Breaking Down the Barriers to Innovation Quality: The Impact of Digital Transformation

Abstract

While the influence of digital technologies on firms’ innovation performance has been examined in the digital transformation literature, the mechanism by which digital transformation affects innovation quality has remained largely unexplored. By analyzing a longitudinal sample of 17,216 China’s A-share listed companies from 2009 to 2021 (excluding real estate and financial firms), we employed a fixed-effects regression model to investigate the impact of digital transformation on strategic risk-taking behavior. The findings indicate that digital transformation significantly enhances innovation quality. Market competition enhances the positive effect of digital transformation on innovation quality. Further analysis reveals that digital transformation has a positive impact on dynamic capability, which in turn mediates the relationship between digital transformation and innovation quality. Furthermore, digital transformation breaks down the barriers to innovation quality by reducing financing costs and financing constraints. These findings have implications for firms’ digital strategy in emerging economies.

1. Introduction

Is digital transformation significant? The evolution of the digital economy has led to continuous enhancements in digital infrastructure and platforms, which have impacted companies’ innovation endeavors [1]. Through digital transformation, companies can alleviate financial constraints and enhance corporate governance, thereby overcoming barriers to innovation [2]. Technology plays a critical role in influencing innovation quality, and as digital technology becomes increasingly pervasive within firms [3], leveraging the advantages offered by the digital economy to enhance innovation quality is becoming an imperative issue to address.

Digital transformation commonly refers to the transformative or disruptive influence of digital technology on businesses in the business world [1]. It can potentially enhance firms’ absorptive and transformative capacities and resolve the innovation dilemma characterized by increased R&D investment without a corresponding increase in total factor productivity [4]. Facilitating innovation is the primary avenue through which digital transformation enhances business performance, enabling firms to significantly spur innovation momentum, reduce costs, and enhance efficiency by leveraging digital technologies through learning and application [5]. While it is recognized that digital transformation influences innovation activities, the specific pathways of its impact constitute a complex system that remains incompletely studied.

Numerous studies have examined the economic impact of digital transformation, focusing on firms’ innovation performance [6] and the number of innovations they produce. However, less attention has been paid to its effect on innovation quality. Traditional measures of innovation performance, often assessed through financial metrics, fail to fully capture the qualitative dimensions of innovation. While recent studies have explored the heterogeneous influences of political connections on innovation quantity and quality, as well as the role of factors such as customer knowledge management [6] and cross-border knowledge management [7] in shaping innovation quality, the unique and transformative nature of the digital economy necessitates a focused examination of how digital transformation influences firms’ innovation quality. This study, therefore, addresses a critical gap by investigating the relationship between digital transformation and innovation quality, thereby contributing to a more comprehensive understanding of the impact of digitalization on firms’ innovation capabilities and innovation quality.

However, current research suggests that the influence of digital transformation on innovation quality is not a straightforward process. Understanding the underlying mechanisms that illustrate how digital transformation contributes to innovation quality is essential for drawing comprehensive cause-and-effect results regarding their relationship. The intermediary factors linking digital transformation to innovation quality remain unclear. Digital empowerment has the potential to transcend spatial, social, and technological constraints, reducing the resource barriers to implementing innovation and facilitating the aggregation of knowledge, products, services, and technical resources across various industries, as well as influencing firms’ dynamic capabilities. This study addresses this theoretical gap by proposing an argument through which digital transformation influences innovation quality.

Through the lens of dynamic capability, the continual changes brought about by digital technology reshape firms’ competitive advantages. Only by possessing the ability to integrate and restructure internal and external resources and by swiftly adapting to evolving environments can firms transcend existing path dependencies and market positions to achieve sustainable competitive advantages [8]. Firms leverage digital technologies like big data to enhance dynamic capabilities, improve supply chain management, drive business process transformations, and generate value [9]. A study also indicates that digital transformation cultivates a stronger demand for technology, which enhances a firm’s innovation capabilities and strengthens its ability to withstand risks [10]. Artificial intelligence can eliminate information barriers, optimize the efficiency of information exchange, lay a solid foundation for technological progress, and ultimately reshape the innovation model [11]. Dynamic capabilities enable firms to stay attuned to changes in their environments and to flexibly mobilize and allocate resources to address various challenges [12].

This study focuses on the scope of digital transformation’s impact on innovation quality outcomes. Building upon Cannas’ research [13], we explore the key connections within dynamic capabilities, including absorptive capability, adaptive capability, and innovative ability, to elucidate how digital transformation influences innovation quality. Moreover, this paper examines the moderating roles of market competition and highlights the boundary conditions that influence the main effect. Our research supplements prior studies on digital transformation outcomes, particularly those centered on managerial discretion [14] and knowledge management capability [15].

This study makes three key contributions to understanding the impact of digital transformation on innovation quality. First, this study expands the literature on the driving factors of innovation quality by identifying the positive impact of digital transformation, offering new insights into the theoretical development of digital transformation [16]. Second, the paper systematically investigates the moderating roles of market competition on digital transformation and innovation quality and provides a new perspective on understanding how market competition in the external environment affects the effectiveness of digital transformation strategies [15]. Third, the findings demonstrate that digital transformation enhances innovation quality through dynamic capabilities and enrich the research on pathways in which digital transformation affects innovation quality [13]. Furthermore, the study reveals that financing constraints and equity financing costs mediate the impact of digital transformation on innovation quality. Digital transformation helps lower financing costs, alleviate constraints, and enhance dynamic adaptability, ultimately improving innovation quality. These findings build on prior research, highlighting how digital technology overcomes resource limitations to drive high-quality innovation output.

The rest of this study is organized as follows: Section 2 presents a literature review and hypothesis development. Section 3 outlines the data and methods. Section 4 details the results. Section 5 depicts the discussion. Section 6 provides implications, limitations, and suggestions for future research.

2. Literature Review and Hypothesis Development

2.1. Literature Review

2.1.1. Digital Transformation

Vial et al. (2019) define digital transformation as a process aimed at enhancing an entity by instigating significant changes across its attributes through the integration of information, computing, communication, and connectivity technologies [17]. This transformative process entails organizations reshaping their value creation processes by leveraging digital technologies in response to evolving environmental dynamics. It is not merely about adopting individual digital tools but rather about employing a combination of digital technologies to drive value creation. For instance, the utilization of big data can empower firms to enhance decision-making, innovate products and services, and generate greater social and economic value [18]. Digital transformation enables firms to generate innovative ideas, identify new competitive advantages in the market, and improve overall business performance and market competitiveness.

Digital transformation represents a significant organizational shift that involves stakeholders, customers, and business operations. Through digitalization, firms can fully leverage the advantages offered by new technologies, reshaping how they create, deliver, and capture value and drive business model innovation [19]. Particularly, the application of digital technology in supply chain management has seen widespread and deepening adoption in recent years. This technology infusion enhances the stability, agility, and connectivity of production processes, enabling firms to better navigate risks and capitalize on opportunities [20]. Moreover, increased corporate investment in information and communication technology, coupled with employee adoption of technology, fuels the expansion of a technology market driven by digital transformation [21].

Digital transformation enhances productivity by streamlining production and operational processes, leading to cost reductions and increased resource efficiency [22]. Technologies like big data and cloud computing enhance information transparency in IT systems and mitigate the transaction costs associated with information asymmetry. Moreover, digitization reduces expenses associated with search, shipping, tracking, and validation. The replication and dissemination of knowledge, information, and experiences at minimal cost, facilitated by digital technology, significantly curtail production and management costs, and this, in turn, leads to substantial savings [22]. Furthermore, digitalization enables customized production, precise marketing efforts, and the development of intelligent products and services, ultimately driving revenue growth and bolstering overall business performance.

2.1.2. Innovation Quality

Most research on innovation quality has traditionally approached the topic from the perspective of the quality and integration of innovation. Haner (2002) was among the pioneers in proposing a conceptual framework for innovation quality, defining it as the assessment of innovation performance across various domains encompassing the new technologies, products, and services offered by a firm [23]. This framework delineates innovation quality through dimensions such as quantity, performance, efficiency, and value to customers. Innovation quality signifies the tangible impact resulting from innovation outputs and serves as an indicator of a firm’s innovation capability. The influence of innovation outputs is chiefly manifested in the frequency of citations received, emphasizing the significance of the real-world impact [24].

When viewed through the perspective of quality management theory, innovation quality represents the market adaptability of new products, technologies, and processes developed by firms utilizing diverse innovation resources, along with stakeholders’ satisfaction levels with the resulting innovations. This perspective captures both the process and the outcomes of firms’ innovation activities, functioning as a key indicator to evaluate the effectiveness of their innovation efforts [25]. Despite extensive research on innovation performance, relatively little attention has been directed toward exploring the influence of innovation quality. Furthermore, conceptual ambiguity persists between innovation quality and innovation performance, underscoring the need for further clarification in scholarly work. Innovation performance is defined by the efficient allocation of resources dedicated to innovation, reflecting the benefits that firms gain from adopting new technologies or processes. In contrast, innovation quality refers to the impact created by a firm’s innovation outputs, evaluating the overall excellence of its innovation efforts. Although innovation activities can impact firm performance, changes in performance within a certain range do not necessarily lead to changes in innovation quality [26].

Studies have examined the impact of different types of technology on innovation quality. Firms engage in external environment scanning to identify diverse technologies and knowledge, fostering open innovation and enhancing innovation quality [25]. Digital technology plays a critical role in influencing innovation quality. For instance, blockchain technology is essential for firms to reduce costs, enhance operational efficiency, and promote innovation quality [14]. Digital platforms enable firms to promote knowledge sharing, optimize internal and external resource allocation networks, and enhance their innovation performance [27].

2.2. Hypothesis Development

2.2.1. Digital Transformation and Innovation Quality

In the Industry 4.0 era, digital technologies drive firms’ new product development (NPD) and innovation by integrating diverse technologies and creating new business models [28]. As competition intensifies, an increasing number of companies are embracing digital transformation to sustain their competitiveness [29]. Digital technologies enhance innovation by fundamentally transforming the way firms organize their operations, manage their resources, and execute research and development activities. This transformation enables more efficient collaboration, faster prototyping, and the integration of advanced analytics, thereby driving higher-quality and more impactful innovations [30]. The impact of digital transformation on innovation quality is examined through three key aspects.

First, digital transformation promotes innovation quality by enhancing the heterogeneity of innovation resources, facilitating distributed innovation, and creating differentiated innovation at the organization’s periphery [31]. Through digital transformation, firms have accumulated substantial technical experience and capabilities, enabling them to anticipate future trends and identify new opportunities in dynamic and competitive markets. Digital transformation enhances innovation quality by enabling precise product and service positioning. Through the integration of advanced analytics and real-time data, digital platforms can forecast production information and consumer preference trends [32]. This capability enables firms to tailor their offerings more effectively, thereby enhancing customer satisfaction and loyalty. Additionally, by leveraging AI-driven predictive analytics, companies can quickly adapt to market changes and emerging consumer demands, thereby driving higher-quality innovation.

Second, by facilitating knowledge sharing and cross-boundary collaboration, digital technologies enhance firms’ innovation capabilities and quality, enabling them to gather valuable insights from diverse stakeholders and integrate these into their innovation processes. Firms can enrich and update their existing knowledge base, overcome limitations, and better cope with the complex issues in technological innovation by acquiring and integrating external technological knowledge [33]. In an increasingly open and volatile market environment, information acquisition based on digital technology has become a significant means of accessing innovation resources, which integrate internal and external heterogeneous resources through digital transformation to achieve “creative collision” and produce high-quality output [34].

Third, digital transformation enhances new product development processes by improving information transparency and expediting the integration of information, thereby significantly improving innovation quality. The transparency of information disclosure brought about by digital technology reduces information asymmetry between management and shareholders, creating a governance effect that prevents management short-sightedness from impeding high-quality innovation activities. Moreover, it reduces information processing costs for investors, and it assists companies in securing more investment to support their innovation endeavors [2]. Therefore, digital transformation facilitates innovation quality by enhancing the efficiency of technology innovation, promoting knowledge sharing, and streamlining new product development (NPD) processes. Thus, we propose the following hypotheses:

H1: 

Digital transformation has a positive impact on innovation quality.

2.2.2. Moderating Effect of Market Competition

Market competition implies that demand and technological changes occur rapidly [35], and it is an important external governance mechanism. Firms need to employ digital technologies to enhance the quality of their innovations and respond to market competition, where digital technology capability is crucial. Therefore, competitive market conditions significantly influence the relationship between digital transformation and innovation quality [35]. The adoption of digital technology has led to a gradual decline in the costs of imitation and learning, thereby exacerbating the homogenization of firms and reducing the longevity of competitive advantages. In this context, the intensity of market competition emerges as a pivotal determinant influencing firms’ allocation of innovation resources [36].

High market competition means that companies face more intense competition, greater diversity in customer needs, and more rapid technological change. Firms must continually enhance their technological capabilities, optimize resource allocation, and improve the quality of innovation to maintain a competitive advantage and market share [35]. Digital transformation has emerged as a key driver for firms to enhance their market responsiveness and innovation capability. Digital transformation enables firms to gather market demand information and to incorporate consumer creativity and ideas into product enhancements, aligning innovation activities with market expectations and consistently delivering high-quality products and services. Thus, the positive relationship between digital transformation and innovation quality is strengthened.

However, under low-intensity market competition, firms often gravitate toward conservative strategies, which may stifle their motivation to innovate and lead them to avoid undertaking challenging innovation initiatives. This tendency is partly due to the reduced pressure to differentiate and outperform competitors. Under such conditions, digital technologies exhibit a constrained capacity to optimize R&D processes, thereby attenuating a positive correlation between digital transformation initiatives and innovation quality metrics. According to the above analysis, this paper puts forward the following hypothesis:

H2: 

Market competition positively moderates the relationship between digital transformation and innovation quality.

2.2.3. Mediating Role of Dynamic Capability

Dynamic capability theory posits that companies have to continually adapt their resources and capabilities to secure and sustain a competitive advantage in an ever-evolving environment [37]. Dynamic capability constitutes an organizational competence that enables a systematic reconfiguration and renewal of resource portfolios to maintain strategic agility amid market volatility, environmental disruptions, and technological paradigm shifts.

It includes intangible assets that empower firms to cultivate, implement, and uphold superior performance across time [38]. Rather than constituting a solitary capability, dynamic capability represents a multifaceted construct facilitating firms’ rapid adaptation to environmental shifts. Dynamic capability mainly consists of three dimensions, including absorptive capacity, adaptive capability, and innovation capability. These three dimensions reflect the typical characteristics of firms’ dynamic capability and reveal how firms transform resource advantages into market advantages [8]. We primarily analyze how digital transformation affects dynamic capability from the above three dimensions.

First, digital transformation enhances organizational absorptive capacity by dismantling structural barriers to resource mobility and fostering decentralized architectures that optimize access to intra- and inter-organizational knowledge repositories. Digital transformation constructs interconnected data ecosystems across stakeholder networks, enabling an instantaneous synchronization of operational intelligence that expands organizational data universes, generating granular insights into consumer behavior patterns and emerging technological pathways. This expansion effectively enhances the depth and breadth of firms’ information resources [39], enabling them to assimilate customer knowledge for product innovation. Thus, the application of digital technology effectively enhances absorptive capacity.

Second, digital transformation can significantly bolster firms’ adaptive capabilities. Leveraging digital technologies, firms can detect changes in market demand, identify business opportunities within vast information landscapes, and utilize digital transformation to enhance the quality of their products and services [25]. Firms leverage digital technology to disrupt and reshape existing business models, operational processes, and innovation systems. Furthermore, digital tools facilitate product and technological innovation activities throughout the value chain, enabling firms to adapt flexibly and proactively to the complexities and dynamics of the evolving business environment [32]. Hence, digital transformation significantly boosts firms’ adaptive capabilities.

Third, digital transformation significantly boosts firms’ innovative capabilities. With its powerful information acquisition and data analysis capabilities, digital technology enables rapid environmental scanning, enabling firms to quickly identify potential opportunities and threats. This capability is the foundation for firms to develop and refine their innovation strategies, enabling them to flexibly adapt their innovation decisions and enhance their adaptability across diverse market environments [40]. Digital transformation enhances a firm’s innovation ability by introducing a wealth of heterogeneous knowledge that disrupts existing paradigms, fostering breakthroughs and enhancing overall innovation capabilities. Therefore, digital transformation primarily enhances firms’ dynamic capabilities by strengthening their absorptive capacity, adaptive capability, and innovative capability. We propose the following hypothesis:

H3: 

Digital transformation can improve the dynamic capability of firms.

In H1 and H3, we propose that digital transformation influences dynamic capability and innovation quality. Research indicates that dynamic capabilities can enhance innovation quality [41]. Firms require dynamic capabilities to reconfigure and update their existing resources, enabling better adaptation of their resource base to the digital transformation environment. This dynamic alignment is the foundation for firms to establish a sustainable competitive advantage in a volatile and complex environment.

In the digital economy, traditional organizations are transitioning into knowledge-intensive entities [42], where knowledge is a major resource for value creation. Big data analytics facilitates knowledge integration and enhances knowledge management processes. Companies can establish dynamic capabilities to sense and capture employees’ latent knowledge behaviors using data-driven digital infrastructure, promote knowledge sharing, and foster employee innovation [43]. The dynamic capability enables translating market-acquired knowledge into resources, skills, and activities that align with customer needs, supporting companies in their product innovation endeavors.

Based on the above analysis, digital transformation enables organizations to detect and respond to environmental changes by leveraging digital technologies and data, thereby enhancing their dynamic capabilities. Consequently, dynamic capabilities enhance innovation quality by integrating digital technologies with organizational structural enhancements and operational process improvements, distinguishing their technologies, products, and services in the marketplace and yielding high-quality innovation outputs. This paper posits a sequential impact pathway, namely “digital transformation-dynamic capability-innovation quality”. Therefore, we propose the following hypothesis:

H4: 

Dynamic capability plays a mediating role between digital transformation and innovation quality.

Based on the above research hypotheses, the theoretical framework of this study is shown in Figure 1.

3. Data and Methods

3.1. Sample and Data

China initiated the issuance of third-generation mobile communication licenses in 2009 to propel enterprises’ digital transformation. Patent citation data are available up to 2021 due to statistical delays. Our empirical study used data from A-share listed companies in the Shanghai and Shenzhen stock exchanges from 2009 to 2021. Following the data collection approach of a prior study [5], we sourced company and patent characteristics data from the China Securities Market and Accounting Research (CSMAR) and the Wind databases. Company characteristics consist of firm age, size, and ownership concentration, while patent information includes patent citation frequency, proportion of granted invention patents, and application proportions. We extracted relevant data from the listed companies’ annual reports to measure firms’ digital transformation indicators [44], focusing on extracting keywords associated with firms’ digital transformation.

Subsequently, we excluded financial and real estate companies due to differing accounting standards and specific financial attributes of Chinese real estate firms, resulting in a final sample comprising the 16 industries, including manufacturing, information transmission, software, information and communication technology, and agriculture. Companies labeled as Special Treatment and those exhibiting abnormal financial data were excluded to guarantee the precision of the analysis results.

3.2. Variables and Measurements

The dependent variable was innovation quality (IQ). Innovation quality is the actual impact of innovation output, representing an important indicator of innovation capability [24]. Traditionally, studies evaluating innovation output have often focused on metrics such as the number of new products or the number of patents. However, these measures may not fully capture the level of innovation quality, as larger firms tend to generate more new products and patents compared to smaller firms. Prior research has used the logarithm of granted patents plus one divided by capital expenditure as a proxy for innovation quality [45]. While this approach enhances comparability across firms of different sizes, it remains a quantitative indicator.

Innovation quality is heavily influenced by practical application value and impact. However, measuring innovation quality solely based on these factors may fail to bridge the gap between R&D achievements and practical applications, thereby leading to inaccuracies in assessing innovation quality. Alternatively, some scholars gauge innovation quality output by considering the total number of citations per patent for a firm. This metric accounts for a patent’s practicality, technical content, and significance, as higher citation frequencies typically indicate greater innovation quality.

Following the study of Singh (2008), this paper adopted the frequency of citations as a measure of patented technology’s impact; a higher frequency of citations suggests higher patent quality [46]. The number of patent citations not only reflects the technological influence but also embodies the practicality of the technology. Patents with a high number of citations are usually widely recognized in practical applications and possess high market value. Moreover, the number of patent citations is objective, using data based on the patent database, featuring quantifiability and comparability. This makes it a reliable indicator for measuring the quality of innovation. The number of patent citations has been widely used by scholars at home and abroad to measure the quality of innovation [47].

The independent variable was the firm’s digital transformation (DT). Digital transformation refers to a process that aims to improve an entity by triggering significant changes in its attributes through a combination of information, computing, communication, and connectivity technologies [17]. Text mining techniques and keyword frequency analysis of the firm’s annual reports were utilized to measure digital transformation. Keywords such as artificial intelligence, big data, cloud computing, and blockchain were analyzed for their frequency, serving as objective indicators of a firm’s digital transformation.

First, we utilized Python 3.11 and the Java PDFBox library tools to extract text from the firm’s annual reports and to identify keywords related to digital transformation [43]. Second, we eliminated any keywords on the front or title page that did not indicate digital transformation, such as “no big data”. Finally, we calculated the natural logarithm of the total word frequency of the text plus one, using it as a proxy to measure the firm’s level of digital transformation.

Teece (2007) defined dynamic capability as a firm’s capacity to create and modify its resources and assets to adapt to market shifts, business environments, and technological opportunities [37]. According to previous research [8], dynamic capability is measured by a composite index through principal component analyses. This index integrates three core dimensions, namely absorptive capacity, adaptive capability, and innovation capability. Absorptive capacity refers to an enterprise’s ability to identify and effectively utilize valuable external information proxied by R&D intensity, defined as the ratio of R&D investment to total sales revenue [48]. Adaptive capability reflects an enterprise’s ability to recognize and exploit market opportunities. It is measured by the coefficient of variation of R&D expenditure intensity, capital expenditure intensity, and advertising expenditure intensity [49]. Innovation capability refers to an enterprise’s ability to rapidly develop new products and enter new markets, proxied by the proportion of R&D personnel.

Absorptive capacity refers to a firm’s ability to recognize the value of external information based on existing knowledge and to effectively apply the absorbed knowledge to practical business applications. In this study, we measured absorptive capacity using R&D investment [50]. R&D investment enables firms to acquire more extensive and higher-quality external resources, thus enhancing absorptive capacity and expediting the transformation of resources into innovative outcomes [51]. To facilitate comparisons across firms of varying sizes, we utilized R&D intensity to measure absorptive capacity.

Adaptive capability denotes a firm’s aptitude for identifying and capitalizing on market opportunities. Firms with high adaptive capability can recognize and seize opportunities while effectively reallocating corporate resources. In this paper, we measured adaptive capacity using the coefficient of variation of R&D expenditure intensity, capital expenditure intensity, and advertising expenditure intensity. To ensure alignment with the direction of adaptive capability, we assigned a negative value to the coefficient of variation, where a higher coefficient of variation indicates greater adaptive capability within a firm [49].

Innovation capability denotes a company’s proficiency in developing new products and penetrating new markets. We computed the standardized values of R&D expenditure intensity and the proportion of technical personnel, respectively, and then we amalgamated them to derive a comprehensive innovation capability index for measurement. This yielded an index that included the three dimensions of dynamic capability.

The moderating variable is market competition, for which we employed the Herfindahl index (HHI) as a measure of market competition intensity, as utilized in previous studies [36]. The HHI captures the distribution of a company’s market share, providing a reflection of its relative size within an industry and describing the dispersion degree of its market share in the industry, serving as a better indicator of industrial market concentration. We multiplied the Herfindahl index by −1 to ensure a positive treatment to obtain the HHI indicator. A higher HHI indicates smaller sales differences, lower market concentration, and higher competition degree, while a lower HHI signifies greater sales differences among firms in the market, higher market concentration, and lower competition degree.

The Herfindahl index was calculated by squaring the ratio of an individual company’s main business revenues to the total business revenues of the company’s industry and then adding these values. The calculation formula is HHI = Σ(X_i/X)², where X_i is the revenue from the main business of an individual company. X = ΣX_i denotes the total revenue from the industry’s main business to which the company belongs. X_i/X represents the company’s market share in the industry.

We controlled firm size, age, ownership, financial leverage, ROE, and revenue growth rate, all of which may influence innovation quality. First, we controlled for firm-level variables, including firm size and age. The natural logarithm of the total number of employees measured firm size (Size). Firm age (Age) was calculated by subtracting the current year from the year of listing plus one [52]. We also included ownership concentration (OwnC), a quantitative measure of how equity is concentrated or dispersed [53]. We measured concentration by the proportion of the largest shareholder. Return on equity (ROE) reflects the efficiency of a firm in using its capital. A higher value indicates a higher return on investment. The degree of financial leverage (DFL), which evaluates a firm’s financial risk level, was measured as the ratio of the change in earnings per ordinary share to the change in EBITDA [54]. We included the revenue growth rate (RGR) to control for changes in the increase or decrease in a firm’s operating income.

3.3. Model Specification

To test the relationship between digital transformation and innovation quality, referring to the prior study [55], we used a fixed-effects model to develop multiple linear regression models (1) to (4).

Model (1) was specified to test Hypothesis 1 (H1), with digital transformation as the independent variable, innovation quality as the dependent variable, and necessary control variables incorporated. We also set the disturbance term ε, while controlling the year and industry effects if the coefficient α₁ of ${DT}_{i,t}$ was significantly positive. This indicates that the firm’s digital transformation can promote innovation quality.

$${IQ}_{i,t}={\alpha }_0+{\alpha }_1{DT}_{i,t}+\sum \beta {Controls}_{i,t}+\sum Year+\sum Ind+\epsilon$$

(1)

In order to test the moderating effect of market competition on the main effect in H2, we also added the interaction term (DT × MC) of the independent variable and the moderator variable in model (1). If the coefficient ${\alpha }_3$ of the interaction term DT × MC is significantly positive, it indicates that the higher the intensity of market competition, the more conducive firms’ digital transformation is to promoting innovation quality.

$$\begin{gathered} {IQ}_{i,t}={\alpha }_0+{\alpha }_1{DT}_{i,t}+{\alpha }_2{MC}_{i,t}+{\alpha }_3{DT}_{i,t}\times {MC}_{i,t}+\sum \beta {Controls}_{i,t} \\ +\sum Year+\sum Ind+\epsilon \end{gathered}$$

(2)

We adopted the sequential testing regression coefficients method to test the mediating effect of dynamic capability. Model (1) tested the positive relationship between digital transformation and innovation quality, which met the first condition of the mediation effect test. Therefore, model (3) was then constructed to test the relationship between digital transformation, dynamic capability, and the coefficient. ${\alpha }_1$ of ${DT}_{i,t}$ was significant, Hypothesis 3 was supported, and the mediation test’s second condition was met. Model (4) examined the impact of dynamic capability on innovation quality after controlling for the effects of digital transformation. ${\alpha }_2$ represents the indirect effect of dynamic capability on innovation quality, and α₁ represents the direct effect of digital transformation on innovation quality. Model (4) was developed for the third step of the sequential testing method for mediating mechanisms.

$${DC}_{i,t}={\alpha }_0+{\alpha }_1{DT}_{i,t}+\sum \beta {Controls}_{i,t}+\sum Year+\sum Ind+\epsilon$$

(3)

$${IQ}_{i,t}={\alpha }_0+{\alpha }_1{DT}_{i,t}+{\alpha }_2{DC}_{i,t}+\sum \beta {Controls}_{i,t}+\sum Year+\sum Ind+\epsilon$$

(4)

4. Results

4.1. Descriptive Statistics

Table 1. Descriptive statistics and correlation matrix.

Variable	Mean	Std. Dev	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
IQ	2.312	1.500	1
DT	1.276	1.341	0.066 ***	1
MC	−0.039	0.080	0.01	−0.173 ***	1
Size	7.909	1.224	0.413 ***	0.038 ***	−0.050 ***	1
OwnC	3.463	0.456	0.034 ***	−0.124 ***	−0.014 *	0.163 ***	1
ROE	0.090	0.074	−0.031 ***	0.031 ***	0.00	0.122 ***	0.077 ***	1
DFL	1.388	0.987	0.063 ***	−0.106 ***	0.036 ***	0.118 ***	−0.027 ***	−0.289 ***	1
RGR	0.302	4.085	−0.01	0.016 **	0.00	0.019 **	0.00	0.040 ***	−0.01	1
Age	10.660	7.104	0.206 ***	0.00	0.01	0.358 ***	−0.055 ***	−0.024 ***	0.146 ***	0.014 *

* p < 0.1, ** p < 0.05, *** p < 0.01.

presents the descriptive statistical results and correlation coefficient matrix of the variables in this study. It is observed that digital transformation (DT) exhibits a significant and positive correlation with innovation quality (IQ). The results align with the earlier hypotheses and substantiate the notion that digital transformation can enhance a firm’s innovation quality. Moreover, the correlation coefficients among the explanatory variables in Table 1 are below 0.5, which indicates the absence of significant multicollinearity in the regression model.

4.2. Regression Analysis

Table 2. The results of the regression.

	(1) IQ	(2) IQ	(3) IQ
DT	0.044 *** (5.25)	0.043 *** (5.11)
Size	0.299 *** (20.38)	0.293 *** (19.97)	0.308 *** (21.10)
OwnC	−0.203 *** (−6.37)	−0.198 *** (−6.21)	−0.209 *** (−6.53)
ROE	−0.613 *** (−6.10)	−0.594 *** (−5.91)	−0.608 *** (−6.03)
DFL	0.0230 *** (3.15)	0.022 *** (3.06)	0.023 *** (3.18)
RGR	−0.006 *** (−2.59)	−0.006 ** (−2.56)	−0.006 ** (−2.54)
Age	0.051 *** (7.85)	0.049 *** (7.65)	0.055 *** (8.62)
MC		0.650 *** (4.57)
DT×MC		0.264 *** (3.87)
Cons	−1.494 *** (−8.14)	−1.429 *** (−7.76)	−1.543 *** (−8.41)
Industry FE	Yes	Yes	Yes
Year FE	Yes	Yes	Yes
N	17216	17216	17216
R2	0.392	0.390	0.385

t statistics in parentheses. ** p < 0.05, *** p < 0.01.

displays the regression outcomes concerning the primary effects of digital transformation on innovation quality. Model (3) exhibits the findings of the fundamental regression, including control variables. Model (1) incorporates a fixed-effects model by introducing core and control variables while adjusting for industry and year effects. The coefficient of DT (β = 0.044, p < 0.01) is positive, suggesting that digital transformation enhances firms’ innovation quality. Thus, Hypothesis 1 is validated.

In Hypothesis 2, we posited that the relationship outlined would become more significant in a more competitive market environment. The coefficient for the interaction between market competition (MC) and digital transformation (DT) was significantly positive (β = 0.264, p < 0.01) in model (3). Instrumental variable estimates indicate that the positive impact of digital transformation on innovation quality is amplified in markets with higher competition levels. Figure 2 illustrates that the slope is steeper in high-competition contexts, indicating that market competition positively moderates the effectiveness of digital transformation in enhancing innovation quality. Therefore, Hypothesis 2 is supported.

4.3. Robustness Checks

4.3.1. Instrumental Variable Estimate

The instrumental variable method addresses endogeneity by introducing a variable that is correlated with the explanatory variable but uncorrelated with the error term, thereby restoring accurate causal estimation. This study employed a two-stage least squares (2SLS) regression using instrumental variables to address endogeneity concerns [56]. We utilized two instruments correlated with digital transformation but were unlikely to be correlated with the error term. Specifically, we employed the lagged one-period digital transformation (L1-DT) and digital policy (DP) from the provincial government work report as instrumental variables.

Table 3. Robustness test: Instrumental variable method and PSM-DID.

Variables	First Stage		Second Stage		PSM-DID
	(1) (DT)	(2) (DT)	(3) (IQ)	(4) (IQ)	(5) (IQ)
L1.DT	0.417 *** (42.83)
DP		0.051 *** (3.58)
DT			0.132 *** (3.46)	4.117 *** (3.41)
TREAT × POST					0.180 *** (5.90)
Controls (Same as Table 2)	YES	YES	YES	YES	YES
Industry FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Underidentification test	1727.169 ***	1727.169 ***	1727.169 ***	11.605 ***
N	10516	10516	10516	14956	15110
R2	0.0582	0.125	0.0582	0.125	0.004

t statistics in parentheses. *** p < 0.01.

presents the results of the instrumental variables approach. In Column (1), we observe the first-stage results, indicating a significant positive correlation between L1-DT and DT. Column (2) demonstrates that DP has a positive impact on DT. Moving to the second stage, Columns (3) and (4) illustrate that DT significantly enhances innovation quality. Thus, the relationship between digital transformation and innovation quality remains robust when controlling for endogeneity using a two-stage least squares specification.

4.3.2. PSM-DID

This paper employs the Propensity Score Matching with Difference-in-Differences Estimation (PSM-DID) method for policy evaluation in non-randomized experiments, which allows for a more accurate estimation of the causal effects of interventions. This study employs the exogenous shock of digital economy policies to address the endogeneity issue. In 2013, China introduced the “Broadband China” policy to stimulate Internet infrastructure development and enterprise digital transformation. Over the subsequent three years, some cities implemented the “Broadband China” policy, while others did not, creating a natural experiment. Thus, we designated 2013 as the baseline year for policy shock and introduced a dummy variable (POST) to represent policy impacts. POST equals 0 for the year preceding the implementation of the “Broadband China” policy in the city where the enterprise is located, and it switches to one post-policy implementation.

Furthermore, we introduced a dummy variable (TREAT) to signify firms in pilot cities of the “Broadband China” policy. This study employed propensity score matching (PSM) to mitigate self-selection bias in the experimental group. Innovation quality is the outcome variable, with company-level control variables as covariates. We used radius matching to identify experimental and control group firms. Figure 3 illustrates the balance test results for PSM, indicating a significant reduction in differences between control and experimental groups post-match.

Model (4) in

Table 4. Intermediate effects test (sequential test).

Variables	(1) IQ	(2) DC	(3) IQ
DT	0.027 * (1.94)	0.267 *** (2.99)	0.026 * (1.83)
Size	0.345 *** (11.06)	−0.458 ** (−2.30)	0.348 *** (11.15)
OwnC	−0.170 *** (−2.89)	0.421 (1.12)	−0.172 *** (−2.93)
ROE	−0.509 *** (−3.08)	−9.635 *** (−9.16)	−0.452 *** (−2.72)
DFL	0.019 (1.46)	−0.166 ** (−2.06)	0.020 (1.54)
RGR	−0.007 (−1.61)	0.006 (0.21)	−0.007 (−1.62)
Age	−0.139 *** (−2.99)	−0.049 (−0.17)	−0.138 *** (−2.99)
DC			0.006 *** (2.92)
N	8643	8643	8643
R2	0.064	0.125	0.065

t statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

presents the robustness regression results using PSM-DID. The coefficient for TREAT × POST is significantly positive (β = 0.10, p < 0.01), confirming that digital transformation positively impacts innovation quality. Thus, digital transformation fosters innovation quality.

4.4. Mechanism Results

We initially employed the sequential test method to examine the relationship. The outcomes are detailed in Table 4. Model (1) scrutinizes the association between digital transformation and innovation quality (Step 1 of the sequential test method), yielding a total effect of 0.027 (p < 0.1). This supports Hypothesis 1, meeting the initial condition for testing the mediating effect. Model (2) showcases the findings concerning the impact of digital transformation on dynamic capability (Step 2 of the sequential test method). The coefficient of digital transformation (β = 0.267, p < 0.01) was notably positive, signifying that deeper digital transformation correlates significantly with enhanced dynamic capability within the firm.

Therefore, Hypothesis 3 is validated, fulfilling the second condition for testing mediating effects. In Model (3), following the inclusion of dynamic capability (Step 3 of the sequential test method), the significant influence of digital transformation on innovation quality remained consistent. However, the coefficient decreased slightly from 0.027 in the initial step to 0.026. Specifically, dynamic capability exhibited a significantly positive relationship with innovation quality, evidenced by a coefficient of 0.006. This suggests that dynamic capability mediates this process, supporting Hypothesis 4.

This study further explores the intermediary mechanism of “digital transformation-dynamic capability-innovation quality” using the bootstrap method. The findings are detailed in

Table 5. Intermediate effects test (bootstrap test).

Type	Estimated Value	Standard Error	Z	p	LLCI	ULCI
Indirect effect	0.060	0.005	12.49	0.000	0.051	0.070
Direct effect	−0.013	0.013	2.49	0.316	−0.037	0.012

. Specifically, the 95% confidence interval for dynamic capability exhibits an indirect effect of [0.051, 0.070], with zero not falling within this interval. This observation indicates that dynamic capability fully mediates the relationship.

4.5. Additional Analysis

To better understand how digital transformation can break through barriers to innovation quality, we focus on the positive effects of digital transformation on acquiring innovation resources. Our assumption posits that digital transformation facilitates enhanced innovation quality by dismantling barriers to resource flow and bolstering dynamic capability, enhancing the caliber of innovation and necessitating substantial R&D investments. Financing constraints (FCs) and equity financing costs (EFCs) emerge as significant barriers impeding enterprises’ access to innovative resources. Consequently, we investigate the mediating role of financing constraints and equity financing costs across diverse contexts to discern the dominant barrier. The results are shown in

Table 6. Intermediate effects results.

Variables	The Mediation Effect of CEF			The Mediation Effect of FC
	(1) IQ	(2) CEF	(3) IQ	(4) IQ	(5) FC	(6) IQ
DT	0.036 *** (4.08)	−0.001 (−0.35)	0.036 *** (4.07)	0.036 *** (4.08)	−0.011 *** (−7.26)	0.030 *** (3.41)
CEF			−1.904 *** (−4.66)
FC						−0.553 *** (−10.52)
Controls (Same as Table 2)	Yes	Yes	Yes	Yes	Yes	Yes
N	15,892	15,892	15,892	15,892	15,892	15,892
R2	0.393	0.187	0.394	0.393	0.622	0.403
Indirect effect	−0.035 **			−0.001 *
Sobel test	48.472%			1.659%

t statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

.

Models (1)–(3) in Table 6 show that financing constraints partially mediate the relationship between digital transformation and innovation quality. Digital transformation has enhanced enterprises’ data processing capabilities, improving information processing accuracy, timeliness, and comprehensiveness. This, in turn, enhances the quality of accounting information disclosure. Moreover, digital transformation plays a crucial role in reducing information asymmetry, significantly improving financing availability, and alleviating liquidity constraints. As a result, financial institutions and external investors gain better insights into enterprise development, enabling informed investment decisions and, ultimately, mitigating corporate financing constraints.

Models (4)–(6) in Table 6 illustrate that equity financing costs partially mediate the relationship between digital transformation and innovation quality. Enterprises can establish agile digital management systems, facilitating improved communication with external stakeholders and expanding viable channels for solvency [57]. Consequently, they gain access to a wealth of pricing information regarding financing products, enabling optimal financing decisions and mitigating decision-making biases arising from information asymmetry [58], thus averting additional costs. Digital technology mitigates the risk of intertemporal financing mismatches, optimizes corporate financing channels within shorter timeframes, and ultimately reduces financing costs.

5. Discussion

This study investigates the impact of digital transformation on innovation quality, focusing on the mediating effect of dynamic capability and the moderating effect of market competition. Our analysis confirms all five hypotheses (H1–H4) and develops a theoretical model of how digital transformation impacts innovation quality (

Table 7. Summary of findings and hypotheses tests.

Hypothesis	Contents of the Hypothesis	Result
H1	Digital transformation→Innovation quality.	Supported
H2	Market competition positively moderates the relationship between digital transformation and innovation quality.	Supported
H3	Digital transformation→Dynamic capability.	Supported
H4	Dynamic capability mediates the gap between digital transformation and innovation quality.	Supported

).

The results demonstrate that digital transformation has a direct and significant impact on enhancing innovation quality, aligning with a prior study by Li et al. [16]. The findings underscore digital technology as a significant factor in achieving high-quality innovation outcomes. Despite existing research indicating that digital adoption can directly increase the number of patents [59], innovation quality is a more effective driver of corporate growth than innovation quantity. The results have deepened our understanding of the relationship between digitalization and innovative output by investigating the factors that influence innovation quality. However, our findings diverge from Wang and Zhang’s research conclusions, indicating that merely adopting digital technologies does not guarantee improved innovation performance [60]. This discrepancy may be due to the adoption of digital technology, which requires strategic support and implementation to drive innovation. The findings suggest that companies should leverage digital technology in their innovation processes to implement digital strategies and generate high-quality innovations. Additionally, governments should provide strong digital infrastructure and encourage enterprises to adopt digital transformation strategies through incentive policies.

Hypothesis H2 affirms the significant moderating role of market competition within the framework of digital transformation and innovation, indicating that firms in highly competitive markets have a stronger relationship between digital transformation and innovation quality. The market competition as a moderator provides valuable insights highlighting the importance of external environment factors in facilitating digitalization. This study is consistent with Ma and Li [61], who underscored the positive role of market competition in corporate innovation output. Our study identifies market competition as a crucial enabler of the influence exerted by digital transformation on innovation quality, providing practical insights into assessing the external environment for effective digital transformation efforts. The results imply that managers engaged in digital transformation must account for external market competition. In highly competitive markets, businesses can more effectively stimulate innovation by rapidly embracing digital technology. Meanwhile, policymakers play a crucial role in fostering a healthy competitive environment. They can improve legal efficiency, combat unfair competition, and implement incentive programs to encourage companies to adopt digital transformation strategies.

The findings validate the mediating effect of dynamic capability and further strengthen its link to innovation quality, aligning with the previous study by Mehrabi et al. [62]. This study provides novel insights into how dynamic capability serves as an intermediary between digital transformation and innovation quality, thereby enriching the literature on pathways to innovation. Contrary to prior studies which examine how digitalization affects innovation performance through knowledge collaboration [11], absorptive capacity [4], and the efficiency of resource allocation [59], our findings indicate that the dynamic capability is an essential mechanism that enhances the interplay between digital transformation and innovation quality, highlighting the necessity of integrating technology into the construction of innovation capability. Additional analysis further validates the mediating effect of financing constraints and equity financing costs and specifies the positive impact of digital transformation on overcoming barriers to innovation outcomes. The findings are consistent with the results of prior studies by Niu et al. [2] and Neumeyer et al. [33]. This conclusion suggests that managers should utilize digital technology to enhance their absorptive capacity, adaptive capacity, and innovative capabilities, thereby responding effectively to the external dynamic environment and fostering high-quality innovation. Furthermore, focusing on information system construction during digital transformation can improve communication efficiency with stakeholders such as investors and the public, reduce information asymmetry, lower equity financing costs, and alleviate financing constraints, thereby creating more resources to boost innovation.

6. Implications, Limitations, and Future Research

6.1. Theoretical Implications

The theoretical contributions of this study are as follows. Firstly, this study enriched the research on the driving factors of innovation quality by confirming the positive impact of digital transformation. Digital technologies such as AI and cloud computing enhance innovation quality by driving firms to strengthen the high-tech added value of their products and services [14]. Existing research mainly focuses on the impact of firm-level factors, such as leadership [6] and patent transfer [26], on innovation quality but lacks a discussion on the important factor of digital transformation. Although some studies have focused on the promoting effect of digital empowerment [16] and blockchain technology [14] on innovation quality, they lack a digital system perspective. This study presents a novel approach to enhancing the quality of corporate innovation in the digital economy era, thereby filling the research gap between digital transformation and innovation quality.

Secondly, this study sheds light on how market competition can shape strategic management by examining the moderating roles of market competition on digital transformation and innovation quality. Our research contributes to previous studies on digital transformation outcomes, primarily focusing on managerial discretion [14] and knowledge management capability [15]. The results reveal diverse outcomes, indicating that firms undergoing digital transformation are more likely to introduce new products and technologies [36], resulting in higher-quality innovation outputs in increasingly competitive markets. In contrast, digital transformation offers a limited positive impact on innovation quality in contexts of low-level market competition. These findings offer crucial guidance for identifying the most suitable contexts to implement digital transformation and effective strategies to address innovation barriers.

Finally, this study reveals the underlying mechanism of digital transformation on innovation quality and extends the literature on pathways leading to enhanced innovation quality. This study refines the digital theoretical framework [13] by demonstrating that dynamic capability mediates the relationship between digital transformation and innovation quality. The research highlights the mechanism by which digital transformation affects firms’ core capabilities, extending prior studies [62,63]. Additionally, this study focuses on how digital transformation affects innovation quality through the other two key pathways of financing constraints and equity financing costs, systematically analyzing these mechanisms. The findings reveal that digital transformation enables companies to lower financing costs, alleviate financing constraints, and enhance dynamic adaptability, thus elevating innovation quality. The findings build on the prior research by Zhu and Cheng [59], emphasizing that digital technology enhances enterprise capabilities, overcomes resource constraints, and leads to high-quality innovation output.

6.2. Practical Implications

The results also have significant practical implications for managers and policymakers. Firstly, the results confirm the positive impact of digital transformation on innovation quality. Managers should recognize the important role of digital transformation in enhancing innovation quality and strive to adopt digital technologies to achieve digital transformation. Firms can enhance operational efficiency by formulating a digital transformation strategy, introducing automation and digital intelligence technologies, and establishing a data management system through digital technologies. Additionally, firms can drive growth by leveraging digital technologies, harnessing the innovative potential of data, fostering open innovation ecosystems, and bolstering their innovation capabilities. For example, Chinese electric vehicle companies have developed dynamic capabilities through the adoption of information and communication technologies (ICTs) and consumer behavior analysis systems. As a result, electric vehicle companies quickly capture consumer demand for intelligent driving, respond to external environment changes, and generate high-quality patents. As a result, they have gained a competitive edge over traditional fuel-powered vehicles.

Secondly, the findings show that market competition positively moderates the relationship between digital transformation and innovation quality. Firms in highly competitive markets have a stronger relationship between digital transformation and innovation quality. Managers should accurately assess the external environment to carry out digital transformation better. In fierce market competition, digital transformation is an effective way to boost innovation and gain competitiveness. Meanwhile, policymakers should enhance the efficiency of laws and the institutional environment, crack down on unfair competition practices, and establish a fair market competition environment. Thus, firms face fair market competition in digital transformation. According to the research findings, market competition can enhance the positive effect of digital transformation on innovation quality. This approach will optimize the efficacy of digital technologies, both in driving innovation outputs and fostering the growth of the digital economy.

Thirdly, the findings validate the mediating effect of dynamic capability and further strengthen its link to innovation quality. Managers should build dynamic capabilities through digital technologies to compete better in the market. Digital technologies can help firms better absorb and utilize external knowledge, thereby improving the efficiency of market decision-making. In a dynamic market environment, digital technologies can promptly identify market opportunities, facilitate their development, and build competitive advantages. Moreover, the findings reveal the mediating effect of financing constraints and equity financing costs and specify the positive impact of digital transformation on overcoming barriers to innovation outcomes. Managers should implement digital transformation strategies that leverage digital technologies to mitigate information asymmetry with key stakeholders, including investors and the public. By enhancing transparency and fostering stakeholder trust, firms can reduce equity financing costs and alleviate financing constraints, thereby overcoming barriers to high-quality innovation outcomes.

6.3. Limitations and Future Research

First, the relationship between digital transformation and innovation quality is complex, with this study primarily focusing on exploring the boundary effects of market competition intensity. Nevertheless, research on their boundary-influencing factors remains relatively limited. At the organizational level, future research can focus on factors such as absorptive capacity, organizational slack, and corporate culture. The positive effects of digital technology on innovation depend on the organizational culture that fosters innovation. Firms with a stronger entrepreneurial orientation are more likely to adopt digital technologies and take risks to pursue high-quality innovation.

Second, this study does not discuss the mechanisms underlying the various sub-dimensions of digital transformation, including the digitization of the value chain, business processes, products, services, and virtual meta-universe technologies. Future studies should investigate the impact of multidimensional digital transformation processes on organizational innovation capabilities. For example, how do digital technologies affect business model innovation, and how does digital transformation impact exploratory and exploitative innovation?

Third, the study was conducted in China, which has unique economic, cultural, and regulatory characteristics. Given these specific contextual factors, the findings may be limited in generalizability to other regions or countries with different market dynamics and institutional environments. China’s digital economy has experienced significant growth, supported by various government policies, which sets it apart from other emerging economies. Future research could refine the framework by incorporating the institutional environments of other emerging economies. This would offer a more comprehensive understanding of how digital transformation drives innovation and economic development in diverse contexts. For instance, emerging markets, including those in Africa, Latin America, Asia, and Eastern Europe, are undergoing rapid digital transformation, presenting unique opportunities for growth and innovation. By examining these contexts, researchers can uncover the mechanisms through which digital transformation impacts innovation and economic development and identify potential barriers and enablers. In addition, future research could focus on cross-country comparative studies or sector-specific investigations.