The impact of digital capabilities and dynamic capabilities on business model innovation: the moderating effect of organizational inertia

The emergence of digital technologies (5G, cloud computing, AI) is reshaping traditional business models. Business model innovation (BMI) involves innovating systems of products, services, technology, and information flows that extend beyond the firm. While prior research highlights drivers of BMI (e.g., financial slack, top management boundary-spanning, customers, digital platforms, knowledge management, digital technology, strategic agility), there is a relative shortage of research on the association between digital capabilities and BMI. The costs of not undergoing digital transformation can be substantial. Digital transformation requires resources and competencies, notably digital capabilities that enable experimentation and risk articulation. High environmental dynamism challenges BMI, with many firms struggling to adapt. Scholars emphasize digital capabilities as key sources of firm innovation, yet insights adopting dynamic perspectives on digital development/integration in regional enterprises remain scarce. This gap is important given the rapid digital change and calls for more BMI research. The study asks: What are the influences of digital capabilities on BMI and competitiveness, and how is this competitiveness achieved? The authors propose understanding the impact of digital capabilities on BMI through the dynamic capabilities perspective. Digital capabilities can have paradoxical effects, expanding search landscapes and complicating trade-offs, potentially increasing stakeholder tensions in digital BMI. There is limited knowledge about how firms sense and seize opportunities from digital technologies to initiate circular BMI and what capabilities manage the transition from linear to circular models. Dynamic capabilities (sensing, seizing, transforming) are higher-order capabilities that confront novel challenges and enable BMI. Enhancing circular BMI through digital capabilities requires combining service development, network management, and digital capabilities within BMI processes and linking to higher-order dynamic capabilities. The study also examines organizational inertia as a contextual factor shaping responses to adversity and emergencies. Despite digitally informed decisions, organizations may fall into a capability trap when integrating digital technology, especially in R&D. Organizational structure and process flexibility are crucial; inertia can slow responses and affect BMI effectiveness. The authors posit that shared interpretations of inertia can create strong resource action strategies, amplifying the positive impacts of cultivating digital capability. Addressing calls to examine contextual factors, the study explores boundary conditions of organizational inertia, positing that adaptability and transformability within inertia may shape BMI processes. Contributions: (1) new light on digital capabilities and BMI from a dynamic capability perspective, proposing a “digital–dynamic–BMI” framework; (2) highlighting dynamic capabilities’ role in transforming business models to capture digital opportunities; (3) emphasizing the moderating role of organizational inertia as a contextual stimulus that can strengthen BMI effects.

The literature review and hypothesis development focus on four relationships: - Digital capabilities and BMI (H1): Digital capability is the capacity to harness digital technologies (Internet, cloud, big data, AI) to foster growth and innovative business models. Digitalization reshapes strategies, sales and distribution channels, and value creation across ecosystems, enabling new customer relationships and revenue models. Thus, H1 posits that digital capabilities positively affect BMI. - Digital capabilities and dynamic capabilities (H2): Dynamic capability is the ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments. Data analytics and managerial digital expertise support asset orchestration and renewal, enhancing sensing and seizing of opportunities. Digital capabilities improve demand understanding, resource utilization, and adaptability, leading to H2: digital capabilities positively affect dynamic capabilities. - Mediation of dynamic capabilities between digital capabilities and BMI (H3): Digital tools automate and enhance services, while dynamic capabilities (sensing, seizing, reconfiguring) enable customer-centric business models and competitive advantage. Knowledge networks and organizational search (ambidexterity) contribute to BMI via knowledge management. Hence, H3: dynamic capabilities mediate the effect of digital capabilities on BMI. - Moderation by organizational inertia (H4): Organizational inertia is the propensity to maintain existing practices. Two views exist: it can hinder or facilitate innovation. While inertia may hinder BMI by sticking to past routines, it can also reflect accumulated resources and knowledge that support innovation. Evidence shows positive moderation of inertia in some digital contexts. Organizational inertia is modeled as a second-order construct (insight, action, psychological inertia). H4: organizational inertia positively moderates the relationship between digital capabilities and BMI.

Design: Time-lagged, two-wave survey with a 1-week interval to mitigate common method bias and simultaneity/reverse causality. Participants and setting: 262 entrepreneurs/managers from the Pearl River–West River Economic Belt in China (e.g., Guangzhou, Nanning). Data collected via the Wenjuanxing online platform from January to March 2023. Initial Time 1 responses: 379; matched valid Time 1–Time 2 pairs: 262. Procedure: Time 1 measured enterprise demographics, digital capabilities (independent variable), and organizational inertia (moderator). Time 2 measured BMI (dependent variable) and dynamic capabilities (mediator). Unique IDs matched responses. Ethical approval obtained; confidentiality assured. Measures: - Digital capabilities: 13-item scale (Nasiri et al., 2023), four dimensions (human ability 3, collaboration ability 3, technical ability 4, innovation ability 3). Sample item: “Digital skills development is supported and promoted in our company.” Cronbach’s alpha = 0.941. - Dynamic capabilities: 12-item scale (Wilden et al., 2013), three dimensions (sensing, seizing, reconfiguring; 4 items each). Sample item: “People participate in professional association activities.” Cronbach’s alpha = 0.920. - Organizational inertia: 13 items across insight inertia (4), action inertia (5), psychological inertia (4) (Huang et al., 2013). Sample item: “Our company has difficulty identifying how other firms solve problems.” Cronbach’s alpha = 0.936. - Business model innovation: 6-item scale (Zhao et al., 2021). Sample item: “The business model offers new combinations of products, services, and information.” Cronbach’s alpha = 0.902. Controls: Enterprise location, years, size, type, and ownership; and enterprise application of digital technology as potential influencers of BMI. Analytical strategy: - Common method bias: Harman’s single-factor test; largest factor accounted for 35.065% (<50%). - Confirmatory factor analysis (CFA): Four-factor model fit: χ2 = 1576.533, df = 896, χ2/df = 1.760, CFI = 0.906, TLI = 0.900, IFI = 0.906, RMSEA = 0.054; superior to alternative models. - Convergent/discriminant validity: Factor loadings generally >0.5; CR >0.6; AVE ≥0.5 (dynamic capability AVE ≈ 0.493 rounded to 0.5). Square roots of AVE exceeded inter-construct correlations. - Hypothesis testing: PROCESS macro for SPSS (Model 5), 5000 bootstrap samples, 95% bias-corrected CIs. - Robustness check: Re-estimated model on first 200 cases; results consistent with main analysis.

- H1 supported: Digital capabilities positively predict BMI (β = 0.329, p < 0.001). - H2 supported: Digital capabilities positively predict dynamic capabilities (β = 0.465, p < 0.001). - H3 supported: Dynamic capabilities partially mediate the relationship between digital capabilities and BMI (indirect effect = 0.105, 95% CI [0.034, 0.183]). Dynamic capabilities also positively predict BMI directly (β = 0.225, p < 0.01). - H4 supported: Organizational inertia positively moderates the digital capabilities → BMI relationship (interaction β = 0.127, p < 0.05). Simple slopes show a stronger relationship at higher inertia levels (+1 SD: effect = 0.429, t = 5.811, p < 0.001). - Main effect of organizational inertia on BMI is negative in the model (β = −0.161, p < 0.01), while its interaction with digital capabilities is positive. - Measurement validity and reliability: High internal consistency (alphas: digital capabilities 0.941; dynamic capabilities 0.920; organizational inertia 0.936; BMI 0.902). CFA indicated good model fit (χ2/df = 1.760; CFI = 0.906; TLI = 0.900; RMSEA = 0.054). Harman’s single-factor test suggested CMB not a major concern (largest factor 35.065%). - Robustness: Subsample analysis (N=200) yielded consistent coefficients (e.g., digital capabilities → BMI β = 0.282, p < 0.001; interaction β = 0.156, p < 0.05; indirect effect via dynamic capabilities = 0.076, 95% CI [0.006, 0.162]).

The findings address the research questions by demonstrating that digital capabilities are a significant antecedent of BMI and that their impact operates both directly and indirectly through dynamic capabilities (sensing, seizing, reconfiguring). This clarifies how firms convert digital strengths into innovative business models. The positive moderation by organizational inertia reveals a nuanced contextual mechanism: while inertia can dampen change in general, accumulated routines and resources associated with higher inertia can strengthen the effectiveness of digital capabilities for BMI, especially when firms follow established processes to implement digital initiatives. The results are relevant to the dynamic capabilities literature, confirming that digitalization enhances an organization’s higher-order capabilities that, in turn, enable customer-centric BMI and competitive advantage. They also extend BMI research by situating digital capability effects within a moderated mediation framework, highlighting boundary conditions where organizational inertia enhances the digital–BMI linkage. Practically, firms should build digital skills, collaboration, technical and innovation capabilities, and deliberately cultivate dynamic capabilities to sense market/technology shifts, seize opportunities, and reconfigure resources. Managers should recognize the dual nature of organizational inertia, leveraging its resource accumulation and process stability to support digital-enabled BMI while managing potential rigidities.

In a sample of entrepreneurs from China’s Pearl River–Xijiang Economic Belt, the study shows that digital capabilities play a crucial role in driving business model innovation. Dynamic capabilities are a pivotal mediating mechanism translating digital strengths into innovative business models. Organizational inertia, counterintuitively, can positively moderate the digital capability–BMI link, strengthening digital initiatives’ effectiveness for BMI. The study underscores the importance of developing digital skills, fostering an innovation culture, and building dynamic capabilities to thrive in dynamic markets, advocating a balanced approach to digital innovation.

- Scope of constructs: Future research should unpack dimensions of digital capabilities (human, collaboration, technology, innovation) and map them to specific facets of BMI. Additional mediators could further explain how digital capabilities drive BMI. - Causality and endogeneity: Incorporating instrumental variables or quasi-experimental designs could address endogeneity and strengthen causal inference among digital capabilities, dynamic capabilities, and BMI. - Generalizability and sampling: The sample focuses on firms in the Pearl River–West River Economic Belt and uses an online survey, potentially introducing selection bias. Future studies should examine different regions with varying digital maturity and consider qualitative methods for richer insights.