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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


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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). 
 



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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 



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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 



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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. 
 



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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. 



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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. 
 



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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 

 



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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 



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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 

 



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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. 
 
 



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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. 

Future research could explore expanding the model by introducing moderating variables (such as work stress 
and organizational culture), or conducting longitudinal studies to examine the dynamic process of changes in 
employee emotional intelligence and performance, to gain more comprehensive and in-depth insights. 
 

References 
Aban, C. J. I., Perez, V. E. B., Ricarte, K. K. G., & Chiu, J. L. (2019). The relationship of organizational commitment, job satisfaction, and 

perceived organizational support of telecommuters in the national capital region. Review of Integrative Business and Economics 
Research, 8(1), 162–197. 

Abdirahman, H. I. H. (2018). The relationship between job satisfaction, work-life balance and organizational commitment on employee 
performance [Master’s thesis, University of Malaysia]. University Repository. 

Ahad, R., Mustafa, M. Z., Mohamad, S., Abdullah, N. H. S., & Nordin, M. N. (2021). Work attitude, organizational commitment and 
emotional intelligence of Malaysian vocational college teachers. Journal of Technical Education and Training, 13(1), 15–21. 
https://doi.org/10.30880/jtet.2021.13.01.002 

Ahmad, A., Ibrahim, R. Z. A. R., & Bakar, A. (2018). Factors influencing job performance among police personnel: An empirical study in 
Selangor. Management Science Letters, 8(9), 939–950. https://doi.org/10.5267/j.msl.2018.6.004 

Al Mamun, A., Ibrahim, M. D., Yusoff, M. N. H. B., & Fazal, S. A. (2018). Entrepreneurial leadership, performance, and sustainability of 
micro-enterprises in Malaysia. Sustainability, 10(5), 1591. https://doi.org/10.3390/su10051591 

Alzoubi, H. M., & Aziz, R. (2021). Does emotional intelligence contribute to quality of strategic decisions? The mediating role of open 
innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 130. https://doi.org/10.3390/joitmc7020130 

Amjad, S. (2018). Emotional intelligence, organizational commitment and job performance in Pakistan. Market Forces, 13(1), 28–42. 
Aziz, F., Md Rami, A. A., Zaremohzzabieh, Z., & Ahrari, S. (2021). Effects of emotions and ethics on pro-environmental behavior of 

university employees: A model based on the theory of planned behavior. Sustainability, 13(13), 7062. 
https://doi.org/10.3390/su13137062 

Basheer, M. F., Hameed, W. U., Rashid, A., & Nadim, M. (2019). Factors effecting employee loyalty through mediating role of employee 
engagement: Evidence from PROTON automotive industry, Malaysia. Journal of Managerial Sciences, 13(2), 1–14. 

Boyatzis, R. E. (2018). The behavioral level of emotional intelligence and its measurement. Frontiers in Psychology, 9, 1438. 
https://doi.org/10.3389/fpsyg.2018.01438 

Chong, S. C., Falahat, M., & Lee, Y. S. (2020). Emotional intelligence and job performance of academicians in Malaysia. International Journal 
of Higher Education, 9(1), 69–80. https://doi.org/10.5430/ijhe.v9n1p69 



Asian Business Research Journal, 2025, 10(8): 59-72 

71 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

Drigas, A., & Papoutsi, C. (2019). Emotional intelligence as an important asset for HR in organizations: Leaders and employees. International 
Journal of Advanced Corporate Learning, 12(1), 58–66. https://doi.org/10.3991/ijac.v12i1.9637 

Gong, Z., Chen, Y., & Wang, Y. (2019). The influence of emotional intelligence on job burnout and job performance: Mediating effect of 
psychological capital. Frontiers in Psychology, 10, 2707. https://doi.org/10.3389/fpsyg.2019.02707 

Gunu, U., & Oladepo, R. O. (2014). Impact of emotional intelligence on employees’ performance and organizational commitment: A case 
study of Dangote Flour Mills workers. University of Mauritius Research Journal, 20, 1–32. 

Hamzah, S. R. A., Kai Le, K., & Musa, S. N. S. (2021). The mediating role of career decision self-efficacy on the relationship of career 
emotional intelligence and self-esteem with career adaptability among university students. International Journal of Adolescence and 
Youth, 26(1), 83–93. https://doi.org/10.1080/02673843.2021.1886952 

Haryono, S., Rosady, F., & MdSaad, M. S. (2018). Effects of emotional and spiritual intelligence on job performance among temporary nurses 
at Abdul Riva’i Regional General Hospital, Berau District, East Kalimantan Province, Indonesia. Management Issues in Healthcare 
System, 4(1), 42–54. 

Issah, M. (2018). Change leadership: The role of emotional intelligence. Sage Open, 8(3), 1–6. https://doi.org/10.1177/2158244018800910 
Istianingsih, N., Masnun, A., & Pratiwi, W. (2020). Managerial performance models through decision making and emotional intelligence in 

public sector. Administratie si Management Public, 35, 153–166. https://doi.org/10.24818/amp/2020.35-09 
Jameel, A. S., & Ahmad, A. R. (2019). Leadership and performance of academic staff in developing countries. In Proceedings of the 33rd 

International Business Information Management Association Conference (pp. 6101–6106). IBIMA. 
Jena, L. K. (2021). Does workplace spirituality lead to raising employee performance? The role of citizenship behavior and emotional 

intelligence. International Journal of Organizational Analysis, 29(2), 407–422. https://doi.org/10.1108/IJOA-02-2020-2026 
Khalid, J., Khaleel, M., Ali, A. J., & Islam, M. S. (2018). Multiple dimensions of emotional intelligence and their impacts on organizational 

commitment and job performance. International Journal of Ethics and Systems, 34(3), 284–302. https://doi.org/10.1108/IJOES-03-
2018-0042 

Krisnanda, P. H., & Surya, I. B. K. (2019). Effect of emotional and spiritual intelligence on transformational leadership and impact on 
employee performance. International Research Journal of Management, IT and Social Sciences, 6(3), 70–82. 
https://doi.org/10.21744/irjmis.v6n3.642 

Kumar, J. A., Muniandy, B., & Wan Yahaya, W. A. J. (2019). Exploring the effects of emotional design and emotional intelligence in 
multimedia-based learning: An engineering educational perspective. New Review of Hypermedia and Multimedia, 25(1–2), 57–86. 
https://doi.org/10.1080/13614568.2018.1513251 

Lee, S. B., Liu, S. H., & Maertz, C. (2022). The relative impact of employees’ discrete emotions on employees’ negative word-of-mouth 
(NWOM) and counterproductive workplace behavior (CWB). Journal of Product & Brand Management, 31(6), 912–924. 
https://doi.org/10.1108/JPBM-08-2020-3020 

Loan, L. (2020). The influence of organizational commitment on employees’ job performance: The mediating role of job satisfaction. 
Management Science Letters, 10(14), 3307–3312. https://doi.org/10.5267/j.msl.2020.6.007 

Ma, Z., Li, Y., & Wang, L. (2021). The influence of job autonomy on employee innovation performance: Mediating informal learning on the 
spot and moderating work values. Journal of Lanzhou University of Finance and Economics, 4, 1–9. 

Maishen, & Jihe. (2019). The interactive effects of organizational empowerment and forward-looking personality on employees’ creative 
performance. Science and Technology Progress and Countermeasures, 4, 140–145. 

Mattingly, V., & Kraiger, K. (2019). Can emotional intelligence be trained? A meta-analytical investigation. Human Resource Management 
Review, 29(2), 140–155. https://doi.org/10.1016/j.hrmr.2018.03.002 

Mukhtar, N. A., & Fook, C. Y. (2020). The effects of perceived leadership styles and emotional intelligence on attitude toward organizational 
change among secondary school teachers. Asian Journal of University Education, 16(2), 36–45. 
https://doi.org/10.24191/ajue.v16i2.9005 

Nanda, M., & Randhawa, G. (2019). Emotional intelligence, well-being, and employee behavior: A proposed model. Journal of Management 
Research, 19(3), 150–158. 

Ngui, G. K., & Lay, Y. F. (2020). The effect of emotional intelligence, self-efficacy, subjective well-being and resilience on student teachers' 
perceived practicum stress: A Malaysian case study. European Journal of Educational Research, 9(1), 277–291. 
https://doi.org/10.12973/eu-jer.9.1.277 

Othman, N., & Muda, T. N. A. A. T. (2018). Emotional intelligence towards entrepreneurial career choice behaviours. Education + Training, 
60(9), 953–970. https://doi.org/10.1108/ET-10-2017-0142 

Papoutsi, C., Drigas, A., & Skianis, C. (2019). Emotional intelligence as an important asset for HR in organizations: Attitudes and working 
variables. International Journal of Advanced Corporate Learning, 12(2), 21–27. https://doi.org/10.3991/ijac.v12i2.9629 

Paruchuri, H., & Asadullah, A. B. M. (2018). The effect of emotional intelligence on the diversity climate and innovation capabilities. Asia 
Pacific Journal of Energy and Environment, 5(2), 91–96. https://doi.org/10.18034/apjee.v5i2.512 

Rangarajan, R., & Jayamala, C. (2014). Impact of emotional intelligence on employee performance: An epigrammatic survey. Sumedha Journal 
of Management, 3(1), 76–85. 

Rasiah, R., Turner, J. J., & Ho, Y. F. (2019). The impact of emotional intelligence on work performance: Perceptions and reflections from 
academics in Malaysian higher education. Contemporary Economics, 13(3), 269–283. https://doi.org/10.5709/ce.1897-9254.313 

Sabie, O. M., Bricariu, R. M., Pîrvu, C., & Gatan, M. L. (2020). The relationship between emotional intelligence and human resources 
employee performance: A case study for Romanian companies. Management Research and Practice, 12(3), 36–46. 

Sanchez-Gomez, M., & Breso, E. (2020). In pursuit of work performance: Testing the contribution of emotional intelligence and burnout. 
International Journal of Environmental Research and Public Health, 17(15), 5373. https://doi.org/10.3390/ijerph17155373 

Sembiring, N., Nimran, U., Astuti, E. S., & Utami, H. N. (2020). The effects of emotional intelligence and organizational justice on job 
satisfaction, caring climate, and criminal investigation officers’ performance. International Journal of Organizational Analysis, 29(4), 
991–1007. https://doi.org/10.1108/IJOA-10-2019-1919 

Stoyanova-Bozhkova, S., Paskova, T., & Buhalis, D. (2022). Emotional intelligence: A competitive advantage for tourism and hospitality 
managers. Tourism Recreation Research, 47(4), 359–371. https://doi.org/10.1080/02508281.2021.1897967 

Supriyanto, A. S., Ekowati, V. M., & Masyhuri, M. (2019). The relationship among spiritual intelligence, emotional intelligence, 
organizational citizenship behaviour, and employee performance. Etikonomi, 18(2), 249–258. 
https://doi.org/10.15408/etk.v18i2.7420 

Tai, M. K., & Kareem, O. A. (2018). The relationship between emotional intelligence of school principals in managing change and teacher 
attitudes towards change. International Journal of Leadership in Education, 22(4), 469–481. 
https://doi.org/10.1080/13603124.2018.1450990 

Tuah, K. T. (2018). The relationship between emotional intelligence and job performance among employees at Telekom Malaysia Berhad, 
Kuching, Sarawak [Master’s thesis, Universiti Malaysia Sarawak]. UNIMAS Institutional Repository. 

Wang, H., Wang, L., & Chen, X. (2021). The impact of inclusive mentor style on graduate students’ innovative behavior: The mediating role 
of innovation self-efficacy and the moderating role of deep learning. Contemporary Education Forum, 2, 66–74. 
https://doi.org/10.13694/j.cnki.ddjylt.20210127.001 

Wang, L., & Si, J. (2020). Does high EQ guarantee high performance? The influence mechanism of bank employees’ emotional intelligence on 
job performance. Journal of Anqing Normal University (Social Science Edition), 2, 99–105. 

Xu, H., Guo, P., & Bao, L. (2021). The impact of organizational innovation support on researchers’ innovation behavior: A chain mediation 
effect based on innovation self-efficacy and knowledge sharing. Science and Technology Management Research, 8, 124–131. 

Yang, C. (2019). An experimental study on the relationship between multi-level relationship networks and employee performance: The 
moderating role of employee emotional intelligence and personality traits. Business Economics, 3, 64–71. 

Yang, R., Díaz, V. G., & Hsu, C. H. (2021). Use of emotional intelligence to promote innovation among employees in the work environment 
through qualitative and quantitative analysis. Aggression and Violent Behavior, 58, 101589. 
https://doi.org/10.1016/j.avb.2021.101589 



Asian Business Research Journal, 2025, 10(8): 59-72 

72 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

Yusof, M. M., Ho, J. A., Imm, S. N. S., & Zawawi, D. (2019). Weeding out deviant workplace behaviour in downsized organizations: The role 
of emotional intelligence and job embeddedness. Asian Journal of Business Research, 9(3), 115–144. 
https://doi.org/10.14707/ajbr.190065 

Zhang, H. (2021). Team emotional intelligence from the perspective of social network. Advances in Psychological Science, 29(8), 1381–1395. 
https://doi.org/10.3724/SP.J.1042.2021.01381 

 
 

 


