59 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA Asian Business Research Journal Vol. 10, No. 8, 59-72, 2025 ISSN: 2576-6759 DOI: 10.55220/2576-6759.541 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA A Study on the Impact Mechanism of Emotional Intelligence on Employee Performance: The Mediating Role of Employee Commitment Liu Yulei1  Perema Kumari A/P S. Ponnapalam2 Chih-Seong SU3 Meng Zhou4 1NanJing Tech University Pujiang Institute, SEGI University of Malaysia. 2,3SEGI University of Malaysia. 4Bank Of Guangzhou Co.,Ltd, China Email: 694819358@qq.com Email: perema@segi.edu.my Email: suchihseong@segi.edu.my Email: 936038252@qq.com (Corresponding Author) Abstract With the development of China's economy and the rapid advancement of technology, the demand for well-rounded employees is growing stronger. Due to the intense competitive pressures of contemporary society, many knowledge workers face role stress and burnout. This can lead to a decline in employee performance and a sense of belonging to the organization, and even lead to turnover, which can have significant negative consequences for the company. Emotional intelligence (EI) has a significant impact on employee work enthusiasm, performance, satisfaction, and work engagement. Organizational commitment, reflecting an individual's attitude and positive attitude toward the organization, plays a significant role in influencing employee burnout and engagement. This paper, based on research on employee emotional intelligence and organizational commitment in China, investigates the relationship between emotional intelligence, organizational commitment, and employee performance among employees from various industries. The mediating effect of EI on employee performance is analyzed using various performance parameters, dependent variables, and independent variables. Using a statistical software package for social sciences, the impact of EI on the behavioral, social, and psychological outcomes of employees in this organization is assessed. Keywords: Emotional intelligence, Mployee Performance, Organizational Commitmen, PLS-SEM. 1. Introduction 1.1. Research Background Emotional intelligence (EI or EQ) refers to an individual's ability to understand and regulate their own and others' emotions and manage their behavior appropriately in social environments. In recent years, the importance of emotional intelligence in organizational management and employee development has become increasingly prominent. It has a significant impact on performance, particularly in roles requiring frequent interpersonal interaction, such as sales, customer service, and leadership. Employees with high emotional intelligence tend to have greater empathy and self-control, enabling them to effectively identify the needs of others and regulate their emotional responses, thereby making more rational decisions in complex interpersonal situations. This ability helps improve work relationships, enhance job satisfaction, and increase performance. In the current context of accelerating globalization and digitalization, organizations are placing greater emphasis on employee commitment and stability. Research has found that an employee's ability to manage emotions and adapt to organizational culture is closely related to their performance and organizational commitment. A lack of emotional intelligence can lead to interpersonal conflict, decreased productivity, and employee turnover. This study aims to explore how emotional intelligence influences employees' organizational commitment and, in turn, their performance. By constructing a mediation model and clarifying the interaction pathways between the three, the study provides theoretical support and practical recommendations for organizational employee selection and training. Furthermore, the study recommends that organizations introduce emotional intelligence assessments, particularly during the onboarding phase for new employees, to promptly identify differences in emotional intelligence and provide targeted training, thereby improving overall employee performance and organizational cohesion. mailto:694819358@qq.com mailto:perema@segi.edu.my mailto:suchihseong@segi.edu.my mailto:936038252@qq.com https://doi.org/10.55220/2576-6759.541 Asian Business Research Journal, 2025, 10(8): 59-72 60 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA 1.2. Related Concepts and Theoretical Foundations 1.2.1. Emotional Intelligence Emotional intelligence, like IQ, is crucial for success and happiness. Learn how to improve your emotional intelligence to strengthen your connections with others and achieve your goals. Emotional intelligence is the ability to recognize, regulate, and leverage one's emotions to reduce distress, express empathy for others through clear communication, diffuse conflict, and overcome obstacles. It helps develop stronger relationships, perform well in school, and achieve personal and professional goals. On the other hand, it helps build connections, record emotionally relevant intentions, and make important decisions. (Drigas, A., & Papoutsi, C., 2019) 1.2.1.1. Emotional Intelligence and Intellectual Intelligence We should recognize that the happiest and most prosperous people are not always the most intelligent. There are undoubtedly people in the world who excel academically but struggle at work or in relationships due to social awkwardness. For someone who wants to live a successful life, their IQ or intellectual ability alone is not enough. Yes, IQ can help with college admissions, but emotional intelligence (EQ) allows for managing stress and emotions as final exams approach. When they complement each other, IQ and EQ work hand in hand to achieve their best performance. The impact of emotional intelligence on success in academia or the workplace. Strong emotional intelligence is essential for inspiring others, leading a successful career, and navigating the challenging social dynamics of the workplace. In fact, many companies now prioritize emotional intelligence (EQ) over technical skills when evaluating key job candidates. Physical health. If one cannot control one's emotions, then one may also be unable to manage stress. This can lead to serious health problems. Uncontrolled stress can damage the immune system, accelerate the aging process, affect fertility, increase the risk of heart attack and stroke, and raise blood pressure. The first step in developing emotional intelligence is learning how to manage stress. Emotional states, stress, and unchecked emotions can negatively impact mental well-being, increasing the likelihood of depression and anxiety. If employees cannot understand, tolerate, or manage these feelings, they cannot build strong connections. This can worsen existing mental health issues and make people feel more isolated. If a person has a better understanding of emotions and how to manage them, they can better express their feelings and understand how others are experiencing them. As a result, they are able to speak more clearly and develop stronger relationships in both their personal and professional lives. Social awareness and understanding emotions enable them to interact socially with both themselves and the external world. Thanks to social intelligence, they can distinguish between friend and foe, determine another person's level of interest, reduce tension, control their nervous system through social connection, and feel loved and fulfilled. 1.2.2. Organizational Commitment Employee commitment is the bond they maintain with their employer. Loyal employees typically experience a sense of belonging, an understanding of the company's goals, and a connection to the company. These employees create value by being more committed to their tasks, demonstrating high levels of productivity, and being more proactive in providing assistance. Job commitment has recently received considerable attention in the human resources literature. Employee commitment data is considered a key indicator of employee loyalty and organizational effectiveness. Organizations are under constant pressure to perform. Due to globalization, among other factors, competition is more intense than ever. Due to this growing pressure, a company's commitment to its employees is no longer considered a given. The idea of lifetime employment has similarly lost its relevance. Today, underperforming organizational units are undergoing restructuring. Often, layoffs result from this. Furthermore, underperforming employees are more likely to be laid off. As a result of this trend, employee commitment to their jobs and company has become less of a guarantee, while individuals' individuality has expanded significantly. Consequently, it is more important for employees to feel connected to their company and behave in a certain way. Loyal employees add value to the company through their tenacity, proactive assistance, relatively high productivity, and a strong sense of quality. Employees who are committed to their work are also less likely to lose their jobs or resign. Disloyal employees may turn against the company and hinder its growth. 1.2.3. Employee Performance A company's employees are its driving force. Therefore, it's no surprise that their daily performance has a significant impact on the company's ability to succeed. If businesses want to succeed in today's market, they need to understand how to keep their employees performing at their best and maximize their potential. By assisting employees in developing their roles and responsibilities, in addition to assisting in recruiting, retaining, and developing the best personnel, businesses can also create a pipeline of future leaders. All of this contributes to long- term success. Organizational management has always faced significant challenges regarding employee performance. It has employed persuasive strategies to motivate employees to complete their tasks and deliver enhanced work performance. The primary source of any organization's strength and competitive advantage is its workforce. In other words, an organization's effectiveness and viability are directly linked to the effectiveness and productivity of its employees. Furthermore, productivity and organizational growth depend on employee performance. Therefore, the issue of employee performance is crucial to understanding organizations. The degree of effectiveness and efficiency of a particular organization can be measured by the performance of its personnel, although many factors can influence this. The university sector cannot be categorized as such, as this applies to all organizational systems. In the Chinese context, it has become a common topic that Chinese government administrators care about the performance of their employees, especially administrators. (Abdirahman, H. I. H., 2018). Asian Business Research Journal, 2025, 10(8): 59-72 61 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA 1.2.4. Organizational Commitment Theory The three-component model is a well-known organizational commitment theory (TCM). This concept proposes three basic elements that constitute organizational commitment: An employee's emotional connection to the company is called their "affective commitment." According to the TCM component, employees are more likely to stay with the same company when they demonstrate a higher level of positive dedication and exemplary behavior. Employee participation in organizational responsibilities, such as attending meetings and discussions, providing insightful suggestions or ideas to help the company, and being proactive at work, is sometimes referred to as positive commitment. Outside of the West, only a limited amount of research has been conducted on the impact of organizational commitment on employee job performance. Furthermore, there is also limited research on how job happiness influences this relationship. The purpose of this study was to investigate how job satisfaction mediates the relationship between organizational commitment and job performance. Four hypotheses were generated for this purpose; the first three predicted a positive correlation between organizational commitment, job satisfaction, and job performance, while the fourth hypothesized that job satisfaction may be a mediating factor. Based on this research, it is claimed that a simple positive correlation between organizational commitment and job performance may not always lead to job performance for employers. Therefore, improving job performance by increasing organizational commitment through improving job satisfaction is key to success (Loan, L., 2020). 2. Literature Review (Quantitative) 2.1. Current Developments Lee et al. (2022) found that with the introduction of a range of social media communication channels, employees now have more options for interacting with external stakeholders to support or oppose their organization's brand. Discrete emotions, as referred to in this work, are negative emotions related to negative word-of-mouth, rather than ineffective behaviors at work, focusing on discretionary behaviors related to negative brand-oriented NWOM. This study aimed to determine whether employees' brand awareness directly reduces their CWB and NWOM and mitigates the impact of negative emotions. Relevant information was collected through a questionnaire survey and tested using structural equation modeling. The results showed that envy was more closely associated with brand NWOM CWB than with ordinary employees, and anger was more strongly associated with employees' NWOM than with exit awareness. Negative emotions such as resentment and envy were directly mitigated by employees' CWB, rather than NWOM. Both CWB and NWOM were negatively correlated with employees' perceived brand knowledge. This study examined the relative significance of emotional antecedents on employees' NWOM and standard CWB from a discrete emotion perspective. Furthermore, it confirms earlier research findings regarding the positive and negative effects of perceived brand awareness on employee behavior and its mitigating effects on NWOM and CWB. Istyaninsingh et al. (2020) suggest that organizational performance may be reflected in managerial performance. While many studies primarily consider employee performance, managers' position as company leaders significantly influences decision-making. The achievement of organizational goals is highly correlated with managerial effectiveness. To make decisions consistent with company goals, leaders also need emotional intelligence. This study aimed to understand and assess how emotional intelligence directly and indirectly influences managerial performance through decision-making. The study sample consisted of 44 leaders of regional equipment organizations during the Bangor Regency. Path analysis was used as a data analysis technique. The results showed that both decision-making and emotional intelligence influence managerial performance, but the impact of decision-making is relatively greater. Decision-making can be used as an indirect mediating variable to measure the impact of emotional intelligence on managerial performance, as its influence on decision-making is greater than the direct influence. According to this study, managers with high levels of emotional intelligence are more capable of making informed decisions that impact their managerial effectiveness. 2.2. Dependent Variable Performance refers to how employees perform their duties and accomplish important tasks. It emphasizes the value, quality, and effectiveness of their output. The degree to which individuals are valuable to the organization is determined by their performance. Emotional intelligence (EI) is a term that has been elusive almost from the outset. Despite nearly 20 years of research, there seems to be little consensus on how to define, measure, or apply EI. This article intends to present the current state of research on this recently coined construct. We specifically address three major themes in EI research: conceptualization, assessment, and application, where gaps exist between what is known and what is unknown. Throughout the various sections of this article, we begin with each section by outlining assertions that can be somewhat definitively established, clarifying the primary sources of consensus regarding EI. Next, we explore areas of debate; those areas where EI researchers are less consistent. Yang et al. (2021) Despite its positive impact on human health and career success, the relationship between emotional intelligence and innovation is not well understood. While knowledge about intelligence and invention remains weak and unclear, it is insufficient to understand how these two traits are linked. By considering roles, this paper seeks to understand how emotional intelligence (EI) influences creativity by considering employees' available resources, their motivations, their incentives, and their dedication to success (EI). Apoutsi et al. (2019) found that the function of EI in the workplace has been extensively researched. Empirical findings indicate that EI is crucial for maintaining the smooth functioning of an organization. By compiling data demonstrating favorable relationships between EI, attitudes, and work variables, this study investigates how EI impacts the workplace. More specifically, it demonstrates how EI is related to six factors that are crucial for creating a better, more productive work environment. Jameel & Ahmad (2019) conducted in-depth research on employee performance in corporate organizations. However, research on academic performance (PAS) is limited. This study aims to develop a conceptual framework to analyze PAS in developing countries. Based on the literature, this study argues that leadership style influences Asian Business Research Journal, 2025, 10(8): 59-72 62 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA PAS. Furthermore, job satisfaction has the potential to moderate the impact of leadership style on PAS. The development and discussion of these ideas are presented here. 2.3. Independent Variables An organizational member's psychological relationship with the company they work for is described as having organizational commitment. A key factor in determining whether an employee will persist with a company for extended periods and fully commit to achieving its goals is organizational commitment. Ngui & Lai (2020) Stress is inevitable in the world of teaching and practical training, so student teachers inevitably experience some stress as they are required to apply diverse knowledge and abilities in real-world school and classroom settings. In this study, Sen et al. (2020) examined how organizational justice and emotional intelligence influence job satisfaction, a supportive workplace, and the effectiveness of criminal investigation officers. The population for this study included all criminal investigation officers from the police force and the Metro Police Criminal Investigation Directorate (based on 2016 data). Xu Hui, Guo Pibin, and Bao Liyan (2021) explored the innovative behavior of employees in a research team and concluded that innovative self-efficacy significantly influences employees' innovative behavior. Innovative self-efficacy motivates team members to actively exchange knowledge and innovative ideas. This exchange of ideas strengthens employees' innovative capabilities and encourages them to actively engage in innovative activities. 2.4. Relationships between Variables Amjad (2018) found that faculty members in academic institutions exhibited low levels of organizational commitment and satisfaction. Workplace productivity and organizational commitment are closely related to emotional awareness. This study explored the relationship between emotional intelligence, organizational commitment, and job performance. Tuah (2018) aimed to examine the relationship between emotional intelligence and job performance among Telekom Malaysia employees in Kuching, Sarawak. According to the literature, emotional awareness, self-awareness, and self-confidence are three components of emotional intelligence that influence employee job performance. The study employed a census sampling and questionnaire distribution method. The results showed a strong correlation between workplace effectiveness and emotional intelligence. The research discussion provides ideas for how organizations can improve employee job performance by investigating and understanding the impact of certain workplace emotional intelligence applications. Furthermore, some suggestions are offered for new researchers eager to conduct additional research in this area to delve deeper and gather in-depth knowledge that will be useful to interested organizations. Yusoff et al. (2021) Burnout and stress frequently endanger the mental health of international and Malaysian medical students. This study aimed to explore the relationship between mental illness, emotional intelligence, personality traits, classroom stress, and burnout among medical students. 2.5. Hypothesis Development The hypotheses investigated in this study include: 1. Null Hypothesis: Emotional intelligence has no effect on employee performance. 2. Hypothesis 1: Improving employee performance through emotional intelligence. 3. Hypothesis 2: Dependent variables such as emotional intelligence will definitely have a significant impact on employees. 4. Hypothesis 3: Independent variables such as organizational commitment, stress, coworkers, and work environment will have an impact on the balance between emotional intelligence and employee performance. 3. Data Analysis 3.1. Research Methods This study evaluated the mediating effect of emotional intelligence on employee performance through various parameters of job performance, dependent variables, and independent variables. Using the Statistical Package for Social Sciences, the impact of emotional intelligence on behavioral, psychosocial, and psychological outcomes of employees working in this organization was assessed. The researchers performed various adjustments in this experiment. SPSS facilitated the organization of retrospective production and analysis of observed data in the research study. The experimental group consisted of 289 respondents, and descriptive statistics were evaluated to obtain frequencies and percentages. Statistically significant differences were assessed using various appropriate tests, such as Cronbach's alpha, frequency tables, histograms, collinearity diagnostics, and analysis of variance. The threshold for statistical significance was considered to be P<0.05, while the threshold for statistical insignificance was P<0.05. The study was conducted in Malaysia after obtaining appropriate written consent from the research participants. It was conducted in a naturalistic setting with employees working in various organizations. After obtaining consent, the 289 research participants, who were employees, completed a questionnaire consisting of yes/no questions and a 5-point Likert scale. The collected data was evaluated using SPSS. The questionnaire consisted of four sections: 1) Personal and industry profiles 2) Emotional intelligence, including self-emotions, emotion regulation, and use of emotions, as well as other emotions. 3) Organizational commitment, including affective commitment, continuance commitment, and normative commitment. 4) Employee performance Due to the effectiveness of empirical research techniques in social science research, they are becoming increasingly important within quantitative research methods. Empirical research methods involve the process of Asian Business Research Journal, 2025, 10(8): 59-72 63 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA developing a model to identify the connections between various factors identified in a problem. Models explaining real-world events can be examined and refined by establishing and testing hypotheses. Questionnaire-based surveys should collect data based on the research methodology and identify and interpolate factors and variables. Table 1. Reliability Analysis. Variable Name Cronbach’s alpha (α) No. of items Employee Performance (EP) 0.833 5 Employee Commitment (EC) 0.934 24 Emotional Intelligence (IE) 0.819 16 4. Results and Discussion This study used “reliability analysis” to examine the characteristics of the measurement scale and items associated with the scale. In addition to providing data on the correlations amongst the scale's constituent items, the reliability analysis technique creates a variety of regularly employed scale reliability measures. Building data trust throughout the organization requires a solid foundation of reliable data, which is full and accurate. One of the key goals of backup and recovery programs, which are also used to uphold data security, data quality, and regulatory compliance, is to ensure data dependability. Ensuring reliability basically means making sure that the data are reliable and reproducible and that the outcomes are correct. To ensure the integrity and quality of a measuring equipment, reliability assurance is a must. Likert measures are frequently incorporated into questionnaire surveys to delve deeper into the underlying components that the investigator is attempting to quantify. These might be categorized answers to binary or multiple-choice surveys that are then added together to provide a score that is connected with a specific responder. The creation of these scales often serves as a tool to collect predictors for inclusion in empirical frameworks rather than the conclusion of the research itself. As the role of scales is expanded to include the field of forecasting, nevertheless, the issue of dependability arises. “Cronbach’s alpha (α)” is one of the most often used dependability measures nowadays. To assess the reliability of a questionnaire survey, “Cronbach’s alpha (α)” calculates the average correlation or internal consistency of its elements. “Cronbach’s alpha (α)” is a gauge of a scale's or test's reliability, more precisely its internal consistency dependability or item interconnectivity (e.g., questionnaire). “Internal consistency” indicates how well each item on a scale or test contributes to assessing a certain construct. Internal consistency is pertinent to scores obtained as a result (i.e., the sum of all items of the scale or test). It is indeed crucial to remember that dependability only applies to facts, not scales or test measures. Typically, Cronbach’s alpha (α) varies between 0 and 1. Scores that are nearer to 1.0 suggest a higher degree of “internal consistency” among the scale's components. In other words, more scale dependability is indicated by higher Cronbach’s alpha (α) values. A number of 1.0 means that there is no measurement error and that all of the variation in test results is attributable to actual score differences (i.e., reliable variance). A value of 0.0, on the other hand, denotes the absence of a genuine score (i.e., a consistent variation) and the presence of just imprecision in the items. In other words, a Cronbach’s alpha (α) of 1.0 denotes complete measurement consistency, whereas a value of 0.0 denotes complete measurement inconsistency. Typically, Cronbach’s alpha (α) scores between 0.60 and 0.80 are regarded as moderate yet reasonable. Cronbach’s alpha (α) is considered to be in the very excellent range when it is between 0.8 and 1.00. Results of the alpha for employee commitment (EC), emotional intelligence (EI), and employee performance (EP) are shown in Table 1. Employee performance (EP), which consists of 5 elements, has a Cronbach's alpha of 0.833. Emotional intelligence (EI) covers “self-emotions”, regulation of emotions”, use of emotions”, and “other emotions”. Emotional intelligence (EI) has a Cronbach's alpha of 0.819 and 16 items. Organization commitment comprises of “affective commitment”, “continuance commitment” and “normative commitment”. Employee commitment (EC) has a Cronbach's alpha of 0.934 and 24 elements. All three of the study's variables have an alpha value more than 0.8, indicating that they are trustworthy, reliable, and consistent in their results. Table 2. Frequency table for age. Frequency Percent 18 to 25 131 45.3 26 to 35 80 27.7 36 to 45 41 14.2 46 to 55 27 9.3 56 to 70 10 3.5 Total 289 100.0 Table 2 provides a visual representation of the percentage and frequency of five distinct age categories, including 18 to 25, 26 to 35, 36 to 45, 46 to 55, and 56 to 70. Table 1's results indicate that 289 employees from various organizations participated in the study; of these, 131 are between the ages of 18 and 25; this group represents 45.3% of the total employee population; the remaining 80 are between the ages of 26 and 35; this group represents 27.7% of the total employee population, 14 employees who took part in the survey are between the ages of 36 and 45, making up 14.2% of the total, while 27 employees are between the ages of 46 and 55, making up 9.3% of the total. Ten employees who took part in the study are between the ages of 56 and 70, making up 3.5% of the total.Table 1 findings indicate that most of the study's participants are young people, ranging in age from 18 to 25. Asian Business Research Journal, 2025, 10(8): 59-72 64 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA Figure 1. Pie chart of Age. The first phase in any data analysis is to define each variable individually. This is also referred as univariate analysis at times. Using charts and graphs one may show how the distribution of answers to a problem looks graphically. Using a limited set of categories, a pie chart displays the frequencies or percentages of a variable. It is shown as a circle with several segments cut out of it. The number of situations or the percentage of incidents in each group is directly proportional to the area of each segment. Ordinarily, either nominal or ordinal variables are used with it. In Figure 1, pie chart of age has been visualized, indicating that most of the study's participants are young people, ranging in age from 18 to 25. Table 3. Frequency table of Sex. Frequency Percent Male 181 62.6 Female 108 37.4 Total 289 100.0 Looking at table 3, we can deduce that, out of the 289 participants—employees from various organizations— 181 of them—or the study's participants—are men, and 108 are women. Employee gender ratios are 62.6 percent for men and 37.4 percent for women. Since there are more male workers than female employees, we may assume that most of the employees employed by various student groups are male. Figure 2. Pie Chart of Sex. Asian Business Research Journal, 2025, 10(8): 59-72 65 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA The distribution of sex is display through pie chart in Figure 2, showing that men make up the majority of the study's participants who work for the various organizations. Whereas there are fewer women working. Table 4. Descriptive Measures. Variable EI EC EP Range 1.63 2.17 1.20 Minimum 3.17 2.83 3.40 Maximum 4.79 5 4.60 Mean 4.24 4.7 3.99 Standard error of mean 0.21 0.01 0.26 Standard deviation 0.37 0.27 0.44 Variance 0.13 0.07 0.19 It would be difficult to understand what the data was saying if we just showed our raw data, especially if there was a lot of it, thus descriptive statistics are crucial. As a result, descriptive statistics help us display the data in a more relevant fashion, making it easier to analyse the data. Table 4 contains information about descriptive statistics, it comprises of “range”, “minimum”, “maximum”, “mean”, “standard error of mean”, “standard deviation” and variance of the emotional intelligence (IE), employee commitment (EC), and employee performance (EP). It demonstrates that the range of employees' emotional intelligence (EI) is 1.62, the mean emotional intelligence (EI) of employees is 4.24 with 0.21 standard error of mean, the variance and standard deviation for emotional intelligence (IE) are 0.13 and 0.37, respectively, and the minimum emotional intelligence (EI) for employees is 3.17 while the maximum is 4.79. Table 4 elaborates that the range of employees' employee commitment (EC) is 2.17, the mean employee commitment (EC) of employees is 4.7 with 0.01 standard error of mean, the variance and standard deviation for employee commitment (EC) are 0.07 and 0.27, respectively, and the minimum employee commitment (EC) for employees is 2.83 while the maximum is 5. According to table 4, that the range of employees' employee performance (EP) is 1.20, the mean employee performance (EP) of employees is 3.99 with 0.26 standard error of mean, the variance and standard deviation for employee performance (EP) are 0.19 and 0.44, respectively, and the minimum employee performance (EP) for employees is 3.40 while the maximum is 4.60. Table 5. Sex and Age’s Crosstab. SEX Total Male Female AGE 18-25 83 48 131 26-35 52 28 80 36-45 24 17 41 46-55 14 13 27 56-70 8 2 10 Total 181 108 289 One of the most helpful analytical techniques and a cornerstone of the data analysis sector is “cross-tabulation”. Categorical data on nominal scale items are most frequently analyzed using “cross-tabulation analysis”, also known as “contingency table analysis”. “Cross-tabulations” are essentially just data tables that display the findings from the entire group of people surveyed as well as findings from various subsets of participants. They enable us to investigate data linkages that may not be immediately clear when we merely examine all of the survey replies. Among 289 employees working in different organizations that participated in the study, age of 83 male employees is found to be 18 to 25 years whereas age of 48 female employees is 18 to 25 years. 80 employees had age between 26 to 35 years, among which 52 were male and 12 were female, Age of 41 employees is between 36 and 45 years having 24 male employees and 17 female employees, there were 27 employees that belong to age group 46 to 55 years, and number of males belonging to this age group is 14 whereas as number of female employees is 13. There were 8 male and 2 female employees that belong to age group 56 to 70 years. We may interpret the findings as: majority of male employees are young as compared to female employees that belonged to age group 18 to 25 years. Table 6. Chi-Square. Value df Sig. Pearson. Chi-Square 3.144 4 .534 Likelihood. Ratio 3.230 4 .520 Linear-by-.Linear Association .121 1 .728 N, of Valid Cases 289 Without revealing the degree or direction of the link between the variables, Chi-Square examines the independent row and column hypotheses. According to chi-square’s null hypothesis there is relationship amongst age and sex, whilst alternative states that there is no relationship amongst age and sex, the significance value of Pearson chi-square is 0.534, which is greater than alpha (α=0.05), thus we are unable to reject the null hypothesis of relationship amongst age and sex, thus we infer that there exists a relationship amongst these two i.e. age and sex. The value of likelihood ratio is 3.20, and linear by linear association between these two i.e. age and sex is 0.121 having 1 degree of freedom. Asian Business Research Journal, 2025, 10(8): 59-72 66 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA Figure 3. Histogram of Employee Performance (EP). In Figure 3, there is histogram of employee performance, we plotted histogram to check normality of predictor variable that is employee performance (EP), also there is a curve on it, and the plot clearly demonstrates the presence of normality in predictor having 3.99 mean and 0.4 standard deviation. Figure 4. Normal p-p plot of employee performance (EP). Table 7. Correlations. EI EC EP EI Pearson Correlation 1 .085 .941** Sig. (2-tailed) .151 .000 Sum of Squares and Cross-products 39.846 2.470 44.947 Covariance .138 .009 .156 N 289 289 289 EC Pearson Correlation .085 1 .802** Sig. (2-tailed) .151 .004 Sum of Squares and Cross-products 2.470 21.383 33.559 Covariance .009 .074 .102 N 289 289 289 EP Pearson Correlation .941** .802** 1 Sig. (2-tailed) .000 .004 Sum of Squares and Cross-products 44.947 33.559 57.229 Covariance .156 .102 .199 N 289 289 289 Asian Business Research Journal, 2025, 10(8): 59-72 67 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA To get more insight into the normality of predicted variable i.e. employee performance (EP) we used normal p- p plot as shown in Figure 4, it shows that employee performance (EP) has a normal distribution, thus the regression assumption is full filled now, and we can further proceed for linear regression analysis to evaluate the effect of emotional intelligence and organizational commitment to the employee performance (EP). When using a correlational study design, no variables are within the researcher's direct control or manipulation. The degree and/or direction of the association amongst two (or maybe more) variables is reflected in a correlation. A correlation may go in either a positively or negatively direction. Only when there is a linear relationship amongst the variables may Pearson's correlation be applied. As long as there is a relationship, it can be either favorable or unfavorable. In investigations conducted inside groups, correlation is employed for assessment. The existence of a perfect positive connection between the variables in this predictive model might be a potential study hypothesis. A perfect negative connection is yet another potential study concept. If there isn't a linear connection between the variables, the null hypothesis would still apply. A measure from + 1 to -1 is used to calculate the correlation coefficient. Either + 1 or -1 represents a variable's perfect or we can say complete connection with another. The correlation is positive whenever one variable rises in the same manner as the other rises; it is negative when one variable falls in the same manner as the other rises. Significance value of correlation amongst Emotional intelligence (EI) and employee commitment (EC) is 0.15, which is greater than alpha 0.05, due to which we infer that there is none kind of relationship amongst these two variables i.e. Emotional intelligence (EI) and employee commitment (EC), the amount of covariance between Emotional intelligence (EI) and employee commitment (EC) is 0.009, which is almost equal to zero and sum of squares (SS) and Cross products (SS) between these two is 2.4. Significance value of correlation amongst Emotional intelligence (EI) and employee performance is 0.00, which is lesser than alpha 0.05, due to which are able to reject the null hypothesis stating no relationship between Emotional intelligence (EI) and employee performance and we infer that there is relationship amongst Emotional intelligence (EI) and employee performance. The amount of correlation is 0.94, and sign is also positive, which means there is strong positive relationship, if there will be increase in emotional intelligence (IE), there will be similar increase in employee performance (EP). The amount of covariance between Emotional intelligence (EI) and employee performance (OC) is 0.156, and sum of squares (SS) and Cross products (SS) between these two is 44.94. Significance value of correlation amongst employee commitment (EC) and employee performance is 0.00, which is lesser than alpha 0.05, due to which are able to reject the null hypothesis stating no relationship between employee commitment (EC) and employee performance and we infer that there is relationship amongst employee commitment (EC) and employee performance. The amount of correlation is 0.802, and sign is also positive, which means there is strong positive relationship, if there will be increase in employee commitment (EC) there will be similar increase in employee performance (EP). The amount of covariance between Emotional intelligence (EI) and employee commitment (EC) is 0.106, and sum of squares (SS) and Cross products (SS) between these two is 33.55. Figure 5. Scatter plot between EI and EP. A scatter plot shows a plausible connection between two separate sets of data' observed changes. It gives an analytical and visual way to gauge how strongly two variables are related. In Figure 5, a scatter plot between emotional intelligence (EI) and employee performance (EP) is displayed, employee performance (EP) is on horizontal axis, whereas emotional intelligence (EI) is on vertical axis, the plot shows that there is strong positive relationship, if there will be increase in emotional intelligence (IE) there will be similar increase in employee performance (EP). Table 8. Regression Summary. R R. Square Adjusted R. Square Std. Error. of the Estimate 0.941 0.886 0.886 0.15076 Maybe the most popular statistical method for determining or estimating the connection between a dependent variable and a group of independent regressors is regression analysis. In a qualitative research approach, it is also Asian Business Research Journal, 2025, 10(8): 59-72 68 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA used as a catch-all phrase for a number of data analysis methodologies which are used for modelling and evaluating many different variables. The consequence or the response to a particular question is the predicted or dependent variable in the regression technique, where the independent variable is a predictor variable or sometimes also referred as an explanatory component. Data modelling and analysis frequently employ regression analysis. The majority of survey analysts use it to comprehend how the variables are related, which can then be used to anticipate the precise conclusion. This method is frequently used by survey researchers to look at and determine a connection between various variables of interest. It offers the chance to evaluate the impact of several predictor factors on a predicted variable. “Regression analysis” is a method that spares survey researchers extra work by eliminating the need to arrange several predictor variables in tables and test or calculate each one's impact on a dependent variable. Numerous analytical techniques are frequently employed to assess novel business concepts and arrive at defensible conclusions. One of the most well-known modelling approaches is “linear regression analysis” since it was one of the first advanced regression analysis techniques that individuals learned while learning predictive modelling. Here, the predictor variable is frequently continuous or discrete with a linear regression line, while the predicted variable is continuous. We employed simple linear regression model to investigate how emotional intelligence (EI) and employee commitment (EC) influence or mediate employees performance, here employees performance is the predicted variable, to whom we are going to predict, and emotional intelligence (EI) and employee commitment (EC) serves as predictor or explanatory variables. The model summary of our regression model has been given in table 8, the value of R-squared is 0.88, that elaborates that emotional intelligence (EI) and employee commitment (EC) are explaining 88% variation present in employee performance (EP), which is a good amount, thus we may infer that model is fitted good. The value of Standard error of estimate i.e. for R square is 1.50. Table 9. ANOVA. Sum. of Squares Df Mean. Square F Sig. Regression 50.728 2 25.364 1115.974 0.000 Residual 6.500 286 0.023 Total 57.229 288 Table 9 contains ANOVA findings, which serves as a framework for significance tests and reveals the amounts of variability present in a regression model, it also reveals information about overall fit of the model. Table 9 reveal that the residual's mean square is 6.5 with 286 degrees of freedom and the model's mean square regression is 50.7 with 2 degrees of freedom. The model's overall significance is 0.00, suggesting that it is significant, with F value of 1115.974. Table 10. Regression Coefficients. Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) -.962 0.179 -5.380 0.000 EI 1.126 0.024 0.939 46.967 0.000 EC 0.136 0.033 0.022 1.113 0.006 In Table 10, there are findings of regression coefficients. Regression analysis employs coefficients and significance values to determine if and how strongly the model's relationships are statistically meaningful. The linear coefficient estimates provide an explanation of the statistically significant relationship between each predictor variable i.e. emotional intelligence (EI) and employee commitment (EC) and the predicted variable i.e. employee performance (EP). The statistical significance of these connections is shown by the coefficients' p values. Table 10 reveals that coefficient value of constant is -0.962, and null hypothesis states “constant plays no role in predicting employee performance (EP)”, it is playing significant role since significance value is 0.00, which is less than α=0.05 due to which we rejected the null hypothesis, meaning that average employee performance (EP) will be -0.96, when all other regressors will be zero, the coefficient value of emotional intelligence (EI) is 1.12 with a standard error of 0.02, we infer that IE is playing a significant role in predicting employee performance, as its significance value is less than 0.05 so we reject null hypothesis “Emotional intelligence (IE) plays no role in predicting employee performance (EP)”, and as sign of EI coefficient is positive it means there is positive relationship between EI and EP. By increase in emotional intelligence (EI) of employees, employee performance (EP) also increases in same manner. We may interpret it as unit increase in EI may cause 1.12 unit increase in employee performance (EP) of employees working in different organizations. The coefficient value of employee commitment (EC) is 0.136 with a standard error of 0.033, we infer that employee commitment (EC) is playing a significant role in predicting employee performance, as its significance value is less than 0.05 so we reject null hypothesis “employee commitment (EC) plays no role in predicting employee performance (EP)”, and as sign of employee commitment (EC) coefficient is positive it means there is positive relationship between OC and EP. By increase in employee commitment (EC) of employees, employee performance (EP) also increases in same manner. We may interpret it as unit increase in employee commitment (EC) may cause 0.13 unit increase in employee performance (EP) of employees working in different organizations. Table 11. Collinearity Diagnostics. Dimension Eigenvalue Condition Index Variance Proportions (Constant) EI EC 1 2.993 1.000 0.00 0.00 0.00 2 0.005 23.435 0.03 0.89 0.17 3 0.002 44.592 0.97 0.11 0.83 Asian Business Research Journal, 2025, 10(8): 59-72 69 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA The regression collinearity diagnostics for employee performance has been given in table 10, the eigen value for dimension 1 is 2.99, while for dimension 2 and 3 it is 0.005 and 0.002 respectively. The condition index for dimension 1 is 1, while for dimension 2 and 3 it is 24.43 and 44.59 respectively. The variance proportion of constant for dimension 1, 2 and 3 is 0.00, 0.03 and 0.97, the variance proportion of employee intelligence for dimension 1, 2 and 3 is 0.00, 0.89 and 0.11, whereas the variance proportion of organizational commitment for dimension 1, 2 and 3 is 0.00, 0.17 and 0.83. Figure 6. Histogram of “Regression Standardized Residual”. To determine if the variance is regularly distributed, utilise the histogram of the residual. The normality assumption is likely to be valid if the bell-shaped histogram is symmetric and uniformly distributed about zero. In Figure 6, there is histogram for standardized residuals of regression having dependent variable employee performance (EP), the graph is showing that residuals are following normal distribution , there means is zero, and standard deviation is 1 for 289 sample size. Figure 7. Normal p-p plot of “Regression Standardized Residual”. In Figure 7, there is normal p-p plot for standardized residuals of regression having dependent variable employee performance (EP), on horizontal side there is observed cum probability while on vertical side there is expected cum probability, the graph is showing that residuals are following normal distribution. Asian Business Research Journal, 2025, 10(8): 59-72 70 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA 5. Conclusions and Outlook 5.1. Research Conclusions Table 1 This study uses emotional intelligence (EI) as the independent variable, employee commitment as the mediating variable, and employee performance as the dependent variable to explore how emotional intelligence indirectly affects employee performance through employee commitment and to verify the interplay between the three. By analyzing data from 289 valid questionnaires and combining statistical methods such as reliability testing, correlation analysis, regression analysis, and analysis of variance, the following key conclusions were drawn: First, the study confirms a significant positive relationship between emotional intelligence and employee performance. Regression analysis results show that the coefficient of influence of emotional intelligence on employee performance is as high as 1.126, with a significance level well below 0.05. This suggests that employees with higher emotional intelligence tend to perform better at work, are more effective in regulating emotions, and adapt to the environment, leading to improved performance. Second, employee commitment has a positive impact on employee performance. Research shows that increased employee commitment (especially affective commitment) helps strengthen employees' sense of responsibility and belonging, thereby stimulating greater work motivation and ultimately resulting in improved performance. The regression coefficient for employee commitment is 0.136, also reaching a significant level. More importantly, the study found that employee commitment partially mediates the relationship between emotional intelligence and employee performance. Although the direct correlation between emotional intelligence and employee commitment did not reach significance, the inclusion of employee commitment as a mediating variable significantly enhanced the explanatory power of emotional intelligence on performance, with the model's R² value reaching 0.886, indicating that 88.6% of the variance in employee performance can be jointly explained by emotional intelligence and employee commitment. In summary, this study confirms that emotional intelligence not only directly improves employee performance but also further enhances performance by strengthening employees' organizational commitment. This finding has important practical implications for corporate managers in recruitment, training, and performance management. 5.2. Practical Implications 1. Incorporate emotional intelligence assessments into talent selection: Companies should use emotional intelligence assessment tools to identify high-EQ individuals during the recruitment process, thereby improving the overall quality of their employees. 2. Focus employee training on emotional management and communication skills: Through methods such as emotional intelligence training and situational simulations, employees' empathy, self-regulation, and social skills can be improved. 3. Strengthening the Cultivation Mechanism for Organizational Commitment: By establishing a rational incentive system, cultural identity system, and career development pathways, employees' sense of identification and belonging to the organization can be enhanced, thereby strengthening their commitment. 5.3. Research Limitations and Future Prospects Although this study is rigorous in its model construction and data analysis, it still has the following limitations: Geographical limitations of the sample: This study's sample primarily comes from Malaysia, and the generalizability of the conclusions requires further verification with cross-cultural and cross-industry samples; Limited variable selection: The study focuses on the relationship between emotional intelligence, employee commitment, and performance, excluding other factors that may influence performance, such as leadership style, organizational support, and psychological capital; Simple research methodology: The study primarily uses quantitative questionnaire analysis, lacking in-depth qualitative exploration of behavioral mechanisms. 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