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Cognitive Load on Leadership Decision-Making: Conscious and Unconscious responses

Carga cognitiva en la toma de decisiones de liderazgo: respuestas conscientes e inconscientes

Arnisa Aliqkaj1

Rubén Carvajal2

For cite this articule:

Aliqkaj, A., & Carvajal, R. (2024). Cognitive Load on Leadership Decision-Making: Conscious and Unconscious responses. Journal of Applied Cognitive Neuroscience, 5(1), e5253. https://doi.org/10.17981/JACN.5.1.2024.02

Received: 26-01-2024
Accepted: 24-04-2024

Abstract

Cognitive load significantly influences leadership decision-making, particularly in high-pressure scenarios. Despite its pivotal role, the connection between cognitive load, decision-making, and physiological responses among leaders remains inadequately understood. Our study aimed to explore the influence of age, sex, years of experience, educational level, and the interplay of reasoning versus intuition on both conscious and unconscious cognitive load during and after a leadership decision-making simulation task. We utilized Galvanic Skin Response (GSR) to evaluate unconscious responses. Notably, our findings revealed a noteworthy negative correlation between leadership experience and perceived cognitive load (r = -0.787, p < 0.007), a robust positive correlation between GSR during the task and decision-making (r = 0.999, p < 0.000), and a significant link between the application of Kahneman’s System 2 thinking – characterized by comprehensive analysis and critical thinking – and the quality of decision-making (r = -0.678, p < 0.031). However, we did not find significant correlations between age, sex, and educational level with perceived cognitive load during and after the decision-making task. In summary, our study suggests that seasoned leaders exhibit superior capabilities in managing cognitive load during complex decision-making scenarios and proposes physiological arousal as a potential indicator of cognitive load during leadership decision-making tasks.

Keywords: Cognitive load; Leadership, Decision-making; Physiological arousal; Galvanic skin response; Kahneman’s System 2.

Resumen

La carga cognitiva influye significativamente en la toma de decisiones de liderazgo, particularmente en escenarios de alta presión. A pesar de su papel fundamental, la conexión entre la carga cognitiva, la toma de decisiones y las respuestas fisiológicas entre los líderes sigue sin comprenderse adecuadamente. Nuestro estudio tuvo como objetivo explorar la influencia de la edad, el sexo, los años de experiencia, el nivel educativo y la interacción del razonamiento versus la intuición en la carga cognitiva consciente e inconsciente durante y después de una tarea de simulación de toma de decisiones de liderazgo. Utilizamos la respuesta galvánica de la piel (GSR) para evaluar las respuestas inconscientes. En particular, nuestros hallazgos revelaron una correlación negativa notable entre la experiencia de liderazgo y la carga cognitiva percibida (r = -0,787, p < 0,007), una correlación positiva sólida entre el GSR durante la tarea y la toma de decisiones (r = 0,999, p < 0,000), y un vínculo significativo entre la aplicación del pensamiento del Sistema 2 de Kahneman – caracterizado por un análisis integral y un pensamiento crítico – y la calidad de la toma de decisiones (r = -0,678, p < 0,031). Sin embargo, no encontramos correlaciones significativas entre la edad, el sexo y el nivel educativo con la carga cognitiva percibida durante y después de la tarea de toma de decisiones. En resumen, nuestro estudio sugiere que los líderes experimentados exhiben capacidades superiores en el manejo de la carga cognitiva durante escenarios complejos de toma de decisiones y propone la excitación fisiológica como un indicador potencial de la carga cognitiva durante las tareas de toma de decisiones de liderazgo.

Palabras claves: Carga cognitiva; Liderazgo; Toma de decisiones; Excitación fisiológica; Respuesta galvánica de la piel; Sistema 2 de Kahneman.

Introduction

Cognitive load

Cognitive load can be understood as the extent to which working memory resources are utilized (Orru & Longo, 2019). The cognitive load theory is a widely spread theory that talks about the problem of learning being impaired when the processing amount requirements do exceed the human working memory capacity. The also assumes that knowledge can be divided into (i) biologically primary knowledge (related to knowledge we have evolved to acquire) and (ii) biologically secondary knowledge (the type of knowledge important for cultural reasons that requires a larger information store) ().

Sources of cognitive load encompass a wide range, spanning from the instructions humans receive—regardless of their value or futility—to the complexity of the tasks that necessitate resolution (Brünken et al., 2010). Cognitive load is therefore divided in three different sources, intrinsic cognitive load (ICL), extraneous cognitive load (ECL), and germane cognitive load (GCL) (Bannert, 2002).

Atiomo (2020) further re-contextualizes this within the framework of Cognitive Load Theory. According to the theory, intrinsic cognitive load (ICL) relates to the inherent complexity of the information being learned. Extraneous cognitive load (ECL), on the other hand, encompasses external factors that have the potential to hinder learning. Lastly, germane cognitive load (GCL) represents the influence of previously acquired patterns from prior learning on the processing of new information.

When examining the Cognitive Load Theory, we encounter recommendations emphasizing the significance of effectively reducing extraneous load, optimizing germane load, and efficiently managing intrinsic load. This is crucial because when the cumulative cognitive load exceeds the limits of working memory capacity, it can have detrimental effects on both learning and performance (Swell, 2021).

In relation to performance, Sweller et al. (2011) highlight the adverse effects of elevated cognitive load on task accuracy. Their research reveals that increased cognitive load corresponds to higher error rates, particularly during periods of intense decision-making and when individuals are required to consider multiple variables simultaneously.

A notable study conducted by Reis et al. (2023) discovered an intriguing association between cognitive load and honesty. Their findings suggest that cognitive load can actually promote honesty, as subjects exhibited increased dishonest behavior under low cognitive load conditions.

The measurement of cognitive load holds substantial theoretical importance, as highlighted by Paas et al. (2003). Moreover, in recent times, measuring cognitive load has gained even more significance within the context of the workplace, playing a vital role in understanding performance and productivity.

Although the statement by Ripp (2021) pertains specifically to healthcare workers, it emphasizes that high cognitive load is consistently linked to burnout regardless of the specialty of the healthcare professional. This underscores the universal impact of cognitive load on well-being in high-demand professions like healthcare.

Leadership

Leadership is a person’s ability to develop human potential around him or her
(Zak, 2017). Leadership encompasses multiple dimensions and roles. It involves self-awareness and personal growth, as well as a focus on others and their development. Leadership extends to managing processes and driving organizational success. It operates within the context of enterprises and impacts individuals and societies. Leadership involves navigating challenges and conflicts, while also fostering followership. It drives change and is guided by a sense of purpose. Leadership is both direct and paradoxical, embodying a combination of simplicity and complexity. It is a multifaceted and demanding journey, filled with pressures and intricacies that defy comprehensive capture (Luedi, 2022).

Leaders hold a pivotal position within organizations, exerting a substantial impact on the well-being of their employees through their leadership behaviors (Kim & Cruz, 2022). Leaders play a vital role within a team or organization, influencing the psychology and work engagement of employees, which in turn impacts the overall efficiency and innovation of the team or organization.

While previous studies have predominantly focused on the leadership role model, they have overlooked the influential guidance that leaders provide through their diverse efforts including decision-making (Fu, et al. 2022). A new field connecting leadership with neuroscience is emerging and being called neuroleadership.

There is growing interest in exploring the integration of emotion and cognitive process management within the work environment to foster effective leadership and create healthier workplaces. A promising emerging approach is the management of happiness, which emphasizes the importance of promoting well-being. Additionally, the field of neuroleadership offers a fresh perspective on management practices. The combined application of these approaches has the potential to significantly impact business management practices (Ruiz-Rodríguez, et al. 2023).

Decision-making

Decision-making is a problem-solving process aimed at finding an optimal or satisfactory solution (Brockmann & William, 2016). Decision-making is not a simple process. It takes quite some steps starting from having options, weighing the evidence, considering the value, making the choice and evaluating the outcome (Platt, 2020). This study aims to investigate the intricate relationship between cognitive load and leadership decision-making process, and how cognitive load affects both conscious and unconscious leadership decision-making.

Emotions play a significant role in decision-making (Fosslien & West Duffy, 2019). Not only that, leaders within organizations must also assess whether the decision-making environment values diverse evidence and stakeholder perspectives (Turner, et al. 2017). The process of decision-making involves quickly assessing a range of potential outcomes and considering the future consequences linked to each available action (Purves, et al. 2001).

Damasio’s “somatic marker hypothesis” (Damasio, 1994) posits that human reasoning and decision-making entail multiple levels of neural functioning, encompassing both conscious and explicitly cognitive processes as well as non-conscious ones.

The hypothesis suggests that conscious cognitive processes heavily rely on sensory representations generated by early sensory cortices. These representations provide the foundation for higher-level cognitive operations. Additionally, cognitive operations, regardless of their specific content, rely on supporting processes such as attention, working memory, and emotion. These processes play a crucial role in shaping our thinking and decision-making abilities.

Moreover, according to Damasio, reasoning and decision-making are intricately connected to the availability of knowledge pertaining to various aspects such as the situational context, individuals involved, potential courses of action, and the anticipated outcomes associated with those actions. This knowledge base serves as an essential resource that informs our cognitive processes and guides our decision-making.

Overall, Damasio’s somatic marker hypothesis offers insights into the multifaceted nature of human cognition, emphasizing the interplay between conscious cognitive processes, supporting mental operations, and the integration of knowledge in reasoning and decision-making. Decision-making plays a vital role in our everyday lives as a fundamental cognitive process and flexible decision requires both: integration between brain and body (Forte, et al. 2021).

Conscious and unconscious decisions

The concept of consciousness remains elusive and resistant to precise definition, challenging both philosophical and scientific endeavors in their attempts to create a measurable framework that accurately captures the intricacies of human experience (Giacino, et al., 2014).

The nature of consciousness remains a topic of ongoing debate, yet numerous scientists and philosophers perceive it as an emergent property arising from the intricate interactions among billions of interconnected neurons in the human brain. Although exclusive self-reporting of consciousness is unique to humans, there is a growing understanding that consciousness exists to varying extents in numerous other organisms. The level of consciousness is believed to be contingent upon the intricacy and arrangement of their respective nervous systems (Liljenström, 2021).

Wang, et al. 2012 suggest that the process of making decisions without conscious awareness may involve the influence of emotional feelings of risk, which are governed by mechanisms separate from those involved in cognitive evaluation of risk.

According to Queen & Hess (2010) unconscious thought is proved to be more suitable when individuals rely on intuitive processing rather than deliberate processing for decision-making, while conscious thought shows an opposite pattern. When we speak of the unconscious, we can connect that to Kahneman’s system one, and when we talk about the conscious, we can connect that to system two (Kahnemann, 2011).

System 1 refers to the rapid cognitive processes that involve immediate reactions, such as instinctive responses to stimuli like a person’s angry facial expression or a sudden loud noise. These processes occur without significant conscious awareness or deeper contemplation. According to Kahneman, these impressions and emotions play a significant role in guiding routine behaviors in everyday life. For instance, when making judgments about others or deciding whether to continue cooperating with someone, we often rely on our instinctual intuition. Kahneman suggests that many of our daily actions and perceptions are predominantly influenced by System 1 cognition, functioning almost like an automatic pilot (Hartwigsen, et al., 2021).

System 2 thinking is responsible for the cognitive processes that are distinct to humans, such as deliberate reasoning and careful reassessment. It enables us to override instinctive emotional reactions and impulsive behaviors through conscious effort. System 2 thinking involves engaging in thoughtful rationalization and actively manipulating our own understanding of other people’s perspectives and beliefs (Hofman, 2015).

System 1 and System 2 can be likened to operating systems, functioning as software rather than hardware. They utilize the same underlying resources and information, can work simultaneously, and tasks can transition between them. System 1 primarily relies on automatic and mostly unconscious processes associated with associative memory. It produces initial impressions, intuitions, and response tendencies, which are then monitored, occasionally disregarded, and sometimes adjusted through the slower and predominantly conscious operations of System 2 (Morewedge & Kahneman, 2010).

Objectives

To investigate the effects of age, sex, years of experience, and educational level on leaders’ performance in a leadership decision-making simulation.
To assess the level of cognitive load perceived by leaders after completing a leadership decision-making simulation.
To examine the correlation between years of experience in leadership and perceived cognitive load after a leadership decision-making simulation.
To determine the effects of cognitive load on leadership decision-making at a unconscious level via physiological responses.
To establish the effects of rationalization and intuition on leadership decision-making.

Material and Methods

Sample

Purposive sampling was used to recruit 10 participants (4 women, 6 men) who held leadership positions in 9 local and 1 international company in Pristina, Kosovo. The participants met specific criteria relevant to the research questions and were representative of the target population. They held a variety of leadership positions, including department director, departmental manager, executive director, and owner. The ages of the leaders ranged from 27 to 56 years old (mean: 43), with experience levels ranging from 3 to 18 years (mean: 7.8). The number of employees under their supervision ranged from 1 to 70 (mean: 20). The types of companies represented included consulting, insurance, telecommunications, and others (Table 1).

Decision-making Questionnaire

Table 2 presents the 10 questions and the four possible answers to the decision-making questionnaire. Each question was scored from 1 (poor decision-making) to 4 (excellent decision-making) using an algorithm created with the help of ChatGPT 3.5. The distribution of the expected answers from options a, b, c, and d, are shown in Table 3.

Table 1. Demographics of the sample

Table 2. Decision-making questionnaire with 1 to 4 scoring

Table 3. Distribution of the expected responses in the decision-making questionnaire

Perceived Cognitive Load

After the decision-making task participants were asked to rate their perceived cognitive load on a scale of 1 to 10. This question allowed participants to provide a subjective assessment of the mental effort required to complete the task. The scale ranged from 1 (low perceived cognitive load) to 10 (high perceived cognitive load). The reported cognitive load was then tabulated.

Physiological responses during decision-making questionnaire

A GSR device (Polygraph Bio, https://www.polygraph.bio) was used to measure the level of stress and arousal while corporate leaders were answering the above-mentioned decision-making questionnaire. Figure 1 shows the arrangements of the equipment during the test for two participants.

First, all participants’ GSR was measured in a relaxed state. This was done to account for the fact that all participants have different resting GSR levels. To obtain significant measurements, the change in GSR from the relaxed state to the task state was calculated for each individual participant. The mean GSR for the tasks was then correlated with the GSR during decision-making using Pearson’s correlation formula.

Figure 1. Equipment layout. Polygraph Bio device used to measure the physiological responses (electrodermal activity) of 10 leaders while making decisions at different levels of cognitive load.

The polygraph images were analyzed as follows:

1. Establishing baseline levels to gauge individuals’ overall arousal.

2. Tracking GSR and HRV changes in response to different questions.

3. Identifying spikes in GSR and HRV, indicative of increased arousal or stress.

4. Assessing recovery time, indicating individuals’ stress management and arousal recovery by observing how quickly GSR and HRV return to baseline levels after each question.

Data analysis

Statistical Package for Social Sciences (IBM SPSS Statistics 21) and Microsoft Excel 2021 (version 2310) were used to process the statistical data.

Ethical considerations

Prior to conducting the individual interviews and test, informed consent from all participants was obtained. Participants were fully informed about the purpose of the study, the scope of their involvement, and the confidential handling of their data. They were explicitly notified that their participation was voluntary, and they had the right to withdraw at any point without any repercussions.

The processing of data from the Galvanic Skin Response was carried out with strict adherence to ethical guidelines. The quantitative data collected was securely stored on password-protected servers, accessible only to the research team. The data was used exclusively for academic purposes and was not shared with any external parties or organizations.

Results

Effect of age on leadership decision-making

The ages of the individuals were compared with their corresponding results in the decision-making questionnaire, grouped into two categories: 5 young executives (33.0 ± 3.7 years) and 5 middle-age executives (52.2 ± 3.6 years). No statistically significant differences (p = 0.5105) were found between the decision-making questionnaire results of junior executives (3.5 ± 0.7) and senior executives (3.4 ± 0.8) (Table 4).

Table 4. Effect of age on leadership decision-making

Effect of gender on leadership decision-making

The results of the decision-making questionnaire were compared for male and female executives. The 4 women obtained an average score of 3.3 ± 0.8, while the 6 men obtained an average score of 3.6 ± 0.8. No statistically significant differences were found between the two groups (p = 0.0678). Gender was the only criterion that was closer to being statistically significant than the other criteria, with a p-value of 0.0678.

Effect of years of experience on leadership decision-making

The years of managerial experience were compared with the corresponding results in the decision-making questionnaire, in two groups: 7 low-experienced executives (5.4 ± 1.9 years) and 3 middle-experienced executives (13.3 ± 4.2 years). No statistically significant differences were found (p = 0.4616) between the decision-making questionnaire results of executives with low experience (3.5 ± 0.8) and those with intermediate experience (3.4 ± 0.8) (Table 5).

Table 5. Effect of years of experience on leadership decision-making

Effect of educational level on leadership decision-making

The educational level of the executives was compared with the corresponding results in the decision-making questionnaire, in two groups: 3 executives with a bachelor’s degree and 7 executives with a postgraduate degree (Master’s or PhD). No statistically significant differences were found (p = 0.6662) between the results of the decision-making questionnaire of executives with a bachelor’s degree (3.5 ± 0.7 points) and those with a postgraduate degree (3.4 ± 0.8 points).

Conscious Assessment of Perceived Cognitive Load and Decision-Making Score

Table 6 presents all the variables related to perceived cognitive load and decision-making scores for each participant after completing the 10-question questionnaire. This conscious assessment reflects each individual’s deliberate consideration of how the decision-making process impacted their perception of cognitive load.

No significant correlation was found (R2 = 0.0008) between perceived cognitive load and the score obtained in the business decision-making questionnaire. Only years of experience showed a negative and statistically significant correlation with cognitive load as presented in the next section.

Table 6. Perceived cognitive load and decision-making score

Relationship Between Leadership Experience and Perceived Cognitive Load

The participants’ tenure as corporate leaders correlated with their perceived cognitive load during decision-making using Pearson’s correlation formula (r = -0.787,
p < .007), suggesting that longer tenure as a corporate leader is associated with lower perceived cognitive load (Table 7).

Table 7. Correlation between leader experience and perceived cognitive load

To predict perceived cognitive load from leadership duration, a simple linear regression was calculated. A significant regression equation was found for the impact of leadership duration on perceived cognitive load (F=13.032, p<.007). Therefore, leadership duration can predict 49% of the variance in perceived cognitive load.

Effects of cognitive load on leadership decision-making at unconscious level

We measured the effects of cognitive load on decision-making at unconscious level. Figure 2 shows one participant’s physiological responses during the polygraph test. Both the GSR and the HRV increased throughout the test. This suggests that the participant became more aroused as the test progressed.

The graph also shows that the participant’s GSR and HRV spiked at certain points during the test. These spikes may be indicative of increased arousal or stress. This could be because the participant was asked a particularly difficult or challenging question at that time.

Overall, the graph suggests that the participant experienced increased arousal and stress throughout the polygraph test. This could be due to a variety of factors, such as anxiety, stress, deception, or the difficulty of the questions.

Figure 2. One participant’s physiological responses. GSR and HR during the polygraph test.

Correlating the mean GSR for the tasks with the GSR during decision-making revealed a very strong and significant correlation (r = 0.999, p < .000), suggesting that unconscious cognitive load is associated with changes in GSR (Table 8, Figure 3).

Table 8. Correlation between mean GSR during task and GSR during decision-making

Figure 3. Correlation between GSR mean during task and GSR during decision-making.

Effects of rationalization and intuition on leadership decision-making

Descriptive analysis revealed that participants with lower GSR levels during decision-making (Table 9) were more likely to be using Kahneman’s System 2 thinking.

Table 9. Mean GSR during decision-making for all participants

Descriptive analysis revealed that participants using Kahneman’s System 2 thinking in decision-making had higher quality decision-making outcomes (Table 10). Pearson’s correlation analysis confirmed a significant correlation between utilizing Kahneman’s System 2 and decision-making outcome quality (r = 0.678, p <.031).

A simple linear regression model was used to predict decision-making outcome quality based on Kahneman’s System 2 thinking. A significant regression equation was found for the impact of using Kahneman’s System 2 on decision-making outcome quality (F=6.823, p <.031), with an R² of 0.460. This indicates that 46% of the variance in decision-making outcome quality can be predicted by Kahneman’s System 2 thinking (Table 10).

Table 10. Correlation between Kahneman’s system 2 and decision-making quality

Discussion

Measuring cognitive load has gained even more significance within the context of the workplace, playing a vital role in understanding performance and productivity. Our primary focus was to investigate how cognitive load impacts decision-making outcomes, both at the conscious and unconscious levels, for leaders in various professional settings.

The data collected from the participants’ responses to the 10 questions, carefully designed to increase their cognitive load and test their decision-making abilities under pressure, has provided us with valuable insights. By measuring the level of stress and arousal through galvanic skin response, we gained an understanding of the physiological responses that accompany decision-making in leadership roles.

Differences in cognitive load management strategies and decision-making outcomes

Our findings revealed a remarkably strong and significant positive correlation between cognitive load during task and during decision-making (r = 0.999, p < .000). This indicates that an increase in cognitive load during the task, which involved answering 10 questions with four possible options in increasing order of difficulty, was directly associated with elevated cognitive load responses during decision-making. This finding corroborates that high cognitive load during task significantly impacts a leader’s cognitive load during decision-making, leading to increased physiological response rates.

Elevated cognitive load during task suggests that leaders’ working memory resources are being extensively employed to process information, evaluate options, and formulate decisions. This intensified cognitive load during task can persist into the subsequent decision-making stage. Consequently, several factors contribute to the robust positive correlation observed between cognitive load during task and during decision-making.

Challenging questions can induce emotional arousal in leaders as they face heightened cognitive load during the task. This can affect their decision-making process and result in increased physiological reactions and cognitive load in subsequent decisions.

Leaders’ working memory resources are extensively engaged in processing information, evaluating options, and making decisions during the task, resulting in elevated cognitive load. This high cognitive load can carry over to the subsequent decision-making stage. Therefore, cognitive load during task and during decision-making are strongly and positively correlated.

Another possibility is that high cognitive load during the task can create heightened perceptual expectations in leaders about the complexity and difficulty of the subsequent decision-making. These expectations can produce a self-fulfilling prophecy, where they experience the decision-making process as more demanding and cognitively taxing.

In conclusion, the strong positive correlation between cognitive load during the task and during decision-making suggests that the cognitive demands of the task spill over into the decision-making process. Leaders who experience high cognitive load during the task are more likely to encounter increased cognitive load during decision-making, resulting in higher physiological response rates. These findings support the fact that high cognitive load during task significantly impacts the leader’s cognitive load during decision-making.

Regarding the relationship between cognitive load and years of experience in leadership positions we examined how time spent in a leadership role influences perceived cognitive load during decision-making deepens our understanding of the decision-making process as leaders gain expertise over time. Leaders with more experience in leadership positions reported lower perceived cognitive load during decision-making. This finding is supported by a significant negative correlation between time spent in a leadership position and perceived cognitive load (r = -0.787, p < .007).

Our findings indicate that leaders with more experience in leadership positions reported lower perceived cognitive load during decision-making. A linear regression analysis revealed that time spent in a leadership position can predict 49% of the variance in perceived cognitive load.

These findings can be attributed to several factors that influence the relationship between time spent in a leadership position and perceived cognitive load during decision-making. One key factor is accumulated experience and expertise. As individuals gain more experience in leadership roles, they develop a deeper understanding of decision-making processes and acquire effective strategies for handling complex tasks. This accumulated knowledge and expertise enable them to navigate decision-making scenarios with greater ease, resulting in a lower perceived cognitive load.

Prolonged exposure to decision-making challenges fosters the development of enhanced decision-making skills in leaders. They cultivate a sharper intuition, enabling them to make decisions with greater ease, often with a quick assessment. Additionally, leaders gain a deeper understanding of their strengths and weaknesses, empowering them to approach decision-making tasks with greater confidence and efficiency.

This increased confidence can alleviate the cognitive load associated with decision-making. Over time, as is characteristic of human nature, leaders may develop adaptive cognitive processes that enhance their ability to process information efficiently and make streamlined decisions. They may refine cognitive shortcuts or mental models that enable them to rapidly assess situations and make judgments, thereby reducing the cognitive burden.

Moreover, as leaders accumulate experience, they develop a deeper familiarity with the contexts in which decisions are made. This familiarity empowers them to anticipate potential challenges and identify relevant information more efficiently. Consequently, the decision-making process becomes more focused and less taxing.

Generally, experienced leaders often face high-stakes and time-sensitive decisions. This is why they may develop coping mechanisms to handle pressure and stress more effectively. As a result, they may perceive decision-making situations as less burdensome, contributing to a lower perceived cognitive load. Overall, the combination of accumulated experience, improved decision-making skills, adaptive cognitive processes, familiarity with decision contexts, reduced perceived pressure, and continuous learning likely contributes to the observed negative correlation between time spent in a leadership position and perceived cognitive load during decision-making.

Comparative Analysis of Rationalization and Intuition in Leadership Decision-Making

Analyzing the effects of rationalization (Kahneman’s System 2) versus intuition (Kahneman’s System 1) on decision-making outcomes revealed a significant positive correlation between employing System 2 and decision quality (r = 0.678, p < .031). This indicates that participants who engaged more deeply in analytical and critical thinking processes achieved better decision-making outcomes. These findings support that Kahneman’s System 2 plays a positive role in influencing a leader’s decision-making outcomes, leading to more informed and effective decisions. Additionally, a simple linear regression analysis indicated that System 2 utilization can predict 46% of the variance in decision-making outcomes.

Understanding the neuroscientific foundation of Kahneman’s System 2 is crucial in elucidating how this approach improves decision-making. System 2 requires training or experience, and its effectiveness is influenced by genetic, environmental, and educational factors. Further research should explore how these factors interact to shape the development and utilization of System 2, ultimately enhancing decision-making proficiency in leaders.

Kahneman’s System 2 engages in deliberate and rational analysis of available information, carefully considering potential consequences and meticulously weighing various options. Leaders who prioritize this approach are more likely to make decisions grounded in objective assessments and thorough evaluations, ultimately leading to superior decision quality.

The observed differences in decision-making outcomes can be attributed to the distinct cognitive processes associated with Kahneman’s System 2 (rationalization) and System 1 (intuition). When leaders prioritize System 2, they engage in more in-depth analysis, critical thinking, and systematic information processing, which can lead to better-informed and more effective decision-making outcomes. Understanding the neuroscientific foundation of Kahneman’s System 2 is crucial in elucidating how this approach improves decision-making. System 2 requires training or experience, and its effectiveness is influenced by genetic, environmental, and educational factors. Further research should explore how these factors interact to shape the development and utilization of System 2, ultimately enhancing decision-making proficiency in leaders.

In conclusion, the significant positive correlation between utilizing Kahneman’s System 2 and the quality of decision-making outcomes strongly suggests that leaders who engage in rational analysis and critical thinking tend to make better decisions. The application of Kahneman’s System 2 positively influences decision-making outcomes, leading to more informed and effective decisions. These findings support that rationalization positively influences a leader’s decision-making outcomes, with the utilization of Kahneman’s System 2 predicting 46% of the variance in decision quality. The primary contribution of this research lies in the comprehensive understanding it provides of how cognitive load, rationalization (Kahneman’s System 2), and accumulated leadership experience influence decision-making outcomes among leaders. By investigating these factors, the study sheds light on the intricate interplay between cognitive processes, decision-making, and leadership performance.

Limitations

While our study contributes valuable insights into the relationship between cognitive load and decision-making among leaders, it is essential to acknowledge its limitations. The small sample size and lack of diversity in our participant pool may restrict the generalizability of our findings to a broader population of leaders. Future research endeavors should prioritize recruiting a larger and more diverse sample to enhance the external validity of the results and facilitate broader applicability in various professional settings.

Furthermore, our study’s cross-sectional design captures data at a single point in time, limiting our ability to assess how decision-making processes may evolve over time. Longitudinal studies could provide a more comprehensive understanding of the dynamic nature of decision-making and the influence of cognitive load across different stages of leadership experience.

Our study’s sample size may limit the generalizability of the results to a broader population of leaders. Future research with a larger and more diverse participant pool would enhance the study’s external validity. Our research utilized a cross-sectional design, which only captured data at a single point in time. Longitudinal studies could provide a more comprehensive understanding of how decision-making processes change over time and how cognitive load may impact decision outcomes across different stages of leadership experience.

Despite these limitations, our research may contribute to the growing body of knowledge on decision-making under cognitive load in leadership roles. Future studies addressing these limitations can build upon our findings and provide a more comprehensive understanding of how cognitive load impacts decision outcomes in diverse leadership settings.

Another option could be conducting longitudinal studies that follow leaders over an extended period can provide valuable insights into how decision-making processes evolve and change with experience. This approach can offer a more comprehensive understanding of the long-term effects of cognitive load on leadership decisions. Also, the designing and implementing interventions or training programs aimed at improving decision-making under cognitive load can be beneficial. Evaluating the effectiveness of such interventions can also offer practical implications for leadership development.

In terms of study design incorporating further Neuroscientific approaches such as functional magnetic resonance imaging (fMRI) or electroencephalogram (EEG), can provide neurobiological insights into the brain mechanisms involved in decision-making under cognitive load. Also, team decision-making, by investigating how cognitive load impacts decision-making in team settings can provide valuable insights for collaborative decision-making processes. According to Johannessen et. al. (2020) multiple physiologic measures should be employed for a more accurate measurement of cognitive load in a real-world setting.

While our study provides valuable insights into the relationship between cognitive load and decision-making among leaders, it is essential to consider potential confounding variables that may affect decision-making outcomes. Although participants were presumed to have been selected through standard psychological tests to ensure they met the study’s criteria, factors such as anxiety, depression, bipolar disorder, or fatigue were not directly assessed. These factors have been shown in previous research to impact cognitive function and decision-making abilities (Berggren & Derakshan, 2013; Eysenck et al., 2007; Gotlib & Joormann, 2010; Lorist & Tops, 2003).

Anxiety, for example, can lead to cognitive biases and impairments in information processing, potentially resulting in suboptimal decision-making under high cognitive load. Similarly, mood disorders such as depression or bipolar disorder can influence individuals’ cognitive processes, affecting their ability to evaluate options and make decisions effectively. Additionally, fatigue, whether physical or mental, can compromise attentional control and executive functioning, leading to errors in judgment and decision-making (López et al., 2016).

Considering the potential impact of these factors is crucial for interpreting the study’s findings accurately. Future research endeavors should incorporate measures to assess participants’ mental state and psychic abilities comprehensively, ensuring that these extraneous variables are controlled for effectively. By addressing these potential confounds, future studies can provide a more nuanced understanding of the interplay between cognitive load and decision-making in leadership contexts.

Conclusions

This research holds significant potential for both academic and practical implications. Academically, it contributes to the burgeoning field of neuroleadership by examining the relationship between cognitive load and leadership decision-making through measuring physiological responses. As an exploratory study, it lays the groundwork for future investigations in this area, encouraging further research and scholarly discourse.

Practically, this research aims to equip leaders with valuable insights into their decision-making processes. By understanding how cognitive load impacts decision-making outcomes, leaders can tailor their approaches to handle high-pressure situations effectively. Additionally, organizations can utilize these findings to design better decision support systems, enhance leadership training programs, and optimize decision-making processes.

While our study provides valuable insights into the cognitive processes underlying decision-making among leaders, there are opportunities for further investigation to enhance the depth of understanding in this area. In future research endeavors, the incorporation of additional neuroscientific methodologies, such as EEG or functional magnetic resonance imaging (fMRI), could offer valuable insights into the neural mechanisms underlying decision-making under cognitive load. These neuroimaging techniques can provide real-time data on brain activity, allowing for a more nuanced exploration of how cognitive processes unfold during decision-making tasks.

Additionally, complementing traditional cognitive assessments with measures of executive functions could provide a comprehensive understanding of the cognitive resources involved in decision-making among leaders. By employing a multi-method approach that integrates neuroscientific techniques with behavioral assessments, future studies can gain a more holistic understanding of the interplay between cognitive load, decision-making processes, and leadership performance.

As noticed, there are opportunities for further research. For other researchers interested in further exploring the topic of decision-making and leadership under cognitive load, there are several valuable avenues to consider. While our research provides valuable insights into the relationship between cognitive load and decision-making in leadership roles, it is essential to acknowledge its limitations to ensure the validity and generalizability of the findings.

Conflicts of Interest

Authors reports no conflicts of interest to report in relation to this study.

Acknowledgments

To Professor Ilir Mazreku, Department of Biology, University of Hasan Prishtina in Prishtina, Kosovo, for allowing us to use the physiological equipment described in this work.

Contributor Roles

Arnisa Aliqkaj: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Project administration, Resources, Software, Visualization, Writing – original draft.

Rubén Carvajal: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Supervision, Validation, Software, Visualization, Writing – review & editing.

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1 Arnisa Aliqkaj, Neuro Business School, Barcelona, Spain. ORCID: https://orcid.org/0000-0001-8304-6933 Email: arnisa.aliqkaj@universitetiaab.com

2 Rubén Carvajal, Universidad Católica Andrés Bello, Caracas, Venezuela, and Neuro Business School, Barcelona, Spain. ORCID: https://orcid.org/0000-0002-3039-0922 Email: rcarvaja@ucab.edu.ve.