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Does cognitive flexibility enhance decision-making in sports?
1Department of Physical Education and Sports, Faculty of Sport Sciences, Nevşehir Haci Bektaş Veli University, 50300 Nevşehir, Türkiye
2Faculty of Sport Sciences, Mersin University, 33110 Mersin, Türkiye
3Department of Sport Management, Faculty of Sport Sciences, Ağrı İbrahim Çeçen University, 04100 Ağrı, Türkiye
4Department of Sport Management, Faculty of Sport Sciences, Sivas Cumhuriyet University, 58140 Sivas, Türkiye
5Ministry of Youth and Sports, 06000 Ankara, Türkiye
6Department of Physical Education and Sports, Yaşar Doğu Faculty of Sport Sciences, Ondokuz Mayis University, 55280 Samsun, Türkiye
7Department of Physical Education and Sports, School of Physical Education and Sports, Harran University, 63000 Şanlıurfa, Türkiye
8Coaching Education Department, Faculty of Sport Sciences, Hitit University, 19030 Çorum, Türkiye
*Corresponding Author(s):furkancamici@hitit.edu.tr (Furkan Çamiçi)
| History | Submitted: 28 October 2024 | Accepted: 21 February 2025 | Published: 30 May 2025 |
| Copyright: | ©2025 The Author(s). Published by MRE Press. |
Background: Decision-making is a multifaceted process crucial in both individual and organizational settings, particularly in high-pressure environments like sports. Cognitive flexibility plays a vital role in enhancing athletes’ decision-making abilities. This study examines the relationship between cognitive flexibility and decision-making processes among athletes, focusing on the sub-dimensions of “Alternatives” and “Control”. Methods: A quantitative research design utilizing a relational survey model was employed. Data were collected from 743 male athletes across various sports disciplines in Türkiye. The Sports Effective Decision-Making Scale and the Cognitive Flexibility Inventory were used as measurement tools. Confirmatory factor analysis confirmed their validity and reliability in the sports context. Regression analysis was conducted to examine the predictive relationships between cognitive flexibility sub-dimensions and decision-making effectiveness. Results: Regression analysis revealed significant findings. The “Alternatives” dimension positively predicted effective decision-making in sports, particularly in external decision-making scenarios (β = 0.207, t = 5.427, p < 0.001). In contrast, “Control” was a stronger determinant of intrinsic decision-making (β = 0.210, t = 5.607, p < 0.001). The R2 values indicated that cognitive flexibility factors explained 18.1% of the variance in effective decision-making and 10.9% in intrinsic decision-making, highlighting the need to explore additional contributing factors. Conclusions: These findings provide practical implications for coaches and sports psychologists. Coaches can enhance athletes’ decision-making abilities by fostering a sense of control and offering diverse alternatives in training and competition. Sports psychologists may develop interventions to strengthen cognitive flexibility and perceived control among athletes. The study emphasizes the necessity of a broader framework for understanding decision-making, suggesting that future research should explore the interplay of emotional, social and cultural factors in athletic decision-making.
Cite this article
Burhan Başoğlu, Erdil Keyf, Mustafa Vural, Mehmet Gül, Abdullah Doğan, Fatih Karakaş, Gökhan Arıkan, Furkan Çamiçi. Does cognitive flexibility enhance decision-making in sports?. Journal of Men's Health. 2025; 21(5): 39-45. doi: 10.22514/jomh.2025.066
1. Introduction
Decision-making, a complex and multifaceted process, serves as a cornerstone in both individual and organizational contexts [1, 2]. It has been a subject of great importance throughout human history, evolving alongside societal advancements and becoming increasingly intricate in modern conditions. While decision-making has been widely studied, there is a need to contextualize these theories within specific domains, such as sports, where high-pressure and dynamic environments uniquely shape decision-making processes. Cognitive flexibility, in particular, has emerged as a critical factor in understanding how individuals adapt their decision-making strategies in such settings. This study contributes to the theoretical framework by examining how the subdimensions “Alternatives” and “Control” interact with decision-making in sports contexts, offering novel insights into their specific roles and implications.
The quality and quantity of these decisions are predicted to vary according to the current development of the individual and the quality of the decision-making process. Carneiro et al. [3] (2019) developed a general decision-making scale to assess perceived decision quality in web-based group decision support systems, pioneering the analysis of decision-making aspects. Similarly, a decision-making style measure is presented that aims to systematically assess and categorize individual decision-making behaviors. These styles are shaped by individual characteristics, external environmental factors, and organizational contexts, underscoring the complexity of decision-making behaviors [4].
The influence of various factors, such as religion, race, educational status and gender, on decision-making has been widely discussed in the literature [5]. For instance, research by Sanchez et al. [6] (2009) revealed that female basketball players do not experience significant pressure when making decisions during games, suggesting that their decision-making processes may differ contextually from their male counterparts. Similarly, Buckley et al. [7] (1998) observed that women demonstrate a lower tendency to engage in unethical decision-making behaviors compared to men, indicating potential gender-based differences in decision-making ethics. However, while these studies offer valuable insights, the relationship between gender and decision-making remains underexplored and inconclusive, with findings varying across different contexts and populations. Despite the descriptive understanding of factors influencing decision-making, there is limited critical examination of how these factors interact and why they impact decision-making outcomes [7]. For example, while it is acknowledged that decision-making styles are influenced by individual and external factors, the specific mechanisms through which these factors shape decision effectiveness, particularly in athletic environments, remain unclear. Moreover, the literature lacks a focused discussion on the relevance of decision-making theories to athletes and practitioners, who must navigate high-pressure and dynamic situations [7, 8, 9]. This study seeks to address these gaps by exploring the specific ways in which decision-making processes, informed by cognitive flexibility, contribute to performance in sports.
In addition, cognitive flexibility is considered to have significant implications for decision-making styles. It has been defined from various perspectives in the literature, but it is generally described as the ability to adapt to change, think about multiple concepts and factors, and perceive diverse opinions and perspectives [8, 9]. Cognitive flexibility is a valuable trait for athletes, enabling them to reorganize their cognitive structures in response to suggestions from the sports environment or to devise new solutions when faced with unique challenges [10]. While some studies have highlighted positive associations between cognitive flexibility and decision-making skills, such as openness to discussion, conflict tolerance [11], interpersonal communication skills [12], assertiveness, communication self-efficacy [13], openness to cooperation, adaptability [14], and self-sensitivity [15], the specific nature of these relationships remain underexplored. Moreover, although cognitive flexibility is often linked to decision-making styles, its direct impact on gendered perceptions of decision-making processes has been scarcely investigated. In this context, this study seeks to address this gap by examining whether cognitive flexibility provides an advantage in decision-making processes, particularly from a gender perspective.
2. Materials and methods
2.1 Research model
This study adopts a quantitative research approach to systematically examine the relationship between cognitive flexibility and decision-making processes among athletes. Quantitative methods were chosen for their ability to provide objective and measurable insights into complex phenomena, ensuring replicability and generalizability of findings. Specifically, survey models were employed, as they enable the collection of data from a broad population, facilitating a comprehensive understanding of the variables under investigation. In the literature, general survey models are described as arrangements designed to derive judgments about an entire population or a representative sample. These models are particularly effective for identifying patterns, relationships, and trends across diverse groups. The recruitment period and collection of participant data took place between 28 September and 26 October 2024, and ensured a comprehensive representation of athletes from various levels of competition and sport disciplines.
For this study, a relational survey model was selected to explore the existence and magnitude of relationships between key study variables such as cognitive flexibility and decision-making outcomes. This approach allows for the analysis of correlations, which reveals the strength and direction of associations between interest variables, as well as comparative analyses to identify differences or changes across groups. By utilizing relational analysis, the study seeks to uncover the underlying dynamics that link cognitive flexibility and decision-making, providing actionable insights for both researchers and practitioners in the field of sports psychology. This model’s capacity to quantitatively assess attitudes, tendencies, and interactions between interest variables makes it particularly suitable for addressing the research questions posed in this study [16, 17].
2.2 Participants and procedure
The study involved 743 male athletes competing in various sports disciplines across Turkiye, representing different branches such as football, basketball, volleyball, and athletics. Athletes were recruited from local, regional, and national-level clubs to ensure diversity in competitive experience and skill levels. Participants ranged in age from 18 to 35 years, with an average age of 24.5 ± 3.80 years. The data collection process utilized a questionnaire distributed to 956 athletes. Among these, 865 athletes voluntarily completed the survey, adhering to ethical principles, and after removing incomplete or erroneous responses, 743 valid questionnaires were included in the analysis.
All participants were informed about the purpose of the study and their rights, including the voluntary nature of their participation and the ability to withdraw at any stage without consequences. No financial incentives were provided to ensure unbiased responses. Ethical approval was obtained, and an informed consent form was signed by all participants prior to data collection. These forms highlighted the confidentiality and anonymity of the data collected, ensuring compliance with ethical standards.
2.3 Data collection
The survey method was used as a data collection technique for this research. One of the data collection methods used in quantitative research is the questionnaire. The questionnaire is a method of preparing question lists to be answered by the people from whom the information is to be collected. First, questions related to the research subject were identified or created. All questions in the questionnaire must be directly related to the research problem and subject, and must be consistent within themselves. The variables for these questions should also be determined, and the target group that answers the questions should be identified. Thus, information about the attitudes and opinions of the target group can be obtained [18].
2.4 Data collection tools
2.4.1 The scale of effective decision-making in sport
The Sports Effective Decision Making Scale developed by Çetin and Kara (2024) was designed to measure effective decision-making ability in sports. The scale consists of 15 items and 2 sub-dimensions categorized as internal decision making and external decision making. The two-factor structure of the scale with 15 items was confirmed by confirmatory factor analysis (CFA) as a model. An examination of the convergent and divergent validity as well as the convergent reliability values of the scale items showed that the scale fulfills the relevant criteria. The internal consistency coefficient of Cronbach’s alpha was calculated at 0.87 for the external decision-making sub-dimension and 0.85 for the internal decision-making sub-dimension [19].
2.4.2 The cognitive flexibility ınventory
The Cognitive Flexibility Inventory (CFI), originally developed by Dennis and Vander Wal (2010), has been adapted to sports environments in recent research [8]. A Turkish adaptation study was conducted by Sapmaz and Doğan (2013), which confirmed that the inventory retained the original structure and content, consisting of 20 items [20]. The inventory is composed of two sub-dimensions—Alternatives and Control—and is evaluated on a 5-point Likert scale. More recently, Yarayan, Turhan and Demir (2023) further adapted and validated the CFI specifically for athletes, ensuring its applicability within sports settings [21].
2.5 Data analysis
In this study, multiple linear regression analysis was used to analyze the data. Before data analysis, the validity of normality, linearity, and multiple assumptions of linear regression were confirmed. This study examined the relationship between a dependent variable and one or more independent variables through regression analysis. There is a cause-and-effect relationship between these variables in the regression analysis, which is explained by a mathematical model. This model is called a regression equation or regression model. A model consisting of one dependent variable and one independent variable is called a simple regression model, whereas a model consisting of one dependent variable and multiple independent variables is called a multiple regression model. The aim here is to provide information about the dependent variable with the help of the independent variable(s), and to construct a prediction equation that can make accurate forecasts [22]. The normality of the distribution of athlete cognitive flexibility scale scores and effective decision-making in sport scale scores were examined using Skewness and Kurtosis coefficients. Spss program was used for the analyses.
As a result of the normality analysis performed to determine the normality of the distribution of the data, Skewness and Kurtosis coefficients were between −1 and +1 [23]. The data were normally distributed (Table 1).
| Measurement tools | Min | Max | S.d. | Skewness | Kurtosis |
| Athlete cognitive flexibility inventory | 49.00 | 79.00 | 5.85 | −0.347 | −0.046 |
| Effective decision-making in sport | 39.00 | 65.00 | 5.70 | 0.882 | 0.299 |
Min: Minimum; Max: Maximum; S.d.: Standard deviation. |
3. Results
The R value (0.318) indicates a moderate positive correlation between the independent variables (both “Alternatives” and “Control”) and effective decision-making in sports. The R2 value of 0.111 suggests that 18.1% of the variance in effective decision-making can be explained by the variables “Alternatives” and “Control”. This indicates that while the model has significant explanatory power, other factors are likely to contribute to effective decision-making. The F-value of 23.21 for “Alternatives” indicates that the overall regression model is statistically significant. A high F value suggests that the model fits the data well for this independent variable. For “Alternatives”, the β coefficient is 0.207, indicating a positive impact of alternatives on effective decision-making. This means that, as the availability of alternatives increases, effective decision-making in sports improves. For “Control”, the β coefficient is −0.023, suggesting a very slight negative relationship between control and decision-making, though the effect size is minimal. The t-value for “Alternatives” is 5.427, which is high and statistically significant. This suggests that “Alternatives” is a strong predictor of effective decision-making in sports. The t-value for “Control” is 2.031, but because the p-value is not significant (as explained below), this relationship is not meaningful in this context.
The R value (0.242) indicates a weak positive correlation between the independent variables (“Alternatives” and “Control”) and extrinsic decision-making. The R2 value of 0.058 means that 9.5% of the variance in extrinsic decision-making can be explained by “Alternatives” and “Control”. While this model has some explanatory power, other factors likely contribute to extrinsic decision-making, which is not accounted for in this analysis. The F-value of 21.88, suggests that the overall regression model is statistically significant. This indicated that the model fit the data well. For “Alternatives”, the β coefficient is 0.143, meaning that an increase in alternatives is associated with a small but positive increase in extrinsic decision-making. This suggests that the availability of alternatives positively affects decisions based on external factors. For “Control”, the β coefficient is 0.235, indicating that having a greater sense of control has a stronger positive effect on extrinsic decision-making than alternatives. The t-value for “Alternatives” is 3.792, which is statistically significant (t > 2). This suggests that “Alternatives” is a meaningful predictor of extrinsic decision-making. The t-value for “Control” is 6.218, which is quite high, indicating that “Control” is a stronger and highly significant predictor of extrinsic decision-making compared to “Alternatives”.
The R value of 0.270 suggests a moderate positive correlation between both “Alternatives” and “Control” with Intrinsic Decision-Making, meaning that both factors have some positive association with intrinsic decision-making processes. The R2 value of 0.073 indicates that 10.9% of the variance in intrinsic decision making can be explained by the combination of these two variables. This shows that the model has modest explanatory power. The F-value for “Alternatives” is 27.78, which indicates that the regression model is statistically significant. A high F value suggests that the model fits the data well. For “Alternatives”, the β coefficient is 0.124, suggesting that an increase in the availability of alternatives leads to a small positive increase in intrinsic decision-making. For “Control”, the β coefficient is 0.210, indicating a stronger positive effect on intrinsic decision-making compared to “Alternatives”. This suggests that a greater sense of control significantly enhances intrinsic decision making. The t-value for “Alternatives” is 3.456, which is statistically significant (t > 2), meaning “Alternatives” is a meaningful predictor of intrinsic decision-making. The t-value for “Control” is 5.607, which is much higher, indicating that “Control” is a more significant and impactful predictor of intrinsic decision-making compared to “Alternatives”.
4. Discussion
This study provides valuable insights into the relationship between cognitive flexibility and decision-making processes among athletes, with a particular focus on the sub-dimensions of “Alternatives” and “Control”. By integrating these dimensions into the broader theoretical framework of cognitive flexibility, the study advances our understanding of how decision-making effectiveness is shaped in high-pressure environments. Specifically, “Alternatives” emerged as a significant predictor of effective decision-making, particularly in external scenarios, where the availability of diverse options enhances adaptability and creativity in problem-solving. In contrast, “Control” was identified as a stronger determinant in intrinsic decision-making, emphasizing the role of autonomy and self-regulation in optimizing internal choices (Table 2). These findings extend the works of Dennis and Vander Wal (2010) by illustrating how these subdimensions operate within athletic environments, where rapid and adaptive decisions are critical [24]. Furthermore, they align with existing literature that underscores the importance of cognitive flexibility in fostering effective and innovative decision-making processes [25, 26, 27].
| Independent variables | Dependent variable: effective decision-making in sport | |||||
| R | R2 | F | β | t | p | |
| Alternatives | 0.318 | 0.111 | 23.21 | 0.207 | 5.427 | 0.001 |
| Control | −0.023 | 2.031 | 0.550 |
The extrinsic decision-making findings of this study align with previous research that highlighted the role of external factors in decision-making processes. The weak positive correlation (R = 0.242) suggests that while “Alternatives” and “Control” contribute to extrinsic decision-making, their impact is relatively limited (Table 3). This result supports the findings of Epstein and Gramling (2015), who also reported that decision-making often involves multiple complex factors beyond those measured in their model [26]. The low R2 value (0.058) reinforces this notion, indicating that a substantial portion of the variance in extrinsic decision-making remains unexplained (Table 3). Future studies could explore additional variables, such as social influences or emotional states, as potential contributors to extrinsic decision-making [28].
| Independent variables | Dependent variable: extrinsic decision-making | |||||
| R | R2 | F | β | t | p | |
| Alternatives | 0.242 | 0.058 | 21.88 | 0.143 | 3.792 | 0.001 |
| Control | 0.235 | 6.218 | 0.001 |
The statistically significant F-value (21.88) demonstrates the robustness of the model despite its limited explanatory power (Table 3). This is consistent with the conclusions of Mõttus et al. [29] (2019), who emphasized that even models with modest explanatory capacities can yield valuable insights into specific behavioral tendencies. The positive β coefficients for both “Alternatives” (0.143) and “Control” (0.235) further illustrate the nuanced interplay between these predictors and extrinsic decision-making (Table 3). Specifically, the stronger impact of “Control” compared to “Alternatives” highlights the importance of autonomy in shaping decisions influenced by external factors, as noted by Morelli et al. [30] (2022).
The findings indicate that while the “Control” factor shows a stronger contribution to extrinsic decision-making (β = 0.235) compared to “Alternatives” (β = 0.143), the overall explanatory power of the model remains low (R2 = 0.058), suggesting that other factors may play a significant role (Table 3). This relatively weaker contribution of “Control” in explaining extrinsic decision-making aligns with previous studies highlighting the complex interplay of external influences and individual autonomy in decision-making processes [31, 32, 33]. Future research could investigate additional variables, such as emotional state or social pressures, to better understand the conditions under which “Control” becomes a more or less dominant factor. This would provide a deeper theoretical understanding of its role in extrinsic decision-making and offer practical insights for designing targeted interventions.
Overall, while this study provides valuable insights, its limitations should be acknowledged. The weak correlation and low R2 value point to the need for a more comprehensive model that incorporates additional predictors (Table 3). Furthermore, the reliance on self-reported measures may introduce biases, as highlighted by Brown et al. [34] (2020). Future research could benefit from using experimental designs or longitudinal approaches to capture the dynamic nature of decision-making processes. Additionally, cultural and contextual factors should be examined to determine their moderating effects on the relationship between “Alternatives”, “Control” and extrinsic decision-making [35].
The intrinsic decision-making findings of this study also align with previous research that emphasizes the importance of intrinsic factors in decision-making (Table 4). The moderate positive correlation (R = 0.270) between “Alternatives” and “Control” and intrinsic decision-making supports prior findings by Schaefer et al. [36] (2020), who highlighted the role of autonomy and available options in enhancing decision-making processes. The R2 value of 0.073, while modest, suggests that these factors contribute meaningfully to intrinsic decision-making (Table 4), echoing the conclusions of Morelli et al. [30] (2022) that decision-making is influenced by a complex interplay of variables.
| Independent variables | Dependent variable: intrinsic decision-making | |||||
| R | R2 | F | β | t | p | |
| Alternatives | 0.270 | 0.073 | 27.78 | 0.124 | 3.456 | 0.001 |
| Control | 0.210 | 5.607 | 0.001 |
The statistically significant F-value for “Alternatives” (F = 27.78) underscores the robustness of the regression model (Table 4). This finding is consistent with Mahdavi’s (2018) research, which demonstrated that even modest explanatory models can provide critical insights into behavioral tendencies [37]. The positive β coefficients for “Alternatives” (0.124) and “Control” (0.210) indicate that both predictors positively influence intrinsic decision-making, with “Control” exerting a more substantial impact. This aligns with the works of Ratcliff et al. [38] (2018), who found that perceived control plays a pivotal role in fostering intrinsic motivation and decision-making.
The significant t-values for “Alternatives” (t = 3.456) and “Control” (t = 5.607) further validate their predictive relevance (Table 4). The stronger impact of “Control” aligns with findings from Nie et al. [27] (2015), who emphasized the critical role of autonomy and control in intrinsic motivation. This suggests that strategies aimed at enhancing intrinsic decision-making should prioritize fostering a sense of control while also ensuring the availability of alternatives.
While these findings provide valuable insights, the study’s limitations should be considered. The moderate correlation and modest R2 value highlight the need for further exploration of additional variables that may contribute to intrinsic decision-making (Table 4). Future research could investigate the role of individual differences, cultural factors, or emotional state, as suggested by Lim (2016). Moreover, incorporating longitudinal or experimental designs could provide deeper insights into the dynamic and contextual nature of intrinsic decision-making [39].
5. Conclusions
This study provides valuable insights into the relationship between athletes’ decision-making processes and the sub-dimensions of “Alternatives” and “Control”. The findings demonstrate that while both factors positively influence decision-making, their impact varies depending on the context. Specifically, “Control” emerged as a stronger determinant in intrinsic decision-making, underscoring the importance of athletes’ perceived autonomy and self-regulation in making effective choices.
For coaches, the findings suggest creating an environment where athletes feel a sense of control and are provided with diverse alternatives, such as incorporating decision-making drills and allowing input in training routines. Sports psychologists could design interventions like mindfulness exercises or role-playing sessions to enhance cognitive flexibility and perceived control. These approaches can improve athletes’ self-regulation and adaptability, fostering better decision-making under high-pressure conditions.
Additionally, this study contributes to the broader framework for understanding effective decision-making by emphasizing the roles of “Alternatives” and “Control” across different contexts. Future research should explore how emotional and social variables, such as stress management and team dynamics, interact with cognitive dimensions to influence decision-making. Integrating these factors into a broader theoretical framework could provide insights into their interplay with cultural and competitive contexts, highlighting how these elements evolve over time. Such an approach would deepen the understanding of the mechanisms underpinning effective decision-making in sports and offer a more holistic perspective for both researchers and practitioners. The findings of this study are based exclusively on a sample of male athletes from Turkiye, which limits the generalizability of the results to broader populations. Future research involving participants from diverse genders and cultural contexts would provide a more comprehensive evaluation of the findings. Such an approach would enhance the study’s external validity and contribute more broadly to sports psychology literature.
Availability of data and materials
Due to the limitations specified in the ethics committee approval, the data collected for this research cannot be shared with third parties.
Author contributions
BB, EK, MV, MG, AD, FK, GA, FÇ—designed the research study; wrote the article. MV, MG—carried out the research. AD, FK—provided help and advice on the research. GA, FÇ—analysed the data. All authors contributed to the editorial changes in the manuscript. All authors read and approved the final version of the article.
Ethics approval and consent to participate
This article complies with the journal’s writing and publication principles, research and publication ethics, and journal ethical guidelines. The responsibility for any violations that may arise regarding the article belongs to the authors. The study was deemed ethically appropriate by the Ethics Committee of Ağrı İbrahim Çeçen University Graduate Education Institute, with the decision dated 26 September 2024 and numbered 333. Informed consent was obtained from all participants prior to their inclusion in the study, and participation was entirely voluntary.
Acknowledgment
Not applicable.
Funding
This research received no external funding.
Conflict of interest
The authors declare no conflict of interest.
