DOI: doi.org/10.21776/ub.ijds.2023.10.02.5
179
The Effect of Diversity Management on Employee Engagement in the Manufacturing Sector
Ali Amran Al Afif, Universitas Indonesia, Indonesia
Corresponding author:
Ali Amran Al Afif,
Article history:
Received: 14 October 2022 Revised: 1 August 2023 Accepted: 5 November 2023
Published online at ijds.ub.ac.id
Copyright © 2023 Author(s) Licensed under CC BY NC
Abstract
In Indonesia's manufacturing industry, which employs the third- largest workforce in the country, employee engagement is a critical measure of labor's contribution. Despite the industry's importance, diversity management—a key driver of engagement—lags, with low representation of women and persons with disabilities and persistent ethnic and racial discrimination in a nation known for its multiculturalism. Additionally, ageism is prevalent, manifesting as discrimination across different age groups. This study investigates the influence of four principal dimensions of diversity management—
ethnic, gender, disability, and age—on employee engagement within the sector. Conducted as a field study in a manufacturing industry in Cilegon with 218 participants, the research provides insightful findings. Notably, it identifies age diversity management as the sole dimension with a significant impact on employee engagement, while the other three—ethnic, gender, and disability diversity management—do not show a substantial effect.
Keywords: diversity management, employee engagement, structural equation modeling, PLS-MGA
1. Introduction
According to the International Labor Organization (ILO), the manufacturing sector holds a significant position in the Asia Pacific workforce, accounting for 14.5% of all employment. In Indonesia, this sector's prominence is even more pronounced. In 2021, it was the third-largest employer in the country, absorbing a substantial workforce of 17.82 million people (BPS, 2021). This underscores the manufacturing industry's role as a significant labor-intensive sector within the Indonesian economy.
As an industrial sector dominated by the workforce, the workforce is essential for the manufacturing industry—Wambui et al. (2013) state that humans are one of the crucial factors for organizations. Organizations need people to manage and operate the factors of production. Therefore, companies require high employee engagement to ensure that they can achieve their goals effectively. Employee engagement is a measure of the energy and dedication of workers toward their organization (Hewitt Associates, 2004). High employee engagement increases the involvement of workers in the company's business
processes. It has a positive impact on productivity (Tuner, 2019) and significantly influences employee performance (Jagannathan, 2014). Engaged employees also contribute to organizations with high company performance (Yeh, 2013).
One of the crucial factors for employee engagement is diversity management (Alshaabani, Hamza, Rudnak, 2021; Goswami and Goswami, 2018; Mistry, Okumus, Orlowsky, 2021). Effective diversity management ensures that all workers have equal opportunities, leading to a positive work environment and a competitive advantage (Femi and Prasetya, 2017). Furthermore, effective diversity management enhances worker performance and promotes innovative behavior (Mistry, Okumus, Orlowsky, 2021). In the urgency of implementing diversity management, gender, and disability diversity in the manufacturing industry are still relatively low. In Indonesia, the involvement of women in the manufacturing industry is only 42.8%, trailing behind several other industries such as education services, health services, social activities, and other services (TirtoID).
Additionally, according to Lokadata (2020), the number of people with disabilities working in the manufacturing sector is lower compared to other sectors. Ethnic and age factors also need to be considered. In the public sphere, issues related to race/ethnicity often arise, such as discrimination against the Chinese community in Yogyakarta (BBC Indonesia, 2016) and violence against Papuan students in 2018 (TirtoID). Companies must ensure that these issues do not infiltrate their internal environment. Furthermore, Indonesia is a country rich in ethnicity, race, customs, and culture, with 1,340 ethnic groups, which leads to companies in Indonesia having a diverse workforce with various ethnic backgrounds. Moreover, there is the phenomenon of ageism, which involves stigma and discrimination between younger and older age groups (Burnes, at al., 2019).
The workplace is a setting characterized by high age diversity.
Several studies have utilized structural equation modeling (SEM) to analyze the impact of diversity management on employee engagement (Alshaabani, Hamza, Rudnak, 2021; Mistry, Okumus, Orlowsky, 2021; Ashikali, Groeneveld, 2015; Dastane, Eshegbe, 2015; Li at al., 2021; Luu, 2018; Onwuchekwa at al., 2019). SEM is a multivariate statistical method that examines the relationship between latent variables, measured by the indicators encompassed within them. Furthermore, SEM enables the simultaneous testing of multiple hypotheses (Hair, Babin, Anderson, 2014). However, despite Indonesia being a country with a high level of diversity, there is a scarcity of research on diversity management within the country. Consequently, this study aims to investigate the impact of diversity management on employee engagement in a manufacturing industry in Indonesia.
2. Theoretical Review
2.1 Employee Engagement
Employee engagement has evolved to encompass a broader scope. Presently, other departments are recognizing the positive impact of employee engagement on various facets of the company, including business outcomes, services, and operations [5].
Consequently, research on employee engagement has become increasingly prolific over time. Practitioners continuously seek insights into how their organizations can enhance competitiveness through effective workforce management, with employee engagement regarded as a pivotal component. Employee engagement refers to an employee's perspective on their work, organizational leadership, recognition and rewards, and organizational communication (Sanchez, 2007). Kahn (1990) introduced a well-known definition of employee engagement, describing it as the extent to which workers invest themselves in their roles as members of the organization. Engaged employees actively and wholeheartedly contribute to their work physically, cognitively, and emotionally.
High levels of employee engagement increase employee involvement in the company's business processes. Turner (2015) highlights that employee engagement positively influences productivity. Additionally, employee engagement has a significant impact on employee performance (Jagannathan, 2014), and engaged workers contribute to the success of high-performing organizations (Yeh, 2013). Moreover, research by Santhanam and Srinivas (Santhanam, Srinivas, 2019) in the manufacturing industry demonstrates that employee engagement reduces burnout and turnover intentions among workers. This is particularly crucial for labor-intensive manufacturing companies aiming to retain their workforce.
2.2 Diversity in the Workplace
Diversity occurs due to variations in the personal attributes of individuals within the same group. These attributes typically include age, gender, and race (Jackson, at al. 2003).
According to Yadav & Lenka (2020), diversity encompasses differences in gender, ethnicity, nationality, culture, and educational backgrounds. Hsiao, Auld, and Ma (2015) provide a more comprehensive perspective, stating that diversity comprises primary and secondary dimensions. The primary dimensions of diversity consist of race, ethnicity, gender, age, religion, disability, and sexual orientation (Diversity Task Force, 2022; Loden and Rosener, 1991). On the other hand, the secondary dimensions of diversity are typically less visible but have a diverse influence on personal identity (Diversity Task Force, 2022).
Secondary dimensions often include communication style, work style, role in the organization, economic status, and geographic origin (Loden and Rosener, 1991).
Therefore, it is evident that the primary dimensions of diversity encompass more specific factors, while the secondary dimensions involve broader factors. Furthermore, primary dimensions tend to be more challenging to control than the factors within the secondary dimension. Given the sensitivity of several diversity factors in Indonesia, this study defines diversity based on the primary factors, as defined by Rijamampianina and Carmichael (2005), which include race/ethnicity, gender, age, and disability.
2.3 Diversity Management
Diversity management is a strategy that promotes equal opportunities and values diversity to create a positive work environment and achieve a competitive advantage (Femi and Prasetya, 2017). It involves adopting an inclusive approach within companies
by hiring individuals with diverse backgrounds and fostering an understanding of these differences across various factors (Bleijenbergh, Peters, Poutsma, 2010). Diversity management encompasses various practices, such as hiring, promoting, providing compensation and benefits, offering training and development opportunities, and evaluating employee performance, irrespective of background (Alshaabani, Hamza, Rudnák, 2021). However, diversity management goes beyond treating all employees equally, as certain individuals may require specific accommodations due to their unique circumstances. For example, female employees may need dedicated spaces for breastfeeding, employees with disabilities may require wheelchair ramps, and older employees may benefit from ergonomic work tools. Therefore, implementing particular practices to support employees from diverse ethnic, gender, age, and disability backgrounds is an integral part of diversity management. The management of ethnic diversity is a crucial aspect of diversity management, particularly in Indonesia, where there are 1,340 ethnic groups. Effectively managing ethnic diversity is an essential factor influencing employee engagement (Chandani, Mehta, Mall, Khokhar, 2016).
Hypothesis 1: Ethnic diversity management has a positive impact on employee engagement.
Gender is another crucial factor in diversity management. While the gender ratio in the population may vary, companies need to strive for gender balance in their workforce.
Traditionally, women have faced biological conditions that require special attention and facilities, leading some companies to hesitate in hiring female employees. However, research suggests that gender diversity can have positive impacts on employee engagement (Onwuchekwa at al. 2019), employee satisfaction (Dastane and Eshegbe, 2015), and employee performance (Oljaca, Firdousi, Akram, 2021; Alghazo and Shaiban, 2016). Companies need to recognize the value of gender diversity and create an inclusive work environment that provides equal opportunities for both men and women. By promoting gender diversity management, companies can harness the unique perspectives, skills, and talents that individuals of different genders bring to the table.
This can contribute to improved employee engagement, increased job satisfaction, and enhanced overall organizational performance.
Hypothesis 2: Gender diversity management has a positive impact on employee engagement.
Age diversity management is another crucial factor to consider in diversity management practices. The workforce comprises individuals from various age groups, with younger individuals often requiring extensive education before entering the workforce and older individuals possessing a wealth of experience and continued participation. Companies must actively recruit young employees to ensure a sustainable labor supply and retain their top senior talents while recruiting experienced older employees (Sousa, Ramos, Carvalho, 2019). Managing age diversity is essential because employees of different age groups have unique needs and perspectives. Companies
should strive to create an inclusive work environment that values and respects individuals of all age groups. By implementing age diversity management practices, companies can ensure that all employees feel valued, engaged, and involved in the company's business processes. This can lead to improved teamwork, knowledge sharing, and innovation, ultimately benefiting the overall performance and success of the organization (Sousa, Ramos, Carvalho, 2019).
Hypothesis 3: Age diversity management has a positive impact on employee engagement.
The diversity of disability is a significant factor that many companies now recognize and address in their diversity management efforts. According to the World Health Organization (WHO), people with disabilities account for only 10% of Indonesia's total population. Unfortunately, many individuals with disabilities face barriers such as limited access to education, vocational training, and job opportunities, often resulting in their isolation and economic challenges. To address this issue, the Indonesian government implemented Law No. 13 of 2003, which mandates companies to employ individuals with disabilities. This not only provides employment opportunities for persons with disabilities but also has positive implications for businesses. Research conducted by Luu (2018) in information technology companies highlights the positive impact of disability diversity management on employee engagement. By embracing disability diversity and implementing inclusive practices, companies can create a more inclusive and equitable work environment. This fosters a sense of belonging, enhances employee engagement, and promotes the unique perspectives and talents of individuals with disabilities.
Moreover, embracing disability diversity not only fulfills legal requirements but also contributes to a more socially responsible and inclusive business culture.
Hypothesis 4: Disability diversity management has a positive impact on employee engagement.
Fig. 1: Conceptual Model
In this study, a multi-group partial least squares structural equation modeling (PLS- SEM) analysis was conducted to examine the differences between male and female groups regarding the impact of diversity management on employee engagement. The analysis aimed to investigate if there were variations in the magnitude of the effects between the two groups based on occupation, gender, ethnicity, and other factors, as proposed by Chandani, Mehta, Mall, Khokhar (2016). However, it is essential to note that a multi-group analysis was not performed for ethnicity and age diversity in the workforce, as the proportions of employees based on these factors did not present any issues or discrepancies. It is worth mentioning that Banihani et al. (2013) argue that gender can significantly influence employee engagement, with men generally experiencing higher levels of engagement compared to women. This observation is attributed to the perceived higher value and utility often associated with male workers. The findings of the multi- group PLS-SEM analysis will provide insights into the potential differences in the impact of diversity management on employee engagement between male and female employees, contributing to a better understanding of the factors influencing engagement within different gender groups.
Hypothesis 5a: There is a difference in the positive impact of ethnic diversity management on employee engagement between males and females.
Hypothesis 5b: There is a difference in the positive impact of gender diversity management on employee engagement between males and females.
Hypothesis 5c: There is a difference in the positive impact of age diversity management on employee engagement between males and females.
Hypothesis 5d: There is a difference in the positive impact of disability diversity management on employee engagement between male and female.
3. Research Method
3.1 Questionnaire Design
This research employs a field study using a questionnaire design in a manufacturing company in Cilegon. In this study, 34 statements were included in the questionnaire, and they were translated into Indonesian. The Likert scale in all the questions ranged from one (strongly disagree) to six (strongly agree). By using an even Likert scale, as suggested by Chang (1994), the inclusion of a neutral option is eliminated, thereby encouraging respondents to express their attitudes more clearly without the option of remaining neutral. The purpose of this approach is to ensure that respondents provide more decisive responses and avoid the tendency to choose a neutral option when they may have a preference or opinion regarding the statement. By removing the neutral option, the Likert scale prompts respondents to indicate their agreement or disagreement more explicitly, leading to a clearer understanding of their attitudes and perceptions of the study variables.
3.2 Data Collection
In this study, the G*Power software version 3.1.9.4 was utilized to determine the sample size, as mentioned in [40]. A priori power analysis was conducted to estimate the sample size required for the study. The effect size used in the analysis was 0.24, as indicated by Paterson at al. (2015). The significance level was set at 0.05, and a statistical power 0.95 was desired. Additionally, considering there are five predictor variables (independent variables) in the study, the recommended minimum sample size is 89 samples for each group. It is important to note that the sample size determination is based on several assumptions, such as the effect size, significance level, statistical power, and number of predictor variables. These assumptions help estimate the sample size needed to detect the expected effects or relationships in the study. Researchers can utilize the recommended sample size as a guideline to ensure an adequate number of participants for their study.
Table 1: Demographics of respondents.
Variable Category Frequency (n) Percentage
Gender Male 122 56%
Female 96 44%
Education Level
High school 32 15%
Diploma 22 10%
Bachelor 147 67%
Master 16 7%
Doctor 1 1%
Age (in years)
21-30 40 18%
31-40 94 43%
41-50 64 29%
51-60 20 9%
Length of Work (in years)
1-10 140 64%
11-20 57 26%
21-30 20 9%
31-40 1 1%
In the study, 400 questionnaires were distributed to respondents, and 218 completed questionnaires were returned, resulting in a response rate of 55%. Among the returned questionnaires, there were 122 male and 96 female respondents. However, it is essential to note that only one respondent in the sample identified as having a physical disability. Due to the limited representation of employees with disabilities, it is not feasible to conduct a multi-group PLS analysis comparing employees with disabilities to those without disabilities.
3.3 Data Analysis
Structural equation modeling (SEM) is a powerful statistical method that combines elements of factor analysis and multiple regression to analyze complex relationships among variables (Hair, Babin, Anderson, Black, 2014). Schumacker and Lomax (2010) further describe SEM as a statistical approach integrating confirmatory factor analysis and path analysis to examine the relationships between latent variables. SEM can be classified into two types: covariance-based SEM (CB-SEM) and variant-based SEM, commonly known as partial least squares SEM (PLS-SEM). CB-SEM is typically used for confirmatory purposes to test pre-existing theories, while PLS-SEM is considered more exploratory and is used to develop new theories. PLS-SEM focuses on the correlation relationships between variables, and the analysis assumes a variance-based data distribution.
It is important to note that PLS-SEM is often applied under data homogeneity, which assumes that the data is derived from a homogeneous population. However, in practice, data heterogeneity is common, and assuming homogeneity can lead to biased results and erroneous conclusions (Hair, Sarstedt, Ringle, Gudergan, 2017). To address this limitation, PLS-SEM multi-group analysis has been introduced to examine potential differences in the relationships between groups. By conducting PLS-SEM multi-group analysis, researchers can explore how the relationships between variables differ across different groups, thereby enhancing the understanding of the underlying dynamics and providing valuable insights into the effects of diversity management on employee engagement within specific subgroups.
4. Findings
4.1 Assessment of the Measurement Model
At this study stage, confirmatory factor analysis (CFA) was performed on the entire sample and each gender group separately (male and female). During the CFA, some indicators were identified as having low factor loadings (less than 0.7) and were subsequently removed from the analysis. This step ensures that only the indicators with strong relationships to their corresponding latent variables are retained in the final model as shown in Table 2.
However, in terms of the latent variables, no indicators were eliminated as all variables demonstrated good reliability and construct validity. This was assessed using measures such as composite reliability (CR), which measures the internal consistency of the latent variables, and average variance extracted (AVE), which evaluates the amount of variance captured by the latent variables. In this study, all latent variables exhibited satisfactory values, with a composite reliability (CR) above 0.70 and an average variance extracted (AVE) above 0.40, indicating good reliability and construct validity (Alshaabani, Hamza, Rudnák, 2021).
Table 2: Item loadings, reliability, and convergent validity.
Latent Variable Indicator Loadings CR AVE Loadings CR AVE Loadings CR AVE Full Sample (n=218) Male (n=122) Female (n=96)
Ethnic Diversity Management
EDM1 Eliminated
0,945 0,812
Eliminated
0,935 0,783
Eliminated
0,973 0,901
EDM2 0,886 0,873 0,949
EDM3 0,928 0,914 0,973
EDM4 0,902 0,872 0,931
EDM5 0,887 0,880 0,943
Gender Diversity Management
GDM1 Eliminated
0,902 0,755
Eliminated
0,888 0,725
Eliminated
0,934 0,825
GDM2 0,852 0,826 0,905
GDM3 0,856 0,818 0,922
GDM4 0,899 0,907 0,898
GDM5 Eliminated Eliminated Eliminated
GDM6 Eliminated Eliminated Eliminated
GDM7 Eliminated Eliminated Eliminated
Age Diversity Management
ADM1 Eliminated
0,925 0,757
Eliminated
0,915 0,728
Eliminated
0,949 0,825
ADM2 0,898 0,903 0,888
ADM3 0,902 0,878 0,962
ADM4 0,794 0,795 0,819
ADM5 0,881 0,834 0,956
ADM6 Eliminated Eliminated Eliminated
Disability Diversity Management
DDM1 Eliminated 0,977 0,914 Eliminated 0,954 0,807 Eliminated 0,990 0,960
DDM2 0,957 0,940 0,972
DDM3 0,957 0,936 0,987
DDM4 0,966 0,939 0,988
DDM5 0,943 0,931 0,973
DDM6 Eliminated 0,727 Eliminated
DDM7 Eliminated Eliminated Eliminated
Employee Engagement
EE1 0,940 0,964 0,795 0,921 0,956 0,755 0,968 0,973 0,838
EE2 0,943 0,937 0,952
EE3 0,906 0,912 0,902
EE4 0,816 0,846 0,783
EE5 0,875 0,822 0,929
EE6 0,863 0,802 0,903
EE7 0,891 0,835 0,959
EE8 Eliminated Eliminated Eliminated EE9 Eliminated Eliminated Eliminated
Following the confirmatory factor analysis (CFA), the next step involved conducting a test for discriminant validity. This test was performed on the overall sample and within
each gender group. The purpose of the test was to assess whether the latent variables in the model had unique characteristics that distinguish them from one another. The results of the discriminant validity test indicated that all latent variables, both in the overall sample and within each group, exhibited distinctiveness as shown in Table 3. This suggests that each latent variable measures a different construct and is not redundant with other variables in the model. The presence of discriminant validity ensures that the latent variables are capturing unique aspects of the constructs they represent, further supporting the validity and reliability of the measurement model.
Table 3: Discriminant validity.
Variable ADM DDM EDM EE GDM
Full Sample (n=218)
ADM 0,854
DDM 0,766 0,785
EDM 0,585 0,228 0,906
EE 0,619 0,299 0,515 0,841
GDM 0,667 0,414 0,727 0,578 0,927
Male (n=122)
ADM 0,853
DDM 0,592 0,898
EDM 0,534 0,215 0,885
EE 0,500 0,332 0,338 0,869
GDM 0,547 0,267 0,729 0,387 0,852
Female (n=96)
ADM 0,908
DDM 0,791 0,980
EDM 0,801 0,680 0,949
EE 0,715 0,583 0,677 0,915
GDM 0,837 0,761 0,832 0,736 0,908
4.2 Assessment of the Measurement Invariance
Based on the conducted permutations, it was observed that the correlation values, mean differences, and variances between the male and female groups fell within the 95%
confidence level as shown in Table 4. This implies that the two groups exhibited similar composite variances, no significant differences in means, and no significant differences in variances. These findings suggest that the data collected from both groups can be considered comparable and suitable for further analysis using the PLS-SEM multi-group analysis approach. Therefore, the data processing can proceed to the next stage, which involves conducting the PLS-SEM multi-group analysis to examine the differences in the structural relationships between the variables across the male and female groups. This analysis will provide insights into any variations or similarities in the relationships and their respective strengths within each group.
Table 4: Measurement invariance of composite models (MICOM).
Variable c-Value 95% Confidence
Interval
Compositional Invariance Ethnic Diversity Management 0,999 [0,997; 1,000] Yes Gender Diversity Management 0,996 [0,989; 1,000] Yes
Age Diversity Management 0,997 [0,992; 1,000] Yes
Disability Diversity Management 1,000 [1,000; 1,000] Yes
Employee Engagement 1,000 [0,999; 1,000] Yes
Variable
Difference of the composite’s mean
value
95% Confidence
Interval Equal mean values Ethnic Diversity Management -0,005 [-0,276; 0,254] Yes
Gender Diversity Management -0,004 [-0,288; 0,257] Yes Age Diversity Management -0,006 [-0,286; 0,260] Yes Disability Diversity Management -0,001 [-0,264; 0,254] Yes
Employee Engagement 0,002 [-0,269; 0,268] Yes
Variable
Difference of the composite’s variance ratio
95% Confidence
Interval Equal variances Ethnic Diversity Management 0,023 [-0,614; 0,621] Yes Gender Diversity Management 0,026 [-0,628; 0,593] Yes Age Diversity Management 0,014 [-0,356; 0,382] Yes Disability Diversity Management 0,010 [-0,712; 0,652] Yes
Employee Engagement 0,000 [-0,454; 0,460] Yes
4.3 Assessment of the Structural Model
According to the results of the hypothesis testing, it was found that in the overall sample and the male group, only hypothesis H3 (age diversity management affects employee engagement) was supported, meaning there is a significant relationship between age diversity management and employee engagement in these groups as shown in Table 5. On the other hand, in the female group, two hypotheses were supported.
Hypothesis H2 (gender diversity management influences employee engagement) and H3 (age diversity management influences employee engagement) showed significant relationships between gender diversity management, age diversity management, and employee engagement in the female group. It is important to note that the non-support of specific hypotheses does not necessarily indicate the absence of any relationship or effect. Other factors or variables not considered in this study may influence employee engagement. However, based on the results obtained, it can be concluded that gender diversity management and age diversity management have a significant impact on employee engagement in the female group.
Table 5: Results for direct relationships.
H Path Full sample (n=218) Male (n=122) Female (n=96) β t-Value Result β t-Value Result β t-Value Result H1 EDM -> EE 0,061 0,381 Not
Supported 0,011 0,065 Not
Supported 0,108 1,059 Not Supported H2 GDM -> EE 0,226 1,610 Not
Supported 0,156 0,991 Not
Supported 0,428 2,819 Supported H3 ADM -> EE 0,352 2,990 Supported 0,376 2,231 Supported 0,333 2,368 Supported H4 DDM -> EE 0,053 0,601 Not
Supported 0,055 0,460 Not
Supported -0,080 0,756 Not Supported
According to the analysis, the Q2 values for all groups were greater than zero, indicating that the research model has relevant predictive value in each group. This suggests that the independent variables in the model have predictive power in explaining the variance of the dependent variable.
Additionally, the coefficient of determination (R2) was analyzed to assess the ability of the independent variables to explain the variance of the dependent variable. The results showed that the predictive power of the research model was moderate in the overall sample (R2=0.370), weak in the male group (R2=0.270), and moderate in the female group (R2=0.580). These R2 values indicate the proportion of variance in the dependent variable that the independent variables in each group can explain.
It is essential to interpret the R2 values within the context of the specific research domain and the complexity of the variables involved. While the overall sample and female group showed moderate predictive power, the male group had a weaker predictive power. This suggests that other factors or variables not considered in the model have a stronger influence on employee engagement in the male group. Further research and investigation could explore these factors to enhance the predictive power of the model in the male group.
Table 6: Result of R2, Q2, and f2.
Variable Full Sample (n=218) Male (n=122) Female (n=96)
R2 Q2 f2 R2 Q2 f2 R2 Q2 f2
EDM 0,002 0,000 0,008
GDM 0,030 0,014 0,092
ADM 0,076 0,090 0,058
DDM 0,003 0,003 0,005
EE 0,370 0,286 0,270 0,186 0,580 0,473
4.4 Assessment of Group Differences
Based on the results of the PLS multi-group analysis, it was found that there was no significant difference in the coefficients between the two groups. This indicates that the effects of the independent variables on the dependent variable are not significantly different between the male and female groups. The p-values, greater than the 0.05 significance level, suggest that the hypotheses H5a, H5b, H5c, and H5d are not supported.
These results indicate that gender diversity management (H5a), age diversity management (H5b), disability diversity management (H5c), and ethnicity diversity management (H5d) do not have a significant differential effect on employee engagement between the male and female groups. The impact of these diversity management practices on employee engagement is similar for both genders. Further analysis or exploration could be conducted to investigate other potential factors or variables that may influence employee engagement differently between the male and female groups.
Table 7: Differences in path coefficients between groups.
Hypothesis Path Male Female
t-Value p-Value Result
β β
H5a EDM -> EE 0,011 0,108 0,482 0,631 Not Supported H5b GDM -> EE 0,156 0,428 1,247 0,215 Not Supported H5c ADM -> EE 0,376 0,333 0,195 0,845 Not Supported H5d DDM -> EE 0,055 -0,080 0,850 0,397 Not Supported
5. Discussion
The results of the analysis indicate that in the overall sample, all independent variables have a positive influence on employee engagement, with age diversity management having the highest β coefficient (0.352). This is followed by gender diversity management (0.226), ethnic diversity management (0.061), and disability diversity management (0.053).
In the male group, the order of the β coefficients from highest to lowest is age diversity management (0.376), gender diversity management (0.156), disability diversity management (0.055), and ethnic diversity management (0.011). However, in the female group, disability diversity management has a negative β coefficient (-0.080), indicating a negative effect on employee engagement. The sequential β coefficients for the female group are gender diversity management (0.428), age diversity management (0.333), ethnic diversity management (0.108), and disability diversity management (-0.080).
These results differ from the findings of [34], which suggested that effective workforce management, including job characteristics, gender diversity, and ethnic diversity, influences employee engagement. In this study, ethnic diversity management did not have a significant effect on employee engagement. Furthermore, in the overall sample, gender diversity management did not show a significant effect on employee
engagement. This discrepancy may be attributed to the more significant number of male respondents, which influenced the overall results and led to a non-significant effect of gender diversity management.
These findings highlight the importance of considering specific contexts and sample characteristics when studying the impact of diversity management on employee engagement. Further research could explore other potential factors or variables that may contribute to the differences observed in the impact of diversity management on employee engagement across different groups.
The lack of significant effect of disability diversity management on employee engagement in this study may be attributed to the low number of respondents with disabilities and the company's non-compliance with the minimum employment requirement for disabled individuals as stipulated by Law Number 13 of 2003. The limited representation of individuals with disabilities in the sample size made it infeasible to conduct a multi-group PLS-SEM analysis comparing workers with and without disabilities.
Therefore, the findings regarding the impact of disability diversity management on employee engagement should be interpreted cautiously in light of these limitations.
However, the significant effect of age diversity management on employee engagement aligns with the findings of [36]. In the manufacturing industry, where the study was conducted, the proportion of workers across different age groups was balanced. However, ageism, which refers to discriminatory behavior based on age, persists and can affect employee engagement. The existence of age-diversity practices in organizations can mitigate ageism and contribute to higher energy levels, more outstanding dedication to work, and increased absorption in job tasks, ultimately leading to higher employee engagement.
The significant effect of gender diversity management observed only in the female group can be attributed to the historical disparity in opportunities and experiences between men and women. Women have often faced challenges in experiencing psychological significance, security, and availability in the workplace compared to men [38]. As a result, gender diversity management is highly valued by women and has a more excellent and significant impact on their level of employee engagement.
It is important to note that these findings are specific to the sample and context of the study. Further research with more extensive and diverse samples, including a sufficient representation of individuals with disabilities, could provide a more comprehensive understanding of the impact of disability diversity management on employee engagement and further investigate the factors influencing gender diversity management and its effects on employee engagement.
The results of the PLS multi-group analysis indicate no significant difference in the β coefficient between the male and female groups, particularly regarding the effect of gender diversity management on employee engagement. Despite gender diversity management having a significant effect on employee engagement in the female group,
the analysis did not find a significant difference in the coefficient between the two groups.
This finding suggests that the presence or absence of significance in the effect of a variable across different groups does not necessarily translate to a significant difference in the coefficient.
However, it is worth noting that the β coefficient of gender diversity management on employee engagement is higher in the female group (0.428) compared to the male group (0.156). This indicates that an increase in the level of gender diversity management will result in a more significant increase in employee engagement in the female group compared to the male group. In other words, gender diversity management has a more substantial impact on employee engagement in the female group.
6. Conclusions
This study aims to examine the impact of diversity management on employee engagement in manufacturing companies, identify the most influential diversity management factor, and investigate any differences in the effect of diversity management between male and female groups. However, due to the limited number of respondents with disabilities who met the requirements for the analysis, multi-group analysis could only be conducted for differences between male and female groups and not for other factors such as physical condition (disability), ethnicity, and age.
Based on the data analysis for the entire sample, the order of importance for diversity management factors, from most to least influential, is as follows: age diversity management, gender diversity management, ethnic diversity management, and disability diversity management. Among these factors, only age diversity management was found to have a significant effect on employee engagement.
The results of the multi-group analysis revealed no significant difference in the effect of all independent variables (ethnic diversity management, gender diversity management, age diversity management, and disability diversity management) on employee engagement between the male and female groups.
7. Recommendation
In future research, it is recommended to include a more comprehensive set of variables related to HR management practices. While this study focused on diversity management and employee engagement, numerous other essential variables could be incorporated into the research model, such as organizational culture, work climate, innovation, leadership, creativity, training, and behavior, among others [41]. Including these variables would provide a more holistic understanding of the factors contributing to employee engagement.
Additionally, due to the limited number of respondents with disabilities in this study, a multi-group analysis explicitly addressing the impact of disability diversity management on employee engagement was not feasible. However, in future research, it
is crucial to include a larger sample size of employees with disabilities to facilitate a more comprehensive analysis of the effects of disability diversity management. This would offer valuable insights into the unique challenges and opportunities associated with disability inclusion in the workplace.
To summarize, future research endeavors should consider the inclusion of additional HR management practice variables and conduct a thorough analysis of the disability factor to enhance our understanding of employee engagement and diversity management within the workplace.
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