Factors Affecting the Indifference of Players in Training for Competitions

Introduction

The performance of professional sports teams is fundamentally shaped by the motivation and engagement of individual players. In the realm of competitive sports, players' commitment and seriousness during training sessions are essential for their preparedness and eventual success in competitions. However, a recurring challenge reported by many coaches and managers is the emergence of apathy or indifference among players during training, which often undermines team cohesion and performance. Understanding the roots of such indifference and finding effective ways to address it are critical to sustaining success in high-level sports.

Indifference among athletes—characterized by a lack of emotional investment, reduced motivation, and disengagement from team goals—can manifest as poor effort in training, disinterest in competitions, or even emotional withdrawal from the fate of the team. This psychological state, often rooted in prolonged dissatisfaction or unmet expectations, leads to detrimental effects not only on the individual but also on the morale and performance of the entire team (DesClouds & Durand-Bush, 2023; Freedman et al., 2021; Harris et al., 2024).

One of the theoretical frameworks often used to explain motivational deficits is equity theory Doehler (2022) which posits that individuals evaluate their level of input (effort, time, commitment) in comparison with the outcomes they receive (e.g., recognition, rewards). When they perceive this balance to be unfair, feelings of frustration, disengagement, or indifference may emerge. In modern sports organizations—especially those with commercial structures such as football, basketball, and volleyball—this sense of fairness is closely related to distributive justice, which refers to the perceived fairness in the distribution of rewards, resources, and responsibilities (Doehler, 2022; Friel & Garber, 2020; Hosseini et al., 2025; Martin & Hong, 2022).

Among various psychological and organizational variables, distributive justice—which refers to the fair allocation of resources, rewards, and responsibilities—has been repeatedly highlighted as a central factor in influencing players’ sense of motivation and preventing apathy. Athletes are more likely to invest effort when they believe their contributions are fairly valued by their clubs. Conversely, when players perceive unjust treatment—particularly in financial compensation or opportunities—they may gradually become indifferent, regardless of their physical abilities or prior achievements (Hauck et al., 2020; Kuklick & Gearity,2022; O’Brien et al., 2021).

Furthermore, studies in social psychology have identified different types of apathy: affective apathy (lack of emotional responsiveness), behavioral apathy (reduced goal-directed actions), and general apathy (a pervasive sense of detachment and low engagement) (Bai et al., 2024; Han & Ha, 2025; McLeod et al., 2023). These categories help frame the various expressions of indifference observed in athletes, and provide a psychological lens through which their behaviors can be interpreted. While these typologies were developed in general psychological contexts, they have direct implications in sports, especially in understanding why some players withdraw from training activities despite their potential.

Although a number of studies have investigated motivation and burnout in athletes, limited attention has been paid to apathy as a distinct and structured phenomenon, especially in the context of training sessions. Even fewer studies have linked it with perceived organizational justice—particularly distributive justice—as a key explanatory variable. As such, a gap exists in the literature regarding the interplay between these constructs in professional sports environments.

Two foundational theories underpin the present research: Self-Determination Theory (SDT) and Organizational Justice Theory (OJT). SDT suggests that individuals are more likely to maintain motivation when their basic psychological needs for autonomy, competence, and relatedness are satisfied (Ryan & Deci, 2000). On the other hand, OJT emphasizes the perceived fairness of outcomes and processes within organizations, particularly how distributive and procedural justice influence individuals’ engagement and satisfaction (Colquitt et al., 2001). These frameworks are especially relevant in the context of professional sports, where players' motivation and commitment can be strongly shaped by both internal psychological dynamics and external organizational practices.

Therefore, the main objective of this study is to identify and prioritize the factors contributing to players’ apathy in training and competitions, with a specific focus on the role of distributive justice within sports clubs. By doing so, this research aims to contribute to both theory and practice: offering a more nuanced understanding of motivational barriers in sports, and providing actionable insights for sports managers, coaches, and policy-makers seeking to foster a fairer and more engaging training environment.

Research Methods

This research employed a mixed-methods approach, combining qualitative and quantitative phases. The qualitative phase was conducted based on the grounded theory methodology of Strauss and Corbin (1990), which emphasizes systematic procedures for data collection and analysis. The quantitative phase utilized an interpretive structural modeling approach through MICMAC analysis.

Given the need to first explore and identify the factors contributing to athletes' apathy in training and then evaluate their relative influence and interrelationships, the research was conducted in two sequential phases. In the qualitative phase, purposeful sampling was applied based on clearly defined criteria, including (1) at least five years of professional experience in top-tier leagues, (2) active involvement in competitive training environments, and (3) availability and willingness to participate. The theoretical saturation point was reached after 16 interviews, as no new concepts or categories emerged beyond the twelfth interview.

The participants in the qualitative phase included 13 professional athletes, 3 club managers, 6 coaches, and 4 referees. Data were collected through semi-structured interviews, guided by the core research question: "What are the factors affecting the occurrence of players' apathy in training for competitions?" Interviews were conducted in person, online, and by phone, each lasting approximately 30 to 45 minutes. Informed consent was obtained, and interviews were recorded for accuracy.

Following Strauss and Corbin’s coding procedures, data analysis involved open coding (identifying key concepts), axial coding (connecting categories), and selective coding (integrating the core themes). The emergence of concepts was tracked systematically, and the process of saturation is summarized in Table 1.

To ensure validity, expert feedback from sport management scholars was incorporated. Reliability was established through inter-coder agreement, with two researchers independently analyzing the data and resolving discrepancies.

In the second phase, a structural self-interaction matrix was developed for the identified factors. Experts who met the same inclusion criteria as in the qualitative phase were asked to perform pairwise comparisons of the factors in terms of influence and dependence. Although the MICMAC technique relies on expert judgment, efforts were made to minimize bias through a consensus-building process and by employing the test–retest method to evaluate the consistency of responses.

The influence levels were rated on a scale from 0 (no influence) to 3 (strong influence). With six final components identified, a 6×6 matrix was constructed and analyzed using MICMAC software. The variables were then categorized into four groups: autonomous, dependent, linked, and independent (driving) factors. The interpretive structural model provides a visual representation of these relationships and informs strategic prioritization of interventions.

Table 1- The Process of Emergence of Concepts and Categories up to the Limit of Theoretical Adequacy

Data Analysis Process

In order to analyze the qualitative data, grounded theory was employed using a systematic coding procedure. The data were gathered through 16 in-depth semi-structured interviews, which were audio-recorded, transcribed verbatim, and subsequently analyzed in multiple stages:

1. Open Coding

Initially, the entire body of interview texts was carefully read several times to gain a comprehensive understanding. Open coding was conducted manually by breaking down the data into discrete parts and assigning codes to meaningful units. This process led to the extraction of 189 initial concepts (codes). These codes were derived based on recurring phrases, behaviors, and patterns mentioned by participants in their narratives.

2. Code Refinement and Validation

To ensure rigor and reduce redundancy, similar or overlapping codes were compared, merged, or eliminated in a collaborative review process involving the research team and subject matter experts. This step included multiple rounds of discussion and validation, resulting in the refinement of the 189 codes down to 31 final conceptual codes. Special attention was paid to avoid the loss of key meanings or the overlooking of distinct concepts.

3. Axial Coding and Category Formation

In the next phase, secondary coding (axial coding) was applied. During this step, conceptual codes were grouped into broader thematic categories based on their relationships and similarities. These categories represented higher-order constructs explaining the main dimensions of athlete apathy. Ultimately, six main categories were identified: Managerial factors, Club policy and structure, Infrastructure, Motivational factors, Environmental factors & Financial factors.

4. Construction of the Direct Influence Matrix (ISM Method)

To explore the interrelationships among the identified factors, a Structural Interpretive Modeling (ISM) approach was used. In this process, an interactive matrix was created by conducting pairwise comparisons of the six main categories to determine whether and how much each factor influences another. Scores were assigned as follows: 0: No effect, 1: Weak effect, 2: Moderate effect, 3: Strong effect.

The matrix served as the foundation for constructing a hierarchical structural model and enabled the identification of driving and dependent factors within the system.

5. MICMAC Analysis

To validate and further interpret the influence-dependence relationships among factors, MICMAC (Cross-Impact Matrix Multiplication Applied to Classification) analysis was performed using specialized software. This step involved calculating direct and indirect influences of each factor and plotting them on a two-dimensional graph. The resulting graphic revealed the system stability, highlighted the key influencing and influenced components, and confirmed the categorization of variables into independent, dependent, linkage, or autonomous types.

Findings

The present section presents the refined outcomes of the qualitative and interpretive structural modeling analyses that aimed to identify the major factors contributing to player indifference in competitive sports training, specifically within the context of Taekwondo League preparation.

The open and axial coding processes resulted in 31 finalized conceptual codes, which were categorized into six main thematic groups: managerial factors, club policy and structure, infrastructure, motivational factors, environmental factors, and financial factors. These categories helped summarize the core drivers of athlete apathy.

Table 2- An Example of the Initial Coding of the Interviews

Although some codes such as "loss of motivation," "lack of interest," and "lack of energy" appear conceptually similar, they were retained as distinct codes due to meaningful differences in the way participants described their experiences. These nuances reflect diverse forms of indifference and thus were preserved to maintain theoretical richness and avoid oversimplification

In the subsequent phase following open coding, similar conceptual codes were systematically grouped into broader categories to provide a clearer and more organized structure to the data. This process involved reviewing and refining the initial codes to ensure conceptual coherence and reduce redundancy. Ultimately, a total of 31 refined conceptual codes were classified into six primary categories: managerial factors, club policy and structure, infrastructure, motivational factors, environmental factors, and financial factors. This categorization facilitates a comprehensive understanding of the multifaceted influences contributing to athlete indifference by highlighting the key thematic areas and their interrelationships within the studied context.

Table 3-Secondary Coding and Formation of Effective Factors in the Occurrence of Players' Indifference

To explore the interrelationships among the six identified categories affecting athlete indifference, the Interpretive Structural Modeling (ISM) method was employed. As part of this process, an interactive matrix of direct effects was developed, in which each category was compared pairwise with others to determine the presence and strength of influence. The relationships were scored using a four-level scale:

0 = No effect,
1 = Weak effect,
2 = Moderate effect, and
3 = Strong effect.

This scoring system is not merely numerical but conveys practically meaningful differences. A score of 3, for instance, indicates a significantly stronger and more direct influence than a score of 2. While both reflect meaningful connections between factors, the difference between “moderate” and “strong” effects can highlight distinctions in how urgently or critically one factor impacts another. These distinctions are essential in structural analysis, as they inform the prioritization of policy interventions, helping decision-makers focus on the most influential elements when designing strategies to address athlete indifference.

Table 4 presents the matrix of direct effects among the six categories: managerial, environmental, motivational, financial, infrastructure, and club structure. Each row reflects the effect of one factor (the influencer) on the others (the recipients in the columns).

Table 4- Interactive Matrix of the Direct Effects of Factors Influencing the Occurrence of Players' Indifference

• Zero effects: 13 cells
• Weak effects (score = 1): 15 cells
• Moderate effects (score = 2): 6 cells
• Strong effects (score = 3): 2 cells
• Matrix completion rate: 88.63%.

To better understand the systemic role and position of each factor influencing players' indifference, a total direct effects matrix was developed (Table 5). This matrix summarizes the total influence each factor exerts on other factors (row total), as well as the extent to which each factor is influenced by others (column total).

In this context, two types of leveling were used:

• Leveling based on effectiveness reflects the degree to which a factor influences other variables. Factors with higher row totals are considered more effective.
• Leveling based on influenceability (or dependency) refers to the extent a factor is influenced by others—i.e., its sensitivity within the system. Factors with higher column totals are more influenced by other components.

These two forms of leveling are essential to the MICMAC analysis, which classifies variables into four categories: autonomous, dependent, independent, and linkage variables

Table 5-Summary of the Total Matrix of the Direct Effects of Factors on Players' Indifference

Based on the total matrix, it is evident that managerial factors possess the highest effectiveness (row total = 9) and thus occupy the first level in terms of impact. Financial factors follow with high effectiveness and lower influenceability, placing them at the second level. Club structure is placed at the third level due to moderate effectiveness and low influenceability. The infrastructure and environmental factors are categorized at the fourth level. Finally, the motivational factor, with the highest influenceability and lowest effectiveness, is situated at the fifth level—making it a highly dependent component in the system.

These leveling forms the basis for drawing the Interpretive Structural Model shown in Figure 1.

Figure 1. The Structural-Interpretive Model of the Factors Influencing Players' Indifference in Preparation Exercises for Competitions

Following the ISM model, the MICMAC method was used to further assess the systemic dynamics among the factors. In this method, the sum of direct and indirect effects of each component is mapped onto two axes: effectiveness (X-axis) and influenceability or dependency (Y-axis). This produces a visual classification of variables into four quadrants:

• Independent variables (high influence, low dependence)
• Dependent variables (low influence, high dependence)
• Linkage variables (high influence and high dependence)
• Autonomous variables (low influence, low dependence)

Following the previous model, the MICMAC method was used to further assess the systemic dynamics among the factors. In this method, the sum of direct and indirect effects of each component is mapped onto two axes: effectiveness (X-axis) and influenceability or dependency (Y-axis). This produces a visual classification of variables into four quadrants:

• Independent variables (high influence, low dependence)
• Dependent variables (low influence, high dependence)
• Linkage variables (high influence and high dependence)
• Autonomous variables (low influence, low dependence)

The MICMAC graphical output reveals that the motivational factor is highly influenced by other factors but exerts little influence itself—indicating a strong dependency and classifying it as a dependent variable. In contrast, managerial, financial, environmental, and club structure factors exhibit high influence and low dependence, making them independent variables that can initiate changes within the system. The infrastructure factor shows both high influence and high dependence, identifying it as a linkage variable—meaning that any change in this factor could create ripple effects across the entire system.

This configuration, resembling the letter “L” on the MICMAC map, signifies that the system is structurally stable. If the components had been scattered across the diagonal axis, the system would have been deemed unstable.

Figure 2. MICMAC Map of Influence and Dependence

In Figure 3, the direct relationships between factors are shown using red (strong), blue (moderate), and dotted (weak) lines:

• Red lines indicate the strongest relationships, showing that managerial and financial factors have the greatest direct influence on motivational factors.
• Blue lines represent moderate effects, such as the managerial factor influencing financial, infrastructure, and club structure, and the club structure influencing motivation.
• Dotted lines reflect weak or insignificant direct effects among other factor pairs

Figure 3- Diagram of Direct Effects in 100% Path Mode

These findings confirm that managerial and financial factors are the most critical drivers of player motivation and overall systemic balance. Improving these key areas is likely to have cascading benefits across other dependent and linked variables, such as infrastructure and motivation. Consequently, strategic interventions should prioritize strengthening managerial practices and financial reliability to effectively reduce player indifference in competitive training contexts.

Discussion

The present study aimed to identify and prioritize the factors contributing to players' indifference in training for competitions, a phenomenon that poses a serious risk to professional sports performance. Through a mixed-methods approach, including qualitative coding and interpretive structural modeling (ISM), followed by MICMAC analysis, the study revealed a complex interplay of managerial, motivational, structural, and individual factors contributing to athlete disengagement.

One of the most influential categories identified was managerial and leadership-related factors. Poor communication, disregard for athletes' needs and opinions, lack of respect for their dignity, and failure to provide adequate welfare support emerged as key issues. These findings align with the work of (Davison & Bing, 2008; Martin & Hong, 2022; Mignano, 2024), who emphasize that managerial responses to athlete demotivation vary widely—from negligence to lack of competence in crisis management.

From the lens of Organizational Justice Theory, the perception of fairness—both distributive and procedural—was shown to play a crucial role in athlete motivation. Players reported a decline in engagement when they felt that their efforts were not fairly rewarded or that managerial decisions were arbitrary. This supports the work of (Friel & Garber, 2020; Kim et al., 2024; Soto-García et al., 2023), highlighting that respect and dignity in management interactions can enhance intrinsic motivation and reduce psychological disengagement.

Another significant category was motivational dynamics, particularly the role of extrinsic incentives such as bonuses and contractual payments. In line with Self-Determination Theory (Skarlicki et al., 2008; Zhao et al., 2024), athletes reported that timely and fair financial rewards reinforced their sense of autonomy and competence, thereby reducing indifference. However, under unstable economic conditions and rising inflation, players' reliance on financial rewards has grown stronger, making them more sensitive to delays and inconsistencies. These findings mirror previous studies (Doehler, 2022; Miller & Fry, 2018; Patrick et al., 2008; Raabe et al., 2022) that show economic pressures magnify the effects of financial motivators.

Moreover, the players' perception of justice in how rewards are distributed based on skill, role, and performance was found to be essential. As confirmed by (Jaarsma et al., 2019; Wu et al., 2024), when athletes perceive inequity in rewards, even high-performing individuals may develop apathy or withdraw mentally from training commitments.

Although welfare infrastructure, such as access to medical care, housing, and standard facilities, was mentioned by participants, it appeared less influential in the hierarchy of factors. This suggests that in top-tier leagues, basic facilities are assumed, and their absence is more tolerated or compensated for by athletes in other ways. Yet, it remains a foundational expectation that clubs must meet to prevent deeper dissatisfaction over time (Le & Nguyen, 2023; Marjit et al., 2023).

The findings also highlight the role of individual differences, including personality traits, self-confidence, and family support. Athletes with higher resilience, stable personalities, and strong support systems were more likely to sustain motivation under pressure. These results point to the importance of considering psychological capital in recruitment and development processes, as low intrinsic motivation and indecisiveness were associated with higher levels of indifference (Arneson, 2022; Kovarik et al., 2023).

The integration of ISM and MICMAC analyses provided a structured understanding of how these factors interact. Managerial factors emerged as key driving variables, suggesting that changes in leadership style and management practices can have cascading effects on other variables such as motivation and player engagement. In contrast, rewards and motivational concerns were more dependent variables, strongly influenced by upstream components. This structured modeling offers a useful tool for clubs seeking to diagnose and address the root causes of player disengagement. It also strengthens the study’s theoretical contribution by operationalizing qualitative insights into a hierarchical framework of influence.

Conclusion

To reduce indifference and enhance commitment among professional athletes, clubs and coaches are advised to practice respectful and inclusive leadership, ensure athletes' voices are heard in decision-making, fulfill contractual obligations promptly and transparently, design reward systems perceived as equitable and performance-based, provide psychological support and nurture mental resilience in athletes, and monitor and respond proactively to early signs of disengagement. Furthermore, merit-based selection, regular performance evaluations, and the avoidance of favoritism are essential to maintaining a motivated and cohesive team culture.

This study, while comprehensive, has certain limitations. The sample size was limited, and the reliance on expert opinion in ISM and MICMAC modeling may introduce subjective bias. Moreover, the findings are context-specific and may not fully generalize across countries with different league structures, economic systems, or sports governance models. Future research could expand on these findings using longitudinal designs, incorporate psychometric tools for motivation assessment, and compare indifference factors across different levels of sport (amateur vs. professional) or across gender lines.

Acknowledgments

We sincerely thank all the organizations and individuals who assisted and supported us in carrying out this study.

Conflicts of Interest

There is no conflict of interest.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.