Improving Corporate Environmental Performance Through Big Data Analytics Implementation: The Role of Industry Environment

Abstract

Big data analytics (BDA) has recently received significant public interest and is widely considered as a transformative technology set to improve organizations’ environmental performance. However, prior empirical studies have yielded inconsistent findings. Based on organizational learning theory, our paper utilized a longitudinal approach to understand the relationships between big data analytics implementation and corporate environmental performance. This project also investigates the role of industry environment in influencing on these relationships. This project employed longitudinal data from 172 firms covering 2408 firm-year observations from Fortune 200 firms. We employed “the generalized method of moments (GMMs) technique” to test the study assumptions. Our analysis shows that a one-unit improvement in BDA leads to, on average, a 2.8% improvement in corporate environmental performance (CEP). In addition, the impact of BDA on CEP is greater in more complex and dynamic settings. This project offers meaningful implications for scholars and managers to understand the influence of BDA on CEP across various settings. Moreover, this study provides a more refined comprehension of the performance ramifications of BDA, consequently addressing the essential enquiries of how and when BDA can improve environmental performance.

1. Introduction

Big data has the capacity to revolutionize business, necessitating comprehension of its implications regarding organizational responses to the changes and concerns of environmental [1] and sustainable issues [2]. Nonetheless, conventional methods for acquiring, accessing, and evaluating massive data are now obsolete [3,4,5]. A study conducted by “NewVantage Partners” indicates that the percentage of prominent firms adopting BDA is on the rise, attaining 99% in 2021 [6]. Despite the exceptional challenges posed by the COVID-19 pandemic, a significant majority of organizations (90.6%) indicate that their expenditure on big data analytics has either improved or stayed stable [7]. The heightened readiness of organizations to spend in deriving value from BDA is also seen in the revenue projections for the BDA sector. “The International Data Corporation (IDC)” projects that global revenues for big data analytics solutions will rise from “$189.1 billion in 2020 to $274.3 billion by 2022”, reflecting a “compound annual growth rate (CAGR) of 13.2%” [8].

Managers employ BDA to formulate paradigms that enhance corporate performance via green innovation and the creation of value [9]. BDA has recently garnered substantial public interest and is widely seen as a transformational technology poised to influence the future of firms and competitive advantage [10]. When combined with artificial intelligence, big data analytics may improve industrial production efficiency, facilitating the transformation and development of production [11,12]. A burgeoning body of literature underscores BDA’s capacity to augment precision in industrial sectors [13], diminish production expenses [14], substitute human labor in perilous sectors, and improve productivity and resource utilization effectively [15,16]. Notwithstanding these studies results, the environmental ramifications of BDA technologies have garnered comparatively scant attention. Although its capacity to mitigate pollution emissions was acknowledged [17,18], the extent of its effect and the fundamental mechanisms are predominantly unexamined.

Despite significant progress in attaining sustainable development goals, industrial sectors still pose challenges to achieving environmental performance. This is intensified by the reliance of economic growth on energy-intensive, carbon-emitting industrial processes and the expansion of ecologically detrimental businesses [19]. In an environmental setting, nations pursue excellence within a dynamic setting, resulting in heightened energy demand. Swift advancement and motorization exacerbate environmental sustainability challenges [20]. As a result, global challenges like pollution, capability limitations, and sustainable concerns prompt issues over organizational sustainability. These issues necessitated organizations to use sustainable initiatives that provide various benefits. The amalgamation of different environmental initiatives results in a new concept termed sustainable organizational performance [21]. Organizations must confront the problem of minimizing pollution and attaining enduring sustainability, compelling senior management to investigate sustainable strategies for improved profitability [22]. Therefore, organizations must prioritize environmental sustainability, adopt eco-friendly initiatives, and consolidate capabilities for environmental capacities and competitive advantages [23,24,25]. Thus, this exploration seeks to address the following research questions:

RQ1: “Does big data analytics have a significant impact on corporate environmental performance?”

RQ2: “How does industry environment (i.e., complexity, dynamism, and munificence) influence the relationships between big data analytics and corporate environmental performance?”

This paper provides scholars and policymakers with significant contributions on BDA and CEP across various settings. First, to the best of the authors knowledge, our exploration is the first to examine the longitudinal relationships between BDA and CEP. Our project employed longitudinal data from Fortune 200 firms to examine the actual value of BDA. Therefore, our paper makes contributions to BDA literature [26,27]. It also adds to the emerging literature on the influence of BDA on CEP [28,29]. Our study also contributes to the link between BDA and CEP by exploring the moderating role of industry setting (i.e., complexity, dynamism, and munificence) on these relationships. Moreover, this research contributes to organizational learning theory in the context of environmental performance. We employed an organizational learning view to explore the BDA–environmental performance relationship. The rest of this document is conceptualized as outlined below. Section 2 shows the theoretical background of the key definitions and discusses the advancement of the proposed model. Section 3 shows the employed method, including research constructs, associated scales and measurements, and the data collection process. Section 4 demonstrates the research analysis and results. Section 5 shows the key findings and contributions to theory and practice, followed by directions for additional investigation in Section 6.

2. Literature Review

2.1. Big Data Analytics Conceptualization

The concept of BDA has transitioned from a focus on data and analytical techniques and procedures to include a wider array of elements [30]. Numerous classifications of big data analytics have been suggested in the previous studies. According to the resource-based view, previous examinations identify three categories of big data analytics capabilities essential for achieving effectiveness and competitive advantage [31,32]. Based on the literature of BDA, prior exploration delineates three components of BDA capacities that organizations must develop to derive value of investing in big data analytics (i.e., BDA management capability) [33]. A previous study proposes, via a thorough literature analysis, that organizations concentrate on three primary aspects of emphasis for big data analytics to achieve superior performance enhancements [34]. These domains encompass concrete capabilities, intangible capabilities, and people’s competencies and expertise. Despite variations in nomenclature throughout the literature regarding the dimensions of BDA, the taxonomy systems exhibit similarities as they collectively represent BDA technology, human capabilities, and management-focused characteristics.

Our project examines the technological dimension of big data analytics broadly, with a specific emphasis on organizations’ adoption of BDA-related technologies. Firms’ adoption of big data analytics-related technology encompasses (1) initiatives that introduce novel BDA-related technologies, and (2) initiatives that utilize existing big data analytics-related technology in innovative manners or other domains [35]. Big data analytics-related technologies can be categorized into the following three primary types: (1) essential database technology for the collection, integration, processing, storage, and management of BD; (2) sophisticated data techniques solutions for assessing BD to generate valuable implications and outcomes; and (3) data visualization techniques [36,37]. The initial group includes different databases. The second group encompasses sophisticated types software for “predictive analytics, social media analytics, stream analytics, and similar functions, operating either on-premises or in the cloud”. The third group encompasses visualization programs for executing fundamental analytical queries, retrieving important performance metrics, and producing custom reports via self-service or mobile interfaces. These three groups establish a robust framework for identifying all pertinent big data analytics technology utilized by each organization during the data gathering phase.

2.2. Corporate Environmental Performance

CEP denotes the substantial influence of a company’s activities, encompassing diverse methods designed to mitigate adverse consequences and enhance positive contributions to environmental well-being [16,17,21]. CEP components encompass multiple dimensions, including waste management, resource efficiency, emission regulation, and sustainable supply chains. These aspects not only signify a company’s ecological impact but also their dedication to corporate social responsibility (CSR) and adherence to ethical business practices. In recent years, the significance of the CEP has been heightened within social sustainability and ethical frameworks. With the rising worldwide awareness of environmental issues, firms are becoming more accountable for their ecological footprint. Stakeholders, such as consumers, investors, and regulatory bodies, are increasingly including environmental factors into their assessment of corporate performance [18,19,38]. The acknowledgement of CEP as a crucial component of the commercial strategy arises from its direct impact on the overall performance of the company. Previous studies have shown that efficient environmental initiatives can enhance operational efficiency, decrease costs, and improve market competitiveness, hence fostering increased profitability [39].

The relationship between CEP and financial performance is emphasized by empirical investigations. Previous research demonstrates that firms displaying elevated levels of environmental stewardship acquire a positive reputation and reap advantages such as customer loyalty and brand distinctiveness [22,40]. Findings indicate that organizations recognized as environmentally friendly are more likely to attract a socially conscious consumer demographic, potentially resulting in increased sales and market share. A meta-analysis indicated that firms with robust environmental performance demonstrated, on average, a 3–5% greater profitability margin than their less environmentally responsible peers [24,41]. This association is especially evident in certain areas, such as consumer products, where brand perception is significantly shaped by sustainable measures. The ramifications of CEP encompass not only profitability but also regulatory compliance. Given stringent global environmental standards, organizations that emphasize environmental management are typically more adept at addressing compliance difficulties.

Prior investigations demonstrated that proactive environmental management is crucial for alleviating the risks linked to sanctions and legal liability [26,42]. In mature economies, where rules are well established, corporations with the highest corporate environmental performance (CEP) typically have fewer regulatory impediments. Conversely, in developing nations, the increasing focus on formalizing environmental legislation has rendered corporate environmental performance (CEP) an essential strategic asset for corporations seeking to attain legitimacy and trust from stakeholders in rapidly evolving markets. The surrounding landscape of market dynamics influences the significance of the CEP. In sectors with significant environmental effects, such as energy, manufacturing, and agriculture, the advantages of robust environmental management can be especially evident. Conversely, sectors with a reduced environmental footprint may have distinct competitive pressures; yet, the incorporation of sustainability into commercial models is becoming progressively pertinent [28,43]. The increasing trend of corporate transparency about environmental policies has compelled companies across all industries to implement more stringent environmental standards, impacting their reputation and consumer trust.

In summary, corporate environmental performance is a crucial predictor of overall company performance, closely linked to profitability, consumer perception, and regulatory compliance. As developed and developing nations acknowledge the significance of CEP, its strategic worth is progressively enhancing across various market scenarios and industries. The complex nature of this relationship emphasizes the necessity for corporations to have comprehensive environmental plans, highlighting a shifting paradigm in which sustainable practices are deemed increasingly vital for long-term success. The correlation between corporate environmental performance and profitability has garnered considerable attention, particularly in light of the increasing demand for sustainable practices across numerous industries. Previous studies indicate that superior environmental practices enhance corporate profitability, primarily through improved operational efficiency and risk management [30,44,45]. The results demonstrate that enterprises incorporating environmental factors into their operating structure frequently realize cost savings related to resource utilization and waste management, hence enhancing profitability. Furthermore, the mediating function of operational performance emphasizes that enhanced environmental practices not only optimize internal operations inside organizations but also bolster their overall efficiency. This interaction indicates that firms with superior environmental performance are more adept at using operational enhancements for financial benefits.

2.3. Organizational Learning Theory

Prior research has contended that enduring competitive advantage relies on the ongoing process by which businesses investigate, retain, disseminate, and utilize information [31]. Previous examination in research has revealed in that organizational learning serves as a crucial source of enduring competitive advantage in a dynamic setting [12,18]. Previous study has asserted that knowledge does not diminish, yet it may become obsolete over time due to the advent of new technology [33]. Therefore, companies must consistently adjust in response to market demand. Companies with a predisposition for learning may sustain competitiveness over time [29]. The process of organizational learning involves gaining information or insight that can enhance performance and lead to changes in organizational behavior [25]. Theory of organizational learning explains how four processes—information acquisition, distribution, interpretation, and organizational memory—contribute to organizational learning, which in turn leads to improved organizational performance [14,17]. Data processing, including data acquisition, distribution, and interpretation, is the foundation of organizational learning according to this theory [31]. This processing then leads to insights and the updating of organizational knowledge. With this foundation, businesses may determine where their resources will yield the highest return.

Organizational learning theory posits that a firm’s performance is significantly dependent on its systematic acquisition of information, which improves its capacity to cope with and innovate across products, processes, and services [16,33]. According to an organizational learning view, big data analytics could facilitate organizational learning within firms. Within the framework of environmental sustainability, an organizational learning perspective provides a theoretical perspective to comprehend how the utilization and processing of extensive information from big data analytics can enhance environmental performance. Prior research indicates that big data possesses considerable potential to influence environmental studies [11,16,23]. Big data analytics functions as a firm’s approach to enable the collecting, organization, and evaluation of vast data from various sources to discern trends and diagrams [22,35]. These insights can be employed to strategically prioritize resource effectiveness and sustainability assessment, including material reuse, waste reduction, and product recycling [10,23,26]. Therefore, according to organizational learning theory, our study suggests that BDA could improve corporate environmental performance.

3. Conceptual Framework and Hypotheses Development

Based on organizational learning theory and prior studies on BDA and CEP [46,47], we developed a conceptual framework that explains the relationships between big data analytics and corporate environmental performance. Our study also analyzes the moderating role of industry environment (i.e., complexity, dynamism, and munificence) on these relationships. Figure 1 demonstrates the study conceptual framework.

3.1. The Relationship Between BDA and CEP

The incorporation of BDA technologies across several industries has prompted a thorough analysis of their sustainable and socio-economic effects. Prior exploration indicates that BDA’s effects on sustainability are significant, however, its ramifications are intricate and varied. Previous research has been crucial in delineating the correlation among investing in BDA and carbon emissions [31]. The investigation is furthered by previous research [48,49,50], which examines how AI might facilitate environmental entrepreneurship via enhanced decision-making and optimizations. A prior examination evaluates studies on artificial intelligence, machine learning, and BDA tools for improving the management of natural resources. The increasing emphasis on the management of resources is supported by studies on the actual value of big data analytics in enhancing climate resilience. This facet is also examined concerning big data analytics utilization for environmental activities within business environments [51,52].

The advancement of BDA constitutes not merely a simple technological application but rather a multifaceted organizational transformation endeavor [53,54]. Firms can impact sustainable performance in three key methods. Initially, through horizontal integration via value creation to enhance collaboration among firms. Because of limited intelligence, most organizations struggle to consolidate extensive, fragmented data and achieve information exchange [55,56]. BDA technologies have created an internet platform that connects IS through many organizations, hence improving information exchange among service providers and consumers. Prior research has explored the link among environmental and energy variables and their influence on other environmental advancement elements, concluding that these variables are significantly connected with urban development aspects [57]. The internet platform, which firms utilize for efficient sustainable information exchange, provides a competitive edge in sustainable data, facilitating the application of sustainable innovation throughout the supply chain and advancing its transformation towards sustainability. A prior study conducted a questionnaire survey including 124 organizations across 8 sectors and discovered that the adoption of BDA by firms facilitates green innovation across the supply chain, hence enhancing environmental performance [58,59]. Secondly, enterprises develop intelligent production systems by vertically integrating paradigms within the manufacturing facility. Prior research asserts that smart manufacturing paradigms in organizations need substantial data centers to meet their dynamic demands; yet, these paradigms are often fueled by diverse sources, many of which are sustainably detrimental, resulting in ecological degradation [60]. An exploration contends that smart production paradigms enhance sustainable performance by minimizing production waste and energy consumption via lean manufacturing, with the reduction in energy consumption significantly surpassing the energy utilized by these paradigms [61].

Previous exploration asserts that firms facilitate product customization via chain integration [62]. The information asymmetry among customers and service providers complicates enterprises’ ability to detect shifts in customers’ needs and preferences and accordingly make timely modifications to production [63,64]. BDA technology has significantly mitigated the asymmetry of market data, enabling firms to utilize customers’ internet platforms to acquire real-time preferences and behaviors data and alter marketing strategies in response to fluctuations in customers’ preferences and needs. As the quality of life improves and society advances, the demand for green consumption has been increasing for some time [19,37]. Firms rapidly adapt to shifts in customers’ needs and preferences via market informatization, focusing not only on their own energy consumption and decreasing emissions in operations and management but also mandating suppliers to enhance manufacturing technologies to decrease energy conservation and utilize eco-friendly materials for the production of sustainable products [65,66,67]. Thus, we suggest the following hypothesis:

H1: 

“Big data analytics implementation has a significant impact on corporate environmental performance.”

3.2. The Influence of Industry Environment

Previous studies on BDA implementation underscore the importance of investigating external settings that may impede or facilitate BDA’s influence on performance results [23,47,51,68]. Previous studies indicate that the alignment between big data analytics capabilities and external variables can improve performance outcomes from big data analytics [63]. Prior research indicates that the capabilities of big data analytics are more significantly associated with radical innovative potential in organizations that have established information governance policies [69,70].

Multiple studies have explored the impact of external environmental factors, such as environmental dynamism [71,72], heterogeneity [73,74], competitiveness [75,76,77], and hostility [51,78], on the amplification or diminishment of the organizational performance effects of big data analytics. A prior study indicates that big data analytics assets are associated with substantial improvements in corporate productivity in competitive settings, while no notable productivity increase is evident in non-competitive contexts [79,80]. Previous research demonstrates that environmental dynamics might amplify the positive effects of big data analytics on innovation [81]. Following prior study conceptualization [82,83], our study conceptualizes industry environment into three main dimensions (i.e., complexity, dynamism, and munificence).

Despite the examination of various environmental aspects in relation to big data analytics, there is a lack of understanding regarding the influence of distinct industry environments on the forms of big data analytics value linked to the exploitation and exploration processes of organizational learning. It is essential to comprehend the circumstances under which big data analytics might enhance or hinder company environmental performance, considering that big data analytics initiatives necessitate substantial financial investments [23] yet sometimes fail to yield anticipated business value [34,72]. In simpler situations, companies typically compete by gradual enhancements of current processes, products, and services [84]. In these circumstances, deriving insights from data to facilitate exploitation activities is paramount [23,85,86]. Therefore, in scenarios of modest complexity, BDA implementation should focus primarily on exploitation to improve operational efficiency. This notion is bolstered by prior studies examining the influence of environmental variables within the framework of BDA. Prior study indicates that in service industries with minimal complexity, BDA resources are mostly focused on optimizing existing processes to achieve incremental process enhancements [56,69,71,87]. Previous study suggests that enhancements in operational efficiency due to data analytics are more significant in less complicated contexts, as such improvements may be undermined by a higher number of competitors in more complex settings [88,89]. Consequently, we anticipate that enterprises operating in low complex industries will utilize big data analytics implementation to enhance environmental performance. Thus, we propose the following assumption:

H2: 

“Industry environment (i.e., complexity, dynamism, and munificence) moderates the relationship between big data analytics implementation and corporate environmental performance.”

4. Research Methodology

4.1. Sample and Data Collection

The data for this research were obtained from two sources. Initially, in accordance with previous research [90,91], we gathered firm-level announcements regarding BDA implementation from various prominent U.S. news sources with the help of Python’s PyPDF2 library (Package), including major wire services (e.g., “Business Wire, PR Newswire, ENP Newswire, GlobeNewswire, and USA Today”), leading newspapers (e.g., “The New York Times and Wall Street Journal”), and well-known technology publications (e.g., “Computerworld, eWeek, Computer Weekly, InformationWeek, Network World, and Infoworld”). Secondly, we acquired data concerning industry environment (e.g., “complexity, dynamism, and munificence”) and corporate environmental performance from the “COMPUSTAT database”. We identified the Fortune 200 firms as our target organizations due to their significant media coverage [90].

Table 1. Keywords used in search for BDA implementation.

Company Name w/25

w/50

(announc* or select* or choos* or chose* or deploy* or “work with” or “working with” or “works with” or “worked with” or collaborat* or partner* or “rolling out” or “rolled out” or “rolls out” or “roll out” or “uses” or “using” or “used” or tap* or “turned to” or “turns to” or “turning to” or “turn to” or implement* or agreement* or contract* or purchas* or signs or sign or signing or signed or award* or licens* or install* or acquir* or “goes live” or “going live” or “went live” or launch* or unveil* or adopt* or align* or add* or build* or built or establish* or expan* or form* or spend* or spent or ink* or leverag* or exten* or utiliz* or compact* or deal* or hir* or employ* or increas* or join* or alliance* or complet* or introduce* or “in effect” or “in use” or “apply” or “applies” or “applied” or “applying” or “application” or arrang* or procur* or cooperat* or “work together” or “working together” or “worked together” or “works together”)

(“analytics” or “data virtualization” or analysis technique* or “data warehouse” or decision support system* or “Hadoop” or “HANA” or “artificial intelligence” or “high performance analytics” or “API” or “high performance computing” or “behavioral analytics” or “historical data analytics” or “information analysis software” or “big data” or “infrastructure-as-aservice” or “Iaas” or “big data analytics” or “in-memory database” or “algorithms” or “intelligence technology” or “data mining” or “intelligent diagnostics system” or intelligent system* or “machine learning” or “marketing analytics” or “marketing promotion optimization” or “data visualization” or “natural language” or “business analytics” or “optimize processes” or “business intelligence” or “parallel processing” or “predictive analytics software” or “predictive modeling” or “security intelligence” or “business process analysis” or “software as a service” or “Saas” or “cloud computing” or “SQL” or “cloud” or “social intelligence” or “collaboration tools” or “collaboration tool” or “text mining” or “crowdsourcing solution” or “visual analytics” or “data center” or “Watson” or “data driven” or “warehouse miner” or “data processing” or “warehousing technology” or “data science” or “workforce analytics” or “data stewardship model” or “NoSQL” or “in-database” or “non-relational database” or “data lake” or “platform as a service” or “Paas” or “database as a service” or “Daas” or “social listening” or “complex event system” or “event processing” or “unstructured data” or “web analytics” or “stream analytics” or “web mining” or “social network analysis” or “social network analytics” or “rule-based system” or “mobile intelligence” or “internet of things” or “IoT”)

shows the list of searches. The data gathering period spans from 2010 to 2023. We designated 2010 as the initial year due to the onset of mass media’s interest in BDA during that time [92]. We omitted companies in the IT sector due to their significant reliance on IT, which could obscure the impact of big data analytics adoption [87]. Private enterprises were also omitted due to the unavailability of their financial data to the public. This index allowed us to incorporate significant firms from both the manufacturing and service sectors, covering a diverse array of industrial groups. The primary data were processed in two phases. Samples with substantial missing data were excluded, and all constructs are winsorized at the top and bottom one percent of their distributions to avoid the influence of outliers. In line with prior research [93], we utilized 50 key words on big data analytics use for each company. This method resulted in 172 firms with 2408 sample observations being retained.

Table 2. Industry breakdown of sample firms.

Industry	Number of Firms	Percentage of Sample (%)	SIC Code
Manufacturing Retail Financial and Insurance Wholesale Transportation Utility Healthcare Communication Others Total	31 29 26 22 18 15 12 10 9 172	18 17 15 13 10 9 7 6 5 100	10–37 52–59 61–66 51 4–45 49 80 48 Others

shows the categorization of our sample companies by industry.

4.2. Measures

4.2.1. Big Data Analytics

Following prior research [90,94,95], our study measured BDA implementation by counting the cumulative number of BDA implementations in each year for each company.

4.2.2. Corporate Environmental Performance

In line with prior research [96,97], our study employs reliable measures to assess CEP. Previous studies employed valid indices (i.e., “pollution emissions, and ecological benefit”) techniques to assess CEP. We gather information on different aspects (i.e., “air pollutants, water pollutants, solid waste, and noise payable taxes”) from 2010 to 2023 through various sources, including their annual reports and the websites. We take into account the data availability and measurement validity during this process. Enterprise ecological impact is represented by the real environmental protection tax (ET) payment, which is then subtracted by the matching tax exemption amount. Additionally, the revenue of businesses serves as a surrogate for the worth of their products or services. The final step in quantifying CEP is to compute the logarithm of the product or service value (worth) as a ratio to the ET data. Here is the exact formula for the calculation:

$$CEP={\displaystyle \frac{Ln \left(Worth\right)}{Ln \left(ET\right)}}$$

(1)

4.2.3. Industry Environment

Dynamism was measured as the sales volatility within a leading industry over a five-year span [98,99,100]. We employed the sales growth in a prominent industry over a period of 5 years to evaluate munificence [101,102]. The Herfindahl index was utilized to evaluate complexity [103,104].

Table 3. Variables operationalization.

Variable	Definition and Measure	Sources
BDAi,t	We counted the cumulative number of BDA implementations in each year firm to measure this variable	[90,94,95]
CEPi,t−1	Logarithm of the sum of enterprise operating income/Logarithm of enterprise pollutant discharge fees	[96,97]
COMi,t−1	We used the Herfindahl index to measure complexity.	[103,104]
DYMi,t−1	We measured dynamism as the volatility of sales in a dominant industry over a period of 5 years	[98,99,100]
MUNi,t−1	We used the sales growth in a dominant industry over a period of 5 years to measure munificence	[101,102]
CashFlowi,t−1	A fund from operations over sales for firm i at time t − 1 as a percentage	[105]
Leveragei,t−1	Total debt over total assets for firm i at time t − 1 as a percentage	[105]
Sizei,t−1	Natural log of assets of each firm i at time t − 1	[105]
R&Di,t−1	Research and development expenses over total sales revenue at time t − 1 charged to firm i as a percentage	[105]

shows the study variables operationalization.

4.2.4. Control Variables

We utilize a number of control factors that are often used to look at the link between BDA and corporate environmental performance. This way, we do not obtain inaccurate estimates due to leaving out important factors. In line with previous research [105], our study includes various control factors (e.g., “firms’ size, firms’ slack resource, firms’ leverage, R&D, and cash flow”).

4.2.5. Model Specification

We employed a longitudinal technique to assess the collected data. We developed the “panel regression models” to assess each hypothesis. The following equations are employed to test the suggested hypothesis:

CEP_it = β₀ + β₁BDA_it−₁ + β₂SIZE_it−₁ + β₃SLR_it−₁ + β₄LEV_it−₁ + β₅R&D_it−₁ +
β₆CF_it−₁ + β₈BDA_it−₁ x IDE _it−₁ + ε_it

(2)

where CEP refers to corporate environmental performance; BDA refers to big data analytics; SIZE represents the firm size “assessed as the log of sales”; LEV “indicates levels of leverage, proxied as long-term debt divided by the market value of the firm”; CF refers to the cash flow; IDE refers to industry environment (i.e., complex, dynamism, and munificence); and SLR represents a company’s slack resource.

4.2.6. Assessment of Causality, Heterogeneity, and Statistical Analytics Technique

Although strict exogeneity constraints are essential for OLS estimation, it is likely that unobserved variables affect both the dependent and independent variables, and some independent variables may be correlated with lagged values of the dependent variables in the analysis. To mitigate estimation issues potentially stemming from unobservable heteroskedasticity and endogeneity, we employed a dynamic generalized method of moments (GMMs) panel estimator with instruments [106]. The “GMM estimation procedure” consists of two components. Initially, we employed the initial variance across all aspects of the estimate models to eliminate any potential bias arising from “unobserved heterogeneity”. Following the initial variance, we utilized a GMMs to estimate each model, employing lagged values of the dependent, explanatory, and control variables as instruments. Consistent with prior research, two lagged values of the pertinent components were employed as instruments in the “GMM estimation” process [107]. This approach was employed for many reasons. The system GMMs estimator mitigates dynamic panel bias by utilizing lagged dependent variables as instruments, which remain uncorrelated with the fixed effects [107]. Secondly, “the system GMM estimator is considered one of the most robust methods for unbalanced panels with endogenous variables” [107]. The system GMMs estimator addresses the endogeneity problem by generating instruments from the lags of the variables incorporated in the model [107]. Furthermore, we assessed variance inflation factors using a regression model (3) to examine multicollinearity arising from the inclusion of several interaction components. To address multicollinearity issues, we standardized the primary effect variables prior to generating interactions [106]. To mitigate simultaneity issues, all independent and control variables were delayed by one period to confirm their temporal priority over the dependent variables [106].

Table 4. Means, standard deviations, and correlations.

Variables	Mean	SD	CEPit−1	BDAi,t	COMi,t	DYMi,t	MUNt−1	CashFlowit−1	R&Dit−1	SIZEi-t	LEVi-,t
CEPii,t−1	27.32	16.29	1.00
BDAi,t	0.217	0.108	0.41 **	1.00
COMi,t	0.021 *	0.310 **	0.227 **	0.409 **	1.00
DYMi,t	0.172 **	0.206 **	0.129 **	0.083	0.106 *	1.00
MUNt−1	0.094	0.024 *	0.012 *	0.105 *	0.075	0.011 *	1.00
CashFlowii,t−1	14.230	11.215	0.21 **	0.105 *	0.322 **	0.410 **	0.023 *	1.00
R&Dii,t−1	4.023	2.658	0.08	0.06	0239 **	0.226 **	0.329 **	0.199 **	1.00
SIZEi,t−1	89.344	62.319	0.084	0.077	0.0931	0.031 *	0.021 *	0.07	0.09	1.00
LEVi,t−1	23.120	16.328	0.328 **	0.217 **	0.053 *	0.312 **	0.429 **	0.112 *	0.143 **	0.329 **	1.00

Note: * p < 0.05. ** p < 0.01.

presents descriptive data for the research variables.

5. Results

5.1. Descriptive Statistics

Table 2 presents descriptive statistics about the research variables, namely big data analytics, corporate environmental performance, dynamism, complexity, and munificence. The total number of observations for firms across the fourteen-year period is 2408. The table indicates that the mean for big data analytics is 0.217, with a standard deviation of 0.108. The mean corporate environmental performance is 27.32, with a standard deviation of 16.29. This investigation demonstrated a significant correlation between big data analytics and corporate environmental performance (B = 0.410 ***). Consequently, practitioners may utilize these descriptive statistics to conduct comparisons across industries or sectors.

5.2. Testing the Relationship Between BDA and CEP

H1 investigates the impact of BDA on CEP. The “dynamic panel GMM estimator with instruments” is utilized to evaluate “regression model” (1). The “Hansen J test” is conducted to evaluate the validity of our estimators [108].

Table 5. Big data analytics, environmental performance, and industry environment.

Variables	Main Effect Model 1	Moderating Effect
		Model 2	Model 3	Model 4	Model 5
Constant	0.503 (0.310)	0.793 (0.196)	0.805 (0.208)	0.863 ** (0.437)	0.816 ** (0.402)
Lagged CEP	0.714 ** (0.267)	0.394 ** (0.104)	0.612 ** (0.251)	0.593 ** (0.308)	0.162 ** (0.415)
BDA	0.028 ** (0.031)	0.034 ** (0.072)	0.042 ** (0.016)	0.029 ** (0.026)	0.016 * (0.021)
Firm size	0.08 (0.003)	0.07 (0.004)	0.05 (0.001)	0.07 (0.003)	0.08 (0.004)
Time period	0.05 (0.002)	0.06 (0.006)	0.08 (0.004)	0.03 (0.005)	0.02 (0.009)
Leverage	0.01 * (0.031)	0.02 * (0.051)	0.02 * (0.049)	0.04 * (0.049)	0.02 * (0.021)
Cash flow	0.016 * (0.049)	0.03 * (0.041)	0.11 * (0.083)	0.12 * (0.015)	0.18 * (0.019)
R & D	0.12 ** (0.040)	0.15 ** (0.019)	0.16 ** (0.014)	0.03 * (0.019)	0.03 * (0.040)
Profitability	0.29 ** (0.114)	0.18 ** (0.106)	0.22 ** (0.113)	0.13 ** (0.102)	0.16 ** (0.105)
Moderating effect
COM		0.428 ** (0.217)			0.347 ** (0.018)
BDA × COM		−0.082 (0.034)			−0.371 (0.047)
DYM			0.319 ** (0.266)		0.402 ** (0.438)
BDA× DYM			−0.172 ** (0.317)		−0.219 ** (0.510)
MUN				0.017 (0.410)	0.083 (0.472)
BDA × MUN				0.062 (0.379)	0.091 (0.416)
Residual	−0.029 (0.041)	−0.034 (0.027)	−0.020 (0.039)	−0.015 (0.027)	−0.011 (0.092)
Model information
Number of observations	2408	2408	2408	2408	2408
R2	0.792	0.789	0.792	0.789	0.799

Note: * p < 0.05. ** p < 0.01.

shows the findings of regression on the BDA-CEP relationship. All “J-statistics (p > 0.05)” in Table 3 indicate that the “null hypothesis about the validity of our estimators cannot be rejected”. The link between BDA and CEP is positive and significant (p < 0.05), thus supporting H1. In line with prior exploration, this study demonstrated that BDA implementation improves corporate environmental performance by optimizing the allocation and rearrangement of resources.

5.3. The Moderating Effect of Industry Environment

Table 5 presents the regression analysis with digital transformation acting as a moderating factor. In accordance with prior findings, the “dynamic GMM panel estimation” method using instruments was applied to evaluate “regression model” (3). The “Hansen J test” demonstrates that the null hypothesis, which asserts that the moment conditions are correctly specified, cannot be rejected at any significance level. Moreover, all VIF values were below 10, demonstrating the absence of substantial multicollinearity [109]. Implementing BDA has a stronger effect on CEP in less complex contexts compared to more complex ones, as shown by the negative and statistically significant coefficient of complexity interaction (β = −0.082, p < 0.05 in Model 2 and β = −0.371, p < 0.01 in Model 5). It appears that BDA implementation has a stronger impact on CEP in less dynamic contexts compared to more dynamic ones, as indicated by the substantial negative coefficient of dynamism interaction (β = −0.172, p < 0.01 in Model 3, β = −0.219, p < 0.01 in Model 5). Munificence has no significant influence on the BDA-CEP relationship. We assert that the benefits of BDA may be more significant for enterprises functioning in circumstances marked by a low complex and dynamic environment. Therefore, H2 was partially supported.

5.4. Robustness Checks

To corroborate the data, we conducted several additional analyses. Our study findings hold considerable significance for management. Consequently, we performed an additional investigation to meticulously investigate this relationship [110,111]. Initially, we performed an “Ordinary Least Squares (OLS) regression” analysis, integrating a quadratic element to investigate possible non-linear effects of big data analytics. The coefficient for the squared term failed to achieve statistical significance, suggesting an absence of evidence for a curvilinear relationship. Secondly, at the organizational level, both firm size and age were incorporated into our conceptual framework. Firm size was quantified by the number of employees, whilst firm age was assessed as the duration since establishment in years. At the industry level, we incorporated several control variables, including industry stability, evaluated as the three-year lagged standard deviation of median sales growth for the two-digit SIC industry (t − 2, t − 1, t), and industry growth, determined as the three-year lagged average of median sales growth for the two-digit SIC industry (t − 2, t − 1, t). The results demonstrate that none of these variables significantly impact the link between BDA and CEP, and our conclusions remain unchanged.

We validated the robustness of the results by performing various tests utilizing techniques such as winsorization, propensity score matching (PSM), estimating an alternative model, and sample size adjustments. All continuous constructs were winsorized at the 99% level to ensure that outliers did not impact the results of the regression. Column 1 of

Table 6. Robustness test results.

DV/CEP	(1) Winsorization	(2) Alternative Model
BDAt−1	0.027 ** (0.160)	0.029 ** (0.183)
BDAt−12	−	0.079 (0.261)
Control variables	Included	Included
Firm fixed effect	Yes	Yes
Year fixed effect	Yes	Yes
Number	2408	2408
R2	0.781	0.799

Note: ** p < 0.01.

shows that BDA continues to strongly impact CEP (β = 0.027, p < 0.01), confirming the initial conclusion. Companies’ fundamental traits vary according to their degree of BDA adoption, which can explain why they engage in diverse forms of environmental activities. We utilized PSM to acquire matched samples according to the industry year median for high and low BDA adoption; the high BDA sample was employed as the treatment sample, while the low BDA sample was utilized as the control sample, to deal with this possible endogeneity issue. We matched using all control constructs and dummy factors for industry and year to reduce the discrepancy in the two dataset’s fundamental features. We discovered that the BDA level is the sole independent factor that affects the CEP variance among the two samples after controlling for other factors. Moreover, we included the square term of BDA in our alternative model to determine if the link among BDA and CEP shows a quadratic pattern (“U-shaped or inverted U-shaped”). The linear link between BDA and CEP is confirmed by the fact that the coefficient for BDA² is not statistically significant (β = 0.079, p = 0.261), as shown in column 3 of Table 6. This allows us to state with certainty that the model we provided is robust.

6. Discussion

6.1. Key Findings

Hypothesis H1 posits that big data analytics is a key driver of company environmental performance. The analysis demonstrates that BDA implementation exerts a significant and favorable influence on corporate environmental performance, consistent with [14,23,28,51,65,89]. The findings show that companies could improve their environmental performance through making investments in utilizing big data analytics. Consequently, investment in big data analytics may have mutually beneficial outcomes, as indicated by previous examinations [19,34,47,56,88]. The findings corroborate our hypothesis H1, aligning with prior studies on the benefits of using big data analytics in improving corporate environmental performance [34,62]. The analysis indicates that a one-unit enhancement in big data analytics leads to, on average, a 2.8% improvement in corporate environmental performance. The statistical findings support the positive effect of big data analytics on enhancing business environmental performance. These results indicate that the application of big data analytics enhances environmental performance and overall sustainability. The beneficial effect is due to the value derived from heightened green innovation, which improves productivity and reduces costs, strengthening sustainable performance, something which is consistent with societal issues for environmental accountability. These results corroborate the conclusions of previous examinations [23,47,59,71].

Our study empirically demonstrates the moderating impacts of industrial environmental variables on the value creation of big data analytics. At elevated levels of dynamism, the principal function of BDA utilization is to facilitate exploitation operations, hence enhancing corporate sustainable performance. These findings are discordant with the findings of prior investigations, which noted that the impact of big data analytics on business growth is more significant in less dynamic contexts [11,23,34,61,80]. Prior study also indicated that big data analytics improves business performance under conditions of low to medium environmental dynamism, but this impact diminishes under high dynamism [34,61,88]. Concerning elevated dynamism, we illustrate that the fundamental function of big data analytics implementation is to facilitate exploration initiatives, hence augmenting business performance. This research corroborates the results of prior studies [45,67], which discovered that elevated environmental dynamism enhances the beneficial impact of big data analytics on performance outcomes. This study is an inaugural investigation into the moderating influence of environmental complexity on the link between big data analytics and corporate environmental performance. The adoption of big data analytics enhances environmental performance as complexity decreases. Our findings indicate that the long-term value, specifically corporate environmental performance, of big data analytics adoption is enhanced in less complex contexts. This discovery corroborates earlier studies indicating that intricate environments enhance the impact of big data analytics on performance outcomes [34,43,61].

Our analysis revealed that cash flow has a significant positive impact on corporate environmental performance. The findings suggest that companies with substantial cash flow exhibit reduced reliance on (1) “external capital markets”, enabling them to lower financing expenses and enhance corporate environmental performance, and (2) “shareholders”, permitting managers to pursue more ambitious environmental research and development initiatives necessitating extended investment timelines, thereby creating a competitive edge and consequently enhancing long term profitability. Our analysis indicated that leverage has a significant and positive influence on corporate environmental performance. Consequently, companies with minimal leverage are obligated to pay interest on their loan, thereby elevating their financial costs. Moreover, substantial debt compels management to dismiss research and development initiatives that could yield long-term competitive benefits. The decline in research and development investments and the necessity to fulfill financial commitments limits low-leveraged enterprises viewed as excessively risky by the market, adversely affecting their corporate environmental performance.

6.2. Theoretical Implications

Our research enhances the theoretical framework by tackling current issues related to environmental consequences, resource consumption, and industrial waste creation, in accordance with recent trends in sustainability research and the implementation of big data analytics [56,72]. Our paper advocates for sustainable performance and the application of big data analytics as strategic measures to alleviate environmental deterioration, contributing to the discourse on comprehensive and ecologically responsible business activities. The analysis of the direct impact of big data analytics implementation on corporate environmental performance is positioned within the developing theoretical framework of technology-driven environmental practices. Recent research emphasized the transformative capacity of AI in promoting sustainable initiatives, consistent with the foundational tenets of organizational learning theory that inform our research [31,52,61].

Our research was grounded in the theoretical framework of organizational learning theory, positing that emerging technologies (BDA) serve as significant catalysts for societal changes and innovations. Consequently, our research elucidates the impact of BDA adoption in enhancing corporate environmental performance. Consequently, the findings of our research illustrate how digital modernization (BDA) can significantly impact and improve sustainable performance and environmental accountability. Our research presents new evidence about the value that big data analytics contributes to the implementation of sustainable and circular economy activities. Our research introduces three essential moderators—complexity, dynamism, and munificence—affecting the link between big data analytics and corporate environmental performance. Our exploration is founded on recent studies investigating the impact of big data on sustainability [63] and knowledge management systems in innovative domains [34,56]. This paper addresses a theoretical gap by including these factors into an integrated model, highlighting the interrelationship between big data analytics and the industry environment in influencing corporate environmental performance and sustainability results. Recent research underscores the necessity for more comprehensive techniques to comprehend the role of technology in sustainability, hence reinforcing the theoretical framework of our research [21,67].

Our study contrasts with other research that frequently concentrates on discrete elements of technology, innovation, and sustainability. It concurrently examines the influence of big data analytics on business environmental performance and evaluates the function BDA plays in sustainability. Our research simultaneously investigates BDA, CEP, and the industry environment from a holistic approach. Prior examination [47,52] has underscored the necessity for thorough assessments that account for the interconnections among many technical aspects in shaping sustainable results, thereby affirming the distinctive theoretical implications of our study. This work distinguishes itself as a pioneering addition to the developing domain of technology-driven sustainability studies by providing a fresh comprehension of the complex interactions between various technical factors and their collective impact on environmental and performance outcomes. Our research examines current ecological issues and develops a theoretical model, highlighting the interrelation of technological elements in fostering sustainable initiatives across various industrial settings.

6.3. Managerial Implications

The practical implications of our research offer a unique and focused strategy that is very pertinent for policymakers and managers across several sectors (i.e., “financial services, manufacturing, technology, telecommunications, healthcare, and tourism”). Our research’s detailed examination of the direct effects of big data analytics deployment on company environmental performance, with the moderating influences of the industry environment, reveals distinct prospects for policymakers to create revolutionary activities. A primary directive for managers is to promote cross-sector collaboration to create a cohesive technological environment. Managers could promote an integrated model that combines big data analytics and the industry environment to enhance environmental performance. This entails the formulation of incentive frameworks that encourage collaborative initiatives among companies, facilitating the aggregation of BDA expertise and big data analytical competencies through industries. Managers can harness the full potential of these technologies to foster new solutions that tackle environmental concerns through various sectors.

The results emphasize the need for policies that prioritize ongoing upskilling and education to provide the workforce with the skills required to effectively utilize new technologies. Managers could partner with educational institutions to develop programs focused on big data analytics literacy, data analytics expertise, and knowledge management competencies. By cultivating proficient staff, managers may leverage the capabilities of these technologies for sustainable and circular processes, thus guaranteeing the enduring success of these efforts. Our paper presents a unique framework for practitioners by promoting an integrated and collaborative strategy to leverage big data analytics implementation through many sectors. This integrated model offers a comprehensive solution designed to address specific difficulties and possibilities, eventually promoting sustainability, circularity, and innovation from an economic standpoint. The government can also set aside specific funds to encourage the research, development, and widespread use of digital environmental protection technologies; this will help ease the financial burden on businesses, encouraging them to adopt greener practices, and will also encourage the integration of BDA into environmental protection efforts.

Our investigation encourages managers in organizations that have not yet used big data analytics to make an investment in it. This results not just from the improvement of environmental performance measures using big data analytics but also from the lasting nature of these benefits. Our research can assist practitioners in organizations that have implemented big data analytics in evaluating the need for further investments. By assessing the influence of each increment in big data analytics implementations on performance outcomes, we illustrate that managers should invest in additional big data analytics initiatives by either adopting innovative big data analytics technologies or applying existing big data analytics technologies to new domains. Moreover, the quantified improvements in environmental performance enabled by big data analytics can aid practitioners in evaluating and measuring the benefits of such analytics before making financial commitments.

This paper proposes that governance strategies for big data analytics must align capabilities with industry features and business objectives. Before investing in big data analytics, managers must integrate industry-specific characteristics into their evaluation, identify appropriate application domains for big data analytics, and define relevant KPIs to assess the returns on such investments. Managers who view industry environments as “dynamic, complex, and munificent” ought to organize big data analytics technologies, proficient personnel in big data analytics, and other relevant capabilities to support exploratory initiatives, including new product development and market entry, while utilizing long-term performance metrics, such as environmental performance, to evaluate the returns on big data analytics. In conditions of low environmental dynamism and complexity, managers may leverage big data analytics capabilities for exploitation projects, such as optimizing existing business processes, and employ short-term outcome measurements, such as efficiency, to assess the value of big data analytics.

7. Limitations and Future Research Directions

This research, like other empirical studies, has limitations that future investigations should address. Firstly, akin to earlier evaluations of this type, we analyze environmental performance quantitatively. The indicators of environmental performance may not accurately represent actual environmental activity. Further study may provide enhanced insights through interviews and case studies with managers to elucidate their operations. Secondly, our research largely concentrates on U.S. enterprises; therefore, the findings may not be directly relevant to other countries with distinct legislative and industrial environments. Future research should investigate the impact of BDA on CEP within a multinational setting, taking into account differences in institutional and regulatory structures. Moreover, our research acknowledges the necessity of addressing the intricate character of CEP. Future research may explore additional aspects and examine the responses of specific stakeholder groups, including employees and consumers. This study investigated the direct effect of BDA on environmental performance. Future research may examine the mechanism via which BDA affects CEP. Further mediating variables, including creativity and dynamic abilities, require investigation. Ultimately, we have not examined the determinants of BDA. Therefore, future studies may examine the principal factors influencing BDA.

8. Conclusions

Recent years have seen a surge in interest in big data analytics, which many see as a game-changing technology with far-reaching implications. Although it has the ability to decrease emissions of industrial pollutants, very little is known about the extent of its effects or the processes that cause them. In order to better understand how to improve corporate environmental performance in various environmental circumstances, this research looked at the function of implementing big data analytics. Consequently, we assess BDA’s effect on environmental performance in depth, tracing its causes and determining the factors that mitigate its effects. The S&P 500 index in the United States was the source of the longitudinal data used in this study.

The results demonstrate that corporate environmental performance (CEP) improves by 2.8% on average for every 1 unit increase in BDA. Also, in more complicated and dynamic settings, BDA has a bigger effect on CEP. Even after subjecting the system to severe resilience testing, this beneficial environmental consequence has not changed. Our findings are shown to be reliable and resilient by the results. Furthermore, the fundamental processes are revealed: we give a thorough theoretical analysis of the ways in which BDA affects the environmental performance of corporations. The theoretical framework is supported by our empirical studies, which also shed light on the quantitative impact of each process. Hence, to improve environmental performance, it is suggested that different sectors integrate BDA. A considerable decrease in the intensity of pollutant emissions can be achieved through policies that encourage the use of BDA. Businesses that use BDA to reduce their environmental impact can be eligible for grants, tax breaks, or other forms of public funding from regulatory agencies and governments. The government can play a role in fostering collaboration by offering financial support for joint research initiatives that investigate the potential of BDA in environmentally sustainable contexts.