Frontiers in Business, Economics and Management ISSN: 2766-824X | Vol. 21, No. 2, 2025 115 A study of the Impact of Digital Capability on Business Model Adaptation: The Mediating Role of Strategic Orientation and the Moderating Role of Organizational Inertia Zhen Li School of Management, Chongqing University of Technology, Chongqing 400054, China Abstract: Against the backdrop of the booming digital economy, this study explores the impact mechanism of digital capabilities on business model adaptation based on the dynamic capability theory. Using SPSS 27.0 and Amos 24.0 software, we analyzed 463 questionnaire responses from companies in the manufacturing, electronic information, and retail industries. This study found that digital sensing capability, digital seizing capability, and digital transforming capability all have a posit ive impact on business model adaptation; strategic orientation plays a positive mediating role in the relationship between digital capabilities and business model adaptation; organizational inertia plays a negative moderating role in the relationship between digital capability and business model adaptation. The research results expand the relevant studies on the impact mechanism of digital capabilities on business model adaptation, enrich the application scenarios of dynamic capability theory, and provide importa nt practical insights on how enterprises can leverage digital capabilities to promote business model adaptation, build competitive advantages, and foster value creation. Keywords: Digital capability; Business model adaptation; Strategic orientation; Organizational inertia; Empirical research. 1. Introduction With the rapid development of the digital economy, emerging digital technology represented by the Internet of Things (IoT), cloud computing, big data, blockchain and artificial intelligence is reshaping the market environment and the rules of business operations (Volberda et al., 2021), while profoundly affecting the activities and processes of enterprises. And digital technology has become an important source of future competitiveness for contemporary organizations, influencing the mechanisms of enterprise value creation, delivery, and capture (Ancillai et al., 2023). For example, emerging digital technology helps organizations to transform and upgrade functions such as business processes, resource allocation, and internal and external collaboration (Warner et al., 2019); by empowering dynamic capabilities through digital technologies, organizations are able to achieve precise management and effective collaboration (Vial, 2019) to cope with the increasing environmental volatility, complexity, and uncertainty of the digital scenario (Matt etal.,2015).In this context, digital capability has rapidly received extensive attention from research in the fields of strategic management, information systems, and innovation management (Li et al., 2022). Digital capability is the ability of an enterprise to adapt to dynamic environmental changes, maintain sustainable competitive advantage, and use digital technology to integrate digital resources in the value network with other organizational resources to drive systematic digital change in the enterprise, so that digital value creation can be achieved (Hu YC et al., 2023). Digital capability can not only change the efficiency of inter-organizational and inter-business behaviors linkages as well as the way resources are allocated (Nambisan et al., 2018; Liu XL et al., 2020), but can also further lead to the adaptive development of business models (Ma L et al., 2021).Creating disruptive or overthrowing established business models is challenging for most firms, so they prefer to leverage digital capability to adapt existing activities in an incremental way (Cavalcante et al., 2011; Foss and Saebi, 2018).The elements of the business model are fully integrated from the demand side to the product side, from the client side to the production side, in order to adapt to the new market rules (Ancillai el., 2023; Cozzolino el., 2018).Therefore, digital competence and business model adaptation have become important topics (Saebi el., 2017; Foss el., 2017; Cozzolino el., 2018; Warner et al., 2019; Yi JB et al., 2022; Ancillai et al.) With the continuous deepening of research, scholars gradually open the black box of business model adaptation (Wang BC et al., 2023; Wang M and Zhang BC, 2022). According to the dynamic capability theory, digital capability is a strategic behavior of business organizations to use digital technology in depth (Wang M and Zhang BC, 2022), and the impact of this capab ility on BMA is constrained by the constraints from the organization's capabilities and resources (Vial, 2019). This implies that strategic orientation, as an important component of strategic behavior, plays an important mediating role between digital capabilities and BMA. In addition, Pisano (2015) points out that the integration of BMA with digital technology also needs to consider the impact of situational factors. However, current research only starts from conventional boundaries such as firm size and industry type, and lacks research that further explores the situational factors in the process of digital capability's role in business model adapt ation. Digital technology is not only an external opportunity, but also an important input element for enterprises to build new inertia (Vial, 2019). What’s more, due to the existing regulations, procedures, habits and other factors that have shaped the organizational inertia, it is difficult for enterprises to change the organization's existing cognition, structure, resources, etc. 116 when facing external digitalization (Ginsberg and Buchholtz, 1990), which may result in "slow reaction" when adapting to the drastic changes in the environment. "or delayed" phenomenon (McKinley et al., 2014; Shi XX and Zhang QP, 2017). Given the above research gaps, we urgently need to deeply explain the connotation and characteristics of digital capability from the theoretical level and analyze the internal motivation and process mechanism of business model adaptation, to open the black box of the process of business model adaptation and make up for the shortcomings of the existing research. Therefore, this paper focuses on the core issue of "how digital capability drives business model adaptation," expands the research boundaries of dyn amic capability theory, and provides theoretical discoveries and inspirations for enterprises to build and enhance their digital capabilities in the digital economy, to better empower their high-quality development, build competitive advantages, and promote value creation. 2. Literature Review 2.1. Business Model Adaptation This study follows the lineage of business model literature (Saebi et al., 2017; Cozzolino et al., 2018), which defines a business model as the design or architecture of a firm's mechanisms for creating, delivering, and capturing value (Teece, 2010), while Business Model Adaptation (BMA) is defined as the process by which management actively adapts a firm's existing business model to changes in the environment" (Saebi et al., 2017). In the existing research on BMA, three key directions are focused: performance impact effects, adaptation process and influencing factors. Studies have shown that BMA improves firm performance through resource allocation (Zhang et al., 2021); its adaptation process faces many challenges (Saebi et al., 2017), such as willingness to learn from experimentation, and leadership play a decisive role(Snihur et al., 2023; Snihur et al., 2022; McDonald et al., 2020; Cozzolino et al., 2018; Achtenhagen et al., 2013; Demil et al., 2010), path dependence (Cozzolino et al., 2018; Saebi et al., 2017) is the main obstacle; in terms of influencing factors, covering non - technological factors such as external stakeholder needs (Ferreira et al., 2013; Miller et al., 2014), customer portfolio diversity (Denoo, 2021), and disruptive technologies, new technological opportunities and other technological factors (Cozzolino et al., 2018; Teece, 2010; Casadesus-Masanell el., 2013; Sabatier el., 2012; Saebi el., 2017), the network of influencing factors of the BMA and the link with digital capabilities is gradually being opened. 2.2. Digital Capability Accompanying the rise of enterprise digital transformation research, digital capability has developed from the dynamic capability and resource base view in the field of capability theory, which is a deepening of the research in the field of enterprise capability (Annarelli et al., 2021).Based on different application scenarios and research perspectives, scholars have continuously deepened their understanding of the connotation and characteristics of digital capabilities. After a literature review, this study finds that scholars at home and abroad mainly define it from three perspectives: release of resource value, application of digital technology and dynamic change response. From the perspective of dynamic change response, scholars define digital capability as the ability to respond quickly to environmental changes (Li et al., 2022), which is a kind of higher-order capability (Annarelli et al., 2021). This perspective stems from the consideration of the increasing volatility, complexity and uncertainty of the environment in the digital context, and argues that firms need to have the ability to respond quickly to the opportunities and challenges posed by high rates of demand, competition and technological change in order to maintain sustainable competitiveness (Warner and Wäger, 2019). While both the perspective emphasising the release of value from resources and the application of digital technologies focus on the connotations and qualities of only one aspect of digital capability, the conceptual definition under the dynamic change response perspective comprehensively integrates the characteristics of dynamic adaptation, resource integration and technological enablement. Therefore, this paper deconstructs the conceptual connotation from the dynamic capability theory, and argues that digital capability contains the following three dimensions: digital perception capability, digital acquisition capability and digital transformation capability (Warner and Wäger, 2019). The relationship between digital capability and business model adaptation has been studied in the literature using qualitative analyses to examine how digital capability can provide a new impetus and a new way for business model adaptation, but there is a lack of empirical tests to support this view. Numerous case studies of enterprises indicate that companies need to establish digital capabilities to quickly adapt their business models, thereby maintaining high relevance in the wave of the digital economy (Achtenhagen et al., 2013; Teece, 2018; Teece and Linden, 2017). Digital capabilities are not only a prerequisite for being a digital business, but also provide new ways for companies to interact with their customers in terms of resources, processes and outputs in order to co-create value (Lenka et al., 2017). Ancillai et al. (2023) further demonstrated that digital capabilities based on digital technologies can influence value creation in business models through the SLR approach, delivery and capture mechanisms, among other important components (Ancillai et al., 2023). Through the review of the above literature, this paper identifies the following research gaps. First, existing research lacks empirical studies on the impact of digital capabilities on business model adaptation (Cozzolino et al., 2018; Foss et al., 2017). Currently, in the field of research related to business model adaptation, longitudinal case study methods are the primary choice for most researchers to analyze corporate business models. Second, the black box of business model adaptation mechanisms has not yet been opened. Although studies have identified the potential impact of factors such as strategic orientation (Saebi et al., 2017), organizational inertia (Yi JB et al., 2022), and organizational capability development (Achtenhagen et al., 2013; Demil et al., 2010) on business model alignment, the interactions between these factors have not yet been fully explored and empirically tested. This study employs a quantitative research method, introducing strategic orientation as a key mediating variable, while incorporating organizational inertia into the research framework as an indispensable moderating variable. The aim is to comprehensively and deeply explore and elucidate the intrinsic mechanisms by which digital capabilities influence business model adaptations, providing novel perspectives and 117 theoretical references for the research. 3. Research hypothesis 3.1. Digital Capabilities and Business Model Adaptation Digital capability is a strategic behavior for enterprises to utilize digital technology and a new driving force for promoting the adaptation of business models (Trabucco et al., 2021; Ceipek, 2021). Businesses leverage digital capabilities to revitalize various resources and continuously adjust their operational structures and management styles to adapt to the volatility, complexity, and uncertainty of the digital environment. By harnessing the strong driving force of digital technology, enterprises effectively deploy and closely connect the value elements of their business models. This alignment helps them adapt to changing market conditions while steadily enhancing their organizational resilience (Liu ZY and Wu ML, 2019). Digital technology is not only a critical component of the external environment, but also a key factor in driving business model adaptation (BMA) (Lantano, 2022; Balocco, 2019; Ritter and Pedersen, 2019). The impact of digital capabilities on business model adaptation is mainly reflected in the following three aspects: First, digital sensing capability endows enterprises with the ability to scan the external environment and identify unexpected trends that may impact the organization's interests (Loebbecke et al., 2015). Teece (2007) also clearly pointed out that "perceiving new opportunities and threats is largely a systematic activity of scanning, creating, learning, and interpreting." Sensing capability permeates all levels of the organizational system (Teece et al., 2017), which coincides with the adaptive process of business model adaptation. Secondly, digital capabilities also include the ability to collect, process, analyze, and apply data. These capabi lities enable enterprises to obtain market information more quickly and accurately (Wang M et al., 2022), thereby achieving the goals of optimizing products or services and improving user experience. In the process of researching digital capabilities, a key turning point is fully considering customer factors. Xiao JH et al. (2014) pointed out that customers are currently highly empowered, a trend that has prompted the research field to innovate—incorporating customers into the subject category. Good customer relationships can encourage companies to more actively adapt their business models to better meet customer needs and maintain a competitive advantage (Denoo et al., 2021). Therefore, digital capabilities open up broad possibilities for interaction betwee n enterprises and customers, providing customers with more choices, expectations, and demands, forcing enterprises to reassess or supplement traditional customer value propositions with new relationships or multidimensional value propositions (Teece, 2010). This means that as a company's digital seizing capabilities improve, it can create agile and personalized customer relationship management (Eller, 2020; Sun XB et al.,2019), amplify customer effects, acquire customer resources, and subsequently influence internal organizational design (Zhang ZG et al., 2021). Finally, digital capabilities, as higher-order dynamic capabilities, can build and update resources, assets, and ordinary capabilities (Teece et al., 2007), and can reallocate resources according to market needs (Vial, 2019), achieving value transformation. Resources and capabilities are carefully arranged through digital transforming capabilities and coordinated with partners' activities to provide value to customers (Foss et al., 2017). Managers can also coordinate, bundle, and utilize big data resources through digital technology to create value for the enterprise, improve resource conversion rates, and adapt the elements of the business model through sufficient cooperative interaction (Annarelli et al., 2021); by centering on data, mobilizing internal and external resources of the enterprise, reducing the complexity and uncertainty of organizational information, thereby creating greater business value and enhancing the ability to adapt the business model (Ji F et al., 2022). Accordingly, the following hypotheses are proposed: H1a: Digital sensing capability positively impacts business model adaptation. H1b: Digital seizing capability positively impacts business model adaptation. H1c: Digital transforming capability positively impacts business model adaptation. 3.2. The Mediating Role of Strategic Orientation Strategic orientation is an important concept that guides the allocation of resources and strategic actions within a company (Gatignon et al., 1997). It has a positive effect on the company's ability to carry out adaptive activities in both market and technological domains (Zhou, 2005). Therefore, this paper draws on Kevin (2005) and Ciampia (2021), among others, to argue that strategic orientation influences the dynamic process in which firms continually seek new opportunities, utilize new technologies, an d realise value transformation, which will have a significant impact on business models from the aspects of market, technology, and entrepreneurship, guiding firms to achieve oriented interactions with their environments, and further influencing the elements of business models and the direction adjustments. Digital capabilities can efficiently drive enterprises to form a strategic orientation. Companies leverage digital capabilities to maintain a more acute market orientation towards customers and the market, guiding the allocation of dynamic capabilities, thereby efficiently designing and managing resources, achieving value exchange and accumulation, and further effectively adapting business models. Moreover, during the process of exploratory learning, companies enhance their ability to acquire exploratory learning through extensive cross-boundary search activities (Peng ZL and He PX, 2015). This not only allows them to obtain the latest information and knowledge but also further enhances their market perception, meets potential market demands, and thereby achieves targeted adaptations in their business models. In a digital environment, strategic orientation emphasizes the "technology -driven" logic (Jian ZQ et al., 2015). Companies innovate technology to build high-efficiency transaction networks, creating user value and further optimizing and expanding the industrial ecosystem, thereby driving business model adaptations. Therefore, companies also invest more in research and development, actively acquire new technologies, and realise the productization and commercialization of technology (Gatignon H., 1997), driving the renewal and iteration of value propositions (Zott et al., 2011). Therefore, enterprises base their internal decisions on observations and judgments of changes in the external environment, thereby responding specifically to the external environment. In this feedback loop, internal resources are restructured, and the flexible allocation 118 of resources increases both the efficiency and scope of resource utilization (Hu YC et al., 2023), promoting business model adaptations and thereby amplifying the effects of these adaptations (Pang CW et al., 2015). Therefore, this paper posits that the strategic orientation digital capability of enterprises plays an important mediating role between digital capability and business model adaptation. Therefore, the following hypotheses are proposed: H2: Strategic orientation mediates the relationship between digital capability and business model adaptation. H2a: Strategic orientation mediates the relationship between digital sensing capability and business model adaptation. H2b: Strategic orientation mediates the relationship between digital seizing capability and business model adaptation. H2c: Strategic orientation mediates the relationship between digital transforming capability and business model adaptation. 3.3. The Moderating Role of Organizational Inertia The dynamic capabilities view points out that incumbent firms tend to engage in what they have always done or are good at, ultimately falling into the "competitiveness trap," with core capabilities manifesting as core rigidity resisting adaptation. Organizational inertia is the manifestation of an organization maintaining its original state, solidifying, and exhibiting internal inertia (Zhou et al., 2010), which hurts inhibiting adaptation. Then, organizational inertia plays a crucial role in the evolution of resources and capabilities, even directly constraining the generation of digital capabilities, determining the direction of resource operation, and the probability of adaptation. As research deepens, scholars have found that organizational inertia manifests as structural inertia in terms of systems and practices, and cognitive inertia in terms of knowledge and experience (Lv YB et al., 2015). As the lifespan and scale of the organization expand, institutionalized and standardized operational processes, habits, and routines will gradually form within the organization (Hambrick et al., 1987). To some extent, organizational inertia helps companies to avoid overly turbulent change and ensures that the organization runs smoothly. But in the long run, as market competition intensifies, organizational inertia is detrimental to maintaining a keen sense of the environment, reducing the organization's flexibility and adaptability to the enviro nment. This means that established business models may become inert and corrosive over time (Cavalcante et al., 2011), and may even inhibit the perceptual ability to make timely adaptations to the business model (Jiao et al., 2021). Moreover, excessive organizational inertia can lead to a high degree of path dependence, creating an overly conservative atmosphere within the organization that resists the digital development needs of the external environment, suppressing the development of digital sensing capabilities, and hindering the adaptation of business models (Lv YB et al., 2015). Moreover, organizational inertia greatly hinders managers from exercising autonomy within the organization, reducing the flexibility for adjusting business models. Resource dependence and the rigidity formed by the accumulation of organizational routines make it difficult for incumbent firms to achieve adaptive development on paths that are non - linearly related to their traditional development trajectories in response to external environmental changes. It is evident that if a company focuses on a certain type of resource operation and capability, once the market shifts to a completely new model, it will be difficult to achieve an effective strategic transition, and the construction and functioning of digital seizing, transforming, and capability will be hindered. Therefore, the following hypotheses are proposed: H3: organizational inertia negatively moderates the relationship between digital capabilit y and business model adaptation. H3a: Organizational inertia negatively moderates the relationship between digital sensing capability and business model adaptation. H3b: Organizational inertia negatively moderates the relationship between digital seizing capability and business model adaptation. H3c: Organizational inertia negatively moderates the relationship between digital transforming capability and business model adaptation. Figure 1. Theoretical model 119 4. Methodology 4.1. Sample Selection and Data Collection This study employs a questionnaire as a data collection instrument, disseminated through both online and offline modalities, targeting firms within the manufacturing, electronic information, and retail sectors that have developed and utilized digital capabilities over the past two years. The majority of respondents comprised middle and senior-level decision-makers, department heads, and other key senior personnel. To ensure the validity of the questionnaire, the research initially examines the BMA status of the participating companies through their official websites and relevant news articles, establishing filtering criteria to ascertain the authenticity of the samples. To mitigate linguistic ambiguities, the questionnaire items were iteratively refined within the research team, integrating insights from a pre-survey and participant feedback to reduce measurement bias. A total of 532 questionnaires were distributed, yielding 497 responses, of which 463 were deemed valid after excluding those with significant inconsistencies, omissions, or other issues. The distribution and characteristics of the surveyed companies are presented in Table 1. Table 1. Sample characteristics Form Quantities Percentage Age of business Under 3 years 66 14.3% 3-6 years 87 18.8% 7-10 years 57 12.3% More than 10 years 253 54.6% Industry characteristics Manufacturing industry 197 42.5% Information Technology Service Industry 93 20.1% Retail industry 36 7.8% Others 137 29.6% Company size <100 45 9.7% 100-500 117 25.3% 501-1,000 46 9.9% 1,001-5,000 77 16.6% More than 5,000 178 38.4% Nature of the company state-owned business 94 20.3% private business 305 65.9% Others 64 13.8% 4.2. Measures To ensure the measurement scale exhibits strong reliability and validity, this study utilizes established scales and relevant literature from both domestic and international sources to assess the selected scales, followed by necessary adaptations and refinements to the measurement items. Digital competence is categorized into distinct dimensions based on the frameworks proposed by Warner (2019), Ciampia (2021), and other pertinent research, resulting in three items each for measuring digital sensing competence, digital seizing competence, and digital transforming competence, culminating in a total of nine items. Strategic orientation is similarly delineated into three dimensions—market orientation, technology orientation, and entrepreneurial orientation—drawing from the works of Wei LQ (2010), Zhou (2016), and other relevant studies, with each dimension assessed through three items, leading to a total of nine items designed for this purpose. The construct of organizational inertia is measured using the scale developed by Dang (2016) and colleagues, which evaluates organizational inertia through the lenses of structural inertia and cognitive inertia, resulting in six items. Furthermore, the measurement of BMA is informed by the research of Saebi (2017) and others, with six items specifically designed for this construct. To enhance the reliability of the research findings, control variables such as the number of years since the establishment of the enterprise, enterprise size, industry classification, and the nature of the enterprise are incorporated. The number of years since establishment is categorized into four levels based on the duration of operation, while enterprise size is similarly classified into four levels according to employee count. All items, with the exception of the control variables, are measured using Likert 5-level scale. Table 2. Explanatory variables Variable name Symbol Business Model Adaptation Y Digital Capability X Digital Sensing Capacity X1 Digital Seizing Capacity X2 Digital Transforming Capability X3 Strategic Orientation W Organizational Inertia Z 4.3. Reliability and Validity In this research, the reliability and validity of the questionnaire were assessed utilizing SPSS 27.0 and AMOS 24.0 software, with the findings presented in Table 2. The Cronbach's alpha coefficients and composite reliability (CR) values for the constructs of digital sensing ability, digital accessibility, digital conversion ability, strategic orientation, organizational inertia, and BMA all exceeded the threshold of 0.7, indicating a satisfactory level of internal consistency and reliability. Regarding validity, the subscales pertaining to organizational inertia and BMA have been acknowledged for their strong content validity in both national and international research contexts. Furthermore, the convergent validity of each latent variable was evaluated through confirmatory factor analysis (CFA), revealing that the majori ty of standardized factor loadings (λ) for the questionnaire items were above 0.6, and most average variance extracted (AVE) values surpassed the acceptable criterion of 0.5, thereby demonstrating robust convergent validity. Additionally, the model fit indices were reported as follows: χ2/df = 2.972, IFI = 0.932, CFI = 0.932, NFI = 0.902, TLI = 0.920, and RMSEA = 0.065. Collectively, these results indicate that the design of the questionnaire and the structural modeling employed in this study achieved a commendable standard across various critical indicators. 120 Table 3. Measurement items and reliability tests for variables Variable Item Standard factor loading Cronhach's α CR AVE X1 Focus on digital technology and its trends (Warner&Waeger,2019). 0.802 0.902 0.899 0.749 Capture changes in market demand through data analytics and forward-thinking technologies (Warner&Waeger,2019). 0.908 Focus on developing digital thinking in business scenarios (Warner&Waeger,2019). 0.882 X2 Insight into customer needs through the deep application of digital technology (Warner&Waeger,2019). 0.909 0.924 0.924 0.801 Rapid and efficient reallocation of resources through digital technology (Warner&Waeger,2019). 0.896 Seek effective integration with external resources through digital technology (Warner&Waeger,2019). 0.88 X3 Adoption of cutting-edge digital technologies to achieve significant business improvements (Warner&Waeger,2019). 0.886 0.910 0.912 0.775 Building novel business models with digital technology (Warner&Waeger,2019). 0.896 Identify the maturity level of the digital workforce. 0.858 W Business objectives express a commitment to customer satisfaction (Wei&Lau,2008). 0.706 0.901 0.930 0.603 Close co-operation between departments to meet customer needs (Wei&Lau,2008). 0.825 Quickly identify competitors' strategies (Wei&Lau,2008). 0.839 Investing resources in technological research and development and fostering innovation capacity (Zhou&Tse,2005). 0.809 Strong ability to acquire, learn and build on technical knowledge (Zhou&Tse,2005). 0.864 Effective management and use of technological resources (Zhou&Tse,2005). 0.864 Emphasis on seizing business opportunities in a more volatile external environment (Ciampia et al. 2021). 0.808 Favouring high-risk, high-yield developments over the past three years (Ciampia et al. 2021). 0.492 Over the past 3 years, the company has introduced many new products or services (Ciampia et al. 2021). 0.705 Z Decision-making is largely informed by historical experience and existing institutions (Dang et al. 2016). 0.52 0.740 0.840 0.478 In the choice of work content, new methods or tools may be rejected out of habit (Dang et al. 2016). 0.684 Prefer improvements to existing products rather than new designs (Dang et al. 2016). 0.763 Focusing on the dynamics of the environment that directly affects their ability to innovate (Dang et al. 2016). 0.798 Faster change of strategy in response to market changes than partners (Dang et al. 2016). 0.831 Clearly positioned in the field of innovation and not competing with partner companies in the same industry (Dang et al. 2016). 0.466 Y Importance of introducing new types of products or services in response to environmental change (Saebi et al. 2017). 0.78 0.906 0.907 0.619 Importance of increasing sales to new domestic and international customers to cope with the changing environment (Saebi et al. 2017). 0.797 Importance of building closer partnerships to address environmental change (Saebi et al. 2017). 0.799 Importance of exploring and selecting new suppliers to cope with environmental changes (Saebi et al. 2017). 0.804 Importance of recorganising resources within the company to cope with environmental changes (Saebi et al. 2017). 0.761 Importance of adjusting the price of a product or service in response to changes in the environment (Saebi et al. 2017). 0.779 4.4. Distinguishing Validity and Common Method Bias In this study, various competing models were employed to assess variable differentiation, with the results presented in Table 3. The fit indices for the six -factor model were as follows: χ²/df = 3.758, CFI=0.899, IFI=0.900, TLI=0.888, and RMSEA=0.077. These indices indicate that the six -factor model provided the best fit among all tested models, thereby demonstrating a high level of variable discriminant validity. To address the potential for common method bias among the variables, the study utilized the Harman one-factor method for testing. The unrotated first factor accounted for 47.957% of the explained variance, which is below the 50% threshold, 121 suggesting that serious common method bias was not present. Additionally, a one-way validated factor analysis (VVA) was conducted on the variables, revealing that the one -way VVA exhibited a poorer fit (χ²/df=7.291, CFI=0.738, IFI=0.739, TLI=0.719, RMSEA=0.122). These findings indicate that common method bias was effectively controlled in this study. Table 4. Results of Confirmatory Factor Analysis Model χ 2 /df CFI IFI TLI RMSEA Single- factor model 0.738 0.739 0.719 Two-factor model 6.187 0.804 0.805 0.789 0.106 Three-factor model 4.305 0.876 0.876 0.866 0.085 Four-factor model 5.636 0.748 0.749 0.729 0.126 Five-factor model 3.985 0.890 0.890 0.879 0.080 Six-factor model 3.758 0.899 0.900 0.888 0.770 5. Data analysis and Results 5.1. Correlation Analysis and Multiple Covariance Test To enhance the analysis of the data, this study calculated the mean of several question items corresponding to each latent variable within the sample data set. Subsequently, descriptive statistics and correlation analyses were conducted using SPSS version 27.0. Table 4 presents the correlation coefficient matrix along with the variance inflation factors for the examined variables. The results of the descript ive statistical analysis, which includes means and standard deviations, are also reported. The correl ation coefficients among digital sensing capability, digital seizing capability, digital transforming capability, strategic orientation, organizational inertia, and BMA range from 0.212 to 0.752, indicating a significant association that provides preliminary support for the regression analysis. Furthermore, the variance inflation factor (VIF) values for the variables are substantially below the critical threshold of 10, suggesting that the regression model does not exhibit serious multicollinearity issues. Table 5. Correlation analysis and variance inflation factor analysis Y X1 X2 X3 W Z Age Industry Size Nature VIF Y 1 X1 0.416 ** 1 2.028 X2 0.450 ** 0.660 ** 1 2.994 X3 0.457 ** 0.639 ** 0.752 ** 1 2.801 W 0.623 ** 0.572 ** 0.698 ** 0.693 ** 1 2.427 Z 0.405 ** 0.212 ** 0.346 ** 0.319 ** 0.404 ** 1 1.227 Age 0.074 0.084 0.001 0.002 -0.022 -0.017 1 1.086 Industry -0.110 * -0.046 -0.078 -0.050 -0.097 * 0.022 -0.043 1 1.122 Size 0.161 ** 0.266 ** 0.268 ** 0.241 ** 0.192 ** 0.034 0.254 ** -0.276 ** 1 1.265 Nature 0.120 ** 0.075 0.033 0.030 0.072 -0.030 -0.007 -0.159 ** 0.037 1 1.038 Mean 4.027 4.211 3.864 3.851 3.917 3.591 3.070 2.500 3.490 1.950 Std.Dev. 0.664 0.808 0.931 0.884 0.618 0.638 1.141 1.651 1.453 0.633 5.2. Hypothesis Testing 5.2.1. Main effect test The hypothesis testing was conducted utilizing stratified regression analysis, with the results presented in Table 5. To assess the impact of digital capabilities on the adaptation of business models, models 5 and 6 were developed. Model 6 indicates that digital perception, seizing, and transforming capabilities exert a positive influence on BMA, with coefficients of β=0.118 (p<0.05), β=0.133 (p<0.01), and β=0.168 (p<0.001), respectively. Consequently, hypotheses H1, H1a, H1b, and H1c are supported. Furthermore, the coefficient reflecting the influence of digital transforming capability on BMA is greater than that of both digital sensing and digital seizing capabilities. This suggests that the impact of digital transforming capability on enterprise performance is more significant than that of digital sensing and seizing capabilities. 5.2.2. Mediation effect test This research employs the stepwise methodology outlined by Baron and Kenny (1986) to examine the mediating effect of strategic orientation and to ascertain whether the proposed model meets the criteria for the presence of such an effect. The initial step entails evaluating the importance of the relationship between the independent variables, specifically digital sensing capability, digital seizing capability, and digital transforming capability, and their impact on BMA. The subsequent step entails evaluatin g the impact of these independent variables on the mediating variable, strategic orientation. Finally, the analysis investigates the influence of the independent variables on business performance. The regression analysis results are presented in Table 5. A comprehensive evaluation of the mediation effect validation steps, as illustrated in Models 2, 4, 6, and 7 in Table 5, leads to the conclusion that strategic orientation serves as a full mediator between digital competence and business model adaptations, thereby supporting Hypothesis H1. Additionally, it is confirmed that strategic orientation also functions as a full mediator in relation to hypotheses H1a, H1b, and H1c. 5.2.3. Test of moderating effect The investigation into the moderating effect of organizational inertia is conducted utilizing the hierarchical regression approach, as referenced in the work of Wen Zhonglin et al. In this analysis, centering is applied to the independent, moderating, and dependent variables, and interaction terms are created accordingly. As presented in Table 6, Models 8-11 illustrate the regression models assessing the impact of digital capability, digital sensing 122 capability, digital seizing capability, and digital transforming capability on business model adaptation. Subsequently, Models 12-15 incorporate the interaction terms involving digital capability, digital sensing capability, digital seizing capability, digital transforming capability, and organizational inertia. The findings indicate that organizational inertia significantly and negatively moderates the relationship between digital capability, digital sensing capability, and business model adaptation (β=0.105, p<0.05; β=0.103, p<0.05), thereby supporting hypotheses H3 and H3a. However, while the regression coefficients of Models 12 and 13 align with the theoretical model's expectations, the moderating effect does not achieve statistical significance, leading to the conclusion that hypotheses H3b and H3c are not supported. Table 6. Regression model and results Variable BMA W BMA M1 M2 M3 M4 M5 M6 M7 Age 0.022 0.049 * -0.040 -0.015 0.022 0.037 0.046 * Industry -0.022 -0.013 -0.013 -0.018 -0.022 -0.027 -0.016 Size 0.060 ** 0.004 0.085 -0.011 0.060 ** -0.008 -0.001 Nature 0.112 * 0.074 0.057 0.034 0.112 * 0.091 0.071 X1 0.087 ** 0.114 * 0.060 X2 0.238 *** 0.132 ** -0.015 X3 0.248 *** 0.172 *** 0.020 W 0.661 *** 0.614 *** R 2 0.043 0.403 0.048 0.563 0.043 0.264 0.407 △R2 0.043 0.360 0.048 0.516 0.043 0.222 0.143 F 5.123 ** 61.696 *** 5.727 *** 83.839 *** 5.123 ** 23.355 *** 38.925 *** Note: ***, **, and * indicate P < 0.01, P < 0.05, and P < 0.1, respectively Table 7. Regression Model and Results Variable BMA M8 M9 M10 M11 M12 M13 M14 M15 Age 0.026 0.018 0.041 0.039 0.028 0.023 0.041 0.039 Industry -0.028 -0.029 -0.022 -0.027 -0.032 * -0.033 -0.026 -0.030 Size -0.002 0.011 0.002 0.007 0.003 0.013 0.009 0.011 Nature 0.072 0.082 0.101 * 0.101 * 0.086 0.094 0.111 ** 0.111 ** X 0.389 *** 0.293 *** X1 0.327 *** 0.246 *** X2 0.315 *** 0.233 *** X3 0.336 *** 0.257 *** Z 0.356 *** 0.385 *** 0.324 *** 0.324 *** X×Z -0.105 * X1×Z -0.101 * X2×Z -0.041 X3×Z -0.031 R 2 0.233 0.189 0.223 0.230 0.326 0.305 0.300 0.311 △R2 0.190 0.146 0.180 0.187 0.093 0.116 0.077 0.081 F 27.723 *** 21.283 *** 26.166 *** 27.325 *** 31.384 *** 28.562 *** 27.794 *** 29.296 *** Note: ***, **, and * indicate P < 0.01, P < 0.05, and P < 0.1, respectively 5.3. Robustness Testing 5.3.1. Substituting key variable measures to assess the robustness of findings Building upon the research conducted by Li XC et al. (2016), Liu XY and Lin S (2015), this study further evaluates the robustness of its findings by employing alternative measurement approaches for business model innovation. In alignment with the definition of a business model proposed by Amit and Zott (2001) and others, three specific questions are utilized to assess the extent of adaptations made to the business model in relation to the transaction structure. The measurement scale has successfully undergone reliability and validity testing, demonstrating both credibility and validity. The regression outcomes derived from this methodology are consistent with the results obtained from hypothesis testing, with detailed findings presented in Tables 7 and 8, thereby reinforcing the research conclusions of this paper. 123 Table 8. Replacing key variable measures to test the robustness of findings Variable BMA W BMA M16 M17 M18 M19 M20 M21 M22 Age -0.026 0.004 -0.040 -0.015 -0.026 -0.007 0.002 Industry -0.019 -0.009 -0.013 -0.018 -0.019 -0.025 -0.014 Size 0.087 *** 0.024 0.085 *** -0.011 0.087 0.002 0.008 Nature 0.098 0.055 0.057 0.034 0.098 0.072 0.053 X1 0.087 ** 0.136 ** 0.085 * X2 0.238 *** 0.340 ** -0.006 X3 0.248 *** 0.256 *** 0.111 ** W 0.748 *** 0.587 *** R 2 0.050 0.486 0.048 0.563 0.050 0.385 0.508 △R2 0.050 0.436 0.048 0.516 0.050 0.335 0.123 F 5.986 *** 86.418 *** 5.727 *** 83.839 *** 5.986 *** 40.712 *** 58.666 *** Note: ***, **, and * indicate P < 0.01, P < 0.05, and P < 0.1, respectively Table 9. Replacing key variable measures to test the robustness of findings Variable BMA M23 M24 M25 M26 M27 M28 M29 M30 Age -0.021 -0.031 -0.003 -0.004 -0.019 -0.027 -0.003 -0.004 Industry -0.0026 -0.027 -0.019 -0.024 -0.030 -0.031 -0.023 -0.027 Size 0.010 0.026 0.016 0.018 0.015 0.028 0.022 0.022 Nature 0.048 0.060 0.085 0.083 * 0.062 0.072 0.094 * 0.093 X 0.485 *** 0.401 *** X1 0.409 *** 0.328 *** X2 0.389 *** 0.315 *** X3 0.435 *** 0.364 *** Z 0.321 *** 0.375 *** 0.290 *** 0.288 *** X×Z -0.072 X1×Z -0.108 * X2×Z -0.032 X3×Z -0.033 R 2 0.329 0.265 0.309 0.346 0.402 0.369 0.368 0.406 △R2 0.279 0.215 0.259 0.297 0.073 0.104 0.059 0.060 F 44.835 *** 32.937 *** 40.799 *** 48.402 *** 43.689 *** 38.037 *** 37.782 *** 44.433 *** Note: ***, **, and * indicate P < 0.01, P < 0.05, and P < 0.1, respectively 5.3.2. Utilization of the process plug-in to evaluate the robustness of findings Table 10. Robustness test regression results Variable BMA M31 M32 M33 M34 Age 0.0490 * 0.0437 ** 0.0477 * 0.0478 * Industry -0.0196 -0.0197 -0.0176 -0.0185 Size 0.0030 0.0019 0.0052 0.0044 Nature 0.0781 * 0.0776 * 0.0823 * 0.0842 * X 0.0222 X1 0.0456 X2 -0.0131 X3 0.0173 W 0.5414 *** 0.5270 *** 0.5812 *** 0.5527 *** Z 0.2411 *** 0.2320 *** 0.2319 *** 0.2166 *** X×Z -0.1212 ** X1×Z -0.1002 * X2×Z -0.0737 X3×Z -0.0435 R 2 0.4439 0.4438 0.4384 0.4360 F 45.2977 ** * 45.2777 ** * 44.3055 ** * 43.8635 ** * All effects were re-evaluated employing the Process plug - in in SPSS version 27.0, as recommended by Hayes (2017). Model 5 was chosen to reassess the research model, and as illustrated in Table 9, the results of the tests corroborated the anticipated hypotheses, thereby reaffirming the assumptions established in the cascade regression analysis. 6. Conclusion and Discussion 6.1. Research Conclusion This study employs dynamic capability theory to analyze the relationship between digital capability, strategic orientation, organizational inertia, and business model adaptation through empirical research methods and draws the following conclusions. First, digital capability has a significant positive impact on business model adaptation. Digital sensing, seizing and transforming capabilities, as the important components of digital capabilities, all positively affect business model adaptation, of which digital transforming capability has a relatively greater impact. Digital sensing capability enables enterprises to scan the environment and analyze information, which is in line with the process of business model adaptation; digital seizing capability enhances the ability of enterprises to obtain market information, which helps optimize product services and improve user experience, and prompts 124 enterprises to interact with customers, thus affecting the elements of the business model; as a higher-order dynamic capability, digital transforming capability can redistribute resources, realise value transformation, and adapt the business model; and digital transforming capability has a significant positive impact on the adaptation of the business model. As a higher-order dynamic capability, digital transforming capability can redistribute resources, realise value transformation, adjust the elements of t he business model, and enhance the capability of business model adaptation. Second, strategic orientation plays an important mediating role between digital capabilities and business model adaptation. Strategic orientation guides firms' resource allocation and strategic actions, affects firms' adaptation to the external environment, and influences business model adaptation in terms of markets, technology and entrepreneurship. Digital capabilities promote the formation of strategic orientation, through which enterprises can keenly perceive the market, enhance exploratory learning capabilities, promote technological innovation, and then optimize their business models to enhance their competitive advantages. Third, organizational inertia negatively regulates the relationship between digital capabilities and business model adaptation. Organizational inertia is manifested in structural and cognitive inertia, which can maintain the smooth operation of the organization to a certain extent, but in the long run, it will reduce the organization's flexibility and ability to adapt to the environment. Organizational inertia inhibits the development of digital capabilities and hinders business model adaptation. When organ izational inertia is high, the effect of digital capabilities on business model adaptation is weakened; conversely, when organizational inertia is low, the positive effect of digital capabilities on business model adaptation is strengthened. 6.2. Theoretical Contributions Firstly, based on the real situation of enterprise strain and adjustment driven by the digital economy, we constructed a theoretical model of "digital capability -strategy orientation- business model adaptation" based on dynamic capability theory, which provides an integrated framework for the research related to digital capability, strategy orientation and business model adaptation. It provides an integrated framework for the research on digital capability, strategy orientation and business model adaptation. Second, from the perspective of research on the impact of digital capabilities on strategic orientation and business model adaptation, this study found that firms' digital capabilities show a positive impact on business model adaptation, which theoretically reveals the need for firms to pay attention to the positive role of digital capabilities in the process of business model adaptation. Third, by introducing the important variable of strategic orientation force, it confirms the role of strategic orientation in facilitating and mediating the relationship between digital capability and business model adaptation, and argues that strategic orientation has a conductive role between digital capability and business model adaptation, which opens up the "black box" in the evolution path from digital capability to business model adaptation fourth, it discovers that organizational inertia has a positive impact on the relationship between digital capability and business model adaptation. Fourth, the moderating effect of organizational inertia on the relationship between digital capability and business model adaptation is found, which answers the questions of universality, specificity, selectivity and decision -making of enterprises with different degrees of organizational inertia in the digital economy era based on the adjustment of digital capability-driven enterprise business model. 6.3. Practical Insights First of all, in the context of the digital era, enterprises must build and enhance digital capabilities to deepen the application of digital technology in the process of enterprise operations. Enterprises can then fully utilize existing resources and capabilities to enhance the efficiency and value of business model adaptation. In addition, enterprises should build a comprehensive digital capability system by enhancing digital sensing, seizing and transforming capabilities. For enterprises that have not yet fully utilized their digital capabilities, enterprises consciously guide their organizations to learn and apply digital technologies to seize the dividends brought by the digital economy. Secondly, enterprises need to use strategic orientation to grasp the direction of business model adaptation, identify the match between market demand and their own conditions, sort out the development path of resources and capabilities, and improve operational efficiency and market competitiveness. Finally, organizationa l inertia can prevent paradigm adjustments in companies with insufficient digital capabilities. Companies need to look at their own organizational inertia, strengthen its positive effects, reduce its negative impacts, and use the positive impacts to drive dynamic adjustments in their business models. 6.4. Research Limitations and Perspectives This paper still has the following shortcomings: firstly, this paper does not carry out in-depth research on the frequency and degree of business model adaptation, and the subsequent can carry out case studies on the degree of business model adaptation as the research topic, and further analyze the influencing factors that can deepen or hinder the degree of adjustment; secondly, this paper does not completely reveal the influence of the digitization ability on the adjustment path of the business model, and the adjustment mechanism is still to be excavated in-depth, and in the future, it is needed. 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