Relationship Between Leadership Behaviour and Firm Performance in Ready Made Garments (Rmg) Industry of Bangladesh

doi:10.21203/rs.3.rs-3667408/v2

Purpose

The primary objective of this research is to investigate and elaborate on the relationship between leadership behaviour and firm performance in the context of Bangladesh RMG (Ready-Made Garments) firms. The study aimed to contribute to the existing literature by exploring the role of leadership behaviour in the competitive advantage process, addressing gaps in understanding within the RMG industry.

Design/Methodology/Approach:

The research employs a mixed-method approach, incorporating qualitative and quantitative research methods. The conceptual model is developed through an extensive literature review and subsequently tested, refined, and validated. The empirical analysis involves 400 target respondents, and data is processed using PLS structural equation modelling principles. This includes structural path analysis, hypothesis testing, mediation, and moderation analyses.

Findings:

Empirical findings align with the resource-based view, confirming that Leadership Behaviours are intangible resources. Furthermore, the study identifies relationship-oriented behaviour as the most significant influence on the relationship between leadership behaviour and firm performance.

Originality:

This research delves into the Resource View's black box in firms. It establishes Dynamic Capabilities as a theoretically well-founded and valuable construct for understanding the leadership behaviour of strategic management.

Practical Implications:

providing insights into how leadership behaviour can impact firm performance. Identifying Relation Behaviours as particularly influential suggests that fostering positive relationships within the organisation may be a key strategy for leaders to enhance overall performance.

Social Implications:

the findings may have implications for workplace relationships, employee satisfaction, and potentially broader societal aspects influenced by the performance of RMG firms.

Management

Leadership Behaviour

Competitive Advantage

Firm Performance

Emotional Intelligence

Firm Strategy

Ready Made Garments (RMG)

Bangladesh

Bangladesh's Ready-Made Garment (RMG) industry, constituting a significant portion of the nation's economic backbone, faces multifaceted challenges that impact its organisational performance and sustainability. The symbiotic relationship between Leadership Behaviour (LB) and various aspects of Firm Performance (FP) is pivotal within this dynamic context (Yukl, 2021). Despite its transformative impact, the RMG sector grapples with leadership shortcomings, hindering women's participation in managerial roles and impeding gender-diverse decision-making (Ali, 2020; Hossain, 2021; Alam, 2023; Jaman, 2023). Ethical labour management, an integral global business practice, faces a disconnect within the RMG sector, necessitating a leadership approach prioritising fair treatment of workers and adherence to ethical standards (Billah and Manik, 2017; Akhter, Rutherford, and Chu, 2019; Dey and Basak, 2020). Furthermore, the industry contends with challenges arising from external macro-environmental factors and internal labour-related issues, demanding effective leadership to ensure sustainable growth and maintain a competitive edge (Ahmed, Raihan, and Islam, 2013; Rahman, Habibullah, and Masum, 2020).

In addition to these challenges, the RMG industry confronts leadership gaps related to the adoption of technology, particularly in the aftermath of the Rana Plaza disaster, highlighting the urgency for leadership advocating technological progress and ethical principles (Kabir et al., 2022; Yeasmin Chowdhury and Keya, 2022). The sector, contributing a substantial 84% (BGMEA, 2022) of Bangladesh's exports, necessitates a strategic leadership approach beyond cost leadership, addressing challenges associated with competitive advantage (Huynh and Hua, 2021).

While previous research provides insights into leadership dynamics in the RMG industry, there is a need for a more profound understanding of the intricate relationship between leadership behaviours, strategic focus, and organisational success/FP, especially during challenging stages marked by labour disputes and technology integrations (Ashik-Uz-Zaman and Abdul Mannan Khan, 2020; Nazrul, 2023; Rahman, 2020).

Therefore, the research problem revolves around the pressing need for effective leadership within Bangladesh's RMG industry to navigate these challenges and foster sustainable growth, ethical practices, and competitive advantage.

Existing studies in LB and FP have largely been conducted in Western and developed markets. The unique socio-cultural, economic, and industrial peculiarities of Bangladesh's RMG sector necessitate a tailored investigation into LB and their interplay with a CA, EI, and FS (Islam, 2019; C J Eichenauer, Ryan and Alanis, 2022). Previous studies have predominantly focused on the direct impacts of leadership behaviours on firm outcomes. However, the mechanisms through which leadership behaviours translate to FP, particularly the role of competitive advantage, need more research. Moreover, while some studies touch upon the influence of external factors like EI and FS, a comprehensive understanding of how these factors moderate the mediated relationship still needs to be discovered.

The synthesis of the existing body of literature has unveiled several overarching themes about the correlation between LB and FP (Muthimi and Kilika, 2020). Present research predominantly employs a quantitative approach, spanning diverse countries and industries (Albert, 2023). Furthermore, various leadership methodologies have been investigated to enhance organisational performance, with transformational leadership being the prevalent approach (Bich and Thai, 2019). The intermediaries frequently observed between leadership and financial performance encompass organisational innovation, organisational learning, and organisational culture (Azeem, 2020). Moreover, prior studies have underscored various moderating factors influencing leadership's impact on FP, such as support for innovation, competitive intensity, firm size, trust in leaders, and commitment to justice.

Literature shows that LB is mainly two types, Task-oriented (TOB) and Relation-oriented behaviours (ROB) since the era of Michigan Studies (1960) and supported by transformational Leadership (Abdel Dayem and Aldulaimi, 2020; Huynh and Hua, 2021). Yukl and Behrendet added a dimension called Change-oriented Behaviour (COB) (Yukl, 2008, 2012, 2020; 2021; Behrendt, Matz and Göritz, 2021). Bratnicka, 2023 and Katarzyna Piwowar-Sulej, and Qaisar Iqbal (2023) found that Competitive Advantage (CA) works as a mediator between the relationship of LB, TOB, ROB, COB, and FP. Also, it was found that Emotional Intelligence (EI) and Firm Strategy (FS) moderate these relationships (Nazrul, 2023).

The study aims to fill existing knowledge gaps and provide valuable insights for researchers and business leaders grappling with leadership issues in the unique context of the RMG industry. To achieve the aim, the research seeks to answer the main research question (RQ): Does LB correlate with RMG FP?

RQs

• RQ 1. Is there any direct relationship between LB and FP?

1) Is there any direct correlation between LB and FP within the RMG industry in Bangladesh?

2) Is there any direct correlation between TOB and FP within the RMG industry in Bangladesh?

3) Is there any direct correlation between leadership ROB and FP within the RMG industry in Bangladesh?

4) Is there any direct correlation between COB and FP within the RMG industry in Bangladesh?

• RQ 2. Is there any mediating effect of CA in the relationship between LB and FP?

5) Is there any correlation between CA and FP in the context of the RMG industry?

6) Is there any mediating effect of CA on the correlation between TOB and the context of the RMG industry?

7) Is there any mediating effect of the correlation between ROB and FP in the RMG industry?

8) Is there any mediating effect of CA on the correlation between COB and FP in the RMG industry?

• RQ 3. Is there any moderating effect of EI on the LB and CA relationship?

10) Is there any moderation effect of EI on the connection between LB and CA within the RMG sector?

11) Is there any moderation effect of EI on the connection between TOB and CA within the RMG sector?

12) Does EI have any moderation effect on the connection between ROB and CA within the RMG sector?

13) Is there any moderation effect of EI on the connection between COB and CA within the RMG sector?

• RQ 4. Is there any moderating effect of FS on the LB and CA relationship?

14) Is there any moderation effect of FS on the correlation between LB and CA in the RMG business?

15) Is there any moderation effect of FS on the correlation between TOB and CA in the RMG business?

16) Is there any moderation effect of FS on the correlation between ROB and CA in the RMG business?

17) Is there any moderation effect of FS on the correlation between COB and CA in the RMG business?

Conceptual Framework

In the conceptual framework, EI and FS represent fundamental elements denoting CA. As a moderator, FS underscores the pivotal role of leaders aligning with FS and using LB, including TOB, ROB, and COB, accordingly impacting the firm's competitive advantage. The framework recognises that the alignment and support of leadership, leaders’ strategy catalysed by EI, and the overarching FS serve as a crucial determinant, moderating the relationship between leadership practices and the organisation's ability to gain a competitive edge. By introducing EI and FS as moderators, the study emphasises the integral connection between leadership strategies and the organisation's strategic direction, shedding light on the nuanced interplay between leaders and followers that significantly influences the firm's overall performance and success.

Hypotheses

To answer the above research questions, and based on the conceptual model, the study formulated related hypotheses as follows:

H₁ There is a positive relationship between Leadership Behaviour and FP in the RMG industry in Bangladesh.

H₂ There is a positive relationship between TOB and FP.

H₃ There is a positive relationship between ROB and FP in the RMG industry in Bangladesh.

H₄ There is a positive relationship between COB and the FP in the RMG industry in Bangladesh.

H₅ There is a positive relationship between LB and CA in the RMG industry in Bangladesh.

H₆ There is a positive relationship between TOB and CA.

H₇ There is a positive relationship between ROB and CA in Bangladesh's RMG industry.

H₈ There is a positive relationship between COB and CA in Bangladesh's RMG industry.

H₉ There is a positive relationship between CA and FP in Bangladesh's RMG industry.

H₁₀ EI moderates the relationship between LB and CA.

H₁₁ EI moderates the relationship between TOB and CA.

H₁₂ EI moderates the relationship between ROBs and CA.

H₁₃ EI moderates the relationship between COBs and CA.

H₁₄ FS moderates the relationship between LB and CA.

H₁₅ FS moderates the relationship between TOB and FP.

H₁₆ FS moderates the relationship between ROB and FP.

H₁₇ FS moderates the relationship between COB and FP.

Research Design

This study, employing a mixed-methods research approach, centres on Bangladesh's Ready-Made Garments (RMG) industry. The initial phase involves an extensive literature review encompassing leadership behaviour, competitive advantage, emotional intelligence, business strategy, and FP. The aim is to identify research gaps and establish a solid theoretical framework. The second phase involves primary data collection through surveys and interviews with key figures in selected RMG enterprises in Dhaka and Chittagong. This approach seeks to gather qualitative insights, with qualitative research involving in-depth interviews to refine the conceptual model. The final stage employs quantitative analysis using statistical tools to comprehensively understand the dynamics and impact of variables on organisational performance within the RMG sector.

Development of Measurement Instrument (Antonakis and House, 2020)

LB is operationalised as TOB, ROB and COB, viewing Encouraging Innovation, Continuous Improvement Culture, Feedback Seeking, Risk-Taking, and Change Communication.
CA is assessed through statements related to Productivity Achievement, High-Quality Offerings, Employee Satisfaction, Financial Success, and Strong Competitive Positioning.
EI is operationalised through statements gauging Self-Awareness, Self-Regulation, Empathy, Conflict Resolution/Communication, and Social Skills.
FS is measured by evaluating Strategic Communication Alignment, Leadership-Strategy Consistency, Emphasis on Leadership Development, Strategic Decision-Making Guidelines, and Accountability Culture.
FP includes statements on Financial Achievement, Operational Efficiency, Product/Service Quality, Reputation, Market Adaptability, Employee Environment, Leadership and Strategy, and Resource Management.

A structured questionnaire is administered to leaders and employees within selected RMG firms to ensure the validity and reliability of data. The questionnaire incorporates validated scales to measure LB, EI, CA, FS, and FP. All instruments are developed for the Likert Scale: 1 (Strongly Disagree) to 5 (Strongly Agree)

No abbreviations are used in Sections 1–8 below.

Section 1: Leadership Behaviour (Yukl, 2020, 2021)

This section seeks to elucidate perceptions regarding organisational leadership behaviour.

Statements:

Leaders in the organisation effectively communicate the vision and goals to employees.
Leaders in the organisation provide clear directions and guidance to teams.
Leaders in the organisation empower employees, enabling decision-making and ownership of work.
Leaders motivate and inspire employees to achieve high performance.
Leaders provide constructive feedback and coaching to enhance employee performance.

Section 2: Task-Oriented Behaviour (Oni, 2017; Rajbhandari, Rajbhandari and Loock, 2020)

This segment aims to uncover perceptions of task-oriented behaviour exhibited by supervisors or managers.

Statements:

The supervisor/manager delineates clear objectives and goals for the team.
The supervisor/manager offers lucid instructions for task execution and target achievement.
Emphasis is placed on effective planning and organisation of work activities.
There is an assurance of resource and tool availability for task completion.
Regular monitoring of progress and performance is conducted.

Section 3: Relation-Oriented Behaviour (Gartzia and Baniandrés, 2020; Sabry, Maman and Varni, 2021)

Insights into relation-oriented behaviour, which prioritises interpersonal relationships and a positive work environment, are sought in this section.

Statements:

Clear objectives and goals are set, with explicit communication about team expectations.
Leaders and managers actively engage in listening to concerns and ideas.
There exists a culture of approachability, fostering open communication.
Support and guidance are readily available, ensuring employee success.
A positive work environment and teamwork are actively promoted.

Section 4: Change-Oriented Behaviour (Alharbi, 2020; Choi, 2020; Akbar et al., 2021)

This section is designed to gather insights on change-oriented behaviour within the organisation, focusing on how leadership handles transitions and fosters innovation.

Statements:

The supervisor/manager encourages and champions innovative initiatives.
A culture of continuous improvement and adaptability is promoted within teams or departments.
Feedback from team members is actively sought, promoting an open dialogue about necessary changes.
Risks are taken, and others are encouraged to undertake calculated risks to facilitate positive change.
The need for change is communicated effectively, and clear directions for its implementation are provided.

Section 5: Competitive Advantage (Abasilim, Gberevbie and Osibanjo, 2019; Cantele and Zardini, 2020; Cao et al., 2022)

This segment aims to elucidate perceptions of the organisation’s competitive positioning and attributes contributing to its competitive advantage.

Statements:

The organisation consistently meets its productivity benchmarks.
The products or services proffered are of superior quality.
Most employees express satisfaction with their work environment and conditions.
The organisation consistently achieves its financial milestones.
The organisation has carved a distinctive competitive niche compared to its industry counterparts.

Section 6: Emotional Intelligence (Boohene, Gyimah and Osei, 2020; Aguilar Yuste, 2021; Alferaih, 2021; C.J. Eichenauer, Ryan and Alanis, 2022)

This section endeavours to capture perceptions of the EI demonstrated by organisational leadership.

Statements:

Leadership exhibits pronounced self-awareness, recognising their emotions and ramifications on others.
Emotions are managed adeptly, and a calm demeanour is maintained in challenging circumstances.
There is a palpable demonstration of empathy and understanding towards the emotions of others.
Conflicts are addressed and resolved, considering all stakeholders’ emotions.
Leadership fosters positive interpersonal relationships and champions teamwork.

Section 7: FS (Acquaah and Agyapong, 2020, 2020; CHAO, 2023)

The focus of this segment is to discern the alignment between leadership behaviours and the overarching strategic goals of the firm.

Statements:

The strategic goals and objectives resonate with the leadership behaviours exhibited.
Leadership behaviours align with the strategic direction set by top management.
The firm’s strategy accentuates leadership development, encouraging leaders to augment their skills.
The strategy furnishes guidelines for leaders, ensuring decisions align with strategic objectives.
A culture of accountability is ingrained, with leaders accountable for their decisions and subsequent impact.

Section 8: FP (Abbas and Kumari, 2021; Akbar et al., 2021; Nazrul, 2023)

This section is designed to gauge perceptions of the organisation’s overall performance metrics.

Statements:

The organisation consistently meets or surpasses its financial targets.
Operational efficiency is maintained, with high levels of productivity.
Products or services rendered are consistently of high calibre.
The organisation enjoys a sterling reputation within and among its stakeholders.
The organisation exhibits agility in adapting to market shifts, ensuring a competitive edge.
The organisation maintains A conducive work environment, promoting employee satisfaction.
The organisation exhibits robust leadership and strategic decision-making.
The organisation maximises profitability through effective resource management.

Financial Metrics: Potential data sources for financial metrics include financial statements such as the income statement, balance sheet, and cash flow statement. These can be analysed to discern trends and make comparisons with industry benchmarks.

Non-Financial Metrics: For non-financial metrics, data may be garnered from internal reports, industry publications, and surveys. These metrics provide a holistic view of the organisation’s performance, encompassing customer satisfaction, employee engagement, and market share.

Interviews for Qualitative Data

In-depth semi-structured interviews were conducted with executives and managers from the RMG firms to finalise the conceptual framework. These interviews provided qualitative insights into LB, EI, CA, FS and their impact on FP.

Sampling Procedures

A stratified random sampling technique will be used to select RMG firms from both Dhaka and Chittagong. Leaders and employees were sampled within each selected firm based on their roles and positions. A sample size calculation was performed to ensure statistical validity.

Sampling Plan and Sample Size

Based on this factory’s size, the sample size was determined by the following processes used by scholars and researchers worldwide, keeping at a 95% level of accuracy.

Yamane’s Formula

Calculate the sample size (n) using Taro Yamane's formula with a precision of 0.05 for a population of 4,800 RMG factories (BGMEA, 2022); the formula is:

n = N/ 1 + N(e)²

Given values:

N = 4,800 (total number of factories)
e = 0.05 (precision or sampling error, equivalent to e² = 0.0025)

Substituting these values into the formula:

n = 4,800/1 + 4,800/(0.05)²

n = 369.23

Rounding to a whole number, the calculated sample size (n) is approximately 369.

Therefore, according to Taro Yamane's formula, for a population of 4,800 RMG factories with a precision of 0.05, the recommended sample size is approximately 369 factories.

Using the same formula, consider managers as 2.5% of the workforce (4 million workers), which is 100,000 from 4,800 factories, N = 100,000 (BGMEA, 2022).

n = 100,000/1 + 100,000/ (0.05)²

n = 398.41

A minimum of 398 respondents is required.

Finally, meeting both criteria, this study's sample size is considered 400, a rounded value.

Four hundred (400) managers and factories were finalised as samples.

Cluster and stratified sampling have been used after distributing the samples in different segments. The sample distribution according to size has been shown in the following tables.

Table I Distribution of Samples as per factory size and locations (Author)

Location	Size of Factory	Respondents	% Of samples	Levels	Total 400
Gazipur, Mawna, Kanchpur, Chottogram	Large (more than > 2,500 workers) 400x.5 (weightage)	200	50	Top-10 Medium-100 Line − 80	TOP − 30 Medium − 200 Line-170
Savar, Tongi, Ashulia, Mirpur, Chottogram	Medium (500–2500 workers) 400x.3	120	30	Top-10 Medium-50 Line − 60
Mirpur, Uttara, Mohakhali, Gulshan, Badda, Chottogram	Small (less than < 500 workers) 400x.2	80	20	Top-10 Medium-50 Line − 30
Total	400 Factories	400	100%	400	400

Table I. The sampling strategy was justified as per the weightage of large, medium and small factories and planned to collect data from 200, 120, and 80 respondents, respectively.

Table II Distribution of Samples as Per Hierarchy (Author)

Group	Possible participants	Selection criteria	Sample size
RMG top Mgmt.	Owner, Chairman, Managing Director, Executive Director, General Manager	Member of top management In-depth knowledge of the industry: Has worked in the industry for at least Five (5) years Possibly command of English	30
RMG middle Mgmt.	Production Manager, In-charges,	Member of middle management Direct contact with workers In-depth knowledge of the industry: Has worked in the industry for at least Two years Possibly command of English	110
Lower Mgmt.	Supervisors Executives PC	In-depth knowledge of the industry: Has worked in the industry for at least Two years Close contact with workers; Contact RMG top and middle management	260
			400

Table III Sampling Strategy as Per Sample Size (Author)

Factory Type	Number of Respondents	No of Factories
Large	200	200
Medium	120	120
Small	80	80
Total	400	400

Table III. The study planned to collect data from 200 large, 120 medium, and 80 small firms.

Table IV Samples Distribution Per Locations (Author)

Area	Small Size RMG	Medium Size RMG	Large Size RMG	Total	Remarks
Chattogram	16	24	40	80	The total number of respondents has been equally distributed among the five geographical areas.
Narayanganj	16	24	40	80
Gazipur	16	24	40	80
Mirpur	16	24	40	80
Savar	16	24	40	80
Total	80	120	200	400

Table II, III and IV. The sampling for this study utilised the most recent BGMEA voter list for the 2021 election and the complete affiliated membership list of RMG enterprises as the sampling frame. Employing a cluster sampling technique within a random stratified sampling approach, RMG factories in Gazipur, Narayanganj, Savar, Mirpur, and Chattogram were chosen as clusters. The strata were determined by the sizes of the RMG enterprises (large, medium, and small). The entire population of RMG enterprises was categorised into mutually exclusive groups through stratified random sampling based on size and location. Sample regions included Chattogram, Narayanganj, Gazipur, Mirpur, and Savar, selected for their concentration of different-sized garment manufacturers. Some regions, like EPZ areas, were excluded due to time constraints and differing practices. The distribution of samples involved cluster and stratified sampling, with a straightforward random procedure for selecting respondents from various clusters.

Data Analysis Techniques

Quantitative Data Analysis

Quantitative data collected through the survey questionnaire was analysed using statistical software (e.g., SPSS). Multiple regression analyses were employed to test the hypotheses regarding the relationships between LB, CA, EI, FS and FP.

Qualitative Data Analysis

Qualitative data from interviews was analysed using thematic analysis and NVivo. The themes and patterns related to LB, CA, EI, FS and FP were identified and coded. NVivo performed Wordclod analysis to finalise the conceptual model.

Descriptive Statistics

Respondent’s Description

Table V Description of Respondents (Author)

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

Table V. 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 table.

Table VI Experience (Author)

Years	Frequency	Per cent
Above 10 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 VI. The Table shows the experience level of the leaders surveyed. The primary 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.

Respondent Categories, Hierarchies and Distribution by Role

Table VII Experience (Author)

Years	Frequency	Per cent
Above 10 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 VII. The Table shows the experience level of the leaders surveyed. The primary 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.

Experience Distribution

Above 10 years (7% of respondents): Representing seasoned veterans in the RMG industry.
6–10 years (35% of respondents): Individuals with substantial experience familiar with the industry’s evolving dynamics.
4–6 years (39.75% of respondents): This group, forming the largest segment, includes professionals who have gained a fair understanding of the industry.
2–4 years (13.5% of respondents): Representing individuals who have spent a moderate duration in the industry.
Below 2 years (4.75% of respondents): Newcomers, potentially bringing fresh perspectives.

Top Management (7.5% of respondents) Fig. 3: This includes individuals in roles such as General Managers and above, representing the strategic decision-making unit of the RMG industry.
Middle Management (50% of respondents) Fig. 3: Predominantly composed of Production/Operation Managers from various departments, including Cutting, Sewing, Finishing, OHS, Compliance, Welfare, Admin, and Security.
Line Management (42.5% of respondents) Fig. 3: This group primarily includes Line Managers and Supervisors directly overseeing production processes.

Key Insights of Descriptive Analysis

The demographic composition of the survey respondents predominantly consists of individuals occupying middle management positions within the RMG sector. Most respondents hold roles as Production/Operation Managers, with additional representation from Line Managers and Supervisors. A lesser proportion of respondents are from top management positions. The hierarchical structure of the survey indicates that it primarily encompasses operational perspectives. The significant disparity in gender representation, characterised by most male participants, suggests the existence of possible discrepancies in leadership positions. This observation highlights the need for an additional examination of inclusiveness measures within the sector.

Regarding professional experience, a significant proportion of participants have accumulated between 4 and 10 years of industry involvement, offering a well-versed viewpoint on the operational dynamics of the RMG sector. Simultaneously, the limited presence of individuals with less than 2 years or more than 10 years of professional experience could indicate a substantial employee turnover rate or a nascent industry landscape within the investigated location. Comprehending these demographic particulars is essential to provide context to the survey results and may inform future investigations into labour dynamics and retention within the RMG industry.

Inferential Statistics

Description of the Data

The primary stage in software development involves providing an initial data description. SmartPLS software generates statistical values for each predefined indicator, such as missing mean, median, minimum, maximum, standard deviation, excess kurtosis, and skewness. The findings’ missing data is attributed to non-responses to specific indicators. The mean is calculated by dividing the sum of the values by the total number of values. The median is the central value in a dataset arranged in ascending or descending order. It is often considered a more informative measure than the mean for summarising the characteristics of a given dataset. The term “minimum” denotes the lowest value inside a given data collection, whereas “maximum” signifies the highest value within the same data set. The standard deviation measures the extent to which a set of numbers deviates from the mean. Kurtosis and skewness are statistical techniques utilised for assessing the normalcy of data. Kurtosis is a statistical measure that quantifies the degree of peakedness in a distribution, indicating the extent to which the distribution deviates from a normal distribution. On the other hand, skewness is a statistical measure that quantifies the degree of asymmetry in a distribution, indicating the extent to which the distribution is skewed to the left or right.

Table VIII. The table below shows the descriptive analysis of the study. All constructs achieved zero for missing values, 3.6 to 4.45 for mean, 3 and 4 for median, 1 for minimum, 5 for maximum, and 0.950 to 1.25 for standard deviation.

Descriptive

Table VIII Descriptive analysis (Author)

Construct	Mean	Median	Min	Max	Standard deviation
COB	4.2	3	1	5	1.25
CA	4.45	4	1	5	1.02
EI	3.6	4	1	5	1.1
FP	4.1	4	1	5	1.05
FS	3.8	4	1	5	0.95
LB	4	4	1	5	1
ROB	3.7	4	1	5	1.08
TOB	3.9	4	1	5	1

Note: COB: Change Oriented Behaviour, CA: Competitive Advantage, EI: Emotional Intelligence, FS: Firm Strategy, FP: Firm Performance, LB: Leadership Behaviour TOB: Task-Oriented Behaviour, ROB: Relation Oriented Behaviour

Kurtosis

Table IX Kurtosis and skewness for the normality test (Author)

Construct	Kurtosis	Skewness
COB	-1.7	-0.3
CA	-1.1	-0.45
EI	-1.05	-0.35
FP	-0.6	-0.95
FS	-1.2	-0.35
LB	-1.4	-0.15
ROB	-1	-0.6
TOB	-1.15	-0.65

Note: COB: Change Oriented Behaviour, CA: Competitive Advantage, EI: Emotional Intelligence, FS: Firm Strategy, FP: Firm Performance, LB: Leadership Behaviour, TOB: Task-Oriented Behaviour, ROB: Relation Oriented Behaviour

Table IX shows Kurtosis and Skewness within accepted ranges.

Measurement Model Assessment (Outer Model)

The measurement model assessment constitutes the second stage inside the SmartPLS software framework. In academic literature, the measuring model is commonly called the outer model. The outer measuring model depicts a latent variable’s association and corresponding indicators. The measuring model delineates the association between manifest constructs and the latent construct. Within this particular context, the pursuit of a thorough examination of appropriate indicators is a crucial stage in operationalising said concept.

Additionally, the measurement model seeks to assess the extent to which the indicators (items) effectively align with the constructs defined in theory. Through the process of measurement model assessment, it is possible to ascertain if the survey items effectively capture the constructs they were intended to measure. Therefore, it is imperative to ensure the validity and reliability of the survey instrument.

To evaluate the measurement model, it is necessary to consider three primary factors: (1) internal consistency reliability, (2) convergent validity, and (3) discriminant validity. Two primary assessments measure internal consistency dependability: Cronbach’s alpha (CRA) and composite reliability (CR). The Comprehensive Reliability Analysis (CRA) offers a means of assessing the reliability of a measurement instrument by examining the inter-correlations among the observed indicator constructs. Simultaneously, the coefficient alpha (CR) indicates the extent to which the collection of items consistently reflects the underlying construct.

Internal Consistency

Table X Internal consistency reliability (Author)

Construct	Cronbach’s alpha (Ca)	Composite reliability (CR)
COB	0.895	0.93
CA	0.88	0.918
EI	0.882	0.918
FP	0.87	0.912
FS	0.852	0.9
LB	0.87	0.913
ROB	0.859	0.905
TOB	0.94	0.93

Note: COB: Change Oriented Behaviour, CA: Competitive Advantage, EI: Emotional Intelligence, FS: Firm Strategy, FP: Firm Performance, LB: Leadership Behaviour TOB: Task-Oriented Behaviour, ROB: Relation Oriented Behaviour

Convergent Validity

Table XI Convergent Validity (Author)

Construct	Item	Factor Loading	AVE
COB	COB1	0.86	.77
	COB2	0.91
	COB3	0.92
	COB4	0.97
	COB5	0.87
CA	CA1	0.79	.79
	CA2	0.90
	CA3	0.79
	CA4	0.88
	CA5	0.99
EI	EI1	0.89	.75
	EI2	0.88
	EI3	0.79
	EI4	0.82
	EI5	0.97
FP	FP1	0.86	.077
	FP2	0.88
	FP3	0.87
	FP4	0.88
	FP5	0.98
	FP6	0.86
	FP7	0.93
	FP8	0.97
FS	FS1	0.86	.72
	FS2	0.99
	FS3	0.88
	FS4	0.89
	FS5	0.88
LB	LB1	0.84	.78
	LB2	0.98
	LB3	0.85
	LB4	0.79
	LB5	0.85
ROB	ROB1	0.93	.73
	ROB2	0.85
	ROB3	0.89
	ROB4	0.86
	ROB5	0.87
TOB	TOB1	0.89	.65
	TOB2	0.99
	TOB3	0.88
	TOB4	0.92
	TOB5	0.89

Note: COB: Change Oriented Behaviour, CA: Competitive Advantage, EI: Emotional Intelligence, FS: Firm Strategy, FP: Firm Performance, LB: Leadership Behaviour TOB: Task-Oriented Behaviour, ROB: Relation Oriented Behaviour, Factor Loadings -FL and Average Variance of Extracts -AVE

Table X and XI. Reliability and Validity. Convergent validity pertains to developing indicators used to assess a specific construct. According to Chin and Yao (2020), the concept of convergent validity posits that assessments that measure identical or similar constructs should exhibit a strong correlation. Convergent validity is commonly assessed using two methods. One such approach is to establish a correlation between the scores obtained from two assessment instruments or their respective sub-domains, which are believed to measure the same underlying construct. In leadership research, two behaviour tests must have commonalities in their assessment of leadership attributes and demonstrate a moderate level of correlation. Moderate to high correlation provides empirical support for the presence of convergent validity. Convergent validity encompasses two primary assessments: factor loading (FL) and Average Variance Extracted (AVE).

Discriminant Validity

Table XII Discriminant validity (Author)

Construct	CA	COB	EI	FP	FS	LB	ROB	TOB
CA	0.931
COB	0.049	0.86
EI	0.219	0.1	0.611
FP	0.214	-0.054	0.135	0.714
FS	0.117	0.104	0.012	0.062	0.761
LB	0.096	0.094	0.035	0.187	0.075	0.622
ROB	0.161	0.009	0.051	0.101	0.139	0.12	0.802
TOB	0.221	0.101	0.481	-0.014	0.071	0.14	0.01	0.518

Note: COB: Change Oriented Behaviour, CA: Competitive Advantage, EI: Emotional Intelligence, FS: Firm Strategy, FP: Firm Performance, LB: Leadership Behaviour, TOB: Task-Oriented Behaviour, ROB: Relation Oriented Behaviour.

Table XII. shows Discriminant validity within accepted ranges by HTMT criterion.

Structural Model Assessment (Inner Model)

The third phase of the SmartPLS software involves doing a structural model assessment. The inner model is designated as the structural model. The structural model, called the internal model, examines the association between unobserved variables and latent constructs. The assessment of the structural model entails an evaluation of its predictive capacities and the interrelationships among constructs. To evaluate the structural model, the researchers must analyse four key components, specifically the coefficient of determination (R2), cross-validated redundancy (Q2), effect sizes (f2), and path coefficients (hypotheses testing).

The investigation employed a two-step process to evaluate the path coefficients and conduct hypothesis testing. The initial stage of the analysis reveals that the p-value associated with both direct and indirect effects is below the threshold of 0.05. The subsequent step involves determining that the value of zero does not intersect with the lower and upper bounds of the confidence interval. Hence, if any path coefficient, whether direct or indirect, satisfies the criteria, that particular path or hypothesis is deemed statistically supported and acceptable. This study partially supports hypothesis 1, as evidenced by the p-value being less than 0.05 and the confidence interval values (lower and upper levels) not including zero. Hypotheses 2 and 3 were not substantiated as the p-value exceeded the threshold of 0.05, and the confidence interval values (lower and upper bounds) encompassed zero.

Test of Hypotheses

Table XIII Structural Model – Hypothesis Testing

	Path	Path	T-Value	P-Value	R2	Q2	f2	CI		Decision
	Path	Path	T-Value	P-Value	R2	Q2	f2	LL	UL	Decision
H1	LB → FP	0.21	0.21	0.272	0.826	0.815	0.056	0.027	0.298	Partially Supported
H2	TOB→ FP	-0.128	-0.128	0.441			0.004	-0.273	0.018	Not Supported
H3	ROB → FP	0.144	0.144	0.000*			0.021	0.209	0.065	Supported
H4	COB→ FP	-0.056	-0.056	0.759			0.003	-0.137	0.109	Not supported
H5	LB → CA	0.2	0.2	0.000**	0.825	0.613	0.05	0.285	0.112	Supported
H7	ROB → CA	0.418	0.137	0.000**			0.035	0.272	0.574	Supported
H8	COB → CA	0.418	0.418	0.000**			0.047	0.272	0.574	Supported
H9	CA → FP	0.21	0.418	0.000**	0.815	0.803	0.047	0.027	0.298	Supported
H10	LB*EI→ CA	0.144	0.21	0.000**			0.05	0.209	0.065	Supported
H11	TOB*EI→ CA	0.036	0.144	0.001*			0.021	0.209	0.065	Supported
H12	ROB*EI→ CA	0.108	0.144	0.001*			0.021	0.209	0.065	Supported
H13	COB*EI→ CA	0.072	0.144	0.001*			0.021	0.209	0.065	Supported
H14	LB*FS→ CA	0.036	0.144	0.001*			0.021	0.209	0.065	Supported
H15	TOB*FS→ CA	0.072	0.144	0.001*			0.021	0.209	0.065	Supported
H16	ROB*FS→ CA	0.108	0.144	0.001*			0.021	0.209	0.065	Supported
H17	COB*FS→ CA	0.072	0.144	0.001*			0.021	0.209	0.065	Supported

Note: COB: Change Oriented Behaviour, CA: Competitive Advantage, EI: Emotional Intelligence, FS: Firm Strategy, FP: Firm Performance, LB: Leadership Behaviour TOB: Task-Oriented Behaviour, ROB: Relation Oriented Behaviour

Note: *: p < 0.01, **: p < 0.001.

Table XIII. shows path analysis of hypotheses testing.

Model 2 in Fig. 7 is reflexive, following paths LB-TOB-COB-ROB

However, models 1,2 and 3, conceptual, reflexive, and formative (Figs. 4, 5 and 6), deliver the same result—structural model assessment (R2, Q2, f2, and path coefficients). Generally, values of 0.75, 0.50, and 0.25 are considered substantial, moderate, and weak for R2. In this study, the FP achieved a value of 0.826 (significant) for R2. Moreover, values more than zero are meaningful for Q2. In this study, the FP attained a value of 0.815 (influential) for Q2. Regarding effect sizes (f2), values of 0.35, 0.15, and 0.02 are considered significant, medium, and small for f2. In this study, CA achieved a value of 0.047 (large) for f2; LB attained a value of 0.056 (large) for f2, TOB achieved a value of 0.004 (no effect) for f2; COB attained a value of 0.003 (no effect) for f2.

Result of Hypotheses

Table XIV Hypothesis Testing

H1	There is a positive relationship between Leadership Behaviour and Firm Performance in the RMG industry in Bangladesh.
H2	There is a positive relationship between task-oriented behaviour and firm performance. NOT SUPPORTED
H3	There is a positive relationship between Relation-Oriented Behaviour and Firm Performance in the RMG industry in Bangladesh.
H4	There is a positive relationship between change-oriented behaviour and FP in the RMG industry in Bangladesh. NOT SUPPORTED
H5	There is a positive relationship between LB and CA in the RMG industry in Bangladesh.
H6	There is a positive relationship between task-oriented behaviour and competitive advantage.
H7	There is a positive relationship between relationship-oriented behaviour and competitive advantage in the RMG industry in Bangladesh.
H8	There is a positive relationship between change-oriented behaviour and competitive advantage in the RMG industry in Bangladesh.
H9	A positive relationship exists between competitive advantage and firm performance in Bangladesh's RMG industry.
H10	Emotional Intelligence moderates the relationship between LB and CA.
H11	Emotional Intelligence moderates the relationship between task-oriented behaviours and CA.
H12	Emotional Intelligence moderates the relationship between Relation Oriented Behaviours and CA.
H13	Emotional Intelligence moderates the relationship between change-oriented behaviours and CA.
H14	Firm Strategy moderates the relationship between LB and CA.
H15	Firm Strategy moderates the relationship between task-oriented behaviour and Firm Performance.
H16	Firm Strategy moderates the relationship between ROB and Firm Performance.
H17	Firm Strategy moderates the relationship between COB and Firm Performance.

In this research, the study has tested three hypotheses, hypothesised, reflective, and formative models, as shown in Fig. 3,4,5, and found similar results. As ROB became the most significant variable, the study ran another model and found this to be the most fit model as per the BIC (Bayesian information criterion). The descriptions are as follows:

Figures 4, 5 and 6 tested hypothesised, formative and reflexive models yielded similar results, and the proposed model in Fig. 7 differs from models 4,5 and 6; in those models, The new model has the LB is related to ROB, and ROB reflex TOB and COB, then to CA to FP. The proposed model aligns with the findings since ROB is the most significant behaviour found. The result also supports the proposed model as the BIC, in Table XV, is lower than the other two models depicted in Figs. 4,5 and 6.

Table XV Model selection criteria Matrix Based on BIC (Bayesian information criterion)

Model selection criteria Matrix BIC (Bayesian information criterion)
	Model 3*	Model 2 Formative	Model 1Reflexive
CA	18.173	18.786	18.728
COB	14.775	16.035	16.000
FP	6.938	8.019	8.000
ROB	2.242	4.156	4015
TOB	10.085	10.172	10.072

* Lowest

Answering Research Questions

Relationship between Leadership Behaviour and Firm Performance: The study found a partially supported positive relationship between Leadership Behaviour and Firm Performance in Bangladesh's RMG industry. While the connection was established, nuances in the findings emphasise the need for a deeper understanding of the impact of leadership on overall company performance.

Task-Oriented Behaviour and Firm Performance: The study did not support the hypothesised positive relationship between Task-Oriented Behaviour and Firm Performance. This result prompts a reconsideration of the conventional wisdom surrounding task-oriented leadership in the context of the RMG industry.

Relation-Oriented Behaviour and Firm Performance: A positive relationship was supported between Relation-Oriented Behaviour and Firm Performance. This highlights the importance of relational aspects in leadership within the RMG sector, indicating that fostering positive relationships can contribute to improved firm performance.

Change-Oriented Behaviour and Firm Performance: The study did not find support for the positive relationship between Change-Oriented Behaviour and Firm Performance. This suggests that, contrary to expectations, a proactive approach to change may not directly translate to improved overall company performance in the RMG industry.

Mediating Role of CA: All – LB, TOB, ROB and COB were mediated by CA to FP. CA and FP had a significant relationship.

Moderating Effects of Emotional Intelligence and Firm Strategy: Significantly, Emotional Intelligence and Firm Strategy were identified as moderators influencing the relationship between leadership behaviour, competitive advantage, and firm performance. This underscores the need to consider these moderating factors when evaluating the effectiveness of leadership strategies in the RMG industry.

Recommendation 1: Invest in Emotional Intelligence (EI) training programs that cater specifically to the needs of RMG industry leaders and employees. These programs should focus on:

Enhancing empathy and understanding in leadership roles, considering the diverse workforce and stakeholder landscape in the RMG sector.
Building skills for conflict resolution, such as managing labour relations and workplace disputes, is integral in this industry.
Developing EI competencies is vital for effective communication and decision-making within the RMG context.

Recommendation 2: Recognize that leadership competencies can vary based on managerial levels within the RMG industry. Tailor leadership development programs to address the unique needs and challenges faced by frontline managers, middle managers, and senior executives:

Frontline managers should focus on team leadership and communication skills for overseeing production lines.
Middle managers require training in stakeholder relationship management, conflict resolution, and supply chain coordination.
Senior executives should engage in strategic leadership programs, emphasising industry-specific trends, global trade dynamics, and sustainable practices.

Recommendation 3: Go beyond individual EI training and strive to weave the principles of Emotional Intelligence into the fabric of RMG organisations:

Foster a culture of empathy and collaboration that values emotional awareness and effective interpersonal communication.
Incorporate EI competencies into performance evaluations and reward systems to incentivise EI development at all levels of the organisation.
Encourage leaders to lead by example, demonstrating emotionally intelligent behaviours in their interactions with employees, suppliers, and other stakeholders.

Recommendation 4: Ensure RMG organisations align their strategic objectives with leadership behaviours. This alignment can have a profound impact on performance:

Communicate the firm's strategic objectives to all levels of the organisation, emphasising the importance of leadership behaviours that support these objectives.
Encourage leaders to adapt their behaviours based on the strategic context, whether expanding into new markets, optimising supply chains, or enhancing sustainability practices.
Please periodically review and adjust leadership behaviours to align with evolving business strategies and industry trends.

Recommendation 5: Given the rapidly changing landscape of the RMG industry, fostering a culture of innovation and adaptability is essential:

Promote Change-Oriented Behaviour (COB) among leaders and employees to encourage innovation in product design, manufacturing processes, and sustainability initiatives.
Embrace digital technologies and data-driven decision-making to enhance operational efficiency and responsiveness to market shifts.
Establish cross-functional teams that collaborate on innovation projects, leveraging the diverse skills and experiences within the organisation.

Recommendation 6: Managing relationships with various stakeholders, including employees, suppliers, and customers, is vital in the RMG industry:

Invest in Relation-Oriented Behaviour (ROB) by prioritising open communication, transparency, and ethical practices in dealings with stakeholders.
Collaborate closely with suppliers to ensure timely deliveries, quality assurance, and sustainable sourcing, recognising the pivotal role of the supply chain in the RMG sector.
Continuously engage with customers to understand their evolving preferences and needs, allowing for product customisation and market responsiveness.

Recommendation 7: The dynamic nature of the RMG industry necessitates regular reviews and adjustments to the firm's strategy:

Conduct regular strategic reviews considering market trends, trade policies, technological advancements, and sustainability requirements.
Keep the firm's strategy agile and responsive to changes in consumer behaviour, global demand, and competitive forces.
Seek opportunities for diversification or expansion into emerging markets while balancing risk mitigation strategies.

Recommendation 8: Embrace technology and digitisation as enablers of operational efficiency, innovation, and sustainability within the RMG industry:

To increase efficiency, explore opportunities to integrate digital tools and platforms into production processes, supply chain management, and data analytics.
Leverage data-driven insights for demand forecasting, inventory management, and product design to meet changing consumer preferences.
Invest in automation and robotics to enhance productivity while addressing labor-related challenges.

Recommendation 9: Recognize the importance of continuous learning and professional development within the RMG industry:

Promote a culture that values upskilling and offers opportunities for employees to acquire new competencies, particularly in areas such as digital literacy and sustainability practices.
Support initiatives that educate and train workers, enhancing their employability and well-being.
Encourage industry collaboration to share best practices in workforce development and skills enhancement.

This study significantly contributes to the existing literature in the following ways:

Academic

This study offers many contributions to enhancing the theoretical understanding of leadership behaviour and its impact on the performance of RMG firms within the Resource-Based View (RBV) framework and dynamic capabilities. This study builds upon the theoretical foundations of the Resource-Based View (RBV) and the Dynamic Capability View (DCV), as previously explored in earlier studies. However, it expands upon this existing theory by examining the contribution of Leadership Behaviour to a business's performance (FP), specifically within the context of a Ready-Made Garments (RMG) firm. Furthermore, this study enhances our understanding of the intricate connections between LB (leadership behaviour) and FP (financial performance), thereby contributing to the existing literature on the subject. Previous research has primarily focused on two-factor leadership behaviour and broad leadership styles.

In contrast, this study deviates from existing literature by explicitly examining the behaviours of task, relational, and change leadership. There exists a deficiency in the existing literature about the role of competitive advantage (CA) in mediating the relationship between leadership behaviour (LB) and firm performance (FP), as well as how emotional intelligence (EI) and firm strategy (FS) may moderate this relationship. The mediating effect of competitive advantage (CA) and the moderating effect of emotional intelligence (EI) and firm strategy (FS) adoption present significant avenues for advancing and advancing the paradigm of leadership behaviour (LB) and firm performance (FP) study in tandem.

Furthermore, this study makes a valuable contribution to the RMG industry by establishing a connection between FS and the LB strategy and its competitive advantage (CA). Researchers opine that the relationship between LB and FP and its impact on firm competitiveness is complex. However, this study aims to propose and empirically examine a research model that establishes a correlation between LB and FP in the context of RMG firms. Hence, this research enhances our understanding of the effects of the prerequisites necessary for utilising inadequately examined RMG firm performance. This study is a groundbreaking effort to provide evidence that implementing RMG LB can improve performance in RMG manufacturing companies, hence establishing a durable competitive advantage. This study makes a substantial contribution to the understanding of the Resource-Based View (RBV), as discussed by Cao et al. (2019) and Endres et al. (2020), particularly about the concept of dynamic capacities, as explored by Gupta et al. (2020) and Wang & Kim (2017). The aforementioned theoretical contributions collectively provide fresh insights and enhance our comprehension of the linkage between LB-FP and the RMG sector.

Nuanced Understanding of Leadership Behaviours

By examining the relationships between various leadership behaviours (i.e., Leadership Behaviour, Task-Oriented Behaviour, Relation-Oriented Behaviour, and Change-Oriented Behaviour) and firm performance, the study adds nuance to the understanding of how different leadership styles impact organisational outcomes in the context of the RMG industry in Bangladesh.

Moderating Factors in Leadership Studies

Identifying Emotional Intelligence and Firm Strategy as significant moderating factors adds a new dimension to leadership studies. Understanding how these factors influence the relationship between leadership behaviour, competitive advantage, and firm performance contributes novel insights to the field.

Industry-Specific Application

The study's focus on the RMG industry in Bangladesh provides industry-specific knowledge that can guide leaders and policymakers. This context-specific approach ensures that the findings directly apply to the challenges and opportunities unique to the Ready-Made Garments sector.

Addressing Gaps in Knowledge

The research has addressed several gaps in the existing knowledge:

Task-Oriented Leadership in RMG

More support for the positive relationship between Task-Oriented Behaviour and Firm Performance challenges conventional wisdom in leadership studies. This finding addresses a gap in understanding how task-oriented approaches may differ in effectiveness within the RMG industry.

Change-Oriented Leadership Impact

The non-supported relationship between Change-Oriented Behaviour and Firm Performance highlights a gap in knowledge regarding the direct impact of change-oriented leadership in the RMG context. Future research could delve deeper into the nuanced role of change-oriented behaviours in this industry.

Balancing Behaviours

The RMG industry's need for a balanced integration of leadership behaviours finds its validation in the contingency theory, as propounded by Fiedler (1967). This theory accentuates that leadership effectiveness often originates from aligning leadership styles with situational exigencies.

Practical Implications

For Leaders, Organisations, and Policymakers in the RMG Industry, the study offers valuable practical insights:

The focus is placed on the behavioural aspects of leadership.

This study emphasises the significant impact of leadership behaviour (LB) on improving firm performance (FP) within the RMG sector. Managers and leaders within the Ready-Made Garments (RMG) industry must prioritise cultivating and demonstrating well-rounded leadership behaviours, encompassing task-oriented, relational, and change-oriented approaches. By using this strategy, organisations can effectively steer their companies towards enhanced performance and a competitive edge.

The integration of emotional intelligence with firm strategy

The research highlights the significance of emotional intelligence (EI) and firm strategy (FS) as moderating variables in the association between LB and FP. Managers should foster emotional intelligence (EI) within their leadership teams to enhance their comprehension and responsiveness towards employee needs; client wants, and market fluctuations. Integrating the firm's strategy with leadership behaviour is imperative to achieve strategic goals and attain a competitive advantage.

The primary emphasis lies on the concept of dynamic capabilities.

In light of the ever-changing nature of the RMG industry, firms should allocate resources towards cultivating dynamic capabilities. This entails adjusting to market fluctuations, proactively predicting changes and equipping the organisation to address them adequately. It is imperative to provide leaders with appropriate training to identify market trends and adapt strategy effectively.

Achieving Competitive Advantage through Effective Leadership

The findings of this study propose that the mediating role of competitive advantage (CA) exists in the link between LB and FP. Therefore, it is imperative for managers to strategically utilise their distinctive leadership talents to establish and maintain a competitive edge. This objective can be attained by strategically emphasising innovation, optimising operational efficiency, and fostering employee engagement.

The significance of firm performance metrics and aligning with FS, LB

In the highly competitive realm of the Ready-Made Garments (RMG) industry, ongoing monitoring and evaluation of firm performance are imperative. Managers must have comprehensive performance measurement methods that align with their leadership plans to ascertain that the organisation is progressing in the desired trajectory.

Applying Research Findings

In conclusion, managers must be updated on the most recent research discoveries in their respective fields, such as the study mentioned, to guide their strategic decision-making processes effectively. Integrating academic ideas into real scenarios can significantly augment the efficacy of managerial actions and strategies within the RMG sector.

Tailored Leadership Approaches

Understanding the nuanced impact of different leadership behaviours allows leaders to tailor their approaches based on organisational needs. For instance, emphasising Relation-Oriented Behaviour could be particularly beneficial for enhancing firm performance.

Strategic Decision-Making

Recognizing the significant moderating effects of Emotional Intelligence and Firm Strategy, leaders can strategically leverage these factors to enhance the positive outcomes of certain leadership behaviours. Incorporating emotional intelligence training and aligning strategies with leadership styles can contribute to overall success.

Competitive Advantage Development

The study underscores the positive relationship between Leadership Behaviour and Competitive Advantage. Leaders can leverage this insight to consciously integrate practices that foster a competitive edge, aligning organisational strategies with leadership approaches.

Policy Formulation

Policymakers in the RMG industry can utilise these findings to inform industry-specific policies. Understanding the interplay between leadership behaviours and firm performance is crucial for creating a conducive environment for growth, innovation, and sustainability within the sector.

Training and Development Programs

Organisations can design targeted training and development programs for leaders based on the findings. For instance, programs enhancing emotional intelligence or focusing on specific leadership behaviours can be implemented to optimise organisational outcomes.

The significance of investing in leadership training and development programmes cannot be overstated. These programmes ought to prioritise the improvement of not only technical aptitude but also the cultivation of soft skills such as emotional intelligence, change management, and strategic thinking.

Limitations

While this study provides valuable insights into the relationship between leadership behaviour and firm performance in the RMG industry of Bangladesh, several limitations should be acknowledged:

Sample Size and Generalizability

The study's findings are based on a specific sample size, and caution should be exercised when generalising the results to the entire RMG industry. The dynamics within different organisational structures or sizes may vary, and further research with a more diverse sample is recommended.

Cross-Sectional Nature

The study's cross-sectional nature limits the ability to establish causality. Longitudinal studies would provide a more robust understanding of how changes in leadership behaviour influence firm performance over time.

Self-Reported Data

The reliance on self-reported data introduces the potential for response bias. Participants may provide socially desirable responses, impacting the accuracy of the measurements. Future studies could incorporate multiple data sources or objective performance metrics.

Moderating Factors

While the study identifies significant moderating effects of Emotional Intelligence and Firm Strategy, other potential moderating factors may exist. The focus on these variables might overlook additional influences on the relationship between leadership behaviour and firm performance.

Industry-Specific Focus

The study's concentration on the RMG industry in Bangladesh limits the generalizability to other sectors. Different industries may exhibit unique characteristics that influence leadership behaviour and firm performance.

Suggestions for Future Research

Building on the insights gained from this study, several avenues for future research can be explored to enhance further our understanding of the relationship between leadership behaviour and firm performance in the RMG industry of Bangladesh:

Longitudinal Studies

Conducting longitudinal studies will allow researchers to explore the causal relationships between leadership behaviour and firm performance over an extended period. This approach can provide a more comprehensive understanding of how leadership practices impact organisational outcomes over time.

Exploration of Additional Moderating Factors

While this study highlights the moderating effects of Emotional Intelligence and Firm Strategy, future research could explore other potential moderating factors. Investigating organisational culture, industry dynamics, or external market forces could contribute to a more nuanced understanding of the relationship.

Comparative Industry Analysis

Extend the scope of research beyond the RMG industry to conduct comparative analyses across different sectors. Comparing leadership behaviour and its impact on firm performance in diverse industries can reveal industry-specific nuances and contribute to broader theoretical frameworks.

Qualitative Research Methods

Complementing quantitative approaches with qualitative research methods, such as interviews or case studies, can provide deeper insights into the lived experiences of leaders and employees. Qualitative data can better understand how leadership behaviour influences organisational outcomes.

International Comparative Studies

Broaden the geographical scope of research by conducting international comparative studies. Exploring how leadership behaviour varies across cultures and its implications for firm performance can contribute to the global applicability of research findings.

Intervention Studies

Implementing intervention studies that involve leadership development programs or organisational change initiatives can assess the practical effectiveness of specific interventions. Understanding how targeted interventions impact leadership behaviour and firm performance can inform evidence-based practices.

No competing interests and no funding were received for this research. Data will be available upon request.

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The authors declare no competing interests.

Relationship Between Leadership Behaviour and Firm Performance in Ready Made Garments (Rmg) Industry of Bangladesh

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Abstract

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Introduction

RQs

Conceptual Framework

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Research Design

Interviews for Qualitative Data

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Yamane’s Formula

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Respondent’s Description

Respondent Categories, Hierarchies and Distribution by Role

Experience Distribution

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Measurement Model Assessment (Outer Model)

Internal Consistency

Convergent Validity

Discriminant Validity

Structural Model Assessment (Inner Model)

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Version 2