Understanding the Impact of Big Data Analytics and Knowledge Management on Green Innovation Practices and Organizational Performance: The Moderating Effect of Government Support

Abstract

Understanding and predicting the relationship between big data analytics (BDA) and knowledge management (KM) and how they complement each other is still an ambiguous and strategically crucial issue. This study aims to investigate the interrelationships between BDA and KM in fostering green innovation practices (GIP) and organizational performance and, in addition, to explore the mediation effect of KM and how it can boost the impact of BDA on GIP. Based upon the underpinning theories that cover the study’s research model, several hypotheses were proposed and then examined. Using a cross-sectional research design, 174 questionnaires were collected from medium and large Algerian manufacturing firms. The study applied smart PLS-SEM for data analysis. The sorted results show that BDA positively impacts KM and GIP. The results also indicate that GIP positively influences and increases firm performance. In addition, the findings reveal that government support plays a significant part in leveling up firms’ GIP. Furthermore, the study emphasizes the critical impact of KM to polish the impact of BDA on GIP. This study highlights the necessity of improving the technological and managerial aspects of BDA by determining valuable capabilities, such as KM processes, to enhance organizational performance. Tracking business opportunities and predicting their relevant threats has become a severe concern to knowledge-extensive firms. Therefore, BDA can enable managers to absorb a large amount of data to increase the efficiency of a business decision to ensure survival and advancement. The study discusses both practical and theoretical implications.

1. Introduction

Nowadays, modern firms have noticed and valued the strategic economic and environmental benefits associated with the digital economy [1]. Big data analytics (BDA) is one of the critical pillars of this new era of revolution based on technological development [2]. New firms born in the age of Economic Digital Transformation (EDT) are engaged in digitalizing their businesses and use large-scale data for decision-making [3]. Using large-scale data needs technical support and management to generate, store, and share valuable knowledge in order to better predict threats and opportunities, along with adopting to eco-business changes [4,5]. BDA helps firms reduce time, formulate accurate decision making, track customer needs and understand their behavior, analyze complex data, predict market demands and changes, and react to competitors’ actions and alternative products [6]. These advantages that might be yielded by leveraging BDA capabilities can occur in support of knowledge management processes (KM) [7]. Indeed, BDA enables top management decision making based on evidence and facts rather than intuition [8].

Therefore, BDA capabilities are beneficial for firms to transform their traditional daily business operations and the technology used for collecting large-scale data efficiently and with fewer costs. In this context, BDA empowers firms to analyze a large amount of data to yield valuable knowledge to absorb potential threats and capture eco-friendly business opportunities.

BDA capabilities facilitate factories by comprehending the massive amount of data variety in order to filter it and generate useful information and knowledge that need to be categorized and interpreted to enhance management performance [9]. Collecting and analyzing a large amount of data helps to develop in-depth insights related to knowledge-intensive businesses. Hence, BDA is becoming a key driver of business performance. A study by Liu [10] argued that BDA differentiates between firms’ high and low performance, since it yields a long-term strategic vision and results in high responsiveness and organizational agility. Hence, top managers used large-scale data to improve their decision-making efficiency, track customer and rival behavior, and sense the trends of business opportunities to change and transform the entire business processes into a knowledge-intensive business [8,11]. However, the close interaction between knowledge yielded by BDA and other organizational capabilities must be combined and integrated. In Algeria, there is a lack of studies examining the impact of BDA and KM processes and their outcomes on organizational performance. Literature addressed the factors that influence the adoption of BDA among Algerian manufacturers. It is suggested that firms are struggling to acquire enough resources and capabilities to build and benefit from BDA.

This study built on and aimed to extend the following major past studies that examined the interrelationships between BDA, KM, business analytics, green innovation, co-innovation, and organizational performance. Using PLS-SEM, study [12] examined the impact of BDA on co-innovation among Colombian firms, and the results indicated that BDA positively and significantly impacted co-innovation. The study argued that BDA used data from the different firm stakeholders to represent co-innovation, which led to close network collaboration, especially in the stage of product development based on ecosystem requirements. Thus, BDA can enhance eco-friendly product requirements. Another study, [13], introduced and explored the indirect impact of BDA capabilities on incremental and radical innovation in the existence of the mediation effect of dynamic capabilities.

The study suggested that firms that acquire strong BDA capabilities can benefit from effective strategic decision responsiveness, high forecasting of customer and competitors’ actions, and radically and innovatively restructuring businesses, resulting in highly superior performance. These strategic and economic benefits also come with environmental benefits as well. Thus, exploring the impact of BDA on green innovation and firm performance is crucial. An empirical study by Ferraris and Mazzoleni [8] found that developing the technological and managerial aspects of BDA increased firm performance, and that KM plays a significant part in improving the beneficial outcomes of BDA on organizational performance. At the same time, another study by Obitade [7] found that BDA capabilities significantly improve KM capabilities and firm agility. The study suggested that traditional KM technologies possess limited powers to effectively process and analyze these large-scale data, which help managers to make better decisions and forecast threats [14]. Therefore, BDA is a promising new tool that overcomes these associated problems. An empirical investigation by El-Kassar and Singh [6] explored that big data and predictive analysis significantly impact green innovation. In addition, the study shows that management commitment possesses a considerable effect in the increasing impact of big data and predictive analysis on green innovation practices.

Abbas and Sağsan [15] examined the relationship between KM capabilities, green innovation, and firm sustainable development. The study shows that KM positively impacts green innovation, whereas the latter mediates and improves economic and environmental performance. Similarly, another study by [16] shares the same finding as [15]. Last but not least, Zameer and Wang [11] explored the critical impact of business analytics on green innovation and competitive advantage. The study indicates that business analytics positively impacts green innovation, which improves the firm’s economic benefits, resulting in green competitive advantages.

This research is conducted because of the need to understand how and why BDA is a strategic resource that enables firm KM creation and leveraging to improve, upgrade, and deploy green innovation on the one hand, and to enhance firm performance on the other hand [17,18,19]. In addition, managing and searching for knowledge creation in light of massive data is a critically severe issue, mainly when firms must track, anticipate, and respond to customers’ behavior, market changes, and competitors [19]. The existence and survival of modern firms are determined by the extent to which firms’ BDA generate valuable insights. KM, as a result, can polish GIP. This study is responding to several calls intending to examine the BDA–KM relationship [8] and the value added that can be created.

Moreover, due to the increasing impacts of environmental and institutional pressures, BDA is necessary to enhance the creation of green knowledge; this last would support firms’ green practices, employees’ green behavior, green products, and green business processes. Consequently, organizational performance is fostered. Additionally, firms can generate knowledge, but it is uncertain whether knowledge creation can be disseminated and benefited from effectively [20]. BDA helps convert knowledge creation to knowledge application, turning it to commercialized ends [21]. Therefore, this study emphasizes BDA’s strategic role as the leading key to KM creation, dissemination, and conversation to commercialized ends. Outcomes generated from BDA–KM are crucial to improving GIP and organizational performance [19]. Contextually, studies conducted in developing economies such as Algeria and the insights that can be gathered from understanding the BDA–KM relationship are needed. The study examined an important theoretical gap that is still not comprehensively addressed and seen as a crucial and ambiguous managerial issue hurting the entire business process. Specifically, studies that address the BDA–KM relationship and its outcomes on GIP are still limited, and not clear whether this relationship leads to improving firms’ green innovation practices.

An empirical study by Dahiya and Le [21] examined the strategic role of BDA on firms’ competitive advantage by elaborating on firm-specific knowledge; the study found that BDA solutions provide high-level firm-specific knowledge and potentially result in a sustained competitive advantage. Another study by Mikalef and van de Wetering [22] emphasizes the role of building dynamic capabilities to foster the impact of BDA on different organizational performance measures. This implies that KM is one of the critical outcomes that benefits and improves from the deployment of BDA to create business value. Meanwhile, the study by [7] concluded that deployed BDA significantly improved cyber KM capabilities. Shaqrah and Alzighaibi [23] stated that a positive association between KM and DBA capabilities and the outcomes generated increases the value added to the process systems. Ferraris and Mazzoleni [8] indicated that developing the technological and managerial aspects of BDA enhances firm performance and KM capabilities; this promotes BDA’s capability to improve firm performance. Studies such as [13,17,18,19,24,25,26,27,28,29,30,31] have introduced and addressed BDA in different aspects and perspectives and applying different methodological approaches to anticipate the strategic role of BDA on different firm organizational measures, decision making, dynamic capabilities, competitive advantages, and green innovation. However, the literature so far is still not clear whether and how BDA can promote and level up the capacities of firms’ specific knowledge in order to drive and enhance green innovation outcomes. Very few studies, if any, have examined the association impact of BDA–KM on firm environmental outcomes. Hence, this study responds to and advances existing literature about the potential impact of BDA on both green dynamic capabilities of firms (e.g., green knowledge) and green innovation. This helps to increase a firm’s green image and brand reputation through delivering eco-friendly products and reducing environmental degradation.

Following the studies mentioned above, gaps in the literature emerged. A lack of studies addressing BDA, KM, green innovation, and firm performance has occurred. Most studies have been focused on the BDA–GI and KM–GI relationships while addressing how the BDA–KM relationship is still overlooked and ambiguous. This requires researchers to understand and explain how (1) BDA can act as a major source of upgrading firm knowledge creation, which leads to supporting organizational decision making and fostering green practices, (2) it is not clear whether and how BDA would influence green innovation as a prerequisite to respond to the three central pillars of the NRBV lens, and (3) examining the BDA–KM relationship is crucial because it leads us to understand the process of knowledge creation and how it can be converted to knowledge application, primarily when it addresses green issues. To the best of our knowledge, few studies [8], if any, have used KM as a predictor of BDA concerning firm performance. Meanwhile, focusing on BDA–KM to predict green innovation still needs serious attention, especially in light of environmental pressures and customer environmental awareness. This study, therefore, aims to anticipate whether knowledge creation by BDA can be converted and enhance green innovation outcomes. Drawing upon the natural-resource-based view, dynamic capability, and the emerging literature on the BDA view, the study’s main objective was to address the critical impact of BDA on KM and green innovation, and how this would enhance firm performance.

2. Theory and Hypotheses Development

Knowledge has been defined as a set of justified beliefs that can be arranged and managed to enhance an organization’s performance through effective action [32]. Firms acknowledged three main KM processes: acquiring, converting, and applying knowledge [19]. Knowledge acquisition is utilized to create new knowledge from existing data and information, whereas knowledge conversion transform knowledge from tacit to explicit knowledge [8]. Knowledge application is one of the leading drivers of the KM process, which converts the developed knowledge into business applications [16]. Firms seek to generate valuable and impactful knowledge leading to advanced business intelligence, increased employee cognitive skills, and upgraded and restructured business processes.

Due to the strategic importance of data, which provide information and valuable insights that might enhance firms’ green businesses while continuously reducing environmental degradation, BDA is an essential tool for anticipating future business trends, customer behavior, competitors’ responsiveness, and market demands [26]. Hence, upgrading BDA technological applications to foster knowledge creation is needed [23]. Many firms still struggle to obtain, assimilate, and deploy sufficient knowledge to derive value added [33]. Big data analytics empower firms’ KM to establish new insights, track customers, and anticipate market demands and competitors’ behavior, which leads to covering the holistic business environment [34].

BDA helps firms create new knowledge to advance business processes, upgrade employees’ skills, develop new products, and respond to customer needs and know-how, reducing cost and time [18]. Furthermore, BDA capabilities enable firms to use established knowledge effectively to support strategic decision making. Cao and Duan [35] acknowledged that BDA is an essential capability that helps firms to enhance their organizational agility and create, share, and store valuable knowledge from diverse sources. The core focus of large firms on BDA capabilities is due to its perceived benefits in building effective decision making to face unexpected business changes and sense future business opportunities [27]. BDA capabilities enable firms to yield new insights and knowledge that help to formulate valuable business strategies [30].

Studies such as [7,8] addressed the critical impact of BDA capabilities on creating KM processes and its relation to business agility and organizational performance. Obitade [7] found that BDA capabilities significantly impact the creation and leveraging of KM processes, leading to organizational agility. At the same time, Ferraris and Mazzoleni [8] concluded that there was significant evidence of the critical role of BDA on KM capabilities. The latter increased the impact of BDA capabilities on firm performance. Another study [23] found that BDA mediates the relationship between KM and business value added. These studies indicated that BDA influences firms to acquire and leverage new knowledge and that BDA capabilities play a significant part in enhancing firm performance.

Similarly, BDA helps firms formulate valuable decision making, detect threats, and sense changes in business conditions, thus enabling the firm to deploy accumulated knowledge to yield business value [36]. From the literature, it is not known whether and how BDA would influence firms’ capacities to create green knowledge on the one hand, and how the coupled impact of BDA–KM will extend green business growth while prioritizing environmental sustainability on the other hand. Overall, understanding how the BDA–KM relationship is improved and its consequences on developing green practices is crucial and needs serious attention. This study, as a result, aims to bridge this gap from the perspective of the NRBV and DCV. Through effective analytics implementation, firms might decrease the timing process, increase responsiveness, develop new ways of resource conservation, and thus govern production costs. Therefore, we hypothesize that:

H1: 

Big data analytics positively influence knowledge management.

Ferraris and Mazzoleni [8] argued that BDA fosters data-driven decision making and yields innovative ways to organize tasks, learn routines, and innovate new ideas. This leads to improving the management of different business processes (e.g., green practices, production efficiency, resource conservation, know-how, and ecofriendly products), which results in superior innovation performances [17,18]. So, the firm’s capacity to acquire, share, and analyze big data might make differences in processes and eco-friendly production quality that fulfill the ecosystem requirements and improve the firms’ image and performance [12]. Firms must combine and leverage several technological resources, human and managerial skills, organizational learning, and a data-driven perspective to foster BDA capabilities; this could improve eco-innovation practices by analyzing features of clean technology processes, products’ design and quality, customer expectations, and market demands [12,18].

Through the use and analysis of large-scale data, it is beneficial and vital to track customer purchases, green behavior, market trends, know-how, resource conservation, and green processes to advance green chemistry and apply green engineering, which foster green innovation performance [17]. Furthermore, BDA capabilities help firms formulate business models, marketing decisions, market segments, and product optimization based on large-scale data stores [8,37,38]. Feng and Guo [39] argued that business analytics help firms to forecast, build effective decision making, continuously develop green practices, and improve eco-friendly products [18]. Kamble and Gunasekaran [40] stated that one of the greatest advantages of BDA is creating new knowledge, establishing new management rules, tasks, and routines, and reconfiguring business structures and processes to fit eco-innovation strategy. In a recent study, Niebel and Rasel [41] emphasized the importance of implementing strategies based on tested outcomes of BDA’s capabilities to successfully foster green innovation [42]. Ferraris and Mazzoleni [8] suggest that firms focus on data management and innovation opportunities based on knowledge created by BDA. Additionally, the authors of [43] stressed the strategic role of digital technologies in improving innovation capabilities and business value. Even though several studies tried to examine and link BDA capabilities to innovation, few studies have attempted to study the BDA–GI relationship and its outcomes on firm performance. Thus, this study aimed to cover this emerging gap and support the NRBV lens from the context of a developing country such as Algeria. Therefore, the study produced the following hypothesis:

H2: 

Big data analytics positively influence green innovation.

Green innovation is characterized by an organization that undertakes the processes and procedures to reduce the negative impacts of their operations on different environmental aspects through their inventions in the production system, product development, and management practices [44] to control hazardous waste and resource consumption, and provide a clean work environment [8]. Green management innovation (GMI) reflects firms’ new management-based green practices to reconfigure business processes and restructure management procedures to improve product functionalities to fit customer environmental awareness [44]. These improvements lead to economic and environmental benefits. Firms seeking to strengthen and green their management practices must base their efforts upon environmental structures such as ISO 14001 [45]. GMI offers firms the capacity to sustain competitive advantages such as customer loyalty, trust, and profitability.

Knowledge management is the core center of innovation processes and products [46]. It supports firms in acquiring and analyzing large-scale data to improve their operations [8,47]. Breznik [48] concluded that the linkage between KM and innovation activities occurred when firms sought to restructure their management practices toward green ones. An empirical study by Abbas and Sağsan [15] found that KM positively impacts green innovation and environmental sustainability. The study also found that green innovation increases the effect of KM on environmental, economic, and social sustainability [15]. According to Li and Huang [44], to deploy green innovation, KM supports firms in innovating new ways to reduce environmental impact, resource consumption, and hazardous waste, and to create eco-friendly products and enhance firms’ green image [15]. Furthermore, KM increases the capacity of a firm to innovate new techniques, practices, and procedures to improve the production system, upgrade product functions, and reduce resource use, enhancing the overall firm image [15,16]. Based on the above discussion, the following hypothesis is assumed:

H3: 

Knowledge management positively influences green innovation.

Scholars measured and examined green innovation in several dimensions and concepts, such as green products, processes, and managerial structures. Green products and processes reflect firms that apply green technologies to enhance production systems and processes, which lead to reduced resource consumption, a clean workplace, enhanced product functionalities, and compliance with the environmental standard [8,15,49]. Thus, an innovation based on improving production techniques and management procedures usually enhances the eco-innovation system and thus decreases environmental impacts. Green innovation increases customer loyalty and profitability and improves the green image, resulting in a firm’s increased financial and non-financial performance [50,51].

Empirical studies’ results differ in addressing the impact of green innovation on environmental and organizational performance [15,50,51]. The results differ for several reasons, such as differences in environments, market conditions, government roles, economic statuses, and societies’ cultures. Tang and Walsh [50] found that green products and processes positively impact firm performance. Driessen and Hillebrand [52] empirically concluded that green innovation is associated with low financial performance. Aguilera-Caracuel and Ortiz-de-Mandojana [53] stated that non-green innovation firms achieved high financial performance compared to those applying green innovation initiatives. Tang and Walsh [50] noted that green innovation led to increasing production costs, which led to decreased competitiveness of firms. Recent studies found that green innovation (green product, process, and management) influences economic, environmental, and social performance [15,16]. Examining the effect of green innovation on Algerian firm performance is of utmost importance to provide additional evidence about the benefits behind greening business operations. Thus, this study hypothesized the following:

H4: 

Green innovation positively influences organizational performance.

Due to the reluctance of top management to apply green practices because of uncertainty about achieving economic benefits and a lack of resources, firms need great assistance to deploy and meet eco-innovation system requirements [51]. Such aid includes funds, infrastructure facilities, technologies, training, collaboration, and R&D expenditure [15]. Past studies found inconclusive results on the relationship between green innovation and its different outcome measures [50]. These consequences remain the call for examining green innovation to predict its outcomes within other contexts, cultures, and economic conditions. Explicitly, the government’s support is essential to encourage firms to apply green practices and achieve super performance [51].

Environmental degradation is the core concern of modern societies and public leaders worldwide; therefore, government support is crucial to reduce environmental impact and ensure socioeconomic sustainability [54]. The support that should be provided for firms could be technological, training, consultant, management, or funds [55]. Governmental support is crucial to advancing firms’ technological progress and thus improving production systems, enhancing the workplace environment, controlling hazardous waste and resource consumption, and promoting product functionalities through green subsidies [55]. Financial incentives, pilot projects, tax facilities, technical resources, and consultants also significantly leverage green practices.

Green innovation implementation needs to extend the availability of resources. The munificence of resources encourages firms to apply green innovation practices to fit environmental management practices [56]. The government plays important role in the munificence of resources enabling organizations through low banking loan rates to upgrade eco-friendly technologies, tax facilities, and R&D expenditures [57]. Lee [58] concluded a positive association between the impact of Korean government support and green initiatives among SMEs. Researchers believe that government support is a critical factor for increasing the chances of green innovation adoption and improving firms’ financial and environmental performance. However, the interplay among the firm’s context, green innovation, government support, and organizational performance remains unclear. Examining these interrelationships is essential, especially within a developing context such as Algeria. Since past studies pointed out inconclusive findings, this study intends to answer whether or not governmental support plays a role in deploying green practices within the Algerian context.

H5: 

Government support positively influences organizational performance.

H6: 

Government support positively moderates the relationship between green innovation and organizational performance.

Prior studies found that investment in information systems and BDA significantly contributed to the business value added [59]. Debate on the relationship between BDA and KM still exists. Scholars and managers believe that investment in BDA remains uncertain to enhance operational efficiency [60]. The absence of understanding of the relationship between BDA and KM and its effect on innovation performance and firm performance is due to several reasons, such as a lack to predict IT benefits [61], poor KM orientation, a lack of data, and useless information [8,62]. KM plays a strategic role in leveraging relevant knowledge and BDA practices used by management to yield and formulate appropriate policies, determine employees’ tasks, upgrade production systems and product functions, segment markets, and improve firm performance [6]. This integration between KM and BDA is critical, and the success depends on a firm’s management capabilities to achieve close interaction between these two strategic resources [5]. Large-scale data are a severe challenge for modern firms based on the knowledge of how to manage the relevant knowledge generated by BDA (e.g., the complexity of data integration, data security and privacy, lack of IT personnel skills, and poor IT infrastructure) [63]. KM, therefore, is more important to enable the firm’s BDA capabilities [4]. Indeed, BDA supports changes in KM processes and improves the role of individual knowledge, which might influence employees’ tasks and routines. El-Kassar and Singh’s [6] empirical study found that extensive data assimilation positively impacts green innovation processes and products. Khan and Vorley [5] highlighted the significant integration between KM and BDA, which leads to sharing and storing relevant knowledge to business intelligence and enables human business knowledge, resulting in a different type of innovation performance improvement. Thus, we propose the following hypothesis:

H7: 

Knowledge management mediates the relationship between big data analytics and green innovation.

The study introduces a two-dimensional view of BDA in terms of management and technological aspects to support KM processes which lead to improving green practices and organizational performance, to predict the research model’s ultimate outcomes or business value. Based on the literature, the study believes that firms acquiring well-established BDA capabilities result from leveraging and achieving a close integration of well-skilled IT personnel with other organizational capabilities for better predicting green innovation performance. BDA management and technological capabilities help refine KM processes (acquiring, sharing, and application), thereby improving the total effect on green innovation (product, processes, and management). These critical linkages between BDA, KM, and green innovation resulted in superior performance.

This research stands on the NRBV lens, and using BDA literature, the study aims to clarify and discuss the importance of BDA–KM in tackling three major environmental issues highlighted by the NRBV. The relationship between BDA and KM is unclear and still ambiguous among researchers, especially concerning green innovation and firm performance. The study was conducted in Algeria because it seems appropriate to explore the development of BDA, and how managerial practices are aware of it and benefit from it in responding to green issues. It is not sure whether firms in developing economies such as Algeria are mastering, deploying, and allocating valuable resources to generate valuable insights from BDA, which lead to fostering knowledge creation, and leveraging and incorporating it into green business strategies. This study, as a result, attempts to overcome these theoretical and practical issues by elaborating on the role of BDA and KM in enhancing GIP and firm performance in the context of developing economies.

Building on the NRBV and DCV [64,65,66] and extensive data analytics literature [5,59,67], this study proposes an evolutionary research model by which BDA empowers KM processes to reconfigure themselves in the dynamic business environment. The study assumes that firms must obtain and combine unimitated resources with other organizational capabilities to build strong BDA capabilities. BDA capabilities can be seen from the sensing, coordinating, learning, and reconfiguring of the structure of business operations, which ultimately improve the different levels of managerial, operational, and functional capabilities that typically result in superior performance. The theoretical framework is presented in Figure 1.

This study is conducted in the context of Algeria because (1) the majority of current studies are conducted in developed economies, in which differences in terms of business environment conditions, culture, and managerial practices are expected to generate several findings and implications, (2) understanding how the BDA–KM relationship can be influenced by firms’ possession of resources and capabilities is crucial, and thus, the power of deploying BDA is not identical in developing economies such as Algeria, and (3) determining how BDA–KM would improve green innovation is essential, especially in the context of developing countries.

3. Methodology and Research Setting

3.1. Sample and Data Collection

Drawing upon the NRBV and DCV perspectives, this study addresses the relationship between BDA, KM, and GI among Algerian firms. This study is conducted in the context of Algeria because (1) the majority of current studies are conducted in developed economies, in which differences in terms of business environment conditions, culture, and managerial practices are expected to generate several findings and implications, (2) understanding how the BDA–KM relationship can be influenced by firms’ possession of resources and capabilities is crucial, and thus, the power of deploying BDA is not identical in the developing economies such as Algeria, and (3) determining how BDA–KM would improve green innovation is essential, especially in the context of developing countries.

The study covers a population of 17,865 firms registered at the Business Directory of Algeria (KOMPAS). Based on the suggestion made by Krejcie and Morgan [68], the appropriate sample size is 375. As suggested by [69], a low response rate and missing surveys may occur. Hence, this study added 40% to the sample size. The study sent 525 questionnaires targeting well-educated respondents from lower to upper-level management.

The questionnaires were provided in both English and Arabic versions and a cover letter was attached. Later, a kind reminder through calls and emails was implemented, informing respondents that their responses were confidential. This study collected 174 questionnaires, recording a participation rate of 33.1%.

Table 1. Backgrounds of respondents and firms.

Respondents’ Profile	Frequency	Percentage (%)
Industry type
Wood-based	06	3.44
Chemistry, plastic, and health	22	12.64
Energy and environment	34	19.54
Food and mineral industry	52	29.88
Construction and habitat	16	9.19
Electrical and electronic	44	25.28
Education
Undergraduate	122	70.11
Postgraduate	52	29.88
Years of experience
1 to 5 years	43	24.71
5 to 10 years	71	40.80
More than ten years	59	33.90
Firm’s age
1–5 years	16	9.19
6–10 years	37	21.26
10–15 years	68	39.08
More than 15	53	30.45
Position
Middle-level management	98	56.32
Upper-level management	76	43.68
Number of employees
Less than 100	18	10.3
100 to 250	40	22.98
251 to 500	68	39.08
More than 500	48	27.58

illustrates the respondents’ and firms’ information. A high education level with long experience and training offered by firms strongly impacts employees’ skills. The descriptive information below stated that most firms have substantial business experience; hence, they have acquired solid knowledge about green practices and the importance of data.

3.2. Measurement of Variables

To make sure that the items were adequate, clear, and the content was easy to understand, three academic professors and two professionals were invited to implement content and face validities. This was intended to ensure that each item’s content was distinguished and reflected only one particular variable. After that, the study incorporated the experts’ feedback into the final survey version to enhance the quality of measurement items. To this end, a pilot study was conducted by reaching 12 firms. The results indicate that all constructs’ internal consistency and composite reliability ranged between 0.816 and 0.924, denoting a high-reliability coefficient.

By using past studies relevant to this research, the measurement tool was developed to fit the Algerian firms’ context. Big data analytics consists of two dimensions representing firms’ management and technological capabilities to use data for their daily businesses. To measure big data analytics, this study used six items for each dimension adopted [8]. Three dimensions of knowledge management capability, namely knowledge acquisition, knowledge conversion, and knowledge application, consisting of 4, 5, and 6 items, respectively, were adopted from the studies by Obitade [7] and Abbas and Sağsan [15]. Green innovation contained three dimensions: green processes, green products, and green management, measured by items adopted from Abbas and Sağsan [15] and Wong [70]. Government support was measured by three items adopted from [71]. Organizational performance was measured by four items, namely sales volume, market share, return on investment, and customer satisfaction, as adopted from [50]. The constructs were measured through a 7-point Likert scale ranging from 1 (=strongly disagree) to 7 (=strongly agree). Due to the differences in terms of resources and capabilities owned from company to another, the study used two control variables, namely years of operation and the number of employees, to confirm whether these factors are free of impact on firm performance or not [50,72,73].

4. Data Analysis

The study applies Smart PLS to anticipate the structural model and measure the measurement model [74]. The rationale behind using Smart PLS is: (1) PLS software assists research in maximizing the prediction relevancy of independent constructs, while at the same time keeping more indicators of constructs, (2) the non-normal distribution shown in [75], (3) the software works well when the sample size is relatively small, and (4) it is able to handle complicated models (mediation and moderation paths).

4.1. Validity and Reliability Assessment

This research uses Smart PLS to test the convergent validity, which consists of the indicator’s outer loading, factor loading, composite reliability, and average variance extracted (AVE).

Table 2. Measurement model.

Constructs
1st Order	2nd Order	Items	Loadings	CA	CR	AVE
Knowledge Acquisition		KACQ 1	0.886	0.912	0.938	0.791
		KACQ 2	0.881
		KACQ 3	0.894
		KACQ 4	0.896
Knowledge Conversion		KC 1	0.909	0.923	0.943	0.768
		KC 2	0.924
		KC 3	0.888
		KC 4	0.912
		KC 5	0.735
Knowledge Application		KAP 1	0.873	0.936	0.909	0.795
		KAP 2	0.892
		KAP 3	0.915
		KAP 4	0.889
		KAP 5	0.890
		KAP 6	-
Knowledge Management		Knowledge Acquisition	0.730	0.734	0.747	0.503
		Knowledge Conversion	0.754
		Knowledge Application	0.813
Big Data Management Capabilities		BDMC 1	0.761	0.828	0.875	0.542
		BDMC 2	0.790
		BDMC 3	0.799
		BDMC 4	0.835
		BDMC 5	0.760
		BDMC 6	0.741
Big Data Technological Capabilities		BDTC 1	0.789	0.862	0.897	0.594
		BDTC 2	0.848
		BDTC 3	0.862
		BDTC 4	0.891
		BDTC 5	0.775
		BDTC 6	0.740
Big Data Analytics		Big Data Management Capabilities	0.953	0.893	0.949	0.904
		Big Data Technological Capabilities	0.949
Green Product		GPRD 1	0.809	0.895	0.922	0.704
		GPRD 2	0.769
		GPRD 3	0.879
		GPRD 4	0.892
		GPRD 5	0.841
Green Process		GPRC 1	0.735	0.772	0.847	0.529
		GPRC 2	0.794
		GPRC 3	0.825
		GPRC 4	0.639
		GPRC 5	0.622
Green Management		GRM 1	0.889	0.880	0.918	0.737
		GRM 2	0.867
		GRM 3	0.794
		GRM 4	0.881
Green Innovation		Green Product	0.728	0.791	0.876	0.704
		Green Process	0.934
		Green Management	0.843
Government Support		GS 1	0.730	0.706	0.835	0.629
		GS 2	0.779
		GS 3	0.864
Organizational Performance		OP 1	0.870	0.833	0.887	0.664
		OP 2	0.812
		OP 3	0.813
		OP 4	0.759

Notes: CA = Cronbach’s Alpha; CR = Composite Reliability; AVE = Average Variance Extracted.

indicates that all items’ loading values were greater than 0.70 [76]. In contrast, all constructs recorded a composite reliability value greater than 0.70 [75]. The value of AVE was more significant than 0.50, as recommended by [77].

Discriminant validity is another test that validates and ensures the measurement tool’s adequacy. This requires testing the Fornell and Larcker criterion.

Table 3. Fornell and Larcker criterion and HTMT ratio.

	BDMC	BDTC	GPRC	GPRD	GRM	GS	KACQ	KAP	KC	OP
BDMC	0.736
BDTC	0.607 (0.831)	0.771
GPRC	0.578 (0.736)	0.539 (0.67)	0.727
GPRD	0.42 (0.462)	0.371 (0.412)	0.692 (0.809)	0.839
GRM	0.536 (0.467)	0.523 (0.603)	0.666 (0.820)	0.316 (0.344)	0.859
GS	0.648 (0.842)	0.589 (0.746)	0.575 (0.789)	0.401 (0.489)	0.593 (0.762)	0.793
KACQ	0.224 (0.297)	0.234 (0.251)	0.333 (0.391)	0.545 (0.612)	0.256 (0.280)	0.222 (0.264)	0.889
KAP	0.469 (0.530)	0.495 (0.548)	0.431 (0.515)	0.306 (0.327)	0.451 (0.489)	0.362 (0.441)	0.266 (0.248)	0.892
KC	0.425 (0.525)	0.378 (0.432)	0.479 (0.578)	0.108 (0.133)	0.711 (0.788)	0.475 (0.613)	0.079 (0.101)	0.392 (0.417)	0.876
OP	0.435 (0.517)	0.507 (0.609)	0.419 (0.515)	0.273 (0.306)	0.512 (0.575)	0.407 (0.504)	0.213 (0.246)	0.451 (0.497)	0.464 (0.498)	0.815

indicates that the bolded values on the diagonals were greater than those in their respective rows and columns, inferring that the items were discriminant. In addition, the Heterotrait–Monotrait (HTMT) ratio (Henseler et al., 2015) [74] is another advanced test to confirm the discriminant validity. Table 3 states that the presented values in parentheses are below 0.85, denoting that they fulfill the HTMT.85 criterion. [78]. Thus, the discriminant validity of this study is achieved.

4.2. Structural Model

Figure 2 and

Table 4. Structural model results.

H	Relationship	Beta	T-Value	p-Value	R2	f2	Decision
H1	BDA→KM	0.623	14.964	0.000	38.8%	0.634	Supported
H2	BDA→Ginn	0.159	2.382	0.000		0.057	Supported
H3a	KM→Ginn	0.203	2.899	0.002	10.6%	0.068	Supported
H4	GInn→OP	0.448	9.665	0.000		0.235	Supported
H5	GS→OP	0.095	2.191	0.021	26%	0.091	Supported
H6	GS*GI→OP	0.069	1.754	0.049		0.086	Supported

show a positive relationship between BDA and KM (β1 = 0.623, t = 14.96, p < 0.001), denoting that H1 is supported. BDA positively impacts GInn (β2 = 0.159, t = 2.38, p < 0.001), validating H2. Furthermore, KM positively affects GInn (β3 = 0.203, t = 2.89, p < 0.001), confirming that H3a is supported. In addition, GInn positively influences OP (β4 = 0.448, t = 9.66, p < 0.001), denoting that H4 is supported. Moreover, GS positively impacts OP (β5 = 0.095, t = 2.19, p < 0.001), which means H5 is supported. Additionally, the interaction effect of government support with GI has a positive and significant impact on OP (β6 = 0.069, t = 1.75, p < 0.001). Hence, GS positively moderates and strengthens the relationship between GInn and OP; thus, H6 is supported.

The effect size (f²) of predictor constructs on a specific criterion demonstrates the connectedness of these variables and how it predicts the dependent constructs simultaneously, indicating the strength of the model [76]. Constructs recording f² values of 0.02, 0.15, and 0.35, are seen as weak, average and substantially large effect sizes, respectively [79]. Table 4 stated that the effect size of BDA on KM was substantial, whereas GI recorded an average effect size on OP. Meanwhile, other construct effects, namely BDA on GI and GS on OP, were small. As recommended by Cohen [79], R² values between 0.02 and 0.13 are considered weak, whereas values ranging between 0.13 and 0.26 are considered moderate, and values above 0.26 are considered substantial. Table 4 indicates that the R² of KM, GI, and OP were 38.8%, 10.6%, and 26.4%, respectively. Thus, the explained variance by the predictor factors of KM, GI, and OP was substantial for KM, weak for GI, and moderate for OP, suggesting the reliability of relationships with all dependent variables.

The effect of the control variables was estimated. The results indicate that the firm size was statistically significant on OP (β = −0.270, t = 5.50, p > 0.05), inferring that these firms possess the power to leverage sufficient capabilities responding to green issues. The firm’s age (β = 0.030, t = 0.591, p < 0.10) recorded an insignificant impact on the firm’s performance. It means that years of operation do not reflect on if Algerian firms possess enough experience and knowledge to apply green practices. This is probably because of poor awareness of the economic and operational benefits of deploying big data analytics to improve their performance outcomes.

4.3. Testing Mediation Effect

Using the approach laid out in [80,81], the mediation effect of KM between big data analytics and GI was estimated.

Table 5. Indirect effect results.

H	Relationship	Std-B	T-Value	p-Value	Confidence Interval		Decision
					2.50%	97.50%
H3b	BDA→KMGI	0.126	2.861	0.002	0.047	0.194	Supported
Variance Accounted For (VAF) of the Mediator Variable for KM
IV	Mediator	DV	Indirect effect	Total effect	VAF (%)	Type
BDA	KM	GI	0.126	0.285	44.21	Partially

Note: IV: independent variable, DV: dependent variable, BDA: big data analytics, KM: knowledge management, GI: green innovation.

illustrates that the indirect impact of BDA on GI has a beta value of 0.126 and a t-value of 2.861, respectively. As recommended by (Hair et al., 2013) [76], the variance accounted for (VAF) that determines the indirect effect size concerning the total effect was calculated. In this study, the VAF = direct effect/total effect has a value of 0.126/0.285 = 0.442, indicating that 44.2% of the BDA effect on GI is explained via the existence of the mediation effect of KM. Since the VAF is greater than 20%, but less than 80%, the authors infer that KM partially mediates this relationship, and hence H3b is supported.

4.4. Testing the Moderation Effect

The study examined the moderation effect of government support between GI and OP. The study applied the two-stage approach [82] to test the strength of the moderation effect. Table 4 details that the positive interaction between GS and OP (β = 0.069, t = 1.754, p < 0.001) was statistically significant, indicating that GS enhances firms’ capacities to achieve better performance through applications of green innovation practices; hence, H6 is supported.

Figure 3 indicates that government support affects the intensity of the positive relationship between green innovation and organizational performance. The results show that the more involvement and support is offered by the Algerian government (i.e., funds, facilities, infrastructure building, patent protection, collaboration, training and workshops, consultancy, clean environment, etc.), the greater the improved green practices that ultimately influence the overall firm performance become.

By applying PLS-SEM and through the blindfolding procedures suggested by [83], the study predicts the relevance of the model (Q²). Values greater than zero or near 1 indicate that the research model is relevant, demonstrating the power of interrelationships [75]. The Q² values presented in

Table 6. Results of the predictive relevance of the research model.

Variables	Predictive Relevance Q2
Knowledge Management (KM)	0.342
Green Innovation (GI)	0.16
Organizational Performance (OP)	0.25

show that knowledge management, green innovation, and organizational performance have values of 0.342, 0.16, and 0.25, respectively, which are greater than zero, showing the strong connectedness among exogenous constructs, thus predicting that the core issue of the study is empirically relevant.

5. Discussion

This study examines the effect of BDA capabilities and KM processes on green innovation and organizational performance moderated by government support. The study’s findings indicate that BDA capabilities are positively associated with KM processes and green innovation, inferring that both hypotheses H1 and H2 are supported. The study’s results align with the past findings [5,7,8]. This emphasizes that having a well-developed infrastructure and proficient IT skills might help foster the dynamic capabilities that promote KM processes [31], and demonstrates that Algerian firms are aware of the importance of deploying BDA to digitize their businesses. BDA capabilities help firms to generate valuable insights, support decision making based on green problem solving, and develop green business processes [28]. BDA, therefore, provides substantial assistance in tracking, anticipating, and responding to customer needs, market demands, and competitors’ reactions [19]. BDA is one of the strategic sources that enables firms to search for and collect valuable data to respond to the changes occurring in the business environment [21]. Firms’ capacity to create, store, and disseminate knowledge is highly associated with the extent to which BDA is utilized and developed [19]. In responding to increased green issues, the core focus of BDA capabilities is to generate valuable knowledge that leads to upgrading green organizational culture, green business processes, eco-friendly products, green employee behavior, and green training, and then finally, promoting firms’ green image [17,18].

Drawing upon the NRBV and responding to their three environmental concerns [17], BDA capabilities can be seen as an essential strategic green dynamic capability that upgrades the entire business process by incorporating green knowledge into green business strategies [18]. Furthermore, the study found that KM positively influences green innovation (H3a). The study’s findings are supported by previous studies [8,15], indicating that the sampled firms are effectively using knowledge resources and their management to motivate and enable their employees to introduce green practices. Firms are advised to search and acquire relevant knowledge to address green issues to reduce environmental impacts generated by the massive use of resources and unfriendly business processes [31]. Hence, knowledge that is created, stored, and leveraged must prioritize green concerns to level up green processes, increase employee green awareness, reduce CO2 emission, and deliver eco-friendly products, resulting in an improvement of firms’ green images and business green growth. Several studies [84,85,86,87] asserted that knowledge is one of the leading dimensions of green business, and the application of innovative green solutions is affected by the extent to which an organization is developing and leveraging green knowledge.

In addition, the results showed that KM recorded a partial mediation effect on the relationship between BDA and green innovation (H3b), which means that KM increases BDA integration in business intelligence to improve green practices [17]. In turn, resource consumption is reduced, enhancing green business techniques and delivering eco-friendly products [19]. It is important to note that the more BDA is incorporated into business processes, the more KM plays a significant role in polishing the impact of BDA on green business [18]. KM mediation explains the indirect effect of BDA on green innovation, since BDA might generate new insights, but it cannot affect business processes unless they are converted to knowledge application. BDA is the source of new insights, analysis, and predictions of future business trends (Lozada et al., 2023) [31], while KM applies the created ideas and insights to make changes in the business processes [88].

To conclude, developing innovative green solutions and applying solid green strategies to limit environmental impacts and natural resource degradation is the subject matter of BDA–KM. The results state that green innovation positively influences organizational performance (H4). This result aligns with past research [50,51]. Greening businesses strategically support firms to reduce the use of natural resources, decrease CO₂ emissions, train employees in green practices, develop green techniques, deliver eco-friendly products, and enhance firms’ green image [89]. Investing in green technologies allows an organization to promote its brand reputation in the long term, resulting in increased business growth and revenue [90].

Moreover, the findings revealed that government support positively impacts green innovation and strengthens the relationship between green innovation and firm performance, denoting that H5 and H6 are supported. Previous studies suggest that governmental support considerably impacts firms to adopt go-green behavior [91]. This increases the business’s green image [92] and brand reputation [93]. Undoubtedly, worldwide governments’ financial and non-financial subsidies are playing an imperative role in encouraging businesses and entrepreneurs to continue and keep their businesses alive regardless of environmental factors [94]. Across the globe, governments and other institutions are allocating massive budgets to support firms to go green and develop and upgrade their green practices to reduce environmental impacts and the massive degradation of the environment [95]. Governments are advised to leverage sufficient green initiatives to drive and promote green economic growth by stimulating businesses and entrepreneurs to go green [96].

6. Conclusions and Implications

The capability of BDA is one of the leading drivers that can improve green business innovation and respond to environmental concerns. In this study, the authors believed that addressing the link between BDA capabilities, KM, green innovation, and organizational performance is theoretically essential. Drawing upon the NRBV and DCV lenses, this research is the first empirical study that links and extends past studies [6,7,8] by examining the crucial link between BDA–KM and BDA–GI to predict different measures in organizational performance. By doing so, this study assumes that firms’ KM is developed by the extent to which BDA is incorporated and leveraged across business units. Thus, it is essential to note that BDA is the main driving force that can foster green practices and respond to the three central pillars of the NRBV lens (e.g., product stewardship, pollution, and sustainable development). Few studies, if any, have incorporated the NRBV approach into the BDA–KM literature to explain and anticipate the outcomes that can be raised to tackle green issues and enhance green practices.

The theoretical assumption of this study builds on the natural-resource-based view, dynamic capability view, and big data analytics literature to address the effect of green issues on organizational performance. The study advances past studies by enriching the literature and extending the view of the BDA–KM relationship and their outcomes on green innovation and organizational performance. This is determined by how well firms can deliver eco-friendly products, reduce environmental degradation, and improve business processes. Past studies on BDA and its strategic, operational, and environmental outcomes are examined from the dynamic capabilities, innovation diffusion, and knowledge management theories. This study differs from existing literature by extending the body of the NRBV lens by incorporating the BDA literature to address how green issues must be solved by prioritizing the three central pillars of the NRBV (product stewardship, pollution, and sustainable development). This leads to the question of whether firm dynamic capabilities must go green to avoid the three environmental concerns or not. Hence, combining both theories helps researchers understand BDA and KM’s importance in promoting a business going-green orientation.

Firms and entrepreneurs increasingly value BDA [97] and its environmental benefits [98]. In this study, empirical evidence shows that to gain increasing GI outcomes, firms must acquire sophisticated BDA capabilities that interact with KM processes [18]. This might improve decision-making quality and place relevant knowledge in the right place [22]. In addition, BDA’s influence on green innovation is substantial in regard to the improved KM, especially concerning new knowledge about unexpected threats and tracking green business opportunities; this process supports firms in absorbing external and internal business changes involving GI upgrades. Overall, KM capabilities are enhanced by BDA capabilities, leading to a response to green requirements and resulting in superior organizational performance [17].

This study provides several implications. First, it highlights the relationship between BDA, KM, and GI, and how it improves firm performance among Algerian factories. The study suggests that to achieve OP, firms must achieve the close interaction of BDA and KM at all stages to develop green practices. Second, the study emphasizes and predicts the outcomes behind GI on firm performance through the empowerment of KM. Through GI, firms upgrade their eco-friendly technologies and update human IT skills to match the newly developed technologies. This results in high eco-friendly product quality, reduced resource consumption, minimized production costs, and increased financial benefits [12,13,17]. Third, the study advises Algerian leaders and managers to benefit from leveraging BDA capabilities and KM to foster green practices, reduce environmental impact, and increase financial performance.

This study also discusses some theoretical implications, namely, (1) the study addresses the relationship between BDA, KM, GI, and organizational performance, which calls for subsequent studies in order to extend the present model and include other factors that might improve the model’s practical and theoretical aspects; (2) the study emphasizes the KM process and how it facilitates green innovation practices among Algerian firms with the support of BDA to enhance OP; therefore, future research could examine other factors that might enable green practices rather than KM in the lens of BDA capabilities; (3) the study is a pioneer that examines the crucial path between BDA, KM, and green innovation which has been overlooked by past studies; thus, future research would extend the model of the study within other contexts and industries.

This study has some limitations that must be addressed for future studies. The study focused on large companies due to their capability to acquire enough resources for applying green technologies, providing IT investment, and building BDA infrastructure. Future studies might target IT personnel and managers to predict BDA and its consequences on green practices and OP. Furthermore, studies must include SMEs and explore the development progress of the BDA and KM processes through the lens of top management and entrepreneurs. Addressing the relationship between BDA, KM processes, green innovation development, and firm performance by conducting a study from a longitudinal approach would be better and more beneficial to demonstrate the development of such practices and resources and their impacts on firm performance.