The purpose of this study is to determine how individual competencies, team collaboration and clear assignment of tasks and roles predict the decision speed of temporary task forces employed to handle the coronavirus disease 2019 (COVID-19) crisis.
This study analyzes COVID-19 task forces and bases of previous research on the performance of temporary teams and human and social capital literature. Linear multilevel regression is used with data from a factorial survey, covering six different continents, various industries and leadership levels from chief executive officers (CEOs) to specialist task force members.
The study reveals that all analyzed predictors (individual competencies, team collaboration and assignment of tasks and roles) are positively associated with the task force decision speed, with the individual competencies standing out in importance.
Team member selection is likely to be one of the first tasks necessary when such a temporary task force is created. Additionally, competent individuals might be successful even when not embedded in clear structures and even when not working in their own specialist roles.
The sudden pandemic outbreak of COVID-19 disrupted governments, corporations and individual people alike. As handling a pandemic is new for most involved people, and many such task forces are shown to be manned by personnel not formally trained in crisis management, this study's results represent important contributions to team performance, decision-making and crisis management literature in general. Furthermore, avenues for further research into the comparative analysis of predictors are revealed.
Wegmann, R.M. and Schärrer, L. (2020), "Outpacing the pandemic? A factorial survey on decision speed of COVID-19 task forces", Journal of Organizational Effectiveness: People and Performance, Vol. 7 No. 2, pp. 191-202. https://doi.org/10.1108/JOEPP-05-2020-0089Download as .RIS
Emerald Publishing Limited
Copyright © 2020, Emerald Publishing Limited
In spring 2020, the sudden global spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), followed by a pandemic outbreak of the coronavirus disease 2019 ( COVID-19) disrupted governments, corporations and individual people alike. By April 2020, more than 200,000 people have died, and preliminary estimates of the global economic impact remain abyssal (Roser et al., 2020). From governments to corporations and public schools, affected organizations suddenly had newly emerging tasks to manage. Measures had to be taken to provide effective duty of care, customers had to be informed of delays, buildings had to be closed, equipment had to be purchased, the health situation had to be monitored and planned projects had to be rescheduled. As the severity of the situation became clear, many organizations decided early to install some variant of a task force. Even though the naming seems elusive, and in some organization similar organizational bodies may be called as crisis management group, COVID-19 staff, working group or entirely different names, their function is similar: they prepare options for the line management and implement plans and concepts. These task forces are essentially temporary organizations, ad hoc structures tailored to solve one specific problem, COVID-19.
There are tremendous amounts of research on both professional crisis management and temporary organizations. However, the task forces tackling COVID-19 are different in two ways. First, many of them are manned by personnel not professionally trained to solve crisis situations. From small and medium-sized enterprises to primary schools and grocery stores, every organization is suddenly forced to create such task forces. Leaders suddenly occupy roles and run processes they are not experienced in. Second, the pandemic is new for everyone except for some few specialists. In most cases, these task forces have no antecedents. They need to be effective very early and create their structures and processes during their first days of operation.
Understanding how these ad hoc structures function and how their performance is influenced may provide crucial insights into how unexpected crises can be managed more effectively. But it is not the practical field alone that stands to profit. As Hällgren et al. pointed out, “[s]ubstantial contributions to management and organization studies were originally derived from extreme contexts” (2018, p. 111). Thus, this study's results can make a contribution to existing performance and decision-making literature, both in crisis settings and otherwise.
In order to avoid misconceptions and wrong interpretations due to different contexts and industries, this study measures the performance of these task forces by how quickly and decisively action is taken. It uses a factorial survey to address international leaders of various industries and backgrounds in order to ask about potential predictors for this decision speed. Such predictors could be individual competencies (Becker, 1964), team collaboration (Coleman, 1988) or unambiguously defined tasks and roles (Wally and Baum, 1994). This study attempts to answer the following research question: how do individual competencies, team collaboration and assignment of tasks and roles influence the decision speed of temporary COVID-19 task forces?
To answer this question, this study starts by summarizing the literature on crisis management and temporary organizations. Subsequently, this study analyzes data from a factorial survey distributed to individuals whose work environments were directly affected by COVID-19.
Crisis management groups, task forces and temporary organizations
About four decades ago, Billings et al. (1980, p. 300) criticized that “[a]nalyses of crisis situations have proceeded without a complete understanding of the concept of crisis.” One reason why this is still partly true is that crises exist in different phenotypes, depending whether they occur in economy, technology, politics or even individual lives.
This study addresses organizational crises, meaning crises which occur in an organization, such as a firm or a public authority. In an attempt to conceptualize and frame the phenomenon, Pearson and Clair (1998, p. 60) defined an organizational crisis as “a low-probability, high-impact event that threatens the viability of the organization and is characterized by ambiguity of cause, effect, and means of resolution, as well as by a belief that decisions must be made swiftly.” These events inherently influence psychological, sociopolitical and technological-structural dimensions of the organization concerned (Pearson and Clair, 1998, p. 59), which results in both research and practical treatment often being cross-disciplinary in nature. Bundy et al. (2017) integrated many of these disciplines and identified the following characteristics for such crises: (1) crises create uncertainty, disruption and change; (2) crises are harmful or threatening; (3) crises are behavioral phenomena, meaning that the involved actors socially construct them and (4) crises are not discrete events but parts of larger processes (Bundy et al., 2017, p. 1663).
COVID-19 and many crises before showed that organizations often choose to manage the situation and its consequences by installing task forces. Task force is an elusive term with scarce academic literature using it. The Cambridge Dictionary defines a task force as “a group of people who are brought together to do a particular job, or a large military group who have a military aim to achieve” (CALD, 2008, p. 1460). While this definition illustrates the term's military legacy, it also clearly reveals that the central attribute is shared by the military and the civil explanation: a task force consists of people who work toward a particular goal or fulfill a task. Assuming that task forces cease to exist once their goal has been achieved, task forces can be described as a subset of temporary organizations, a term much more common in academia and much more precisely described.
Temporary organizations have ascended in diverse fields, from software development to theater production. Burke and Morley distinguished them from permanent organizations as being “transient,” with “an ex ante-determined termination point.” A “temporally bounded group of interdependent … actors, formed to complete a complex task” (2016, p. 1237). Burke and Morley also asserted that crisis response is one important field where temporary organizations occur. Subsequently, they identified multiple reasons for this, such as “superior energy output,” “higher levels of creativity and innovation” and “holding the promise of a hyper-efficient organizational form freed from any organizational slack” (2016, p. 1236).
Lundin and Söderholm (1995) compared such temporary organizations in their “theory of the temporary organization” with the behavioral theory of the firm (March and Cyert, 1963). They distinguished the temporary organizations from permanent organizations along four dimensions: First, time as a discreet period of existence rather than a continuous flow. Second, a behavior geared around the presence of a task “that calls for attention [and] is the main reason for creating a temporary organization” in the first place – a clear difference to permanent organizations with a regularly revised strategy. Third, they identify a team with the urgent need for interpersonal relations and commitment building. Fourth, they identify the existence of temporary organizations as a transition, meaning that a specific goal has to be achieved before the temporary organizations successfully dissolve again (Lundin and Söderholm, 1995, p. 450).
Clearly, COVID-19 task forces studied are temporary organizations. They are meant to exist as long as COVID-19 influences the parent organization (time) because they are teams tailored to the task of responding to the pandemic outbreak. They are transitive as they focus on the temporary measures to be taken, without necessarily making permanent decision for the parent organization. Consequently, their ad hoc nature evokes questions about their effectiveness, their intra-team mechanics and structural and procedural particularities which are potentially not witnessed in permanent organizations.
Performance and decision speed
Various studies have focused on team performance and its predictors (e.g. De Dreu and Weingart, 2003; Guimerà et al., 2005; Mesmer-Magnus and DeChurch, 2009). However, financial or other external performance indicators are of little use for this study. As COVID-19 unfolded in different speeds and severities, varying vastly over locations, industries and organizational contexts, this study needs a simpler and more tangible handle on performance. Baum and Wally (2003) suggested to focus on decision speed. In a four-year study with 318 chief executive officers (CEOs), they found that particularly in “unpredictable environments,” “fast decision-making is beneficial, even given the negative force of environmental dynamism upon performance.” (2003, p. 1122). Finding the balance between speed and carefulness in cognition and decision-making is called as the speed–accuracy trade-off and has been an object of interest for more than a century (Förster et al., 2003; Garrett, 1922; Woodworth, 1899). Baum and Wally's strong vote for speed in this trade-off also is clearly in line with two other cornerstones on crises and the handling of dynamic situations: Hermann's three variable model (1972) and Boyd's observe–orient–decide–act (OODA) loop (Osinga, 2007). The former refers to the three variables, surprise, short decision time and threat to valued goals, with the first two indicating the value of time in crisis situations. The latter asserted that speed of iteration beats quality of iteration when cycling through the OODA loop.
A task force performing well in the sense of this study thus means that it takes a quick and decisive action. It ignores whether the action itself remains correct in hindsight and it automatically includes the necessary antecedent conditions (like team information processing) to be equally quick and effective.
Studying different ways to effectively measure and steer individual performance management, Schleicher and colleagues deducted that the organizational performance is a function of “unit-level human capital resources (HCRs)” (2019, p. 866). People and their capacities influence the performance of any organization. Using and expanding human capital theory (Becker, 1962), Crook and colleagues specified that human capital in the form of the “best and brightest” must be nurtured and retained. They specified that human capital even gains importance when the talent in question is not readily available in the labor market, an assumption which is likely to be true in the case of temporary COVID-19 task forces (2011, p. 453). Combined with the focus on decision speed as handle on performance, we hypothesize:
Individual competencies are positively related to the decision speed of a task force.
Coleman argued that expanding the traditional understanding of capital by the concept of human capital is important but not good enough. When human capital represents the “skills and knowledge acquired by an individual,” then it needs to be complemented by social capital representing the “relations among persons” (Coleman, 1988, p. 100). In an experimental setup, Grand et al. (2016) examined team cognition. They showed that both external observers and the individuals themselves recognized that performance is not a function of the aggregated competencies alone. They observed that the participants, each possessing distinct expertise and information, “needed to work collaboratively to fully acquire the knowledge needed to make effective decisions” (Grand et al., 2016, p. 1368). From this, we hypothesize:
Team collaboration is positively related to the decision speed of a task force.
Next to the individual competencies and the team collaboration as two possible drivers of decision speed, research has also established leadership as a crucial influence factor (e.g. Dionne et al., 2010; Dubno, 1965). While leadership as such is a wide term difficult to operationalize, one core aspect is more tangible: Wally and Baum (1994, p. 947) asserted that more “centralized” organizations have a higher decision speed. In their study, “centralization” does not mean having decisional power focused in one place but rather having clearly defined tasks and roles with unambiguous delegation of power. In a study on the performance of temporary disaster response teams, Wegmann endorsed that view by proposing that “Not arcane [leadership] skills seem to be crucial [performance] drivers…, but seemingly simple things, like ‘assignment of clear tasks and roles” (Wegmann, 2020). From this, we hypothesize:
Assignment of tasks and roles is positively related to the decision speed of a task force.
The factorial survey
The study presented in this paper is based on a factorial survey administered to individuals whose work environments are directly affected by COVID-19. A factorial survey (also called “vignette study”) consists of short descriptions of hypothetical situations illustrating a series of components that are randomly manipulated by scholars (Aguinis and Bradley, 2014). A factorial approach is particularly useful analyzing human actions and situations that are difficult to access by traditional methods (Rossi and Anderson, 1982).
In our study, a 3 × 3 × 3 within-subjects factorial design was used. Participants were required to assess the decision speed of the task force under specific conditions (individual competencies, team collaboration and assignment of tasks and roles). Being in line with the definition of task forces as temporary organizations as previously explained, this study introduced the term “task force” in the survey as follows: “… by task force we mean a temporary organization or structure, specifically tasked with handling all challenges in relation to COVID-19. In your organization, this could also be called as working group, crisis management group or it could have any other label.” The three conditions (individual competencies, team collaboration and assignment of tasks and roles) possessed three different degrees each (e.g. “low”, “medium” and “high”). A summary of the 27 different vignettes is displayed in Table 1.
The data of this cross-national study were collected from March 18 until April 19 during the COVID-19 crisis. In order to globally reach participants competent enough to judge task forces, we used a purposeful, non-probability strategy drawing on a combination of targeted contacting of eligible participants (task force members, chiefs of staff and leaders in crisis management organizations) and snowballing. In total, we distributed our survey to 701 experts in the field, ranging from public authorities like military, civil protection and health to the corporate sector with task force managers responsible for managing the COVID-19 crisis. About eight of the 107 returned questionnaires did not satisfy our criteria, yielding a final sample of 99 participants (completion rate of 15.26%). Each participant evaluated four different vignettes; thus, the total number of observations for the factorial survey was 396.
The majority of the participants in this study were male (73.7%) and had had a higher education degree (i.e. university or university of applied science) (93%). On average, they were 41 years of age (SD = 9.06). The participating individuals came from various industries and 17 different countries from six different continents.
This study measured “decision speed” by using a 10-point scale ranging from “very unlikely” to “very likely.” Participants responded to the following question: “how likely would it be that this task force acts quickly and decisively?”
In each vignette, three-level scales described the value of “individual competencies” (poor vs intermediate vs excellent), “team collaboration” (low vs intermediate vs high) and “assignment of tasks and roles” (unclearly vs more or less clearly vs clearly). Figure 1 shows a sample vignette.
Because handling a crisis does not necessarily need the same competencies, nor call for the same leadership style than day-to-day operations (Boin et al., 2013; James, 2011; Weick, 1988), we controlled on the respondent level for crisis management training (0 = no or less than 100 days of crisis management training; 1 = more than 100 days of crisis management training) and task force membership (0 = no task force member; 1 = task force member).
The data analysis
Typically, factorial surveys have a clustered data structure because the unit of analysis and each respondent rates multiples vignettes (Wallander, 2009). In our study, each participant rated four vignettes yielding a total number of observations of 396. Consequently, the data were designed to have a hierarchical structure at the respondent level and to consider two sources of variance: between and within levels (Lazazzara et al., 2013). We estimated two models according to the best practice recommendations for analyzing experimental vignette methodology models (Aguinis and Bradley, 2014). The null model does not include the independent variable and was used to break down the variance between the two levels of analysis: the respondent level (level 2) and the vignette level (level 1). In the second and full model, we extended it by considering all independent variables (i.e. control variable and vignette characteristics).
Table 2 displays the means, standard deviations and correlations for each variable used in this study. Decision speed is positively correlated with individual competencies (r = 0.470, p < 0.001), team collaboration (r = 0.400, p < 0.001) and assignment of tasks and roles (r = 0.322, p < 0.001). Furthermore, decision speed is positively correlated with task force membership (r = 0.169, p < 0.001). Assignment of tasks and roles is negatively correlated with crisis management training (r = −0.102, p < 0.005). Additionally, crisis management training is positively correlated with task force membership (r = 0.142, p < 0.001).
Results of the multilevel analysis are presented in Table 3. Model 1 contains only fixed and random effects of the intercepts' indications of whether the decision speed differs between the task forces or not. Variance in the score at the level of the participating field experts was 5.55. The intraclass correlation (ICC), which provides an indication of the similarity between values from the same individual, was 16.6%. According to a meta-analysis on articles published in Journal of Applied Psychology, applying multilevel analyses is typically appropriate if the ICC is between 15 and 30% (Mathieu et al., 2012). The intercept showed that the mean of the decision speed of the task forces was 5.41 (on a scale ranging from 1 to 10). Moreover, model 1 allowed us to ascertain the baseline−2-log-likelihood (i.e. a measure used for selecting and comparing mixed models) of 1,856.54.
The full model tested the effects of all independent variables, including our two control variables, and the three vignette characteristics. We found that on respondent level (level 2), only task force membership was significantly associated with decision speed (γ = 0.745, p < 0.001). Crisis management training had a nonsignificant effect on the dependent variable (γ = −0.213, p > 0.1). On the vignette level (level 1), all three characteristics were positively associated with a task force's decision speed: individual competencies (γ = 1.466, p < 0.001), team collaboration (γ = 1.167, p < 0.001) and assignment of tasks and roles (γ = 1.100, p < 0.001). Therefore, hypotheses 1, 2 and 3 are fully supported. Overall, the full model was a better fit than the null model ( Δ−2-log-likelihood = 488.793, p < 0.001) and can explain 26% of the total variance (pseudo R2 = 0.2633).
After a literature review on the crisis context and team performance, resulting in the development of three hypotheses and a study focused on decision speed as effective handling on performance, this study conducted a factorial survey among field experts affected by COVID-19 in the current work environment. This resulted in a multilevel model illustrating the relationships between individual competencies, team collaboration, assignment of tasks and roles and task force decision speed.
The following insights can be gained:
Insight 1: All independent variables contribute to a high decision speed.
However, the results yielded from hypotheses 1, 2 and 3 are unlikely to satisfy one central criterion of relevant research: nonobviousness (Thomas and Tymon, 1982). The assertion that competent individuals, team collaboration and task and role assignment are generally beneficial for any given organization is not particularly novel and unlikely to be disputed. Therefore, we conducted a post hoc analysis to compare the predictors in order to increase the value of the contribution for both theory and practice. Multiple authors asserted that nonoverlapping confidence intervals (CIs) are a good measure to express statistically significant differences (Knol et al., 2011, p. 253; Tryon, 2001). A respective comparative analysis showed that individual competencies are more positively related to the decision speed of the task force than team collaboration and assignment of roles and tasks. However, the results demonstrated that there are no statistically significant differences between team collaboration and assignment of roles and tasks. We summarize this in insight 2:
Individual competencies stand out as seeming to be the single most important performance predictor, at least when decision speed is taken as a handle on performance. This is surprising as it stands against the assumption that structures and processes (leading to tasks and roles) are central in crisis management training (Elliott and Smith, 2006; Smith, 1995). If tasks and roles are secondary compared with the individual competencies, then the question must be asked why crisis management training focuses on structures in the first place.
Insight 3: The negative correlation between having clarity in tasks and roles (on vignette level) and previous crisis management training (on respondent level) suggests that crisis management training shapes higher expectations in clarity in tasks and roles. A likely reason for this is that crisis management training and analysis tends to feature structures, roles and unambiguous flows of information as a core element for success.
Insight 4: Schaubroeck and colleagues focused on transformational leaders (2007, p. 1027) when they proposed that leaders shape team potency, which in turn provides the conditions for high-performing teams. While it seems reasonable that competent individuals coincide with effective transformational leaders (because competent people may have a choice to choose their workplace and thus their superiors), but it is equally possible that the influence exerted by competent individuals is independent of their (transformational) leaders. In this case, the transformational leaders' in Schaubroeck and colleagues' study would rather be important because they surround themselves by these competent individuals than by fostering team potency of given personnel.
Implications for theory
Deriving findings from data collected during the current COVID-19 crisis contributes to existing literature on performance and decision-making theory as it confirms (the value of competencies, collaboration and structures) and refines (the specification that individual competencies might matter most) existing knowledge (e.g. Dionne et al., 2010; Dubno, 1965; Grand et al., 2016; Schleicher et al., 2019; Wally and Baum, 1994). The global sample, rapidly collected while still targeting a relevant population, underlines the value of the findings derived from it. Furthermore, the timely availability of insights on how current COVID-19 task forces view their own morphology and their decision speed created a credible starting point to refine academic understanding on how COVID-19 is tackled by organizations further. As many of the survey's participants voluntarily shared their contact information, follow-up studies and deeper examination, by quantitative and qualitative methods alike, are made possible.
Implications for practice
Team member selection is likely to be one of the first tasks necessary when such a temporary task force is created. The displayed value of individual competencies raises the question whether team member selection enjoys the necessary weight (Flin and Slaven, 1994; Littlejohn, 1983). Bearing in mind that competent individuals seem to outrank clear assignment of tasks and roles in their prediction power, they might possibly be successfully selected without being limited by organizational charts or job profiles. Competent individuals might be successful even when not embedded in clear structures and even when not working in their own specialist roles.
Limitations and strengths
This study and its results are subject to some critical limitations. First, we measured decision speed of task forces using only one item, which led to a narrow analysis of performance (Baum and Wally, 2003). To capture the team performance more wholesomely, a more elaborated study could assume a wider stance on this. Additionally, the measurement of decision speed is based on the subjective perception of the respondents and limits the ability to generalize inferences from our findings. However, as COVID-19 unfolded in different speeds and severities, varying vastly over locations, industries and organizational contexts, a simple and more tangible measurement seems reasonable. Second, by using snowballing techniques, the study is confronted with general method biases (e.g. little control over the sampling method or lack of representativeness) (Goodman, 1961). However, this method allowed us to reach field experts affected by COVID-19 from all over the world in a very short time. The rapid implementation of our study and new evidence contribute to a better understanding of crisis management and temporary organizations.
This study shows three promising avenues for further research. First, an explicit comparative analysis of the predictors could shed light on the weight with which they can be prioritized. If future findings include approximate ratios of the prediction power, practitioners could use them to focus their improvement more accurately (e.g. by focusing resources on task force member selection instead of task force structures).
Second, this study's sample was quick, wide and global and encompassed a wide range of participants whose work environments were affected by COVID-19. A more targeted approach, aiming at a perhaps smaller sample but covering only task force members, could complement the findings with an inside perspective.
Third, follow-up studies collecting data from the same participants after the COVID-19 crisis would add a hindsight perspective and possibly yield more objective results from the participants.
|Vignette||Individual competencies||Team collaboration||Assignment of tasks and roles||N||M||SD|
|2||Poor||Low||More or less clearly||21||2.90||2.01|
|5||Poor||Intermediate||More or less clearly||10||2.80||1.49|
|8||Poor||High||More or less clearly||9||5.44||1.28|
|11||Intermediate||Low||More or less clearly||15||4.67||2.56|
|14||Intermediate||Intermediate||More or less clearly||19||4.74||1.22|
|17||Intermediate||High||More or less clearly||20||6.80||1.48|
|20||Excellent||Low||More or less clearly||17||5.47||1.80|
|23||Excellent||Intermediate||More or less clearly||15||7.47||1.47|
|26||Excellent||High||More or less clearly||10||8.10||1.06|
Mean, standard deviation and correlation values
|1. Decision speed||5.42||2.58||–|
|2. Individual competencies||2.07||0.79||0.470***||–|
|3. Team collaboration||1.99||0.83||0.400***||0.028||–|
|4. Assignment of tasks and roles||1.90||0.80||0.322***||−0.008||0.006||–|
|5. Crisis management traininga||0.18||0.38||−0.071||0.007||−0.065||−0.102**||–|
|6. Task force membershipb||0.47||0.50||0.169***||0.097||−0.010||−0.029||0.142***||–|
Note(s): *p < 0.05; **p < 0.01; ***p < 0.001
a0 = no or less than 100 days of crisis management training; 1 = more than 100 days of crisis management training
b0 = no task force member; 1 = task force member
Multilevel models predicting decision speed (level 1: N = 396, level 2: N = 99)
|Model 1 (Null model)||Model 2 (full model)|
|γ||SE||95% CI||γ||SE||95% CI|
|Intercept||5.414||***||0.159||[5.010, 5.729]||−2.298||***||0.432||[−3.147, −1.448]|
|Crisis management training||−0.213||0.367||[−0.944, 0.517]|
|Task force membership||0.745||**||0.286||[0.177, 1.313]|
|Individual competencies||1.466||***||0.119||[1.232, 1.700]|
|Team collaboration||1.167||***||0.110||[0.950, 1.385]|
|Assignment of tasks and roles||1.100||***||0.113||[0.878, 1.322]|
|Within-person variance (σ2)||5.551||2.399|
|Intercept variance (τ00)||1.101||1.107|
|Number of estimated parameters||3||8|
Note(s): *p < 0.05; **p < 0.01; ***p < 0.001
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