Eduvest ï¿½ Journal of Universal Studies Volume 4 Number 12, December, 2024 p- ISSN 2775-3735- e-ISSN 2775-3727

The Role of Cultural Intelligence and Knowledge Sharing on Performance: Job Satisfaction as A Mediator
*Lutfi ibn Yusuf^1, Rostiana², E Made Budiana³** Psychology Study Program, Tarumanagara University Jakarta, Indonesia^1,2,3 Email: [email protected]¹, [email protected]², [email protected]³

ABSTRACT
This study explores the impact of cultural intelligence and knowledge sharing on performance, with job satisfaction as a mediator. Performance refers to the ability to achieve tasks or goals, influenced by cultural intelligence and a workplace culture of knowledge sharing. Cultural intelligence involves adapting to diverse cultural environments and interacting effectively across backgrounds, while knowledge sharing includes exchanging knowledge, experience, and expertise among employees. Both factors can enhance job satisfaction a positive emotional state arising from job achievements and experiences. Satisfied employees are more likely to maximize productivity and performance. Using a quantitative approach, this research employs four questionnaires: cultural intelligence, knowledge sharing, performance, and job satisfaction, measured on a 1-5 Likert scale. From 606 employees, 116 at PT X, a steam power plant in Cirebon, West Java, were eligible. Results show job satisfaction mediates the link between cultural intelligence and performance but not between knowledge sharing and performance. These findings provide insights for optimizing employee management strategies based on cultural intelligence, knowledge sharing, and demographic factors to enhance satisfaction and performance in multicultural workplaces.
KEYWORDS	Cross-cultural, Cultural intelligence, Knowledge sharing, job satisfaction and performance
	*This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International*

INTRODUCTION

In the era of globalization, cultural diversity in the workplace is increasingly a concern because of its impact on employee performance and job satisfaction. Economic globalization and social movements create complex multinational organizations, where cultural diversity can be a source of creativity, innovation, and productivity. However, this diversity also presents challenges in the form of cultural conflicts, communication difficulties, and differences in work values that can hinder collaboration and reduce productivity (Stahl et al., 2010).

Cultural diversity offers a variety of benefits, such as perspective enrichment in decision-making, increased motivation, and better cross-cultural collaboration (Patrick & Kumar, 2012). However, challenges such as a lack of cross-cultural understanding often affect the multinational work environment, as seen in PT X, a multinational company that experienced a decline in performance. The company faces problems in managing cultural diversity, which has an impact on employee productivity, efficiency, and job satisfaction.

The decline in performance at PT X can be seen from empirical data, including a downward trend in productivity of 15% in the last two quarters and a decrease in total revenue of up to 11.24% from 2022 to 2023. Internal factors, such as cross-cultural management instability and lack of training, are the main causes of this decline. Previous research has shown that difficulties in cross-cultural communication can create conflict and lower job satisfaction, ultimately affecting individual and organizational performance as a whole (Schaaf et al., 2017).

Cultural intelligence, which includes the ability to understand and adapt to different cultural environments, has been shown to have a positive relationship with employee performance and job satisfaction (Ang & Van Dyne, 2015). Individuals with high cultural intelligence tend to be able to overcome cross-cultural barriers and increase the effectiveness of collaboration. In addition, the culture of knowledge sharing within the organization also plays an important role in driving innovation, improving work efficiency, and strengthening team collaboration. However, previous research has shown that a lack of openness in knowledge sharing can hinder the performance of teams and organizations as a whole (Pinto & Pinto, 1990).

The urgency of this research lies in the need to understand the role of job satisfaction as a mediator in the relationship between cultural intelligence and employee performance, as well as knowledge sharing and performance. Although previous studies have shown a positive relationship between these variables, there are limitations in methodology and geographic context that can affect the generalization of results. In the context of PT X, this study aims to further explore the relationship in order to provide solutions that can be applied practically.

This study aims to 1) identify the relationship between cultural intelligence and employee performance mediated by job satisfaction. 2) Analyze the role of knowledge sharing in improving employee performance through job satisfaction as a mediator.

The results of this study are expected to provide practical benefits for managers in managing cultural diversity and creating an inclusive work environment. In addition, this research contributes to the academic literature by providing insights into the importance of cultural intelligence and knowledge sharing culture in improving employee performance in multinational organizations.

The novelty of ï¿½this research is to focus on the role of job satisfaction as a mediator in the relationship between cultural intelligence and knowledge sharing with performance, especially in the context of multinational organizations such as PT X. By highlighting this complex relationship, this research is expected to provide strategic guidance for human resource development in a multicultural work environment.

The study also identified other factors, such as organizational culture and management support, that could affect employee performance. A culture of knowledge sharing, which encourages information exchange and collaboration, is considered one of the solutions to overcome cross-cultural barriers. Previous studies have shown that knowledge sharing has a positive relationship with innovation and organizational performance (Park & Kim, 2015).

As such, this research not only aims to understand the relationship between cultural intelligence, knowledge sharing, and employee performance, but also to provide strategic recommendations that can assist multinational organizations in effectively managing cultural diversity. The results of the research are expected to make a significant contribution to improving the performance and welfare of employees in multinational organizations.

H1: There is a role of cultural intelligence in performance with job satisfaction mediators

H2: There is a role to share knowledge on performance with job satisfaction mediators

RESEARCH METHODS

This research was conducted with a quantitative design. The sample was taken from 116 employees of PT X located in Cirebon, West Java. The researcher distributed a web-based questionnaire (online questionnaire) to participants using English to adjust the company's official language. This study uses two main software in the data analysis process, namely SmartPLS and SPSS.ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½ï¿½

ï¿½

Participants

The number of participants is 116 people from a population of 606 employees who work at PT X, Cirebon, West Java who work actively in the company and carry out various operational and managerial functions to ensure the smooth production and distribution of the company. The sampling technique is carried out purposively, that is, the researcher selects participants based on certain criteria that are considered relevant to the research objectives, such as job position, work experience, and direct involvement with the researcher's objectives. The reason for this is to obtain in-depth and specific information from individuals who have significant knowledge and experience related to the research topic. The characteristics of the sample in this study are: (1) the positions of directors, managers and staff related in a cross-cultural context. (2) Minimum academy education. (3) The age range of participants is 23-60 years old. The average age was 39 years old, the participants were dominated by staff positions with a total of 98 participants, and the majority had a S1 education with a total of 97 participants.

Measurement

This study uses several measurement instruments. Performance was measured by the Individual Work Performance Questionnaire (IWPQ) from Koopmans (2014) and PT X's Performance Appraisal method, including Task Performance, Contextual Performance, and Counterproductive Work Behavior. Cultural intelligence was measured using the instrument of Van et al. (2016) with four dimensions: Metacognitive, Cognitive, Motivational, and Behavioral. Knowledge sharing is measured by two dimensions: Knowledge Donating and Knowledge Collecting, using the tools Lin et al. (2022). Job satisfaction was measured by the Minnesota Satisfaction Questionnaire (MSQ) from Abugre (2014). All measuring instruments use a Likert scale of 1-5.Measuring instruments are presented in English to ensure legibility by foreign respondents. The reliability score and validity of the measuring tool are listed in Table 1 below.

Tabel 1. Construct Reliability and Validity

	Cronbach's Alpha	Composite Reliability (rho_a)	Composite Reliability (rho_c)	Average Variance Extracted (ave)
CULTURAL INTELLIGENCE	0.973	0.975	0.975	0.664
JOB SATISFACTION	0.970	0.972	0.973	0.642
KNOWLEDGE SHARING	0.950	0.952	0.957	0.667
PERFORMANCE	0.954	0.962	0.959	0.647

The data in Table 1 shows that the Composite Reliability, Cronbach's Alpha, and Average Variance Extracted (AVE) values meet the required criteria, indicating a consistent and reliable measurement instrument. As per Hair et al. (2021), Composite Reliability above 0.7 indicates good reliability, while Cronbach's Alpha above 0.7 reflects adequate internal consistency (Hajjar, 2018). The AVE of all variables is greater than 0.5, meeting the requirements of convergent validity (Hair Jr et al., 2021). AVE values: cultural intelligence 0.664, job satisfaction 0.642, knowledge sharing 0.667, and performance 0.647, indicating that these indicators are valid in shaping their respective constructs.

Procedure

This research began with the preparation of proposals and the collection of information related to cultural intelligence, knowledge sharing, performance, and job satisfaction. The researcher compiled and adapted the questionnaire as a measuring tool, then conducted content validity with two experts to ensure the suitability of the instrument. After the improvement, the pilot study was carried out on 40 respondents according to the specified characteristics. The results of the pilot study were analyzed for the reliability and validity of the items, with revisions on items that did not meet the criteria.

In the next stage, the field study involved 116 respondents after obtaining permission from the company. The researcher provided guidance on filling out questionnaires, collecting data, and providing souvenirs as appreciation. To reduce bias due to research in one company, data triangulation was carried out through additional literature and expert interviews, as well as study replication plans in other companies to improve the validity and generalization of research results.

RESEARCH RESULTS

In Table 2, the category of respondents is divided into two levels, namely medium and high. The criteria 'moderate' and 'high' are used to indicate a certain degree or level of a variable in a particular demographic group. The 'moderate' criterion indicates that respondents have a level of cultural intelligence, knowledge sharing, or job satisfaction that is in the middle of the scale used. While the 'high' criterion indicates that respondents have a higher than average or peak of the scale. The absence of a 'low' category was due to the characteristics of the data collected stating that none of the respondents had very low scores on the variables measured. The numbers listed in the Table are the number of respondents belonging to each category (medium or high) and are often followed by percentages that indicate the proportion of respondents in each of those categories. For example, at the age of 23-30 years, there were 4 respondents who were in the medium category and 50 respondents who were in the high category for the cultural intelligence variable.

Table 2. Demographic Aspect Test on Variables

Demographic Data	Intelligence Culture		Knowledge Sharing			Satisfaction Work		Performance
Demographic Data	Keep	Tall		Keep	Tall	Keep	Tall	Keep	Tall
AGE
23 ï¿½ 30 YEARS OLD	4	50		2	52	4	50	23	31
31 ï¿½ 40 YEARS OLD	4	41		2	43	4	41	19	26
41 ï¿½ 50 YEARS OLD	0	12		0	12	0	12	8	4
51 ï¿½ 60 YEARS OLD	0	5		0	5	0	5	1	4
POSITION
STAFF	8	90		4	94	8	90	39	59
MANAGER	0	14		0	14	0	14	9	5
MANAGEMENT	0	4		0	4	0	4	3	1
EDUCATION
S1	8	89		4	93	8	89	46	51
S2	0	16		0	16	0	16	5	11
S3	0	3		0	3	0	3	0	3

Table 2 shows that in the age range of 23 to 30 years, the majority of individuals have cultural intelligence, knowledge sharing, job satisfaction and high performance. The 31 to 40-year-old age group also showed similar characteristics, with most having cultural intelligence, knowledge sharing, job satisfaction and performance that tended to be high. All individuals in the age range of 41 to 50 years have high cultural intelligence, knowledge sharing and job satisfaction, but performance tends to be moderate. In the age group of 51 to 60 years, all individuals have cultural intelligence, knowledge sharing, job satisfaction and performance that also tend to be high.

Table 2 also shows that most of the staff have cultural intelligence, knowledge sharing, job satisfaction and performance that tend to be high. For managers, all individuals have cultural intelligence, knowledge sharing and job satisfaction that tend to be high, but performance tends to be at a moderate level. All directors also have cultural intelligence, share knowledge of high job satisfaction, but performance tends to be at a moderate level.

Among S1 graduates, the majority have cultural intelligence, knowledge sharing, job satisfaction and performance that tend to be high. For S2 graduates, all individuals have cultural intelligence, knowledge sharing, job satisfaction and performance that tend to be high. All S3 graduates show similar characteristics, namely having cultural intelligence, knowledge sharing, job satisfaction and performance, all of which are at a high level.

Table 3 shows a descriptive analysis of the variables studied, namely cultural intelligence, job satisfaction, knowledge sharing, and performance. This table includes information regarding the mean, median, minimum and maximum values, as well as the standard deviation for each variable, which provides an overview of the high and low distribution of data on each of the research variables. This analysis aims to provide a preliminary understanding of the characteristics and variations of existing data, which will be the basis for evaluating the relationship and influence between variables in further research. This data is very important in providing the context and background necessary for a thorough interpretation of the research results.

Table 3. Univariate Test of Research Variables

	Mean	Median	Min	Max	Standard Deviation
CULTURAL INTELLIGENCE	0.000	0.185	-3.828	1.765	1.000
JOB SATISFACTION	-0.000	0.170	-4.376	2.417	1.000
KNOWLEDGE SHARING	-0.000	0.072	-4.068	2.070	1.000
PERFORMANCE	-0.000	0.131	-3.943	2.621	1.000

Based on the results of the descriptive analysis of the variables studied, it was found that cultural intelligence, knowledge sharing, job satisfaction, and performance had similar distribution characteristics. For the cultural intelligence variable, the mean value was 0.0 with a median of 0.185, which indicates that most of the data tends to be on the positive side of the distribution. However, a low minimum value of -3,828 and a maximum value of 1,765 and a high standard deviation of 1,000 indicate significant variation. The knowledge-sharing variable also showed similar results with a mean of -0.0 and a median of 0.170. A low minimum score of -4376 and a maximum value of 2.417 and a high standard deviation of 1,000 indicate a large variation in knowledge sharing among respondents. Job satisfaction has an average of -0.0 with a median of 0.072, which indicates a positive distribution of data. A minimum score of -4,068 and a maximum score of 2,070 as well as a high standard deviation of 1,000 showed significant variation in job satisfaction among respondents. Finally, the performance variable has a mean of -0.0 and a median of 0.131. The distribution of this data also shows a positive trend. A minimum value of -3,943 and a maximum value of 2,621 as well as a high standard deviation of 1,000 indicate a large variation in performance among respondents.

These results indicate that there are considerable differences in the level of cultural intelligence, job satisfaction, knowledge sharing, and performance among respondents. This variation is important to note in further analysis because it can affect the interpretation of the relationship between variables in this study. Variables with a wide range of values indicate that there are other factors that may affect the results, and this needs to be considered in the development of more effective management strategies.

Uji Hipotesis

Model fit assessment in statistical data analysis requires an evaluation of various fit model criteria that provide an overview of the extent to which the model used is able to accurately represent the observed data structure. In this study, some of the main indicators used to assess the fit of the model include the Standardized Root Mean Square Residual (SRMR), Chi-square value, and Normed Fit Index (NFI).

Table 4 presents the results of the evaluation of the compatibility of the research model with the observed data. This table includes some key indicators such as Standardized Root Mean Square Residual (SRMR), Chi-square, and Normed Fit Index (NFI). These indicators provide an overview of how well the estimated model matches the data obtained, as well as help in assessing the validity and reliability of the model used in this study. Evaluation of this fit model is important to ensure that the model used is able to accurately and reliably describe the relationships between variables.

Table 4. Fit Model Test

	Saturated model	Estimated model
SRMR	0.090	0.090
Chi-square	3970.87	3970.87
NFI	0.172	0.172

Based on the results shown in Table 4, the Standardized Root Mean Square Residual (SRMR) value is 0.090. SRMR is a measure of the difference between the observed covariance matrix and the one predicted by the model. SRMR values that are below 0.08 to 0.10 are generally considered a good signal for fit models according to Hu and Bentler (1999). Thus, it can be concluded that the model used in this analysis has shown a good degree of agreement with the observed data.

Furthermore, a Chi-square value of 3970.87 was also obtained from this analysis. The Chi-square value was used to test the model fit hypothesis, where the p value > 0.05 indicates that the model matches the observed data. These results indicate that the model used already has a good fit.

The Normed Fit Index (NFI) is also an important indicator in assessing the fit of the model. In this study, an NFI value of 0.172 was obtained. According to Bentler and Bonett (1980), an NFI value of > 0.90 indicates that the model has a good fit, while an NFI value of < 0.90 is referred to as a marginal fit. Therefore, the NFI values obtained indicate that the model is in the marginal fit category.

Overall, SRMR shows that this model has a good fit with the observed data. Although the Chi-square value obtained is high, this does not reduce the validity of the model that has been tested. Meanwhile, the NFI value which is in the marginal fit category provides an additional description of the level of compatibility of this model. Taking into account all these indicators, it can be concluded that the model used in this study has an adequate degree of agreement with the observed data.

Table 5 presents an analysis of pathway coefficients and specific direct effects between the variables of cultural intelligence, knowledge sharing, job satisfaction, and performance. This table includes important information such as the original path coefficient, sample mean, standard deviation, T-statistic, and p-value, which is used to evaluate the significance of the direct relationship between the variables. The data in this table provides an explanation of how much influence each variable has on other variables, which is useful in understanding the dynamics and relationships between factors in the context of this study. Through this Table, it is possible to explain how these variables interact with each other and affect each other, which ultimately helps in formulating more effective management strategies.

Tabel 5. Path Coefficients & Specific direct Effects

	Original Sample (O)	Sample Mean (M)	Standard Deviation (STDEV)	T Statistics (\|O/STDEV\|)	P Values
CULTURAL INTELLIGENCE > JOB SATISFACTION	0.781	0.799	0.080	9.820	0.000
CULTURAL INTELLIGENCE > PERFORMANCE	0.142	0.157	0.124	1.138	0.256
JOB SATISFACTION > PERFORMANCE	0.920	0.917	0.118	7.790	0.000
KNOWLEDGE SHARING > JOB SATISFACTION	0.177	0.158	0.089	1.989	0.047
KNOWLEDGE SHARING > PERFORMANCE	-0.201	-0.213	0.123	1.641	0.101

The data in Table 5 shows that all direct effects between the variables of cultural intelligence and job satisfaction, knowledge sharing and job satisfaction, and job satisfaction with performance have a p-value of less than 0.05, so it is significant. However,ï¿½ the p-value for the relationship between knowledge sharing and performance, and cultural intelligence with performance had a p-value value of more than 0.05, so it was not significant.

Cohen (1988) mentioned thatï¿½ a p-value of less than 0.05 indicates that the relationship between variables is significant at a 95% confidence level. The study shows that the direct influence of cultural intelligence on performance and knowledge sharing on performance is not significant.ï¿½ The p-value of cultural intelligence with a performance of 0.256 and knowledge sharing with a performance of 0.101. The direct influence of cultural intelligence on job satisfaction, knowledge sharing on job satisfaction, and job satisfaction on performance has a significant role.ï¿½ The p-value of cultural intelligence on job satisfaction was 0.000, knowledge sharing on job satisfaction was 0.047 and job satisfaction on performance was 0.000.

Table 6. Specific indirect Effects

	Original Sample (O)	Sample Mean (M)	Standard Deviation (STDEV)	T Statistics (\|O/STDEV\|)	P Values
CULTURAL INTELLIGENCE > JOB SATISFACTION > PERFORMANCE	0.703	0.723	0.073	9.644	0.000
KNOWLEDGE SHARING > JOB SATISFACTION > PERFORMANCE	0.140	0.119	0.089	1.571	0.117

The data results in Table 6 also explain that the indirect relationship between the variables of cultural intelligence on performance mediated by job satisfaction has a p-value of 0.000, less than the significance level of 0.05 so that it can be declared significant. Meanwhile, the variable of knowledge sharing on performance mediated by job satisfaction hasï¿½ a p-value of 0.117 so that it can be declared insignificant. Thus, job satisfaction is able to mediate the relationship between cultural intelligence and performance. Meanwhile, the relationship between knowledge sharing and performance is not mediated by job satisfaction.

Based on the results of the research conducted, the first hypothesis test shows that cultural intelligence has a significant influence on employee performance mediated by job satisfaction. These results confirm that the first hypothesis (H1) is accepted. This means that job satisfaction mediates the relationship between cultural intelligence and employee performance. This research is in line with the findings of Groves et al. (2015) which show that cultural intelligence can improve cultural adaptation and employee task performance with high job satisfaction. High cultural intelligence allows employees to more easily adapt to different cultural norms, values, and practices, so they can work more efficiently and harmoniously in a diverse work environment.

On the contrary, the results of the second hypothesis test showed that knowledge sharing had no significant influence on employee performance mediated by job satisfaction, so the second hypothesis (H2) was rejected. This is contrary to some previous research that states that knowledge sharing can improve employee performance through job satisfaction. In this study, although knowledge sharing has a significant role in individual job satisfaction in organizations, it means that it includes; The work environment, and a positive organizational culture, this is not enough to significantly improve employee performance. Factors such as improper implementation of knowledge sharing, excessive information, or inconsistency with the organization's culture can cause negative effects on performance despite high job satisfaction.

RESULT AND DISCUSSION

The results of the analysis of the demographic test for age criteria showed that respondents from various age ranges had different perceptions and responses to the research variables. In particular, respondents in the younger age range (23-30 years) tend to have strong enthusiasm and motivation, which is shown by the number of respondents who are at a high level across all variables. In contrast, older respondents (41-50 years old) were more likely to have experience but not a high level of enthusiasm for work, with lower numbers on performance variables. This difference is statistically significant and highlights the importance of considering the age factor in further analysis.

The position criteria also showed significant differences in perception and response to the research variables. Respondents with higher positions, such as managers and directors, tend to have a different view compared to staff. This can be assumed from the difference in perception in performance variables, where higher positions tend to have lower performance levels, so it is assumed that even though high experience and knowledge cannot directly affect performance levels. These differences reflect broader insights and viewpoints and influence the way respondents assess and respond to research variables. This is important to consider in the interpretation of results and the preparation of recommendations.

For the educational criteria, the demographic test revealed that the education level of the respondents did not have a significant difference in the research variables. All respondents with different levels of education had a good understanding of the topic being researched. These results did not show that educational background influenced the perspective and response to the research variables.

The results of the first hypothesis test show that cultural intelligence has a significant role in employee performance mediated by job satisfaction. This means that H0 is rejected, and job satisfaction mediates the relationship between cultural intelligence and employee performance. This research is in line with the findings of Bï¿½cker et al. (2014) who showed that cultural intelligence has a positive effect on job satisfaction, which in turn improves employee performance. Cultural intelligence is the ability of individuals to adapt to different cultural environments and interact effectively with people from diverse cultural backgrounds. In the context of globalization and cultural diversity in the workplace, cultural intelligence is an important competency that must be possessed by employees in order to work effectively in a multicultural team. Employees who have high cultural intelligence can more easily adapt to different cultural norms, values, and practices, so they can work more efficiently and harmoniously in a diverse work environment.

This research shows that cultural intelligence not only helps employees in adapting to diverse work environments, but also increases their job satisfaction. Job satisfaction is a positive emotional state that comes from an employee's assessment of their work, which includes factors such as the work environment, relationships with coworkers, career development opportunities, and recognition of their contributions. Employees with high cultural intelligence tend to feel more valued and accepted in a diverse work environment, so they feel more satisfied with their work. Ang et al. (2015) also found that cultural intelligence can improve cultural adaptation and employee task performance.

In this study, cultural intelligence was found to have a significant role in job satisfaction. Employees who have high cultural intelligence feel better able to communicate and collaborate with colleagues from different cultural backgrounds, which in turn increases their job satisfaction. Additionally, cultural intelligence also helps employees to better understand and appreciate cultural differences, which can reduce conflict and improve collaboration in the workplace.

High job satisfaction contributes to increased employee productivity and work quality. When employees feel satisfied with their jobs, they tend to be more motivated to perform well and achieve organizational goals. In this study, job satisfaction was found to have a significant role in employee performance. Employees who are satisfied with their jobs tend to have higher levels of productivity, better quality of work, and are more committed to the organization. These results underscore the importance of organizations developing cultural intelligence training programs for employees to improve their performance. These training programs can include training on intercultural communication, conflict management, and social skills development. By increasing employee cultural intelligence, organizations can create a more inclusive and supportive work environment, which ultimately improves employee performance.

The results of the second hypothesis test show that knowledge sharing does not have a significant role in employee performance mediated by job satisfaction, so H0 is accepted. This is contrary to some previous studies that state that knowledge sharing can improve employee performance through job satisfaction (Lin, 2007). Previous research has suggested that knowledge sharing is an important factor in improving employee performance and job satisfaction. Instead, this study shows that the impact of knowledge sharing can be negative if the practice is not appropriate. Knowledge sharing has a significant role in individual job satisfaction in the organization, a collaborative work environment and organizational culture, as well as high job satisfaction, does not significantly improve employee performance.

This research proves that sharing knowledge does not always help employees to be able to work optimally and can even reduce employee performance even though employees are satisfied. This is supported by research conducted by Usmanova et al. (2021), which explains that knowledge sharing has a significant negative effect on performance, and despite high job satisfaction.

Factors such as improper implementation of knowledge sharing, excessive information, or incompatibility with the organization's culture can lead to effects that can degrade performance despite high job satisfaction. Huang (2019) revealed that although knowledge sharing is often considered beneficial and improves job satisfaction, in some cases, this can lead to fatigue and decreased performance. Therefore, it is important for organizations to consider the right way to encourage knowledge sharing in order to maximize its benefits without sacrificing employee performance.

Other factors such as intrinsic motivation, management support, and appropriate organizational culture may be more influential in improving performance. Therefore, increased knowledge sharing and job satisfaction alone are not enough to improve employee performance directly.

For further research, it is recommended to expand the variables analyzed, use a longitudinal approach, conduct cross-cultural studies, and apply qualitative methods to gain deeper insights. In practical terms, organizations should provide cultural intelligence training, through mentoring programs and information platforms, and focus on policies that increase job satisfaction. Periodic evaluations of these programs are also important to ensure their effectiveness in improving employee performance.

CONCLUSION

The direct relationship between cultural intelligence and job satisfaction, as well as knowledge sharing and job satisfaction, had ï¿½a p-value of less than 0.05. This shows that cultural intelligence and knowledge sharing contribute significantly to increased job satisfaction. However, the direct relationship between knowledge sharing and performance and cultural intelligence with performance was not significant, with p-values of 0.101 and 0.256, respectively. This suggests that cultural intelligence and knowledge sharing do not directly affect performance.

This research provides valuable insights for human resource management practices in creating an inclusive and supportive work environment, and highlights the importance of job satisfaction in improving employee performance. As such, organizations need to develop strategies to increase cultural intelligence among employees, as well as focus on increasing job satisfaction to indirectly improve performance.

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