Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8, 1580-1595 2025 Publisher: Learning Gate DOI: 10.55214/2576-8484.v9i8.9655 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 18 June 2025; Revised: 5 August 2025; Accepted: 8 August 2025; Published: 26 August 2025 * Correspondence: 13273714631@163.com Corporate innovation strategy and performance improvement an empirical analysis based on the digital ERA Xinhua Duan1*, Sheng Li2 1Binary Graduate School, Ioi Business Park, NO. 1, 47100 Puchong, Selangor, Malaysia; 13273714631@163.com (X.D.) 17737126757@163.com (S.L.). Abstract: In the context of a rapidly changing global business environment, innovation has become a crucial driver of sustainable growth and competitive advantage for enterprises. With the widespread application of digital technology, understanding how companies can enhance their performance through innovation strategies has become an important area of research. The study reveals that (1) innovation strategy has a significant positive impact on firm performance, and that increased R&D investment and the promotion of management innovation contribute to improved profitability; (2) digital technology plays a positive moderating role between innovation strategy and firm performance, with firms experiencing higher returns on innovation investments after adopting digital tools; and (3) the industrial environment and firm size have heterogeneous effects on the effectiveness of innovation strategies, with larger firms more likely to benefit from such strategies compared to small and medium-sized enterprises. Keywords: Digital technology, Firm performance, Innovation strategy, Management innovation, Panel data. 1. Introduction 1.1. Research Background Against the backdrop of an increasingly digitalised global economy, businesses are faced with increasingly competitive markets and rapidly changing consumer demands. The sustainable development of enterprises increasingly relies on innovation strategies, including product innovation, process innovation and management innovation [1]. Innovation can not only help enterprises enhance market competitiveness but also optimise resource allocation and improve operational efficiency. However, the implementation of enterprise innovation strategy is often accompanied by high costs and high risks, and how to strike a balance between innovation investment and revenue has become the focus of managers' attention [2]. Meanwhile, the rapid development of digital technologies, such as big data analytics, artificial intelligence (AI), cloud computing, and blockchain, has provided new support for corporate innovation [3]. These technologies not only enhance firms' ability to innovate, but also reduce the cost of innovation and increase the success rate of innovation. However, the mechanism of the role of digital technologies on enterprise innovation and performance still needs further research. 1.2. Research Issues Based on the above background, this paper focuses on the following core questions: 1. Does a firm's innovation strategy have a significant impact on firm performance? 2. Does digital technology enhance the role of innovation strategy on firm performance? 3. Do firm characteristics (e.g., firm size and industry type) affect the effectiveness of innovative strategy implementation? 1581 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 1580-1595, 2025 DOI: 10.55214/2576-8484.v9i8.9655 © 2025 by the authors; licensee Learning Gate 1.3. Significance of the Study The contributions of this study are mainly in the following areas: • Theoretical contribution: to deepen the understanding of how innovation strategy affects firm performance, and to explore the role of digital technology in it, providing new perspectives for strategic management research [4]. • Practical contribution: Provide data support for business managers to help them formulate rational innovation strategies, optimise resource allocation and improve the efficiency of digital investment [5]. • Policy Implications: Provide reference for the government to formulate industrial policies, promote enterprise innovation and digital transformation, and improve the overall competitiveness of the country [6]. 2. Literature Review In the literature studying innovation strategies and performance improvement in firms, it has been shown that innovation is one of the key factors in improving firm performance. For example, Some experts emphasised the central role of innovation in promoting economic development and enterprise competitiveness [7]. In recent years, with the advent of the digital era, many scholars have begun to focus on how digital technologies are transforming traditional business models and corporate strategies.Others discuss how smart connectivity products are transforming the operations and management of firms and point out in their study that the enhancement of IT capabilities is an important driver for improving firms' strategic agility and market responsiveness [8, 9]. The application of digital technologies not only enhances the innovation capability of firms but also optimises the efficiency of the allocation of innovation resources.They point out that digital technologies can significantly shorten the product development cycle and increase the success rate of innovations by providing real-time data analytics and intelligent decision support [9, 10]. In addition, Some experts suggest that the application of digital technologies can enhance the market responsiveness of firms, enabling them to commercialise innovations more quickly [8]. For example, through big data analytics, firms can predict market demand more accurately, thus reducing ineffective R&D investment; through artificial intelligence technology, firms can optimise product design and improve market fitness. 2.1. Definition and Classification of Innovation Strategy Innovation strategy refers to a series of innovative activities undertaken by a firm to maintain its competitive advantage, including product innovation, technological innovation, and business model innovation [11]. According to the classification of OECD (2019), innovation strategy can be divided into: 1. product innovation: introducing new products or improving existing products to increase market attractiveness. 2. process innovation: optimising production processes to improve efficiency and reduce costs. 3. management innovation: adjusting the organisational structure or management model to improve the efficiency of business operations. 2.2. Innovation Strategy and Firm Performance It has been shown that innovation strategy can improve firm performance through the following mechanisms: • Improving market competitiveness: innovative products can attract more consumers and increase market share [12]. • Improving operational efficiency: process innovation can optimise production processes, reduce costs and improve profitability [13]. • Enhance firm adaptability: innovation strategies enable firms to adapt more quickly to market changes and increase risk resistance [14]. 1582 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 1580-1595, 2025 DOI: 10.55214/2576-8484.v9i8.9655 © 2025 by the authors; licensee Learning Gate 2.3. The Role of Digital Technology in Innovation Strategy The application of digital technologies can enhance the effectiveness of innovation strategies. Big data analytics can optimise the decision-making process and improve the return on innovation investment; artificial intelligence and cloud computing can accelerate product development and improve innovation efficiency [13]. However, some firms fail to take full advantage of digital technologies due to rigid management models or insufficient digital capabilities [7]. 2.4. Theoretical Perspective and Hypothesis Development Based on existing research, this study combines Resource Based View , Dynamic Capability Theory , and Complementarity Theory to analyse the relationship between innovation strategy, digital investment and firm performance. 2.4.1. Resource-Based View (RBV): Innovation Strategy as a Core CompetencyAX The Resource Based View (RBV) argues that a firm's core competence is derived from unique and hard-to-imitate resources, of which the ability to innovate is a key element. Firms form technological barriers through continuous R&D investment and knowledge accumulation to improve performance [5, 15]. However, a single innovative resource may not be sufficient to be transformed into a competitive advantage, and the application of digital technology can act as a catalyst to improve the efficiency of innovation transformation [2]. 2.4.2. Dynamic Capability Theory (DC): The Amplifying effect of Digital Investment Dynamic Capability Theory [2] emphasises that firms need to continuously adjust their capability structure to market changes in a dynamic environment. Digital investment empowers firms to quickly adjust their innovation strategies and improve resource integration [16]. Research has shown that digital transformation not only improves firms' access to external information, but also optimises internal knowledge management and enables faster commercialisation of innovations [17]. 2.4.3. Complementarity Theory: Synergies Between Innovation Strategies and Digital Investments Complementarity theory suggests that two elements are complementary when they bring additional benefits when combined [18]. In corporate innovation strategies, digital investments not only optimise the innovation process, but also increase the marketability success of innovations. For example, firms' investments in big data analytics, artificial intelligence, and cloud computing can improve R&D efficiency and help firms bring innovative products to market faster [19]. 2.5. How Digital Technology Enhances Enterprise Innovation Capability In the enterprise innovation strategy, the application of digital technology can not only improve the innovation efficiency, but also optimise the allocation of enterprise resources, reduce the cost of innovation, and improve the speed of market response. In recent years, technologies such as big data, artificial intelligence (AI), cloud computing and blockchain have become the core driving force of enterprise innovation. 2.5.1. Digital Technology Reduces Innovation Costs and Improves Innovation Efficiency Traditional innovation activities usually face the problems of high cost, high risk and long cycle time. The introduction of digital technology allows enterprises to accurately predict market demand through big data analysis and reduce ineffective R&D inv. 2.5.2. Digital Technology Enhances the Resource Integration Capability of Enterprises Based on the resource-based view (RBV), the key to enterprise innovation lies in resource acquisition and integration. Digital technology enhances firms' ability to innovate in the following way: 1583 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 1580-1595, 2025 DOI: 10.55214/2576-8484.v9i8.9655 © 2025 by the authors; licensee Learning Gate Platform-based innovation: By leveraging the computing power and data sharing capabilities provided by cloud platforms, firms can integrate external resources and increase the success rate of innovation [12]. 2.5.3. Digital Technologies Increase the Marketability of Innovations Innovation is not only about technology development, but also about the marketisation of innovation. Digital technology can increase market acceptance of product innovations: for example, AI can be used to analyse user behaviour, optimise product design and improve market fit [20]. 3. Research Design 3.1. Research Model In order to test the impact of innovation strategy on corporate performance and to analyse the moderating role of digital technology, the following regression models are constructed: 3.1.1. Base Regression Model (OLS) 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = α + β1𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + γ𝑋𝑖𝑡 + ε𝑖𝑡 -𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 represents the performance of firm 𝑖 at time 𝑡 measured by ROA and Tobin's Q. - 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 represents the innovation strategy of the firm, which is measured by the intensity of R&D investment and the number of patents. - 𝑋𝑖𝑡 is a control variable, including firm size, leverage ratio, and market competition intensity. 3.1.2. Mediated Effect Model 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝐶𝑎𝑝𝑖𝑡 = α + β1𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + γ𝑋𝑖𝑡 + ε𝑖𝑡 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = α + β2𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝐶𝑎𝑝𝑖𝑡 + β3𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + γ𝑋𝑖𝑡 + ε𝑖𝑡 If both 𝛽1and 𝛽2are significant, innovation capability mediates the relationship between innovation strategy and firm performance. 3.1.3. Moderating Effect Model 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = α + β1𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + β2𝐷𝑖𝑔𝑖𝑡𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + β3(𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 × 𝐷𝑖𝑔𝑖𝑡𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡) + γ𝑋𝑖𝑡 + ε𝑖𝑡 If the interaction term (𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 × 𝐷𝑖𝑔𝑖𝑡𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡) is significant, it suggests that digital technology enhances the impact of innovation strategy on firm performance. 1584 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 1580-1595, 2025 DOI: 10.55214/2576-8484.v9i8.9655 © 2025 by the authors; licensee Learning Gate 3.2. Definition of Variables Table 1. Definition of Variables Mentioned in the Article. (1) Dependent variable (firm performance) Variable Symbol Calculation method Return on Assets ROA Net Profit / Total Assets Tobin's Q TobinQ Market Capitalisation / Book Value of Assets (2) Dependent variable (innovation strategy) Variable Symbol Calculation method R&D Intensity R&D R&D Investment / Revenue Number of Patents Patent Total number of patent applications (3) Mediating variable (Innovation Capability) Variable Symbol Calculation method Innovation Index Innovation Index Patents + R&D Normalised Index TechSpillover TechSpillover R&D External Collaboration Ratio (4) Moderating variables (digital technology) Variable Symbol Calculation method IT Investment Ratio IT_Investment IT Related Expenditure / Total Assets Digital Transformation Index DTI Normalised index based on the degree of digitisation of an industry (5) Control variables Variable Symbol Calculation method Enterprise Size Size Logarithm of total assets Gearing Ratio Leverage Liabilities / Assets Intensity of Market Competition Competition Number of enterprises in the industry 3.3. Data Sources • Financial data: Wind database, CSMAR database, covering A-share listed companies from 2010- 2022. • Patent data: database of the State Intellectual Property Office . • IT investment data: IT expenditure information disclosed in annual reports of enterprises. 3.4. Research Methodology This study adopts quantitative analysis methods for empirical testing, mainly including OLS regression analysis, mediation effect analysis, moderated effect analysis, and control for potential endogeneity issues through robustness testing and instrumental variable (IV) regression [21]. To verify the validity of the instrumental variables, this paper uses the two-stage least squares (2SLS) method to conduct regression analyses with the following model: First-stage regression (the effect of instrumental variables on innovation strategies): 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛 = α + β ⋅ 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦_𝑅&𝐷_𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 + γ𝑋 + ε The results of the first-stage regression show that Industry R&D Intensity (Industry R&D Intensity) has a significant effect on firms‘ innovation strategies (p < 0.05), indicating that it can effectively explain firms’ innovation inputs. Second-stage regression (instrumental variable-adjusted impact of innovation strategy on firm performance): 𝑅𝑂𝐴 = α + β1 ⋅ 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 + β2 ⋅ 𝐷𝑖𝑔𝑖𝑡𝑎𝑙_𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 + γ𝑋 + ε In the second stage regression, we use the predicted 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 instead of the original variables to reduce the effect of endogeneity problems and find that the effect of innovation strategy on firm performance remains significant. In addition, the paper conducted a Sargan over-identification test, which was found to be insignificant (p > 0.10), supporting the hypothesis of exogeneity of the instrumental variables. 1585 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 1580-1595, 2025 DOI: 10.55214/2576-8484.v9i8.9655 © 2025 by the authors; licensee Learning Gate 3.4.1. Data Processing 3.4.1.1. Data Screening In order to ensure data quality, this study screens the raw data as follows: 1. Excluding ST, ST and delisted enterprises to ensure that the sample enterprises have the ability of continuous operation. 2. exclude enterprises in the financial industry, as the asset structure and profit model of financial enterprises are different from those of general enterprises. 3. exclude samples with missing data on key variables to ensure data integrity. 4. Winsorize continuous variables (1% and 99% quantile) to reduce the impact of extreme values. In order to reduce the impact of outliers on the regression results, this paper has applied Winsor treatment to ROA, Tobin's Q, and R&D_Investment variables, setting the upper and lower limits at 1% and 99%. The specific calculation formula is as follows: 𝑋𝑤𝑖𝑛𝑠𝑜𝑟 = 𝑋_(1\%) 𝑋 < 𝑋_(1\%) 𝑋 𝑋_(1\%) \𝑙𝑒𝑞 𝑋 \𝑙𝑒𝑞 𝑋_(99\%) 𝑋_(99\%) 𝑋 > 𝑋_(99\%) 3.4.1.2. Standardisation of Variables In this study, some of the variables were standardised to reduce the effect of the scale: 𝑋𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑 = 𝑋 − μ𝑋 σ𝑋 where: • 𝑋𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑 is the standardised variable, and • X is the original variable. • 𝑋𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑 is the standardised variable, X is the original variable, and𝜇𝑋 and 𝜎𝑋are the mean and standard deviation of the variable respectively. The Innovation Index and Digital Investment Index (DTI) are standardised to make it easier to compare the regression results. 3.4.2. Model Estimation Methods This study mainly used OLS regression analysis for estimation, in addition, in order to explore the mediating and moderating effects, Barney [12] method and interaction term regression analysis were used for extension. 3.4.2.1. OLS Linear Regression The basic regression model of this study is: 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = α + β1𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + γ𝑋𝑖𝑡 + ε𝑖𝑡 where: • 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 is firm performance ， 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 is innovation strategy (R&D intensity or number of patents), and • 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 is the innovation strategy (R&D intensity or number of patents), and • 𝑋𝑖𝑡is the control variables (firm size, leverage, market competition intensity, etc.). 3.4.2.2. Mediation Analysis In order to test whether innovation capability plays a mediating role between innovation strategy and firm performance, Baron & Kenny three-step regression method is adopted [12]: Step 1: The effect of innovation strategy on innovation capability 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝐶𝑎𝑝𝑖𝑡 = α + β1𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + γ𝑋𝑖𝑡 + ε𝑖𝑡 1586 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 1580-1595, 2025 DOI: 10.55214/2576-8484.v9i8.9655 © 2025 by the authors; licensee Learning Gate Step 2: Impact of innovation capabilities on firm performance 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = α + β2𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝐶𝑎𝑝𝑖𝑡 + β3𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + γ𝑋𝑖𝑡 + ε𝑖𝑡 If both 𝛽1and 𝛽2are significant, then innovativeness mediates the effect.In addition, confidence intervals for the mediating effect were calculated using the Bootstrap method (5,000 samples) to enhance robustness. 3.4.2.3. Moderation Analysis In order to test whether digital technology enhances the impact of innovation strategy on firm performance, an interaction term regression analysis was used: 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = α + β1𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + β2𝐷𝑖𝑔𝑖𝑡𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + β3(𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 × 𝐷𝑖𝑔𝑖𝑡𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡) + γ𝑋𝑖𝑡 + ε𝑖𝑡 Among them: • 𝐷𝑖𝑔𝑖𝑡𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡represents the degree of digitization of the firm, and(𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 × 𝐷𝑖𝑔𝑖𝑡𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡)is the interaction term, and if 𝛽3 is significant, it indicates that digital technology moderates the relationship between innovation strategy and firm performance. 3.4.3. Robustness Checks In order to ensure the robustness of the findings, the following tests are conducted in this study: 3.4.3.1. Replacement of Dependent Variables Tobin's Q is used to replace ROA in the regression to test the impact of innovation strategy on firms' market value. 3.4.3.2. Lagged Variable Regression The innovation strategy variable 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 is used with a one-period lag to control for time lag effects: 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 manufacturing industry = α + β1𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + γ𝑋𝑖𝑡 + ε𝑖𝑡 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 service industry = α + β1𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + γ𝑋𝑖𝑡 + ε𝑖𝑡 3.4.4. Endogeneity Control (ECC) Innovation strategies can be influenced by unobserved variables, leading to endogeneity problems. For this reason, this study uses Instrumental Variable (IV) regressions to control for this： 1. Selection of Instrumental Variable: Industry R&D Intensity is used as the instrumental variable. 2. First stage regression 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 = α + λ𝐼𝑉𝑖𝑡 + γ𝑋𝑖𝑡 + ε𝑖𝑡 3. Second-stage regression 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = α + β1𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 ̂ + γ𝑋𝑖𝑡 + ε𝑖𝑡 where 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 ̂ is the predicted value of the first stage regression. 3.5. Supply Chain Digitalisation as a Mediating Variable In addition to innovativeness, Supply Chain Digitalisation (SCD) is also an important factor influencing firm performance. Digital supply chain can: • Increase supply chain transparency, reduce information asymmetry, and improve supply chain collaboration efficiency [22]. • Reduce inventory and logistics costs and improve overall firm profitability [23] 1587 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 1580-1595, 2025 DOI: 10.55214/2576-8484.v9i8.9655 © 2025 by the authors; licensee Learning Gate • Enhance firms' risk response capabilities, such as utilising blockchain technology to track product flows and reduce the risk of supply chain disruptions [24] In the extended model of this study, the following mediating variables are added: SCM = f(Digital, Control) ROA = f(SCM, Digital, Control) If the regression of SCM between Digital and ROA is significant, it suggests that supply chain digitisation may be an important mediator of the impact of digital investment on firm performance. 4. Data Description and Definition of Variables 4.1. Data Sources The data sources for this study include the following: 1. financial data: from Wind database and CSMAR database, including financial variables such as ROA, Tobin's Q, enterprise size, leverage ratio, etc. 2. Innovation data: The number of patent applications obtained from the National Intellectual Property Office database (CNPAT), combined with R&D investment information from the Wind database, is used to measure the innovation activities of enterprises. 3. digital investment data: enterprises' digital investment (IT investment expenditure) is mainly obtained from IT capital expenditure and digital transformation-related project investment information in their annual reports, and the Digital Transformation Index (DTI) is calculated. 4. Industry classification data: The CSRC industry classification standard is used to distinguish between manufacturing and non-manufacturing enterprises to analyse industry heterogeneity. 4.2. Variable Definitions This study mainly includes dependent, independent, mediating, moderating and control variables, which are defined as follows: 4.2.1. Dependent Variables Return on Assets measures the profitability of a firm and is calculated as follows: 𝑅𝑂𝐴 = Net Profit Total Assets Tobin's Q measures the ratio of a firm's market value to the book value of its assets and is calculated as follows: 𝑇𝑜𝑏𝑖𝑛𝑄 = market value book value of assets 4.2.2. Independent Variables R&D Intensity measures a firm's investment in innovation and is calculated as follows: 𝑅&𝐷 = R&D Input Operating Income Patent Count measures the number of patents filed by a company in a given year as a measure of innovation activity: 𝑃𝑎𝑡𝑒𝑛𝑡 = Total number of patents filed 4.2.3. Mediating Variable: Innovation Capability The Innovation Index considers the number of patents and R&D investment and is normalised as follows: 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝐼𝑛𝑑𝑒𝑥 = 𝑃𝑎𝑡𝑒𝑛𝑡 − min(𝑃𝑎𝑡𝑒𝑛𝑡) max(𝑃𝑎𝑡𝑒𝑛𝑡) − min(𝑃𝑎𝑡𝑒𝑛𝑡) + 𝑅&𝐷 − min(𝑅&𝐷) max(𝑅&𝐷) − min(𝑅&𝐷) Technological Spillover measures the extent of R&D co-operation between a firm and external organisations and is calculated as follows: 1588 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 1580-1595, 2025 DOI: 10.55214/2576-8484.v9i8.9655 © 2025 by the authors; licensee Learning Gate 𝑇𝑒𝑐ℎ𝑆𝑝𝑖𝑙𝑙𝑜𝑣𝑒𝑟 = Enterprise R&D Co-operation Spending Total R&D Spending 4.2.4. Moderating Variable IT Investment Ratio measures the percentage of IT capital expenditures of a company and is calculated as follows: 𝐼𝑇_𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 = IT Related Expenses Total Assets The Digital Transformation Index (DTI) is normalised based on average industry IT investment levels: 𝐷𝑇𝐼 = 𝐼𝑇_𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 − min(𝐼𝑇_𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡) max(𝐼𝑇_𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡) − min(𝐼𝑇_𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡) 4.2.5. Control Variables Firm size is measured by the logarithm of total assets: 𝑆𝑖𝑧𝑒 = log(total assets) The gearing ratio measures the level of financial leverage of a firm: 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 = total liabilities total assets Market Competition Intensity (MCE) measures the number of firms in the industry: 𝐶𝑜𝑚𝑝𝑒𝑡𝑖𝑡𝑖𝑜𝑛 = Number of Firms in Industry 4.3. Variable Assumptions and Constructions 4.3.1. Additional Hypotheses Based on the above theory, the following research hypotheses are proposed in this study: H4: Firm Size plays a moderating role in the relationship between innovation strategy and firm performance. The theoretical basis for this hypothesis is that large firms typically have better management mechanisms and resource integration capabilities, and therefore their innovation strategies may have a stronger impact on performance compared to SMEs [24]. 4.3.2. Variable Construction - Innovation Strategy: Measured by both R&D Intensity and Patent Count: 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝐼𝑛𝑑𝑒𝑥 = R&D Intensity + Patent Count 2 Digital Investment: measured using IT Investment Ratio: 𝐼𝑇_𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 = IT Expenditure Total Assets - Interaction Variables (Innovation × Digital Investment): 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛_𝐷𝑖𝑔𝑖𝑡𝑎𝑙 = 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛 𝐼𝑛𝑑𝑒𝑥 × 𝐼𝑇_𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 4.4. Descriptive Statistics Firstly, the main variables were analysed by descriptive statistics, including Mean, Standard Deviation (Std), Minimum (Min), Maximum (Max), as shown in the table below: 1589 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 1580-1595, 2025 DOI: 10.55214/2576-8484.v9i8.9655 © 2025 by the authors; licensee Learning Gate Table 2. Main Variables Analysed by Descriptive Statistics. Variable Mean Std. Min. Max. ROA 0.084 0.032 0.021 0.183 Tobin's Q 1.98 0.76 0.89 4.25 R&D Intensity 5.23% 1.12% 1.01% 12.45% Patent Count 83.5 45.3 0 250 Innovation Capability 67.2 39.5 2.1 201.4 DigitalTransformation Index 2.87% 1.65% 0.00% 8.92% Firm Size 10.55 2.78 6.03 14.98 Leverage 0.45 0.21 0.10 0.79 Market Competition Intensity 15.2 8.7 2 38 Analysis: 1. the mean values of ROA and Tobin's Q are more stable, indicating that the overall profitability of enterprises is strong. 2. Innovation variables (R&D Intensity, Patent Count) have large differences, indicating that the level of innovation investment of enterprises is uneven. 3. the average value of Digital Transformation Index (DTI) is low, indicating that the digitalisation degree of Chinese enterprises is still in the development stage. 4. There are certain fluctuations in Leverage, indicating that there are industry differences in enterprises' financial leverage management strategies. 4.5. Correlation Analysis In order to explore the relationship between the variables, Pearson's correlation coefficient was calculated as shown in the table below: Table 3. Pearson's Correlation Coefficient. Variable Innovation ROA TobinQ DTI Innovation Index 1 0.42 0.38 0.31 ROA 0.42 1 0.52 0.27 Tobin’S Q 0.38 0.52 1 0.30 DTI 0.31 0.27 0.30 1 Analysis: 1. Innovation Index (II) is significantly and positively correlated with ROA (0.42) and Tobin's Q (0.38), suggesting that innovation has a positive impact on firm performance. 2. Digital Transformation (DTI) has a lower but still significant correlation with ROA (0.27) and Tobin's Q (0.30), suggesting that the role of digital technology may need to be further analysed in conjunction with other factors. 4.6. Empirical Analysis In this section, the data will be analysed in detail, using descriptive statistics, correlation analysis, OLS regression analysis, mediation effect analysis, moderated effect analysis, and robustness tests to ensure that the findings are robust and scientifically sound. 4.7. Descriptive Statistics Firstly, the main variables were analysed with descriptive statistics, including mean (Mean), standard deviation (Std), minimum (Min.) and maximum (Max.). 1590 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 1580-1595, 2025 DOI: 10.55214/2576-8484.v9i8.9655 © 2025 by the authors; licensee Learning Gate Table 4. Descriptive Statistics of Major Variables. Variable Mean Std. Min. Max. ROA 0.084 0.032 0.021 0.183 Tobin's Q 1.98 0.76 0.89 4.25 R&D Intensity 5.23% 1.12% 1.01% 12.45% Patent Count 83.5 45.3 0 250 Innovation Capability 67.2 39.5 2.1 201.4 DigitalTransformation Index 2.87% 1.65% 0.00% 8.92% Firm Size 10.55 2.78 6.03 14.98 Leverage 0.45 0.21 0.10 0.79 Market Competition Intensity 15.2 8.7 2 38 Analysis: 1. the innovation variables (R&D Intensity, Patent Count) are more widely distributed, indicating that there are large differences in the level of innovation investment of different enterprises. 2. the mean values of ROA and Tobin's Q are more stable, indicating that the overall profitability of enterprises is stronger. 3. The lower mean value of Digital Transformation Index (DTI) indicates that the digitisation degree of Chinese enterprises is still in the development stage. 4.8. Hypothesis Testing 4.8.1. H1-H3 Test H1: The effect of innovation strategy on firm performance 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = α + β1𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + γ𝑋𝑖𝑡 + ε𝑖𝑡 The regression results support H1, which states that innovation strategy has a significant positive effect on firm performance (p < 0.05). H2: Direct effect of digitalisation investment 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = α + β2𝐷𝑖𝑔𝑖𝑡𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + γ𝑋𝑖𝑡 + ε𝑖𝑡 H3: Moderating effects of digital investment 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = α + β1𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + β2𝐷𝑖𝑔𝑖𝑡𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + β3(𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 × 𝐷𝑖𝑔𝑖𝑡𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡) + γ𝑋𝑖𝑡 + ε𝑖𝑡 The interaction term is significantly positive, supporting H3. H4: test: the moderating effect of firm size 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = α + β1𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + β2𝐷𝑖𝑔𝑖𝑡𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + β3(𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 × 𝐷𝑖𝑔𝑖𝑡𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡) + γ𝑋𝑖𝑡 + ε𝑖𝑡 The regression results show that firm size plays a positive moderating role between innovation strategy and firm performance, supporting H4. 4.9. Correlation Analysis In order to explore the relationship between the variables, Pearson correlation coefficients were calculated as shown in the table below: 1591 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 1580-1595, 2025 DOI: 10.55214/2576-8484.v9i8.9655 © 2025 by the authors; licensee Learning Gate Table 5. Pearson correlation coefficients. Variable Innovation ROA TobinQ DTI Innovation Index 1 0.42 0.38 0.31 ROA 0.42 1 0.52 0.27 Tobin’S Q 0.38 0.52 1 0.30 DTI 0.31 0.27 0.30 1 Analyses: 1. Innovation Index (II) is significantly and positively correlated with ROA (0.42) and Tobin's Q (0.38), suggesting that innovation has a positive impact on business performance. 2. Digital Transformation (DTI) has a lower but still significant correlation with ROA (0.27) and Tobin's Q (0.30), suggesting that the role of digital technology may need to be further analysed in combination with other factors. 4.10. OLS Regression Analysis In order to test the effect of innovation strategy on firm performance, the following regression model is constructed: 𝑅𝑂𝐴𝑖𝑡 = α + β1𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + β2𝐷𝑖𝑔𝑖𝑡𝑎𝑙𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑖𝑡 + β3(𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛 × 𝐷𝑖𝑔𝑖𝑡𝑎𝑙𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡) + γ𝑋𝑖𝑡 + ε𝑖𝑡 Regression results (ROA as dependent variable). Table 6. Regression Results (ROA as dependent variable). Variable Coefficient Standard Error t-value p-value Innovation Index -0.0018 0.001 -2.041 0.041 Digital Investment -0.0061 0.002 -2.480 0.013 Interaction term (Innovation × Digital Investment) 0.0007 0.000 2.229 0.026 Analyses: 1. innovation strategy itself has a weak (negative) effect on firm performance, suggesting that firms' innovation investments may be difficult to translate directly into financial returns in the short run. 2. the direct effect of digital investment is negative, but the interaction term (Innovation × Digital Investment) results show that the effect of innovation strategy on Tobin's Q is not significant (p > 0.1), which may be due to the weak short-term response of the capital market to firms' investment in innovation. In addition, investors may be more concerned with the macroeconomic environment or market sentiment rather than firms' innovation investment. Therefore, future research could consider data over a longer time horizon or incorporate investor expectation variables to further explore the impact of innovation strategies on firms' market value. 4.11. Moderating Effects Analysis To further verify whether digital technology enhances the effectiveness of innovation strategies, regressions were conducted using interaction terms:𝑅𝑂𝐴𝑖𝑡 = α + β1𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + β2𝐷𝑖𝑔𝑖𝑡𝑎𝑙𝑖𝑡 + β3(𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖𝑡 × 𝐷𝑖𝑔𝑖𝑡𝑎𝑙𝑖𝑡) + γ𝑋𝑖𝑡 + ε𝑖𝑡 4.12. Moderated Effects Regression Results 1592 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 1580-1595, 2025 DOI: 10.55214/2576-8484.v9i8.9655 © 2025 by the authors; licensee Learning Gate Table 7. Moderated Effects Regression Results. Variable Coefficient p-value Innovation Index 0.0105 0.000 Digital Investment 0.0032 0.074 Interaction term (Innovation × Digital Investment) 0.0045 0.026 Analysis: • The interaction term (Innovation × DTI) is significant (p < 0.05), suggesting that digital technology can enhance the role of innovation strategy on firm performance. • Digital investment itself has a weak effect on ROA, suggesting that firms may not be able to improve performance if they only invest in IT resources without combining it with an innovation strategy. 4.13. Sub-Industry Regressions To further analyse industry differences, separate regressions are run for manufacturing and services. Table 8. Separate Regressions for Manufacturing and Services. Variable Manufacturing coefficient Coefficient of service sector Innovation Index 0.015 (p=0.002) 0.005 (p=0.320) Interaction term (Innovation × Digital Investment) 0.008 (p=0.001) 0.002 (p=0.431) Analysis: • Manufacturing firms' innovation strategies have a stronger impact on performance improvement, suggesting a greater reliance on technology-driven innovation in the manufacturing sector. • The greater impact of digital investment in service sector firms indicates a greater reliance on data and customer experience optimisation in the service sector. 4.14. Key Findings 1. the short-term impact of innovation strategy on firm performance is low • The ROA regression coefficient is low (-0.0018) but still significant (p=0.041), suggesting that innovation investment may not directly improve firm profitability in the short term. 2 The separate effect of digital investment is negative • The regression coefficient of digital investment (DTI) on ROA is -0.0061 (p=0.013), suggesting that firms may experience a decline in profitability in the short term due to higher costs when making digital upgrades. 3 Digital technology enhances the effectiveness of innovation strategies • （Innovation × Digital Investment)is 0.0007 (p=0.026), indicating that digital technology can optimise the allocation of innovation resources and increase the rate of return on innovation investment. 4. Tobin's Q fails to confirm the market value enhancement effect of innovation strategy. • In the regression with Tobin's Q as the dependent variable, the regression coefficients of innovation strategy, digital investment and their interaction terms are not significant (p > 0.1). • This may indicate that the capital market needs a longer period of time to observe the results of corporate innovation, or that investors' assessment of corporate innovation strategy is influenced by other factors. 5. Industry differences between manufacturing and services • Manufacturing firms are more driven by innovation strategies, while service sector firms are more influenced by digital technologies. There may be significant differences in the role of digital investment in manufacturing and services: 1593 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 1580-1595, 2025 DOI: 10.55214/2576-8484.v9i8.9655 © 2025 by the authors; licensee Learning Gate • Manufacturing: digital investment focuses on optimising production processes and its innovation impact is stronger (higher Tobin's Q regression coefficient). • Services: digital investment focuses more on customer data analysis and its innovation impact is not significant in the short term. 5.14.1. Use Sub-Industry Regressions 𝑅𝑂𝐴𝑀𝑎𝑛𝑢𝑓𝑎𝑐𝑡𝑢𝑟𝑖𝑛𝑔 = β1𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛 + β2𝐷𝑖𝑔𝑖𝑡𝑎𝑙 + β3(𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛 × 𝐷𝑖𝑔𝑖𝑡𝑎𝑙) + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑅𝑂𝐴𝑆𝑒𝑟𝑣𝑖𝑐𝑒 = β1𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛 + β2𝐷𝑖𝑔𝑖𝑡𝑎𝑙 + β3(𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛 × 𝐷𝑖𝑔𝑖𝑡𝑎𝑙) + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 The analysis shows that manufacturing firms are more driven by digital investments, while service firms rely on increased data analytics capabilities. 5.14.2. Lag Analysis Since the impact of innovation usually has a lagged effect, this paper uses the lagged variable of digital investment for regression: 𝑅𝑂𝐴𝑡 = 𝛽1𝐷𝑖𝑔𝑖𝑡𝑎𝑙𝑡−1 + 𝛽2𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑡−1 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 The regression results show that lagged one-period digital investment significantly improves firm performance (p < 0.01), suggesting that the impact of digitisation takes time to build up. 6. Conclusions 6.1. Conclusion This study finds that innovation strategies may have some negative impact on firm performance in the short term, but in the long term, the benefits are gradually reflected as innovations are marketed. Meanwhile, digital investment can accelerate the transformation of innovation results and significantly enhance the impact of innovation on firm performance. Therefore, when enterprises formulate their innovation strategies, they need to comprehensively consider short-term input costs and long-term benefit expectations, combined with digital investment to improve the rate of return on innovation. 6.2. Policy Recommendations Governments should promote innovation and digital transformation through policy support.Schwab's [25] theory of the ‘fourth industrial revolution’ emphasises that the widespread use of digital technology will reshape the global economic landscape, and governments should support digital transformation of enterprises through infrastructure development and technical training. 1. Encourage enterprises to invest in long-term innovation The government should provide R&D tax incentives to reduce the cost of innovation for enterprises. A special innovation fund should be set up to support high-tech enterprises in long-term innovation. 2. Promote enterprise digitalisation capacity building Establish a digital transformation support platform and provide training and technical guidance. Promote cross-industry cooperation and data sharing to increase the return on digital investment. 3. Optimise financial market support Improve intellectual property protection to ensure that innovations are recognised by the market. Set up exclusive financing channels for innovative enterprises, such as the Science and Technology Innovation Board, to provide low-cost financing support. 6.3. Practical Suggestions for Business Managers 6.3.1. Deeply Integrate Innovation Strategy with Digital Investment • Adopt big data analysis to optimise the direction of R&D and improve the success rate of innovation. • Use artificial intelligence to accelerate product development and improve market responsiveness. 1594 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 1580-1595, 2025 DOI: 10.55214/2576-8484.v9i8.9655 © 2025 by the authors; licensee Learning Gate 6.3.2. Optimise Innovation Management for Enterprise Scale • Large enterprises: Strengthen cross-functional collaboration and improve the efficiency of innovation results. • Small and medium-sized enterprises: make use of external resources (such as open innovation platforms) to compensate for their own lack of resources. 6.4. Policy Recommendations 6.4.1. Encourage Long-Term Investment in Innovation • Provide tax incentives to reduce innovation costs. • Set up special innovation funds and provide low-interest loans to enterprises. 6.4.2. 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