects that persist during the pandemic, our specific focus is on the potential for far training transfer across different domains of leadership support behaviors (i.e. from family and sleep support to COVID-19 support). Some past studies have investigated distal outcomes of interventions beyond their intended targets, including evaluations of the present intervention on well-being and workplace safety outcomes (Brossoit et al., 2023; Hammer et al., 2021), and we extend this work even further to assess distal intervention effects during a crisis, and over a longer period of time (i.e. 1–2 years following the intervention).

Last, we examined a working population of United States (U.S.) National Guard personnel who experienced compounded stressors during the pandemic (e.g. by serving as first responders), and therefore may have had an especially high need for support from leadership (e.g. Reger, 2021). Additionally, most studies in the crisis leadership literature have examined upper-level strategic leaders, like CEOs, resulting in calls for studies of frontline leader responses to crises (Collins et al., 2023), which is the focus of in the present study.

Transfer of training during the COVID-19 pandemic

For training to be successful, trainees must be able to transfer what they learned to relevant job-related activities (Aguinis and Kraiger, 2009). Training transfer refers to the process through which knowledge, skills, and/or behaviors learned in training are successfully applied at work (Aguinis and Kraiger, 2009; Baldwin and Ford, 1988). The two primary dimensions of training transfer include generalization (i.e. the application of training content across settings or situations) and maintenance (i.e. persistence of learned changes over time; Baldwin and Ford, 1988; Blume et al., 2010; Ford et al., 2018). Scholars have also differentiated between near and far transfer of training, with near transfer characterized as the application of training content to similar contexts and tasks, whereas far transfer occurs when individuals can apply training content to different contexts and tasks (Barnett and Ceci, 2002). Ideally, training content can be extrapolated and applied across other work-related contexts, tasks, and over time.

Barnett and Ceci (2002) proposed a transfer taxonomy in which near versus far transfers are distinguished on continuums according to content (i.e. what information is transferred) and context (i.e. when and where learning is transferred). Drawing from this taxonomy, we examined features of the knowledge domain (i.e. application of knowledge to another context; like generalization) and temporal context (i.e. time elapsed between learning and application; like maintenance) as elements of far training transfer. One of the key components of the intervention we evaluated in the present study involved domain-specific leadership training on FSSB and sleep leadership behaviors. The information leaders learned in the training was applied to the intended target (i.e. behaviors that support sleep; Hammer et al., 2021), providing evidence for near transfer in the knowledge domain.

Accordingly, we explored whether learning family- and sleep-specific support behaviors generalized and transferred to support for employees during the COVID-19 pandemic (i.e. knowledge domain) 1–2 years following the intervention (i.e. temporal context). The intervention included opportunities for behavioral self-monitoring, in which leaders practiced and tracked their supportive behaviors (e.g. helping employees balance their work and nonwork demands, encouraging healthy sleep behaviors, role-modeling). Successfully enacting behaviors can lead to mastery experiences and subsequent feelings of self-efficacy in those behaviors (Bandura, 1978), which is critical to training transfer (Colquitt et al., 2000; Ford et al., 2018; Salas et al., 2012). Moreover, leaders who enact family- and sleep-specific support behaviors signal that they care about their employees’ nonwork life and health. In line with organizational support theory, employees will appreciate leadership support as a form of organizational support and reciprocate in positive ways (e.g. commitment to the organization, performance outcomes; Rhoades and Eisenberger, 2002). Indeed, there is evidence that the intervention’s effects on perceived leadership support pre-pandemic increased employees’ job satisfaction and reduced their turnover intentions (Hammer et al., 2021) and enhanced their workplace safety behaviors and outcomes (Brossoit et al., 2023). These favorable responses from employees are positive reinforcers that should further increase leaders’ motivation to continue providing support, including in more challenging and high-stakes contexts where self-efficacy is even more pertinent, such as during a crisis. For these reasons, we expected to find evidence of far training transfer.

  • There will be a direct effect of the intervention on COVID-19 leadership behaviors at the pandemic follow-up, such that employees in the treatment group will report that their supervisor engaged in greater COVID-19 leadership behaviors during the pandemic, compared to those in the control group.

More specifically, learning to support employees across personal areas of their life (i.e. family, sleep) was expected to translate to the ability to enact support behaviors during the COVID-19 pandemic. Supervisors who learned how to make employees feel comfortable discussing conflicts between work and family demands (i.e. FSSB) and to ask employees about their sleep (i.e. sleep leadership) may translate those behaviors to the provision of emotional support and ability to have open conversations about the COVID-19 pandemic. Role modeling behaviors that were learned during the intervention (e.g. personally demonstrating how to effectively juggle work and family responsibilities; conveying the importance of sleep) could transfer to role modeling in the context of the pandemic (e.g. leading by example by following health guidelines). Learning how to provide instrumental support (i.e. practical or tangible resources), such as solving conflicts between work and family or reorganizing work within a unit, could translate to effectively adjusting work arrangements during the pandemic (e.g. modifying tasks to prevent employees from working in close proximity to one another). Further, the sleep leadership training content may facilitate knowledge transfer to another health-promoting context. Leaders in the intervention learned how to encourage healthy sleep behaviors (e.g. getting adequate sleep, avoiding caffeine and nicotine) which could transfer to disseminating health-promoting guidelines and support specific to COVID-19. By integrating the crisis leadership, domain-specific leadership, and training transfer literatures, we expect the domain-specific leadership support behaviors learned in the intervention (i.e. family support and sleep support) to transfer to leadership behaviors during the pandemic, thereby explaining the effects of the intervention on supportive COVID-19 leadership behaviors.

  • The effects of the intervention on COVID-19 leadership behaviors will be mediated by increased reports of (a) family-supportive supervisor behaviors (FSSB) and (b) sleep leadership behaviors from supervisors.

Methods

Recruitment, study design, and data collection

We examined a cluster randomized controlled trial in which an intervention was developed, implemented, and evaluated prior to the pandemic. Recruitment efforts included obtaining support from top National Guard leadership and unit leaders in the participating state. Unit leaders were responsible for distributing information about how to participate in the study with their full-time personnel. The intervention was implemented on a rolling basis between Fall 2017 – Fall 2019. Questionnaires were administered online using the REDCap platform and completed during off-duty time. Survey data were collected from Army and Air National Guard service members at baseline (N = 704) and at 4-months (N = 584) and 9-months (N = 549) post-baseline to assess the effectiveness of the intervention. Then, a third supplemental wave of follow-up data was collected from service members in Fall 2020 during the COVID-19 pandemic (N = 346). The supplemental data collection was not included in the original scope of the project but was conducted to understand the potential intervention effects during the COVID-19 pandemic. The Fall 2020 data collection involved a new consent process and a separate online survey. All surveys were completed outside of work hours and participants were compensated $25 for the completion of each survey. The study design included a waitlist control group, which had the opportunity to receive the intervention after the 9-month data collection was complete.

Nesting and randomization

The Army and Air National Guard service members who participated in the study were nested within 20 groups (10 groups in each branch of service: Army or Air National Guard). Research staff met with National Guard leadership to discuss the randomization strategy. After several discussions, the construction of treatment and control groups was determined by geographic location to reduce intervention contamination effects (i.e. groups were separated by working location). Randomization occurred following the baseline survey data collection. Within each branch, five matched groups of comparable sizes and with similar job types were created and then randomized to either the treatment or control conditions. Matched groups participated in the study in close succession (i.e. data collections for each matched group occurred within one to two months).

Participants

Participants included full-time service members in the Army (43.5%) and Air (56.5%) National Guard in a state in the Pacific Northwest region of the U.S. A total of 704 service members participated from units which were randomized into intervention (N = 358) or control (N = 346) conditions. Of these participants, 74.7% were men, 25.1% women, and 0.1% other gender. The average age of service members was 36.2 years, and most were white (80.7%). The majority were married or living with a partner (77.1%), most were parents (69.1%), and few had elder caregiving responsibilities (4.3%). Service members worked an average of 42 hours per week and had been working in the National Guard for an average of 10.9 years. Supervisors were identified by National Guard leadership and confirmed in the baseline surveys, in which participants were asked to identify their supervisory status and indicate the name of their direct supervisor. In the present study, only survey responses provided by service members are evaluated.

During the third data collection period, which occurred between November 12, 2020, and December 1, 2020 (i.e. 1–2 years following the intervention), service members worked in locations that were differentially affected by the COVID-19 pandemic. Service members worked in 16 different counties throughout the Pacific Northwest state where the study took place [1]. Drawing from state-wide publicly available data, across these 16 counties, the confirmed COVID-19 case rates ranged from an average of 156.4–727.5 per 100,000 residents during the data collection period [2] (M = 482.4; SD = 65.5).

Intervention components [3]

All intervention activities were administered approximately one month following the baseline data collection. The intervention was designed using a Total Worker Health® framework and participants included both service members and their supervisors. First, the health protection component of the intervention involved training supervisors on domain-specific leadership support behaviors. The evidence-based training content drew on FSSB training developed by Hammer et al. (2011), as well as sleep leadership principles in training developed by Adler et al. (2021), and content was tailored to be relevant to the military. These previously developed domain-specific leadership trainings were informed by current research and best practices in the work-family and sleep literatures resulting in the training examined in the present study. Supervisors learned about why work-life balance and sleep are critical for health and well-being, and then learned how to enact behaviors to support their employees’ life outside of work and sleep health, including training on how to enact role modeling (e.g. openly talking about taking care of personal demands), instrumental support (e.g. implementing a flexible work schedule), win-win management (e.g. re-arranging team work tasks), and emotional support behaviors for employee family/personal and sleep challenges, such as showing genuine concern for employee work-nonwork demands. The training was computer-based and took approximately one hour to complete. Supervisors were encouraged to practice enacting family-supportive and sleep leadership behaviors and tracked their support behaviors for two weeks following the training. The health promotion component of the intervention involved tracking, education, and goal setting around sleep. Drawing from methodology used by Adler et al. (2017), participants in the treatment group received individualized sleep feedback reports based on actigraphic sleep data that were collected over a 21-day period at baseline. Participants reviewed their sleep feedback reports with trained research assistants and set two goals to improve their sleep duration and sleep quality.

Service members and supervisors randomized to the control group were waitlisted to participate in the intervention after the conclusion of the study. During the initial study period (between 2017 and 2019), none of the health protection or health promotion components of the intervention were delivered to the control group participants. Shortly after the primary study concluded 9-months post-baseline (i.e. during months 10–11), the control group participants had the option to receive the components of the intervention (i.e. supervisor training on FSSB and sleep leadership, and sleep feedback reports). By the time the supplemental follow-up survey was administered in 2020, almost all service members in the control group had been exposed to the intervention; few (6.6%) had supervisors who chose to complete the leadership training and most service members requested to receive sleep feedback reports (99%), but they were delivered in a less personalized manner compared to how the reports were shared with the treatment group (e.g. reports were either emailed or presented in groups, rather than one-on-one with a member of the research team). Thus, the intervention that was provided to the waitlist control participants was considerably less comprehensive and rigorous compared to the intervention that service members in the treatment group received.

Measures

Family supportive supervisor behaviors.

Service members rated the extent to which their supervisor supported and helped them manage their work and nonwork lives across four items (e.g. “[supervisor’s name] demonstrates effective behaviors in how to juggle work and non-work issues”; Hammer et al., 2013; baseline α = 0.95, 4-month α = 0.96). Response options were on a five-point scale (1 = Strongly Disagree; 5 = Strongly Agree).

Sleep leadership.

An eight-item modified version of the sleep leadership measure was used to assess supervisor support for sleep (Gunia et al., 2015). Service members rated how frequently their supervisor enacted sleep leadership behaviors (e.g. “[supervisor’s name] encourages subordinates to get adequate sleep”; baseline α = 0.92, 4-month α = 0.94). Response options were on a five-point scale (1 = Never; 5 = Always).

COVID-19 leadership.

A five-item modified measure of the COVID-19 leadership behavior measure was used to assess the extent to which supervisors enacted supportive behaviors related to the COVID-19 pandemic (e.g. “[supervisor’s name] has shared useful and accurate information about the COVID-19 pandemic”; Adler et al., 2022; α = 0.86). Response options were on a five-point scale (1 = Strongly Disagree; 5 = Strongly Agree).

Analytic approach

Descriptive statistics and correlations are provided in Table 1. All analyses were performed in Mplus Version 8. All available data were included, and the full-information maximum likelihood estimation method was used. Although the intraclass correlation coefficient (ICC) for COVID-19 leadership was small (0.04), multilevel modeling was used to account for the nesting of service members within 20 groups. In the indirect effect analyses, bias-corrected bootstrapping with 5,000 bias-corrected bootstrapped samples was used (Fritz and MacKinnon, 2007; Preacher and Hayes, 2008). A weight variable was defined in the indirect effects analyses to enable the use of complex modeling with replicate weights, in line with other intervention analyses from the same study (Hammer et al., 2021). Following Bodner and Bliese’s (2018) recommendations, an analysis of covariance approach was used across analyses, in which models controlled for grand mean centered baseline levels of the mediators. Baseline levels of the COVID-19 leadership outcome measure were not controlled for because it was not assessed at baseline [4], [5].

Table 1.

Descriptives and correlations among study variables

N M (SD) 1 2 3 4 5 6
1. Condition 704 0.51(0.50) –
2. Sleep Leadership (B) 693 2.23(0.98) 0.05 –
3. Sleep Leadership (4 m) 567 2.43(1.00) 0.11** 0.59** –
4. FSSB (B) 702 4.07(0.97) 0.04 0.37** 0.32** –
5. FSSB (4 m) 572 4.05(0.92) 0.08 0.28** 0.47** 0.46** –
6. COVID-19 Leadership (COVID) 296 4.19(0.71) 0.16** 0.11 0.27** 0.19** 0.32** –
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Note(s): Nesting of the participants is not accounted for in this table. Condition: 0 = control group, 1 = treatment group. B = baseline, 4 m = 4-month post-baseline follow-up, COVID = follow-up during COVID-19 pandemic. FSSB = Family-supportive supervisor behaviors

*

p < 0.05,

**

p < 0.01

Source(s): Created by authors

Results

Hypothesis tests

There was a significant main effect of the intervention on service member reports of their supervisors’ COVID-19 leadership behaviors (b = 0.23, SE = 0.09, p < 0.01, pseudo ΔR2 = 0.03, d = 0.33). Thus, hypothesis 1 was supported (see Table 2). There was a significant indirect effect of the intervention on service member reports of their supervisors’ COVID-19 leadership support behaviors through sleep leadership (b = 0.04, SE = 0.02, [95% CI = 0.01,0.08]). However, FSSB did not mediate the effects of the intervention on COVID-19 leadership behaviors (b = 0.02, SE = 0.02, [95% CI = −0.01, 0.07]). Therefore, hypothesis 2a was not supported and hypothesis 2b was supported (see Table 3).

Table 2.

Main effect of the intervention on COVID-19 leadership

Est. SE
Intercept 4.07*** 0.07
Condition 0.23** 0.09
Intercept Variance 0.01 0.01
Residual Variance 0.49 0.05
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Note(s): Est. = unstandardized regression coefficient. SE = standard error. Condition: 0 = control group, 1 = treatment group

*

p < 0.05,

**

p < 0.01,

***

p < 0.001

Source(s): Created by authors

Table 3.

Indirect intervention effects

FSSB at 4 m (Mediator) COVID-19 leadership (Outcome) Indirect effects
Predictor b (SE) b (SE) ab SE 95% CI
FSSB (B) 0.45 (0.05)*** 0.03 (0.05)
FSSB (4 m) 0.23 (0.08)**
Condition 0.10 (0.07) 0.13 (0.09) 0.02 0.02 −0.01, 0.07
Sleep leadership at 4 m (Mediator) COVID-19 leadership (Outcome) Indirect effects
Predictor b (SE) b (SE) ab SE 95% CI
Sleep Leadership (B) 0.59 (0.05)*** −0.06 (0.04)
Sleep Leadership (4 m) 0.22 (0.04)***
Condition 0.18 (0.07)** 0.16 (0.09)† 0.04 0.02 0.01, 0.08
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Note(s): Indirect effects account for nesting within randomized groups as well as baseline values of the mediator variables. ab = unstandardized indirect effect. SE = standard error. CI = confidence interval. 95% CI obtained from 5,000 bias-corrected bootstrap samples. Condition: 0 = control group, 1 = treatment group. B = baseline, 4 m = 4-month post-baseline follow-up

†

p < 0.10,

*

p < 0.05,

**

p < 0.01,

***

p < 0.001

Source(s): Created by authors

Discussion

The present study contributes to the leadership, training transfer, and workplace intervention literatures, as our findings suggest that domain-specific leadership training can transfer beyond the primary training content and extend over time. We found a direct effect of an intervention targeting leadership support for family and sleep health, which was implemented approximately 1–2 years prior to the COVID-19 pandemic, on greater leadership support behaviors during the pandemic. Service members who were randomized to the intervention group reported greater COVID-19 support from their supervisors, despite the fact that the training their supervisors received occurred up to two years prior to the pandemic and was not specifically focused on crisis leadership or COVID-19. These effects were also detected even after some of the waitlist control group participants received components of the intervention.

The practicing and tracking of behaviors that was built into the intervention design likely facilitated mastery experiences, thereby enhancing leaders’ self-efficacy in providing support. It is also possible that leaders’ self-efficacy and motivation to support employees were augmented by positive reactions and responses they received from employees following the provision of support (Rhoades and Eisenberger, 2002). Thus, although the skills were not developed specifically to address a crisis, the training enabled supervisors to hone soft skills relevant to a crisis by practicing them with more mundane topics like family and sleep support. Together, the findings suggest that domain-specific leadership training interventions can transfer across domains, time, and be applicable and relevant during crises.

An indirect mediated effect was only detected with one of the two primary domain-specific leadership targets – sleep leadership, but not FSSB. It is possible that support behaviors for sleep more closely resemble support behaviors for COVID-19 because both include the encouragement of health-promoting behaviors. FSSB, on the other hand, emphasizes balancing demands between work and nonwork life. The transfer of sleep support to COVID-19 support could be considered “far” in terms of the knowledge domain and temporal context (Barnett and Ceci, 2002), but the transfer of knowledge of family support behaviors to COVID-19 support may be “farther”. Alternatively, the lack of an indirect intervention effect with FSSB as a mediator may also be because there was not a direct effect of the intervention on FSSB. Although there were not intervention effects on FSSB, FSSB was significantly related to greater COVID-19 leadership, suggesting that developing skills for supporting employees’ family demands may in fact transfer across domains and be applicable during crises. Therefore, it is plausible that interventions that can successfully increase FSSB may also have downstream impacts on other forms of leadership support.

There are likely other unexplored mediating mechanisms that explain why the intervention resulted in greater COVID-19 leadership support. We focus on behavior change mechanisms, though intervention mechanisms that facilitate training transfer can also include changes to emotions and cognitions (Nielsen and Shepherd, 2022). For example, the intervention may have led to changes in leaders’ thoughts and beliefs surrounding the importance of work-life balance and sleep health, which may generalize more broadly to prioritizing behaviors that support employees’ health and well-being. It is also possible that leaders developed greater feelings of empathy and concern for their employees through the training intervention, which may have been heightened during the pandemic. Relatedly, enhanced self-efficacy of leaders to provide support to their employees might also explain the relationships we found. We encourage researchers to investigate other potential mechanisms of change in future intervention work.

Practical implications

Our results highlight the utility of training frontline leaders in behaviors that support their employees, as the benefits appear to extend beyond the mere intended targets. Indeed, leaders who received the intervention, even 1–2 years prior, were better equipped to support their direct employees during a crisis (the COVID-19 pandemic), despite the training content being unrelated to crises. It may therefore be especially worthwhile to prioritize health-promoting domain-specific leadership approaches, as the knowledge and skills developed in these trainings can transfer and be applicable across contexts and time. We found evidence that leadership support training can transfer to support during a public health crisis, which has important implications for supporting the workforce in future crises. This is particularly important given an increasing understanding that illness, conflicts around the world, and climate change have only worsened since the onset of COVID-19 and will lead to future challenges for organizational leaders (United Nations, 2022). Considering the small effect sizes found in our study though, explicitly incorporating crisis leadership strategies (e.g. building trust) into leadership training may be particularly useful and could be integrated within existing domain-specific support trainings. Our findings suggest that leadership support training may be more valuable than originally thought and that the return on investment of these programs may be extended across domains, including during a crisis, and over time. Organizations should invest in resources to develop and implement evidence-based leadership trainings and evaluate their effectiveness on both primary outcomes of interest as well as more distal outcomes to capture potential “far transfer”. It may also be worthwhile to provide periodic refresher trainings to remind leaders how to practice support behaviors, and to signal the importance of leadership support.

Limitations and opportunities for future research

Unlike existing crisis leadership studies (as reviewed by Collins et al., 2023), we evaluated behaviors of frontline leadership during a crisis, examined outcomes of a leadership support intervention over time (i.e. 1–2 years following the intervention), and used an experimental RCT design in an organizational setting (Eden, 2021), which are strengths of our study. One limitation is that we were not able to perform analysis of covariance models exactly as recommended by Bodner and Bliese (2018) in the main intervention analyses, because COVID-19 leadership was not measured at baseline (as the pandemic had not occurred yet). Additionally, the magnitudes of the significant effects were small, which is unsurprising as small effect sizes are typical in applied psychology (e.g. Bosco et al., 2015), and considering that the intervention effects were assessed on a type of support upon which leaders did not receive training and were evaluated 1–2 years later. Moreover, we used a conservative intent-to-treat approach and assessed waitlist control group participants after they had the opportunity to receive the intervention – both of which would underestimate true intervention effects (McCoy, 2017) – suggesting that the actual intervention effects may have been stronger than what we detected in the present study. During periods of uncertainty, the degree to which leaders can influence their employees becomes even more compelling and their lack of preparation may take a particular toll. Thus, even small statistical effect sizes can yield significant practical effects (e.g. Prentice and Miller, 1992; Rosenthal and Rubin, 1982), particularly in the context of public health crises.

Another methodological limitation is the use of a shortened version of the COVID-19 leadership scale, which was abbreviated for efficiency. It was advantageous to use a COVID-19 leadership measure that was developed in military populations (Adler et al., 2022). Yet, our adapted version of the measure may have limited the extent to which we could comprehensively assess COVID-19 support behaviors. For instance, some measures of COVID-19 leadership include behaviors related to promoting and encouraging positivity during the pandemic (Adler et al., 2022; Gray et al., 2023), that were not reflected in our assessment. Further, scholars have acknowledged that the provision of support, including supervisor support during COVID (Gray et al., 2023), can sometimes be unhelpful to employees (e.g. Gray et al., 2020; Hughes et al., 2022), though we did not consider potentially unhelpful forms of leader support in the present study.

It would be useful for both science and practice to uncover whether domain-specific leadership training transfers to other contexts beyond COVID-19 support. In their review of crisis leadership, Collins et al. (2023) describe that external crisis leaders are characterized as “shepherds” who provide sensemaking, protection, and quick decision making for employees, and “saints” who provide comfort, support, and a sense of normalcy. It would be interesting to understand if leadership support training may also transfer to other similar types of crisis contexts (i.e. those that are external and unintentional, like natural disasters, or those that are external and intentional, like wars). Collins et al. (2023) further differentiated external crises from internal crises (i.e. those occurring within an organization), and describe how internal and intentional crises (e.g. organizational scandals) and internal and unintentional crises (e.g. product defects and failures), produce leaders who are considered “sinners” or “spokespersons”, and therefore require different types of behaviors (e.g. apologies, rebuilding of trust) (Collins et al., 2023). Future work could investigate the extent to which supervisors who receive training on domain-specific leadership behaviors are better equipped to support employees and lead effectively during other types of crises.

Finally, in the present study we focus on employee perceptions of their supervisors, but it would also be worthwhile to understand leaders’ perceptions of their own behaviors. Evaluations of leaders would also allow for the assessment other psychological (e.g. self-efficacy) or emotional components of providing support. For instance, leaders are also impacted by external crises, and may need to regulate their emotions to an even greater extent to effectively support their followers and employees, which is an area that would be worth exploring (Collins et al., 2023; Wu et al., 2021b). We encourage researchers to continue investigating other outcomes and mechanisms of leadership support and crisis leadership in future work.

Conclusion

Considering present crises and those that may arise in the future (e.g. pandemics, natural disasters, domestic and international terrorism, social unrest, financial crises), it is necessary to develop approaches for supporting workers’ health and well-being, such as leadership support training. Our study provides evidence that domain-specific leadership trainings can transfer to new and relevant contexts, even 1–2 years following training. Specifically, we found that participating in a FSSB and sleep leadership support training and a sleep health intervention prior to the COVID-19 pandemic predicted greater leadership support behaviors specific to the COVID-19 pandemic in 2020. In addition to direct intervention effects on COVID-19 leadership, sleep leadership also acted as a mediator. These results have practical implications for how organizations and leaders can protect employees’ health and well-being during critical times, such as during a public health crisis.

Acknowledgments

This work was supported by Office of the Assistant Secretary of Defense for Health Affairs, through the Psychological Health and Traumatic Brain Injury Research Program - Comprehensive Universal Prevention/Health Promotion Interventions Award, under Award No. W81XWH-16-1-0720. Material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication. Opinions, interpretations, conclusions and recommendations are those of the author and are not necessarily endorsed by the Department of the Army or the Department of Defense. This work was also partly supported by the Oregon Institute of Occupational Health Sciences at Oregon Health and Science University via funds from the Division of Consumer and Business Services of the State of Oregon (ORS 656.630). Portions of this research were supported by the Grant #T03OH008435 awarded to Portland State University, funded by the Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health. Total Worker Health® is a registered trademark of the U.S. Department of Health and Human Services (HHS). The contents of this article are solely the responsibility of the authors and do not imply endorsement by the U.S. Department of Health and Human Services, the Centers for Disease Control and Prevention, or the National Institute for Occupational Safety and Health. Leslie Hammer has a financial interest in Work Life Help, LLC., a company that may have a commercial interest in the results of this research and technology.

Footnotes

This potential conflict of interest has been reviewed and managed by OHSU. The authors have no other conflicts of interest to disclose.

1.

Approximately one-third of participants worked in a different county from where they lived. Here, we report on work location rather than home location because participants were informed that their home location data would not be used for research purposes.

2.

Confirmed COVID-19 case rates were reported in two-week intervals. Thus, the range of average case rates was calculated by taking an average of the confirmed COVID-19 cases per 100,000 residents in a given county across two date ranges (i.e. the cumulative rate between 11/8/20–11/21/20 and the cumulative rate between 11/22/20–12/05/20), that captured the entire data collection period.

3.

Additional information about the intervention design can be found in Hammer et al. (2021).

4.

Of note, 56 service members (8%) completed the 9-month post-baseline data collection (i.e. the second follow-up data collection, prior to the data collection in Fall of 2020) on or after March 8, 2020, which was the date in which a State of Emergency was declared due to COVID-19. A flag variable (i.e. 0 = completed 9-month survey prior to March 8, 2020; 1 = completed 9-month survey on or after March 8, 2020) was included as a statistical control variable in the analyses. All results were substantively the same regardless of the inclusion of the flag variable, so all reported analyses reflect models with the flag variable excluded.

5.

Analyses were also performed with FSSB and sleep leadership at 9-months modeled as mediators and the results mirror those found with 4-month mediators (i.e. the indirect effect is not significant with FSSB as a mediator and is significant with sleep leadership as a mediator). Throughout the paper, only results with the mediators modeled at 4-months post-baseline are reported for parsimony.

Contributor Information

Rebecca M. Brossoit, Department of Psychology, Louisiana State University, Baton Rouge, Louisiana, USA Department of Psychological Sciences, Rice University, Houston, Texas, USA.

Leslie B. Hammer, Oregon Institute of Occupational Health Sciences, Oregon Health & Science University, Portland, Oregon, USA

Todd E. Bodner, Department of Psychology, Portland State University, Portland, Oregon, USA

Cynthia D. Mohr, Department of Psychology, Portland State University, Portland, Oregon, USA

Shalene J. Allen, Department of Psychology, Portland State University, Portland, Oregon, USA Department of Psychological Sciences, Kansas State University, Manhattan, Kansas, USA..

Tori L. Crain, Department of Psychology, Portland State University, Portland, Oregon, USA

Krista J. Brockwood, Oregon Institute of Occupational Health Sciences, Oregon Health & Science University, Portland, Oregon, USA

Amy B. Adler, Center for Military Psychiatry and Neuroscience, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA

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