The rapid advancements in digital technologies like 5G, cloud computing, and AI have significantly disrupted traditional business models. Digital capability-driven business model innovation is now a key driver of digital economic development. While existing research has explored various drivers of BMI, including financial resources, top management behavior, and knowledge management, there's a gap in understanding the specific link between digital capabilities and BMI. Many firms face high costs associated with failing to undergo digital transformation, including decreased revenue growth and loss of competitive advantage. Digital transformation is multifaceted and requires firms to possess and utilize digital capabilities effectively to experiment with new approaches while managing associated risks. The dynamic nature of the digital environment creates challenges for BMI, requiring adaptability and responsiveness. Current research lacks a multi-regional analysis of how digital capabilities contribute to BMI, particularly within regional enterprises. This study addresses this research gap by focusing on the dynamic capabilities perspective, exploring how firms sense, seize, and reconfigure resources to leverage digital technologies for business model innovation. The research aims to understand the influence of digital capabilities on BMI and the mechanisms through which this influence operates. Existing literature also indicates a dark side to digital capabilities, such as unexpected tensions and paradoxical effects that may hinder value creation. The study also considers the role of organizational inertia, investigating how an organization's resistance to change impacts the relationship between digital capabilities and BMI.

The literature review examines the existing research on digital capabilities, dynamic capabilities, business model innovation, and organizational inertia. The positive effect of digital capabilities on business model innovation is established, noting that digital capabilities encompass service development, network management, and digital technology. The mediating role of dynamic capabilities, encompassing sensing, seizing, and reconfiguring resources, is also discussed, drawing on the dynamic capabilities framework. The review then explores the contrasting perspectives on organizational inertia, acknowledging its potential to both hinder and facilitate BMI. Some research shows it impedes innovation while other research finds that it supports innovation due to accumulated resources and knowledge.

This study employs a time-lagged questionnaire-based survey design with a one-week interval between two data collection waves. This approach helps mitigate common method bias and address simultaneity issues. The sample consists of 262 entrepreneurs located in the Pearl River-West River Economic Belt in China. Data were collected using the Wenjuanxing online platform from January to March 2023. The questionnaire underwent a content validity test by five enterprise experts. The first wave of questionnaires collected data on digital capabilities and organizational inertia, while the second wave collected data on BMI and dynamic capabilities. After matching the two waves using unique IDs, 262 valid questionnaires were analyzed. The study uses several established scales to measure the key variables: digital capabilities (13 items, Cronbach's alpha = 0.941), dynamic capabilities (12 items, Cronbach's alpha = 0.920), organizational inertia (13 items, Cronbach's alpha = 0.936), and business model innovation (6 items, Cronbach's alpha = 0.902). Control variables include enterprise location, year, size, type, and application of digital technology. Harman's single-factor test addressed common method bias. Confirmatory factor analysis (CFA) tested the reliability and distinctiveness of the variables. Convergent and discriminant validity were assessed. Hypothesis testing utilized PROCESS Model 5 in SPSS with 5000 bootstrap samples and 95% bias-corrected confidence intervals. Simple slope analysis was conducted to further illustrate the moderating effect of organizational inertia. A robustness check involved repeating the analysis with a subsample of 200 observations.

The results support all four hypotheses. First, digital capabilities significantly and positively affect BMI (β = 0.329, p < 0.001). Second, digital capabilities positively impact dynamic capabilities (β = 0.465, p < 0.001). Third, dynamic capabilities partially mediate the relationship between digital capabilities and BMI (indirect effect = 0.105, LLCI = 0.034, ULCI = 0.183). Fourth, organizational inertia positively moderates the relationship between digital capabilities and BMI (β = 0.127, p < 0.05). Simple slope analysis reveals that the positive effect of digital capabilities on BMI is stronger at higher levels of organizational inertia. Robustness checks using a subsample of 200 observations confirmed the findings.

The findings confirm the significant role of digital capabilities in driving BMI, supported by prior research. The study extends this understanding by highlighting the mediating role of dynamic capabilities. Firms that effectively sense, seize, and reconfigure resources based on digital opportunities experience greater BMI. The positive moderating effect of organizational inertia is a key contribution. While often perceived negatively, organizational inertia, in this context, seems to leverage accumulated resources and knowledge to enhance the impact of digital capabilities on BMI. This suggests that established structures and processes can, under certain conditions, facilitate digital transformation and innovation. This finding challenges the conventional view of organizational inertia as purely detrimental to innovation.

This study provides valuable insights into the complex relationship between digital capabilities, dynamic capabilities, organizational inertia, and business model innovation. It emphasizes the importance of cultivating digital capabilities, fostering dynamic capabilities, and strategically managing organizational inertia for successful BMI. Future research could explore the specific dimensions of digital capabilities and their differential effects on BMI, investigate additional mediating variables, and address potential endogeneity concerns using instrumental variables. Furthermore, replicating the study across diverse regional contexts with varying levels of digital maturity would strengthen generalizability.

The study's sample focuses on entrepreneurs in a specific region of China, potentially limiting the generalizability of the findings to other geographical areas or types of businesses. The use of an online questionnaire may introduce sampling bias by excluding entrepreneurs with limited digital literacy or internet access. Future research should address these limitations by expanding the geographical scope and utilizing diverse data collection methods to ensure greater representativeness and robustness.