The Dynamic Role of Green Innovation Adoption and Green Technology Adoption in the Digital Economy: The Mediating and Moderating Effects of Creative Enterprise and Financial Capability

Abstract

This study investigates the critical roles of green innovation adoption (GIA) and green technology adoption (GTA) in advancing the digital economy (DE). It explores how creative enterprises (CE) and financial capability (FC) mediate and moderate the relationships between green innovation, technology adoption, and the digital economy. Data were collected from 400 respondents in the automotive industry in China through a structured survey questionnaire. Structural Equation Modeling (SEM) was conducted using Smart PLS 4.0 to analyze the data and test the proposed hypotheses. The findings indicate that green innovation and technology adoption significantly and positively impact the digital economy. Moreover, these factors positively influence creative enterprises, enhancing the digital economy. Creative enterprises were also found to mediate the relationship between green innovation/technology adoption and the digital economy. In addition, financial capability significantly moderated the link between creative enterprises and the digital economy. This research contributes to the literature by examining the synergistic effects of environmental sustainability, innovation adoption, and technological integration within the digital economy. It provides actionable insights into embedding sustainable practices in the evolving digital landscape while highlighting the intricate dynamics among innovation, technology adoption, and financial resources in driving economic transformation. This study contributes to the literature by exploring the synergistic effects of green innovation, technology adoption, and financial capability in driving the digital economy, focusing on creative enterprises as a mediating factor. It provides actionable insights for integrating sustainability into the digital landscape, offering a novel framework for economic transformation in the automotive industry.

1. Introduction

The digital economy has fundamentally reshaped industries worldwide, unimaginably driving innovation, efficiency, and sustainability [1]. Recently, the digital economy has been driving global economic growth, with businesses leveraging digital tools to enhance productivity, reach new markets, and create value in once-unimaginable ways. As digital technologies evolve, they reshape industries and challenge traditional economic structures, making the digital economy a central element in shaping the future of global business and sustainability [2]. It transformed traditional business models by enabling more excellent connectivity, efficiency, and scalability. Among the various forces in digital transformation, green innovation, and technology adoption have emerged as pivotal factors in advancing sustainability [3]. As the global awareness of environmental issues intensifies, businesses are increasingly called upon to integrate environmentally friendly practices into their operations. Adopting green innovations and technologies contributes to ecological sustainability and aligns with the growing demand from consumers, governments, and stakeholders for businesses to embrace more responsible and sustainable practices [4]. In the context of the digital economy, these green practices are often fueled by advancements in digital technologies, making it critical to understand how they interact and impact overall business success [5].

A prime example of this transformation can be seen in BYD (Build Your Dreams), one of the world’s largest electric vehicle (EV) manufacturers [6]. BYD has successfully integrated green innovation and technology adoption into its business strategy. The company’s adoption of cutting-edge battery technology and energy-efficient manufacturing processes has significantly reduced carbon emissions while enhancing operational efficiency. By leveraging digital economy integration (DEI), BYD has optimized its supply chain and improved production forecasting through AI-driven analytics. These green innovations have strengthened the company’s financial performance and positioned it as a leader in sustainable mobility, enhancing its corporate reputation and competitive advantage [7]. This case exemplifies how green innovation and technology can drive environmental and financial success within the digital economy.

China is the largest manufacturer in the world and a global leader in technological innovation because of its potential to influence sustainable practices on a massive scale. The country is increasingly adopting digital solutions, such as artificial intelligence, big data, and cloud computing, to optimize manufacturing processes and reduce environmental impact [8]. At the same time, China’s commitment to green development and environmental sustainability has led to policies encouraging the adoption of green technologies, particularly in the energy, transportation, and industrial sectors [9]. The convergence of these digital and green initiatives presents a powerful opportunity for businesses to integrate sustainability into their operations while navigating the pressures of a competitive global market. This dynamic environment makes China ideal for studying how traditional industries can innovate and implement green technologies within the evolving digital economy. The automotive industry in China is undergoing a significant transformation driven by rapid digitalization, sustainability, and innovation advancements [10]. With the government’s strong push towards reducing carbon emissions, promoting energy efficiency, and accelerating the adoption of electric mobility, Chinese automakers are increasingly investing in green technologies and digital solutions [11].

For instance, state-owned and private automotive firms in China operate under different regulatory frameworks, ownership structures, and financial incentives, which shape their ability to invest in and implement green technologies. State-owned enterprises (SOEs) often benefit from government subsidies and policy support, enabling large-scale EV production and infrastructure development [12]. In contrast, private firms may face higher financial constraints and competitive pressures, influencing their investment strategies and risk appetite in adopting green innovations. Additionally, the transition to electric vehicles (EVs) is heavily shaped by national policies, such as subsidies, carbon credit trading, and infrastructure investments, which mediate the speed and scope of green technology adoption across the sector. By contextualizing these industry-specific factors that mediate GIA/GTA, we can better understand how state ownership, market orientation, and regulatory environments interact with corporate entrepreneurship and financial capabilities to influence digital and green transformation.

The relationship between green innovation and technology adoption is complex and multifaceted. While GIA typically introduces novel ideas and practices that reduce environmental impact, GTA focuses on implementing and using technological solutions to minimize adverse environmental effects. Together, they form the foundation for companies aiming to operate sustainably within the digital economy. A creative enterprise characterized by a company’s capacity for innovative thinking and entrepreneurial spirit is crucial in transforming abstract green innovations into practical, marketable technologies [13]. Moreover, the financial resources available to an organization significantly affect its ability to adopt and scale green technologies, as these technologies often require substantial investment in research, infrastructure, and implementation. However, the successful adoption of green innovations and technologies does not occur in isolation [14]. Factors such as creative enterprise and financial capability influence how these innovations and technologies are embraced and integrated into business practices.

The automotive industry in Guangdong, China, is a compelling case study for this research due to its significance in manufacturing and technological advancement. Known for its robust industrial base and leadership in technological development, Guangdong provides a unique context to examine how companies within traditional industries, such as automotive manufacturing, can leverage digital and green technologies to address pressing environmental challenges [15]. While several studies have explored the relationship between green innovation adoption (GIA) and green technology adoption (GTA) within industries such as textiles and fashion or have examined the construction industry to explore innovative design and digital transformation, there is a notable gap in research focusing on GTA and GIA within the automotive sector, particularly in Guangdong’s rapidly evolving digital economy. This gap presents an opportunity to extend the existing literature by exploring how creative enterprise (CE) and financial capability (FC) mediate and moderate the relationship between GIA and GTA in the context of the digital economy. While there has been substantial research on creative enterprises, many studies overlook their role as a mediator in the relationship between green innovation and technology adoption. Moreover, several studies have considered financial capability primarily as a dependent variable, particularly with Corporate Social Responsibility (CSR), without exploring its moderating role in adopting sustainable practices and technologies. This leaves a significant gap in understanding the combined impact of environmental sustainability, innovation adoption, and technology integration within the digital economy. This study addresses these gaps by examining the role of green innovation and technology adoption within the digital economy. It focuses on how creative enterprise mediates and how financial capability moderates its effects. The conceptual framework of Figure 1 illustrates the dynamic interplay between GIA, GTA, creative enterprise, and financial capability within the context of the DE. The research will provide novel insights into how these factors influence adopting sustainable practices and technologies in the automotive industry, specifically in Guangdong, China. By doing so, this study will contribute to both theoretical understanding and practical applications for companies aiming to navigate sustainability challenges in a rapidly digitalizing world. In light of the above discussion, this study seeks to address the following research questions:

• How do green innovation adoption (GIA) and green technology adoption (GTA) influence the digital economy in the automotive industry?
• In what ways does creative enterprise (CE) mediate the relationship between GIA, GTA, and the digital economy?
• How does financial capability (FC) moderate the relationship between creative enterprise and the digital economy?

This study contributes significantly to three key areas: First, this study addresses the gaps in understanding green innovation adoption (GIA) and green technology adoption (GTA) within the digital economy, particularly in the automotive industry. By integrating environmental sustainability, innovation adoption, and digital transformation, the research offers a holistic perspective on how traditional industries can leverage green and digital technologies to achieve sustainability goals. This aligns with existing literature on sustainable innovation but expands the discussion by contextualizing it within the automotive sector’s transition toward a greener future. Second, this research investigates the mediating role of creative enterprise and the moderating effect of financial capability in this relationship. While previous studies have explored innovation adoption and financial capability separately, this study uniquely positions creative enterprise as a mediator and financial capability as a moderator. The findings provide fresh insights into their impact on adopting sustainable practices and technologies, highlighting the synergistic link between these elements. By integrating these perspectives, this study builds on prior research while offering novel insights into how these factors collectively influence the digital economy. Third, grounded in the Dynamic Capability Theory (DCT), our findings emphasize the need for a holistic approach to the digital economy. By integrating GIA and GTA, this study provides a robust framework to understand how firms respond to the dual imperatives of sustainability and digital transformation. This theoretical perspective enhances previous discussions on dynamic capabilities by demonstrating how firms develop resilience through green innovation. Moreover, by offering region-specific insights, this study contributes to both theoretical advancements and practical strategies for fostering sustainable development in the rapidly evolving digital economy.

The structure of this paper is as follows: The next section provides a comprehensive review of the relevant literature, laying the foundation for the study. The third section introduces the proposed mechanism and hypotheses. The fourth section presents the development of the research model and the selection of key variables. The fifth section examines the variations in the progress of the digital economy across different regions. The sixth section details the empirical research, including the methodology, findings, and interpretations of the data analysis. Finally, the last section summarizes the key research findings and offers practical recommendations and insights.

Conceptual Model Legends

Table 1. Definitions of key constructs.

Constructs	Definition
Green Technological Adoption (GTA)	Implementation of energy-efficient and low-emission technologies.
Green Innovation Adoption (GIA)	Development of sustainable products, processes, and business models.
Creative Enterprise (CE)	An organization’s ability to foster innovation and adaptability.
Financial Capability (FC)	The firm’s proficiency in managing financial risks and investments.
Digital Economy (DE)	The extent to which a firm leverages digital platforms and technologies.

provides an overview of the key constructs and their definitions.

2. Theoretical Foundation and Hypotheses Development

2.1. Dynamic Capability Theory

DCT (Dynamic Capability Theory) is a conceptual framework that elucidates how organizations navigate and adapt to evolving environments by effectively developing, deploying, and reconfiguring their internal and external resources [16]. This theory underscores the importance of an organization’s capacity to identify opportunities and threats, capitalize on these opportunities, and transform its resources accordingly. DCT is particularly valuable for understanding how organizations navigate complex challenges, such as technological advancements and sustainability demands, in rapidly evolving industries. In the context of the dynamic role of green innovation and green technology adoption within the digital economy, this theory offers a robust framework to understand how firms respond to the dual imperatives of sustainability and digital transformation. The theory is particularly relevant in exploring the mediating role of creative enterprise and the moderating role of financial capability in this process.

Firstly, GIA and GTA are inherently dynamic processes requiring firms to adjust their resource base and operational strategies to integrate environmentally sustainable practices into the digital economy. Adopting green innovations (e.g., eco-friendly manufacturing practices) and green technologies (e.g., renewable energy systems) reflects an industry’s ability to sense and seize opportunities in response to external pressures, such as regulatory frameworks and changing consumer preferences [17]. These dynamic capabilities enable firms to align with sustainability goals while maintaining competitiveness in the digital economy.

Second, CE is a dynamic enabler, mediating the relationship between GIA, GTA, and DE. Creative enterprises foster an environment of entrepreneurial innovation, driving the successful implementation of green initiatives by leveraging creativity, adaptability, and resourcefulness. By reconfiguring organizational processes and mindsets, CE ensures that green innovations and technologies translate into tangible improvements in organizational performance and digital integration [18]. A creative enterprise might enable redesigning production systems to incorporate green technologies while creating value through novel business models that align with the digital economy.

Lastly, FC acts as a moderating factor, amplifying the effectiveness of CE in facilitating green innovation and technology adoption. Financial capability reflects the industry’s ability to mobilize and allocate financial resources effectively, which is critical for supporting the high costs and risks associated with implementing green technologies. Firms with robust financial capabilities can invest in advanced technologies, research, development, and employee training, enhancing their dynamic capabilities. The interplay between FC and CE highlights the importance of tangible (financial resources) and intangible (creativity and entrepreneurship) assets in enabling firms to thrive in a complex and dynamic digital economy.

2.2. Hypotheses Development

2.2.1. Green Innovation Adoption, Digital Economy, and Creative Enterprise

Environmental degradation and climate change, exacerbated by rising emissions and pollution, present significant challenges to achieving a globally sustainable economy [19]. Addressing these challenges necessitates the adoption of green innovation and the integration of green technology adoption. These innovations involve creating and implementing environmentally friendly products, processes, or services to minimize ecological impacts [20]. However, transitioning from conventional production methods to sustainable practices involves complexities and uncertainties, requiring organizations to develop dynamic capabilities to adapt to evolving environmental demands [21].

Dynamic Capability Theory provides a robust theoretical foundation for understanding how firms adapt to environmental sustainability and technological integration challenges. The theory underscores the need for firms to sense, seize, and transform opportunities and resources to maintain competitiveness. For organizations engaging in GIA, sensing opportunities involves identifying the need for sustainable practices, seizing opportunities requires integrating innovative solutions into their operations, and transforming involves restructuring processes to align with sustainability goals.

Previous research highlights the facilitators and barriers to GIA in various industrial settings. For example, aligning with Sustainable Development (SD) metrics, encompassing environmental, social, and economic goals, has been instrumental for firms seeking to enhance sustainability outcomes [22]. Industries that successfully incorporate GIA and GTA, such as the automotive sector, demonstrate a dynamic capability to balance environmental costs while improving operational efficiencies [23]. These capabilities enable firms to gain a competitive edge by innovating their product offerings and integrating green practices into their value propositions [24].

Creative enterprises play a pivotal role in this context as they drive innovation by fostering adaptive strategies and resource reconfiguration, enabling firms to implement green initiatives effectively [25]. Furthermore, businesses that adopt GIA can differentiate themselves by incorporating green principles into their operations, enhancing their reputation, and creating value in the digital economy [26].

The adoption of green innovations represents a firm’s sensing capability, where it identifies emerging sustainability challenges and digital opportunities [27]. Firms that engage in GIA (e.g., eco-friendly manufacturing processes) are actively detecting and responding to regulatory shifts, consumer preferences, and environmental concerns. This aligns with DCT’s sensing function, which involves scanning the environment, anticipating changes, and positioning the firm for sustainable digital transformation.

Based on the integration of literature with DCT, the following hypotheses are proposed:

H1: 

Green innovation adoption positively and significantly impacts the digital economy.

H3: 

Green innovation adoption positively and significantly impacts creative enterprises.

2.2.2. Green Technology Adoption, Digital Economy, and Creative Enterprise

Green technology adoption refers to integrating environmentally friendly technologies, such as renewable energy sources, energy-efficient equipment, and eco-friendly manufacturing processes. GTA is vital in addressing environmental sustainability challenges while enhancing operational efficiencies across industries. The digital economy (DE) has further advanced the adoption of GTA, improving industrial processes and spurring economic growth [28]. However, the adoption of such technologies is often uneven, with economically developed regions benefiting more from early digital technology adoption, which fosters a higher number of digital enterprises, a more digitally skilled workforce, and government support for technological development [29]. Conversely, in economically impoverished areas, the pace of technology adoption is slower, leading to disparities in the expansion of DE [30].

DCT offers a valuable perspective for understanding how businesses can utilize Green Technology Adoption (GTA) to address environmental challenges and maintain their relevance in the fast-changing digital era. DCT emphasizes the ability of organizations to identify opportunities, capitalize on them, and adapt their internal resources and processes to meet evolving conditions. Specifically, in the context of GTA, companies develop the necessary capabilities to incorporate green technologies into their workflows, thereby advancing environmental sustainability and achieving digital transformation.

Previous studies have underscored the role of the GTA in fostering creative solutions aimed at sustainability. Companies that adopt GTA can innovate new products and processes that minimize environmental harm while boosting operational efficiency. This, in turn, provides them with a competitive edge [31,32]. By integrating eco-friendly innovations, firms can simultaneously enhance sustainability and competitiveness [33]. Creative enterprises, in particular, benefit from green technologies as they provide the resources, expertise, and information necessary to develop sustainable products and services. Studies have shown that high levels of GTA correlate with increased creativity and entrepreneurship, especially in manufacturing firms, where the integration of green technologies can lead to innovative product development and sustainable manufacturing processes [34,35].

Linking these insights with DCT, it becomes clear that the GTA enables firms to sense environmental opportunities, seize them by adopting green technologies, and reconfigure their resources to maintain a competitive edge. Creative enterprises play a crucial role by transforming green innovations into sustainable products and business models that align with the evolving digital economy. Furthermore, investments in green technologies equip firms with the knowledge and tools to design eco-friendly products, thus positioning them as leaders in sustainability and innovation [36].

Firms that adopt green technologies (e.g., renewable energy integration) proactively address environmental concerns while enhancing their strategic positioning in the digital economy [37]. Companies with higher GTA capabilities are better equipped to align with digital economy integration, as they can effectively sense external market shifts and environmental demands. This aligns with DCT’s sensing function, which involves scanning the business environment, anticipating changes, and positioning the firm for sustainable digital transformation.

Based on this literature, the following hypotheses are proposed:

H2: 

Green technology adoption positively and significantly impacts the digital economy.

H4: 

Green technology adoption positively and significantly impacts creative enterprises.

2.2.3. Creative Enterprise, Green Innovation Adoption, Green Technology Adoption, and Digital Economy

The challenges facing the global economy in the twenty-first century highlight the importance of technological advancements, including nanotechnology and sustainable innovations [38]. In addressing these challenges, creative enterprises—firms that leverage innovation and new ideas—play a critical role [39]. The concept of ‘creative enterprise’ has been widely used in innovation and entrepreneurship literature, often referring to a firm’s ability to develop novel products, processes, and business models [40]. Creative enterprises can generate and implement novel solutions, enabling organizations to adapt to dynamic market conditions and drive sustainable development. It underscores the significance of green innovation and technology in achieving long-term sustainability, asserting that adopting creative business models is central to economic growth in the digital economy [41]. These creative ventures are pivotal in fostering innovation and aligning economic activities with sustainable practices [42].

Drawing from DCT, which emphasizes an organization’s ability to sense opportunities, seize them, and reconfigure its resources to maintain competitiveness, creative enterprises are positioned as key drivers in adopting green technologies and GIA. Dynamic capabilities allow creative enterprises to integrate these green technologies into their operations, contributing to environmental sustainability and economic development within the digital economy (DE). By sensing the need for sustainable practices, seizing opportunities to innovate with green technologies, and transforming business models to integrate these technologies, creative enterprises become central to driving sustainable innovation [43].

The literature highlights that creative enterprises foster innovation and mediate the impact of green technologies on the digital economy [44]. Creative enterprises significantly enhance firms’ financial and operational performance by promoting sustainable innovation and aligning business practices with environmental goals, particularly when the organization formally integrates sustainable processes [45]. These synergies between creativity, innovation, and sustainability align well with the principles of Dynamic Capability Theory, suggesting that creative enterprises are vital in mediating the relationship between green innovation adoption, green technology adoption, and the growth of the DE.

A creative enterprise fosters entrepreneurial creativity, resourcefulness, and adaptability, which are critical for the effective deployment of green technologies and innovation into digital platforms [46]. For instance, CE enables organizations to redesign production systems, integrate digital tools for sustainability, and develop novel business models that align with both ecological and economic objectives. CE mediates the relationship between GIA/GTA and DE, as firms with strong creative capabilities are better able to seize sustainability opportunities and translate them into competitive advantages in the digital economy.

Based on the integration of literature and Dynamic Capability Theory, the following hypotheses are proposed:

H5: 

Creative enterprise positively and significantly impacts the digital economy.

H6a: 

Creative enterprise mediates the relationship between green innovation adoption and the digital economy.

H6b: 

Creative enterprise mediates the relationship between green technology adoption and the digital economy.

2.2.4. Financial Capability, Creative Enterprise, and Digital Economy

Financial capability refers to an organization’s ability to access and manage financial resources effectively, essential for achieving long-term sustainability and success. Recent studies have highlighted the growing interest in financial capability, noting that it encompasses not only the ability to access financial resources but also the awareness, knowledge, behaviors, and attitudes that contribute to sound financial decision-making [47,48]. Fu (2020) [49] expands this concept, asserting that financial capacity involves the skills and behaviors necessary to make informed financial choices and achieve financial well-being. Research suggests that financial capability plays a significant role in influencing business performance, entrepreneurship, and decision-making [50,51], as it enables firms to manage resources efficiently, secure financing, and undertake strategic investments.

From the perspective of Dynamic Capability Theory, financial capability is a vital intangible asset that enables organizations to adapt, seize opportunities, and maintain a competitive edge in the digital economy. As described by [16], dynamic capabilities empower firms to reconfigure resources and skills to meet evolving market demands. In this context, financial capability supports organizations in overcoming technological challenges, such as adopting green innovations, while ensuring effective resource allocation to sustain long-term innovation. Organizations with strong financial capabilities can invest in research and development, implement sustainable practices, and navigate financial constraints, all critical for success in a digitally and environmentally focused economy [52].

The literature also suggests that financial capability enhances the effectiveness of creative enterprises by providing them with the resources necessary to innovate and implement sustainable solutions [53]. Financially capable creative enterprises can leverage green technologies and innovations, manage risks more effectively, and drive performance in the digital economy. Therefore, integrating financial capability into the dynamic capability’s framework highlights its moderating role in fostering creative enterprises in green innovation adoption and technology integration.

Companies with strong financial capabilities are better positioned to invest in advanced green technologies, employee training, and R&D. This financial flexibility enhances their ability to sense, seize, and transform opportunities and is crucial for adapting innovating and sustaining their position in the digital economy [54]. FC moderates the relationship between CE and DE, such that firms with greater financial resources can more effectively leverage their dynamic capabilities to transform green innovation into digital economic success.

Based on this literature, the following hypothesis is proposed:

H7: 

Financial capability significantly moderates the relationship between creative enterprises and the digital economy.

2.3. Research Methodology

2.3.1. Research Design

The present study adopted a quantitative approach to systematically investigate the relationships between GIA, GTA, creative enterprise, and financial capability within the digital economy. This methodology is well-suited for examining specific, measurable experiences and drawing generalizable conclusions from a representative sample. Using quantitative methods allows for empirical analysis of the complex dynamics between sustainable practices, technological adoption, and their mediating and moderating effects in the context of the DE.

The quantitative design uses advanced statistical techniques, such as Structural Equation Modeling (SEM) via Smart PLS 4.0, to test the proposed hypotheses. This approach ensures robust analysis and enables the generalization of findings to a larger population of firms engaged in green innovation and digital technology adoption. Ultimately, this research design contributes to the empirical understanding of how green practices, technological advancements, and financial strategies can drive sustainable business success in the evolving digital economy.

2.3.2. Sampling Technique

A cross-sectional survey approach was utilized in the quantitative phase of this research to examine the dynamic role of GIA and GTA within the context of the DE. A non-probability sampling technique was employed to ensure adequate representation across various sectors and organizational sizes. This sampling method allows for flexibility and practical feasibility in gathering data from various organizations engaged in DE [1].

We selected a sample of 70 automotive firms operating under the Guangdong Provincial Department of Industry and Information Technology (GDIIT) and the Guangdong Securities Regulatory Bureau (CSRC) to ensure regulatory compliance and industry representation. After excluding companies that had discontinued operations or those with incomplete or inaccurate contact information, a final total of 50 automotive firms were included in the survey. Firms were chosen based on their engagement in green technological adoption (GTA), green innovation adoption (GIA), creative enterprise (CE), financial capability (FC), and digital economy integration, ensuring a diverse and comprehensive dataset. To enhance data reliability, multiple questionnaires were distributed within each firm, targeting respondents across different management levels and key functional roles directly involved in strategic decision-making. This approach ensured insights were obtained from those with the most relevant expertise and influence within the industry, strengthening the validity of our findings.

Participants were selected from a diverse pool of industries operating within the DE, specifically focusing on industries in the Guangdong region of China. These participants were asked to assess their levels of GIA, technology adoption, creative entrepreneurship, and financial capability. As outlined by [55], convenience sampling involves selecting participants based on accessibility and proximity, ensuring that those chosen can provide relevant insights without the need for a full population study. He also emphasized that non-probability sampling methods are often more pragmatic and cost-effective, especially when the aim is to gather data from a manageable population subset [56].

For this study, 400 samples were drawn from the Guangdong region to fulfill the research objectives. The selection of this sample size ensures sufficient power for the statistical analysis while also allowing for meaningful insights into the relationships between GIA, GTA, creative enterprise, and financial capability within the digital economy. This approach also enables the research to generalize findings to a wider array of firms, providing a valuable contribution to understanding sustainability and innovation in the evolving digital landscape.

2.3.3. Data Collection

This study included participants from various roles, such as engineers, manufacturing managers, operations managers, production managers, quality managers, executive directors, and managing directors within the automotive sector in Guangdong, China. Data were collected through a field survey conducted by trained personnel under the researcher’s guidance. Multiple communication platforms, including emails, WeChat, and QQ, were utilized to distribute the survey and ensure broad participation.

To minimize measurement error and common method bias, the questionnaire was pre-tested with industry experts to refine wording and clarity. Additionally, anonymous responses were encouraged to reduce social desirability bias, and different scale formats were used where applicable to mitigate response pattern tendencies. Potential constraints within the automotive industry were considered throughout the study. These constraints include regulatory pressures, supply chain complexities, technological adoption barriers, and significant capital investment required for green innovation and digital transformation. Such constraints may impact firms’ ability to fully leverage their dynamic capabilities and could influence the generalizability of our findings to firms outside of the automotive sector. Nonetheless, we believe that the methods employed in this study provide a robust framework for future research in both the automotive industry and other sectors pursuing digital and green innovation.

A close-ended-structured questionnaire was designed to ensure the clarity and accuracy of the responses. It consisted of carefully crafted questions to capture participants’ insights regarding GIA, GTA, creative enterprise, and financial capability within the digital economy. A 5-point Likert scale was employed, with response options ranging from “strongly disagree” to “strongly agree”, which provided a transparent and standardized way to measure the participants’ attitudes and experiences.

Out of 450 distributed questionnaires, 400 valid responses were collected, resulting in an approximate response rate of 88.9%. This high response rate demonstrates the participants’ engagement with the research topic and supports the reliability of the data for further analysis. The data were analyzed using Structural Equation Modeling (SEM) with Partial Least Squares (PLS), a robust statistical method for evaluating complex relationships between variables and testing hypotheses.

2.3.4. Measurement Items

The present study employed a quantitative survey methodology, utilizing a structured questionnaire divided into five sections, containing 20 measurement items. Likert scales were used to assess respondents’ perceptions of various constructs, including green technological adoption (GTA), green innovation adoption (GIA), creative enterprise (CE), financial capability (FC), and digital economy integration. For measuring the scope of the digital economy, four items were adapted from [57]. The creative enterprise (CE) construct was assessed using four items, modified from [58]. To capture green innovation adoption (GIA), four items were adapted from [59]. Similarly, green technology adoption (GTA) was measured using four items derived from the same source. Finally, four items were modified from [60] to evaluate financial capability (FC).

Respondents rated each statement on a scale from “Strongly Disagree” to “Strongly Agree”, providing nuanced insights into their perceptions of company engagement with these constructs. Before the main survey, a pilot test was conducted with a small sample to evaluate the clarity, relevance, and appropriateness of the questions. Feedback from pilot participants was used to refine and revise the measurement instruments to better align with the study’s objectives. The finalized questionnaire, as detailed in the Appendix A, accurately reflects the study’s focus on the intersection of GTA, GIA, CE, FC, and digital economy, ensuring reliable measurement of the targeted constructs.

2.3.5. Demographic Characteristics

Table 2. Descriptive statistics of the respondents.

Demographics	Classifications	Frequency	Percentage
Gender	Male	275	68.75%
	Female	125	31.25%
	18–25	95	23.75%
Age	26–35	102	25.5%
	36–45	151	37.75%
	Over 45	52	13%
	Undergraduate	135	33.75%
Education	Masters	212	53%
	Above Maters	53	13.25%
Positions	Upper Management	85	21.25%
	Middle Management	133	33.25%
	Lower Management	182	45.5%
Experience	1–5 Years	181	45.25%
	6–10 Years	132	33%
	11 Years and above	87	21.75%
Total samples		400	100%

presents the demographic details of the study participants. Among the respondents, 68.75% were male, and 31.25% were female. Participants ranged from 18 to over 50 years, offering a broad representation across various age groups. Regarding educational attainment, most respondents (53%) held a master’s degree, 33.75% had completed undergraduate studies, and 13.25% possessed advanced degrees. Data were collected from management staff, with 45.5% of the contributions coming from lower management, 33.25% from middle management, and 21.25% from upper management. Regarding professional experience, 45.25% of participants reported having 1–5 years of experience, 33% had 6–10 years, and 21.75% possessed 11 or more years of experience, demonstrating a wide range of expertise in the automotive industry. These demographics suggest a broad representation of younger and more experienced individuals with varying educational backgrounds, contributing to the richness and reliability of the data collected.

2.4. Data Analysis

2.4.1. Common Method Bias

The risk of common method bias (CMB) is a notable concern when data are gathered through a cross-sectional design from a single source. The research utilized two established techniques to address this issue: a full collinearity assessment test [61] and Harman’s single-factor test [62]. These methods are widely endorsed in social sciences for managing CMB [63]. The findings revealed that all Variance Inflation Factor (VIF) values were below the critical threshold 5, indicating no significant multicollinearity. Additionally, Harman’s single-factor test showed that the leading factor explained only 40.9% of the total variance, which is below the accepted limit of 50%. Consequently, it can be concluded that common method bias (CMB) does not pose a significant threat to the integrity of the research model.

2.4.2. Measurement Model Assessment

The first step in SEM analysis involves assessing the outer model, or measurement model, to determine the relationship between items (questions) and their associated conceptual constructs. This step focuses on the one-way predictive relationships between latent constructs and their indicators [64]. Becker et al. (2012) [65] outlines two key approaches for measuring indicators in PLS-SEM: reflective and formative outer models. For reflective models, the assessment includes indicator reliability, latent variable reliability, internal consistency (evaluated using Cronbach’s alpha and composite reliability), construct validity (examined via loadings and cross-loadings), convergent validity (calculated by average variance extracted, AVE), and discriminant validity (using the Fornell–Larcker criterion, cross-loadings, and the HTMT method) [66]. These evaluations confirm that the indicators accurately represent the constructs and ensure the model’s robustness for further analysis.

2.4.3. Indicator Reliability

In PLS-SEM (Partial Least Squares–Structural Equation Modeling), indicator reliability assesses the extent to which an individual measurement item (indicator) reflects the latent variable it is supposed to measure. This is quantified by outer loadings, which range between 0 and 1. A higher loading indicates that the indicator is strongly associated with its latent construct [67]. According to [68], outer loadings should ideally be above 0.70. Indicators with loadings between 0.40 and 0.70 may be considered for removal if this improves composite reliability and average variance extracted (AVE). Indicators with loadings below 0.40 should be excluded to enhance the model’s overall accuracy [66]. Beyond indicator reliability, it is also essential to assess multicollinearity, which occurs when indicators within a model are too highly correlated, leading to redundancy and potentially distorting the results. This is measured using the Variance Inflation Factor (VIF). A VIF value above 5 suggests problematic multicollinearity, indicating that an indicator shares too much variance with other indicators in the model. Ideally, VIF values should be below 3, ensuring that the indicators contribute unique information rather than redundant signals [69]. As detailed in

Table 3. Outer loadings and variance inflation factors.

Constructs	Items	Outer Loadings	VIF
Creative Enterprise	CE1	0.759	1.499
	CE2	0.774	1.592
	CE3	0.785	1.554
	CE4	0.820	1.704
Digital Economy	DE1	0.873	2.536
	DE2	0.779	1.714
	DE3	0.674	1.366
	DE4	0.843	1.972
Financial Capability	FC1	0.845	1.968
	FC2	0.823	2.068
	FC3	0.819	2.015
	FC4	0.833	1.892
Green Innovation Adoption	GIA1	0.785	1.632
	GIA2	0.797	1.641
	GIA3	0.802	1.642
	GIA4	0.785	1.554
Green Technology Adoption	GTA1	0.793	1.672
	GTA2	0.817	1.752
	GTA3	0.820	1.784
	GTA4	0.773	1.536

and Figure 2, all outer loading values exceed 0.70, validating the reliability and relevance of the indicators in the model.

2.4.4. Internal Consistency and Composite Reliability

Internal consistency is measured using two key methods: Cronbach’s alpha (CA) and composite reliability (see

Table 4. Reliability tests.

Constructs	Cronbach’s Alpha	Composite Reliability (CR)	Average Variance Extracted (AVE)
Creative Enterprise	0.792	0.865	0.616
Digital Economy	0.806	0.872	0.633
Financial Capability	0.851	0.899	0.689
Green Innovation Adoption	0.802	0.871	0.628
Green Technology Adoption	0.813	0.877	0.641

). These metrics assess the reliability of a construct by examining the relationships among observed variables. As noted by [70], these values generally range from 0 to 1, with higher values indicating greater reliability. For exploratory research, Cronbach’s alpha and composite reliability values between 0.60 and 0.70 are deemed acceptable, while values exceeding 0.70 are recommended for more advanced stages of analysis. However, values above 0.90, especially those nearing 0.95, may indicate redundancy issues [66]. In this study, Cronbach’s alpha values range from 0.60 to 0.85, confirming satisfactory internal consistency. In practical terms, these reliability measures ensure that the survey or measurement tool produces consistent and dependable results. In this study, Cronbach’s alpha values range from 0.60 to 0.85, confirming satisfactory internal consistency, meaning that the constructs are measured reliably without excessive overlap or redundancy.

2.4.5. Average Variance Extracted (AVE)

Convergent validity evaluates the extent of correlation among multiple indicators representing the same construct. It is established using metrics such as factor loadings, composite reliability (CR), and average variance extracted (AVE) [66]. The AVE ranges from 0 to 1, with a threshold of 0.50 or higher indicating sufficient convergent validity. A higher AVE signifies that the construct accounts for a substantial portion of the variance in the indicators, reinforcing the model’s validity (see Table 4). This means that the measured indicators effectively represent the intended construct, ensuring that the model captures meaningful relationships rather than random noise.

2.5. Discriminant Validity

2.5.1. Heterotrait–Monotrait (HTMT) Ratio

Discriminant validity assesses whether constructs in a model are truly distinct. One approach to evaluating discriminant validity is the Heterotrait–Monotrait (HTMT) ratio, which compares the correlation between constructs to a threshold. When the HTMT value exceeds 0.85, it suggests potential issues with multicollinearity and a lack of discriminant validity [71]. Researchers typically recommend a threshold of 0.85, though some advocate for a more stringent cutoff of 0.90 [72]. In this study, the HTMT ratios for each pair of constructs were statistically significant, with the highest observed value being 0.882. This value is below the 0.90 threshold, indicating that discriminant validity was maintained in the model. Maintaining discriminant validity ensures that each construct in the model measures a unique concept rather than overlapping with others. This study confirms that the constructs are sufficiently distinct.

Table 5. Analysis of discriminant validity (Heterotrait–Monotrait).

Constructs	CE	DE	FC	GIA	GTA
CE
DE	0.762
FC	0.713	0.528
GIA	0.882	0.736	0.669
GTA	0.846	0.833	0.562	0.874

CE: creative enterprise; DE: digital economy; FC: financial capability; GIA: green innovation adoption; GTA: green technology adoption.

presents the results, demonstrating that the constructs are sufficiently distinct.

2.5.2. Fornell and Larcker Criterion

Discriminant validity was evaluated using the Fornell–Larcker criterion by comparing each construct’s square root of the Average Variance Extracted (AVE) with its correlations to other constructs. As per [73], a construct should explain more variance in its indicators than in other constructs. Franke and Sarstedt (2019) [71] emphasized that the square root of the AVE for each construct must be greater than its correlations with other latent constructs to establish discriminant validity. The results in

Table 6. Analysis of discriminant validity (Fornell and Larcker criterion).

Constructs	CE	DE	FC	GIA	GTA
CE	0.785
DE	0.62	0.796
FC	0.588	0.448	0.830
GIA	0.705	0.61	0.56	0.792
GTA	0.68	0.703	0.471	0.707	0.801

CE: creative enterprise; DE: digital economy; FC: financial capability; GIA: green innovation adoption; GTA: green technology adoption.

confirm that all constructs in this study satisfy the Fornell–Larcker criterion, indicating clear discriminant validity. This ensures that each construct measures a distinct concept rather than overlapping with others. The results in Table 6 confirm that all constructs satisfy the Fornell–Larcker criterion, ensuring clear discriminant validity and reinforcing the model’s robustness.

2.5.3. Assessment of Structural Model

The structural model was evaluated after the satisfactory results from the measurement model. The model’s predictive accuracy was assessed by examining the explained variance, revealing that it accounts for 56.4% of the variation in creative enterprise and 55.4% of the variation in the DE. Barroso and Cepeda-carrion (2010) [74] emphasized the importance of predictive relevance as an additional model fit measure alongside R². To evaluate this, a Stone–Geisser Q² value was computed using a PLS blindfolding technique, which assesses the model’s predictive power for each latent construct’s manifest indicators [75]. Chin et al. (2020) [76] states that a Q² value greater than zero indicates predictive relevance. As shown in

Table 7. Model-predictive-power metrics.

Constructs	R Square	Q Square
Creative Enterprise	0.564	0.554
Digital Economy	0.554	0.516

, the Q² values of 0.554 and 0.516 are significantly above zero, confirming the model’s strong predictive relevance (see Table 7).

The structural model’s path significance was assessed using nonparametric bootstrapping with 5000 replications, revealing significant paths that supported the proposed hypotheses. Additionally, f-Square was used to evaluate the change in R² when an exogenous variable was removed from the model. A study [77] defined f-Square as an effect size indicator, with values greater than or equal to 0.02 considered small, 0.15 or greater as medium, and 0.35 or higher as large. As seen in

Table 8. Effect size.

F Square
CE -> DE	0.043
GIA -> CE	0.231
GIA -> DE	0.014
GTA -> CE	0.151
GTA -> CE	0.216

CE: creative enterprise; DE: digital economy; GIA: green innovation adoption; GTA: green technology adoption.

, the f-square values for the model fall within these effect size categories, further validating the structural model’s robustness and explanatory power.

As the Standardized Root Mean Square Residual (SRMR) indicates, the estimated model shows an adequate fit. With an SRMR value of 0.07, below the commonly accepted threshold of 0.08, the model meets the criteria for a good fit, as [78] recommended. Furthermore, the Root Mean Square Error of Approximation (RMSEA) of 0.077 also falls within the acceptable range (<0.08), reinforcing that the model has a reasonable approximation to the population data. The Tucker–Lewis Index [79] of 0.906 and the Comparative Fit Index (CFI) of 0.908 both exceed the 0.90 threshold, supporting the overall acceptability of the model (

Table 9. Goodness of fit.

Goodness-of-Fit Criteria	Structured Model	Recommended Value
RMSEA	0.077	<0.08
SRMR	0.054	<0.08
TLI	0.906	>0.90
CFI	0.908	>0.90

).

These results indicate that the model effectively explains relationships between variables, provides meaningful predictions, and fits the data well, making it a strong framework for further analysis.

2.5.4. Hypotheses Testing

The outputs of the structural model, presented in

Table 10. Path coefficient and hypothesis testing.

Relationship	Hypotheses	Beta Values (β)	STD	T Statistics	p Values	Remarks
GIA -> DE	H1	0.128	0.063	2.048	0.041	Supported
GTA -> DE	H2	0.471	0.055	8.608	0.000	Supported
GIA -> CE	H3	0.449	0.06	7.469	0.000	Supported
GTA -> CE	H4	0.363	0.064	5.632	0.000	Supported
CE -> DE	H5	0.224	0.062	3.640	0.000	Supported

CE: creative enterprise; DE: digital economy; GIA: green innovation adoption; GTA: green technology adoption.

and Figure 2, demonstrate positive relationships among the variables. The research findings confirm that Green Innovation Adoption (GIA) significantly and positively affects the digital economy (DE). Specifically, the beta value of 0.128, standard deviation (STD) of 0.063, T-statistic of 2.048, and p-value of 0.041 provide strong statistical evidence for accepting Hypothesis 1 (H1). Additionally, the results indicate that green technology adoption (GTA) significantly impacts the DE. The beta value of 0.471, STD of 0.055, T-statistic of 8.608, and p-value of 0.000 provide conclusive evidence supporting Hypothesis 2 (H2). This suggests that when companies adopt eco-friendly technologies and innovations, they experience a measurable boost in their digital growth and overall business performance.

Furthermore, the research reveals that GIA positively influences creative enterprise, as evidenced by the beta value of 0.449, STD of 0.060, T-statistic of 7.469, and p-value less than 0.001. These results strongly validate Hypothesis 3 (H3). Similarly, GTA is found to have a positive impact on the creative enterprise, with a beta value of 0.363, T-statistic of 5.632, STD of 0.064, and p-value of 0.000, supporting Hypothesis 4 (H4). Finally, this study demonstrates that creative enterprise positively and significantly influences the DE, as reflected by the beta value of 0.224, STD of 0.062, T-statistic of 3.640, and p-value of 0.000. This finding strongly supports Hypothesis 5 (H5).

Additionally, the positive relationship between creative enterprise and the digital economy suggests that fostering innovation-driven businesses can further accelerate digital growth.

2.5.5. Mediation and Moderation Tests

The analysis presented in

Table 11. Mediation and moderation test.

Relationship	Hypotheses	Beta Values(β)	(STD)	T Statistics	BCI		p Values
					2.5%	97.5%
GIA -> CE -> DE	H6a	0.101	0.031	3.211	0.035	0.145	0.001
GTA -> CE -> DE	H6b	0.081	0.028	2.955	0.035	0.145	0.003
FC ×CE -> DE	H7	0.131	0.037	3.555	0.060	0.206	0.000

CE: creative enterprise; DE: digital economy; FC: financial capability; GIA: green innovation adoption; GTA: green technology adoption.

highlights that creative enterprise positively mediates the relationship between green innovation adoption (GIA) and the digital economy (DE). The findings are supported by significant beta values of 0.101, a standard deviation (STD) of 0.031, a t-statistic of 3.211, and a p-value of 0.001, which strongly validates Hypothesis 6a (H6a). Additionally, this study confirms that creative enterprise also mediates the relationship between green technology adoption (GTA) and the DE, as demonstrated by the beta value of 0.081, STD of 0.028, a t-statistic of 2.954, and a p-value of 0.003, providing substantial evidence for Hypothesis 6b (H6b).

Furthermore, Hypothesis 7 (H7) posits that financial capability significantly moderates the relationship between creative enterprises and the DE. The results support this hypothesis robustly, with a beta value of 0.131, a standard deviation of 0.037, a t-statistic of 3.555, and a p-value of 0.000, offering strong empirical evidence favoring H7.

This means that businesses with stronger financial resources are better positioned to leverage creative enterprises for digital transformation, further amplifying their impact.

The moderation analysis in Figure 3 reveals that FC significantly moderates the relationship between CE and DE. At low levels of FC, the effect of CE on DE is weaker, indicating limited financial resources hinder the ability of creative enterprises to drive economic contributions. In contrast, at high levels of FC, the impact of CE on DE is much stronger, demonstrating that sufficient financial resources amplify the positive influence of creative and innovative enterprises on the digital economy. This highlights the critical role of financial capability in enhancing the potential of green innovation adoption and creative enterprise activities in fostering digital economic growth.

3. Discussion

This study explores the dynamic role of green innovation adoption (GIA) and green technology adoption (GTA) within the digital economy (DE), focusing on how creative enterprise and financial capability mediate and moderate the implementation of environmentally friendly innovation and technology. Grounded in Dynamic Capability Theory (DCT), which emphasizes a firm’s ability to sense opportunities, seize them, and reconfigure resources to adapt to environmental and technological changes, the research investigates how businesses can strategically address sustainability challenges in the evolving digital landscape. By examining the interconnectedness of green practices, creativity, and financial resources, this study aims to provide insights into how firms can leverage their dynamic capabilities to drive environmental sustainability and economic growth. The hypotheses proposed were empirically validated, with significant results showing how these capabilities enhance businesses’ ability to innovate, adapt, and thrive in the digital era.

The findings of this study indicate that green innovation adoption (GIA) has a positive and significant impact on the digital economy (DE). This aligns with the work of [80], which emphasizes the importance of integrating environmentally sustainable technology to drive economic progress within the digital environment. Similarly, Hashem and Aboelmaged (2023) [81] organizations adopting environmentally responsible technologies showed superior performance in DE, further reinforcing that sustainability practices play a crucial role in the modern, technology-driven landscape. The increasing importance of sustainable practices in the digital age underscores their contribution to innovation and economic growth. These results deepen our understanding of the complex relationship between the adoption of green innovation and the successful transformation of businesses within the DE, highlighting how sustainability drives both competitive advantage and long-term performance.

The results also reveal that green technology adoption (GTA) significantly impacts the digital economy (DE). These findings align with previous research, such as the work by [82], highlighting eco-friendly technologies’ transformative potential across various sectors. Similarly, the research conducted by [83] emphasized the role of sustainable practices in driving digital advancements, establishing a favorable correlation between adopting environmentally friendly technology and economic development. Furthermore, Sahoo and Kumar (2023) [84] underscored the importance of integrating green technology for achieving long-term economic and environmental sustainability, advocating for governments and businesses to prioritize such technologies. Building on these prior studies, our research reinforces the argument for incorporating environmentally responsible technologies into the digital realm, thereby contributing to both the academic literature and the practical understanding of sustainable digital innovation.

The findings of this study demonstrate that green innovation adoption (GIA) has a positive and significant impact on creative enterprises. This result is consistent with existing literature, which emphasizes the catalytic role of environmental innovation in fostering creativity and promoting sustainable business practices, as evidenced by the work of [85]. Additionally, it aligns with empirical studies, such as those by [86], which shows the positive impact of green innovation on organizational performance, particularly in enhancing environmental sustainability and internal innovative practices. The results highlight how green innovation stimulates creative thinking within organizations, driving the development of new ideas and solutions that contribute to environmental goals and improve long-term sustainability and operational efficiency. This underscores the potential of GIA to serve as a key driver of innovation and sustainability in modern business environments.

Based on the available evidence, green technology adoption (GTA) positively and significantly impacts creative enterprises. The results of [87] highlight the significant role of incorporating environmentally sustainable technology in fostering innovation and creativity within organizations. These findings are consistent with the present study, demonstrating the beneficial effects of green technology adoption on organizational creativity. Moreover, the favorable correlation between green technology adoption and creativity, as highlighted by [88], aligns with the broader body of research on this topic. The results indicate that using green technology leads to positive environmental outcomes and acts as a catalyst for driving innovative initiatives within organizations. This underscores the dynamic relationship between sustainability, innovation, and organizational creativity, positioning GTA as a key driver in the evolving landscape of sustainable business practices and innovation.

The findings reveal that creative enterprises significantly and positively influence the digital economy (DE). This is consistent with earlier studies, such as those by [89], which highlight the crucial role of creative activities in promoting digital economic growth. Similarly, Zhou et al. (2022) [90] emphasizes the strong connection between creative entrepreneurship and digital economic success, suggesting that creative efforts play a key role in fostering economic development in the digital era. To provide a more comprehensive understanding, this study acknowledges that CE is not a unidimensional construct but rather consists of multiple interrelated aspects, including entrepreneurial creativity, technological adaptability, and innovation capacity. These dimensions enable organizations to navigate digital transformation, implement green innovations, and enhance market responsiveness. While our study measured CE through four key items, we recognize the need for more refined, multi-dimensional scales that capture its dynamic and evolving nature. The positive impact observed in this study underscores the importance of encouraging creativity within organizations as a strategic method for adapting to and thriving in the ever-evolving DE. This highlights the value of creative enterprises in establishing a competitive edge, helping organizations navigate the complexities of the digital world, and driving sustained economic growth.

The findings of this study show that creative enterprise has a positive and significant impact on the digital economy (DE). Previous research, such as the study by [91], has demonstrated that creativity plays a crucial role in developing novel digital technologies and driving economic growth. This aligns with earlier studies by [92], highlighting the positive relationship between creative entrepreneurship and DE. The confirmed positive impact further strengthens the argument that creative pursuits are integral to economic expansion in the digital era. It underscores the importance of nurturing creative processes within organizations as a key strategy for achieving success and maintaining competitiveness in the rapidly evolving digital landscape.

The results indicate that creative enterprise mediates the relationship between green innovation adoption (GIA) and the digital economy (DE). This aligns with previous research, such as the study by [93], which emphasizes the significant role of creative processes in converting sustainable practices into economic advantages. Additionally, it supports the existing body of work on innovation and sustainability, as demonstrated by [94], highlighting innovative enterprises’ crucial role in linking green innovation to positive economic outcomes. These findings strengthen the argument that creative enterprises serve as intermediaries, facilitating the positive impacts of green innovation on digital economic outcomes. The mediating role of creative business practices has been empirically validated, offering a deeper understanding of the complex dynamics between green innovation adoption and DE. This underscores the essential function of creativity in bridging the gap between sustainability and economic prosperity in the digital era.

The study’s findings indicate that creative enterprise positively mediates the relationship between green technology adoption (GTA) and the digital economy (DE). This aligns with existing scholarly research, such as the investigation by [23], which emphasizes the intermediary function of creative processes in driving the positive outcomes of incorporating green technology to advance the DE. Similarly, the results align with the [95] study, which underscores the positive correlation between creative entrepreneurship and DE. The confirmed significance of creative entrepreneurship as an intermediary between the adoption of green technology and the DE adds a layer of complexity to this relationship, highlighting the influential role of creativity in linking sustainable practices with economic prosperity. These findings reinforce the idea that creative enterprises are essential for fostering the transition to a more sustainable and economically robust digital economy.

The findings indicate that financing capability significantly moderates the relationship between creative enterprises and the digital economy (DE). This conclusion aligns with previous research by [96], highlighting the importance of financial capabilities in determining the impact of creative endeavors on digital economic outcomes. Additionally, this aligns with the study by [21], which emphasized financial capabilities’ influential role in shaping corporate strategies and broader economic dynamics. The moderating influence of financing capabilities further enriches our understanding of how financial resources can impact the performance of creative enterprises within the DE. These findings underscore the crucial role that financial support plays in fostering the success of creative activities, ultimately driving economic growth and innovation in the digital era.

4. Theoretical and Practical Implications of the Study

4.1. Theoretical Implications

This study enriches the existing body of knowledge by illustrating the synergies between sustainable practices, innovation adoption, and economic growth in the DE. The findings underscore the pivotal role of green innovation in promoting economic prosperity through environmentally sustainable practices. Similarly, the transformative potential of adopting sustainable technologies to foster creativity within organizations aligns with this study’s findings on green technology’s positive impact on enterprise creativity.

As revealed in this study, the mediating role of creative enterprises contributes to a more nuanced understanding of how green innovation drives economic outcomes ascertaining that innovation serves as a bridge between sustainability and economic growth. Furthermore, the moderating role of Financial Capability introduces new dimensions to theoretical frameworks, emphasizing its significance in amplifying the impact of creative endeavors on economic outcomes.

This study contributes to multiple theoretical domains, including environmental sustainability, innovation adoption, and the DE. It underscores the necessity of incorporating creative enterprises into theoretical models that explore the green-digital nexus, particularly given the role of financial capacity in enhancing this relationship. Integrating these perspectives deepens our understanding of how businesses can navigate the complexities of sustainability and digital transformation.

4.2. Practical Implications

From a practical standpoint, the findings offer actionable insights for organizations striving to balance sustainability with economic growth in the digital era. The demonstrated positive impact of GIA and GTA on the DE highlights the importance of prioritizing environmentally sustainable practices and technological advancements. This entails integrating green innovation as a core strategy to enhance creativity and organizational performance for businesses.

The mediating role of creative enterprises suggests that fostering innovation within organizations can amplify the benefits of green practices, enabling businesses to achieve greater economic and environmental outcomes. Furthermore, the moderating role of financial capability underscores the necessity of robust financial strategies to support creative processes. Organizations are encouraged to allocate financial resources effectively to strengthen the link between creativity and digital economic success.

These insights are particularly valuable for enterprises operating in digital commerce, where sustainability challenges and opportunities coexist. Businesses can optimize their operations by leveraging green innovation, creative thinking, and financial resources while contributing to broader environmental and economic objectives.

5. Conclusions

This study sheds light on the dynamic roles of green innovation adoption (GIA) and green technology adoption (GTA) in the digital economy (DE), emphasizing the mediating influence of creative enterprises and the moderating role of financial capability. The findings highlight GIA’s positive and significant effects on both the DE and creative enterprises, reinforcing the value of integrating sustainable practices within the digital business landscape. Prior studies, such as [97], underscore the catalytic role of creativity in transforming sustainable practices into measurable economic benefits, a notion further supported by this research. Creative enterprises act as intermediaries, enhancing the impact of GIA on the DE, as proposed by [98]. This underscores the importance of fostering innovation within organizations to maximize the benefits of environmentally friendly initiatives on economic outcomes. While GIA demonstrated significant effects, the results suggest that GTA has an insignificant direct impact on the DE, as evidenced by the lack of statistical significance. This finding calls for a deeper and more nuanced exploration of the mechanisms by which the adoption of environmentally sustainable technology influences the DE. Future research should aim to unravel these complex relationships, furthering our understanding of how green technologies can contribute to digital economic growth.

The study also highlights the critical role of financial capability in moderating the relationship between creative enterprises and the DE. Organizations operating in the DE must strategically allocate financial resources to amplify the impact of creative initiatives on long-term economic development. By addressing this interplay between financial capability, creativity, and sustainability, businesses can navigate the challenges of the digital era while maintaining their commitment to environmental stewardship.

In conclusion, this study significantly contributes to understanding how creative and environmentally sustainable business practices can drive success in the digital age. Organizations can achieve economic growth by integrating innovation, sustainability, and strategic resource management while addressing global environmental challenges. Future research should explore additional dimensions of this interplay to provide a more comprehensive understanding of sustainable business practices in the DE.

6. Limitations and Future Study

This study acknowledges several limitations that warrant careful consideration when interpreting its findings. Firstly, the research relies on a cross-sectional questionnaire survey to test and confirm the hypotheses. While this approach provides valuable insights, it limits the ability to infer causation among the variables. The evolving dynamics of Green Innovation Adoption (GIA) and Green Technology Adoption (GTA), particularly across different stages of growth in the creative industry in China, are not fully captured. To address this limitation, future research should adopt a longitudinal approach to examine these relationships over time, offering a more comprehensive understanding of how these factors interact and evolve. Additionally, external factors, like government policies and market competition, could influence the relationship between green innovation adoption (GIA) and the digital economy (DE), yet they were not considered in the model. Future studies could employ instrumental variable techniques or control variables to account for unobserved factors like government regulations, industry dynamics, and market competition. Secondly, this study’s sample is confined to automotive industries in China. This focus, while insightful, limits the generalizability of the findings to other industries and geographical contexts. Organizations seeking to apply these results should be mindful of potential variations in factors affecting outcomes and performance in different sectors. Technology maturity levels, decision-making processes, and adoption strategies are likely to vary across nations and industries. Future studies should expand the research model to include a broader spectrum of businesses and regions to overcome this limitation. Conducting similar research in diverse national and industrial contexts will facilitate comparative analyses and provide richer insights. By exploring these dynamics across a broader range of organizations, researchers can uncover industry-specific and region-specific factors that influence the impact of GIA and GTA on creative enterprises and the digital economy. Finally, replicating this study in different countries and industries would enhance its applicability and provide a more robust understanding of the interplay between environmental sustainability, innovation, and economic growth. Such efforts would contribute to developing universally applicable frameworks and strategies for fostering green practices and innovation in the global digital economy.