Abstract
This study aims to investigate the impact of digital capabilities on business model innovation, and emphasizes the pivotal mediating role of dynamic capabilities, comprising sensing, seizing, and reconfiguring resources, in translating digital strengths into innovative business models. And also uncovers a nuanced perspective on organizational inertia, showcasing its potential to positively moderate the relationship between digital capabilities and business model innovation. A questionnaire-based, time-lagged study with a 1-week interval. From January 2023 to March 2023, we collected a total of 262 questionnaires from entrepreneurs located in the Pearl River-West River Economic Belt, such as Guangzhou and Nanning, China. To analyse the moderation and mediation model, we utilized The PROCESS Model 5. Digital capabilities have a positive impact on the innovation of enterprisesâ business models. Moreover, the connection between digital capabilities and business model innovation is mediated by dynamic capability. The findings also illustrate that organisational inertia moderates the relationship between digital capabilities and business model innovation. This study contributes to the existing literature on enterprise sustainable development by shedding light on the mediating and moderating mechanisms through which digital capabilities enhance business model innovation in enterprises. These findings offer valuable insights for enterprises aiming to thrive in dynamic markets, emphasizing the importance of a balanced approach to digital innovation, and instrumental for entrepreneurs seeking to refine their organisational strategies, particularly by bolstering their digital capabilities.
Similar content being viewed by others
Introduction
The emergence of digital technologies, such as 5âG, cloud computing, and AI, has largely subverted the traditional business model (Wannakrairoj and Velu, 2021). With its Digital Strategy 2022â2025, 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 âChina Digital Transformation Index 2023â, 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 firmsâ 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 organizationsâ responses to adversity and emergencies. Despite a scientific and accurate decision provided by digital capabilities, organizations are likely to fall into a âcapability trapâ. 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 âdigital-dynamic-BMIâ 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 firmsâ 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 BMIâs 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 inertiaâs contingency role in BMIâs 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.
Literature review and hypothesis development
The positive effect of digital capabilities on business model innovation
Digital capability refers to an enterpriseâs 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 enterprisesâ 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 firmâs 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 enterpriseâs 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 organizationâs business model (Zhou et al. 2022).
Seizing pertains to an organizationâs 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 organizationsâ 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 firmâs 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 organizationâs 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 Wenjuanxingâs network of enterprise managers. The platform posted our survey link online after obtaining approval from the universityâs research ethics committee. Before data collection, our survey questionnaire underwent a content validity test involving five enterprise expertsâ review and unanimous approval.
At Time 1, respondents received a cover letter explaining the studyâs 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 (Nâ=â379) 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 âDigital skills development is supported and promoted in our company.â 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 âPeople participate in professional association activities.â The Cronbachâs 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 âOur company has difficulty identifying how other firms solve problems.â The Cronbachâs 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 âThe business model offers new combinations of products, services, and information.â The Cronbachâs 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 enterprisesâ characteristics.
Common methods bias
We conducted Harmanâs 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 (Ï2â=â1576.533, dfâ=â896, Ï2/dfâ=â1.760; CFIâ=â0.906, TLIâ=â0.900, IFIâ=â0.906, RMESAâ=â0.054) than other models, as shown in Table 2.
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.
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. (βâ=â0.329, pâ<â0.001). Therefore, H1 was supported.
H2 proposed that digital capabilities positively affect dynamic capabilities. As shown in Table 4, digital capabilities positively affected dynamic capabilities (βâ=â0.465, pâ<â0.001); 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 effectâ=â0.105, LLCIâ=â0.034, ULCIâ=â0.183). 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 (βâ=â0.127, pâ<â0.05).
To further explain the moderating influence, we plotted the interaction at low, medium, and high levels of organizational inertia (â1 SD, Mean, +1âSD). 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 (+1âSD from the mean, effectâ=â0.429, tâ=â5.811, pâ<â0.001). This indicates that organizational inertia strengthens the association between digital capabilities and BMI; thus, H4 was supported.
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).
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 SMEsâ 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 studyâs 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.
Data availability
All relevant data are within the paper and its Supporting Information files.
References
Albats E, Podmetina D, Vanhaverbeke W (2023) Open innovation in SMEs: A process view towards business model innovation. J Small Bus Manag 61(6):2519â2560
Ancillai C, Sabatini A, Gatti M, Perna A (2023) Digital technology and business model innovation: A systematic literature review and future research agenda. Technol Forecast Soc Change 188:122307
Bashir M, Verma R (2019) Internal factors & consequences of business model innovation. Manag Decis 57(1):262â290
Bhatti SH, Santoro G, Khan J, Rizzato F (2021) Antecedents and consequences of business model innovation in the IT industry. J Bus Res 123:389â400
Bocken NM, Geradts TH (2020) Barriers and drivers to sustainable business model innovation: Organization design and dynamic capabilities. Long range plan 53(4):101950
Broekhuizen TL, Emrich O, Gijsenberg MJ, Broekhuis M, Donkers B, Sloot LM (2021) Digital platform openness: Drivers, dimensions and outcomes. J Bus Res 122:902â914
Burström T, Parida V, Lahti T, Wincent J (2021) AI-enabled business-model innovation and transformation in industrial ecosystems: A framework, model and outline for further research. J Bus Res 127:85â95
Ceipek R, Hautz J, Petruzzelli AM, De Massis A, Matzler K (2021) A motivation and ability perspective on engagement in emerging digital technologies: The case of Internet of Things solutions. Long Range Plan 54(5):101991
Chatterjee S, Chaudhuri R, Vrontis D, Giovando G (2023) Digital workplace and organization performance: Moderating role of digital leadership capability. J Innov Knowl 8(1):100334
Chen C, Pan J, Liu S, Feng T (2023) Impact of digital capability on firm resilience: The moderating role of coopetition behavior. Bus Process Manag J 29(7):2167â2190
Clauss T (2017) Measuring business model innovation: conceptualization, scale development, and proof of performance. R D Manag 47(3):385â403
Clauss T, Abebe M, Tangpong C, Hock M (2019) Strategic agility, business model innovation, and firm performance: An empirical investigation. IEEE T Eng Manag 68(3):767â784
Danneels E (2008) Organizational antecedents of second-order competences. Strateg Manag J 29(5):519â543
Feng M, Li J-J, Xiong X-Y (2022) Institutional pressures, high-performance work systems, and marketability: The moderating role of organizational inertia. J Appl Behav Sci 0(0) https://doi.org/10.1177/00218863221096164
Fu XR, Luan R, Wu HH, Zhu WT, Pang J (2021) Ambidextrous balance and channel innovation ability in Chinese business circles: The mediating effect of knowledge inertia and guanxi inertia. Ind Mark Manag 93:63â75
Guo H, Guo A, Ma H (2022) Inside the black box: How business model innovation contributes to digital start-up performance. J innov knowl 7(2):100188
Halpern N, Mwesiumo D, Suau-Sanchez P, Budd T, Bråthen S (2021) Ready for digital transformation? The effect of organisational readiness, innovation, airport size and ownership on digital change at airports. J Air Transp Manag 90:101949
Hellemans I, Porter AJ, Diriker D (2022) Harnessing digitalization for sustainable development: Understanding how interactions on sustainabilityâoriented digital platforms manage tensions and paradoxes. Bus Strategy Environ 31(2):668â683
Heubeck T (2023) Managerial capabilities as facilitators of digital transformation? Dynamic managerial capabilities as antecedents to digital business model transformation and firm performance. Digital Bus 3(1):100053
Hill AD, Johnson SG, Greco LM, OâBoyle EH, Walter SL (2020) Endogeneity: A review and agenda for the methodology-practice divide affecting micro and macro research. J Manag 47(1):105â143
Huang H-C, Lai M-C, Lin L-H, Chen C-T (2013) Overcoming organizational inertia to strengthen business model innovation: An open innovation perspective. J Organ Chang Manag 26(6):977â1002
Huo MJ, Li C, Liu RH (2024) Climate policy uncertainty and corporate green innovation performance: From the perspectives of organizational inertia and management internal characteristics. Manag Dec Econ 45(1):34â53
Jantunen A (2005) Knowledgeâprocessing capabilities and innovative performance: An empirical study. Eur J Innov 8(3):336â349
Jiang W, Mavondo F, Zhao W (2020) The impact of business networks on dynamic capabilities and product innovation: The moderating role of strategic orientation. Asia Pac J Manag 37(4):1239â1266
Keiningham T, Aksoy L, Bruce HL, Cadet F, Clennell N, Hodgkinson IR et al. (2020) Customer experience driven business model innovation. J Bus Res 116:431â440
Kim I-HS (2023) The comprehensive unified paradigm for business model innovation. Technol Anal Strateg Manag 35(11):1497â1518
Lei H, Tang S, Zhao Y, Chen S (2024) Enterprise digitalization, employee digital literacy and R&D cooperation: The moderating role of organizational inertia. Chin Manag Stud 18(2):479â505
Li LX, Ye F, Zhan YZ, Kumar A, Schiavone F, Li YN (2022) Unraveling the performance puzzle of digitalization: Evidence from manufacturing firms. J Bus Res 149:54â64
Li S, Gao L, Han C, Gupta B, Alhalabi W, Almakdi S (2023) Exploring the effect of digital transformation on Firmsâ innovation performance. J Innov Knowl 8(1):100317
Liu L, Yu B, Wu W (2019) The formation and effects of exploitative dynamic capabilities and explorative dynamic capabilities: an empirical study. Sustainability 11(9):1â21
Long TB, van Waes A (2021) When bike sharing business models go bad: Incorporating responsibility into business model innovation. J Clean Prod 297:126679
Mancuso I, Petruzzelli AM, Panniello U (2023) Innovating agri-food business models after the Covid-19 pandemic: The impact of digital technologies on the value creation and value capture mechanisms. Technol Forecast Soc 190:122404
Matarazzo M, Penco L, Profumo G, Quaglia R (2021) Digital transformation and customer value creation in Made in Italy SMEs: A dynamic capabilities perspective. J Bus Res 123:642â656
Mikalef P, Krogstie J, Pappas IO, Pavlou PJI (2020) Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Inf Manag 57(2):103169
Mikalef P, van de Wetering R, Krogstie J (2021) Building dynamic capabilities by leveraging big data analytics: The role of organizational inertia. Inf Manag 58(6):103412
Nasiri M, Saunila M, Ukko J, Rantala T, Rantanen H (2023) Shaping digital innovation via digital-related capabilities. Inf Syst Front 25(3):1063â1080
Nedzinskas Å , PundzienÄ A, BuožiÅ«tÄ-RafanaviÄienÄ S, PilkienÄ M (2013) The impact of dynamic capabilities on SME performance in a volatile environment as moderated by organizational inertia. Balt J Manag 8(4):376â396
Nguyen HTT, Pham HST, Freeman S (2023) Dynamic capabilities in tourism businesses: Antecedents and outcomes. Rev Manag 17(5):1645â1680
Nylén D, Holmström J (2015) Digital innovation strategy: A framework for diagnosing and improving digital product and service innovation. Bus Horiz 58(1):57â67
Nyuur RB, Donbesuur F, Owusu-Yirenkyi D, Ampong GOA, Tantawy AA (2023) Owner-managers failure experience and business model innovations in B2B firms: The roles of coopetition, managerial persistence, and financial resource slack. Ind Mark Manag 113:128â137
Podsakoff PM, MacKenzie SB, Lee J-Y, Podsakoff NP (2003) Common method biases in behavioral research: A critical review of the literature and recommended remedies. J Appl Psychol 88(5):879â903
Ranta V, Aarikka-Stenroos L, Väisänen J-M (2021) Digital technologies catalyzing business model innovation for circular economyâMultiple case study. Resour Conserv Recy 164:105155
Ritter T, Pedersen CL (2020) Digitization capability and the digitalization of business models in business-to-business firms: Past, present, and future. Ind Mark Manag 86:180â190
Rusch M, Schöggl JP, Baumgartner RJ (2023) Application of digital technologies for sustainable product management in a circular economy: A review. Bus Strateg Environ 32(3):1159â1174
Soluk J, Miroshnychenko I, Kammerlander N, De Massis A (2021) Family influence and digital business model innovation: The enabling role of dynamic capabilities. Entrep Theory Pr 45(4):867â905
Sousa-Zomer TT, Neely A, Martinez V (2020) Digital transforming capability and performance: A microfoundational perspective. Int J Oper Prod Man 40(7/8):1095â1128
Strohmeier S (2020) Digital human resource management: A conceptual clarification. Ger J Hum Resour 34(3):345â365
Teece DJ (2020) Hand in glove: Open innovation and the dynamic capabilities framework. Strat Manag Rev 1(2):233â253
Teece DJ, Pisano G, Shuen A (1997) Dynamic capabilities and strategic management. Strateg Manag J 18(7):509â533
Trischler MFG, Li-Ying J (2023) Digital business model innovation: Toward construct clarity and future research directions. Rev Manag Sci 17(1):3â32
Velu C, Stiles P (2013) Managing decision-making and cannibalization for parallel business models. Long Range Plan 46(6):443â458
Verhoef PC, Broekhuizen T, Bart Y, Bhattacharya A, Dong JQ, Fabian N et al. (2021) Digital transformation: A multidisciplinary reflection and research agenda. J Bus Res 122:889â901
Wang M-C, Chen P-C, Fang S-C (2021) How environmental turbulence influences firmsâ entrepreneurial orientation: The moderating role of network relationships and organizational inertia. J Bus Ind Mark 36(1):48â59
Wannakrairoj W, Velu C (2021) Productivity growth and business model innovation. Econ Letters 199:109679
Warner KS, Wäger M (2019) Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long range Plann 52(3):326â349
Wei Z, Yang D, Sun B, Gu M (2014) The fit between technological innovation and business model design for firm growth. Evid China R D Manag 44(3):288â305
Wilden R, Gudergan SP, Nielsen BB, Lings I (2013) Dynamic capabilities and performance: strategy, structure and environment. Long Range Plann 46(1-2):72â96
Wilhelm H, Maurer I, Ebers M(2022) When are dynamic capabilities routine? A mixed-methods configurational analysis. J Manag Stud 59(6):1531â1562
Wu L, Sun L, Chang Q, Zhang D, Qi P (2022) How do digitalization capabilities enable open innovation in manufacturing enterprises? A multiple case study based on resource integration perspective. Technol Forecast Soc Change 184:122019
Xie XM, Han YH, Anderson A, Ribeiro-Navarrete S (2022) Digital platforms and SMEsâ business model innovation: Exploring the mediating mechanisms of capability reconfiguration. Int J Inf Manag 65:102513
Xu X, Du J, Gong X (2023) The impact of digital economy on innovation efficiency of new energy enterprises: Evidence from the perspective of innovation value chain. Emerg Mark Financ Trade 59(11):3402â3421
Yan S, Hu BL, Liu G, Ru XJ, Wu QT (2020) Top management team boundary-spanning behaviour, bricolage, and business model innovation. Technol Anal Strateg Manag 32(5):561â573
Yu B, Hao SB, Wang Y (2020) Organizational search and business model innovation: the moderating role of knowledge inertia. J Knowl Manag 24(7):1705â1718
Yuan S, Pan X (2023) The effects of digital technology application and supply chain management on corporate circular economy: A dynamic capability view. J Environ Manag 341:118082
Zhao J, Wei Z, Yang D (2021) Organizational search, dynamic capability, and business model innovation. IEEE Trans Eng Manag 68(3):785â796
Zhen J, Cao C, Qiu H, Xie Z (2021) Impact of organizational inertia on organizational agility: The role of IT ambidexterity. Inf Technol Manag 22(1):53â65
Zhong X, Chen W, Ren G (2023) How and when economic policy uncertainty influences firmsâ strategic change: The role of CEO turnover and organizational inertia. Int J Emerg Mark 18(10):3640â3656
Zhou Y, Zhang LK, Lu RY (2022) The impact of network ties on SMEsâ business model innovation and enterprise growth: Evidence from China. IEEE Access 10:29846â29858
Acknowledgements
This work was supported by the [Key Research Base of Humanities and Social Sciences in Guangxi Universities Scientific Research Fund Project of Pearl River-Xijiang Economic Belt Development Research Institute] under Grant [ZX2022013]; [Guangxi Philosophy and Social Science research project] under Grant [22FSH019; 23FSH022]; [Guangxi Education Science 14th Five Year Plan Special Project, Research on Social Entrepreneurship and Anti-Poverty Innovation Performance of Guangxi Urban Returned Youth under the Background of Rural Revitalization] under Grant [2022ZJY2922]; [Innovation Project of Guangxi Graduate Education] under Grant [XJCY2023010]; [Guangxi Normal University 2024 National Social Science Fund Cultivation Project] under Grant [2024PY016].
Author information
Authors and Affiliations
Contributions
All authors contributed to the studyâs conception and design. Conceptualization: all authors; investigation: LL, LC, and QH; writingâoriginal draft preparation: all authors; writingâreview and editing: all authors; visualization: CZ. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethical approval
This study was conducted in accordance with the Declaration of Helsinki. This study was approved by the Academic Ethics Committee of the Pearl River Xijiang Economic Belt Development Research Institute of Guangxi Normal University reviewed and approved (ID: ZX2022013).
Informed consent
All participants read and signed a consent form before they participated in the study.
Additional information
Publisherâs note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the articleâs Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the articleâs Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Liu, L., Cui, L., Han, Q. et al. The impact of digital capabilities and dynamic capabilities on business model innovation: the moderating effect of organizational inertia. Humanit Soc Sci Commun 11, 420 (2024). https://doi.org/10.1057/s41599-024-02910-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1057/s41599-024-02910-z