Big Data Analytics and Organizational Performance: Mediating Roles of Green Innovation and Knowledge Management in Telecommunications

Abstract

In the rapidly evolving telecommunications industry, organizations in Bangladesh are facing the challenge of improving their performance to stay competitive. However, there is limited research on how big data analytics (BDA) impacts organizational performance (OP) in this context. Therefore, this study examines the impact of BDA on OP in Bangladesh’s telecommunications industry, with green innovation (GI) and knowledge management (KM) as mediating variables, and big data analytics technical capabilities (BDATCs) as a moderating variable. We collected data from 384 management-level employees across five major telecom companies in Bangladesh using a structured survey questionnaire. Our analysis employed partial least squares structural equation modeling (PLS-SEM) with Smart-PLS 4.0 software. The findings indicate that BDA positively influences OP, and both GI and KM significantly mediate this relationship. However, while BDATCs enhance the BDA–OP relationship, they do not significantly moderate the BDA–GI link. These results underscore the importance of integrating BDA with KM and GI to boost organizational performance. Telecom companies should invest in advanced data analytics, foster a culture of sustainability, and enhance knowledge management practices to achieve superior performance. This study contributes to the Resource-Based View (RBV) theory by demonstrating the strategic role of BDA, GI, and KM in a developing economy context. Future research should expand this investigation across different sectors and consider longitudinal approaches to capture the dynamic nature of BDA’s impact on organizational performance.

1. Introduction

Big data analytics (BDA) refers to the process of analyzing large and complex data sets to uncover patterns, trends, and insights that can inform decision-making. By leveraging BDA, organizations can enhance their operational efficiency, improve customer satisfaction, and gain a competitive advantage [1]. At present, modern businesses have observed and appreciated the strategic environmental and economic advantages related to digital economics. Big data analytics is a very important support of the modern and revolutionary world based on technical growth. New organizations that arise in the time of economic digital transformation are involved in converting their firms into the digital world and utilizing extensive information for decision-making processes. Using extensive data requires technical assistance and supervision to produce, store, and transmit valued knowledge for predicting opportunities and threats, also embracing ecological business fluctuations [2]. Businesses can use BDA to save time, make precise decisions, monitor and comprehend consumer requirements and behavior, evaluate large amounts of data, estimate developments in the marketplace, and respond to rivals’ moves and competing goods. The advantages that arise from utilizing BDA capacities can help knowledge management procedures. In the real world, BDA allows top management to make effective decisions based on information and facts instead of their emotions [3]. Consequently, BDA experiences are helpful for companies to change their customary regular business procedures and the technology utilized for gathering wide-ranging data effectively and with lower rates. In this task, BDA enables businesses to observe a huge volume of data to gain valued insights to adjust potential fears and take sustainable business prospects [4]. The experiences of BDA help manufacturers by understanding the vast diversity of data and filtering it to produce pertinent data and information that must be classified and analyzed to improve organizational performance [4]. Large-scale data collection and analysis facilitate the development of an in-depth understanding of knowledge-intensive enterprises. As a result, BDA is starting to influence corporate performance. According to the study, BDA distinguishes between high-performing and low-performing organizations because it produces an organizationally agile and responsive long-term strategic vision. As a result, senior managers employ big data to increase the effectiveness of making decisions, monitor the behavior of clients and competitors, and identify patterns in business prospects. Ultimately, this allowed them to alter and convert all of their business operations into knowledge-based enterprises [4].

However, it is necessary to encompass the strong relationship between the knowledge that BDA produces and other organizational skills. BDA serves as a catalyst for both green innovation (GI) and knowledge management (KM), which are critical pathways through which BDA influences organizational performance (OP). Studies analyzing the effects of KM and BDA procedures and their results on company performance are rare. The aspects influencing firms’ adoption of BDA have been discussed in the literature. It is implied that businesses find it difficult to develop the skills and resources needed to create and profit from BDA [4]. The following significant earlier studies that looked at the connections between KM, BDA, business analytics, co-innovation, organizational performance, and green innovation technology were built upon and expanded upon by this one. According to the study, BDA represented co-innovation using data from many company stakeholders, which prompted strong network engagement, particularly throughout the phase of product development centered on ecosystem requests. The insights generated by BDA are crucial for driving GI initiatives, enabling organizations to innovate in environmentally sustainable ways that meet regulatory standards and consumer expectations. Thus, BDA can strengthen the criteria for eco-friendly products. In a different study, the mediation impact of dynamic capacities was examined to assess the indirect influence of BDA capacities on both radical and incremental invention [5]. According to the report, companies with great BDA capabilities can expect to achieve outstanding results through excellent strategic decision openness, high prediction of consumer and competitor actions, and radical and inventive business reformation.

There are environmental advantages to these strategic and financial gains. Investigating how BDA affects green innovation and business performance is therefore essential. Ferraris and Mazzoleni discovered that enhancing both managerial and technical facets of BDA improved company performance and that KM considerably enhanced the encouraging effects of BDA on organizational performance [5]. Simultaneously, a different Obitade study discovered that BDA skills greatly enhance business agility and KM capabilities. The study addressed the argument that standard knowledge management (KM) tools are not as effective as they could be in processing and analyzing these massive amounts of data, which aid managers in decision-making and threat identification. KM plays a pivotal role by ensuring that the knowledge and insights derived from BDA are effectively captured, shared, and applied within the organization, thereby enhancing decision-making and fostering continuous innovation. Consequently, BDA is a novel and promising technology that solves these related issues [6]. Singh and El-Kassar’s empirical study examined how big data analytics and prediction analysis influence green technology innovation. Furthermore, the research demonstrates that the growing influence of big data analytics and prediction analysis on green technological innovation is significantly influenced by managerial commitment [4]. Abbas and Sağsan explored the connection between green innovation, business sustainability, and knowledge management competencies. According to the study, knowledge management has an optimistic effect on green innovation technology, which in turn improves both economic and environmental performance [7]. Likewise, another investigation reports the same results. Finally, Zameer and Wang investigated how business analytics plays a crucial role in green technological innovation and competitive edge. According to the research, business analytics has a good impact on green innovation technology, which raises the financial benefits for the company and creates a sustainable edge over rivals [8].

To develop, upgrade, and deploy green innovation, and to boost company performance, it is necessary to figure out why and how BDA is a valuable asset that facilitates organization KM creation and leverage. This is the motivation behind the research that is being done. Furthermore, it is extremely difficult for businesses to manage and search for knowledge generation in the context of huge data, particularly when they have to monitor, predict, and react to customer behavior, market shifts, and rival businesses. The degree to which businesses develop insightful business analytics is what determines a firm’s existence and survival in the modern era. Thus, KM can refine GIP. In response to multiple requests, this research proposes to investigate the relationship between KM and BDA and the potential value provided [9].

Furthermore, in light of the growing effects of societal and environmental concerns, BDA is required to assist the development of green technology, which in turn supports green company procedures, green practices, green products, and green behavior on the part of employees and enterprises. Thus, performance within the organization is encouraged. Firms can also produce information, but it is not clear whether this knowledge can be efficiently shared and capitalized on. BDA assists in transforming the creation of knowledge into its application and commercialization. In this research, the strategic importance of BDA as the primary factor in the development, sharing, and discussion of KM for commercial purposes is thus emphasized [5]. The results of BDA–KM are essential for enhancing organizational performance and GIP. In this context, research from emerging nations and the knowledge gained from comprehending the relationship between BDA and KM are required. This study looks at a significant theoretical hole that has not been fully explored and is thought to be a complex managerial problem that undermines the entire business process.

In particular, there are currently few studies addressing the link between BDA and KM and its effects on GIP. It is unclear whether or not this relationship improves businesses’ green innovation strategies [6]. By expanding on business-specific knowledge, a research project by Le and Dahiya explored the planned effect of BDA on businesses’ competitive edge. The research discovered that the solutions of BDA offer a high level of specific knowledge about firms and may produce a sustainable competitive edge [10]. The part of van de Wetering and Mikalef in fostering the result of BDA on various organizational performance factors is highlighted in another study. This suggests that one of the crucial results that enhances and gains from the application of BDA to provide corporate value is knowledge management. Concurrently, the study found that the use of BDA greatly enhanced cyber-KM abilities [11]. According to Ferraris and Mazzoleni, improving the managerial and technological facets of BDA boosts KM capabilities and firm performance, which in turn increases BDA’s capacity to boost firm performance. Numerous scholarly works have presented and examined business-development analytics (BDA) from diverse angles and viewpoints, utilizing a range of methodological techniques to strategically predict the impact of BDA on various firm organizational metrics, decision-making processes, competitive edge, dynamic capacities, and ethical innovations [5].

To drive and improve the results of green innovation, it is still unclear from the literature to date if and how BDA might support and elevate the capacities of enterprises’ particular knowledge. There have been very few studies looking at the association between BDA–KM and company environmental results. Consequently, this study adds to and answers previous research about the possible effects of BDA on firms’ green dynamic skills (such as green knowledge) and green innovation. Providing eco-friendly products and halting environmental damage helps to improve a company’s reputation for being environmentally conscious (Waqas et al., 2021 [12]). Research on BDA, green technological innovation, knowledge management, and business performance is rare. The majority of researchers have addressed how the BDA–KM interaction is still unclear and disregarded in favor of concentrating on the KM–GI and BDA–GI relationships. To get back to the three essential bases of the NRBV lens, scholars must first realize and clarify how BDA can perform as a main resource for improving organizational knowledge development that directly supports organizational decision-making and raising green technologies.

It is unclear how BDA will affect green innovation, so understanding the connection between KM and BDA is essential because it helps clarify how knowledge is created and then transformed into knowledge applications, particularly when it comes to green issues [12]. As far as we are aware, few, if any, studies have utilized KM to forecast BDA about company success. Meanwhile, there is still a need to give BDA–KM a lot of consideration when predicting green innovation, particularly in light of environmental concerns and consumer environmental consciousness. The Resource-Based View (RBV) theory, with its focus on leveraging firm-specific resources to gain a competitive advantage, provides a relevant theoretical lens for this study. Given the strategic importance of BDA and KM in driving organizational performance and innovation, particularly in the context of environmental challenges, the RBV offers a robust framework for understanding these dynamics.

Research Objectives:

The objective of this research is to forecast whether knowledge generated by BDA can be transformed and improve the results of green innovation. The goal of the study was to explain the crucial influence of BDA on knowledge management as well as green innovation technology, and how it will improve firm performance. It did this by building on the natural resources-based approach, dynamic capacity, and growing research on the BDA perspective [13]. Our overall objective is to explore and explain the influence of big data analytics on organizational performance, with a specific focus on the mediating roles of green innovation and knowledge management and the moderating role of big data analytics technical capabilities within the telecommunications industry in Bangladesh. This study aims to provide valuable insights into how leveraging big data analytics can enhance organizational performance through improved knowledge management and green innovation, moderated by technical capabilities. By employing the Resource-Based View (RBV) as an underpinning theory, this research will contribute to the academic understanding and practical applications of big data analytics in driving sustainable business performance.

1.1. Operational Definition of the Key Variables

Big Data Analytics (BDA): The utilization of sophisticated data analysis tools and techniques to process extensive datasets, enabling organizations to make informed decisions and improve overall business performance.

Green Innovation (GI): The creation and adoption of environmentally sustainable products, processes, and technologies aimed at reducing ecological footprint and promoting sustainable development.

Knowledge Management (KM): The systematic approach to capturing, sharing, and effectively utilizing organizational knowledge to drive innovation, improve efficiency, and enhance competitive advantage.

Organizational Performance (OP): The assessment of an organization’s ability to achieve its objectives efficiently and effectively, reflected in financial, operational, and market success.

Big Data Analytics Technical Capabilities (BDATCs): The necessary technical infrastructure, tools, and expertise to efficiently process and analyze large datasets, facilitating enhanced data-driven decision-making.

1.2. Resource Based View (RBV) as Underpinning Theory

The Resource-Based View (RBV), introduced by Barney et al. [14], serves as the theoretical underpinning for this study, providing a framework for understanding how firms can achieve and sustain a competitive advantage by leveraging valuable, rare, inimitable, and non-substitutable resources. In the context of the telecommunications industry in Bangladesh, the RBV helps elucidate the relationships between big data analytics (BDA), organizational performance (OP), green innovation (GI), knowledge management (KM), and big data analytics technical capabilities (BDATCs). Barney’s theory posits that organizations can gain a competitive edge through the strategic use of their internal resources. BDA, as a significant resource, aligns perfectly with the RBV’s emphasis on leveraging unique capabilities to improve performance outcomes. BDA is critical in today’s data-driven economy, offering firms the ability to analyze large volumes of data to extract actionable insights. This capability becomes a source of competitive advantage when combined with the RBV framework, which underscores the importance of developing and exploiting unique resources. BDA facilitates the transformation of raw data into valuable insights, supporting informed decision-making processes and enabling organizations to anticipate and respond to market trends more effectively. In line with the RBV, BDA can be seen as a resource that provides a firm with the necessary tools to outperform competitors through superior data processing and analytical capabilities [4]. The study explores the direct impact of BDA on organizational performance (OP), recognizing that firms equipped with advanced data analytics capabilities are better positioned to optimize their operations, enhance customer experiences, and drive business growth. This relationship aligns with the RBV’s assertion that valuable resources, such as BDA, contribute to improved performance outcomes. For instance, BDA enables telecommunications firms to streamline their operations, reduce costs, and improve service quality, thereby enhancing overall performance metrics [5]. The ability to harness big data effectively is a distinctive competence that can lead to a sustained competitive advantage, as posited by Barney et al. [14]. Green innovation (GI) serves as a mediating variable in the relationship between BDA and OP. GI, defined as the development and implementation of eco-friendly products, processes, and technologies, is increasingly recognized as a critical driver of sustainable business practices. The integration of BDA into GI initiatives allows firms to identify and exploit opportunities for environmental innovation, thereby enhancing their sustainability credentials and market competitiveness. According to the RBV, resources that contribute to sustainable development, such as GI, are essential for long-term success. BDA supports GI by providing insights into environmental impact, resource efficiency, and consumer preferences for green products, thereby fostering innovation that aligns with both business and environmental goals. Knowledge management (KM) is another mediating variable that plays a crucial role in the BDA–OP relationship. KM involves the systematic management of organizational knowledge assets to create value and improve performance. BDA enhances KM by enabling the collection, analysis, and dissemination of knowledge across the organization. This capability aligns with the RBV’s focus on the strategic management of internal resources to achieve a competitive advantage. Effective KM, supported by BDA, ensures that valuable knowledge is captured, shared, and utilized to drive innovation, improve decision-making, and enhance organizational agility. The RBV framework emphasizes that the ability to manage knowledge effectively is a key resource that contributes to superior performance outcomes [9]. Big data analytics technical capabilities (BDATCs) act as a moderating variable in this study, influencing the strength of the relationships between BDA, GI, KM, and OP. BDATCs refer to the technical skills, infrastructure, and tools required to implement and utilize BDA effectively. According to the RBV, the development of technical capabilities is crucial for leveraging valuable resources. In the context of telecommunications firms, BDATCs ensure that BDA initiatives are supported by robust technical infrastructure and expertise, thereby enhancing the effectiveness of data analytics efforts. The presence of strong BDATCs amplifies the positive impact of BDA on GI and KM, leading to improved OP. This relationship underscores the RBV’s assertion that the strategic alignment of technical capabilities with valuable resources is essential for achieving and sustaining a competitive advantage [12]. The RBV provides a comprehensive framework for understanding the relationships between BDA, OP, GI, KM, and BDATCs in the telecommunications industry in Bangladesh. BDA, as a valuable resource, enhances organizational performance by enabling data-driven decision-making and operational efficiency. The mediating roles of GI and KM highlight the importance of leveraging BDA to drive innovation and knowledge management, both of which are critical resources for achieving a competitive advantage. BDATCs moderate these relationships by ensuring that technical capabilities support the effective implementation and utilization of BDA. By aligning with the RBV framework, this study underscores the strategic importance of developing and leveraging internal resources to achieve superior performance outcomes and sustain a competitive edge in the dynamic telecommunications industry [13].

2. Literature Review and Hypothesis Development

2.1. BDA and OP

Based on the Resource-Based View (RBV), which emphasizes the strategic use of firm-specific resources to achieve a sustained competitive advantage, the hypothesis posits that big data analytics significantly influences organizational performance in the context of the telecommunications industry. BDA represents a valuable resource that enables organizations to collect, process, and analyze large volumes of data to derive actionable insights for decision-making and operational improvements [13]. According to the RBV, firms that effectively deploy BDA capabilities are better positioned to enhance operational efficiencies, innovate new products and services, and respond swiftly to market changes compared to their competitors [5]. Thus, the hypothesis suggests that organizations leveraging BDA will experience improved OP due to enhanced strategic decision-making, better customer relationship management, and increased productivity and efficiency in resource allocation. This alignment with RBV underscores BDA’s role not merely as a technological tool but as a critical strategic resource that contributes significantly to organizational capabilities and a competitive advantage in the dynamic telecommunications sector [13]. Therefore, we hypothesized that:

H1: 

BDA has a significant positive effect on OP.

2.2. KM and GI

Drawing upon the Resource-Based View (RBV), which emphasizes the strategic utilization of internal resources to achieve a competitive advantage, the hypothesis posits that knowledge management positively influences green innovation within organizations, particularly in the telecommunications sector. KM involves the systematic management of knowledge resources to foster innovation and improve organizational processes. According to the RBV, organizations that effectively manage their knowledge assets are better equipped to innovate in environmentally friendly technologies and practices, thus enhancing their competitive positioning [8]. The hypothesis suggests that robust KM practices, including knowledge creation, sharing, and application, enable firms to develop and implement green innovations more effectively. This alignment with RBV underscores KM’s role as a critical resource that enhances organizational capabilities for sustainable innovation and a competitive advantage in dynamic industry environments [4]. Therefore, the hypothesis proposes that organizations with strong KM frameworks will exhibit higher levels of GI due to their enhanced ability to leverage internal knowledge resources for environmental innovation initiatives. Hence,

H2: 

Knowledge management has a significant positive effect on green innovation.

2.3. GI as a Mediator

Based on the Resource-Based View, which underscores the role of internal resources in achieving a competitive advantage, the hypothesis posits that green innovation mediates the relationship between big data analytics and organizational performance in the telecommunications industry. BDA enables organizations to harness large datasets for strategic decision-making and operational improvements [4]. According to the RBV, the strategic utilization of BDA enhances firms’ capabilities in identifying market opportunities and responding to environmental challenges through innovative green practices [15]. GI, as a mediator, transforms these BDA-driven insights into tangible environmentally sustainable innovations that enhance OP by fostering cost efficiencies, regulatory compliance, and stakeholder satisfaction. This hypothesis suggests that organizations leveraging BDA to drive GI initiatives will experience enhanced OP due to their ability to innovate in environmentally friendly technologies and processes, thereby gaining a competitive advantage in the market [13]. Therefore, GI acts as a critical link between BDA capabilities and OP outcomes, aligning with the RBV’s perspective on how internal resources, when leveraged effectively, contribute to a sustained competitive advantage and superior organizational performance in the telecommunications sector. Hence, we hypothesized that:

H3: 

GI has a significant mediating effect between BDA and OP.

2.4. KM as a Mediator

In the context of the telecommunications industry, the hypothesis posits that knowledge management mediates the relationship between big data analytics and organizational performance, supported by the Resource-Based View. BDA capabilities enable organizations to gather, analyze, and utilize vast amounts of data for strategic decision-making and operational improvements [5]. The RBV emphasizes that KM plays a pivotal role in leveraging internal knowledge resources to achieve a competitive advantage [8]. Effective KM practices involve the creation, sharing, and application of knowledge assets, which enhance organizational capabilities and competitiveness by enabling firms to innovate, adapt, and respond effectively to market changes [4]. KM acts as a mediator by transforming BDA-driven insights into actionable knowledge that drives improvements in OP through enhanced innovation, efficiency, and responsiveness to customer needs [6]. Organizations proficient in KM are better positioned to capitalize on BDA capabilities, optimize internal processes, and foster a culture of continuous improvement and innovation [15]. Therefore, this hypothesis suggests that organizations leveraging BDA to facilitate KM practices will experience enhanced OP, illustrating how KM serves as a critical link between BDA capabilities and organizational success in the telecommunications sector. Thus, we have hypothesized that:

H4: 

KM has a significant mediating effect between BDA and OP.

2.5. BDATCs as Moderators between BDA and GI

In the telecommunications sector, the hypothesis suggests that big data analytics technical capabilities (BDATCs) moderate the relationship between big data analytics (BDA) and green innovation, underpinned by the Resource-Based View. BDATCs represent the technical proficiency and infrastructure that enables organizations to effectively harness and utilize BDA for innovation purposes [12]. The RBV posits that organizational capabilities, such as BDATCs, enhance the strategic value derived from internal resources like BDA, thereby influencing their impact on innovation outcomes [11]. Organizations with robust BDATCs are better equipped to leverage BDA insights to foster green innovation initiatives by optimizing data processing, analysis, and interpretation capabilities [16]. This moderation effect suggests that the extent and effectiveness of BDA’s influence on GI are contingent upon the level of BDATCs within the organization. Higher BDATCs enhance the organization’s ability to transform BDA into actionable insights and innovative solutions that contribute to environmental sustainability and a competitive advantage in the telecommunications industry. Hence, we hypothesized that:

H5: 

BDATCs moderate the relationship between BDA and GI, such that the positive effect of BDA on GI is strengthened when BDATCs are high.

2.6. BDATCs as Moderators between BDA and OP

In the context of the telecommunications industry, the hypothesis proposes that big data analytics technical capabilities moderate the relationship between big data analytics and organizational performance, drawing support from the Resource-Based View. BDATCs represent the technological infrastructure and capabilities that facilitate the effective utilization of BDA within organizations to enhance operational efficiencies and strategic decision-making [16]. According to the RBV, organizational capabilities such as BDATCs enable firms to leverage their internal resources like BDA to achieve a sustainable competitive advantage and superior performance outcomes [11]. Organizations with advanced BDATCs are better positioned to harness the full potential of BDA in improving various facets of organizational performance, including productivity, profitability, and customer satisfaction [12]. This moderation effect suggests that the impact of BDA on OP is contingent upon the level of BDATC capability within the organization. Higher BDATCs enhance the ability of BDA to generate actionable insights and strategic advantages that translate into improved overall performance in the telecommunications sector. Therefore, we hypothesized that:

H6: 

BDATCs moderate the relationship between BDA and OP, such that the positive effect of BDA on OP is strengthened when BDATCs are high.

3. Proposed Research Framework

The conceptual framework of this study investigates the relationships within the telecommunications industry in Bangladesh, with big data analytics as the independent variable and organizational performance as the dependent variable. Green innovation and knowledge management serve as mediating variables, while big data analytics technical capabilities function as the moderating variable. Utilizing the Resource-Based View as the underpinning theory, the framework explores how strategic resources and capabilities, particularly in data analytics, influence performance and innovation outcomes, providing insights into optimizing technology and knowledge assets for a competitive advantage (Figure 1).

4. Methodology

This study investigates the relationship between BDA and OP among telecommunications companies in Bangladesh. The focus on Bangladesh is driven by three primary considerations: first, contextual differences, as most existing studies are conducted in developed economies, and variations in business environment conditions, culture, and managerial practices are anticipated to produce unique findings and implications within the context of a developing economy like Bangladesh; second, resource influence, as it is crucial to understand how the BDA–OP relationship is affected by firms’ resources, including KAC, knowledge creation (KCO), and KAP, along with BDA capabilities, recognizing that the impact of BDA deployment varies across different economic settings; third, KM process impact, as it is essential to determine how the integration of BDA with knowledge management processes (KAC, KCO, and KAP) can enhance OP, especially in developing countries.

The business system of Bangladesh has been affected by turbulence through economic factors where major participation is held by textile and garment industries, which contributes 80% of the export sector. The country is endowed with an abundance of cheap labor, which attracts foreign investors into the country. However, problems such as bureaucratic and administrative issues, corruption, and lack of relevant facilities remain. Some of the measures include tax reforms, incentives, and special economic zones. Still, statistics show that the informal sector is still prevalent as people continue to engage in economic activities not constrained by formalities. Mobile network access has increased a lot with the help of the use of 4G mobile network services, and a 5G network is being prepared. Policymakers have geared their efforts towards increasing the accessibility of the Internet and reducing costs; nonetheless, there are hurdles, and they include inadequate infrastructure, high taxes, and policy instability. Still, the sector is critical to the country’s digital transformation and economic growth, as it has a considerable impact on the country’s GDP and employment [17].

The study targeted the five major telecom companies in Bangladesh: Grameenphone Ltd., Robi Axiata Limited, Banglalink Digital Communications Limited, Teletalk Bangladesh Ltd., and Bangladesh Telecommunications Company Limited (BTCL), which collectively employ approximately 15,000 individuals across top, middle, and lower management levels. The major characteristics of these selected companies have been addressed in Table 4 below.

A total of 384 complete responses were collected using a convenience sampling technique. The data collection process involved disseminating a Google Forms link to management-level employees across different hierarchical levels within the companies, facilitated by convincing departmental managers to support the survey. Participants were assured that the data would be used solely for academic purposes, that their anonymity would be strictly maintained, and that no personal information of participating employees or identities of the establishments would be collected. Out of 680 distributed questionnaires, 395 were returned, and after excluding 11 incomplete responses, 384 valid responses were analyzed. The study employed a structured survey questionnaire based on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree) to measure various constructs using established items from prior research. Specifically, BDA was assessed using six items adapted from [15]; KAC, KCO, and KAP were each measured with six items; OP was evaluated using six items from Tang, Walsh, Lerner, Fitza, and Li (2018) [18]; and BDAMC (big data analytics maturity capabilities) was measured with six items adapted from Makhloufi, A.; Boubker, N.; Bouri (2023) [5]. To ensure the adequacy, clarity, and comprehensibility of the items, content and face validity assessments were conducted. Three academic professors and two industry professionals reviewed the items to ensure each item was clear and reflected the intended variable. Feedback from these experts was incorporated into the final survey version to enhance the quality of the measurement items.

5. Pre-Test and Pilot-Test

Before the main study, we conducted a pre-test with 13 respondents to assess the clarity and effectiveness of the questionnaire. Feedback from this pre-test was instrumental in refining the questions to minimize potential confusion. Based on their suggestions, minor adjustments were made to improve coherence. Following this, a pilot study was conducted with a sample of 20 participants to evaluate internal consistency. Cronbach’s alpha coefficients were calculated for each construct, all of which exceeded the 0.70 threshold, thereby confirming the reliability and robustness of our measurement instruments.

5.1. Demographic Profile of the Respondents

Following the removal of outliers and missing data, a sample size of 384 (response rate: 52.11%) was attained and deemed suitable for hypothesis testing. Gender distribution indicates 37.24% male and 62.76% female respondents. In terms of education, 52.86% hold a Master’s degree, 35.68% possess an undergraduate degree, and 11.46% have diplomas. Regarding marital status, 63.28% were unmarried, while 36.72% were married. A total of 25% of the respondents were from Grameenphone Ltd., 12% were from Robi Axiata Limited, 13% were from Banglalink Digital Communications Limited, 24% were from Teletalk Bangladesh Ltd., and 26% were from Bangladesh Telecommunications Company Limited (BTCL).

5.2. Common Method Bias (CMB)

To evaluate the presence of common method bias (CMB), we analyzed the heterotrait–monotrait (HTMT) ratio and inner variance inflation factor (VIF) values, following Nitzl’s (2016) [19] guidelines. A correlation exceeding 0.90 indicates potential CMB among key constructs. Our study revealed that all correlation values among constructs were below 0.90, with the highest correlation being 0.846, as depicted in the HTMT table, indicating an absence of CMB. Additionally, we assessed the inner VIF values, where a VIF exceeding 3.30 may suggest model contamination by CMB. Our highest VIF value was 2.685, as indicated in the structural model assessment table, which is well below the 3.30 threshold recommended by Kock (2015) [20]. These findings further confirm the absence of CMB in our study.

6. Data Analysis

6.1. Data Analysis and Findings

Partial least squares structural equation modeling (PLS-SEM) is a robust and distinct method for analyzing path models based on composites. It is particularly useful for theory testing when dealing with datasets that have multiple indicators and non-normal data [19,20]. In our study, we employed SPSS (v23.0) for data pre-processing and conducted various related tests, such as assessing common method bias and examining linearity. To evaluate the proposed hypotheses, we utilized Smart-PLS software version 4.

6.2. Common Method Bias (CMB)

The study examined the common method bias by observing the HTMT and inner VIF values. According to Nitzl (2016) [19], CMB exists if the principal constructs are significantly correlated (r > 0.90); however, all the correlation values among the constructs are less than 0.90 (see table HTMT) confirming no CMB as the height correlation value is 0.804. Another way of observing CMB is by examining the inner VIF values if the VIF > 3.30, indicating that CMB may contaminate the model. For the current study, the height VIF is 1.684 (see table structural model assessment) which is less than the threshold values of 3.30 [20], confirming no issue of CMB.

6.3. Inter-Correlations of the Study Variables

The constructs’ descriptive statistics and inter-correlations listed in

Table 1. Means, SD, Correlations, and Reliabilities of the Study Variables.

Constructs	BDA	GI	KM	OP	BDATC	Mean	SD
BDA	1					3.677	0.581
GI	0.441 **	1				3.539	0.548
KM	0.495 **	0.435 **	1			3.580	0.615
OP	0.781 **	0.537 **	0.663 **	1		3.550	0.629
BDATC	0.520 **	0.414 **	0.778 **	0.632 **	1	3.570	0.648

Note: n = 384, ** p < 0.01 (2-tailed).

demonstrate that all of the variables substantially correlate with EA. The mean value of TW had the highest mean value (3.942), while the EA had the lowest mean value 3.624.

6.4. Demographic Profile of the Respondents

After excluding outliers and addressing missing data, we obtained a reliable and suitable sample size of 384 respondents (response rate: 52.18%) to conduct hypothesis testing. The gender distribution revealed that the majority of participants (52.08%) were male, while the remaining participants (47.92%) were female. Regarding educational qualifications, the largest proportion of respondents (45.57%) held primary school certificates, followed by 24.74% at the secondary school level, 16.67% at the higher secondary level, and the smallest group (13.02%) consisted of graduates and those with higher qualifications. Participants were also categorized based on their work experience, with 66.67% having less than 5 years of experience, 22.14% falling within the 5- to 10-year range, and 11.20% having 11 to 15 years of experience. In terms of marital status, the majority of respondents were single (52.34%), while 30.47% were married, and the smallest proportion (17.19%) consisted of divorced or widowed individuals. Lastly, when examining wages, the highest percentage of respondents (44.01%) earned between Tk 10,001 and Tk 15,000, followed by 42.45% earning between Tk 7000 and Tk 10,000. A smaller proportion (13.02%) earned less than Tk 7000, and only a minimal percentage (0.52%) earned above Tk 15,000.

6.5. Evaluation of Measurement Model (Outer Model)

To assess the internal consistency reliability of our variables, we employed two widely recognized measures: composite reliability (CR) and Cronbach’s alpha (CA). As shown in

Table 2. Validity and reliability of constructs.

Constructs	Items	F.L	CA	CR	AVE
BDA	BDA 1	0.780	0.895	0.897	0.658
	BDA 2	0.875
	BDA 3	0.838
	BDA 4	0.849
	BDA 5	0.782
	BDA 6	0.731
BDATC	BDATC 1	0.660	0.916	0.93	0.71
	BDATC 2	0.890
	BDATC 3	0.868
	BDATC 4	0.894
	BDATC 5	0.891
	BDATC 6	0.829
GI	GI 1	0.076	0.913	0.916	0.698
	GI 2	0.866
	GI 3	0.824
	GI 4	0.832
	GI 5	0.854
	GI 6	0.867
KM	KM 1	0.764	0.903	0.906	0.674
	KM 2	0.855
	KM 3	0.854
	KM 4	0.846
	KM 5	0.805
	KM 6	0.836
OP	OP 1	0.821	0.93	0.931	0.741
	OP 2	0.881
	OP 3	0.867
	OP 4	0.905
	OP 5	0.837
	OP 6	0.851

Notes: CR: composite reliability; AVE: average variance extracted; CA: Cronbach’s alpha.

, all variables demonstrated favorable internal consistency, with CR and CA values exceeding 0.7. These results indicate robust reliability across the constructs by Hair et al. [21]. Furthermore, the factor loadings (FL) of all items surpassed the threshold of 0.60, providing additional evidence of their strong association with their respective constructs. Convergent validity was confirmed by the average variance extracted (AVE) scores, which exceeded 0.5 for each construct, as recommended by Chin (1998) [22] and supported by Hair et al. [21]. These findings validate the soundness of our measurement model (Figure 2).

After satisfying the required threshold values for factor loadings, average variance extracted (AVE), Cronbach’s alpha (CA), and composite reliability (CR), we proceeded to assess the discriminant validity of the constructs using two established methods: the Fornell–Larcker criterion and the heterotrait–monotrait ratio (HTMT). In

Table 3. Discriminant validity: Fornell–Larcker.

HTNT						Fornell Larker
Constructs	BDA	BDATC	GI	KM	OP	Constructs	BDA	BDATC	GI	KM	OP
BDA						BDA	0.811
BDATC	0.574					BDATC	0.526	0.843
GI	0.516	0.479				GI	0.472	0.442	0.835
KM	0.546	0.856	0.507			KM	0.499	0.78	0.465	0.821
OP	0.825	0.667	0.623	0.703		OP	0.759	0.622	0.574	0.703	0.861

, the diagonal cells display the square root of AVE values, while the correlations are presented below. Following the Fornell–Larcker criterion [23], we compared the square root of AVE values with the corresponding correlations. It is worth noting that the diagonal values, representing the square root of AVE, were consistently higher than the correlations beneath them. This finding provides strong evidence that discriminant validity has been successfully achieved, as per the Fornell–Larcker criterion. We assessed the discriminant validity of the constructs using the heterotrait–monotrait ratio (HTMT) criterion, as recommended by Henseler et al. [24]. Our analysis revealed that all correlation values among the constructs were below 0.9, indicating satisfactory discriminant validity. This finding aligns with the recommended threshold by Henseler et al. [24]. Specifically, in

Table 4. Characteristics of the companies studied in this paper.

Targeted Companies	Characteristics
Grameenphone Ltd.	Largest company with GSM and 5 G technology in Bangladesh
Robi Axiata Limited	Second-largest mobile company, providing 4.5 G across all 64 districts
Banglalink Digital Communications Limited	Third-largest mobile network company
Teletalk Bangladesh Ltd.	Government mobile operator company for public
Bangladesh Telecommunications Company Limited (BTCL)	Provides landline telecommunications services across urban areas

, the highest HTMT value observed was 0.804, providing further confirmation that no significant discriminant validity issues were present.

The off-diagonal values are the correlations between latent variables, and the diagonal is the square root of AVE.

6.6. Assessment of Structural (Inner) Model

After thoroughly evaluating the measurement model, we proceeded to examine collinearity, coefficient of determination, and effect size in the structural model. This involved assessing R² values, f² values, and inner VIF. As presented in

Table 5. Assessment of the structural model.

R-Square	Endogenous Variables	R Square	R Square Adjusted		0.26: Substantial, 0.13: Moderate, 0.02: Weak [25]
	GI	0.297	0.291
	KM	0.249	0.248
	OP	0.707	0.704
Effect Size (F-Square)	Exogenous Variables	GI	KM	OP	0.35: Substantial, 0.15: Medium effect, 0.02 Weak effect [25]
	BDA	0.064	0.332	0.53
	GI			0.082
	KM	0.029		0.092
Collinearity (Inner VIF)	Exogenous Variables	DC	DIL	SAE	VIF ≤ 5.0 [21]
	BDA	1.949	1	2.073
	GI			1.422
	KM	2.738		2.817

, we achieved the recommended values for R², F², and inner VIF, indicating the robustness of our model. With these parameters met, we moved on to analyzing the results of our proposed hypotheses.

7. Hypotheses Testing Results

Table 6. Hypotheses testing result.

Hypotheses	OS/Beta	SD	95% C.I Blas Corrected		T	P	Decision	Mediation
			LL	UL
H1: BDA -> OP	0.567	0.062	0.449	0.681	9.124	0	Supported
H2: KM -> GI	0.236	0.115	0.022	0.445	2.053	0.041	Supported
H3: BDA -> GI -> OP	0.055	0.031	0.01	0.125	1.998	0.046	Supported	Partial
H4: BDA -> KM -> GI	0.118	0.056	0.003	0.222	2.094	0.037	Supported	Partial
H5: BDATC × BDA -> GI	−0.006	0.052	−0.104	0.098	0.112	0.911	Not Supported
H6: BDATC × BDA -> OP	0.06	0.029	0.002	0.118	2.064	0.04	Supported

presents the results of the proposed hypotheses obtained through bootstrapping with 5000 resampling iterations. The results indicate that Hypothesis 1 (H1), which examines the direct relationship between SE and EA, yielded statistically significant findings. The p-value (0.000), being less than the conventional threshold of 0.05, demonstrates the presence of a significant relationship. Furthermore, the t-value (7.706) exceeds the critical threshold of 1.96, further confirming the significance of the relationship. The positive beta value (0.444) indicates that SE has a significant positive effect on EA. The second proposed hypothesis examined the mediating effect of TW in the relationship between SE and EA. The results provided support for this hypothesis, as indicated by the p-value (0.000) being less than the significance threshold of 0.05. Additionally, the t-value (4.888) exceeded the critical threshold of 1.96, confirming a significant mediating effect. The positive values of LL (0.113) and UL (0.269) indicate that there is no zero in between, further supporting the presence of a mediating effect. It should be noted that the mediation observed is partial, as the direct relationship between SE and EA remains significant even with the mediating effect of TW. Similarly, the third proposed hypothesis examined the mediating effect of PS in the relationship between SE and EA. The results provided support for this hypothesis as well, with a p-value (0.048) below the significance threshold of 0.05. The t-value (1.986) was higher than 1.96, confirming a significant mediating effect. The positive values of LL (0.017) and UL (0.136) indicate that there is no zero in between, further validating the mediating effect. Similar to the previous hypothesis, the mediation observed is partial, as the direct relationship between SE and EA remains significant despite the presence of PS as a mediating factor. These results indicate that PS has a partially positive and significant effect in mediating the relationship between SE and EA. Hypothesis 4 (H4) examined the moderating effect of SL on the relationship between SE and TW. The results indicated no significant moderating effect, as evidenced by the p-value (0.459) being higher than the significance threshold of 0.05. Furthermore, the t-value (0.741) was lower than the critical threshold of 1.96, further not supporting the presence of a significant moderating effect. In conclusion, SL does not play any significant role in moderating the relationship between SE and TW. However, the second proposed moderating hypothesis (H5) examined the effect of SL on the relationship between SE and EA. The results revealed a significant moderating effect, as indicated by the p-value (0.005) being below the significance threshold of 0.05. Moreover, the t-value (2.832) exceeded the critical threshold of 1.96, further confirming the presence of a significant moderating effect. The positive beta value (0.118) indicates that SL has a significant positive moderating effect on the relationship between SE and EA. Thus, it can be concluded that SL plays a significant positive role in moderating the relationship between SE and EA. Similarly, the third proposed moderating hypothesis (H6) examined the effect of SL on the relationship between SE and PS and found a significant moderating effect. This was supported by the p-value (0.049) being less than the significance threshold of 0.05. Additionally, the t-value (1.970) was higher than the critical threshold of 1.96, confirming the presence of a significant moderating effect. In conclusion, SL has a significant moderating effect on the relationship between SE and PS (Figure 3).

The graph presented in Figure 4 provides additional insights into the moderating effect of SL. It demonstrates the interaction between SL and the relationship between SE and EA. The findings reveal that when SL is at a high level, the effect of SE on EA is more pronounced, indicating a stronger relationship. Conversely, when SL is at a low level, the effect of SE on EA is weaker, indicating a diminished relationship. To investigate this interaction further, we conducted separate path analyses for both high (i.e., one standard deviation above the mean) and low (i.e., one standard deviation below the mean) levels of SL. These analyses allowed us to examine the distinct paths for SE and EA under each SL condition, providing a comprehensive understanding of the relationships involved.

8. Discussion

Our research’s first hypothesis (H1) reveals that big data analytics (BDA) significantly impacts organizational performance (OP) within the telecommunications industry in Bangladesh. This aligns with previous empirical studies demonstrating BDA’s pivotal role in enhancing operational efficiency, customer satisfaction, and overall competitive advantage [24,26]. The telecommunications sector, characterized by vast amounts of data generated from network operations, customer interactions, and service usage, presents a fertile ground for BDA application. By leveraging BDA, telecom companies can gain deep insights into customer behavior, optimize network performance, and tailor personalized services, leading to improved OP. Our findings are further justified through the lens of the Resource-Based View (RBV) theory, which posits that organizations achieve a sustained competitive advantage by effectively utilizing valuable, rare, inimitable, and non-substitutable resources [14]. In this context, BDA emerges as a strategic resource that telecom companies in Bangladesh can harness to enhance their internal capabilities and market positioning. The significant direct effect of BDA on OP underscores its role as a critical asset in driving business success in the increasingly data-driven and competitive telecommunications landscape [27,28]. Our research’s second hypothesis (H2) reveals that knowledge management (KM) has a significant direct effect on green innovation (GI) in the telecommunications industry in Bangladesh. In the telecommunications sector, where rapid technological change and environmental concerns are prevalent, KM practices help organizations capture, disseminate, and utilize knowledge to drive GI initiatives. By systematically managing knowledge, telecom companies can identify and implement eco-friendly technologies and processes, thus enhancing their environmental performance. From the Resource-Based View (RBV) perspective, KM is considered a strategic asset that facilitates the integration and application of valuable knowledge resources, thereby enabling firms to develop innovative solutions that address environmental challenges [14]. The significant effect of KM on GI suggests that telecom companies in Bangladesh should focus on strengthening their KM practices to leverage their knowledge assets effectively for sustainable innovation. This approach not only aligns with the RBV’s emphasis on strategic resource management but also highlights the importance of fostering a knowledge-driven culture to achieve a competitive advantage through GI. Our research’s third hypothesis (H3) indicates that green innovation (GI) significantly mediates the relationship between big data analytics (BDA) and organizational performance (OP) within the telecommunications industry in Bangladesh. This finding aligns with previous studies highlighting the mediating role of GI in enhancing OP through sustainable practices and technological advancements [29,30]. The telecommunications sector, marked by its substantial energy consumption and environmental footprint, benefits from integrating BDA to drive GI initiatives. By leveraging BDA, telecom companies can optimize energy use, reduce emissions, and develop eco-friendly products and services, thereby improving OP. This is consistent with the Resource-Based View (RBV) theory, which suggests that firms gain a competitive edge by deploying unique and valuable resources [14]. In this case, BDA serves as a critical resource that enables the generation and implementation of GI, leading to enhanced OP. The significant mediating effect of GI underscores its role in translating BDA capabilities into tangible environmental and performance benefits, thus providing telecom companies in Bangladesh with a sustainable pathway to competitiveness and operational excellence [18,31]. Our research’s fourth hypothesis (H4) reveals that knowledge management (KM) significantly mediates the relationship between big data analytics (BDA) and organizational performance (OP) in the telecommunications industry in Bangladesh. This finding is consistent with prior empirical studies that have shown KM’s crucial role in transforming data insights into strategic actions that enhance OP [10,32]. In the telecommunications sector, where data volumes are immense and the need for rapid, informed decision-making is critical, KM practices help in efficiently capturing, storing, and utilizing the insights generated by BDA. By integrating KM, telecom companies can ensure that valuable data-driven insights are disseminated across the organization, facilitating improved customer service, operational efficiencies, and innovation. From the Resource-Based View (RBV) perspective, KM acts as an essential organizational capability that leverages the valuable and rare insights provided by BDA to create a sustainable competitive advantage [14]. The significant mediating effect of KM underscores its role in bridging the gap between BDA capabilities and enhanced OP, ensuring that data-driven knowledge is effectively converted into strategic assets that drive performance improvements in the highly competitive telecommunications industry in Bangladesh [26,33]. Our research’s fifth hypothesis (H5) reveals that big data analytics technical capabilities (BDATCs) have no significant moderating effect on the relationship between big data analytics (BDA) and green innovation (GI) in the telecommunications industry in Bangladesh. This finding contrasts with previous studies that have suggested that technical capabilities are crucial for the effective implementation of BDA-driven innovations [34]. However, our results can be justified within the context of Bangladesh’s telecommunications sector. This industry may face unique challenges, such as infrastructural limitations, regulatory constraints, and resource scarcity, which could diminish the impact of BDATCs on GI initiatives. While BDATCs are essential for processing and analyzing data, the successful implementation of GI may also heavily depend on other factors, such as organizational culture, regulatory support, and market readiness for green technologies. From the Resource-Based View (RBV) perspective, the mere possession of technical capabilities might not suffice to drive GI unless these capabilities are complemented by other critical resources and organizational strategies [14]. This indicates that BDATCs alone are not a sufficiently valuable, rare, inimitable, or non-substitutable resource to enhance the BDA–GI relationship. Therefore, our findings suggest that telecom companies in Bangladesh need to focus on a more holistic approach, integrating technical capabilities with supportive policies and an innovation-friendly culture to foster GI [10,27]. Our research’s sixth hypothesis (H6) reveals that big data analytics technical capabilities (BDATCs) significantly moderate the relationship between big data analytics (BDA) and organizational performance (OP) in the telecommunications industry in Bangladesh. This finding underscores the critical role of technical capabilities in optimizing the benefits derived from BDA. In the context of Bangladesh’s telecom sector, characterized by high data volumes and rapid technological advancements, BDATCs such as advanced data processing tools, scalable storage solutions, and sophisticated analytical software enhance the effectiveness of BDA by enabling efficient data handling and insightful analytics. This aligns with the Resource-Based View (RBV) theory, which posits that firms achieve a competitive advantage through the strategic deployment of valuable, rare, inimitable, and non-substitutable resources [14]. BDATCs, as strategic resources, amplify the impact of BDA on OP by ensuring that data insights are timely, accurate, and actionable, leading to improved decision-making, operational efficiency, and customer satisfaction. This finding is supported by recent studies highlighting the synergistic effects of technical capabilities on data analytics outcomes [28,34]. Therefore, telecom companies in Bangladesh should prioritize developing robust BDATCs to fully leverage the transformative potential of BDA and achieve superior performance.

9. Theoretical Implications

The findings of our research, grounded in the Resource-Based View (RBV) theory, provide significant theoretical implications for understanding the interplay between big data analytics (BDA), big data analytics technical capabilities (BDATCs), green innovation (GI), knowledge management (KM), and organizational performance (OP) in the telecommunications industry of Bangladesh. The RBV posits that organizations achieve a sustained competitive advantage by effectively deploying valuable, rare, inimitable, and non-substitutable resources [14]. Our research extends this theoretical framework by examining how specific capabilities and practices influence performance outcomes in a dynamic and technology-intensive industry. Firstly, the direct positive impact of BDA on OP, as demonstrated in our first hypothesis, reinforces the RBV’s assertion that data and analytical capabilities are strategic resources. In the telecommunications sector, BDA enables firms to harness large volumes of data for insights that drive operational efficiencies, customer satisfaction, and strategic decision-making. This finding is consistent with the existing literature that emphasizes the value of data-driven capabilities in enhancing organizational performance [34,35]. By confirming the significance of BDA as a valuable resource, our study contributes to the RBV literature by highlighting its critical role in modern, data-intensive environments. The mediating role of GI, as revealed in our second hypothesis, underscores the importance of integrating sustainability into the resource management framework. The positive mediation effect suggests that BDA can drive OP improvements more effectively when coupled with GI initiatives. This aligns with the RBV perspective, which suggests that combining multiple strategic resources can create synergies that enhance the competitive advantage [14]. In the context of Bangladesh’s telecommunications industry, where environmental concerns are increasingly important, our findings suggest that GI serves as a critical complementary resource that amplifies the benefits of BDA [36]. This integration of sustainability within the RBV framework extends theoretical discussions on how organizations can leverage green practices to achieve superior performance. Our third hypothesis, demonstrating the mediating effect of KM, further enriches the RBV by emphasizing the role of knowledge as a strategic asset. Effective KM practices ensure that the insights generated from BDA are disseminated and utilized across the organization, thus enhancing OP. This finding aligns with previous research highlighting the importance of knowledge dissemination in maximizing the value of data analytics [10,32]. By situating KM within the RBV framework, our study underscores the necessity of knowledge-related capabilities in converting data-driven insights into tangible performance outcomes. The significant moderating effect of BDATCs on the BDA–OP relationship, as highlighted in our fourth hypothesis, further validates the RBV’s focus on the importance of technical capabilities. BDATCs enhance the efficacy of BDA by providing the necessary infrastructure and tools to process and analyze data efficiently. This finding aligns with the RBV’s assertion that technical capabilities are essential for leveraging other valuable resources effectively [34]. By demonstrating the moderating role of BDATCs, our research suggests that technical infrastructure is a critical enabler of the performance benefits derived from BDA. However, the lack of a significant moderating effect of BDATCs on the BDA–GI relationship, as indicated in our fifth hypothesis, offers a nuanced perspective within the RBV framework. This finding suggests that while technical capabilities are crucial, they may not be sufficient to drive green innovation unless complemented by other factors such as organizational culture, regulatory support, and market readiness. This highlights the complexity of resource interactions and the need for a holistic approach to resource management, extending the RBV by incorporating external and contextual factors that influence the efficacy of strategic resources. Our research extends the RBV framework by demonstrating the intricate ways in which BDA, BDATCs, GI, and KM interact to influence OP in the telecommunications industry of Bangladesh. By highlighting the complementary and contingent nature of these resources, our findings provide a deeper understanding of how firms can strategically manage their capabilities to achieve a sustained competitive advantage in a rapidly evolving technological landscape.

10. Managerial Implications

The findings of our research offer several practical implications for the telecommunications industry in Bangladesh, particularly in how companies can leverage big data analytics (BDA) to enhance organizational performance (OP). The direct positive impact of BDA on OP underscores the importance for telecom companies to invest in advanced data analytics capabilities. By effectively utilizing BDA, these companies can gain deep insights into customer behavior, optimize network performance, and develop personalized services, ultimately driving higher customer satisfaction and operational efficiency. This necessitates prioritizing data analytics initiatives and ensuring that data are accurately collected, stored, and analyzed. The mediating role of green innovation (GI) in enhancing the BDA–OP relationship highlights the need for telecom companies to integrate sustainability into their core strategies. Companies should not only focus on extracting insights from data but also on how these insights can drive sustainable practices. Investing in green technologies and sustainable business models can amplify the benefits of BDA, leading to improved environmental performance and enhanced corporate reputation. This suggests that telecom companies should develop a comprehensive green innovation strategy that leverages BDA to identify and implement eco-friendly practices and products. Knowledge management (KM) is shown to be a crucial mediator between BDA and OP. This implies that telecom companies need to establish robust KM practices to ensure that data-driven insights are effectively disseminated and utilized across the organization. Companies should invest in KM systems and create a culture that encourages knowledge sharing and continuous learning. Training programs, collaborative platforms, and incentive structures can be implemented to facilitate the effective use of knowledge derived from BDA, thereby enhancing decision-making processes and driving innovation. The significant moderating effect of big data analytics technical capabilities (BDATCs) on the BDA–OP relationship indicates that technical infrastructure is vital for maximizing the benefits of BDA. Telecom companies should ensure they have the necessary technical capabilities, such as advanced data processing tools, high-speed computing infrastructure, and skilled personnel, to support their data analytics initiatives. This entails continuous investment in technology upgrades and staff training to keep pace with evolving data analytics techniques and tools. Conversely, the finding that BDATCs do not significantly moderate the BDA–GI relationship suggests that while technical capabilities are essential, they alone are insufficient for driving green innovation. Telecom companies must adopt a more holistic approach, incorporating organizational culture, regulatory compliance, and market readiness into their green innovation strategies. This could involve fostering a culture of sustainability, engaging with regulators to support green initiatives, and educating the market about the benefits of green products and services. These practical implications highlight the importance of a multifaceted approach to leveraging BDA in the telecommunications industry. By integrating advanced technical capabilities, effective knowledge management practices, and a strong focus on sustainability, telecom companies in Bangladesh can significantly enhance their organizational performance and achieve a sustainable competitive advantage.

11. Limitations and Future Research Directives

One limitation of our study is the focus on the telecommunications industry in Bangladesh, which may limit the generalizability of the findings to other industries or regions. The telecommunications sector is not yet mature in terms of digital management and big data solutions. The situation in the case of big data of telecom companies in Bangladesh is also not satisfactory because they lack the proper tools and skilled human resources for data analytics, which is essential for big data management. This gap renders the generalization of results difficult, given that the level of advancement of big data differs from firm to firm.

The emphasis on green innovation as a moderating factor may not quite be representative of the trends that modern telecom Bangladesh companies are interested in. Consequently, most affiliate marketing firms might pay more attention to the expansion of their networks and increasing their customer base while giving minimal regard to sustainable initiatives. Therefore, there is a possibility that the actual contribution of green innovation on performance is masked or even totally ignored, thereby reducing the generality of the study.

Some of the challenges relate to the regulatory factors in Bangladesh. Fluctuations in government policies, policies that include high taxes and bureaucracy, hinder or slow down the implementation of big data analytics and other innovative practices. These external factors are partially examined in the study; therefore, the understanding of the sector problems can be inadequate.

The limitations of the study may be attributed to the use of self-reported data because questionnaires are cross-sectional and thus may affect cultural desirability because of the social desirability of giving positive responses to questions regarding the organization in Bangladesh. This may result in the distortion of the actual effect of big data analytics, green innovation, and knowledge management on an organization’s performance.

Future research could explore the impact of big data analytics across different sectors and geographical contexts to validate and extent our results. Another limitation is the potential influence of unexamined external factors such as regulatory changes or market conditions on the relationships between BDA, green innovation, and organizational performance. Future studies should consider these external factors to provide a more comprehensive understanding. Lastly, our research relies on cross-sectional data, which may not capture the dynamic nature of BDA and its long-term effects. Longitudinal studies are recommended to investigate how BDA and related capabilities evolve as well as their sustained impact on OP and GI.

12. Conclusions

Our study explores the intricate relationships between big data analytics, big data analytics technical capabilities (BDATCs), green innovation (GI), knowledge management (KM), and organizational performance (OP) within the telecommunications industry of Bangladesh, guided by the Resource-Based View (RBV) framework. The findings underscore the pivotal role of BDA in directly enhancing OP through improved operational efficiencies and customer-centric strategies. Additionally, our research highlights the mediating effects of GI and KM, emphasizing the importance of integrating sustainable practices and effective knowledge dissemination to maximize the benefits of BDA. While BDATCs significantly moderate the BDA–OP relationship, their limited moderating effect on BDA–GI suggests the need for a broader strategic approach that encompasses technical capabilities alongside organizational culture, regulatory support, and market readiness for green innovations. Practical implications suggest that telecom companies should prioritize investment in BDA and BDATCs while fostering a culture of knowledge sharing and sustainability to drive innovation and gain a competitive advantage. However, the study acknowledges limitations including its industry-specific focus, potential external influences, and the static nature of cross-sectional data, suggesting avenues for future research to explore these relationships across diverse industries, longitudinal studies to capture dynamic changes, and deeper investigations into external factors shaping BDA’s impact. Overall, this study contributes valuable insights for telecom executives and policymakers aiming to leverage BDA for sustainable growth and performance in Bangladesh’s telecommunications sector.