Introduction

The emergence of digital technologies, such as 5 G, cloud computing, and AI, has largely subverted the traditional business model (Wannakrairoj and Velu, 2021). With its Digital Strategy 2022–2025, the United National Development Programme (UNDP) aims to help countries build inclusive, ethical, and sustainable digital societies, as digital ability-driven business model innovation has become the driving force of digital economic development (Burström et al. 2021; Guo et al. 2022; Trischler and Li-Ying, 2023). Business model innovation (BMI hereafter) focuses on the business model, which relates to the innovation of a system of products, services, technology, and/or information flows that go beyond the firm (Clauss, 2017; Mancuso et al. 2023). Researchers have studied financial resource slack (Nyuur et al. 2023) and determined that are leading forces behind BMI; however, BMI is not exclusively driven by finances, also emphasized the need to explore additional drivers of BMI (Albats et al. 2023), for example, the boundary-spanning behaviour of top management teams (Yan et al. 2020), customers (Zhou et al. 2022), digital platforms (Xie et al. 2022), knowledge management (Yu et al. 2020), bricolage knowledge absorptive capacity (Bhatti et al. 2021), digital technology (Ancillai et al. 2023), and strategic agility (Clauss et al. 2019). Although researchers have sought to elucidate the impact of BMI, there is a shortage of research on the association between digital capabilities and BMI. In reality, the costs of not undergoing digital transformation are high: declining revenue growth, increasing costs, and failing to achieve a competitive advantage (Li et al. 2023). Unquestionably, digital transformation is a multifaceted process; thus, a firm must be prepared for its resources and competencies, such as digital capabilities (Verhoef et al. 2021) that experiment with new approaches and articulate the risks and challenges of digital technologies.

The high dynamics of the digital environment have resulted in serious challenges to BMI, which many enterprises are forced to suspend because they cannot adapt to the rapid changes. A growing number of scholars are conducting BMI research using the digital perspective, emphasising digital capabilities as the key source of firm innovation (Bashir and Verma, 2019; Strohmeier, 2020). Accenture released the “China Digital Transformation Index 2023”, and 2% of Chinese enterprises are undergoing comprehensive and sustained digital transformation, leading on financial indexes, and also reinventing BMI. Nonetheless, we lack BMI insights that adopt dynamic perspectives on digital development/integration in regional enterprises. This may be a considerable gap because of the high dynamics of the challenges of the digital environment and the steady call for more research on BMI. To the best of our knowledge, there has been no multiregional discussion on digital capabilities, we utilize as a change in how an enterprise employs digital technologies, to develop BMI that helps to create and appropriate more value for the enterprise. To bridge this research gap, our study focuses on dynamic perspectives on digital development/integration in regional enterprises. Specifically, literature has extensively recognized that digital capabilities have been found to play a critical role in successfully transforming firms’ business models to capture the value generated by integrating new digital opportunities, there are not yet studies that address the role of digital capabilities in exploiting business opportunities. Therefore, we aim to answer the following research questions: what are the influences of digital capabilities on BMI and ultimate competitiveness, and how is this different competitiveness achieved?

We address this gap in the literature by proposing that the determining impact of digital capabilities on BMI is better understood from the dynamic capabilities perspective. Hellemans et al. (2022) suggest that digital capabilities have a dark side as a source of unexpected tensions and paradoxical effects, which may risk value creation for societal actors. The combination of digital and BMI expands the search landscape, making it difficult for many companies to manage the trade-off between knowledge breadth and depth. Finally, the wide variety of competencies involved and the larger access to information may spur even more conflictual relationships between stakeholders in managing digital-base BMI (Bocken and Geradts, 2020). More specifically, we know very little about what drives the capacity of firms to sense and seize opportunities created by digital technologies to initiate circular BMI (Ancillai et al. 2023). Furthermore, we lack knowledge of what capabilities are involved in managing the digital transformation from linear to circular business models (Ranta et al. 2021). Finding solutions to those issues is critical because dynamic capabilities are higher-order capabilities that confront novel challenges (Wilhelm et al. 2022) and enable BMI (Teece, 2020) based on dynamic capability theory (Teece et al. 1997). Enhancing circular BMI through digital capabilities requires the unique capacity to combine service development capabilities, network management capabilities, and digital capabilities within a process of BMI (Nasiri et al. 2023). Thus more insight is needed into how this design and operation connect with higher-order (dynamic) capabilities, such as sensing change, seizing opportunities, and transforming organizations (Nguyen et al. 2023).

This study also uses organizational inertia to investigate organizations’ responses to adversity and emergencies. Despite a scientific and accurate decision provided by digital capabilities, organizations are likely to fall into a “capability trap”. Why do some enterprises fail to achieve the expected results when implementing digital measures? The primary reason for this failure is encountering many hurdles in integrating digital technology into the R&D stage (Lei et al. 2024). Broekhuizen et al (2021) emphasized that the crucial role of digital capabilities involves the flexibility of organizational structure and operation processes, which slows organizational responses to changing environmental conditions and the effectiveness of BMI. Drawing on dynamic capability theory, we suggest that organizations could have disparate interpretations of organizational inertia and that a shared perception is likely to create strong resource action strategies, amplifying the positive impacts of cultivating digital capability with new demands for enterprises to adapt to digital economy development. To heed Halpern et al. (2021) call for research on the contextual factors in organizational inertia-driven BMI research, this study examines the boundary conditions of organizational inertia. In doing so, we aim to extend the current literature by highlighting that adaptability and transformability within organizational inertia may shape the influencing process of BMI.

Overall, the current research contributes to the literature on digital capabilities and BMI in the following ways. First, this study casts new light on digital capabilities and BMI from the dynamic capability perspective. We identified the antecedent influence of BMI, constructing a “digital-dynamic-BMI” framework. Second, despite the literature extensively recognizing the role of digital technologies in fostering digital circular BMI (Rusch et al. 2023), little attention has been paid thus far to the role of dynamic capabilities. In reality, dynamic capabilities are critical in successfully transforming firms’ business models to capture the value generated by integrating new digital opportunities (Heubeck, 2023). Drawing on dynamic theory, this study enriches existing research by addressing the role of dynamic capabilities in exploiting digital capabilities associated with BMI. Third, we highlight the moderating role of organizational inertia. This finding contributes to the digital capabilities and BMI literature by emphasizing that contextual stimulus (organizational inertia) can strengthen BMI’s effect. Organizational inertia makes them less aware of the external information environment (Mikalef et al. 2021), potentially amplifying the effects on the BMI. By investigating organisational inertia’s contingency role in BMI’s influencing mechanism, we aim to explain how to overcome its influence on BMI and extend the current literature by evaluating a motivational process (organizational inertia) underlying the association between digital capabilities and BMI. See Fig. 1.

Fig. 1: Research framework.
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Research framework of digital capabilities impact on business model innovation.

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Literature review and hypothesis development

The positive effect of digital capabilities on business model innovation

Digital capability refers to an enterprise’s capacity to harness digital technologies, such as the Internet, cloud computing, big data, and AI, to foster growth and innovative business models (Warner and Wäger, 2019). These capabilities encompass service development, network management, and digital technology (Nasiri et al. 2023).

BMI holds promise for addressing sustainability challenges, such as sustainable mobility, while delivering substantial business benefits (Long and van Waes, 2021). When Small and Medium-sized Enterprises (SMEs) redefine their business models, they face various challenges that demand tailored strategies contingent on the context and triggers (Albats et al. 2023). Adopting digital technology significantly impacts enterprises’ strategies for future development (Ceipek et al. 2021). Direct shifts can influence BMI in technology and customer needs at the micro-level or indirect technological transformations and evolving macro-level needs (Kim, 2023).

Simultaneously, digital capabilities continually shapes how SMEs present and sell their products and value propositions (Ritter and Pedersen, 2020). Digital technology has transformed sales and distribution channels, enabling firms to integrate their products and services across various mobile operating systems, social media platforms, and app stores (Nylén and Holmström, 2015). SMEs increasingly adopt digital distribution, communication, and market analysis tools, influencing their business models. Tools such as social media, apps, chatbots, and big data have revolutionized consumer value creation through new distribution channels and deeper customer relationships (Matarazzo et al. 2021). The locus of value creation has transitioned from solely relying on a firm’s technological innovation to encompassing the entire business ecosystem (Wei et al. 2014). Ultimately, BMI aims to boost revenue by enhancing the value of products or services and their delivery to customers (Keiningham et al. 2020). Therefore, we propose Hypothesis 1:

H1: Digital capabilities positively affect BMI.

The mediating effect of dynamic capability

As defined by Teece et al. (1997), the dynamic capability is the enterprise’s ability to integrate and adapt swiftly to changing market environments. Firms equipped with sufficient human, organizational, and underutilized resources tend to systematically deploy dynamic capabilities (Wilhelm et al. 2022). As the digital economy expands, businesses must optimize allocating resources through dynamic capabilities to enhance market competitiveness and reform business models (Velu and Stiles, 2013).

Prior research suggests that extensive data analysis capabilities positively influence the development of dynamic capabilities (Mikalef et al. 2020). A crucial managerial function for sustaining dynamic capabilities is achieving semi-continuous asset orchestration and corporate renewal. Hence, managers with digital expertise are better equipped to manage continuous renewal in the digital era (Sousa-Zomer et al. 2020). Organizations must possess dynamic capabilities to effectively sense and seize external opportunities, and leadership must develop IT and innovation skills to enhance these capabilities (Chatterjee et al. 2023).

Digital capabilities empower firms to accurately understand consumer demand, maximize resource utilization, reduce production waste, save on marginal costs, and reduce energy intensity (Yuan and Pan, 2023). By expanding knowledge search across diverse resources, digital capabilities enable organizations to adapt to the growing uncertainty and risk in business operations arising from the constant emergence of new knowledge and opportunities and, unpredictable technological developments. This enhanced adaptability makes organizations more market-sensitive and responsive to challenges (Chen et al. 2023). Accordingly, we hypothesize that:

H2: Digital capabilities positively affect dynamic capabilities.

Digital technologies provide sophisticated and user-friendly tools that offer opportunities for automating products and enhancing services (Nasiri et al. 2023). These digital tools potentially transform business operations and create smarter services.

Dynamic capabilities are pivotal to helping organizations establish innovative, customer-centric business models, thereby gaining a competitive edge (Soluk et al. 2021). Dynamic capabilities are widely recognized for their contribution to sustaining superior performance over time, encompassing three core characteristics: sensing, seizing, and reconfiguring (Wilden et al. 2013). Sensing involves how effectively an organisation and its employees gather external information about technological and market trends. This information is acquired through various means, such as professional affiliations, conference participation, and attendance at trade shows (Danneels, 2008). These knowledge networks forged through connections between enterprises, universities, and research institutions, act as conduits for a wealth of innovation resources. These resources significantly expedite technological innovation and the development of new products and services within SMEs, ultimately fostering innovation in the organization’s business model (Zhou et al. 2022).

Seizing pertains to an organization’s ability to effectively utilize its acquired knowledge, particularly in developing new and improved products. It also involves the capacity to promptly respond to signals or cues from the environment (Jantunen, 2005). Organizational search encompasses the knowledge acquisition behaviour of firms. The ambidexterity of organizational search brings diverse knowledge, leading to specialization and complexity in BMI. Therefore, knowledge management may significantly mediate the relationship between organizational search and BMI (Yu et al. 2020). In a market characterized by highly differentiated consumer demands, SMEs can acquire the latest knowledge and information to promote BMI by building and maintaining strong customer relationships (Zhou et al. 2022). Accordingly, we hypothesize that:

H3: Dynamic capabilities mediate the effect of digital capabilities on BMI.

The moderating effect of organizational inertia

Organizational inertia is characterized by organizations’ propensity to maintain existing practices and processes rather than adapt to environmental changes (Zhen et al. 2021). Studies on organizational inertia present two distinct viewpoints on organizational inertia: that it obstructs BMI or that it facilitates it.

Prior research indicates that organizations often stick to past successful practices and experiences due to inertia, which can hinder BMI (Huang et al. 2013). Additionally, organizational inertia can negatively moderate dynamic capabilities and the relative performance of SMEs (Nedzinskas et al. 2013). It also negatively moderates the relationship between environmental turbulence and a firm’s entrepreneurial orientation (Wang et al. 2021). Although digital is generally associated with improved firm performance, knowledge inertia can negatively moderate this relationship (Li et al. 2022). Furthermore, knowledge inertia and guanxi inertia can significantly hamper innovation ability (Fu et al. 2021). Knowledge inertia results from applying routine problem-solving strategies that rely on redundancy (Li et al. 2022), whereas guanxi inertia refers to an organization’s tendency to maintain existing business relationships rather than establishing new ones (Fu et al. 2021).

Organizations with greater inertia are more likely to adhere to historical paths and established procedures when making strategic decisions (Zhong et al. 2023). This is especially true for companies that rely on digital capabilities for BMI. Research has validated that organizational inertia positively moderates the relationship between the digital economy and the two-stage innovation efficiency of new energy enterprises (Xu et al. 2023). According to the resource-based view, an enterprise consists of various resources, and its sustained competitive advantage is derived from its valuable and scarce resources. Organizations with greater organizational inertia have accumulated a wealth of knowledge, technology, information, and other innovative resources, contributing to corporate innovation (Huo et al. 2024). Organizational inertia significantly positively moderates the relationship between institutional pressures and the perception of high-performance work systems (Feng et al. 2022). Organizational inertia is a second-order latent variable comprising three first-order latent variables: insight inertia, action inertia, and psychological inertia (Huang et al. 2013).

H4: Organizational inertia positively moderates the relationship between digital capabilities and BMI.

Research method

Design

We conducted a time-lagged study, involving two waves of data collection from Chinese entrepreneurs with a one-week interval. Two separate survey waves were chosen to mitigate the common methods bias (CMB) (Podsakoff et al. 2003). Utilizing a time lag in our study design helps address simultaneity or reverse causality issues, ensuring that variables do not influence each other simultaneously (Hill et al. 2020).

Participants

Our sample consisted of 262 entrepreneurs in China, focusing on entrepreneurs in the Pearl River-West River Economic Belt, including cities such as Guangzhou and Nanning. Sample selection was guided by the 2023 China Regional Innovation Capacity Evaluation Report, which highlighted the substantial potential for business cooperation in towns within the Pearl River-West River Economic Belt. Consequently, these enterprise managers were deemed valuable research subjects.

Data collection

We used the Wenjuanxing online platform for data collection from January 2023 to March 2023. A significant portion of our convenience sample was drawn from Wenjuanxing’s network of enterprise managers. The platform posted our survey link online after obtaining approval from the university’s research ethics committee. Before data collection, our survey questionnaire underwent a content validity test involving five enterprise experts’ review and unanimous approval.

At Time 1, respondents received a cover letter explaining the study’s purpose and were assured of the confidentiality of their responses. They were requested to provide basic information about their enterprises, digital capabilities (independent variable), and organizational inertia (moderator variable). A total of 379 respondents completed and returned the Time 1 questionnaire. One week later (Time 2), the same group of respondents (N = 379) received the second-wave questionnaire, which inquired about BMI (dependent variable) and dynamic capability (mediator variable). We matched the two waves of questionnaires using unique IDs and received 262 valid questionnaires. As our survey was distributed online, it was uncertain how many individuals received the survey links, making it challenging to establish an accurate response rate.

Measures

Digital capabilities

We measured digital capabilities with a 13-item scale developed by (Nasiri et al. 2023). Their scale has four dimensions: human ability has three items, collaboration ability has three items, technical ability has four items, and innovation ability has three items. A sample item is “Digital skills development is supported and promoted in our company.” The Cronbach alpha score was 0.941.

Dynamic capability

We measured dynamic capability with a 12-item scale developed by (Wilden et al. 2013). Their scale has three dimensions: sensing, seizing, and reconfiguring, with four items in each dimension. A sample item is “People participate in professional association activities.” The Cronbach’s α was 0.920.

Organizational inertia

The measurement of organizational inertia was adopted from (Huang et al. 2013), comprising 13 items in three dimensions. Insight inertia has four items, action inertia has five items, and psychological inertia has four items. A sample item is “Our company has difficulty identifying how other firms solve problems.” The Cronbach’s α was 0.936.

Business model innovation

We measured BMI with a six item scale developed by (Zhao et al. 2021). A sample item is “The business model offers new combinations of products, services, and information.” The Cronbach’s α was 0.902. The scale items are shown in Appendix 1.

We controlled for enterprise location, enterprise year, enterprise size, enterprise type, and enterprise application of digital technology, as they may influence BMI outcomes (Liu et al. 2019).

Results

Descriptive statistical analyses

Table 1 displays the enterprises’ characteristics.

Table 1 The situation of the respondent enterprises.
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Common methods bias

We conducted Harman’s single-factor test for Common Method Bias (CMB) by subjecting all items for variables to an exploratory factor analysis. The analysis revealed that no single factor accounted for the majority of the variance, as the highest factor accounted for 35.065% of the variance, which is less than the 50% threshold suggested by (Podsakoff et al. 2003).

Confirmatory factor analysis

Confirmatory factor analysis (CFA) was performed to test the distinctiveness and reliability of the variables. Results showed that a four-factor model elicited a better model fit (χ2 = 1576.533, df = 896, χ2/df = 1.760; CFI = 0.906, TLI = 0.900, IFI = 0.906, RMESA = 0.054) than other models, as shown in Table 2.

Table 2 Confirmatory factor analysis.
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Convergent, discriminant validity, and correlational analysis

Table 3 shows the factor loading, composite reliability (CR), average variance extracted (AVE), mean, standard deviation (SD), and Pearson correlation coefficient of variables. As per the threshold level, the factor loading is above 0.5, the CR value should exceed 0.6, and the AVE value should be greater than 0.5. As demonstrated in Table 3, except for dynamic capabilities, the factor loading, CR, and AVE values of all variables examined are higher than the threshold level. If one decimal place is retained, the AVE value of dynamic capability is 0.5, which meets the threshold. In addition, the square root of the AVE value of each variable is above the coefficient of correlations. Hence, discriminant validity was supported.

Table 3 Convergent, discriminant validity, and correlational analysis.
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Hypothesis test

We employed the PROCESS tool (Model 5) for SPSS to test our hypotheses. Following Preacher and Hayes, we set the number of bootstrap samples at 5000 and selected 95 percent bias-corrected confidence intervals.

Table 4 shows the regression results of the hypotheses. H1 expected that digital capabilities have a positive effect on BMI. As demonstrated in Table 4, digital capabilities positively predict BMI. (β = 0.329, p < 0.001). Therefore, H1 was supported.

Table 4 Conditional process analysis (N = 262).
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H2 proposed that digital capabilities positively affect dynamic capabilities. As shown in Table 4, digital capabilities positively affected dynamic capabilities (β = 0.465, p < 0.001); thus, H2 was supported.

H3 posited that dynamic capabilities mediate the effect of digital capabilities on BMI. Table 4 illuminates the result of indirect influence (indirect effect = 0.105, LLCI = 0.034, ULCI = 0.183). Since digital capabilities were significantly related to BMI when dynamic capabilities were entered into the same model, dynamic capabilities partially mediate the relationship between digital capabilities and BMI. Thus, H3 was supported.

H4 expected that organizational inertia positively moderates the relationship between digital capabilities and BMI. As summarized in Table 4, the interaction between digital capabilities and organizational inertia support was significantly correlated with BMI (β = 0.127, p < 0.05).

To further explain the moderating influence, we plotted the interaction at low, medium, and high levels of organizational inertia (−1 SD, Mean, +1 SD). As illustrated in Fig. 2, the slope for the relationship between digital capabilities and BMI was stronger among firms reporting higher levels of organizational inertia (+1 SD from the mean, effect = 0.429, t = 5.811, p < 0.001). This indicates that organizational inertia strengthens the association between digital capabilities and BMI; thus, H4 was supported.

Fig. 2: Simple slope.
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The simple slope of the relationship between digital capabilities and business model innovation, moderated by organizational inertia.

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Robustness check

For the robustness check, we conducted a model variation test by selecting 200 cases of the overall sample of 262. PROCESS tool (Model 5) for SPSS was applied to a sub-sample of 200 initial observations from the entire sample, the results of which are reported in Table 5. The results are consistent with those reported in Table 4, suggesting that our findings are robust. This testing method has been used in previous studies (Nguyen et al. 2023).

Table 5 The regression model with the first 200 cases.
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Discussion

Theoretical implications

This study explores the impact of digital capabilities on BMI through dynamic capabilities with the moderating role of organizational inertia. The key contributions of this research are as follows.

First, the study confirms that digital capabilities are pivotal in BMI. This aligns with previous research indicating that digital capabilities positively influence SMEs’ BMI (Xie et al. 2022). Digital technology has transformed sales and distribution channels, allowing firms to integrate their products and services across various mobile operating systems, social media platforms, and app stores (Nylén and Holmström, 2015). BMI aims to enhance product or service value and their delivery to customers, with the ultimate goal of revenue growth (Keiningham et al. 2020).

Second, digital capabilities have a positive impact on dynamic capabilities. This is consistent with earlier studies that have found digital marketing to positively affect dynamic capabilities (Nguyen et al. 2023). Digital capabilities, by expanding the search for various resources, enable organizations to adapt to the increasing uncertainty and risks in business operations resulting from emerging knowledge, opportunities, and unpredictable technological developments, making them more responsive to market challenges (Chen et al. 2023). Dynamic capability is vital for enterprises to adapt and thrive in rapidly changing market environments (Teece et al. 1997). Furthermore, managers with digital expertise are better equipped to navigate the constant renewal required in the digital era (Sousa-Zomer et al. 2020).

Third, dynamic capabilities mediate the relationship between digital capabilities and BMI. This is consistent with prior research showing dynamic capabilities to fully mediate the relationship between network breadth and product innovation and, between network depth and product innovation (Jiang et al. 2020). The nature of change in business models, whether radical or incremental, is influenced by various factors, including competition, market maturity, and internal organizational needs (Albats et al. 2023). Digital capabilities supporting open innovation are closely linked to internal and external situational factors (Wu et al. 2022). Strong dynamic capabilities empower firms to mobilize internal and external resources flexibly to seize opportunities effectively (Jiang et al. 2020).

Finally, organizational inertia positively moderates the relationship between digital capabilities and BMI. Firms with greater organizational inertia tend to follow established procedures and historical paths in their strategic decisions (Zhong et al. 2023), particularly when companies rely on digital capabilities for BMI. Previous research has demonstrated that organizational inertia significantly and positively moderates the relationship between the digital economy and the two-stage innovation efficiency of new energy enterprises (Xu et al. 2023). From the resource-based view, companies accumulate valuable and scarce resources over time, and those with greater organizational inertia possess rich knowledge, technology, information, and innovative resources, contributing to corporate innovation (Huo et al. 2024).

Practical implications

The practical implications drawn from our research findings are as follows:

First, enterprises should place a significant emphasis on enhancing their digital capabilities in the areas of human resources, collaboration, technology, and innovation. Strengthening these aspects contributes to improving BMI and enhances dynamic capabilities. This investment in digital capabilities can lead to a more competitive and innovative business environment.

Second, our study underscores the importance of dynamic capabilities within enterprises. Encompassing sensing, seizing, and reconfiguring, these capabilities are crucial to driving BMI. Companies should actively work on developing and nurturing these dynamic abilities to adapt to rapidly changing market conditions and seize new opportunities.

Finally, our study shows a correlation between organizational inertia and digital capabilities, dynamic capabilities, and BMI. Notably, organizational inertia positively impacts the relationship between digital capabilities and BMI. Organizational inertia, often associated with resistance to change, sometimes acts as a driving force for successfully implementing digital capabilities and BMI. Companies should be mindful of the dual role of organizational inertia and leverage its beneficial aspects for sustainable development.

Limitations and future research directions

First, while this study has illuminated the positive relationship between digital capabilities, dynamic capabilities, and BMI, further research can delve deeper into the nuances of this relationship. Future studies might consider the dimensions of digital capabilities, such as human capabilities, collaboration capabilities, technology capabilities, and innovation capabilities, and how influence various aspects of BMI. Additionally, examining the role of dynamic capabilities, in driving BMI provides a more comprehensive understanding of this complex interplay. Future research should explore additional potential mediating variables to better understand how digital capabilities drive BMI.

Second, future research can enhance the study’s design by incorporating instrumental variables into the data collection process to address potential endogeneity concerns. This would aid in establishing a causal relationship between digital capabilities, dynamic capabilities, and BMI. By carefully selecting and applying instrumental variables, researchers can enhance the robustness of their findings and provide more precise insights into the impact of these capabilities on organizational innovation.

Third, we intentionally chose firms in the Pearl River-West River Economic Belt due to its well-documented high digital capabilities and supportive innovation environment. In addition, we used an electronic questionnaire (www.wjx.com) instead of a paper questionnaire, which requires respondents to have a certain level of network operation ability. We recognize that this may introduce potential sample selection bias. To mitigate potential bias, we used the time lag method in the design of the study and robustness testing in the analysis. Furthermore, future research could utilize the qualitative method to explore similar relationships in regions with different digital capabilities and innovation environments to provide a more comprehensive understanding of the topic.

Conclusion

This empirical study conducted in the Pearl River Xijiang Economic Belt highlights the crucial role of digital capabilities in driving BMI. It underscores the significance of developing digital skills, fostering an innovation culture, and leveraging technological proficiency within organizations. The research emphasizes the pivotal mediating role of dynamic capabilities, comprising sensing, seizing, and reconfiguring resources, in translating digital strengths into innovative business models. Interestingly, the study uncovers a nuanced perspective on organizational inertia, showcasing its potential to positively moderate the relationship between digital capabilities and business model innovation. These findings offer valuable insights for enterprises aiming to thrive in dynamic markets, emphasizing the importance of a balanced approach to digital innovation.