Antecedents of Learning Agility in Asian Business Environment: The Role of Emotional Intelligence and Leader-Member Exchange

Abstract

Purpose: This study investigates the antecedents of learning agility among employees in Korean organizations, addressing the literature gap in understanding factors that foster learning agility in Asian business environments. Research design, data, and methodology: A survey was conducted with 270 employees from three Korean conglomerate subsidiaries across diverse industries. Hierarchical regression analysis examined relationships between emotional intelligence, core self-evaluation, leader-member exchange social comparison (LMXSC), job involvement, person-job fit, and learning agility. Results: All hypothesized relationships were supported. Emotional intelligence (β = 0.27, p < 0.001) and LMXSC (β = 0.20, p < 0.001) emerged as the strongest predictors, followed by job involvement (β = 0.16, p < 0.01), person-job fit (β = 0.16, p < 0.01), and core self-evaluation (β = 0.13, p < 0.05). Conclusions: The findings highlight the critical role of emotional regulation and high-quality leader-member relationships in enhancing learning agility within Asian organizational contexts. These results provide practical insights for HR practitioners and leaders seeking to cultivate learning-agile talent in rapidly evolving Asian business environments.

1. Introduction

In the context of the Asian business environment, where rapid economic development, technological advancement, and cultural complexity intersect, the importance of learning agility becomes even more pronounced. In recent years, organizations are massively striving at enhancing their efficiency and competitiveness by elevating employees' knowledge and skill sets. Consequently, the capacity to learn from experience has become a vital asset. The concept of learning agility of the willingness and ability to acquire and apply knowledge from past experiences to novel situations has held significant attention among HR scholars and practitioners. Lombardo and Eichinger (2000) define it as the aptitude to absorb the right lessons from experience and effectively apply them in unfamiliar contexts. Highly learning agile individuals actively seek new challenges, embrace feedback, engage in self-reflection, and continuously assess and refine their capabilities. These individuals are characterized by their adaptability, growth mindset, and drive for self-improvement. Lombardo and Eichinger (1997) delineated four key dimensions of learning agility as mental agility, results agility, people agility, and change agility. As a competitive strategic tool, learning agility now plays a pivotal role in talent identification and development within modern organizations. In Asia, where hierarchical structures, collective cultural values, and rapid market transitions coexist, fostering learning agility is crucial for enabling leaders and employees to navigate change, innovate, and sustain organizational performance.

Of the dimensions of learning agility, mental agility reflects an individual's capacity to think critically, adapt to new perspectives, and articulate thoughts clearly in unfamiliar situations. People agility involves emotional intelligence, strong interpersonal skills, and the ability to build empathetic and constructive relationships. Results agility characterizes individuals who excel under pressure, deliver consistent performance in new tasks, and motivate others through their confidence and influence. Change agility denotes a proactive mindset of those who embrace uncertainty and eagerly seek novel experiences. To assess these dimensions, Lombardo and Eichinger (2000) developed the Choices Questionnaire, grounding in their framework of experiential learning. This tool evaluates an individual’s inclination and ability to learn in challenging or unfamiliar contexts. It comprises 76 behavioral items rated by supervisors, peers, or colleagues familiar with the individuals. The instrument effectively measures learning agility and distinguishes it from other learning styles, offering a robust method for identifying high-potential talent within organizations.

Initially, learning agility was defined as a multifaceted construct, integrating emotional, motivational, and interpersonal competencies vital for experiential learning (Lombardo & Eichinger, 2000). However, DeRue et al. (2012) criticized this approach for its vagueness around the concept of "agility," instead framing it narrowly as the cognitive speed to assimilate and apply information. This reductionist view has faced backlash for equating learning agility with mental intelligence. In contrast, Park and Kim (2019) expanded the definition to encompass both intellectual and behavioral dimensions in the context of workplace learning. Despite these varied interpretations, scholars commonly agree that cognitive flexibility and behavioral adaptability are core elements. DeRue et al. (2012) underscored mental tractability and rapid learning as critical enablers, while Schmidt and Hunter (1998) highlighted emotional intelligence as a significant facilitator in learning from experience. Ultimately, higher cognitive capacity is linked to greater success in complex, high-level tasks.

Building on these theoretical insights, previous research on learning agility has predominantly emphasized its outcomes, behavioral traits, and its role as a precursor to effective leadership and job performance. However, recognizing employees' learning agility is now crucial in the talent development process, especially for identifying high-potential individuals aligned with organizational goals. To build up a comprehensive theoretical framework and inform targeted training initiatives, it is essential to explore the antecedents of learning agility. This study addresses that gap by examining key predictors of emotional intelligence, core self-evaluation, leader-member exchange social comparison (LMXSC), person-job fit, and job involvement-drawn from established literature.

Extending this focus to a broader contextual level of the dynamic and diverse Asian business environment of cultural intricacies, technological disruptions, and competitive market pressures, understanding the key drivers of learning agility is essential for organizational resilience and growth. The research aims to determine the influence of these variables on learning agility and identify which among them exert the most significant impact. Such insights would be vital for shaping effective development strategies and fostering agile talent within organizations. In Asia, where businesses often balance tradition with modernization and global integration, pinpointing the most influential factors can help tailor leadership and talent development approaches that align with regional values and strategic needs.

2. Literature Review

2.1. Emotional Intelligence and Learning Agility

Emotional intelligence (EI), as defined by Wong and Law (2002), is the ability to perceive, understand, manage, and regulate emotions. EI has been recognized as a predictor of both employee performance and leadership potential (Schmidt & Hunter, 1998; Palmer et al., 2001). Mayer and Salovey (1997) describe EI as a blend of interconnected skills, including the capacity to accurately perceive and express emotions, generate emotions that enhance cognitive processes, understand emotional knowledge, and regulate emotions to foster both emotional and intellectual growth. From a learning perspective, EI plays a crucial role in integrating emotional and cognitive information for problem-solving and decision-making in real-world contexts. It also emphasizes the use of emotions to motivate others and effectively complete challenging tasks. Building on existing literature, this study proposes that EI influences learning agility, as individuals who can regulate their emotions are better equipped to confront unfamiliar challenges and adapt swiftly to new environments.

H1: Emotional intelligence (EI) may have a positive relationship with learning agility.

2.2. Core Self-evaluation and Learning Agility

Core self-evaluation refers to an individual’s fundamental belief in their own worth and competence (Judge et al., 2003). It encompasses four key components: self-esteem (self-worth), self-efficacy (belief in competence), locus of control (perceived control over life), and emotional stability (ability to regulate emotions). These factors collectively predict job attitudes and performance. Core self-evaluation plays a crucial role in enhancing learning abilities by motivating individuals to reflect on their progress and engage in metacognitive processes to align tasks with learning goals. It encourages individuals to consider how specific tasks contribute to their broader learning objectives. According to Labuhn et al. (2010), self-regulation is essential for learners to plan, seek assistance, and persist through challenges. Given that individuals with high core self-evaluation possess a strong drive for growth and confidence in their capabilities, this study posits that core self-evaluation positively influences learning agility, as both are linked to heightened motivation and effective learning.

H2: Core self-evaluation may have a positive relationship with learning agility.

2.3. Job Involvement and Learning Agility

Job involvement is defined as the degree to which individuals perceive their job as essential for fulfilling their needs, boosting self-esteem, and gaining psychological recognition (Blau, 1964; Kanungo, 1982). It is also described as a cognitive state of psychological identification with one’s role, reflecting the significance of job performance in personal fulfillment (Mathieu et al., 1992). Highly job-involved individuals exhibit a greater commitment to learning and applying new skills in the workplace (Hall, 1971; Morrow, 1983), as they view their job as integral to their personal growth and career success. Such employees are more motivated to invest effort in learning and improving performance. Job involvement influences learning agility, as those who regard their job as a central life component are more driven to learn and grow professionally. Consequently, it can be inferred that job involvement and learning agility are positively correlated, with highly involved employees exhibiting stronger learning capabilities and growth orientation.

H3: Job involvement may have a positive relationship with learning agility.

2.4. LMXSC and Learning Agility

The leader-member exchange (LMX) relationship is posited to influence learning agility, though limited research has explored this connection. Vidyarthi et al. (2010) introduced the concept of leader-member exchange social comparison (LMXSC), suggesting that employees in high-quality LMX relationships engage more actively in learning programs compared to those in low-quality relationships (Colquitt et al., 2000). Supervisors' motivation to learn is linked to their subordinates' empathetic behaviors and mutual understanding. According to Sonnentag and Natter (2004), significant learning occurs in daily work situations or new challenges, with leaders playing a pivotal role in fostering commitment to learning. Studies by Birdi (1997) and Colquitt et al. (2000) further support the notion that leaders actively influence subordinates' participation in learning activities. Thus, the quality of LMX relationships can significantly impact employees' engagement in developmental opportunities.

Based on social exchange theory, Leader-Member Exchange (LMX) posits that high-quality relationships between supervisors and subordinates foster a sense of mutual responsibility and commitment (Graen & Uhl-Bien, 1995; Blau, 1964; Gouldner, 1960). Noe & Wilk (1993) suggested that employees' perception of their supervisor’s support for their learning and growth positively influences their learning agility. However, few studies have explored the relationship between LMX Social Comparison (LMXSC) and learning agility. This research aims to investigate how leader behavior, particularly within high-quality LMX relationships, impacts employees' learning agility. Employees who perceive their relationship with their supervisor as superior are more likely to engage in learning activities, as they anticipate support when faced with new challenges. Therefore, a positive relationship between LMXSC and learning agility is proposed.

H4: LMXSC may have a positive relationship with learning agility.

2.5. Person-job Fit and Learning Agility

Person-job fit refers to the alignment between an individual's characteristics and the demands of a job role (Wong & Tetrick, 2017). Research by Cai et al. (2018) demonstrates a positive relationship between work engagement and person-job fit. Amabile (1988) argues that an individual's expertise and skills within a specific domain are critical for fostering creativity and effective performance. For instance, Dewa (1996) emphasized the importance of specialized knowledge, skills, and abilities for driving innovation. When employees' abilities align with job requirements, they are more likely to engage in organizational learning, which enhances their capacity to learn from experience and apply knowledge in new or challenging contexts. However, poor person-job fit can hinder learning, as employees may lack motivation to acquire new skills. Thus, person-job fit is integral to fostering learning agility, suggesting a positive relationship between the two.

H5: Person-Job fit may have a positive relationship with learning agility.

2.6. Relationship among Emotional Intelligence, Core Self-evaluation, Job Involvement, LMXSC, Person Job Fit and Learning Agility

Previous research suggests that emotional intelligence (EI) enhances employees' knowledge and adaptability. Individuals with high EI are better equipped to navigate challenging social and emotional situations, which aids in learning and adjustment (Goleman, 1996; Salovey & Mayer, 1990). De Meuse et al. (2010) highlight that emotionally intelligent employees excel in maintaining composure, seeking feedback, and managing interpersonal dynamics-key behaviors tied to learning agility. Similarly, individuals with high core self-evaluations (CSE), who possess confidence in their abilities and self-worth, are more likely to embrace challenges and view setbacks as opportunities for growth (Judge et al., 2003; Bono & Judge, 2003). This mindset fosters resilience and a proactive approach to continuous learning. Luthans and Youssef (2007) further emphasize that psychological capital-comprising resilience, optimism, and hope-is essential for thriving in dynamic environments, reinforcing the link between CSE and learning agility.

Job involvement plays a crucial role in driving learning agility. Employees deeply engaged in their work are more likely to embrace learning and development opportunities, as they see their performance as personally significant (Kanungo, 1982). This psychological investment encourages them to seek feedback, reflect on their job performance, and continuously improve (Kahn, 1990; DeRue & Wellman, 2009). Thus, learning agility is not only influenced by personal attributes such as emotional intelligence or cognitive ability but also by the emotional and cognitive attachment employees have to their roles, which fosters a proactive and growth-oriented mindset.

Person-job fit and Leader-Member Exchange Social Comparison (LMXSC) are vital contextual factors that enhance learning agility. Positive perceptions of LMXSC foster trust and validation, encouraging employees to take risks and grow without fear of judgment (Vidyarthi et al., 2010; Edmondson, 1999). When employees feel they are treated better by their leader than others, they are more motivated to pursue challenging goals and step outside their comfort zones. Similarly, a strong person-job fit aligns employees' values and skills with their roles, increasing their confidence and ability to tackle learning-oriented tasks (Kristof-Brown et al., 2005; Cable & DeRue, 2002). This alignment bolsters their sense of security, facilitating greater engagement in development opportunities.

The analysis reveals that learning agility is influenced by a combination of emotional, cognitive, relational, and contextual factors. Emotional intelligence and core self-evaluation empower individuals to adapt, grow, and harness knowledge effectively. Job involvement reflects the motivational drive that compels employees to seek development opportunities. Additionally, learning agility is shaped by contextual elements such as the quality of leader-member relationships (LMXSC) and the alignment between an individual’s attributes and job requirements (person-job fit). These insights underscore the interaction between internal and external factors in fostering learning agility, leading to the proposition of a research model that integrates these theoretical and empirical findings.

Figure 1: Research model proposed

3. Research Methods

3.1. Participants and Data Collection

A survey was conducted over two weeks with 350 employees from three subsidiaries of a conglomerate based in Seoul, Korea. Of the 274 responses received, four were excluded due to outliers or insufficient data, leaving 270 usable responses. Among the respondents, 136 (50.5%) were male and 134 (49.5%) were female. The age distribution included 45 participants (17%) aged 20-29, 118 (44%) aged 30-39, 81 (30%) aged 40-49, and 22 (9%) aged 50-59. In terms of education, 147 (54.5%) held a 4-year university degree, and 56 (21%) completed a 2-year college program. Occupationally, 42 (15.6%) were clerks, 100 (37.1%) were assistant managers, 60 (22.4%) were managers, 42 (15.6%) were deputy managers, and 22 (8.3%) were general managers. Regarding industry, 86 (32%) worked in finance/insurance, 68 (25.4%) in manufacturing, and 29 (11%) in consulting/training. Experience within the current organization varied, with 140 (52%) having over 10 years, 48 (18%) with 7-9 years, 31 (11.7%) with 5-6 years, 20 (7.5%) with 3-4 years, and 21 (7.8%) with 1-2 years of tenure.

3.2. Measurements

This study utilized an online survey with a five-point Likert scale, ranging from "completely disagree" (1) to "strongly agree" (5). Forward-backward translation was held for cross-cultural reliability by first translating the questionnaire from the source to the target language (forward translation from English to Korean), then independently translating it back to the original language (backward translation from Korean to English). Then, we compared the original and back-translated versions to identify discrepancies, ensuring conceptual equivalence, clarity, and cultural relevance. This process helped validate that the translated instrument maintained the original meaning across languages and cultures. Emotional intelligence (EI) was measured using the Emotional Intelligence Scale developed by Wong and Law (2002), comprising 16 items: four items each assessing self-emotion appraisal, others' emotion appraisal, emotion usage, and emotion regulation, with a Cronbach’s alpha of .93. A sample item is, “I have a good understanding of my emotions.” Core self-evaluation was assessed using the Core Self-Evaluation Scale developed by Judge et al. (2003), including three items each for evaluating self-worth, control over one’s environment, and emotional adjustment, with a Cronbach’s alpha of .80. A sample item is, “I am confident, I get success, and I deserve it in my life.”

Job involvement was assessed using the nine-item Short Job Involvement Scale (Reeve & Smith, 2001), which evaluates an individual's perception of job importance and its role in personal life and identity, with a Cronbach’s alpha of .75. A sample item is, “The major satisfaction in life comes from my job.” Leader-member exchange social comparison (LMXSC) was measured with six items developed by Vidyarthi et al. (2010), gauging individuals’ perceptions of their LMX relative to coworkers, with a Cronbach’s alpha of .88. A sample item is, “I have a better relationship with my supervisor compared to the majority of other team members.” Person-job fit was evaluated using six items by Cable & DeRue (2002), addressing needs-supplies and demands-abilities fit, with a Cronbach’s alpha of .82. A sample item is, “There is a good fit between what my job offers me and what I am looking for in a job.” Learning agility was measured by the Workplace Learning Agility Assessment Inventory (Park & Kim, 2019), comprising 16 items across four dimensions-open sensing, strategic exploring, experimental applying, and critical reflecting-yielding a Cronbach’s alpha of .92. A sample item is, “I find the core message of complicated data and information in a short time.”

3.3. Data Analysis

Descriptive statistics and correlation analyses were performed using SPSS to examine the relationships among the variables. The survey data were converted into numerical values, with descriptive statistics calculated initially. Subsequently, correlation analysis was conducted to explore the interconnections among all variables. Finally, hierarchical regression analysis was employed to test each hypothesis.

4. Results

4.1. Descriptive Statistics and Correlation

Table-1 presents the descriptive statistics (mean, standard deviation, and correlation) for all variables in the research model. Emotional intelligence had the highest mean value at 3.67, while LMXSC had the lowest at 3.16. Among the correlations, the weakest was between core self-evaluation and job involvement (r = 0.15), and the strongest was between emotional intelligence and core self-evaluation (r = 0.58). Consistent with the literature review predictions, all variables were significantly correlated with one another.

Table1: Mean, standard deviation, and correlation

Note: N=270. *p<.05. ***p<.001.

4.2. Results of Hierarchical Regression

Table 2 shows that emotional intelligence (β = 0.27, p < 0.001) and LMXSC (β = 0.20, p < 0.001) have significant positive relationships with learning agility, supporting hypotheses 1 and 4. Job involvement (β = 0.16, p < 0.01) and person-job fit (β = 0.16, p < 0.01) also show positive relationships with learning agility, validating hypotheses 3 and 5. Core self-evaluation (β = 0.13, p < 0.05) has a weaker but still positive effect on learning agility, supporting hypothesis 2. Overall, the regression analysis fully supports all hypotheses.

Table 2: Results of hierarchical regression

Note: N=270. *p<.05.**p<.01. ***p<.001.

5. Discussions

In the Asian business environment, where interpersonal harmony, hierarchical relationships, and emotional restraint are often culturally emphasized, the findings of this study carry particular relevance for organizational development. This study identified key antecedents of learning agility, including emotional intelligence, core self-evaluation, LMXSC, job involvement, and person-job fit, all of which exhibited significant positive relationships with learning agility, even when controlling for age, gender, education, and tenure. Among these, emotional intelligence and LMXSC had the strongest effects. Positive emotions were found to enhance the learning process, whereas negative emotions limited employees' cognitive flexibility. Emotional intelligence, particularly the ability to recognize, reason with, and regulate emotions, plays a crucial role in fostering learning interest and organizational learning. Additionally, the study highlights the importance of LMXSC, demonstrating how self-perceptions of supervisor relationships can significantly influence employees' learning agility.

Employees with strong emotional intelligence demonstrate a heightened ability to recognize and regulate both their own emotions and those of others, fostering more effective communication, conflict resolution, adaptability, and leadership capacity. Within teams, EI enhances collaboration, reduces tension, and supports sound decision-making under pressure. Interventions such as targeted EI workshops, individualized coaching, mindfulness practices, and 360-degree feedback systems can cultivate these competencies. Similarly, awareness of LMXSC is vital, as employees often evaluate the fairness of their supervisor relationships relative to peers. Perceptions of bias or favoritism can undermine morale and productivity. To counteract this, organizations can implement leadership development programs, encourage transparent communication, promote inclusive team-building activities, and establish regular feedback channels to ensure equitable treatment and reinforce a culture of trust and engagement.

Improving employees' learning agility requires strong support from leaders, including mentoring, coaching, and networking. In high-quality leader-member exchange (LMX) relationships, leaders provide abundant resources, autonomy, and decision-making power, which foster innovation and learning. Research has shown that organizational citizenship behavior is linked to high-quality LMX and employees' innovative behaviors, both of which are crucial for enhancing learning agility. The leader-member social comparison relationship creates a key psychological context for knowledge acquisition. Employees who perceive a high-quality relationship with their supervisor are more likely to seek guidance and support, thereby gaining valuable resources that enhance their learning capabilities.

Employees who have the opportunity to redesign their jobs to better align with their personal values and job requirements are more likely to exhibit greater learning agility. Studies have shown that when employees’ cognitive and individual values match their job roles, organizational citizenship behavior increases, which in turn motivates learning and development. Job involvement is also a key predictor of learning agility, as highly engaged employees demonstrate greater motivation and adaptability in their learning processes. Supervisors play a critical role by providing support that fosters success, thereby enhancing employees' core self-evaluation. A strong core self-evaluation is essential for learning agility, as self-aware individuals are better equipped to recognize their strengths and weaknesses, whereas those with low self-awareness may struggle to identify mistakes and the need for skill development.

6. Conclusion

This study offers both theoretical and practical implications. Theoretically, it highlights emotional intelligence as the most significant antecedent of learning agility, strongly correlating with other factors such as leader-member exchange social comparison (LMXSC), which directly influences learning agility by improving leader-member relationships. Additionally, person-job fit and job involvement are shown to be interconnected with learning agility, emphasizing their direct impact on employees’ learning processes. Core self-evaluation also plays a crucial role in enhancing learning agility, though further research is needed to fully understand its relationship. Practically, the study underscores the importance of supervisors, subordinates, and leaders in cultivating a learning-agile environment. Employee self-evaluation, intelligence, job motivation, and alignment with job requirements are key to fostering learning agility. Supervisors must offer support and guidance to enhance subordinates' learning capabilities, while HR and leadership should ensure proper job placement and a knowledge-sharing culture. Ultimately, creating a learning-agile environment is a collaborative effort that requires the active involvement of all parties.

7. Limitations

This study has several limitations. Firstly, it is cross-sectional, and future research should employ longitudinal data collection to establish causal relationships between variables. Additionally, studies should broaden their scope by including employees from various industries to enhance generalizability. The learning agility measurement used in this study, though recent and validated, could be complemented by other measurement tools in future studies to test the replicability of the findings. Furthermore, future research should explore additional mediating and moderating variables to provide a more comprehensive understanding of the antecedents of learning agility. Investigating organizational-level antecedents more deeply would also add significant value to the field.

In the Korean cultural context, deeply rooted of Confucian values, rigid hierarchical norms, and a pronounced power distance play a pivotal role in shaping learning agility. These cultural characteristics can influence how individuals engage with key aspects of learning agility, such as receptiveness to feedback, willingness to take risks, and adaptability to change. For example, the traditional respect for seniority and authority may discourage employees from questioning established norms or engaging in open, self-directed learning. Consequently, to gain a more nuanced understanding of learning agility in global work environments, future research should prioritize cross-cultural comparisons. Such investigations can uncover how varying cultural frameworks affect learning behaviors and inform the development of culturally tailored strategies that promote agility across diverse organizational landscapes.

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