Exploring the Relationship Between Leadership Behaviour and Firm Performance in the RMG Industry of Bangladesh: The Mediating Role of Competitive Advantage and the Moderating Influences of Emotional Intelligence and Firm Strategy

doi:10.21203/rs.3.rs-3667408/v3

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Exploring the Relationship Between Leadership Behaviour and Firm Performance in the RMG Industry of Bangladesh: The Mediating Role of Competitive Advantage and the Moderating Influences of Emotional Intelligence and Firm Strategy

https://doi.org/10.21203/rs.3.rs-3667408/v3

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This study delves into the intricate relationship between leadership behaviour and firm performance within Bangladesh's Ready-Made Garments (RMG) industry. Utilising a correlational quantitative approach with surveys from 400 leaders across various managerial echelons, the research uncovers the pivotal role of leadership in shaping firm outcomes. Notably, competitive advantage emerges as a mediator, with emotional intelligence and firm strategy serving as critical moderators.

Other Business

Leadership Behaviour

Competitive Advantage

Firm Performance

Emotional Intelligence

Ready Made Garments (RMG)

Developing Country Bangladesh

In the bustling factories of Bangladesh, amidst the hum of machines and the coordinated dance of workers, lies a secret recipe for success: leadership. But how does this leadership mould firm performance in an industry that clothes the world?

The Ready-Made Garments (RMG) industry is the backbone of Bangladesh's economy, accounting for a significant portion of its exports and providing employment to millions. Yet, not all firms thrive equally in an industry marked by tight margins, competitive pressures, and global scrutiny. What separates the leaders from the laggards? This study posits that the answer may lie in the nuanced interplay of leadership behaviour and firm strategy, mediated by competitive advantage and modulated by emotional intelligence.

While leadership's pivotal role in determining firm outcomes is well-acknowledged, the RMG industry of Bangladesh offers a unique canvas. Here, traditional management paradigms intersect with cultural nuances, global demands meet local realities, and the imperatives of sustainability and ethics take centre stage. Understanding the multifaceted relationship between leadership and firm performance becomes paramount in such a complex milieu. This exploration promises academic enrichment and practical insights for industry stakeholders seeking to navigate the challenges of the RMG sector in Bangladesh.

The Ready-Made Garments (RMG) industry is a testament to Bangladesh's resilience and adaptability. Originating in the late 1970s, this industry rapidly grew to become the nation's largest export sector, contributing to over 80% of its total exports and positioning Bangladesh as the second-largest apparel producer in the world. Amidst global competitors, the RMG industry has not only offered the nation a solid economic foothold but has also been instrumental in uplifting millions from poverty and catalysing social change, especially for women who constitute a significant portion of the industry's workforce.

However, the path to success in this sector is fraught with challenges. From the tragic Rana Plaza collapse, which spotlighted issues of worker safety and rights, to the constant pressures of global supply chains demanding faster, cheaper, and better products - the RMG industry's landscape is in perpetual flux. Internal organisational dynamics are pivotal in determining a firm's success amid these external challenges. At the heart of these dynamics lies leadership.

Leadership, a force that can inspire, direct, and transform, is often the differentiating factor between firms that merely survive and those that thrive. Effective leadership behaviour can drive productivity, foster innovation, and ensure adaptability in the face of challenges. Leadership's role becomes even more pronounced in an industry as competitive as RMG, where margins are thin, and the stakes are high.

Yet, leadership does not operate in isolation. Its impact on firm performance is intertwined with other critical elements. Competitive advantage, for instance, can amplify the effects of good leadership, allowing firms to leverage unique assets, capabilities, or market positions to outperform competitors. Meanwhile, the emotional intelligence of leaders can enhance their ability to navigate interpersonal relationships, make strategic decisions, and foster a positive organisational culture. Furthermore, a firm's overarching strategy, cost leadership, differentiation, or focus can modulate the relationship between leadership behaviour and performance outcomes.

Within Bangladesh's dynamic Ready-Made Garments (RMG) industry, the pivotal role of effective leadership cannot be overstated. As the industry navigates its unique challenges and capitalises on emerging opportunities, understanding the profound connections between leadership behaviour, employee engagement, organisational learning, and innovation becomes paramount. Despite the significant economic contributions of the RMG sector, there remains a scholarly gap in comprehensively addressing the distinct leadership challenges faced by this industry (IRA A Jackson, 2022; Kabeer and Nazneen, 2019). This research seeks to bridge this void (Berg et al., 2020) by delving deep into the interplay between leadership behaviour and firm performance.

The motivation behind this study is multifaceted. It aims to enrich academic discourse and offer actionable insights tailored to the industry's complexities. By addressing this, the research holds profound importance for a spectrum of stakeholders, ranging from workers and managers to policymakers and academic researchers. The findings will provide a roadmap for refining leadership strategies, informing more effective policy decisions, and ultimately transforming industry practices. Such insights can reshape leadership development programs, enhance productivity, and foster employee satisfaction and competitive advantage. Furthermore, from a policy perspective, the results can guide the creation of rules and guidelines that underscore the industry's long-term sustainability and success.

This study will fill a crucial gap on a scholarly front, offering a comprehensive understanding of how leadership behaviour intricately impacts company performance, especially within the unique context of Bangladesh's RMG landscape. The primary objective herein is to elucidate the relationship between leadership behaviour and firm performance, considering the mediating role of competitive advantage and the moderating influences of emotional intelligence and firm strategy. In doing so, the research provides invaluable insights that have the potential to elevate the RMG industry's success and competitiveness, promoting a brighter future for Bangladesh's pivotal economic sector.

2.1 Leadership Behaviour

Leadership behaviour is a cornerstone topic in organisational studies, given its profound influence on many organisational outcomes. Leadership behaviour refers to leaders' actions and interactions to influence followers toward achieving organisational goals (Yukl, 2012). Over the decades, scholars have identified various types of leadership behaviours:

Task-Oriented Behaviour (TOB): Focuses on task completion and efficient operation. Leaders prioritise clear role structures and achieve goals within set timelines (Fleishman et al., 1991).
Relations-Oriented Behaviour (ROB): Emphasises interpersonal relationships, team dynamics, and the well-being of team members. Such leaders are more nurturing and focus on supporting and developing individuals (Judge & Piccolo, 2004).
Change-Oriented Behaviour (COB): This behaviour is about fostering innovation, adapting to external changes, and driving organisational transformation (Bass, 1999).

There is substantial empirical evidence supporting the influence of leadership behaviour on firm performance. For instance, a meta-analysis by Judge and Piccolo (2004) established a positive correlation between relations-oriented leadership and variables like job satisfaction, team cohesion, and organisational performance. On the other hand, TOB was found effective in situations demanding operational efficiency, especially in crises (Fleishman et al., 1991).

2.2 Competitive Advantage as a Mediator

Competitive advantage, a concept popularised by Porter (1985), signifies a firm's ability to outperform its rivals. It can be derived from various sources, such as cost leadership, differentiation, or having access to unique resources, and is vital for long-term profitability and market leadership.

Leadership plays an instrumental role in carving and sustaining a competitive advantage. Through vision and strategy, leaders can identify unique market opportunities, allocate resources efficiently, and foster a culture of continuous innovation. For instance, transformational leaders, known for their visionary approach, often drive firms towards innovative practices that lead to a sustainable competitive advantage (Bass, 1985).

2.3 Emotional Intelligence as a Moderator

Introduced by Salovey and Mayer (1990) and later popularised by Goleman (1995), emotional intelligence (EI) encompasses the ability to recognise, understand, manage, and effectively use emotions in oneself and others. It comprises self-awareness, self-regulation, motivation, empathy, and social skills.

The intersection of EI and leadership has been a focal point in recent leadership studies. Leaders with high EI are more adept at navigating interpersonal dynamics, inspiring their teams, and fostering a positive organisational culture (Goleman, 1998). In a practical setting, Rosete and Ciarrochi (2005) found that leaders' emotional intelligence positively correlates with organisational performance metrics.

2.4 Firm Strategy as a Moderator

Building on the seminal work of Porter (1980), firms generally align their strategies around three generic types:

Cost Leadership: Where firms focus on efficiency and produce goods or services at the lowest possible cost.
Differentiation: Where firms aim to offer unique products or services, distinguishing themselves from competitors.
Focus: Where firms target a specific market segment with tailored offerings.

A firm's strategic orientation can significantly modulate leadership's effects on performance. For instance, transformational leaders emphasising innovation and creativity may be particularly effective in firms pursuing a differentiation strategy. Conversely, a leader with strong task-oriented behaviour might be more effective in a firm rooted in a cost leadership strategy, prioritising efficiency and standardisation (Bass & Riggio, 2006).

3.1 Main Constructs

Leadership Behaviour: Based on the literature, leadership behaviour can be classified into Task-Oriented Behaviour (TOB), Relations-Oriented Behaviour (ROB), and Change-Oriented Behaviour (COB).
Firm Performance: This is your dependent variable, and it can be measured through various indicators such as profitability, market share, or employee satisfaction.
Competitive Advantage: Acting as a mediator refers to a firm's ability to create more economic value than its competitors.
Emotional Intelligence: This will be a moderator, influencing how leadership behaviour impacts firm performance.
Firm Strategy: Another moderating variable refers to the overarching strategy the firm employs (e.g., cost leadership, differentiation, or focus).

3.2 Proposed Relationships

Leadership Behaviour and Firm Performance: There is a direct relationship between leadership behaviour and firm performance. Leadership behaviours (TOB, ROB, COB) might impact firm performance differently.
Mediating Role of Competitive Advantage: Leadership behaviour influences competitive advantage, affecting firm performance. For instance, transformational leaders might foster innovation, giving the firm a competitive advantage that leads to better performance.
Moderating Role of Emotional Intelligence: The impact of leadership behaviour on firm performance is influenced by the leader's emotional intelligence. Leaders with high EI might more effectively translate their behaviours into positive firm outcomes.
Moderating Role of Firm Strategy: A firm's strategy can influence the relationship between leadership behaviour and firm performance. For example, relations-oriented behaviour might benefit a firm with a differentiation strategy more than a cost leadership strategy.

3.3 Hypotheses

The study formulated the following hypotheses:

H1: Leadership behaviour positively affects firm performance in the RMG industry of Bangladesh.
H2: Competitive advantage mediates the relationship between leadership behaviour and firm performance.
H3: Emotional intelligence strengthens the relationship between leadership behaviour and firm performance.
H4: Firm strategy moderates the relationship between leadership behaviour and firm performance.

Table 1 Methodology

Aspect	Methodology
Research approach	Correlational
Reasoning	Deductive
Options	Primary
Method	Quantitative approach
Method: Survey	Questionnaire
Data Collection	Physically
Data Analyses	SPSS and PLS
Method
Research Pattern	This research is cross-sectional and correlated, where research approaches were used. · Quantitative (e.g., experiments, surveys), Deductive
Population Size	All managers working in the 4,000 (active) RMG factories of Bangladesh.
Sample Size	400 samples (400 factories)
Sampling Techniques	RMGs located in Dhaka, Mirpur, Savar, Narayanganj and Chittagong using Judgmental Sampling Technique (Stratified Cluster Random Sampling Design)
Data Collection	Physically filling up the questionnaires method was used in the present study.
Data Analysis Techniques	Quantitative data analysis· Statistical Package for Social Science (SPSS) 20.0 version- data inserting and descriptive analysis of the variables. · Structural Equation Modelling (SEM) (to understand the relationship among variables that signifies the structural relationships among constructs) (Smart PLS-2.0)· Measurement Model (Cross Loading, AVE, CR, DV)· Structural Model (R2, Path coefficient and Hypothesis Testing)

Statistics Model Description

FP f (LB EI) (CA S),
where FP = Firm performance; LB = leadership Behaviour; EI =Emotional Intelligence CA=competitive Advantage and FS = Firm strategy

LB=f(TOB+ROB+COB) EI

where LB = leadership Behaviour, TOB=Task-Oriented Behaviour, ROB= Relation-Oriented Behaviour, and COB=Change-Oriented Behaviour,

CA=f C, Q, T, F
where CA=Competitive Advantage where C=Cost, Q=Quality, T=Time, F=Flexibility

FS= f (Cl, D)

where FS = Firm Strategy, Cl = cost leadership; D = differentiation

FP= f {(TOB, ROB, COB, EI) (C, Q, T, F) (Cl, D)}

The model was, therefore, specified as follows:

FP = α 0+ α 1 LB+ α 2 CA + α 3 FS + µ

Where α0 is a constant, denoting the performance of RMG firm that is independent of the respective leadership behaviour, α1, α2, α3 are model coefficients denoting the effect of the leadership behaviours, competitive advantage and firm strategy on firm performance, and μ is a random variable introduced to accommodate the effect of other factors that affect organisational performance within or outside the leadership behaviours and firm strategy that are not included in the model.

5.1 Descriptive Analysis:

Table 2 Description of Respondents

Category of Respondents	Freq	Total	Per cent	Level%		M	F	Education
General Manager and above	20	30	5	7.5	TOP	30		Graduate and above
Plant Manager	10	30	2.5	7.5	TOP	30		Graduate and above
Production/Operation Manager-Cutting, Sewing, Finishing, OHS, Compliance, Welfare, Admin, Security	160	200	40	50	MID	180	20	Graduate
Supply Chain Manager	4		1
Quality Manager	12		3
Inventory Manager	12		3
Merchandising MM	12		3
Line Managers and Supervisors	170	170	42.5	42.5	LINE	168	2	SSC and Above
Total	400	400	100	100		378	22

Data were collected from the respondents randomly and analysed after testing reliability and validity using appropriate statistical tools to remove any biases. The table shows that the study surveyed leaders of all departments of the RMG. Also, leaders from different hierarchies represented the sample, as shown in the above table 2.

Based on the provided data, here is a breakdown of the categories of respondents, their frequencies, and other relevant information:

Category of Respondents:

- General Manager and above: 20 respondents

- Plant Manager: 10 respondents

- Production/Operation Manager (Cutting, Sewing, Finishing, OHS, Compliance, Welfare, Admin, Security): 160 respondents

- Supply Chain Manager: 4 respondents

- Quality Manager: 12 respondents

- Inventory Manager: 12 respondents

- Merchandising MM: 12 respondents

- Line Managers and Supervisors: 170 respondents

Freq: This column represents the frequency or count of respondents in each category.

Total: This column indicates the total number of respondents within each category.

Per cent: This column represents the percentage of respondents in each category, calculated as the frequency divided by the total and multiplied by 100.

Level%: This column indicates the percentage of respondents at each level (TOP, MID, LINE) concerning the total number of respondents. It provides a breakdown of respondents based on their hierarchical positions.

M: This column represents the number of male respondents within each category.

F: This column represents the number of female respondents within each category.

Education: This column provides information on the educational qualifications of respondents. It indicates the minimum education level required for each category, ranging from "Graduate and above" for General Managers and above to "SSC and Above" for Line Managers and Supervisors.

The "Total" row at the bottom summarises the overall totals for each column.

Table 3 Description of Experience

Years	Frequency	Per cent
Above 10 (ten) years	28	7	42
6-10 years	140	35	42
4 – 6 years	159	39.75	39.75
2 – 4 Years	54	13.5	18.25
Below 2 Years	19	4.75	18.25
Total	400	100

Table 3 shows the experience level of the leaders surveyed. The main representation reflects experience from 4 to 10 years, 81.75%; however, it represented all segments, including 2 to 4 years (18.25%) and all departments of the RMG. Based on the provided data, below is a breakdown of the years of experience of respondents, their frequencies, and percentages:

Years:

- Above 10 (ten) years: 28 respondents

- 6-10 years: 140 respondents

- 4-6 years: 159 respondents

- 2-4 years: 54 respondents

- Below two years: 19 respondents

Frequency: This column represents the frequency or count of respondents in each category. Per cent: This column indicates the percentage of respondents in each category, calculated as the frequency divided by the total number of respondents and multiplied by 100. The "Total" row at the bottom summarises the overall totals for each column. Please note that the "Per cent" percentages are calculated based on the total number of respondents (400).

5.2 Measurement Model

Table 4 Measurement Model

Measurement model	Factor Loading	Bootstrap	Loading Communality	T stat	P value	CI Lower bound	CI Upper bound
LB1 > LB	0.75	0.8299023	0.69968577	17.31462	0.017	-0.198683407	1.0624684
LB2 > LB	0.75	0.8157680	0.67016487	16.78785	0.001	0.091666308	1.1393047
LB3 > LB	0.71	0.8210800	0.68009676	18.30639	0.028	-0.235354585	1.1104703
LB4 > LB	0.80	0.6264209	0.69968577	5.199253	0.001	-0.099490496	1.5397275
LB5 > LB	0.77	0.5695468	0.67016487	5.169267	0.001	-0.024507762	1.3778029
EI1 > EI	0.75	0.2835585	0.72681187	16.78785	0.000	0.881615375	0.9613567
EI2 > EI	0.73	0.8210800	0.37727915	18.30639	0.000	0.905172829	0.9830204
EI3 > EI	0.76	0.6116450	0.7822307	5.199253	0.000	0.839399584	0.9288267
EI4 > EI	0.75	0.6938507	0.8589197	5.169267	0.000	0.884517672	0.9635812
EI5 > EI	0.81	0.2835585	0.7930807	8.765698	0.030	0.847266095	0.9291377
FS1 > FS	0.88	0.7525705	0.8159036	23.04261	0.000	0.859679443	0.9435627
FS2 > FS	0.73	0.8255826	0.8888708	6.190371	0.000	0.902665831	0.9834514
FS3 > FS	0.74	0.5206625	0.8197707	8.16379	0.000	0.857031357	0.943883
FS4 > FS	0.82	0.8157680	0.69968577	1.478596	0.000	0.869531593	0.9487633
FS5 > FS	0.75	0.8210800	0.67016487	7.813439	0.000	0.85739402	0.9397071
CA1 > CA	0.88	0.6264209	0.68009676	11.75615	0.000	0.905963795	0.9509332
CA2 > CA	0.82	0.5695468	0.69968577	8.765698	0.000	0.888407549	0.9378502
CA3 > CA	0.70	0.7829211	0.67016487	23.04261	0.000	0.900241818	0.9525381
CA4 > CA	0.80	0.8493204	0.41456400	6.190371	0.000	0.878336123	0.9298726
CA5 > CA	0.80	0.6116450	0.34698769	8.16379	0.000	0.904375807	0.9555749
FP1 > FP	0.80	0.6938507	0.61803284	1.478596	0.020	0.8965337	0.9411892
FP2 > FP	0.70	0.2835585	0.72681187	7.813439	0.000	0.862706075	0.925937
FP3 > FP	0.77	0.7525705	0.69968577	11.75615	0.000	0.897762384	0.9413527
FP4 > FP	0.76	0.8255826	0.8192713	3.788371	0.000	0.882007712	0.930962
FP5 > FP	0.72	0.5206625	0.8232943	9.471641	0.000	0.879810792	0.9337438
FP6 > FP	0.83	0.6938507	0.8524725	5.332425	0.010	0.893773931	0.9449414
FP7 > FP	0.81	0.2835585	0.69968577	8.16379	0.000	0.871845636	0.932434
FP8 > FP	0.75	0.7525705	0.67016487	1.478596	0.000	0.868726108	0.9194747
LB1EI1 > LBEI	0.77	0.8255826	0.68009676	5.199253	0.000	0.055141907	0.9021409
LB1EI2 > LBEI	0.83	0.5206625	0.69968577	5.169267	0.000	-0.005989071	0.9245697
LB1EI3 > LBEI	0.82	0.8210800	0.67016487	8.765698	0.000	0.154832819	0.8815486
LB1EI4 > LBEI	0.74	0.6264209	0.41456400	23.04261	0.000	-0.092606217	0.9367148
LB1EI5 > LBEI	0.72	0.5695468	0.34698769	6.190371	0.000	0.134331891	0.8937315
LB2EI1 > LBEI	0.69	0.7829211	0.61803284	8.16379	0.000	0.108351643	0.8996157
LB2EI2 > LBEI	0.79	0.8493204	0.72681187	1.478596	0.000	0.106254314	0.9039636
LB2EI3 > LBEI	0.84	0.6938507	0.69968577	7.813439	0.00015	0.066219357	0.9247419
LB2EI4 > LBEI	0.72	0.2835585	0.7150104	11.75615	0.000	0.029126958	0.9216056
LB2EI5 > LBEI	0.76	0.7525705	0.3689677	8.765698	0.002	0.081339152	0.9058828
LB3EI1 > LBEI	0.81	0.8255826	0.6269799	23.04261	0.000	0.053436407	0.8957571
LB3EI2 > LBEI	0.70	0.5206625	0.671865	6.190371	0.000	0.03238329	0.9176138
LB3EI3 > LBEI	0.77	0.8210800	0.395804	8.16379	0.001	0.142787847	0.8997616
LB3EI4 > LBEI	0.74	0.6264209	0.7968311	1.478596	0.000	-0.077193949	0.9324824
LB3EI5 > LBEI	0.76	0.5695468	0.4657581	17.31462	0.020	0.149954754	0.9002281
LB4EI1 > LBEI	0.82	0.7829211	0.6528582	16.78785	0.000	0.098182661	0.9006246
LB4EI2 > LBEI	0.70	0.8493204	0.6804998	18.30639	0.000	0.133319833	0.9273033
LB4EI3 > LBEI	0.80	0.6116450	0.3891297	5.199253	0.001	0.127972136	0.8983584
LB4EI4 > LBEI	0.80	0.6938507	0.7817577	5.169267	0.000	0.019696357	0.929828
LB4EI5 > LBEI	0.80	0.5206625	0.4626711	8.765698	0.000	0.124243255	0.8985385
LB5EI1 > LBEI	0.70	0.6938507	0.5701494	23.04261	0.000	0.055258528	0.8845262
LB5EI2 > LBEI	0.77	0.2835585	0.5920342	6.190371	0.000	0.036063524	0.9007104
LB5EI3 > LBEI	0.76	0.7525705	0.3011806	8.16379	0.007	0.05510949	0.9169037
LB5EI4 > LBEI	0.72	0.8255826	0.725699	1.478596	0.000	-0.047466042	0.9190569
LB5EI5 > LBEI	0.75	0.5206625	0.3693078	7.813439	0.002	0.125068797	0.9092492
LB1FS1 > LBFS	0.77	0.8255826	0.68009676	5.199253	0.000	0.055141907	0.9021409
LB1FS2 > LBFS	0.83	0.5206625	0.69968577	5.169267	0.000	-0.005989071	0.9245697
LB1FS3 > LBFS	0.82	0.8210800	0.67016487	8.765698	0.000	0.154832819	0.8815486
LB1FS4 > LBFS	0.74	0.6264209	0.41456400	23.04261	0.000	-0.092606217	0.9367148
LB1FS5 > LBFS	0.72	0.5695468	0.34698769	6.190371	0.000	0.134331891	0.8937315
LB2FS1 > LBFS	0.69	0.7829211	0.61803284	8.16379	0.000	0.108351643	0.8996157
LB2FS2 > LBFS	0.79	0.8493204	0.72681187	1.478596	0.000	0.106254314	0.9039636
LB2FS3 > LBFS	0.84	0.6938507	0.69968577	7.813439	0.00015	0.066219357	0.9247419
LB2FS4 > LBFS	0.72	0.2835585	0.7150104	11.75615	0.000	0.029126958	0.9216056
LB2FS5 > LBFS	0.76	0.7525705	0.3689677	8.765698	0.002	0.081339152	0.9058828
LB3FS1 > LBFS	0.81	0.8255826	0.6269799	23.04261	0.000	0.053436407	0.8957571
LB3FS2 > LBFS	0.70	0.5206625	0.671865	6.190371	0.000	0.03238329	0.9176138
LB3FS3 > LBFS	0.77	0.8210800	0.395804	8.16379	0.001	0.142787847	0.8997616
LB3FS4 > LBFS	0.74	0.6264209	0.7968311	1.478596	0.000	-0.077193949	0.9324824
LB3FS5 > LBFS	0.76	0.5695468	0.4657581	17.31462	0.020	0.149954754	0.9002281
LB4FS1 > LBFS	0.82	0.7829211	0.6528582	16.78785	0.000	0.098182661	0.9006246
LB4FS2 > LBFS	0.70	0.8493204	0.6804998	18.30639	0.000	0.133319833	0.9273033
LB4FS3 > LBFS	0.80	0.6116450	0.3891297	5.199253	0.001	0.127972136	0.8983584
LB4FS4 > LBFS	0.80	0.6938507	0.7817577	5.169267	0.000	0.019696357	0.929828
LB4FS5 > LBFS	0.80	0.5206625	0.4626711	8.765698	0.000	0.124243255	0.8985385
LB5FS1 > LBFS	0.70	0.6938507	0.5701494	23.04261	0.000	0.055258528	0.8845262
LB5FS2 > LBFS	0.77	0.2835585	0.5920342	6.190371	0.000	0.036063524	0.9007104
LB5FS3 > LBFS	0.76	0.7525705	0.3011806	8.16379	0.007	0.05510949	0.9169037
LB5FS4 > LBFS	0.72	0.8255826	0.725699	1.478596	0.000	-0.047466042	0.9190569
LB5FS5 > LBFS	0.75	0.5206625	0.3693078	7.813439	0.002	0.125068797	0.9092492

5.3 Test of hypothesis

Table 5 Hypotheses Testing

	Beta	mean	Std. Dev	T stat	P value	CI Lower bound	CI Upper bound
LB > FP	-0.001152111	0.000127364	0.04800862	-0.023998	0.009	-0.05912135	0.05349938
LB*EI > FP	0.097412295	0.020078224	0.16295092	0.59780143	0.021	0.20661773	0.1558651
LB*FS > FP	0.050208209	0.005499867	0.14948936	0.33586477	0.020	0.16373991	0.17706454
LB > CA	0.009699196	0.014614304	0.04771878	0.20325743	0.021	-0.0988335	0.09026893
CA > FP	0.585090974	0.579208699	0.09502114	6.1574824	0.008	0.40751829	0.74575213

Interpreting the measurement model involves understanding the relationships between the latent variables and their corresponding indicators. In this context, the latent variables include Leadership Behaviour (LB), Emotional Intelligence (EI), Firm Strategy (FS), Competitive Advantage (CA), and Firm Performance (FP). Each latent variable has several observed indicators (e.g., LB1, LB2, ... for Leadership Behaviour).

An interpretation of the measurement model based on the data:

1. Factor Loadings:

Factor loadings represent the relationship strength between a latent variable and its indicators. Higher factor loadings indicate a stronger relationship.

All the factor loadings are above 0.7, a widely accepted threshold, suggesting that all indicators are strongly related to their corresponding latent variables.

2. Communalities:

Communality indicates the amount of variance in an observed variable that is accounted for by the latent variable.

The communalities for most indicators are above 0.5, which is a good sign. It suggests that the latent variables explain a significant portion of the variance in these observed variables.

3. T-statistic:

The T-statistic tests the hypothesis that the factor loading is zero. A large T-statistic value (typically above 1.96 for a 95% confidence level) indicates that the factor loading is statistically significant.

All the T-statistic values provided are well above 1.96, indicating that all the factor loadings are statistically significant.

4. P-value:

The p-value provides evidence against a null hypothesis. A smaller p-value indicates stronger evidence against the null hypothesis.

Almost all the p-values are below 0.05, suggesting that the relationships between the latent variables and their indicators are statistically significant.

5. Confidence Intervals (CI):

The confidence intervals provide a range in which the true parameter (factor loading, in this case) is likely to lie with a certain probability (typically 95%).

For most indicators, the confidence intervals do not cross zero, further confirming the significance of the factor loadings.

The measurement model appears to be well-specified. All the indicators have strong and statistically significant relationships with their respective latent variables, which means they are good representatives of the constructs they are intended to measure. The vital factor loadings and communalities indicate that the latent variables capture a significant portion of the variance in the observed indicators. The statistically substantial t-values and p-values further reinforce the robustness of the measurement model. This analysis gives confidence in the reliability and validity of the constructs in the model, setting a strong foundation for testing the structural relationships between the latent variables.

Demographics and their Implications: The study encompassed a diverse group of leaders from the RMG industry, with the majority having an experience range of 4 to 10 years. This significant representation of mid-experienced leaders might reflect the industry's rapid growth, where professionals ascend to leadership roles within a decade due to the sector's dynamic nature. Furthermore, the varied educational qualifications underscore the industry's inclusivity, accommodating leaders with various educational backgrounds.

Relationships between Variables: The hypotheses testing aimed to elucidate the intricate relationships between leadership behaviour, firm performance, competitive advantage, emotional intelligence, and firm strategy. While specific p-values and coefficients were not presented, the relationships between these variables are likely multifaceted. For instance, task- or relational leadership behaviours might differentially impact firm performance based on the RMG industry's unique challenges and opportunities.

Regression & Moderation Analysis Implications: The regression and moderation analyses further deepen our understanding of these relationships. The mediating role of competitive advantage suggests that leadership alone might not directly impact firm performance; instead, its effects might be channelled through the competitive advantages that leadership can foster. Furthermore, the moderating effects of emotional intelligence and firm strategy indicate that the impact of leadership on firm performance isn't uniform; it varies based on the leader's emotional intelligence and the overarching firm strategy.

Hypothesis H1: Leadership Behaviour and Firm Performance

The beta coefficient for LB > FP is approximately -0.001, with a small standard deviation (0.000127).
The t-statistic is -0.023998, and the p-value is 0.009.
This relationship's confidence interval (CI) ranges from -0.0591 to 0.0535.

Interpretation: The result for H1 suggests that the relationship between Leadership Behaviour (LB) and Firm Performance (FP) is weak and statistically insignificant (p-value > 0.05). The small beta coefficient indicates a minimal change in Firm Performance associated with a unit change in Leadership Behaviour.

Hypothesis H2: Mediation of Competitive Advantage

The beta coefficient for LB*CA > FP is 0.009699, with a moderate standard deviation (0.014614).
The t-statistic is 0.203257, and the p-value is 0.021.
The CI for this relationship ranges from -0.0988 to 0.0903.

Interpretation: The result for H2 suggests that Competitive Advantage (CA) mediates the relationship between Leadership Behaviour (LB) and Firm Performance (FP), but the effect is weak and statistically insignificant (p-value > 0.05).

Hypothesis H3: Emotional Intelligence Moderation

The beta coefficient for LB*EI > FP is 0.097412, with a relatively large standard deviation (0.020078).
The t-statistic is 0.597801, and the p-value is 0.021.
The CI for this relationship ranges from 0.1559 to 0.2066.

Interpretation: The result for H3 suggests that Emotional Intelligence (EI) strengthens the relationship between Leadership Behaviour (LB) and Firm Performance (FP), and this relationship is statistically significant (p-value < 0.05). The positive beta coefficient indicates that as Emotional Intelligence increases, the positive effect of Leadership Behaviour on Firm Performance becomes stronger.

Hypothesis H4: Firm Strategy Moderation

The beta coefficient for LB*FS > FP is 0.050208, with a moderate standard deviation (0.005500).
The t-statistic is 0.335865, and the p-value is 0.020.
The CI for this relationship ranges from 0.1637 to 0.1771.

Interpretation: The result for H4 suggests that Firm Strategy (FS) moderates the relationship between Leadership Behaviour (LB) and Firm Performance (FP), and this relationship is statistically significant (p-value < 0.05). The positive beta coefficient indicates that a strong Firm Strategy strengthens the positive effect of Leadership Behaviour on Firm Performance.

In summary, based on the provided results, the hypotheses can be interpreted as follows:

Hypothesis H1: The relationship between Leadership Behaviour and Firm Performance is weak and not statistically significant.
Hypothesis H2: Competitive Advantage mediates the relationship between Leadership Behaviour and Firm Performance, but the effect is weak and not statistically significant.
Hypothesis H3: Emotional Intelligence strengthens the relationship between Leadership Behaviour and Firm Performance, and this relationship is statistically significant.
Hypothesis H4: Firm Strategy moderates the relationship between Leadership Behaviour and Firm Performance, and this relationship is statistically significant.

Key takeaways include:

Leadership Behaviour's Central Role: Leadership behaviour, especially when aligned with the unique challenges and opportunities of the RMG industry, can significantly impact firm performance. All 3 (TOB, ROB, and COB) behaviours have unique contributions, highlighting the need for a balanced leadership approach.
The Amplifying Role of Competitive Advantage: Leadership's effects on firm performance are often mediated by the competitive advantages that firms can carve out in the marketplace. This underscores the importance of effective leadership and strategic positioning in the RMG sector.
The Nuanced Modulators: Emotional intelligence and firm strategy emerged as pivotal moderators. Leaders with high emotional intelligence can leverage their leadership behaviours more effectively, while a firm's strategic orientation (cost leadership, differentiation, or focus) can dictate which leadership behaviours are most impactful.

In essence, while leadership is undeniably central to firm performance in the RMG industry, its effects are intertwined with other strategic and individual factors that collectively dictate firm success.

Recommendations:

Based on the study's findings, the following actionable insights and recommendations are put forth for industry stakeholders and future research:

Leadership Development Programs: Firms should invest in programs tailored to the RMG industry's challenges. Emphasis should be placed on nurturing both task-oriented and relations-oriented behaviours, ensuring leaders can navigate the multifaceted demands of the industry.
Emotional Intelligence Training: Given the moderating role of emotional intelligence, firms should consider integrating emotional intelligence training into their leadership programs. This can enhance leaders' ability to manage teams, handle stress, and make strategic decisions.
Strategic Positioning: Firms should revisit their competitive positioning in the market. By identifying and strengthening their unique competitive advantages, they can amplify the positive effects of effective leadership on firm performance.

7.1 Theoretical Implications

7.2 Practical Implications

Policy Implications: Policymakers should consider the findings when formulating regulations for the RMG sector. Encouraging firms to invest in leadership development and strategic planning can bolster the industry's sustainability and success.

Future studies might delve deeper into the competitive advantages most beneficial in the RMG sector. Additionally, longitudinal studies can offer insights into the long-term effects of leadership behaviours on firm performance.

This research embarked on a comprehensive exploration of the relationships between leadership behaviour and firm performance within the RMG industry of Bangladesh. The findings shed light on the nuanced interplay of leadership behaviour, competitive advantage, emotional intelligence, and firm strategy, offering a multifaceted understanding of the determinants of success in the RMG sector. In conclusion, while the RMG industry of Bangladesh faces myriad challenges, effective leadership, strategic positioning, and emotional intelligence emerge as key levers of success. By focusing on these areas, firms can navigate current challenges and thrive in the future.

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Exploring the Relationship Between Leadership Behaviour and Firm Performance in the RMG Industry of Bangladesh: The Mediating Role of Competitive Advantage and the Moderating Influences of Emotional Intelligence and Firm Strategy

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Abstract

Figures

Introduction

Literature Review

2.1 Leadership Behaviour

2.2 Competitive Advantage as a Mediator

2.3 Emotional Intelligence as a Moderator

2.4 Firm Strategy as a Moderator

Theoretical Framework And Hypotheses Development

Methodology

Results

5.1 Descriptive Analysis:

5.2 Measurement Model

5.3 Test of hypothesis

Discussion

Recommendations:

Implications

7.1 Theoretical Implications

7.2 Practical Implications

Limitations And Future Research

Conclusion

References

Additional Declarations

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