The Impact Mechanism of Digital Transformation on Digital Innovation Performance in Service Enterprises

The Impact Mechanism of Digital Transformation on Digital Innovation Performance in Service Enterprises | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact Mechanism of Digital Transformation on Digital Innovation Performance in Service Enterprises Donghua Chen, Zongqi Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8968675/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract In the era of digital intelligence, how digital transformation impacts the digital innovation performance of service enterprises has become a crucial topic of current focus. From the perspective of digital innovation capability and learning orientation, this study investigates the impact mechanism of digital transformation on digital innovation performance in service enterprises by collecting 284 valid samples from logistics companies. Empirical research has found that: (1) digital transformation can promote the improvement of digital innovation performance; (2) In terms of mechanism, digital innovation capability plays a mediating role between digital transformation and digital innovation performance, and learning orientation positively moderates the impact of digital transformation on digital innovation performance. The research conclusion further expands the role path of digital transformation on digital innovation performance, providing theoretical basis and inspiration for the implementation of digital transformation in service enterprises and the improvement of digital innovation performance. Digital Transformation Digital Innovation Capability Learning Orientation Digital Innovation Performance Figures Figure 1 1 Introduction With the development of emerging digital technologies, digital innovation performance has emerged as the times require. Digital innovation performance represents the contribution made by enterprises to themselves through the implementation of digital innovation activities, and it is also the key to evaluating the success of digital innovation activities. However, the impact of digital transformation on the digital innovation performance of enterprises remains unclear. Some studies have shown that digital transformation of enterprises helps to enhance their response speed and adaptability in a dynamic environment, thereby positively promoting enterprise performance (Vial., 2019) [ 1 ] . However, other research indicates that 70% of enterprise digital transformation projects ultimately fail to achieve expected results, severely restricting the production efficiency of enterprises and the innovation capabilities of employees, and resulting in resource waste (Li., 2022; Orlando et al., 2020) [ 2 ][ 3 ] , which may have a negative impact on the digital innovation performance of enterprises. It can be seen from this that although existing studies on the impact of digital transformation on digital innovation performance have achieved certain results, there is still room for further deepening, which is mainly reflected in the following two aspects: First, the academic community still has controversies regarding whether digital transformation has a positive or negative impact on enterprises' digital innovation performance; Second, most of the existing studies focus on the direct effect of digital transformation on digital innovation performance, ignoring the potential impact of mediating effects and moderating effects. In summary, this study selects logistics enterprises as the research objects, and based on the perspectives of digital innovation capability and learning orientation, it aims to systematically analyze the influence mechanism of digital transformation on the digital innovation performance of service enterprises, and explore the following three research questions: (1) What is the influence path of digital transformation on the digital innovation performance of service enterprises? (2) Does digital innovation capability play a mediating role in this path? (3) Does learning orientation play a moderating role in this path? This study makes the following two theoretical contributions: First, it further improves the research on the connotation and measurement dimensions of digital innovation performance. Currently, there are obvious differences in the measurement system of digital innovation performance in the academic community, and there is a lack of a unified standard for dimension division. By exploring the connotation of digital innovation performance, this study innovatively divides it into two dimensions: digital process innovation performance and digital product-service innovation performance, and develops a corresponding measurement index system, which provides a theoretical basis for in-depth research in the future. Second, based on the perspectives of digital innovation capability and learning orientation, this study constructs and expands a theoretical model of the influence mechanism of digital transformation on digital innovation performance, reveals the influence mechanism of digital transformation on the digital innovation performance of service enterprises, and expands the research in related fields. 2 Research Hypotheses 2.1 Digital Transformation and Digital Innovation Performance Digital transformation refers to a process in which enterprises proactively comply with the development requirements of the digital era, actively adapt to the environmental changes brought about by the popularization of digital technologies, and systematically reshape their internal operational processes, production models, and other aspects (Wei et al., 2020) [ 4 ] . Enterprises' promotion of digital transformation helps improve corporate performance (Zhang et al., 2022) [ 5 ] . Digital innovation performance is the evolution of innovation performance in the digital environment and an extension of the concept of innovation performance. Specifically, it refers to the beneficial outcomes that enterprises obtain through digital innovation. Scholars have explored the connotation of digital innovation performance from different perspectives. From a process perspective, Ardito et al. (2021) [ 6 ] argue that digital innovation performance refers to the innovation performance obtained by applying digital technologies to an organization's operational processes and product innovation processes. From an outcome perspective, Khin et al. (2019) [ 7 ] proposed that the essence of digital innovation performance lies in enterprises using digital technologies to empower existing businesses such as products or services, or developing new businesses through digital technologies and endowing them with digital characteristics. This viewpoint has been recognized by many scholars (Ardito et al., 2020; Hanelt et al., 2020) [ 6 ][ 8 ] . Based on this, this study defines digital innovation performance from two dimensions: digital process innovation performance and digital product and service innovation performance. studies have shown that digital transformation can improve corporate innovation performance. Li et al. (2023) [ 9 ] argue that the improvement of an enterprise's innovation level benefits from its high degree of digitalization. A study by Liu et al. (2023) [ 10 ] indicates that digital transformation has a positive and significant impact on corporate innovation. Sarbu (2022) [ 11 ] proposes that Industry 4.0 has played a positive role in promoting the intensity of product innovation in service enterprises. It can be seen that digital transformation can effectively promote corporate innovation. The success of an enterprise largely depends on the resources it owns and controls, which can be reflected in the level of its performance outcomes. As the foundation for digital transformation, digital technology can be regarded as an advantageous resource for enterprises. Some scholars argue that this indicates digital transformation helps improve corporate innovation performance (Sousa-Zomer et al., 2020; Li et al., 2021) [ 12 ][ 13 ] . Through research, Cabrilo et al (2020) [ 14 ] found that advanced digital technologies play an important role in the path of influencing innovation performance. Further research by Blichfeldt et al. (2021) [ 15 ] and others found that digital technology has a direct impact on innovation performance. Wu et al. (2021) [ 16 ] believe that digital technology also helps enterprises integrate information, thereby promoting the improvement of innovation performance. Zhou et al. (2020) [ 17 ] argue that a higher level of digitalization can be transformed into higher innovation performance, and the level of innovation performance depends on the level of digitalization. Based on the above analysis, this study proposes the following hypotheses: H1-1: Digital transformation has a positive impact on digital process innovation performance; H1-2: Digital transformation has a positive impact on digital product and service innovation performance. 2.2 Digital Transformation and Digital Innovation Capability Liu et al. (2021) [ 18 ] defined digital innovation capability as the ability to create new value based on the effective deployment of digital technologies and the in-depth exploration of data resources. Hu et al. (2022) [ 19 ] argued that digital transformation can help enterprises improve production efficiency, market adaptability and product quality, thereby facilitating corporate innovation. Chen et al. (2023) [ 20 ] proposed that digital transformation can promote corporate innovation by enhancing employees' knowledge level, increasing firms’ R&D investment and fostering innovation awareness. Digital transformation is a process in which enterprises leverage digital technologies to carry out an all-round reshaping of their business processes, organizational structures, management models and customer services. Chen et al. (2020) [ 21 ] emphasized that digital technologies have gradually become a key driving force for the development of enterprise technological innovation, and this driving force will continue to strengthen with the deepening of enterprises' digital transformation. The research of Li et al. (2022) [ 22 ] showed that enterprises with a relatively high level of digital transformation tend to pay more attention to the upgrading and iteration of digital technologies, such as big data, cloud computing, blockchain and the Internet of Things, and actively promote the integration of digital technologies with their core advantages to stimulate innovation vitality. Zhou et al. (2018) [ 23 ] affirmed the enabling role of digital technologies and argued that the application of digital technologies can help enterprises improve their innovation capabilities. It is worth noting that digital transformation is not a simple application of digital technologies, but a comprehensive and systematic reform involving multiple dimensions such as strategy, organization and planning. An et al. (2022) [ 24 ] pointed out that such an all-round transformation not only helps improve enterprises' technological level, but also optimizes the internal innovation ecosystem of enterprises, enabling them to have the ability of continuous innovation in the complex and ever-changing market competition. In addition, through an empirical study on state-owned enterprises, Zhang et al. (2025) [ 25 ] found that digital transformation can effectively enhance the innovation capabilities of state-owned enterprises by optimizing resource allocation, breaking organizational inertia and expanding knowledge breadth. Based on the above analysis, this paper proposes the following hypothesis: H2 : Digital transformation has a positive impact on digital innovation capability. 2.3 Digital Innovation Capability and Digital Innovation Performance Zhang Y S. (2024) [ 26 ] drew the conclusion that digital innovation capability has a significant impact on corporate performance through an empirical analysis of 218 enterprise samples. Zheng et al. (2024) [ 27 ] argued that digital innovation capability exerts a significant positive impact on corporate performance based on a study of relevant data from 991 Chinese listed companies. Innovation capability helps enterprises establish competitive advantages. Relying on innovation activities, enterprises effectively integrate, apply and re-develop data resources, technical tools and market opportunities, and then transform them into market-competitive products and services, thereby establishing their own competitive advantages and improving corporate performance. From the internal perspective of enterprises, digital innovation capability can promote the cultivation of enterprises' internal dynamic capabilities, thus further boosting corporate performance growth (Karimi et al., 2015) [ 28 ] . At the same time, Wang et al. (2024) [ 29 ] argued that innovation capability plays a positive role in optimizing enterprises' internal management processes. Enterprises with strong innovation capability can more efficiently embed digital technologies into organizational management processes, thereby breaking through the constraints of traditional organizational structures and promoting cross-departmental collaboration and information sharing. This innovation at the internal management level can not only effectively reduce enterprises' operational costs, but also improve employees' work efficiency. In addition, excellent innovation capability will also prompt enterprises to transform their management philosophy to be more flexible and efficient to adapt to the rapidly changing market environment, thus enhancing corporate performance. From the external perspective of enterprises, innovation capability can help enterprises explore new business opportunities. Through innovative thinking and practices, enterprises can deeply insight into the evolution trend of market demand, and then accurately develop products or services that better meet consumers' expectations (Wang et al., 2024) [ 29 ] . Based on the above analysis, this paper proposes the following hypotheses: H3-1: Digital innovation capability has a positive impact on digital process innovation performance; H3-2: Digital innovation capability has a positive impact on digital product and service innovation performance. 2.4 The Mediating Effect of Digital Innovation Capability Enterprises' pursuit of the renewal, reconfiguration, and re-creation of resources and capabilities is an important way for them to respond to environmental changes and achieve long-term performance. Liu et al. (2021) [ 18 ] argue that digital innovation refers to innovations carried out by enterprises through emerging digital technologies, including product innovation, service innovation, organizational structure innovation, and business innovation. Digital innovation capability is the outcome of digital innovation. As an important intangible asset of enterprises, digital innovation capability is an indispensable dynamic capability for enterprises, covering all aspects of enterprises' digital innovation field. It can empower enterprises to adapt to market changes and serves as a key factor for enterprises to gain advantages in a highly volatile market environment and fierce competition. Some scholars have explored the relationship between digital transformation, innovation capability, and corporate performance. A study by Sun Y. (2023) [ 30 ] shows that the improvement of corporate innovation capability can be regarded as the result of enterprises using digital technologies to carry out digital transformation. On the one hand, digital innovation capability helps enterprises explore new technologies and develop new processes; on the other hand, it helps enterprises reduce R&D investment costs and shorten the R&D cycle of new products or services. Tao et al. (2023) [ 31 ] believe that enterprises with strong digital innovation capability have greater advantages in applying new technologies and new knowledge, and digital technology innovation can effectively improve corporate performance. Zhang et al. (2024) [ 32 ] propose that digital innovation exerts a positive impact on the level of corporate performance through process digitalization, product intelligence, and service digitalization. It can be seen from this that digital transformation can promote the improvement of corporate performance by fostering and strengthening enterprises' innovation capability. Based on the above analysis, this study proposes the following hypotheses: H4-1: Digital innovation capability plays a mediating role between digital transformation and digital process innovation performance; H4-2: Digital innovation capability plays a mediating role between digital transformation and digital product and service innovation performance. 2.5 The Moderating Effect of Learning Orientation Learning orientation is a core value that influences the degree of an organization’s active learning, and it affects the acquisition, transformation, and utilization of knowledge by the organization (D’Angelo et al., 2019) [ 33 ] . Existing studies mainly divide learning orientation into three dimensions: shared goals, inclusive thinking, and commitment to learning. Among these, shared goals refer to a future-oriented blueprint that most employees in an enterprise deeply understand and collectively hold; this blueprint can provide focus and direction for learning, as enterprise managers and employees share the enterprise’s development vision, enabling employees to work toward common goals. Inclusive thinking means that an enterprise is not limited to fixed thinking patterns, but can embrace new ideas and accept new knowledge. Commitment to learning refers to an enterprise regarding learning as a core philosophy and a fundamental activity with core value, thus being willing to invest resources in it, which reflects the enterprise’s willingness to learn. Some scholars have explored the role of learning orientation in enterprises’ digital transformation. Sousa et al. (2019) [ 34 ] argue that learning can serve as a driving force for enterprises to develop digital technologies, thereby promoting their digital transformation. Li et al. (2018) [ 35 ] held that enterprises with a strong learning orientation can enhance their absorptive capacity for knowledge by stimulating employees' learning initiative and promoting communication and collaboration among various departments, thus achieving favorable innovation performance. Gerschewski et al. (2018) [ 36 ] pointed out that learning orientation exerts a significant impact on enterprise performance. It can be seen that learning orientation plays an important role between digital transformation and enterprise performance. Hu Q. (2020) [ 37 ] proposed that enterprises with a strong learning orientation can strengthen the impact of digital transformation on enterprise performance from three aspects. First, they can promote the implementation of digital transformation plans, thereby improving enterprise performance. Second, they can enhance internal communication, encourage employees to question and improve existing technologies and businesses, and accelerate the process of converting digital transformation into enterprise performance. Third, they can improve learning efficiency and enhance cohesion by setting common goals, thus driving the transformation of digital transformation practices into enterprise performance. Through empirical analysis, Yang et al. (2021) [ 38 ] indicated that a learning-oriented corporate culture is conducive to the implementation of digital transformation and can strengthen the positive impact of digital transformation on enterprise performance. Based on the above analysis, this study proposes the following hypotheses: H5-1: Learning orientation positively moderates the impact of digital transformation on digital process innovation performance; H5-2: Learning orientation positively moderates the impact of digital transformation on digital product and service innovation performance. Based on the above analysis and the proposed research hypotheses, this study constructs a research conceptual model, as shown in Fig. 1 . 3 Research Design 3.1 Data Collection This study adopts the questionnaire survey method, taking logistics enterprises as the research objects, and distributes and collects questionnaires through the professional online survey platform "Wenjuanxing" (a well-known Chinese online survey platform). The survey respondents include middle managers and senior leaders of the enterprises, who can answer questions from different levels and perspectives, ensuring the comprehensiveness and representativeness of the survey data. From May 2025 to August 2025, a total of 366 questionnaires were distributed, and 335 were actually collected, with a recovery rate of 91.53%. After strict screening and cleaning of the questionnaire data, invalid questionnaires (such as those with incomplete answers and obviously contradictory responses) were excluded, and 284 valid questionnaires were finally obtained, with an effective recovery rate of 84.78%, which meets the sample quality requirements of empirical research. 3.2 Definition and Measurement of Variables (1) Independent Variable The independent variable in the conceptual model is digital transformation. Vial (2019) [ 1 ] proposed that the essence of digital transformation is a process of applying digital technologies to optimize organizations. Zhu et al. (2022) [ 39 ] emphasized the impact of digital technologies on enterprises' production, services, and business processes in the context of digital transformation. From the perspective of organizational change, Hu Q. (2020) [ 37 ] measured digital transformation using 5 items. Based on existing research, Chi et al. (2020) [ 40 ] designed 3 items, corresponding to enterprises' digital technology-based operation, integration, and transformation respectively. On the basis of Chi et al.'s (2020) [ 40 ] research, Lu et al. (2021) [ 41 ] made appropriate adjustments to the items and measured digital transformation from three perspectives: business model, operation system, and value creation. This study mainly refers to the scales developed by the above-mentioned scholars and measures digital transformation through 4 items, such as "Our enterprise uses digital technologies to improve existing business activities". (2) Dependent Variable The dependent variable in the conceptual model is digital innovation performance. Digital innovation performance refers to the performance achieved by enterprises through innovations in business processes, products, and services using digital technologies. This study defines digital innovation performance from two dimensions, namely digital process innovation performance and digital product and service innovation performance. Among them, digital process innovation performance is used to measure the effectiveness of enterprises' transformation using digital technologies. Ardito et al. (2021) [ 6 ] argued that such transformation aims to improve production and innovation efficiency, and its outcomes are ultimately reflected in the enhancement of enterprises' overall performance and competitiveness. Khin et al. (2018) [ 7 ] and Hanelt et al. (2021) [ 8 ] believed that digital product and service innovation performance is a measure of the beneficial effects on organizational performance and competitive advantage brought about by enterprises embedding digital technologies into existing products and services to make them more responsive to customer needs. Digital process innovation performance was measured using the scales developed by Ardito et al. (2021) [ 6 ] and Zhen et al. (2021) [ 42 ] , including 3 items such as "Our enterprise uses digital technologies to improve the processes and methods for producing products and services". Digital product and service innovation performance was measured with reference to the scale by Pesch et al. (2020) [ 43 ] , including 7 items. (3) Mediating Variable The mediating variable in the conceptual model is digital innovation capability. Liu et al. (2021) [ 18 ] argue that digital innovation capability reflects an organization’s ability to coordinate and allocate digital resources and convert them into innovations. It emphasizes enterprises’ capabilities in digital connection, intelligent analysis, and collaboration of various types of resources, and research on digital innovation capability can be extended to areas such as digital capability and digital innovation. Yi et al. (2022) [ 44 ] designed 15 items by drawing on domestic and international studies related to digital capability. Zhang et al. (2024) [ 32 ] measured digital innovation from three dimensions: process digitalization, product intelligence, and service digitalization. Cao et al. (2023) [ 45 ] divided digital innovation capability into digital process innovation capability and digital reorganization innovation capability, and designed measurement items based on the actual domestic situation. Wang et al. (2024) [ 46 ] categorized digital innovation capability at the organizational level into digital agility capability and reorganization innovation capability, measuring each with 6 items respectively. Wu et al. (2025) [ 47 ] believe that digital innovation capability is an enterprise’s core capability, which highlights the integration of digital technologies and demonstrates rapid iteration and openness. By integrating domestic and international research findings, they finally developed 8 measurement items. Based on the study by Yi et al. (2022) [ 44 ] , this study mainly refers to the scales developed by the aforementioned scholars and measures digital innovation capability through 11 items, such as "The company can timely update the data or relevant information carried by related infrastructure, production equipment, R&D tools." (4) Moderating Variable The moderating variable in the conceptual model is learning orientation. Learning orientation refers to an enterprise’s core values toward learning. Most scholars have developed scales for learning orientation from three dimensions: commitment to learning, shared vision, and open-mindedness (Hu Q., 2020; Mai et al., 2020) [ 37 ][ 48 ] . This study adopts the scale developed by Hu Q. (2020) [ 37 ] and measures learning orientation through 9 items, such as "Our enterprise views learning as an investment rather than an expense". (5) Control Variable Hu Q. (2020) [ 37 ] argued that factors such as the nature and scale of the surveyed enterprises may affect the survey results. Therefore, this study selects the nature of the enterprise and the scale of the enterprise's employees as control variables. 4 Data Analysis and Hypothesis Testing 4.1 Common Method Bias Test To assess common method bias (CMV), this study conducted Harman’s single-factor test. The results showed that the variance explained by the first factor was 31.901%, which is less than the critical value of 40%, indicating that there is no severe common method bias in the data. 4.2 Reliability Test The reliability test of the data was completed using SPSS 27 software, and the test results are shown in Table 1 . Table 1 Results of Reliability Test Variable Digital Transformation Digital Process Innovation Performance Digital Product and Service Innovation Performance Digital Innovation Capability Learning Orientation Cronbach’s α 0.838 0.887 0.923 0.943 0.931 As can be seen from the reliability test results in Table 1 , the Cronbach’s α values of all variables are greater than 0.8, indicating that the scales have good reliability. 4.3 Validity Test The validity test of the data was completed using SPSS 27 software and AMOS 29 software, and the test results are shown in Table 2 . Table 2 Results of Validity Test Variable CR AVE Square Root of AVE The maximum value (in absolute terms) of the correlation coefficient with other variables Digital Transformation 0.839 0.566 0.752 0.614 Digital Process Innovation Performance 0.888 0.725 0.851 0.611 Digital Product and Service Innovation Performance 0.923 0.632 0.795 0.511 Digital Innovation Capability 0.943 0.601 0.775 0.614 Learning Orientation 0.931 0.601 0.775 0.240 All scales used in this study are mature scales developed by domestic and international scholars, thus ensuring good content validity. As can be seen from the validity test results in Table 2 , in terms of convergent validity, the Average Variance Extracted (AVE) and Composite Reliability (CR) of all variables meet the requirements. Specifically, the AVE values of all variables exceed the threshold of 0.5, and the CR values all reach an excellent level of above 0.8. Meanwhile, in terms of discriminant validity, the correlation coefficient between any two variables is lower than the corresponding square root of AVE. All tests meet the requirements, verifying that the scales have an ideal level of validity. This study tested the fit of the model using AMOS 29 software, and the results are shown in Table 3 . Table 3 Fit Indices χ 2 /df RMSEA GFI NFI CFI 1.015 0.007 0.905 0.921 0.999 As can be seen from the fit indices in Table 3 , χ²/df = 1.015 < 3, RMSEA = 0.007 0.90, NFI = 0.921 > 0.90, and CFI = 0.999 > 0.90, which indicates that the scale has a good fit. 4.4 Hypothesis Testing (1) Direct Effect Test This study tests the direct effect using SPSS 27 software. First, a regression analysis is conducted on the direct effect of digital transformation on digital innovation performance, and the results are shown in Table 4 . Table 4 Results of Regression Analysis on the Direct Effect of Digital Transformation on Digital Innovation Performance Variable Digital Process Innovation Performance Digital Product and Service Innovation Performance Model 1 Model 2 Model 3 Model 4 Nature of the Company 0.087 0.100 -0.004 0.011 Company Size -0.080 -0.067 0.008 0.022 Digital Transformation 0.342 *** 0.370 *** Adjusted R-squared 0.009 0.123 -0.007 0.128 F 2.303 14.268 *** 0.013 14.796 *** Note: "*" indicates p < 0.05, "**" indicates p < 0.01, and "***" indicates p < 0.001 As can be seen from the regression analysis results of Model 2 in Table 4 , the regression coefficient of digital transformation is 0.342 ( p < 0.001), which indicates that digital transformation has a significant positive impact on digital process innovation performance, and hypothesis H1-1 is verified. As can be seen from the regression analysis results of Model 4 in Table 4 , the regression coefficient of digital transformation is 0.370 ( p < 0.001), which indicates that digital transformation has a significant positive impact on digital product and service innovation performance, and hypothesis H1-2 is verified. Next, a regression analysis was conducted on the direct effect of digital transformation on digital innovation capability, and the results are presented in Table 5 . Table 5 Results of Regression Analysis on the Direct Effect of Digital Transformation on Digital Innovation Capability Variable Digital Innovation Capability Model 5 Model 6 Nature of the Company 0.025 0.047 Company Size 0.006 0.026 Digital Transformation 0.546 *** Adjusted R-squared -0.007 0.291 F 0.085 39.671 *** Note: "*" indicates p < 0.05, "**" indicates p < 0.01, and "***" indicates p < 0.001 According to the regression analysis results of Model 6 in Table 5 , the regression coefficient of digital transformation is 0.546 (p < 0.001), indicating that digital transformation exerts a significant facilitating effect on digital innovation capability, and Hypothesis H2 is thus verified. Finally, this study conducts a regression analysis on the direct effect of digital innovation capability on digital innovation performance, and the results are presented in Table 6 . Table 6 Results of Regression Analysis on the Direct Effect of Digital Innovation Capability on Digital Innovation Performance Variable Digital Process Innovation Performance Digital Product and Service Innovation Performance Model 7 Model 8 Model 9 Model 10 Nature of the Company 0.100 0.087 0.011 -0.02 Company Size -0.067 -0.075 0.022 0.014 Digital Innovation Capability 0.283 *** 0.288 *** Adjusted R-squared 0.123 0.177 0.128 0.184 F 14.268 *** 16.228 *** 14.796 *** 16.901 *** Note: "*" indicates p < 0.05, "**" indicates p < 0.01, and "***" indicates p < 0.001 According to the regression analysis results of Model 8 in Table 6 , the regression coefficient of digital innovation capability is 0.283 (p < 0.001), indicating that digital innovation capability exerts a significant facilitating effect on digital process innovation performance, and Hypothesis H3-1 is thus verified. According to the regression analysis results of Model 10 in Table 6 , the regression coefficient of digital innovation capability is 0.288 (p < 0.001), indicating that digital innovation capability exerts a significant facilitating effect on digital product and service innovation performance, and Hypothesis H3-2 is thus verified. (2) Mediating Effect Test This study uses the Process v4.0 plug-in in SPSS 27 software and adopts the Bootstrap method to test the mediating effect of digital innovation capability, with the results presented in Table 7 . Table 7 Results of Bootstrap Analysis for Mediating Effect approach Effect Size SE 95% CI BootLLCI BootULCI Digital Transformation→Digital Innovation Capability→Digital Process Innovation Performance 0.172 0.043 0.092 0.262 Digital Transformation→Digital Innovation Capability→Digital Product and Service Innovation Performance 0.148 0.037 0.078 0.224 As can be seen from the analysis results in Table 7 , digital innovation capability exhibits a significant mediating effect in both sets of paths. Specifically, in the path between digital transformation and digital process innovation performance, the indirect effect value of digital innovation capability is 0.172, with a 95% confidence interval (95% CI) of [0.092, 0.262]. Since the interval range does not include zero, it meets the criteria for determining a mediating effect. This indicates that digital innovation capability plays a mediating role between the two, and hypothesis H4-1 is supported. In the path between digital transformation and digital product and service innovation performance, the indirect effect value of digital innovation capability is 0.148, with a 95% confidence interval (95% CI) of [0.078, 0.224]. As the interval range does not include zero, it conforms to the criteria for identifying a mediating effect. This shows that digital innovation capability exerts a mediating role between the two, and hypothesis H4-2 is supported. (3) Moderating Effect Test This study tested the moderating effect of learning orientation using SPSS 27 software. First, the independent variable (digital transformation) and the moderating variable (learning orientation) were centered to reduce the potential impact of multicollinearity on the test results. On this basis, an interaction term between the independent variable and the moderating variable (digital transformation × learning orientation) was constructed. Subsequently, a hierarchical regression analysis method was adopted: in the first layer, control variables, the independent variable, and the moderating variable were introduced; in the second layer, the interaction term was added for regression analysis. The significance level of the regression coefficient of the interaction term was used to determine whether learning orientation exerts a significant moderating effect on the relationship between digital transformation and digital innovation performance. The results are shown in Table 8 . Table 8 Results of Moderating Effect Regression Analysis Variable Digital Process Innovation Performance Digital Product and Service Innovation Performance Model 11 Model 12 Model 13 Model 14 Nature of the Company 0.100 0.076 0.016 -0.006 Company Size -0.067 -0.09 0.017 0.003 Digital Transformation 0.342 *** 0.374 *** 0.360 *** 0.388 *** Learning Orientation -0.001 0.063 0.064 0.121 * Digital Transformation × Learning Orientation 0.354 *** 0.317 *** Adjusted R-squared 0.12 0.238 0.128 0.222 F 10.663 *** 18.651 *** 11.422 *** 17.157 *** Note: "*" indicates p < 0.05, "**" indicates p < 0.01, and "***" indicates p < 0.001 As can be seen from the regression analysis results of Model 12 in Table 8 , the regression coefficient of the interaction term is 0.354 ( p < 0.001), which indicates that learning orientation can enhance the impact of digital transformation on digital process innovation performance. Therefore, hypothesis H5-1 is supported. As can be seen from the regression analysis results of Model 14 in Table 8 , the regression coefficient of the interaction term is 0.317 ( p < 0.001), which indicates that learning orientation can enhance the impact of digital transformation on digital product and service innovation performance. Therefore, hypothesis H5-2 is supported. 5 Conclusions and Discussion 5.1 Research Conclusions and Theoretical Contributions Based on the Perspective of Digital Innovation Capability and Learning Orientation, This study constructs an expanded theoretical model of the influence mechanism of digital transformation on digital innovation performance, systematically analyzes the operational mechanism between them, and draws the following research conclusions through rigorous empirical testing: First, digital transformation exerts a significant positive impact on digital innovation performance. Second, digital transformation not only directly promotes the improvement of digital innovation performance but also indirectly affects digital innovation performance through the transmission path of enhancing enterprises’ digital innovation capability. The mediating effect of digital innovation capability between the two is verified, which reveals the "transformation-capability-performance" influence mechanism. Third, in the process of digital transformation affecting digital innovation performance, enterprises’ learning orientation can significantly strengthen this influence path, indicating that learning orientation plays a positive moderating effect in this path. Specifically, the stronger the enterprise’s learning orientation, the more significant the promoting effect of digital transformation on digital innovation performance. The research conclusions of this study have the following two theoretical contributions: First, this study further improves the research on the connotation and measurement dimensions of digital innovation performance. Currently, there are obvious differences in the measurement system of digital innovation performance in the academic community, and there is a lack of a unified standard for dimension division. By exploring the connotation of digital innovation performance, this study innovatively divides it into two dimensions: digital process innovation performance and digital product-service innovation performance, and develops a corresponding measurement index system, which provides a theoretical basis for in-depth research in the future. Second, this study constructs an expanded conceptual model, reveals the internal relationship and operational mechanism between digital transformation and digital innovation performance of service enterprises through empirical research, and empirically tests the mediating effect of digital innovation capability and the moderating effect of learning orientation in the operational path. It provides a new theoretical perspective and further improves the theoretical research on digital transformation and digital innovation performance of service enterprises. 5.2 Implications and Discussion This study provides the following implications for the digital transformation practice of service enterprises: First, enterprises should establish digital transformation as a strategic priority for improving digital innovation performance and actively promote it. Second, in the process of digital transformation, enterprises must regard the cultivation of digital innovation capability as a core path to advance in synergy with digital transformation, thereby empowering the continuous improvement of digital innovation performance. Digital innovation capability is a manifestation of the achievements of enterprises’ digital transformation; meanwhile, it helps enterprises update their production processes, develop products and services, reduce costs and increase efficiency, and further drive the improvement of their digital innovation performance. Third, enterprises should take learning orientation as a core component of their corporate values. This requires enterprises not only to cultivate employees’ abilities and habits of continuous learning and proactive thinking but also to set common goals, tolerate and support employees’ innovative ideas—thereby improving innovation efficiency and creating favorable conditions for the enhancement of enterprises’ digital innovation performance. This study has certain research limitations. On the one hand, this study takes logistics enterprises as the research object, which leads to limitations in the selection of sample industries. The research conclusions may only be applicable to logistics enterprises. In future research, the scope of involved industries can be further expanded to improve the research conclusions. On the other hand, the data collected in this study through questionnaires are cross-sectional data, which can only reflect the state of enterprises at a specific time point and present static correlations. However, the impact of digital transformation on digital innovation performance has a time lag, and this impact is a dynamic and gradual process. As enterprises are in different development stages, the mechanism of action of digital transformation may also have significant differences. Therefore, future research can consider conducting longitudinal studies to explore the mechanism of action of digital transformation on digital innovation performance in service enterprises more deeply. Declarations Data availability statement : The original contributions presented in the study are included in the article material; further inquiries can be directed to the corresponding author. Ethics approval: The paper involves a questionnaire survey. The survey topic is 'The Impact Mechanism of Digital Transformation on Digital Innovation Performance in Service Enterprises'. The survey respondents include middle managers and senior leaders of the enterprises the survey respondents are aware of it. This study was approved by the Institutional Review Board (IRB) of the School of Logistics and E-commerce at Zhejiang Wanli University. All participants provided informed consent prior to the study. Ethical accordance: This study was conducted in accordance with the Institutional Review Board (IRB) of the School of Logistics and E-commerce at Zhejiang Wanli University. Consent to Participate declaration: All participants involved in the study provided informed consent. All participants ensure objectivity, and the research data is authentic. Clinical trial number : not applicable Funding : This work was supported by the Zhejiang Provincial Philosophy and Social Science Planning Project (24NDJC201YB); Youth Fund Project of the Ministry of Education (22YJC630001); Zhejiang Provincial Key Research Base for Philosophy and Social Sciences - Lin gang Modern Service Industry and Creative Culture Research Center Conflicts of Interest: The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. Authors’ contributions : Conceptualization, D.C., and Z.W.; methodology, Z.W.; writing-original draft preparation, D.C., and Z.W.; writing-review and editing, D.C., and Z.W.; visualization, S.W.; project administration, D.C., and Z.W.; funding acquisition, D.C.. Consent to Publish declaration ： Not applicable. Acknowledgment : Thank all respondents for participating in this survey. References Vial, G. (2019). 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Digital innovation performance represents the contribution made by enterprises to themselves through the implementation of digital innovation activities, and it is also the key to evaluating the success of digital innovation activities. However, the impact of digital transformation on the digital innovation performance of enterprises remains unclear. Some studies have shown that digital transformation of enterprises helps to enhance their response speed and adaptability in a dynamic environment, thereby positively promoting enterprise performance (Vial., 2019)\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. However, other research indicates that 70% of enterprise digital transformation projects ultimately fail to achieve expected results, severely restricting the production efficiency of enterprises and the innovation capabilities of employees, and resulting in resource waste (Li., 2022; Orlando et al., 2020)\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e][\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, which may have a negative impact on the digital innovation performance of enterprises. It can be seen from this that although existing studies on the impact of digital transformation on digital innovation performance have achieved certain results, there is still room for further deepening, which is mainly reflected in the following two aspects: First, the academic community still has controversies regarding whether digital transformation has a positive or negative impact on enterprises' digital innovation performance; Second, most of the existing studies focus on the direct effect of digital transformation on digital innovation performance, ignoring the potential impact of mediating effects and moderating effects.\u003c/p\u003e \u003cp\u003eIn summary, this study selects logistics enterprises as the research objects, and based on the perspectives of digital innovation capability and learning orientation, it aims to systematically analyze the influence mechanism of digital transformation on the digital innovation performance of service enterprises, and explore the following three research questions: (1) What is the influence path of digital transformation on the digital innovation performance of service enterprises? (2) Does digital innovation capability play a mediating role in this path? (3) Does learning orientation play a moderating role in this path?\u003c/p\u003e \u003cp\u003eThis study makes the following two theoretical contributions: First, it further improves the research on the connotation and measurement dimensions of digital innovation performance. Currently, there are obvious differences in the measurement system of digital innovation performance in the academic community, and there is a lack of a unified standard for dimension division. By exploring the connotation of digital innovation performance, this study innovatively divides it into two dimensions: digital process innovation performance and digital product-service innovation performance, and develops a corresponding measurement index system, which provides a theoretical basis for in-depth research in the future. Second, based on the perspectives of digital innovation capability and learning orientation, this study constructs and expands a theoretical model of the influence mechanism of digital transformation on digital innovation performance, reveals the influence mechanism of digital transformation on the digital innovation performance of service enterprises, and expands the research in related fields.\u003c/p\u003e"},{"header":"2 Research Hypotheses","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Digital Transformation and Digital Innovation Performance\u003c/h2\u003e \u003cp\u003eDigital transformation refers to a process in which enterprises proactively comply with the development requirements of the digital era, actively adapt to the environmental changes brought about by the popularization of digital technologies, and systematically reshape their internal operational processes, production models, and other aspects (Wei et al., 2020)\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Enterprises' promotion of digital transformation helps improve corporate performance (Zhang et al., 2022)\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDigital innovation performance is the evolution of innovation performance in the digital environment and an extension of the concept of innovation performance. Specifically, it refers to the beneficial outcomes that enterprises obtain through digital innovation. Scholars have explored the connotation of digital innovation performance from different perspectives. From a process perspective, Ardito et al. (2021)\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e argue that digital innovation performance refers to the innovation performance obtained by applying digital technologies to an organization's operational processes and product innovation processes. From an outcome perspective, Khin et al. (2019)\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e proposed that the essence of digital innovation performance lies in enterprises using digital technologies to empower existing businesses such as products or services, or developing new businesses through digital technologies and endowing them with digital characteristics. This viewpoint has been recognized by many scholars (Ardito et al., 2020; Hanelt et al., 2020)\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e][\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Based on this, this study defines digital innovation performance from two dimensions: digital process innovation performance and digital product and service innovation performance.\u003c/p\u003e \u003cp\u003estudies have shown that digital transformation can improve corporate innovation performance. Li et al. (2023)\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e argue that the improvement of an enterprise's innovation level benefits from its high degree of digitalization. A study by Liu et al. (2023)\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e indicates that digital transformation has a positive and significant impact on corporate innovation. Sarbu (2022)\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e proposes that Industry 4.0 has played a positive role in promoting the intensity of product innovation in service enterprises. It can be seen that digital transformation can effectively promote corporate innovation. The success of an enterprise largely depends on the resources it owns and controls, which can be reflected in the level of its performance outcomes. As the foundation for digital transformation, digital technology can be regarded as an advantageous resource for enterprises. Some scholars argue that this indicates digital transformation helps improve corporate innovation performance (Sousa-Zomer et al., 2020; Li et al., 2021)\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e][\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Through research, Cabrilo et al (2020)\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e found that advanced digital technologies play an important role in the path of influencing innovation performance. Further research by Blichfeldt et al. (2021)\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e and others found that digital technology has a direct impact on innovation performance. Wu et al. (2021)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e believe that digital technology also helps enterprises integrate information, thereby promoting the improvement of innovation performance. Zhou et al. (2020)\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e argue that a higher level of digitalization can be transformed into higher innovation performance, and the level of innovation performance depends on the level of digitalization.\u003c/p\u003e \u003cp\u003eBased on the above analysis, this study proposes the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1-1: Digital transformation has a positive impact on digital process innovation performance;\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eH1-2: Digital transformation has a positive impact on digital product and service innovation performance.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Digital Transformation and Digital Innovation Capability\u003c/h2\u003e \u003cp\u003eLiu et al. (2021)\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e defined digital innovation capability as the ability to create new value based on the effective deployment of digital technologies and the in-depth exploration of data resources.\u003c/p\u003e \u003cp\u003eHu et al. (2022)\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e argued that digital transformation can help enterprises improve production efficiency, market adaptability and product quality, thereby facilitating corporate innovation. Chen et al. (2023)\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e proposed that digital transformation can promote corporate innovation by enhancing employees' knowledge level, increasing firms\u0026rsquo; R\u0026amp;D investment and fostering innovation awareness. Digital transformation is a process in which enterprises leverage digital technologies to carry out an all-round reshaping of their business processes, organizational structures, management models and customer services. Chen et al. (2020)\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e emphasized that digital technologies have gradually become a key driving force for the development of enterprise technological innovation, and this driving force will continue to strengthen with the deepening of enterprises' digital transformation. The research of Li et al. (2022)\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e showed that enterprises with a relatively high level of digital transformation tend to pay more attention to the upgrading and iteration of digital technologies, such as big data, cloud computing, blockchain and the Internet of Things, and actively promote the integration of digital technologies with their core advantages to stimulate innovation vitality. Zhou et al. (2018)\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e affirmed the enabling role of digital technologies and argued that the application of digital technologies can help enterprises improve their innovation capabilities. It is worth noting that digital transformation is not a simple application of digital technologies, but a comprehensive and systematic reform involving multiple dimensions such as strategy, organization and planning. An et al. (2022)\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e pointed out that such an all-round transformation not only helps improve enterprises' technological level, but also optimizes the internal innovation ecosystem of enterprises, enabling them to have the ability of continuous innovation in the complex and ever-changing market competition. In addition, through an empirical study on state-owned enterprises, Zhang et al. (2025)\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e found that digital transformation can effectively enhance the innovation capabilities of state-owned enterprises by optimizing resource allocation, breaking organizational inertia and expanding knowledge breadth.\u003c/p\u003e \u003cp\u003eBased on the above analysis, this paper proposes the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2\u003c/b\u003e: \u003cb\u003eDigital transformation has a positive impact on digital innovation capability.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Digital Innovation Capability and Digital Innovation Performance\u003c/h2\u003e \u003cp\u003eZhang Y S. (2024)\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e drew the conclusion that digital innovation capability has a significant impact on corporate performance through an empirical analysis of 218 enterprise samples. Zheng et al. (2024)\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003eargued that digital innovation capability exerts a significant positive impact on corporate performance based on a study of relevant data from 991 Chinese listed companies. Innovation capability helps enterprises establish competitive advantages. Relying on innovation activities, enterprises effectively integrate, apply and re-develop data resources, technical tools and market opportunities, and then transform them into market-competitive products and services, thereby establishing their own competitive advantages and improving corporate performance.\u003c/p\u003e \u003cp\u003eFrom the internal perspective of enterprises, digital innovation capability can promote the cultivation of enterprises' internal dynamic capabilities, thus further boosting corporate performance growth (Karimi et al., 2015)\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. At the same time, Wang et al. (2024)\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e argued that innovation capability plays a positive role in optimizing enterprises' internal management processes. Enterprises with strong innovation capability can more efficiently embed digital technologies into organizational management processes, thereby breaking through the constraints of traditional organizational structures and promoting cross-departmental collaboration and information sharing. This innovation at the internal management level can not only effectively reduce enterprises' operational costs, but also improve employees' work efficiency. In addition, excellent innovation capability will also prompt enterprises to transform their management philosophy to be more flexible and efficient to adapt to the rapidly changing market environment, thus enhancing corporate performance.\u003c/p\u003e \u003cp\u003eFrom the external perspective of enterprises, innovation capability can help enterprises explore new business opportunities. Through innovative thinking and practices, enterprises can deeply insight into the evolution trend of market demand, and then accurately develop products or services that better meet consumers' expectations (Wang et al., 2024)\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBased on the above analysis, this paper proposes the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3-1: Digital innovation capability has a positive impact on digital process innovation performance;\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eH3-2: Digital innovation capability has a positive impact on digital product and service innovation performance.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 The Mediating Effect of Digital Innovation Capability\u003c/h2\u003e \u003cp\u003eEnterprises' pursuit of the renewal, reconfiguration, and re-creation of resources and capabilities is an important way for them to respond to environmental changes and achieve long-term performance. Liu et al. (2021)\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e argue that digital innovation refers to innovations carried out by enterprises through emerging digital technologies, including product innovation, service innovation, organizational structure innovation, and business innovation. Digital innovation capability is the outcome of digital innovation. As an important intangible asset of enterprises, digital innovation capability is an indispensable dynamic capability for enterprises, covering all aspects of enterprises' digital innovation field. It can empower enterprises to adapt to market changes and serves as a key factor for enterprises to gain advantages in a highly volatile market environment and fierce competition. Some scholars have explored the relationship between digital transformation, innovation capability, and corporate performance. A study by Sun Y. (2023)\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e shows that the improvement of corporate innovation capability can be regarded as the result of enterprises using digital technologies to carry out digital transformation. On the one hand, digital innovation capability helps enterprises explore new technologies and develop new processes; on the other hand, it helps enterprises reduce R\u0026amp;D investment costs and shorten the R\u0026amp;D cycle of new products or services. Tao et al. (2023)\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e believe that enterprises with strong digital innovation capability have greater advantages in applying new technologies and new knowledge, and digital technology innovation can effectively improve corporate performance. Zhang et al. (2024)\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e propose that digital innovation exerts a positive impact on the level of corporate performance through process digitalization, product intelligence, and service digitalization. It can be seen from this that digital transformation can promote the improvement of corporate performance by fostering and strengthening enterprises' innovation capability.\u003c/p\u003e \u003cp\u003eBased on the above analysis, this study proposes the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4-1: Digital innovation capability plays a mediating role between digital transformation and digital process innovation performance;\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eH4-2: Digital innovation capability plays a mediating role between digital transformation and digital product and service innovation performance.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.5 The Moderating Effect of Learning Orientation\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eLearning orientation is a core value that influences the degree of an organization\u0026rsquo;s active learning, and it affects the acquisition, transformation, and utilization of knowledge by the organization (D\u0026rsquo;Angelo et al., 2019)\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Existing studies mainly divide learning orientation into three dimensions: shared goals, inclusive thinking, and commitment to learning. Among these, shared goals refer to a future-oriented blueprint that most employees in an enterprise deeply understand and collectively hold; this blueprint can provide focus and direction for learning, as enterprise managers and employees share the enterprise\u0026rsquo;s development vision, enabling employees to work toward common goals. Inclusive thinking means that an enterprise is not limited to fixed thinking patterns, but can embrace new ideas and accept new knowledge. Commitment to learning refers to an enterprise regarding learning as a core philosophy and a fundamental activity with core value, thus being willing to invest resources in it, which reflects the enterprise\u0026rsquo;s willingness to learn.\u003c/p\u003e \u003cp\u003eSome scholars have explored the role of learning orientation in enterprises\u0026rsquo; digital transformation. Sousa et al. (2019)\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e argue that learning can serve as a driving force for enterprises to develop digital technologies, thereby promoting their digital transformation. Li et al. (2018)\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e held that enterprises with a strong learning orientation can enhance their absorptive capacity for knowledge by stimulating employees' learning initiative and promoting communication and collaboration among various departments, thus achieving favorable innovation performance. Gerschewski et al. (2018)\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e pointed out that learning orientation exerts a significant impact on enterprise performance. It can be seen that learning orientation plays an important role between digital transformation and enterprise performance. Hu Q. (2020)\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e proposed that enterprises with a strong learning orientation can strengthen the impact of digital transformation on enterprise performance from three aspects. First, they can promote the implementation of digital transformation plans, thereby improving enterprise performance. Second, they can enhance internal communication, encourage employees to question and improve existing technologies and businesses, and accelerate the process of converting digital transformation into enterprise performance. Third, they can improve learning efficiency and enhance cohesion by setting common goals, thus driving the transformation of digital transformation practices into enterprise performance. Through empirical analysis, Yang et al. (2021)\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e indicated that a learning-oriented corporate culture is conducive to the implementation of digital transformation and can strengthen the positive impact of digital transformation on enterprise performance.\u003c/p\u003e \u003cp\u003eBased on the above analysis, this study proposes the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH5-1: Learning orientation positively moderates the impact of digital transformation on digital process innovation performance;\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eH5-2: Learning orientation positively moderates the impact of digital transformation on digital product and service innovation performance.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on the above analysis and the proposed research hypotheses, this study constructs a research conceptual model, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Research Design","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data Collection\u003c/h2\u003e \u003cp\u003eThis study adopts the questionnaire survey method, taking logistics enterprises as the research objects, and distributes and collects questionnaires through the professional online survey platform \"Wenjuanxing\" (a well-known Chinese online survey platform). The survey respondents include middle managers and senior leaders of the enterprises, who can answer questions from different levels and perspectives, ensuring the comprehensiveness and representativeness of the survey data. From May 2025 to August 2025, a total of 366 questionnaires were distributed, and 335 were actually collected, with a recovery rate of 91.53%. After strict screening and cleaning of the questionnaire data, invalid questionnaires (such as those with incomplete answers and obviously contradictory responses) were excluded, and 284 valid questionnaires were finally obtained, with an effective recovery rate of 84.78%, which meets the sample quality requirements of empirical research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Definition and Measurement of Variables\u003c/h2\u003e \u003cp\u003e(1) Independent Variable\u003c/p\u003e \u003cp\u003eThe independent variable in the conceptual model is digital transformation. Vial (2019)\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e proposed that the essence of digital transformation is a process of applying digital technologies to optimize organizations. Zhu et al. (2022)\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e emphasized the impact of digital technologies on enterprises' production, services, and business processes in the context of digital transformation. From the perspective of organizational change, Hu Q. (2020)\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e measured digital transformation using 5 items. Based on existing research, Chi et al. (2020)\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e designed 3 items, corresponding to enterprises' digital technology-based operation, integration, and transformation respectively. On the basis of Chi et al.'s (2020)\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e research, Lu et al. (2021)\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e made appropriate adjustments to the items and measured digital transformation from three perspectives: business model, operation system, and value creation. This study mainly refers to the scales developed by the above-mentioned scholars and measures digital transformation through 4 items, such as \"Our enterprise uses digital technologies to improve existing business activities\".\u003c/p\u003e \u003cp\u003e(2) Dependent Variable\u003c/p\u003e \u003cp\u003eThe dependent variable in the conceptual model is digital innovation performance. Digital innovation performance refers to the performance achieved by enterprises through innovations in business processes, products, and services using digital technologies. This study defines digital innovation performance from two dimensions, namely digital process innovation performance and digital product and service innovation performance.\u003c/p\u003e \u003cp\u003eAmong them, digital process innovation performance is used to measure the effectiveness of enterprises' transformation using digital technologies. Ardito et al. (2021)\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e argued that such transformation aims to improve production and innovation efficiency, and its outcomes are ultimately reflected in the enhancement of enterprises' overall performance and competitiveness. Khin et al. (2018)\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e and Hanelt et al. (2021)\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e believed that digital product and service innovation performance is a measure of the beneficial effects on organizational performance and competitive advantage brought about by enterprises embedding digital technologies into existing products and services to make them more responsive to customer needs.\u003c/p\u003e \u003cp\u003eDigital process innovation performance was measured using the scales developed by Ardito et al. (2021)\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e and Zhen et al. (2021)\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e, including 3 items such as \"Our enterprise uses digital technologies to improve the processes and methods for producing products and services\". Digital product and service innovation performance was measured with reference to the scale by Pesch et al. (2020)\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e, including 7 items.\u003c/p\u003e \u003cp\u003e(3) Mediating Variable\u003c/p\u003e \u003cp\u003eThe mediating variable in the conceptual model is digital innovation capability. Liu et al. (2021)\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e argue that digital innovation capability reflects an organization\u0026rsquo;s ability to coordinate and allocate digital resources and convert them into innovations. It emphasizes enterprises\u0026rsquo; capabilities in digital connection, intelligent analysis, and collaboration of various types of resources, and research on digital innovation capability can be extended to areas such as digital capability and digital innovation.\u003c/p\u003e \u003cp\u003eYi et al. (2022)\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e designed 15 items by drawing on domestic and international studies related to digital capability. Zhang et al. (2024)\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e measured digital innovation from three dimensions: process digitalization, product intelligence, and service digitalization. Cao et al. (2023)\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e divided digital innovation capability into digital process innovation capability and digital reorganization innovation capability, and designed measurement items based on the actual domestic situation. Wang et al. (2024)\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e categorized digital innovation capability at the organizational level into digital agility capability and reorganization innovation capability, measuring each with 6 items respectively. Wu et al. (2025)\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e believe that digital innovation capability is an enterprise\u0026rsquo;s core capability, which highlights the integration of digital technologies and demonstrates rapid iteration and openness. By integrating domestic and international research findings, they finally developed 8 measurement items.\u003c/p\u003e \u003cp\u003eBased on the study by Yi et al. (2022)\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e, this study mainly refers to the scales developed by the aforementioned scholars and measures digital innovation capability through 11 items, such as \"The company can timely update the data or relevant information carried by related infrastructure, production equipment, R\u0026amp;D tools.\"\u003c/p\u003e \u003cp\u003e(4) Moderating Variable\u003c/p\u003e \u003cp\u003eThe moderating variable in the conceptual model is learning orientation. Learning orientation refers to an enterprise\u0026rsquo;s core values toward learning. Most scholars have developed scales for learning orientation from three dimensions: commitment to learning, shared vision, and open-mindedness (Hu Q., 2020; Mai et al., 2020)\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e][\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. This study adopts the scale developed by Hu Q. (2020)\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e and measures learning orientation through 9 items, such as \"Our enterprise views learning as an investment rather than an expense\".\u003c/p\u003e \u003cp\u003e(5) Control Variable\u003c/p\u003e \u003cp\u003eHu Q. (2020)\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e argued that factors such as the nature and scale of the surveyed enterprises may affect the survey results. Therefore, this study selects the nature of the enterprise and the scale of the enterprise's employees as control variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Data Analysis and Hypothesis Testing","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Common Method Bias Test\u003c/h2\u003e \u003cp\u003eTo assess common method bias (CMV), this study conducted Harman\u0026rsquo;s single-factor test. The results showed that the variance explained by the first factor was 31.901%, which is less than the critical value of 40%, indicating that there is no severe common method bias in the data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Reliability Test\u003c/h2\u003e \u003cp\u003eThe reliability test of the data was completed using SPSS 27 software, and the test results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Reliability Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital Transformation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital Process Innovation Performance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigital Product and Service Innovation Performance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDigital Innovation Capability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLearning Orientation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs can be seen from the reliability test results in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the Cronbach\u0026rsquo;s α values of all variables are greater than 0.8, indicating that the scales have good reliability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Validity Test\u003c/h2\u003e \u003cp\u003eThe validity test of the data was completed using SPSS 27 software and AMOS 29 software, and the test results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Validity Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSquare Root of AVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThe maximum value (in absolute terms) of the correlation coefficient with other variables\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Transformation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Process Innovation Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Product and Service Innovation Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Innovation Capability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLearning Orientation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll scales used in this study are mature scales developed by domestic and international scholars, thus ensuring good content validity. As can be seen from the validity test results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, in terms of convergent validity, the Average Variance Extracted (AVE) and Composite Reliability (CR) of all variables meet the requirements. Specifically, the AVE values of all variables exceed the threshold of 0.5, and the CR values all reach an excellent level of above 0.8. Meanwhile, in terms of discriminant validity, the correlation coefficient between any two variables is lower than the corresponding square root of AVE. All tests meet the requirements, verifying that the scales have an ideal level of validity.\u003c/p\u003e \u003cp\u003eThis study tested the fit of the model using AMOS 29 software, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFit Indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e/df\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs can be seen from the fit indices in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;1.015\u0026thinsp;\u0026lt;\u0026thinsp;3, RMSEA\u0026thinsp;=\u0026thinsp;0.007\u0026thinsp;\u0026lt;\u0026thinsp;0.08, GFI\u0026thinsp;=\u0026thinsp;0.905\u0026thinsp;\u0026gt;\u0026thinsp;0.90, NFI\u0026thinsp;=\u0026thinsp;0.921\u0026thinsp;\u0026gt;\u0026thinsp;0.90, and CFI\u0026thinsp;=\u0026thinsp;0.999\u0026thinsp;\u0026gt;\u0026thinsp;0.90, which indicates that the scale has a good fit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Hypothesis Testing\u003c/h2\u003e \u003cp\u003e(1) Direct Effect Test\u003c/p\u003e \u003cp\u003eThis study tests the direct effect using SPSS 27 software. First, a regression analysis is conducted on the direct effect of digital transformation on digital innovation performance, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Regression Analysis on the Direct Effect of Digital Transformation on Digital Innovation Performance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDigital Process Innovation Performance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDigital Product and Service Innovation Performance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNature of the Company\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompany Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Transformation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.342\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.370\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.268\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.796\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote: \"*\" indicates\u003c/b\u003e \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.05, \"**\" indicates\u003c/b\u003e \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.01, and \"***\" indicates\u003c/b\u003e \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs can be seen from the regression analysis results of Model 2 in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the regression coefficient of digital transformation is 0.342 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which indicates that digital transformation has a significant positive impact on digital process innovation performance, and hypothesis H1-1 is verified.\u003c/p\u003e \u003cp\u003eAs can be seen from the regression analysis results of Model 4 in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the regression coefficient of digital transformation is 0.370 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which indicates that digital transformation has a significant positive impact on digital product and service innovation performance, and hypothesis H1-2 is verified.\u003c/p\u003e \u003cp\u003eNext, a regression analysis was conducted on the direct effect of digital transformation on digital innovation capability, and the results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Regression Analysis on the Direct Effect of Digital Transformation on Digital Innovation Capability\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDigital Innovation Capability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNature of the Company\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompany Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Transformation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.546\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.671\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eNote: \"*\" indicates\u003c/b\u003e \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.05, \"**\" indicates\u003c/b\u003e \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.01, and \"***\" indicates\u003c/b\u003e \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to the regression analysis results of Model 6 in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the regression coefficient of digital transformation is 0.546 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that digital transformation exerts a significant facilitating effect on digital innovation capability, and Hypothesis H2 is thus verified.\u003c/p\u003e \u003cp\u003eFinally, this study conducts a regression analysis on the direct effect of digital innovation capability on digital innovation performance, and the results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Regression Analysis on the Direct Effect of Digital Innovation Capability on Digital Innovation Performance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDigital Process Innovation Performance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDigital Product and Service Innovation Performance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 10\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNature of the Company\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompany Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Innovation Capability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.283\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.288\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.268\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.228\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.796\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.901\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote: \"*\" indicates\u003c/b\u003e \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.05, \"**\" indicates\u003c/b\u003e \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.01, and \"***\" indicates\u003c/b\u003e \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to the regression analysis results of Model 8 in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the regression coefficient of digital innovation capability is 0.283 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that digital innovation capability exerts a significant facilitating effect on digital process innovation performance, and Hypothesis H3-1 is thus verified.\u003c/p\u003e \u003cp\u003eAccording to the regression analysis results of Model 10 in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the regression coefficient of digital innovation capability is 0.288 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that digital innovation capability exerts a significant facilitating effect on digital product and service innovation performance, and Hypothesis H3-2 is thus verified.\u003c/p\u003e \u003cp\u003e(2) Mediating Effect Test\u003c/p\u003e \u003cp\u003eThis study uses the Process v4.0 plug-in in SPSS 27 software and adopts the Bootstrap method to test the mediating effect of digital innovation capability, with the results presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Bootstrap Analysis for Mediating Effect\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eapproach\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEffect Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBootLLCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBootULCI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Transformation\u0026rarr;Digital Innovation Capability\u0026rarr;Digital Process Innovation Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Transformation\u0026rarr;Digital Innovation Capability\u0026rarr;Digital Product and Service Innovation Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs can be seen from the analysis results in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, digital innovation capability exhibits a significant mediating effect in both sets of paths. Specifically, in the path between digital transformation and digital process innovation performance, the indirect effect value of digital innovation capability is 0.172, with a 95% confidence interval (95% CI) of [0.092, 0.262]. Since the interval range does not include zero, it meets the criteria for determining a mediating effect. This indicates that digital innovation capability plays a mediating role between the two, and hypothesis H4-1 is supported.\u003c/p\u003e \u003cp\u003eIn the path between digital transformation and digital product and service innovation performance, the indirect effect value of digital innovation capability is 0.148, with a 95% confidence interval (95% CI) of [0.078, 0.224]. As the interval range does not include zero, it conforms to the criteria for identifying a mediating effect. This shows that digital innovation capability exerts a mediating role between the two, and hypothesis H4-2 is supported.\u003c/p\u003e \u003cp\u003e(3) Moderating Effect Test\u003c/p\u003e \u003cp\u003eThis study tested the moderating effect of learning orientation using SPSS 27 software. First, the independent variable (digital transformation) and the moderating variable (learning orientation) were centered to reduce the potential impact of multicollinearity on the test results. On this basis, an interaction term between the independent variable and the moderating variable (digital transformation \u0026times; learning orientation) was constructed. Subsequently, a hierarchical regression analysis method was adopted: in the first layer, control variables, the independent variable, and the moderating variable were introduced; in the second layer, the interaction term was added for regression analysis. The significance level of the regression coefficient of the interaction term was used to determine whether learning orientation exerts a significant moderating effect on the relationship between digital transformation and digital innovation performance. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Moderating Effect Regression Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDigital Process Innovation Performance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDigital Product and Service Innovation Performance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 13\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 14\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNature of the Company\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompany Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Transformation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.342\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.374\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.360\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.388\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLearning Orientation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.121\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Transformation \u0026times; Learning Orientation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.354\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.317\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.663\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.651\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.422\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.157\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote: \"*\" indicates\u003c/b\u003e \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.05, \"**\" indicates\u003c/b\u003e \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.01, and \"***\" indicates\u003c/b\u003e \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs can be seen from the regression analysis results of Model 12 in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the regression coefficient of the interaction term is 0.354 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which indicates that learning orientation can enhance the impact of digital transformation on digital process innovation performance. Therefore, hypothesis H5-1 is supported.\u003c/p\u003e \u003cp\u003eAs can be seen from the regression analysis results of Model 14 in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the regression coefficient of the interaction term is 0.317 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which indicates that learning orientation can enhance the impact of digital transformation on digital product and service innovation performance. Therefore, hypothesis H5-2 is supported.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusions and Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Research Conclusions and Theoretical Contributions\u003c/h2\u003e \u003cp\u003eBased on the Perspective of Digital Innovation Capability and Learning Orientation, This study constructs an expanded theoretical model of the influence mechanism of digital transformation on digital innovation performance, systematically analyzes the operational mechanism between them, and draws the following research conclusions through rigorous empirical testing:\u003c/p\u003e \u003cp\u003eFirst, digital transformation exerts a significant positive impact on digital innovation performance.\u003c/p\u003e \u003cp\u003eSecond, digital transformation not only directly promotes the improvement of digital innovation performance but also indirectly affects digital innovation performance through the transmission path of enhancing enterprises\u0026rsquo; digital innovation capability. The mediating effect of digital innovation capability between the two is verified, which reveals the \"transformation-capability-performance\" influence mechanism.\u003c/p\u003e \u003cp\u003eThird, in the process of digital transformation affecting digital innovation performance, enterprises\u0026rsquo; learning orientation can significantly strengthen this influence path, indicating that learning orientation plays a positive moderating effect in this path. Specifically, the stronger the enterprise\u0026rsquo;s learning orientation, the more significant the promoting effect of digital transformation on digital innovation performance.\u003c/p\u003e \u003cp\u003eThe research conclusions of this study have the following two theoretical contributions:\u003c/p\u003e \u003cp\u003eFirst, this study further improves the research on the connotation and measurement dimensions of digital innovation performance. Currently, there are obvious differences in the measurement system of digital innovation performance in the academic community, and there is a lack of a unified standard for dimension division. By exploring the connotation of digital innovation performance, this study innovatively divides it into two dimensions: digital process innovation performance and digital product-service innovation performance, and develops a corresponding measurement index system, which provides a theoretical basis for in-depth research in the future.\u003c/p\u003e \u003cp\u003eSecond, this study constructs an expanded conceptual model, reveals the internal relationship and operational mechanism between digital transformation and digital innovation performance of service enterprises through empirical research, and empirically tests the mediating effect of digital innovation capability and the moderating effect of learning orientation in the operational path. It provides a new theoretical perspective and further improves the theoretical research on digital transformation and digital innovation performance of service enterprises.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Implications and Discussion\u003c/h2\u003e \u003cp\u003eThis study provides the following implications for the digital transformation practice of service enterprises:\u003c/p\u003e \u003cp\u003eFirst, enterprises should establish digital transformation as a strategic priority for improving digital innovation performance and actively promote it.\u003c/p\u003e \u003cp\u003eSecond, in the process of digital transformation, enterprises must regard the cultivation of digital innovation capability as a core path to advance in synergy with digital transformation, thereby empowering the continuous improvement of digital innovation performance. Digital innovation capability is a manifestation of the achievements of enterprises\u0026rsquo; digital transformation; meanwhile, it helps enterprises update their production processes, develop products and services, reduce costs and increase efficiency, and further drive the improvement of their digital innovation performance.\u003c/p\u003e \u003cp\u003eThird, enterprises should take learning orientation as a core component of their corporate values. This requires enterprises not only to cultivate employees\u0026rsquo; abilities and habits of continuous learning and proactive thinking but also to set common goals, tolerate and support employees\u0026rsquo; innovative ideas\u0026mdash;thereby improving innovation efficiency and creating favorable conditions for the enhancement of enterprises\u0026rsquo; digital innovation performance.\u003c/p\u003e \u003cp\u003eThis study has certain research limitations. On the one hand, this study takes logistics enterprises as the research object, which leads to limitations in the selection of sample industries. The research conclusions may only be applicable to logistics enterprises. In future research, the scope of involved industries can be further expanded to improve the research conclusions. On the other hand, the data collected in this study through questionnaires are cross-sectional data, which can only reflect the state of enterprises at a specific time point and present static correlations. However, the impact of digital transformation on digital innovation performance has a time lag, and this impact is a dynamic and gradual process. As enterprises are in different development stages, the mechanism of action of digital transformation may also have significant differences. Therefore, future research can consider conducting longitudinal studies to explore the mechanism of action of digital transformation on digital innovation performance in service enterprises more deeply.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e: The original contributions presented in the study are included in the article material; further inquiries can be directed to the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThe paper involves a questionnaire survey. The survey topic is 'The Impact Mechanism of Digital Transformation on Digital Innovation Performance in Service Enterprises'. The survey respondents include middle managers and senior leaders of the enterprises the survey respondents are aware of it. This study was approved by the Institutional Review Board (IRB) of the School of Logistics and E-commerce at Zhejiang Wanli University. All participants provided informed consent prior to the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical\u0026nbsp;accordance:\u003c/strong\u003e This study was conducted in accordance with the Institutional Review Board (IRB) of the School of Logistics and E-commerce at Zhejiang Wanli University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration:\u0026nbsp;\u003c/strong\u003eAll participants involved in the study provided informed consent. All participants ensure objectivity, and the research data is authentic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This work was supported by the Zhejiang Provincial Philosophy and Social Science Planning Project (24NDJC201YB); Youth Fund Project of the Ministry of Education (22YJC630001); Zhejiang Provincial Key Research Base for Philosophy and Social Sciences - Lin gang Modern Service Industry and Creative Culture Research Center\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e: Conceptualization, D.C., and Z.W.; methodology, Z.W.; writing-original draft preparation, D.C., and Z.W.; writing-review and editing, D.C., and Z.W.; visualization, S.W.; project administration, D.C., and Z.W.; funding acquisition, D.C..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eNot\u0026nbsp;applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e: Thank all respondents for participating in this survey.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVial, G. 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The impact of entrepreneurial orientation and learning orientation on business model innovation in entrepreneurial enterprises. \u003cem\u003eEnterprise Economy, 39\u003c/em\u003e(8), 87\u0026ndash;95. https://doi.org/10.13529/j.cnki.enterprise.economy.2020.08.011\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Digital Transformation, Digital Innovation Capability, Learning Orientation, Digital Innovation Performance","lastPublishedDoi":"10.21203/rs.3.rs-8968675/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8968675/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the era of digital intelligence, how digital transformation impacts the digital innovation performance of service enterprises has become a crucial topic of current focus. From the perspective of digital innovation capability and learning orientation, this study investigates the impact mechanism of digital transformation on digital innovation performance in service enterprises by collecting 284 valid samples from logistics companies. Empirical research has found that: (1) digital transformation can promote the improvement of digital innovation performance; (2) In terms of mechanism, digital innovation capability plays a mediating role between digital transformation and digital innovation performance, and learning orientation positively moderates the impact of digital transformation on digital innovation performance. The research conclusion further expands the role path of digital transformation on digital innovation performance, providing theoretical basis and inspiration for the implementation of digital transformation in service enterprises and the improvement of digital innovation performance.\u003c/p\u003e","manuscriptTitle":"The Impact Mechanism of Digital Transformation on Digital Innovation Performance in Service Enterprises","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 11:20:26","doi":"10.21203/rs.3.rs-8968675/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-10T11:21:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T03:46:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-07T14:05:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22185344923430468639530544205424963616","date":"2026-04-07T00:03:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-06T04:24:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9439390557996684721560054201771811127","date":"2026-04-06T02:35:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244881292622718974758122516592273950502","date":"2026-04-04T10:38:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200382159250774531910941880599427419664","date":"2026-03-30T13:07:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-30T10:36:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-06T06:19:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-06T06:15:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2026-02-25T14:02:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ef38f219-7739-477d-98ca-96ec53a02c8d","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T04:54:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 11:20:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8968675","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8968675","identity":"rs-8968675","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}