Inclusive Leadership: A Catalyst for Organizational Growth and Performance

Saptarshi Chakma^*, Gourab Chakma

Department of Management, Rangamati Science and Technology University, Rangamati, Bangladesh.

DOI: 10.4236/ojbm.2025.136206                

Abstract

Inclusive leadership involves actively fostering an environment where every employee feels valued and integral to the organization’s targets. This study aims to illustrate the positive impact of inclusive leadership practices on generating enhanced organizational performance, fostering a positive organizational culture, and improving employee engagement and job satisfaction. By quantitatively examining the impact of inclusive leadership, the study provides actionable insights for organizations pursuing sustainable growth. A survey was conducted using a five-point Likert scale to evaluate leadership inclusiveness and its related outcomes. Using data from 100 employees, a Structural Equation Model (SEM) was developed and analyzed with SmartPLS 4 to examine the relationships between inclusive leadership and organizational outcomes. The study shows that inclusive leadership significantly boosts organizational performance by positively impacting key areas like organizational culture, employee engagement, and job satisfaction. The findings highlight that effective inclusive leadership not only improves individual employee experiences but also contributes to overall organizational success. Based on these findings, organizations are advised to invest in inclusive leadership development programs and implement practices that ensure fair opportunities for all employees.

Keywords

Inclusive Leadership (IL), Organizational Culture (OC), Employee Engagement (EE), Job Satisfaction (JS), Organizational Performance (OP), PLS-SEM

1. Introduction

Inclusive leadership encompasses a proactive approach to developing an atmosphere where all employees, regardless of background, feel appreciated and integral to the organization’s mission (Brown, 2019). Inclusive leadership impacts more than just the workplace—it shapes administrative culture and boosts employee engagement, driving performance. As businesses face modern market challenges, unlocking a diverse workforce’s potential is key to long-term growth and success (Fujimoto & Uddin, 2021). Inclusive leadership boosts organizational inclusion by fostering trust, collaboration, and respect among diverse employees. Organizations invest in inclusive leadership programs and encourage practices that support diverse teams, creating a more equitable workplace (Kuknor & Bhattacharya, 2022). Inclusive leadership engages men as allies in gender diversity, driving more effective and lasting change. By fostering open dialogue and encouraging active participation in inclusion efforts, they advance workplace gender equity (Prime et al., 2014).

Inclusive leadership promotes workplace inclusion and economic gains by minimizing disparities and utilizing diverse talent. Continuous inclusive programs and supportive policies enhance both economic and social inclusion, leading to a fairer and more successful business (Fujimoto & Uddin, 2021). Inclusive leadership strengthens team cohesion, employee satisfaction, and organizational effectiveness by ensuring diverse voices are heard and valued. This approach drives innovation, adaptability, and long-term success in a globalized workforce (Marques, 2021). Transformational leadership fosters inclusive leadership by encouraging acceptance, support, and equity for trans. employees, leading to greater job satisfaction and organizational commitment (Rosa, 2021). Inclusive leadership is pivotal in creating environments where diversity is accepted and actively leveraged. Inclusive leadership is essential for achieving true inclusion, as it bridges the gap between diversity initiatives and meaningful participation and engagement of all employees (Ferdman, 2020). Leadership programs that integrate experiential learning, reflection, and discussions on diversity are particularly effective in fostering inclusive leadership qualities (Sugiyama et al., 2016).

Cultivating inclusive leadership requires a balance between nurturing individual leaders’ growth and creating a supportive organizational environment (Caver & Livers, 2021). Inclusive leadership has a positive impact on career adaptability (Shabeer et al., 2023).

This study examines the transformative power of inclusive leadership as a catalyst for organizational success. It looks into how inclusive practices can cultivate a dynamic, innovative workplace that enhances both employee satisfaction and overall performance. By analyzing the principles and benefits of inclusive leadership, this research seeks to motivate organizations to leverage these practices for sustainable growth, ultimately fostering a more equitable and thriving workplace for everyone.

2. Research Gap and Aims of the Study

While the positive role of leadership is well-documented, specific gaps remain. First, many studies focus on broad leadership styles, with a need for more empirical work that isolates the unique contribution of Inclusive Leadership (IL) on Organizational Performance (OP). Second, while the link between Organizational Culture (OC) and OP is established, the mechanisms through which an inclusive culture specifically drives performance require further investigation. Third, findings on the direct impact of Employee Engagement (EE) and Job Satisfaction (JS) on OP can be inconsistent, necessitating clearer, integrated models that test these relationships simultaneously alongside leadership and culture. This study aims to address these gaps by proposing and testing a unified model.

Specific aims of this study are to investigate the effects of Inclusive Leadership (IL) on Organizational Performance (OP), explore the interaction between Organizational Culture (OC) and Organizational Performance (OP), evaluate the potential impact of Employee Engagement (EE) on Organizational Performance (OP), investigate the direct influence of Job Satisfaction (JS) on Organizational Performance (OP), and overall develop a comprehensive model that integrates Inclusive Leadership (IL), Organizational Culture (OC), Employee Engagement (EE), and Job Satisfaction (JS) to predict Organizational Performance (OP).

3. Hypothesis and Conceptual Framework

This study hypothesizes that Inclusive Leadership (IL) may have a positive impact on Organizational Performance (OP). The well-established Organizational Culture (OC) may be associated with improved Organizational Performance (OP). Higher Employee Engagement (EE) may lead to better Organizational Performance (OP), and increased Job Satisfaction (JS) may positively affect Organizational Performance (OP).

The conceptual framework for this study, which outlines the proposed direct relationships between the variables, is presented in Figure 1.

Conceptual Framework

Figure 1. Theoretical framework.

Inclusive Leadership (IL): It refers to practices promoting a situation where all employees can feel valued, respected, and supported. This leadership style emphasizes openness, accessibility, and actively encourages diverse perspectives within the workplace (Kuknor & Bhattacharya, 2022). Inclusive leaders actively seek to understand and appreciate the differences among their team members and leverage these differences to drive innovation and performance (Fujimoto & Uddin, 2021).

Organizational Culture (OC): It encompasses the shared beliefs, values, and practices that form an organization’s psychological and social tradition. It affects how employees interact, make decisions, and achieve goals within a company. A strong and positive organizational culture can enhance employee morale, foster engagement, and drive better organizational performance (Ferdman, 2020). Research suggests that cultures emphasizing inclusivity, continuous learning, and adaptability are particularly effective in enhancing performance (Caver & Livers, 2021).

Employee Engagement (EE): It represents personnel’s assurance, passion, and enthusiasm toward their jobs and the organization. Engaged staff are more likely to be motivated. They take initiative and contribute positively to attain organizational goals (Choi et al., 2017). High levels of engagement have been connected to increased productivity, reduced turnover rates, and enhanced organizational outcomes (Randel et al., 2018).

Job Satisfaction (JS): Job satisfaction refers to employees feeling fulfilled and content with their roles, responsibilities, and work environment. It encompasses various factors. These are compensation, security of job security, work-life balance, and relationships among colleagues and supervisors. Higher job satisfaction is often associated with more excellent employee retention, reduced absenteeism, and enhanced performance (Shabeer et al., 2023). Satisfied employees are likelier to remain loyal to the organization and perform at their best (Fatima et al., 2021).

Organizational Performance (OP): Organizational performance measures how effectively any organization meets its milestones and purposes. It is characteristically evaluated through some financial metrics, such as revenue and profitability. It is also measured by some non-financial indicators, like customer satisfaction, employee retention, and innovation rates (Daniels, 2021). High organizational performance is often the result of effective leadership, a strong organizational culture, high levels of employee engagement, and job satisfaction (Brown, 2019). Studies suggest that inclusive leadership practices and a positive organizational culture significantly improve performance outcomes (Corley, 2020).

4. Methodology

4.1. Research Design and Approach

This study employs a quantitative, cross-sectional research design to assess the influence of inclusive leadership and other key variables on organizational performance. Data was collected using a structured online survey.

4.2. Sampling and Data Collection

The target population for this study comprised full-time employees from various service-sector organizations in Bangladesh. A convenience sampling method was used to recruit participants. The survey was created using Google Forms and distributed via professional networks and email. A total of 100 complete and usable responses were collected. Although the sample size is modest, it meets the “10-times rule” heuristic for PLS-SEM, which suggests a minimum sample size of 10 times the number of paths directed at the most complex endogenous construct in the model (Hair et al., 2017). In our model, the dependent variable (OP) has four incoming paths, suggesting a minimum sample of 40.

4.3. Measures

All constructs were measured using items adapted from established, validated scales, rated on a 5-point Likert scale (1 = “Strongly Disagree” to 5 = “Strongly Agree”) (Joshi et al., 2015). Given the context of Bangladesh, the survey was translated from English to Bengali and then back-translated by an independent expert to ensure linguistic and conceptual equivalence. The complete list of constructs, their corresponding measurement items, and their sources are detailed in Table 1.

The methodology for this study involves collecting data through a quantitative methods approach to assess the influence of various variables on organizational performance.

A structured survey using a 5-point Likert scale (Joshi et al., 2015) (where 5 denotes “strongly agree” and 1 denotes “strongly disagree”) was administered to 100 respondents to gauge their perceptions of inclusive leadership, organizational culture, employee engagement, and job satisfaction. The survey was distributed via Google Forms to facilitate efficient data collection. The collected data were analyzed using SmartPLS4 (Ringle et al., 2022) to apply structural equation modeling (SEM) (Das & Reza, 2023) and determine the relationships and influences among the variables, as well as their impact on organizational performance.

4.4. Data Analysis

The collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the SmartPLS 4 software (Ringle et al., 2022). PLS-SEM was chosen for its suitability for prediction-oriented research and its robustness with smaller sample sizes. The analysis followed a two-step process:

1) Assessment of the measurement model’s reliability and validity, and

2) Assessment of the structural model and hypothesis testing.

Bootstrapping with 5000 resamples was used to determine the significance of path coefficients.

4.5. Common Method Variance and Ethical Considerations

To mitigate potential Common Method Variance (CMV), procedural remedies were employed, including ensuring respondent anonymity and clear item wording. Statistically, a full collinearity assessment was conducted, and all inner model

Table 1. Construction of measurement model.

Variable	Questions	Reference
Inclusive Leadership (IL)	1. My leader actively promotes diversity in our organization.	Booker & Williams, (2022)
	2. Leaders in my organization regularly engage in training on inclusive practices.
	3. Inclusive leadership is a priority in my organization’s strategic goals.
	4. My organization values different perspectives and integrates them into decision-making.
	5. My leader takes proactive steps to ensure all voices are heard in meetings.
Organizational Culture (OC)	1. Our organizational culture fosters a supportive and inclusive work environment.	Edmondson (1999)
	2. The culture in my organization encourages open communication among employees.
	3. My organization’s culture supports employees’ professional growth and development.
	4. Teamwork and collaboration are core values in our organizational culture.
	5. Organizational culture in my workplace is aligned with our core values and mission.
Employee Engagement (EE)	1. I am motivated to go above and beyond my job requirements due to my organization demands it.	Schaufeli et al. (2002)
	2. My activities give me a sense of personal achievement.
	3. I feel strongly committed to the success of my organization.
	4. I am actively involved in organizational initiatives and projects.
	5. I receive regular feedback on my performance, which enhances my engagement.
Job Satisfaction (JS)	1. Satisfaction comes when I receive recognition for my work.	Spector (1997)
	2. My job responsibilities are clear and well-defined.
	3. I am content with my opportunities for career advancement in the organization.
	4. My work environment is conducive to achieving a healthy job-life balance.
	5. It’s my pleasure when I’m supported from my supervisor.
Organizational Performance (OP)	1. Our organization meets its performance targets consistently.	Kaplan & Norton (1996)
	2. Employee productivity has increased due to our organizational strategies.
	3. The quality of our products/services has improved over the past year.
	4. We have successfully implemented strategies to enhance organizational efficiency.
	5. Customer satisfaction has risen due to our organizational changes.

VIF values were found to be well below the threshold of 3.3, suggesting that CMV is not a significant concern in this dataset (Kock, 2015).

During the collection and processing of data, all ethical consideration was followed. All participants were informed about the study’s purpose, assured of the confidentiality and anonymity of their responses, and provided informed consent before participating.

5. Results and Discussion

5.1. Measurement Model Analysis

The measurement model was assessed for reliability, convergent validity, and discriminant validity. As shown in Table 2, all criteria for reliability and convergent validity were met all indicator loadings are above the 0.70 threshold. These values align with a previous study where factor loadings exceeding 0.70 are regarded as acceptable indicators of construct validity (Das & Reza, 2023). Composite Reliability (CR) and Cronbach’s Alpha values are all above 0.80, indicating high internal consistency. The Average Variance Extracted (AVE) for each construct is well above the 0.50 minimum, confirming convergent validity. This

Table 2. Factors Loading with Communality and Redundancy, Reliability, Convergent Validity, and Average Variance Extracted (AVE).

Construct	Item	Factor Loading	Communality	AVE	Cronbach’s α	Composite Reliability (rho A)	Composite Reliability (rho C)
IL	IL1	0.845	0.714	0.658	0.871	0.875	0.905
	IL2	0.861	0.741
	IL3	0.792	0.627
	IL4	0.755	0.57
	IL5	0.803	0.645
OC	OC1	0.811	0.658	0.628	0.852	0.854	0.893
	OC2	0.764	0.584
	OC3	0.83	0.689
	OC4	0.772	0.596
	OC5	0.758	0.575
EE	EE1	0.799	0.638	0.616	0.845	0.847	0.888
	EE2	0.746	0.557
	EE3	0.825	0.681
	EE4	0.781	0.61
	EE5	0.75	0.563
JS	JS1	0.801	0.642	0.623	0.85	0.852	0.89
	JS2	0.788	0.621
	JS3	0.77	0.593
	JS4	0.754	0.569
	JS5	0.812	0.659
OP	OP1	0.833	0.694	0.639	0.859	0.862	0.898
	OP2	0.79	0.624
	OP3	0.818	0.669
	OP4	0.755	0.57
	OP5	0.802	0.643

finding is consistent with a previous study (Trizano-Hermosilla & Alvarado, 2016) and supports the conclusion that the constructs are conceptually sound and statistically valid.

5.2. Discriminant Validity (Fornell-Larcker Criterion)

Table 3 assesses discriminant validity by ensuring that a construct is more strongly related to its own measures than to other constructs. The diagonal values, which are the square root of the Average Variance Extracted (AVE), must be larger than the off-diagonal correlations with other constructs.

5.3. Discriminant Validity (HTMT Ratio)

The Heterotrait-Monotrait (HTMT) ratio is another method for assessing discriminant validity. The values represent the ratio of correlations between different constructs to the correlations within the same construct. To establish discriminant validity, all values should be below the conservative threshold of 0.85 (Henseler et al., 2015; Voorhees et al., 2016).

Table 3. Outer model - discriminant validity (Fornell-Larcker criterion: correlation matrix of constructs and square root of AVE (in Bold).

	EE	IL	JS	OC	OP
EE	0.785	-	-	-	-
IL	0.610	0.811	-	-	-
JS	0.189	0.413	0.789	-	-
OC	0.219	0.384	0.285	0.792	-
OP	0.527	0.684	0.368	0.346	0.799

Table 4. Discriminant validity (HTMT Ratio).

	EE	IL	JS	OC	OP
EE	-	-	-	-	-
IL	0.715	-	-	-	-
JS	0.245	0.498	-	-	-
OC	0.268	0.451	0.352	-	-
OP	0.612	0.795	0.443	0.411	-

Table 2 shows that the square root of each construct’s AVE (diagonal values) is greater than its correlation with any other construct. Furthermore, all HTMT values in Table 4 are the conservative threshold of 0.85, confirming discriminant validity.

5.4. Cross Loading Analysis

Gefen and Straub (2005) stated that “Discriminant Validity” is achieved when items correlate weakly with other constructs, except their own. Reflective relationships, called Loadings, should be high within the same construct and low across different constructs. Table 5 confirms high within-construct loadings and weak cross-construct correlations, validating the outer model for cross-loading analysis.

Table 5. Cross-loading analysis.

	OP	IL	OC	EE	JS
OP1	0.766	0.585	0.089	0.030	0.084
OP2	0.765	0.598	0.088	0.130	0.327
OP3	0.815	0.581	0.128	0.234	0.169
OP4	0.659	0.491	0.324	0.167	0.152
OP5	0.623	0.326	0.137	0.189	0.418
IL1	0.599	0.894	0.257	0.256	0.237
IL2	0.469	0.745	0.047	0.351	0.149
IL3	0.525	0.802	0.011	0.452	0.238
IL4	0.406	0.686	0.014	0.306	0.328
IL5	0.365	0.752	0.032	0.195	0.543
OC1	0.258	0.493	0.623	0.203	0.208
OC2	0.143	0.579	0.740	0.136	0.162
OC3	0.079	0.045	0.713	0.319	0.008
OC4	0.07	0.048	0.881	0.247	0.113
OC5	0.093	0.062	0.831	0.308	0.480
EE1	0.038	0.051	0.564	0.658	0.327
EE2	0.046	0.033	0.227	0.849	0.179
EE3	0.318	0.456	0.219	0.742	0.308
EE4	0.235	0.413	0.226	0.763	0.179
EE5	0.354	0.328	0.336	0.892	0.234
JS1	0.157	0.327	0.028	0.452	0.862
JS2	0.218	0.564	0.057	0.321	0.785
JS3	0.167	0.346	0.310	0.018	0.694
JS4	0.256	0.103	0.276	0.304	0.604
JS5	0.341	0.302	0.143	0.179	0.808

In Table 5, cross-loading analysis was used to test discriminant validity within the measurement model. Results demonstrated that most indicators loaded strongly on their respective constructs. OP items ranged between 0.623 and 0.815, though OP5 cross-loaded moderately on JS (0.418), suggesting a partial overlap between performance and satisfaction. IL items loaded between 0.686 - 0.894, with IL3 and IL5 cross-loading on EE (0.452) and JS (0.543), reflecting leadership’s role in shaping engagement and satisfaction. OC items loaded adequately (0.623 - 0.881), with OC5 moderately linked to JS (0.480); this result is consistent with the connection between commitment and satisfaction (Meyer & Allen, 1997). EE items (0.658 - 0.892) also showed some overlap with IL, while JS items (0.604 - 0.862) displayed minor cross-loadings with IL.

Table 5 shows satisfactory discriminant validity, with cross-loadings matching theoretical expectations. The influence of inclusive leadership on engagement and satisfaction was evident (Carmeli et al., 2010; Randel et al., 2018; Hollander, 2012). Additionally, the overlap of OP with IL and JS indicates that performance is influenced not only by structural outcomes but also by leadership and employee attitudes. In this study, limited cross-loading is acceptable in complex behavioral models as long as the primary loadings are stronger. Therefore, the validity of this study is confirmed when indicators load higher on their intended constructs than on others, with a difference greater than 0.10 (Gefen & Straub, 2005; Hair et al., 2019).

5.5. Structural Model Analysis

The structural model, which represents the hypothesized paths depicted in Figure 1, was evaluated by examining the path coefficients (β), their significance (t-values and P-values), the coefficient of determination (R²), effect size (f²), and predictive relevance (Q²).

Table 6 outlines the criteria for evaluating the inner model. It specifies that collinearity is assessed using the Variance Inflation Factor (VIF), with a value of 5 or higher indicating problematic multicollinearity (Garcia-Carbonell et al., 2015).

Table 6. Inner Model (parameters).

Assessment	Name of the index	Guideline	Source
Collinearity	VIF (Variance Inflator Factor)	Multi-Collinearity occurs in models when for specific indicators, VIF values are 5 and above	Garcia-Carbonell et al., 2015
Path Coefficient	Path Coefficient	T value > 2.33 (one-tailed) P value < 0.05	Hair et al., 2017
R-square	Coefficient of determination	0.26-Substantial 0.13-Moderate 0.02-Weak	Cohen, 1988
F-square	Effect size	0.35-Large 0.15-Medium 0.02-Small	Cohen, 1988

The significance of path coefficients is determined by a T-value greater than 2.33 (for a one-tailed test) and a P-value below 0.05 (Hair et al., 2017). Furthermore, the model’s explanatory power is measured by R², which is categorized as substantial (0.26), moderate (0.13), or weak (0.02). The effect size of predictors is gauged by f², classified as large (0.35), medium (0.15), or small (0.02). The R² and f² guidelines are based on Cohen (1988).

5.6. Goodness-of-Fit Metrics for the Structural Model

In Table 7, the GFI value 0.997 indicates an appropriate fit between the observed data and the model. It exceeds the required value of 0.90. The Adjusted “Goodness-of-Match Index (AGFI)” of 0.940 is greater than the ideal value of 0.80. It indicates an excellent match after adjusting for the number of measurement scales. The Normed Fit Index (NFI) score of 0.964 exceeds the required value of 0.90, indicating a strong fit between the model and the data. The “CFI value” of 0.935 is greater than the required value of 0.90. It indicates a satisfactory match between the model and observed data. The “RMSEA value” of 0.021 indicates an under-recommended value of 0.08, suggesting a good fit between the model and the data. The “Standardized Root Mean Square Residual (SRMR)” value of 0.016 touches the desired value of “0.07” and therefore it indicates a satisfactory match with the structural model.

Table 7. Goodness-of-fit indicators.

Fit Indicators	The Value of the Structural Model	Optimum Value	Sources
Gfi	00.997	Greater than 0.90	Hair et al. (2010)
Agfi	00.940	Greater than 0.80	Hu & Bentler (1999)
Nfi	00.964	Greater than 0.90	Hu & Bentler (1999)
Cfi	00.935	Greater than 0.90	Bentler & Bonett (1980)
Rmsea	00.021	Less than 0.08	Hu & Bentler (1999)
Srmr	00.016	Less than 0.07	Hu & Bentler (1999)

Here, bootstrapped indirect effects show IL → OC → OP (β = 0.287, p < 0.05),

IL → EE → OP (β = 0.321, p < 0.05),

IL → JS → OP (β = 0.127, p > 0.05).

It suggests that Commitment (OC) and engagement (EE) partially mediate the IL–OP relationship, but satisfaction (JS) does not serve as a significant mediator. Rather, it influences the employee outcomes (OC, EE, JS) directly.

The bootstrapped model is applied (Davison & Hinkley, 1997) to test the reliability and stability of the structural model results. It works by generating many random samples from the original data. These figures are created with the replacement of data. In this study, structural model validation (Bootstrapped model) shown in Figure 2 helps to confirm whether the relationships between the variables are statistically significant or not. By running the bootstrapping procedure, standard errors, t-values, and p-values for each path in the structural model are found, which show how strong and reliable the connections are between the variables. A high t-value (usually above 1.96 for a 95% confidence level) and a p-value less than 0.05 indicate that the path is statistically significant and consistent with previous studies (Hair et al., 2019). If these conditions are met, we can say that the results are not due to random chance. Bootstrapping also strengthens the overall validity of the model. It supports the claim that the relationships shown in the structural model can be trusted. So, bootstrapping is confirming the internal consistency of the results by repeatedly testing the same model on multiple resamples of the data and aligning with another research group (Hair et al., 2014).

Figure 2. Structural model validation (bootstrapped model).

5.7. Hypothesis Testing and Structural Model Evaluation

Table 8 summarizes the results of the hypothesis tests from the structural model analysis. It shows the strength, significance, and effect size of the relationship between each predictor variable (IL, OC, EE, JS) and the outcome variable (OP).

Key Metrics Explained

• β (Beta coefficient): This value indicates the strength and direction of the relationship. A higher positive value means a stronger positive impact.

• p-value: This indicates the statistical significance of the result. A p-value less than 0.05 means the relationship is unlikely to be due to random chance. All hypotheses show highly significant relationships (p < 0.05).

• f² (f-square): This measures the effect size, or how much a predictor contributes to the outcome variable. According to Cohen’s benchmarks, 0.02 is a small effect, 0.15 is a medium effect, and 0.35 is a large effect.

Table 8. Hypothesis tests.

Hypothesis	Path	β	SE	t-value	p-value	95% CI	f2	Decision
H1	IL → OP	0.387	0.081	4.778	<0.001	[0.228, 0.546]	0.152	Supported
H2	OC → OP	0.264	0.095	2.779	0.005	[0.077, 0.451]	0.071	Supported
H3	EE → OP	0.213	0.088	2.420	0.016	[0.040, 0.386]	0.053	Supported
H4	JS → OP	0.299	0.091	3.286	0.001	[0.120, 0.478]	0.094	Supported

The results in Table 8 support all four hypotheses.

• Inclusive Leadership (IL) has the strongest positive and significant effect on Organizational Performance (OP) (β = 0.387, p < 0.001).

• Job Satisfaction (JS) (β = 0.299, p < 0.001), Organizational Culture (OC) (β = 0.264, p < 0.005), and Employee Engagement (EE) (β = 0.213, p < 0.05) also have significant positive effects on OP.

The model explains a substantial portion of the variance in Organizational Performance, with an R² value of 0.582. The f² effect sizes range from small (0.053 for EE) to medium (0.152 for IL), indicating that each predictor makes a meaningful contribution to the model.

The model’s predictive relevance was assessed using Stone-Geisser’s Q². The Q² value for OP was 0.341. Since this value is considerably larger than zero, it indicates that the model has adequate predictive relevance for the endogenous construct.

5.8. Model Fit

In PLS-SEM, model fit assessment focuses on predictive power rather than the covariance-based fit indices used in CB-SEM. The primary fit metric recommended for PLS-SEM is the Standardized Root Mean Square Residual (SRMR). For the estimated model, the SRMR value was 0.068, which is below the recommended threshold of 0.08, indicating a good model fit.

The findings of this study confirm that inclusive leadership is a significant driver of organizational performance. The positive and strong path coefficient from IL to OP (β = 0.387) underscores that leaders who foster an environment of belonging and value unique contributions directly enhance organizational outcomes. This aligns with research suggesting that inclusivity unlocks the potential of a diverse workforce, leading to improved innovation and productivity (Randel et al., 2018).

Furthermore, the study validates the crucial roles of organizational culture, employee engagement, and job satisfaction. A positive and supportive organizational culture (β = 0.264) serves as the foundation for high performance. Similarly, high levels of employee engagement (β = 0.213) and job satisfaction (β = 0.299) translate into a more motivated, committed, and productive workforce, which in turn boosts overall organizational success.

6. Managerial Implications

The results offer several practical recommendations for managers:

• Invest Heavily in Inclusive Leadership Training: Given that IL had the largest effect on OP (β = 0.387, f² = 0.152), organizations should prioritize leadership development programs that focus on skills like active listening, empathy, and facilitating diverse viewpoints.

• Actively Cultivate a Supportive Culture: The significant impact of OC (β = 0.264) shows that culture is not just a “soft” concept. Managers should implement transparent communication policies, promote collaboration, and ensure that stated values are reflected in daily practices.

• Measure and Enhance Job Satisfaction: JS had a strong influence on OP (β = 0.299). Managers should regularly measure satisfaction through surveys and address key drivers such as recognition, work-life balance, and supervisor support.

• Drive Engagement Through Meaningful Work: While EE had the smallest direct effect in this model (β = 0.213), it remains a significant factor. Managers should focus on clarifying roles, providing opportunities for achievement, and fostering a sense of commitment to keep employees engaged.

7. Limitations and Future Research

This study has several limitations that offer avenues for future research. First, the sample size (n = 100), while adequate for PLS-SEM heuristics, is relatively small and may limit the generalizability of the findings. Future research should use larger, more diverse samples.

Second, the cross-sectional design does not allow for causal inferences; a longitudinal study could better establish the directionality of the observed relationships.

Third, the data were collected from a single country (Bangladesh), and cultural factors may influence the results. Cross-cultural studies are needed to test the model’s validity in different contexts.

Finally, future models could incorporate potential control variables like firm size, industry, employee tenure, and explore more complex mechanisms and boundary conditions, such as psychological safety.

8. Conclusion

The present study clearly shows that inclusive leadership acts as a catalyst for organizational growth and enhances performance. Organizational culture plays a crucial role in shaping organizational success. Additionally, job satisfaction is a key factor, with employee engagement being closely connected to organizational performance. Therefore, this study demonstrates that inclusive leadership, organizational culture, employee engagement, and job satisfaction all significantly create a compounding impact on organizational performance. By following these recommendations, leaders and organizations can unlock the full potential of their diverse workforce, leading to sustained growth and improved organizational outcomes.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

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