Leadership Managment

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An Adaptive Leadership Approach: The Impact of Reasoning and Emotional Intelligence (EI) Abilities on Leader

Scott L. Boyar1 · Grant T. Savage1 · Eric S. Williams2

Accepted: 3 November 2022 / Published online: 14 November 2022

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022

We build on the adaptive leadership framework to include reasoning (i.e., a cognitive ity) and emotional intelligence (EI) (i.e., social ability) factors in predicting potential leader adaptability. We incorporate adaptive and situational leadership theories along with trait process models to examine two types of reasoning abilities, inductive and deductive along with the direct and moderating effect of EI on potential leader adaptability. Using a three wave panel design, we found that inductive reasoning and EI predicted adaptive leadership; we further showed that EI moderated the inductive-adaptive leadership relationship where higher levels of EI and inductive reasoning abilities predicted potential leader adaptability. We discuss the implications of the study findings to theory and practice while offering directions for future research.

abil-Keywords Trait theory · Reasoning abilities · Emotional intelligence · Adaptive leadership

Leadership is a highly interactive process of influencing others to achieve goals and tives (DeRue, 2011); these complex social interactions between leader and followers along with situational demands require adaptability to create meaningful outcomes at the indi-vidual, group, and organization levels (Dinh & Lord, 2012; Vroom & Jago, 2007). Leaders must adapt to ambiguous, uncertain, and changing work situations (Yukl & Mahsud, 2010) and have the abilities to interact effectively with followers (DeRue, 2011) to effect positive change across dynamic work environments (Uhl-Bien & Marion, 2009; Vroom & Jago,

objec-2007). Yet there is paucity of research examining the adaptive nature of leadership (Yukl & Mahsud, 2010).

Note. The data collection involved human subjects and received IRB approval through the University of Alabama at Birmingham (IRB Project Number: IRB-121016004).

* Scott L. Boyar

1 Department of Management, Information Systems and Quantitative Methods, Collat School of Business, University of Alabama at Birmingham, 710 13Th Street South, Birmingham, AL 35294-4460, USA

2 Culverhouse College of Commerce, The University of Alabama, Tuscaloosa, AL, USA

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Leadership researchers continue to examine individual difference models (e.g., Dinh & Lord, 2012; Mumford et al., 2000; Shondrick et al., 2010; Zaccaro et al., 2004, 2018), which have led to a number of studies (e.g., Antonakis et al., 2012; Zaccaro, 2012; Zaccaro et al., 2015) devoted to specifying traits that directly or indirectly relate to leader behav-iors and outcomes (Zaccaro et al., 2004; Zacarro et al., 2013). There have been numerous reviews (e.g., Judge & Long, 2012; Zaccaro et al., 2013, 2018) and meta-analyses (e.g., DeRue et al., 2011) examining traits across leadership approaches, yet with scant exami-nation of the cognitive and social abilities necessary for adaptive leaders (Amdurer et al.,

2014; Pulakos et al., 2000; Yukl & Mahsud, 2010).

Further, there is a need to understand the objective factors influencing potential leaders (Dries & Pepermans, 2012), including more nuanced measures of these broad phenomena (Finkelstein et al., 2018). We hope to address this shortcoming in the literature by exam-ining the relationship between specific components of cognitive and social abilities and potential leader adaptability for a sample of potential leaders. In particular, we argue that two cognitive abilities, inductive and deductive reasoning, involve distinct decision-making processes (Evans & Over, 2013) that may lead to unique effects. Moreover, we suggest that emotional intelligence (EI) is an important social ability that will likewise predict potential leader adaptability. Capturing EI, which is one’s ability to process and organize emotion-related information, should lead to better decisions for potential leaders (Zaccaro & Torres,

2020). Broadly, research needs to integrate process models that focus on clarifying how traits operate to effect intra- and interpersonal behavioral changes (DeRue et al., 2011) that increase cognitive flexibility in achieving outcomes (Dinh & Lord, 2012; Zaccaro et al.,

2018). Therefore, the purpose of this study is to better understand how cognitive and social abilities influence leader behaviors within an adaptive leadership framework.

Theoretical Framework

Research continues to focus on integrative process models, particularly those involving leaders’ abilities to manage the complex interactions with followers (Avolio et al., 2009; Dinh & Lord, 2012; Zaccaro & Torres, 2020). Zaccaro et al. (1991) developed a theoreti-cal model integrating both general and social intelligence; they posited that these factors improve the leader’s ability to interpret, organize and process information from both situ-ational demands and social cues to effectively adapt behaviors, yet few studies have tested these propositions. Zaccaro et al. (2018) advanced this idea in a comprehensive model that includes both leader foundation traits and leader capacities; both categories are needed to effectively respond to situational demands. Foundation traits initiate or predispose an indi-vidual toward effective leader behaviors (e.g., cognitive abilities) and should demonstrate stability across situations. These traits develop leader capabilities to be more adaptive in dynamic work environments. Leader capacities capture factors that accentuate the leader’s ability to adapt to demands, enabling leaders to organize and process relevant information in choosing appropriate behaviors to influence others and achieve desired performance out-comes (Zaccaro et al., 2018).

Consistent with Zaccaro et al.’s (2018) model and information processing theories (Dinh & Lord, 2012), the ability to process information both cognitively and socially in

an adaptive fashion is critical to meeting the needs of followers within an adaptive ship framework; this allows leaders to respond quickly and appropriately to achieve desired outcomes (Dinh & Lord, 2012). Our study models two abilities, reasoning and emotional

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leader-intelligence, to process information and choose appropriate leadership styles in an adaptive fashion (see Fig. 1).

Adaptive Leaders

Adaptive leadership is defined as a dynamic and interactive process between the leader and follower where both respond to existing or potential challenges in complex environments that result in positive outcomes (Heifetz & Laurie, 1997), including career satisfaction and success (Amdurer et al., 2014). Adaptive leadership theory emphasizes adaptive change (Heifetz & Laurie, 1997), creating flexibility to respond to challenges, which is a central tenet of adaptive leadership (DeRue, 2011; Yukl & Mahsud, 2010) and necessary for iden-tifying and developing leadership potential (Dries & Pepermans, 2012). Leaders need to evaluate and accurately identify the challenge, and then incorporate behaviors that engage followers to respond appropriately (Heifetz & Laurie, 1997; Heifetz et al., 2009; Yukl & Mahsud, 2010). Consistent with situational leadership theory (SLT) (Hersey & Blanchard,

1993, 1996), leaders are able to adapt to environmental demands as well as the needs and readiness of their followers.

SLT provides a prescriptive model for understanding the leader–follower relationship (Silverthorne & Wang, 2001) by focusing on the leader’s style flexibility to meet work con-ditions. The theory postulates that leader effectiveness in matching leader behavior with followers’ readiness to complete specific tasks leads to leader and follower effectiveness (Hersey & Blanchard, 1996). Leaders choose the appropriate leadership style, which con-sists of varying degrees of task (i.e., providing direction regarding expected actions and responsibilities) and relationship (i.e., engaging in supportive and interpersonal communi-cation that facilitates relationship building) behaviors depending on follower performance readiness, to effect positive change (Hersey, 2009; Hersey et al., 1982; Hersey & Blan-chard, 1996). While leaders have preferred leadership styles, adaptive leaders demonstrate flexibility in choosing less preferred styles when necessary to influence others. SLT sug-gests that individuals that adapt their style will be more effective and increase individual and organizational performance levels (Hersey & Blanchard, 1993) and are thought to be adaptive leaders (Hersey et al., 1982; Silverthorne, 2000). SLT continues to be a popular approach for both practitioners and researchers (Meirovich & Gu, 2015), although concerns have been raised (Thompson & Vecchio, 2009; Vecchio, 1987), research has demonstrated

Deductive Reasoning

Potential Leader AdaptabilityH1a

Emotional Intelligence

H3a H3b

Emotional Intelligence

H2

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its resilience and promise (Hambleton & Gumpert, 1982; Silverthorne, 2000; Silverthorne & Wang, 2001). Within the adaptive leadership framework, leaders interpret, organize, and process information about others’ abilities to complete a specific task in order to effect behavioral change. Each situation requires leaders to process cognitive (e.g., reasoning abilities) and social information in choosing the appropriate leadership style.

Reasoning and Leadership

General cognitive ability is thought to enable leaders to make better decisions, solve lems, and process information more effectively (Antonakis et al., 2020). A meta-analysis by Judge et al. (2004) found a positive relationship between intelligence and leadership; while significant, it was less than expected even when correcting for range restriction

prob-(p = 0.27). While cognitive ability captures a range of underlying abilities (Johnson et al.,

2008), it may be that specific components of intelligence, such as reasoning, are tive in understanding leader behaviors and effectiveness (Finkelstein et al., 2018). Reason-ing is an important aspect of cognitive ability (Carroll, 1993; Thurstone, 1938) and critical thinking (Ennis, 1993, 1996).

informa-Although reasoning abilities have received less attention in the leadership literature, there is interest in understanding their practical uses (Evans & Over, 2013). Sosik et al. (2011) demonstrated a positive relationship between moral reasoning and transformational leadership, while Zaccaro et al. (2015) did not find a significant effect for a leader’s verbal reasoning. These narrow types of reasoning abilities may have limited application across leadership theories. We fill this gap in the research by examining inductive and deductive reasoning (Rips, 2001) that may be broadly applied and relevant to the range of informa-tion processing leaders experience and which may shed light on unique effects.

Interestingly, both reasoning processes rely on the same information when making sions but are distinct phenomenon (Carroll, 1993; Rips, 2001; Shye, 1988) and process information differently. In fact, they use different cognitive (Heit & Rotello, 2010) and psy-chological functions (Evans & Over, 2013). Advances in fMRI imaging have demonstrated that inductive and deductive reasoning are distinct constructs (Goel & Dolan, 2004). We suggest that inductive and deductive reasoning are essential for leaders’ decision-making, problem-solving, and interactions with others in the organization. Each type of reason-ing requires different levels of confidence in drawing conclusions. This likely affects the speed and timing of decision-making. Inductive reasoning involves the ability to infer rules whereas deductive reasoning is the ability to apply rules (Evans & Over, 2013; Shye,

Inductive reasoning is more open-ended and exploratory where information is collected and organized in generating support for a conclusion and where adding more information provides support for the argument (Bassham et al., 2005). As Bassham et al. (2005) noted,

“if the premises [or statement(s)] are true, then the conclusion is probably true” (p. 56).

With inductive reasoning there is a level of ambiguity and probability, rather than absolute certainty that one might see with deduction (Heit & Rotello, 2010; Shye, 1988). Inductive reasoning may play a more practical role in the leader’s daily cognitions (Kemp & Tenen-baum, 2009). It tends to be more flexible, allowing leaders to assess the situation and utilize information to infer relationships, and then choose a leadership style based on the situation, and with potential time constraints and uncertainty (Evans & Over, 2013). Consistent with information processing theory (Dinh & Lord, 2012), inductive reasoning involves heuris-tics and quicker decision-making where leaders adapt and use their experience to inform

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their decisions (Heit & Rotello, 2010). This lends itself to a more adaptive process where leaders draw conclusions that support their decisions that seem likely, but not certain, since full information is not available nor fully processed (Kemp & Tenenbaum, 2009).

Deduction, however, involves arguments based on laws or rules (Colberg et al., 1985) that are well-defined logical statements (Kemp & Tenenbaum, 2009) to prove conclusions (Bassham et al., 2005) or where “the conclusions follow necessarily from the premise” (Colberg et al., 1985, p. 682). Deductive reasoning involves a more analytic process (Heit & Rotello, 2010) and works better when information is readily available. Drawing valid conclusions can be time-consuming and may work best in stable environments (Evans & Over, 2013). It involves the ability to implement a specific plan, such as choosing a lead-ership style that clearly matches the follower’s readiness and situational demands (Shye,

1988). The process is based on drawing valid inferences based on rules or knowledge that a course of action will result in a desired conclusion and outcome (Heit & Rotello, 2010).

We anticipate that both inductive and deductive reasoning will predict potential leader adaptability because both draw conclusions from the same available information. However, leaders relying on inductive reasoning will make quicker decisions or implement a course of action, such as choosing a leadership style once enough information is gathered to con-fidently draw a reasonable conclusion. While deduction may result in better conclusions and decisions (Heit & Rotello, 2010), it may not provide the level of flexibility needed for leaders working in dynamic environments (Evans & Over, 2013). Therefore, we expect inductive reasoning to provide a stronger relationship than deductive reasoning because it works faster and better in ambiguous situations, which is more pragmatic considering the typical dynamic organizational context. With deductive reasoning, leaders will need strong evidence that a less preferred leadership style is best in order to deviate from a preferred leadership approach. Therefore, we posit the following:

• Hypothesis 1a. Inductive reasoning ability leads to greater potential leader adaptability.• Hypothesis 1b. Deductive reasoning ability leads to greater potential leader adaptabil-

ity, but to a lesser degree (weaker) than inductive reasoning.

Emotional Intelligence

The interactive nature of leadership is an emotional process (Dasborough & Ashkanasy,

2002) requiring leaders to demonstrate high levels of social and emotional intelligence (Zaccaro & Torres, 2020; Zaccaro et al., 1991). There has been extensive study of emo-tions in organizations (see Ashkanasy & Humphrey, 2011) and its importance to affect (Weiss & Cropanzano, 1996). Emotions are the result of the psychological, physical, or cognitive response to an event and may impact leaders’ attitudes, motivations, and behav-iors (Ashkanasy, 2003; Weiss & Cropanazo, 1996; Zaccaro & Torres, 2020). The ability to understand, respond to, and manage emotions in an adaptive fashion to effect change is critical to EI (Mayer et al., 2008; Rothman & Melwani, 2017; Zaccaro et al., 1991).

We focus on ability-based EI rather than trait or mixed models that are often highly related with personality measures (Ashkanasy & Daus, 2005; Caruso et al., 2002; Harms & Crede, 2010). Ability-based models tap into the behavioral and cognitive, rather than dispositional, aspect of EI, and are distinct from personality (Joseph & Newman, 2010). EI is concerned with the capacity to process and think about emotion-related information and to use this information efficiently to make decisions, solve problems, and interact with oth-ers (Mayer et al., 2000, 2008; Zaccaro & Torres, 2020). EI is defined as “the set of abilities

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cor-(verbal and nonverbal) that enable a person to generate, recognize, express, understand, and evaluate their own and others’ emotions in order to guide thinking and action that suc-cessfully cope with environmental demands and pressures” (Van Rooy & Viswesvaran,

2004: 72) and which may be an important determinant of adaptive leaders.

There has been a debate over the distinction between cognitive and emotional abilities (Locke, 2005), but they are generally considered distinct yet related constructs (Cherniss,

2010; Côté & Miners, 2006; Mayer et al., 2000, 2001). Recent studies, including several meta-analyses, provide evidence that EI has incremental validity over personality (Sy et al.,

2006) and cognitive ability (Joseph & Newman, 2010; Rossen & Kranzler, 2009) when predicting job performance (Joseph et al., 2015) and work attitudes (Miao et al., 2016,

2017; O’Boyle et al., 2011). These meta-analyses show promising benefits of EI for viduals and organizations. Although the relationship between EI and leadership has been questioned (Antonakis, 2003, 2004; Locke, 2005), there are practical (Goleman, 1995,

indi-1998) and theoretical (Zaccaro et al., 1991, 2018) arguments for its importance to ship (Ashkanasy & Humphrey, 2011; Ashkanasy et al., 2017; Humphrey, 2002, 2012).

leader-The leadership process involves managing one’s emotions as well as those of the lowers’ (Humphrey, 2002). EI has been shown to positively and incrementally predict leadership emergence and effectiveness (Walter et al., 2011), transformational leadership (e.g., Harms & Credé, 2010) and other leadership styles (George, 2000). More research is needed to better understand its relationship to leadership emergence, styles, and effective-ness (Rothman & Melwani, 2017). We test the proposition that EI is a necessary determi-nant for leader adaptability (Zaccaro et al., 2018). EI should enable leaders to effectively manage emotions that results in less disruptive behaviors (Weiss & Cropanzano, 1996) and greater positive attitudes and effective work outcomes (Humphrey, 2002; Lord et al., 2011). Mayer and Salovey (1997) describe EI as “(t)he ability to perceive emotions, to access and generate emotions so as to assist thought, to understand emotions and emotional knowl-edge, and to regulate emotions so as to promote emotional and intellectual growth” (p. 5). Higher levels of EI should increase leaders’ understanding of their own emotions, emotions of others, and the ability to regulate such emotions in a meaningful way. Specifically, the ability to use and manage emotions should lead to more adaptive behaviors by improving the social-emotional information processing by more effectively appraising the situation and interacting with others to identify the appropriate leader behaviors to affect desired outcomes. Therefore, we posit the following:

fol-Hypothesis 2. Emotional intelligence leads to greater potential leader adaptability.

Moderating Effect of EI

Cognitive and emotional abilities enhance the leader’s perceptions and processing of task and inter-personal level information that may allow for greater behavioral flexibility (Dinh & Lord, 2012; Lord et al., 2011). The ability to critically think and process information through reasoning may work with other variables, such as EI, to predict leader behavior (Zaccaro et al., 2018). According to Zaccaro et al. (2018), leader capacities where “com-bining cognitive and social adaptation skills likely yield higher magnitudes of effects” (p. 7), such models may have a greater influence on leader behaviors when considering moder-ating effects of EI. We posit that EI will strengthen the effect of reasoning when predicting potential leader adaptability. This moderating effect makes sense considering that ability-based EI involves “the ability to carry out accurate reasoning about emotions and the ability

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to use emotions and emotional knowledge to enhance thought” (Mayer et al., 2008, p. 511). Therefore, leaders who demonstrate high levels of inductive reasoning along with EI will adapt to dynamic work environments, and quickly and confidently choose the appropriate leadership style. Similarly, leaders demonstrating high levels of deductive reasoning along with high EI will also adapt to situational demands, but because the information processing may take longer and require more complete information to draw accurate conclusions (i.e., choosing the perfect leadership style), the overall effect will be significant, yet weaker than inductive. Plus, they may find it challenging to choose a less preferred leadership style, which may result in less adaptability. Therefore, we posit the following:

Hypothesis 3a. Emotional intelligence positively moderates the inductive-adaptability relationship.

Hypothesis 3b. Emotional intelligence positively moderates the deductive-adaptability relationship, but to a lesser degree (weaker) than the inductive-adaptability relationship.

Sample and Procedure

The study was conducted with upper-level undergraduate students enrolled in a large public university. Most of the sample were employed (86%) and the average age was 26.5. Forty-seven percent were males and nearly 53% were females. Sixty percent were single, 31% were married, and 9.3% did not identify their marital status. Sixty-three percent were Cau-casian, 27% were African-American, 2.9% were Hispanic, 3.5% were Asian, and 4% did not identify their race. All participants received similar leadership training and were treated as potential leaders (see below). While 307 were invited to participate, 196 participated, providing a 64% response rate. We assessed minimum survey time requirements for the full reasoning assessment by asking four adults to read all the questions, taking, on average, 22 min. We removed 16 participants that completed the assessment in less than 20 min. The current study employed Listwise deletion method, removing 7 participants who did not complete one or more of the assessments, resulting in a final sample size of 173.

Participants were provided an overview of the research study and asked to voluntarily participate. Surveys were emailed to those that agreed to participate at three time periods. In time 1, demographic data and both inductive and deductive reasoning were assessed. In time 2, one week later, emotional intelligence was collected. At time 3, four weeks from time 2, potential leader adaptability scores were captured. For each scale, we used the pub-lisher’s provided instructions to ensure consistent directions were provided. We employed OLS regression, while controlling for age, to assess the posited relationships in the model. Interactions were assessed using the PROCESS Macro version 3.5 (Hayes, 2018).

Leadership Training Protocol.

The goal of the training was to provide a broad overview of leadership. All participants were involved in a two-phase training program that included readings and self-reflection. Phase 1 covered the following topics: leadership definition, Johari Window, goal setting theory, and an overview of leadership (i.e., how is it different than management, its ben-efits to stakeholders, and the impact of leadership on the leader–follower relationship). Phase 2 immediately followed phase 1 and involved participants reflecting and noting their strengths and weaknesses across the topics covered in phase 1. Each participant received general feedback on their completed reflection activity.

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Emotional Intelligence The Mayer-Salovey-Caruso emotional intelligence test (MSCEIT) measure was used (Gohm, 2004; Mayer et al., 2001, 2002). The 141-item scale is an abil-ity-based measure capturing one’s overall EI ability (see Mayer et al., 2008 for sample items). The MSCEIT has shown strong reliability and validity information across a wide range of studies (see Cherniss, 2010; Mayer et al., 2008). The Cronbach’s alpha for the overall measure was 0.91.

Inductive and Deductive Reasoning Reasoning abilities were captured using the Cornell Critical Thinking Test, level Z (Ennis et al., 2005); induction was measured with 18 items and deduction was measured with 24 items. Scores were calculated as follows: total cor-rect answers minus one-half incorrect answers as suggested by Ennis et al. (2005), and such practices prevent us from calculating accurate reliability estimates. However, this is an established assessment (see Ennis et al., 2005 to learn more and view items).

Potential Leader Adaptability We assessed potential leader adaptability using the ship Effectives and Adaptability Description (LEAD) self version assessment by the Center for Leadership Studies© (Hersey, 2009) using their proprietary system. It has been shown to be a reliable (Zigarmi et al., 1997) and valid assessment (Silverthorne & Wang, 2001) consisting of 12 general situational-based items (Hersey & Blanchard, 1988) covering vari-ous and straightforward situations a leader may encounter. The items are worded such that an individual with or without leadership experience can evaluate and choose among the alternatives provided.

The means, standard deviations, and correlations are presented in Table 1. Using OLS regression we assessed over time the direct effect of two types of reasoning abilities, inductive and deductive, and ability-based emotional intelligence on potential leader adaptability. As shown in Table 2, inductive reasoning (H1a) significantly (B = 0.15,

p < 0.05) predicts adaptability as posited, but deductive (H1b) did not (B = -0.01, p = ns); this provides partial support for hypothesis 1. Emotional intelligence was sig-

nificantly (B = 0.04, p < 0.05) related to adaptability, providing support for

hypothe-sis 2. While we posited a direct effect for EI, we were particularly interested in the interactive effect of EI on both the inductive-adaptability and deductive-adaptability

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relationships. We employed Model 1 in Hayes’s (2018) PROCESS Macro to test these interactions and assess the conditional effect (ϴX➔Y) of reasoning and EI on potential leader adaptability.

Table 3 presents the results for these interaction hypotheses. For the

inductive-adapta-bility relationship, EI was positive and significant (B = 0.01, SE = 0.01, p < 0.05).

Specifi-cally, higher levels of EI positively relate to the inductive-adaptability relationship, ing support for hypothesis 3a. In Fig. 2 we graph the regression slopes to demonstrate the significant effect at one standard deviation (sd) below and above the mean for both induc-tive reasoning and EI. The interaction term for the deductive-adaptability relationship was

provid-not significant for EI (B = -0.00, SE = 0.00, p = ns); thus, 3b was provid-not supported.

We conducted additional analysis to better understand the extent EI significantly enced the conditional effect (ϴX➔Y) of inductive reasoning and EI on potential leader adaptability. In other words, we assess the point where the conditional effect of inductive reasoning was significant for potential leader adaptability. As shown in Fig. 3, we plotted the simple slopes around the mean of EI to include low (-1 sd), average, and high (+ 1 sd) to assess significance levels for these groups. Those with low levels of EI did not mod-erate the main effect (EI score = 89.71, ϴX➔Y = 0.00, SE = 0.09, t = 0.05, p = 0.96, 95%

influ-CI [-0.18,0.19]. However, those at the mean (EI score = 101.84, ϴX➔Y = 0.16, SE = 0.06,

t = 2.51, p = 0.01, 95% CI [0.03,0.29]) and high (EI score = 113.97, ϴX➔Y = 0.31,

SE = 0.10, t = 3.13, p = 0.00, 95% CI [0.12,0.51]) levels of EI were significant.

We further probed the interaction using the Johnson and Neyman (1936) technique to identify the point in the distribution of values of EI where it became significant in influ-encing the conditional of effect (ϴX➔ Y) of inductive reasoning ability on potential leader adaptability. For this sample, when EI scores reached 99.33 (and higher) they became a significant moderator creating a region of significance that accounted for 64.16% of the scores above the zero in the 95% confidence interval for the conditional effect (ϴX➔Y) of

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inductive reasoning ability on potential leader adaptability. Therefore, those who scored in this region showed a significant relationship between inductive reasoning and adaptability and those that scored below this region did not.

Low EIHigh EI

Inductive Reasoning

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