Network Ambidextrous Capabilities, Routine Replication, and Opportunity Iteration of Digital Startups—Evidence from China

Abstract

Amid the rapid advancement of digital technology, a surge of digital startups has emerged, intensifying competitive pressures that pose significant challenges to the growth and survival of these enterprises in China. This study constructs a theoretical model based on optimal distinction theory and upper echelon theory, incorporating network ambidextrous capabilities (network exploitation and network exploration), routine replication (general routines replication and flexible routines replication), digital leadership, and opportunity iteration. Utilizing data collected from surveys distributed to 372 digital startups in China and employing hierarchical regression analysis, we investigate the impact and scope of network ambidextrous capabilities on opportunity iteration. The results indicate that both network exploitation and network exploration capability positively influence opportunity iteration. General routines replication partially mediates the relationship between network exploitation capability and opportunity iteration, while flexible routines replication partially mediates the relationship between network exploration capability and opportunity iteration. Furthermore, digital leadership positively moderates the mediating effect of general routines replication on the relationship between network exploitation capability and opportunity iteration, as well as the mediating effect of flexible routines replication on the relationship between network exploration capability and opportunity iteration, demonstrating moderated mediation effects. This study enhances the understanding of opportunity iteration and offers new insights and solutions for digital startups to adapt to opportunities in response to environmental changes.

1. Introduction

The rapid development of digital technologies such as artificial intelligence, cloud computing, blockchain, and big data has significantly lowered the barriers to entrepreneurship in China. This has led to a surge in startups focused on digital products and services, stimulating market vitality and economic growth. The “2022–2023 China Innovation and Entrepreneurship Development Report” highlights that Chinese startups face intense competition from both traditional enterprises and other new ventures. Identifying unique positioning and advantages in this competitive market is a crucial challenge for these startups. Opportunity iteration, an extension of opportunity creation [1,2,3,4], has gained prominence with digital technology advancements, breaking traditional boundaries and demonstrating iterative changes [5]. This process is vital for the growth and survival of digital startups. Successful examples like Meituan and ByteDance illustrate the benefits of opportunity iteration, while the failure of companies like Mobike, which did not adapt, underscores its importance. Therefore, exploring opportunity iteration in startups holds significant practical and theoretical value. The differentiated resource acquisition and integration methods enabled by network ambidextrous capabilities may substantially impact opportunity iteration.

Opportunity iteration is the process of continuously testing and adjusting initial opportunities to achieve a mature and stable outcome, crucial for the growth and competitive advantage of startups [2,3,4]. Some studies indicate that users, as experiencers of digital products or services, can directly influence changes in opportunities through their feedback. Scholars have explored the impact mechanisms of opportunity iteration from various perspectives such as user experience [6], customer orientation [7], and lead user experience [8], reflecting the idea that organizations strive to innovate through differentiated products to facilitate opportunity iteration. Other research suggests that digital startups, constrained by limited resources, need to enhance their capabilities through strategic learning to achieve opportunity iteration. This demonstrates how startups build capabilities to enhance organizational legitimacy and respond to changes in opportunity iteration. Overall, existing studies have explored the factors influencing opportunity iteration development from the perspectives of differentiation and legitimacy, but lack an integrated approach that combines these perspectives to explore the antecedents of opportunity iteration in digital startups. Optimal distinctiveness theory suggests that individuals and organizations need to balance uniqueness and legitimacy to foster innovation. Network ambidextrous capabilities, encompassing network exploration capability and network exploitation capability, plays a crucial role in this balance. Network exploitation capability refers to the ability to acquire and utilize resources through existing relationships [9], which helps maintain external consistency and organizational legitimacy, thereby facilitating opportunity iteration. Network exploration capability involves establishing new relationships to acquire and utilize diverse resources [9], fostering the formation of differentiation strategies, which are key drivers of opportunity iteration. Based on this, the current study examines the role of network ambidextrous capabilities in opportunity iteration from the perspective of optimal distinctiveness theory. The knowledge integration methods of routine replication may illuminate the link between network ambidextrous capabilities and opportunity iteration.

Network ambidextrous capabilities do not automatically lead to the iteration of digital entrepreneurial opportunities; a transformation mechanism, primarily routine replication, is required. Routine replication involves the deconstruction, recombination, and restoration of knowledge templates between dual organizations [10,11], which expands organizational boundaries and enhances innovation capabilities [12]. According to optimal distinctiveness theory [13], network exploitation capability promotes opportunity iteration by obtaining resources through stable connections and facilitating the replication of general routines that emphasize a sense of belonging and standardized operations. This supports organizational consistency and adaptability to external networks. Conversely, network exploration capability seeks differentiation by establishing new network relationships and absorbing heterogeneous resources from potential partners. This is achieved through replication of flexible routines, emphasizing innovation and flexibility, thus facilitating the adjustment and modification of organizational opportunities. For example, Didi Chuxing’s collaboration with Uber China leveraged existing network relationships to replicate Uber’s technical and operational routines, accelerating opportunity iteration and establishing market dominance in China. Similarly, Meituan’s partnerships with various restaurants, hotels, and tourist attractions established new network relationships, achieved flexible routine replication, facilitated opportunity iteration, and expanded its service range. Therefore, this study posits that routine replication mediates the relationship between network ambidexterity and opportunity iteration. An entrepreneur’s prior knowledge, social networks, and personal characteristics influence the opportunity development process in startups [14]. The distinctive leadership style of digital leadership may significantly impact the opportunity iteration process in digital startups.

Leadership style endows leaders with unique qualities that influence their behavior and decision-making, thereby impacting the organization as a whole [15]. The Upper Echelons Theory suggests that leader traits influence corporate strategic choices, which in turn affect organizational behavior [16]. Digital leadership helps organizations navigate risks and uncertainties [17]. Consequently, leadership style may influence the relationship between routine replication and opportunity iteration. Digital leadership promotes positive responses to digital strategies through digital communication, transformation, and team building, thereby altering organizational behavior and fostering innovation [18]. However, there is limited research on digital leadership as a moderating factor in this relationship. In digital contexts, digital leadership aids in identifying opportunities and risks amidst environmental changes. It enhances a firm’s ability to integrate digital resources through routine replication, thus facilitating opportunity iteration and competitive advantage. Therefore, exploring the moderating role of digital leadership between routine replication and opportunity iteration is crucial for clarifying the boundary conditions for opportunity iteration in digital startups and advancing innovative development in enterprises.

In summary, this study employs optimal distinctiveness theory and upper echelons theory to develop a theoretical model of “Network Ambidextrous Capabilities—Routine Replication—Opportunity Iteration” in a digital context. Through empirical analysis, it investigates the interactions among these elements, focusing on the mediating role of routine replication between network ambidextrous capabilities and opportunity iteration, and the moderating effect of digital leadership. This research aims to enhance the theoretical understanding of the impact of network ambidextrous capabilities on opportunity iteration in digital startups, and to provide practical insights for Chinese digital startups in leveraging these capabilities to drive opportunity iteration.

2. Literature Review and Research Hypotheses

2.1. Digital Startups and Opportunity Iteration Digitalization

“Digitalization” refers to the integration of digital technologies into business, economics, and society, facilitating connectivity among objects, individuals, and entire organizations [19,20]. When digital technologies are applied in entrepreneurship, this is known as “digital entrepreneurship” [21]. Digital entrepreneurship includes startups based on digital technology products and those transforming traditional businesses through digital technologies. Startups driven by or leveraging digital technologies to explore opportunities, whose activities or products and services are related to digital technology, are called digital startups [21,22,23]. Digital entrepreneurial opportunities manifest through the creation and utilization of digital technologies, reshaping paths to meet market demands, and generating new market demands [24,25]. The characteristics of digital technology, such as editability and scalability [5], accelerate the pace of technological iterations, prompting discontinuous market changes and iterative opportunity updates. Several studies [2,26,27] have described the process where startups continuously experiment, adjust, refine, and upgrade initial opportunities based on contextual changes to achieve a mature and stable final opportunity that meets the expectations of entrepreneurs and stakeholders as opportunity iteration [1,2]. Opportunity iteration effectively enhances the competitive advantage of entrepreneurial firms and promotes their performance growth [3]. With the deepening research on opportunity iteration theory, its connotations, characteristics, and influencing factors have been gradually refined. Existing studies indicate that opportunity iteration consists of three stages: the generation of iteration momentum, the formulation of iteration strategies, and the formation of iteration outcomes [2]. Factors such as customer orientation [7], strategic learning [28], and dual strategic orientation [3] serve as antecedents to opportunity iteration, all effectively promoting its development.

Previous research has explored the meaning and formation process of opportunity iteration in digital startups, though there has been the limited exploration and empirical examination of these concepts. The rapid advancement of digital technology has intensified market competition for startups, making it essential for them to gain competitive advantages through product differentiation. However, startups face significant challenges due to their inherent lack of legitimacy and insufficient resources. Thus, it is crucial to construct a resource pool from both legitimacy and differentiation to effectively capitalize on opportunities and respond to market pressures effectively.

The openness and interconnectedness of digital technology enable more stakeholders to participate in the opportunity iteration of startups [29]. Digital startups utilize existing network relationships to align with external entities for legitimacy or to develop new network relationships to enhance differentiation. Both approaches provide essential resources for iterating opportunities. To clarify the mechanism of opportunity iteration for startups in a digital context, this study integrates network ambidextrous capabilities to explore how the process of opportunity iteration occurs in startups.

2.2. Network Ambidextrous Capabilities

Network capability refers to a new firm’s ability to identify, establish, coordinate, and develop various relationships with different roles in the market [30,31]. This capability forms the foundation for entrepreneurial success [32]. Previous research has extensively applied the exploration–exploitation dichotomy to the study of alliance networks and portfolios portfolios [33]. Faroque (2021) first categorized network capability from the dual perspectives of exploration and exploitation. Network exploitation capability refers to a firm’s ability to utilize and cultivate existing network relationships to optimize the allocation of its current resource base (e.g., market knowledge) [9]. Early-stage startups can leverage this capability to acquire similar resources and market insights, enabling them to better adapt to market changes and iterate on opportunities [34]. This capability aligns firms with peers and enhances legitimacy [35]. For instance, ByteDance accelerated the development of its TikTok (30.3.0)platform by closely collaborating with existing customers and partners, reducing legitimacy resistance and iterating on business opportunities. As the positive effects of initial relationships may diminish over time, digital startups must seek new relationships to broaden their networks and achieve growth [36]. Network exploration capability involves acquiring and integrating new resources through the establishment of new relationships. By developing new network connections, digital startups can obtain heterogeneous resources for innovation, promoting the iteration and renewal of opportunities. This capability supports the development of differentiated products and services, reflecting a strategy of organizational uniqueness. For example, ByteDance expanded its network through the acquisition of Musical.ly(4.2) and collaborations with various content creators, media companies, and advertisers. This strategy not only gained new users but also resulted in a diverse range of digital products. Building network ambidextrous capabilities benefits startups in identifying and creating opportunities.

For resource-constrained digital startups, balancing network ambidextrous capabilities is crucial. The optimal distinctiveness theory provides theoretical insights for coordinating the conflicting demands of legitimacy and differentiation [37]. This theory emphasizes the need for firms to balance differentiation and legitimacy to achieve optimal performance, suggesting that firms should dynamically adjust this balance over time to foster innovation [38]. The exploitation and exploration dimensions of network ambidextrous capabilities reflect different strategies for pursuing legitimacy and differentiation. Network exploitation capability maintains existing relationships to ensure consistency with the external environment, using trust and support to acquire resources for opportunity iteration [39]. Conversely, network exploration capability drives opportunity iteration through the establishment of new relationships and the acquisition of heterogeneous resources, forming a differentiation strategy [9]. This study considers network ambidextrous capabilities as addressing the “legitimacy” and “differentiation” issues from an optimal distinctiveness perspective, offering insights into the mechanisms by which digital startups leverage network ambidextrous capabilities to iterate on opportunities.

In the digital context, entrepreneurial opportunity iteration relies not only on network capabilities for acquiring knowledge, but also on a firm’s ability to quickly and effectively transmit and integrate information, knowledge, and resources. Routine replication can simplify this transfer process, thereby promoting swift opportunity iteration [40]. Therefore, it is theoretically essential to clarify how digital startups use network capability and routine replication to achieve entrepreneurial opportunity iteration.

2.3. Research Hypothesis

2.3.1. The Direct Effect of Network Ambidextrous Capabilities on Opportunity Iteration

• (1). Network exploitation capability and Opportunities Iteration

According to optimal distinctiveness theory, individuals and organizations must balance uniqueness and legitimacy to foster innovation. Network exploitation capability emphasizes maintaining stable and reliable connections to ensure organizational legitimacy, thereby optimizing existing resources and promoting entrepreneurial opportunity iteration. First, digital startups can reduce legitimacy deficits through network exploitation capability, acquiring digital technology resources from existing relationships. The generative nature of digital technology expands the unique knowledge created during interactions, enabling startups to recognize new opportunities [41]. Opportunity recognition drives entrepreneurs to better understand the value of these opportunities [42], intensifying the process of entrepreneurial opportunity iteration. This provides strategic guidance and value support for the continuous creation of digital opportunities in startup development. Second, startups can more easily mimic advanced technologies and innovative ideas from their industry or beyond by leveraging the legitimacy gained from existing network relationships. This accelerates the learning curve and reduces startup costs [43], facilitating market entry and allowing for timely adjustments to entrepreneurial opportunities based on market feedback. Finally, startups often face information asymmetry with larger partners. The legitimacy obtained through network exploitation capability can effectively coordinate with external groups or individuals and improve internal communication. This helps build trust and reduce opportunism through digital technologies [44], lowering initial risks and receiving diverse knowledge from different sources in a structured manner. Consequently, this increases the likelihood of updating entrepreneurial opportunities. Thus, this study proposes the following hypothesis:

Hypothesis 1a (H1a): 

Network exploitation capability positively influences entrepreneurial opportunity iteration.

• (2). Network Exploration Capability and Opportunity Iteration

The collaborative nature of digital entrepreneurship results in co-creation, rapid iteration, feedforward processes, and high efficiency in identifying entrepreneurial opportunities [26,45]. Digital technologies enable startups to expand their networks with stakeholders at a low cost, creating open organizational boundaries that facilitate frequent interactions [46]. These network exploration capabilities provide heterogeneous resources crucial for the iteration of digital entrepreneurial opportunities. According to the optimal distinctiveness theory, emerging fields such as the digital economy pursue differentiation strategies while achieving legitimacy [47]. Network exploration capability enables the acquisition of heterogeneous resources through new relationships, fostering the development of new products and services and reflecting an organization’s strategic differentiation mindset. These capabilities effectively promote opportunity iteration through the integration of diverse resources. First, network exploration capability continually increases the participation of entities with heterogeneous knowledge and information in the opportunity construction activities of enterprises. Interactions among these entities generate new knowledge combinations, promoting a consensus on differentiation strategies among stakeholders and driving entrepreneurial opportunity iteration [1]. Second, the use of digital technologies facilitates more convenient and closer interdisciplinary connections and interactions [48], accelerating the dissemination and exchange of knowledge and information. Network exploration capability involves acquiring new knowledge and information through the establishment of new relationships. Coupled with data analysis, it allows for an accurate understanding of user experiences and the exploration of both explicit and implicit customer needs, key considerations for the iteration of entrepreneurial opportunities [6]. Finally, social cognition theory posits that cognition and decision-making are influenced by the contextual factors of an enterprise’s external relational network [49]. Network exploration capability aids managers in understanding external development policies and market environments through continuously updated network relationships. This ongoing entrepreneurial learning from external networks enhances managerial cognition and promotes strategic differentiation thinking within organizations. This cognitive adjustment process, along with the rapidly evolving external market environment driven by digital technologies, enables startups to adapt their entrepreneurial opportunity prototypes and make strategic decisions crucial for opportunity iteration. Therefore, this study proposes the following hypothesis:

Hypothesis 1b (H1b): 

Network exploration capability positively influences the iteration of entrepreneurial opportunities.

2.3.2. Mediating Effect of Routine Replication

Routine is defined as a learned behavior pattern that encapsulates organizational capabilities and knowledge, allowing enterprises to repeat these skills [12,50]. Existing research has categorized routines from various perspectives, including cognitive behavior [51], hierarchy [52], process [53], and capability [11,54]. Among these, the capability perspective divides routines into two dimensions: general and flexible. General routines primarily pertain to knowledge and skills related to general operational capabilities within management processes (e.g., manufacturing processes). In contrast, flexible routines pertain to knowledge combinations associated with dynamic capabilities (such as strategic change and vision recognition). Routine replication involves transferring knowledge between organizational dyads. Organizations accomplish replication by deconstructing, recombining, restoring, and applying knowledge from routine templates [55]. These templates consist of a series of systematically compiled and recombined knowledge elements, effectively overcoming knowledge stickiness within routines [56]. The capability perspective’s distinction between general routines replication and flexible routines replication reflects the micro-processes of resource integration, reorganization, and routine generation and deployment. General routines replication involves organizations imitating and adapting clear, stable rules or behavior patterns to achieve incremental capability improvements, thereby continuously enhancing entrepreneurial opportunities for digital startups. In contrast, flexible routines replication entails deconstructing diverse and variable implicit rules or behavior patterns, optimizing and updating them for practical application, and seeking radical capability enhancements to innovate and iterate on entrepreneurial opportunities [57,58]. This study adopts these dimensional distinctions. General routines replication is highly sensitive to stable environments and exploitation capabilities [11], whereas flexible routines replication is more responsive to turbulent environments and exploration capabilities [11]. Knowledge management theory indicates that firms need to seek and acquire innovative knowledge from external sources [59]. Nahapiet and Ghoshal (18) argued that social relationships serve as a conduit for the exchange and combination of knowledge in new ventures, facilitating the iteration of entrepreneurial opportunities through the creation of new knowledge [60]. Therefore, it is essential to further investigate the differentiated mechanisms of general routines replication and flexible routines replication in the context of network ambidextrous capabilities and opportunity iteration.

• (1). Mediating Effect of General Routines Replication

Research on knowledge management theory indicates that no single enterprise possesses all the knowledge resources required for innovative development [53]. Therefore, businesses must seek and acquire innovative knowledge from external sources. Network exploitation capability aids organizations in acquiring resources, technologies, and processes related to operations, thereby stabilizing and consolidating knowledge. Replication of general routines involves transferring relatively stable operational knowledge [53,60]. Network exploitation capability supports new ventures in acquiring routine resources by demonstrating legitimacy, positively impacting the replication of general routines. Firstly, network exploitation capability enhances the depth of general routines replication. It enables organizations to leverage existing relationships to access resources and conduct deep local knowledge searches. This facilitates exploitative learning, leading to the integration and absorption of knowledge, which in turn expands and refines existing knowledge [58]. This process strengthens applications in familiar domains and promotes the replication of general routines. Secondly, network exploitation capability reduces the costs associated with searching for templates for routine replication. Replication of general routines involves the optimized absorption of perceived, recognized, and classified knowledge templates [61]. New ventures can acquire necessary knowledge templates through network exploitation capability, replicating rules, procedures, knowledge, and learning capabilities at a lower cost, thereby building the capabilities of digital startups. Lastly, given the rapid pace of technological change, startups must respond swiftly to dynamic market environments. Network exploitation capability enables timely understanding of market changes, facilitating the replication of knowledge templates that are relatively simple in structure, stable, and continuously evolving. This process addresses knowledge stickiness and enhances the efficiency of general routines replication.

On the other hand, replication of general routines improves and expands the existing knowledge of new ventures [62], enabling the rapid identification and correction of flaws in initial opportunities, thereby positively influencing opportunity iteration. Firstly, replication of general routines focuses on the perception, recognition, and classification of knowledge templates. This optimized absorption of explicit knowledge through fixed cognitive pathways reduces early-stage costs for opportunity iteration in digital startups and increases iteration efficiency. Segarra-Ciprés (2018) argues that in-depth knowledge search allows enterprises to acquire detailed and granular innovative knowledge, facilitating service innovation [63]. Secondly, replication of general routines often relies on vicarious learning to improve and expand existing knowledge. This behavior continually reconstructs an organization’s digital technologies, prompting the improvement of existing routines and generating momentum for opportunity iteration. Benner (2003) points out that the introduction of routines enhances an organization’s technological innovation capabilities through continuous improvement of existing processes, thereby capturing new market opportunities [64]. Finally, replication of general routines depends on the organizational ability to deeply analyze routine templates. By replicating clear and stable templates, organizations enhance their competitive advantage, mitigate risks, and stabilize knowledge transfer, providing assurance for the construction phase of opportunity iteration. Szulanski argues that by replicating successful business practices, enterprises can reduce uncertainty and risk in entering new markets and developing new products [65]. Therefore, this study proposes the following hypothesis:

Hypothesis 2a (H2a): 

Replication of general routines mediates the relationship between network exploitation capability and opportunity iteration.

• (2). The Mediating Effect of flexible routines replication

Research based on knowledge management theory indicates that creating new network connections helps organizations acquire novel information distinct from their existing knowledge base, thereby enhancing organizational performance [66]. Network exploration capability enhances the replication of flexible routines in digital startups. Firstly, the rapid iteration of digital technologies exposes startups to increased uncertainty. Flexible routines, characterized by diverse knowledge templates and rich content, exhibit discontinuous and radical evolution [67]. For startups operating in a turbulent digital environment, network exploration capability facilitates the acquisition of extensive new knowledge, helping organizations to discern and integrate diverse, dynamic implicit rules or behavior patterns, thereby promoting the replication of flexible routines [11]. Secondly, digital technology reduces communication costs, enhancing network connectivity. This increases the heterogeneity of knowledge within the network, requiring digital startups to leverage flexible routines replication to integrate this diverse knowledge and enhance their innovation capabilities. Lastly, network exploration capability enhances inter-organizational relational learning. Flexible routines replication acts as a resource reconfiguration mechanism within organizational experiential learning, with network exploration capability providing additional learning resources for flexible routines replication [68].

On the other hand, the replication of flexible routines adapts and modifies organizational routines in new or volatile environments, aiming fundamentally to acquire and absorb new knowledge. By improving organizational resource allocation, replication of flexible routines effectively promotes the evolution of entrepreneurial opportunities, achieving iterative development. Firstly, opportunity creation theory posits that the exchange and interaction of diverse knowledge lead to opportunity formation [69], thereby shortening research and development cycles. Replication of flexible routines emphasizes the search, adjustment, and modification of knowledge templates, facilitating the exploration and variation of tacit knowledge through improvisational cognitive tracing. This approach helps identify customer needs and improves the efficiency of opportunity iteration. Secondly, replication of flexible routines often relies on experiential learning to acquire breakthrough new knowledge and deconstruct existing resources, promoting the accumulation of diverse knowledge. This, in turn, advances stakeholder consensus and drives motivation for opportunity iteration [6]. Lastly, replication of flexible routines depends on organizational exploration capabilities. When replicating routine templates in new or volatile digital environments, organizations continuously adjust and evolve routines to sustain innovative exploration. This process enables them to acquire the latest customer experiences and target the direction of opportunity iteration. Therefore, this paper proposes the following hypothesis:

Hypothesis 2b (H2b): 

Replication of flexible routines mediates the relationship between network exploration capability and opportunity iteration.

2.3.3. The Moderating Role of Digital Leadership

In developing digital startups, leaders must create a clear and meaningful digital vision. This involves leveraging information technology to induce changes in attitudes, emotions, thinking, behaviors, and performance within the organization, a capability known as digital leadership [18,70]. Digital leaders utilize their skills in digital, market, business, and strategic leadership to manage and lead interdisciplinary teams (both digitally skilled and non-digitally skilled), fostering strategic consensus among employees and enhancing the identification of new opportunities for the organization [18,71]. This paper defines digital leadership as the ability of leaders to drive the transformation of individual, group, and organizational attitudes and behaviors toward digital strategies through five key skills: digital communication and coordination, digital motivation and change management, digital team building and maintenance, digital technical expertise, and digital trust cultivation. This approach aims to foster the growth of startups. Strong digital leadership facilitates employees’ understanding and adoption of the best routines from external organizations, promoting iterative development of opportunities for digital startups to gain a competitive advantage. Therefore, this paper uses digital leadership as a moderating variable in the relationship between the replication of general routines and the iteration of entrepreneurial opportunities.

• (1). The moderating role of digital leadership in the relationship between Replication of general routines and opportunity iteration

The upper echelons theory suggests that leaders’ characteristics influence strategic choices, which in turn affect organizational behavior [16]. Digital leadership, a blend of digital technology and leadership skills, enables leaders to embrace digital transformation and inspire innovation among employees [72]. High digital leadership allows employees to learn from the best general routines of collaborating organizations, optimizing structures and processes, and thus accelerating the identification, evaluation, and validation of entrepreneurial opportunities. Firstly, digital technology expertise in digital leadership effectively promotes innovation in digital technology within startups [73]. The editable nature of digital technologies helps enterprises rapidly adapt to new environments [23], breaking down the boundaries of entrepreneurial opportunities and accessing abundant resources. The resource pools created by digital technologies provide numerous templates for the replication of best general routines in digital startups, facilitating progressive learning and optimization activities, and providing more efficient tools for continuous opportunity refinement, thereby speeding up the opportunity iteration process. Secondly, digital leadership fosters effective communication and coordination within digital teams, establishing robust information channels [74]. This enhances team members’ knowledge integration behaviors during the disassembly, deconstruction, and reconstruction of external templates in exploitative learning, thereby driving the optimization and upgrading of business opportunities. Lastly, digital leadership’s digital incentives and change management measures create an open and innovative cultural environment [15], which helps improve the efficiency of deep searches in routine replication, accelerates the improvement and expansion of existing templates, and promotes opportunity iteration in startups. Therefore, this study proposes the following hypothesis:

Hypothesis3a (H3a): 

Digital leadership positively moderates the relationship between the replication of general routines and opportunity iteration.

• (2). The Moderating Role of Digital Leadership in replication of flexible routines and Opportunity Iteration

Research on upper echelons theory indicates that digital leadership, through the effective use of digital technology, can identify and realize value-creating opportunities [72]. Managers with digital leadership skills continuously adjust their computational abilities, communication skills, and management content to adapt to the digital transformation context [75]. This adjustment ensures that organizations can leverage open innovation through improvisational searches to meet the diverse digital resource needs in digital opportunity innovation, thereby benefiting the opportunity iteration of digital startups.

Firstly, digital leadership provides various information, resources, and training to support the formation and development of organizational digital teams. Digital collaboration technologies enhance communication and information sharing among team members [75], ensuring efficient filtering, variation, and optimization of templates in the replication of flexible routines, thereby reducing internal pressure on organizational opportunity iteration. Secondly, routines serve as repositories of organizational knowledge, embedding substantial tacit knowledge within flexible routine templates. New ventures replicate these flexible routines to acquire new digital technologies and digital knowledge, facilitating the iterative upgrade of existing entrepreneurial opportunities. Managers with digital leadership possess background knowledge that impacts the discovery and innovative use of tacit knowledge [18]. Lastly, flexible routines rely on exploratory learning to acquire discontinuous heterogeneous knowledge. Under digital leadership, the establishment of digital trust enhances organizational members’ identification with the organization [18], promoting collaboration and communication among team members. This enables organizations to efficiently conduct exploratory learning, seeking optimal solutions through continuous experimentation and innovation, thereby capturing opportunities in the market environment. Therefore, this study proposes the following hypothesis:

Hypothesis 3b (H3b): 

Digital leadership positively moderates the relationship between replication of flexible routines and opportunity iteration.

2.3.4. Moderated Mediation Effects

• (1). Moderation of General Routines Replication by Digital Leadership

Based on the preceding theoretical analysis, hypothesis H2a posits that network exploitation capability promotes opportunity iteration through the replication of general routines. Hypothesis H3a further delineates the moderating role of digital leadership between replication of general routines and opportunity iteration. Collectively, this study infers that digital leadership moderates the entire mediation mechanism linking network exploitation capability, replication of general routines, and opportunity iteration, suggesting a presence of moderated mediation. Specifically, when an organization exhibits high levels of digital leadership, communication and coordination among members via digital technologies are more fluid. The integration of knowledge relevant to the organization’s existing innovations, acquired through network exploitation capability, is improved. This enhances the efficiency of routine replication, thereby fostering opportunity iteration under incremental innovation. Fatima (2024) [76] indicates that the knowledge value perspective of digital leadership enhances knowledge sharing among team members, significantly promoting open innovation by strengthening knowledge sharing and improving innovation capabilities. Conversely, when an organization’s digital leadership is low, even if it acquires knowledge related to operations and process structures through network exploitation capability, the lack of a data-driven decision-making culture typically found in such organizations hampers the effective use of data analysis to optimize processes and identify new opportunities. This diminishes the positive effects of opportunity iteration. Wang, T. (2022) [15] suggests that digital leadership conveys a digital vision to followers, empowering them with trust, tolerance, and respect. This motivates members to actively participate in building a unified digital organizational culture, which facilitates the effective application of digital technologies, promotes knowledge sharing and creation, and positively impacts exploratory innovation. The analysis suggests that digital leadership enhances the developmental impact of opportunity iteration, which is mediated by the replication of general routines through network exploitation capability. Consequently, this study proposes the following hypothesis:

Hypothesis 4a (H4a): 

Digital leadership positively moderates the mediating effect of general routines replication in promoting opportunity iteration through network exploitation capability, meaning stronger digital leadership strengthens the mediating role of general routines replication.

• (2). The Moderating Effect of Digital Leadership on the Mediating Role of Replication of Flexible Routines

Based on the theoretical analysis presented, Hypothesis H2b posits that network exploration capability promotes opportunity iteration through the replication of flexible routines. Hypothesis H3b suggests that digital leadership moderates the relationship between flexible routines replication and opportunity iteration. This study integrates these perspectives to propose that digital leadership moderates the entire mediation mechanism involving network exploration capability, replication of flexible routines, and opportunity iteration, potentially exhibiting a moderated mediation effect. Specifically, when an organization has a high level of digital leadership, it establishes more integrated and networked relationships with partners and competitors [77]. The increased looseness and permeability of organizational boundaries enhance innovative capabilities [78]. When a company utilizes network exploration capabilities to acquire external information, the deconstruction and reorganization of implicit rules in the replication of flexible routines align better with organizational characteristics, stimulating the generation of innovative knowledge and facilitating opportunity upgrading in a digital environment. Benitez (2022) [79] demonstrates that digital leadership promotes the development of a company’s digital platform capabilities, integrating knowledge from IT infrastructure, business processes, and data within internal and external networks, thereby enhancing the company’s innovation performance. Conversely, when an organization has low digital leadership, it often struggles to maintain an open, connected, and highly collaborative organizational culture [77], making the company less sensitive to innovative behaviors. When startups bring in resources through network exploration capabilities, there is insufficient motivation to generate new capabilities through the replication of flexible routines, hindering the company’s opportunity iteration. Rudito (2017) [80] points out that companies lacking digital leaders struggle to integrate digital capabilities into their culture and development, which is detrimental to organizational innovation. Therefore, based on the above analysis, digital leadership aids in the development of opportunity iteration by promoting network exploration capability through the replication of flexible routines. This study proposes the following hypothesis:

Hypothesis 4b (H4b): 

Digital leadership positively moderates the mediating effect of flexible routines replication in the relationship between network exploration capability and opportunity iteration. In other words, the stronger the digital leadership, the more enhanced the mediating role of flexible routines replication.

In summary, the theoretical model of this study is constructed as shown in Figure 1.

3. Research Design

3.1. Sample Selection and Data Sources

This study targets digital startups through a questionnaire survey. According to Zimmerman and Zeitz (2002) [81], enterprises established within the last eight years are considered startups. Digital startups are defined as entrepreneurial ventures that leverage digital technologies for primary value creation and have been in existence for no more than eight years. To ensure data quality, the questionnaires were completed by entrepreneurs or senior executives of digital startups who have a deep understanding of the company and are responsible for strategic planning. To ensure the representativeness of the sample, this study selected four regions based on geographical diversity and entrepreneurial activity levels, guided by the “China Digital Economy Innovation and Entrepreneurship Index” published by the Peking University Enterprise Big Data Research Center in December 2021. The regions chosen were Guangdong Province, Zhejiang Province, Beijing, and Jilin Province, representing the southern, northern, and central parts of China. Guangdong Province, Zhejiang Province, and Beijing were identified as regions with high entrepreneurial activity, while Jilin Province was identified as having low entrepreneurial activity. The survey data for this study were collected through a combination of a questionnaire platform, email, and field research. The questionnaire platform has a rigorous quality control system, ensuring targeted distribution to specific users. Measures such as IP restriction, device control, time control, and manual sampling were employed to guarantee data quality. The survey was conducted from December 2023 to May 2024, with a total of 550 questionnaires distributed. The collected questionnaires underwent strict screening to eliminate invalid responses, including those from companies established for over 8 years, those with missing information, and those with uniform or patterned answers. Ultimately, 372 valid questionnaires were obtained, resulting in a response rate of 67.6%. The distribution of the final sample was as follows: among the entrepreneurs, those with prior entrepreneurial experience constituted the largest proportion at 59.7%. In terms of company size, firms with 51–100 employees accounted for the largest share at 25.5%. The internet industry was the most represented sector, comprising 34.1% of the sample. Geographically, Zhejiang Province had the highest representation at 30.4%. Overall, the sample encompassed a variety of digital enterprises, indicating that the survey data were broad and representative.

3.2. Variable Measurement

This study designed measurement items by reviewing the existing literature and using established scales from both domestic and international sources, tailored to the actual circumstances of the survey subjects. We followed Brislin’s (1980) traditional translation-back-translation procedure to create the Chinese version of the scale. Initially, the questionnaire was drafted in English and then translated into Chinese by bilingual, experienced management researchers to ensure validity. We consulted experts in the field to refine the scale structure, removing ambiguous or irrelevant items and adding elements that were initially overlooked. To ensure the quality of the scale, a small-scale pre-survey was conducted with 10–20 middle and senior managers from digital startups to refine the semantic settings and logical expressions of the questionnaire. For instance, the item “We regularly engage in social activities with all partners”, which fell under network utilization capability, was deleted. Most pre-survey respondents selected “completely disagree” for this item, and subsequent consultations with senior managers indicated that such activities are impractical for startups due to limited funds and potential cost implications. Based on pre-survey feedback, the questionnaire was finalized and refined into a formal survey. Apart from control variables, a 7-point Likert scale was used to measure the variables, with 1 indicating “completely disagree” and 7 indicating “completely agree”.

• (1). Network Exploitation Capability: This refers to a company’s ability to leverage and develop existing network connections and relationships to optimize its current resource base (e.g., market knowledge). This paper draws on the studies by Walter (2006) [82], Mitrega et al. (2012) [83], and Faroque (2022) [66], measuring network development capability through inter-organizational and personal coordination, conflict management, and internal communication. A total of 10 items were included.

• (2). Network Exploration Capability: This is the ability to establish new network connections and expand existing ones by maintaining an open attitude toward new relationships. Accordingly, this paper adopts the research of Mitrega (2012) [83], Parida (2009) [84], and Faroque (2022) [66], measuring network utilization capability through new relationship exploration and pairing. A total of seven items were included.

• (3). Replication of General Routines: This involves the incremental dynamic process of knowledge transfer between firms. This paper draws on the measurement methods of general routines replication by Pentland (2003) [40] and Wei Long (2020) [58]. The replication of general routines was measured using a validated five-item scale developed by Wei Long (2023) [57]. Specifically, these were: (1) the technical operation manuals of partner companies, which are task-aligned, are easy to imitate or describe; (2) the company adopts the work methods and practices of experienced partner companies when executing tasks; (3) through regular assessments, the company can participate in the revision of standards; (4) the company relies on clear strategic planning to implement organizational changes and address internal and external challenges; and (5) the company tends to maintain existing partnerships to strengthen knowledge transfer channels.

• (4). Replication of Flexible Routines: This refers to the breakthrough dynamic process of knowledge transfer between firms. Accordingly, this paper adopts the measurement methods for flexible routines replication from Bresman (2013) [53], Chen Yanliang (2014) [11], and Wei Long (2020) [58]. The replication of flexible routines was measured using a validated five-item scale developed by Wei Long (2023) [57]. Specifically, they were as follows: (1) task-related rules acquired from partner companies are difficult to articulate and cannot be clearly documented; (2) task execution methods and practices learned from partner companies require hands-on experience to master; (3) through periodic project feedback, the company actively participates in the revision of standards; (4) the company quickly adopts, promotes, and applies new organizational standards to address internal and external challenges; and (5) the company tends to expand its range of partnerships to establish knowledge transfer channels.

• (5). Opportunity Iteration: This describes the ongoing process of entrepreneurial opportunity development, essentially involving the adjustment, refinement, or upgrading of existing opportunities. This paper, therefore, draws on the studies by Wood and McKinley (2017) [1], Liu Zhiyang et al. (2019) [2], and Guo Runping (2022) [7] to measure opportunity iteration. A total of five items were included: (1) continuously experimenting with and adjusting original entrepreneurial opportunities even after the company is established; (2) quickly modifying and refining original entrepreneurial opportunities in response to environmental changes; (3) rapidly adjusting or improving original entrepreneurial opportunities based on feedback from stakeholders (e.g., customers, employees, investors, government agencies, partners); (4) frequently adjusting and refining original entrepreneurial opportunities; and (5) regularly upgrading and updating original entrepreneurial opportunities.

• (6). Digital Leadership: This is the social influence process in which leaders use digital technologies to transform individuals, groups, and organizations regarding attitudes, emotions, thinking, behavior, and performance towards digital strategies [85] (Avolio et al., 2014). Therefore, this paper draws on the studies by Sawy (2016) [70], Avolio (2014) [85], Van (2019) [86] and Q Yao (2024) [18], measuring digital leadership in five areas: digital communication and coordination, digital motivation and change management, digital team building and maintenance, digital technology expertise, and digital trust cultivation. A total of five items were included as follows: (1) leaders facilitate internal and external communication and coordination through digital technologies; (2) leaders utilize digital technologies for organizational incentives and management transformation; (3) leaders establish and maintain digital teams; (4) leaders possess digital thinking and expertise in digital technologies; and (5) leaders foster digital trust within the organization.

• (7). Control Variables: Research indicates that executives’ entrepreneurial experience positively influences the acquisition of entrepreneurial knowledge, thereby promoting entrepreneurial success [87]. Therefore, this study includes entrepreneurial experience as a control variable at the individual level, measured by whether the entrepreneur has previously founded another company before establishing the current one (1 = yes; 0 = no). At the firm level, Petruzzelli et al. (2018) suggest that company size impacts innovation development [88]. This study controls for company size, as outlined in “Maturity of Knowledge Inputs and Innovation Value: The Moderating Effect of Firm Age and Size”, by measuring the number of employees (1–20, 21–50, 51–100, 101–200, and over 200). Previous research emphasizes that industry type affects firms’ innovation strategy choices and recommends controlling for industry type to avoid bias [89]. Accordingly, this study controls for industry type, categorized into five sectors based on the “2020 China Digital Economy Development White Paper”: software industry, internet industry, telecommunications, electronic information manufacturing, and other industries. Guo (2020) [90] points out that an open external technological environment allows digital startups to access necessary technology and knowledge at relatively low costs, influencing firm growth. This study controls for the openness of the technological environment, measured by the ease of access to technology and the associated costs, based on existing research.

4. Hypothesis Testing and Analysis

This study’s empirical analysis encompassed several key aspects. First, we evaluated the reliability and validity of the questionnaire using SPSS27 and AMOS27 software, following the methodology of Henseler et al. (2015) [91]. Second, we used SPSS to analyze common method bias, variable correlations, and VIF values. We then established and tested regression models for main effects, mediation effects, and moderation effects, following the frameworks of Baron and Kenny (1986) [92] and Shrout and Bolger (2002) [93]. Additionally, we employed PROCESSv4.2 software to examine moderated mediation effects. Finally, we conducted robustness tests to ensure the reliability of our results.

4.1. Common Method Bias Test

Since all questionnaire items were completed by a single individual, there remained a potential for common method bias despite our efforts to mitigate this during data collection. We emphasized confidentiality, anonymity, and the exclusive use of the data for academic research. To assess common method bias, we applied Harman’s single-factor test, performing an unrotated exploratory factor analysis on all variables. The results indicated that the first factor accounted for 29.3% of the variance, which is below the critical threshold of 40%, suggesting that common method bias was not a significant concern in our data.

4.2. Reliability and Validity Analysis

To ensure the validity of the questionnaire design, we conducted reliability and validity analyses using SPSS 27.0 and Amos 28.0. The results are presented in Table 1. The Cronbach’s alpha coefficients and composite reliability (CR) values for all latent variables in the theoretical model were greater than 0.8, surpassing the acceptable threshold of 0.8 for both reliability measures. This indicates that the scale exhibited good internal consistency and reliability.

Regarding validity, the scales for network ambidextrous capabilities, routine replication, digital leadership, technological environment openness, and opportunity iteration have been recognized in previous domestic and international studies for their good content validity. Confirmatory factor analysis (CFA) results, shown in Table 1, demonstrate that all items had standardized factor loadings (λ) greater than 0.6 and average variance extracted (AVE) values exceeding 0.5, indicating good convergent validity. The square root of the AVE for each construct was greater than the correlations between constructs, confirming high discriminant validity. The model fit indices were as follows: χ2/DF = 1.176, RMSEA = 0.022, GFI = 0.894, IFI = 0.983, and CFI = 0.982. These indices suggest that the model fit the data well. Therefore, the reliability and validity of the scales used in this study, as well as the overall fit of the theoretical model, met the required standards.

4.3. Descriptive Statistics and Correlation Analysis

Table 2 presents the descriptive statistics and correlation matrix for the primary variables. The correlations between variables ranged from 0.3 to 0.5, with none exceeding 0.7, indicating significant relationships among the main variables. The variance inflation factors (VIFs) were all below 2, suggesting the absence of severe multicollinearity. The variables are defined as follows: entrepreneurial experience (Experience), firm size (Size), industry type (Industry), technological environment openness (TEO), network exploitation capability (NEIC), network exploration capability (NERC), replication of general routines (RGR), replication of flexible routines (RFR), digital leadership (DL), and opportunity iteration (OI).

4.4. Regression Analysis

• (1). Main Effect Test

A hierarchical regression analysis was conducted using SPSS 27, as shown in Table 3. Models M2 and M3 represent the regression models for network exploitation capability and network exploration capability on opportunity iteration, respectively. The results indicated that both network exploitation capability (β = 0.303, p < 0.001) and network exploration capability (β = 0.287, p < 0.001) have a significant positive effect on opportunity iteration. This supports hypotheses H1a and H1b, suggesting that digital startups can enhance opportunity iteration through the implementation of network exploitation and exploration capabilities.

• (2). Mediation Effect Test

This study employed a three-step method to examine the mediating effects of general routines replication and flexible routines replication. Building on existing regression results, M11 demonstrated a significant positive effect of network exploitation capability on general routines replication (β = 0.251, p < 0.001). Additionally, M4 indicated a positive effect of general routines replication on opportunity iteration (β = 0.32, p < 0.001). Comparing M2 (β = 0.303, p < 0.001) and M6 (β = 0.235, p < 0.001), the influence of network exploitation capability on opportunity iteration decreased when general routine replication was included, suggesting that general routine replication partially mediates the relationship between network exploitation capability and opportunity iteration. M13 showed a significant positive impact of network exploration capability on flexible routines replication (β = 0.23, p < 0.001), while M5 revealed a positive effect of flexible routines replication on opportunity iteration (β = 0.305, p < 0.001). Comparing M3 (β = 0.287, p < 0.001) and M7 (β = 0.229, p < 0.001), the influence of network exploration capability on opportunity iteration decreased with the inclusion of flexible routines replication, indicating that flexible routines replication partially mediates the relationship between network exploration capability and opportunity iteration. Therefore, hypotheses H2a and H2b are confirmed.

• (3). Test of Moderating Effect

M8 represents the moderating effect of digital leadership on the relationship between replication of general routines and opportunity iteration. The results show that the interaction term between replication of general routines and digital leadership has a significant positive impact on opportunity iteration (0.176, p < 0.001), supporting Hypothesis H3a. Similarly, model M9 examined the moderating effect of digital leadership on the relationship between the replication of flexible routines and opportunity iteration. The findings show that the interaction term between replication of flexible routines and digital leadership positively affects opportunity iteration (0.211, p < 0.001), supporting Hypothesis H3b. Additionally, the levels of the moderating variable are represented by the mean and the mean plus or minus one standard deviation. The results of the simple slope analysis are illustrated in Figure 2 and Figure 3.

• (4). Moderated Mediation Effect Test

This study used the PROCESS macro to examine the moderated mediation effect, utilizing the Bootstrap method with 5000 samples and a 95% confidence interval. The results are shown in Table 4. For the indirect effect of general routines replication, the index of moderated mediation was 0.0332, with a 95% confidence interval of [0.0141, 0.0555], which does not include zero. Additionally, as the level of digital leadership increased, the mediated effect of general routines replication also increased, supporting H4a. Regarding the indirect effect of flexible routines replication, the index of moderated mediation was 0.0367, with a 95% confidence interval of [0.0164, 0.0634], which also does not include zero. Similarly, as the level of digital leadership increased, the mediated effect of flexible routines replication increased, supporting H4b.

4.5. Robustness Check

To ensure the reliability of the research conclusions, this study conducted a robustness check. Initially, additional control variables were introduced to reflect the internal characteristics of firms, such as firm nature and establishment years. Firm nature was measured by ownership attributes and classified into five categories: state-owned, joint venture, foreign, private, and other. Firm age was divided into four categories based on previous research on new ventures: 0–2 years, 2–4 years, 4–6 years, and 6–8 years. Using the same regression model, the results of the robustness check are presented in Table 5. These results indicate that, despite some changes in regression coefficients and significance levels with the inclusion of additional control variables, the direct effect of network ambidextrous capabilities on opportunity iteration, the mediating effect of routine replication, and the moderating effect of digital leadership remain consistent with the estimates in Table 3. Additionally, the robustness check of the moderated mediating effect, shown in Table 6, is consistent with the estimates in Table 4. In summary, the robustness check confirms that the research conclusions are highly robust.

5. Discussion

5.1. Research Conclusions

The development of digital technology has lowered market entry barriers, compelling Chinese startups to compete not only with their peers but also with challengers from other industries. Additionally, the widespread adoption of digital technologies has heightened Chinese consumers’ expectations for more personalized, efficient, and immediate products and services. Consequently, Chinese startups must innovate and differentiate themselves more effectively to attract and retain customers. Effective opportunity iteration, as demonstrated by Chinese entrepreneurial practices, enables startups to continuously gain competitive advantages through innovation. While existing research has explored the impact of external customer analysis and internal corporate learning on opportunity iteration, there remains a gap in understanding the influencing factors from the perspective of capability building within firms.

This study addresses this gap by integrating optimal distinctiveness theory and upper echelons theory to develop a research framework encompassing network ambidextrous capabilities, routine replication, and opportunity iteration. This study investigates how network ambidextrous capabilities affect opportunity iteration and identifies the boundary conditions of these effects. The findings are summarized as follows:

• (1). The micro-dynamics of digital startups’ network ambidextrous capabilities are crucial for achieving opportunity iteration. Both network exploitation and exploration capabilities positively impact opportunity iteration under the explore–exploit dichotomy. This finding corroborates previous research by Faroque [66] and Shi et al. [94], which demonstrated that an organization’s ambidextrous capabilities positively impact opportunity development and performance. Specifically, network exploitation capability helps digital startups leverage existing relationships to acquire resources, expand their knowledge base, and iterate opportunities for improving and updating digital products or services. Startups with network exploration capability can reconfigure or restructure existing networks, enabling them to integrate and absorb complementary network resources and capital, thereby creating comprehensive market intelligence, identifying market changes, and adapting through opportunity iteration.

• (2). From a capability perspective, the structural dimensions of general routines and flexible routines replication mediate the relationship between network ambidextrous capabilities and opportunity iteration. This conclusion supports the perspective of Wei [58] that “the impact mechanisms of routine replication on innovation may vary across different dimensions from the capability perspective”. Replication of general routines partially mediates the effect of network exploitation on opportunity iteration, while replication of flexible routines partially mediates the effect of network exploration on opportunity iteration. This suggests that replication of general routines, supported by network exploitation capability, constructs a resource base through fixed cognitive traces, resulting in clear, stable explicit rules or behavior patterns, which are then adjusted and refined through imitation. In contrast, replication of flexible routines, supported by network exploration capability, expands the resource base through improvisational cognitive traces, leading to diverse, dynamic implicit rules or behavior patterns, which promote the improvement and upgrading of entrepreneurial opportunities through optimized practices.

• (3). Digital leadership positively moderates the impact of routine replication on opportunity iteration and mediates the relationship between network ambidextrous capabilities and opportunity iteration. Consistent with Yao et al. [18], who emphasized the role of digital communication, coordination, digital motivation, change management, and digital trust cultivation in facilitating organizational digital transformation, our findings highlight that effective communication and coordination under digital leadership create seamless information channels. Motivational measures foster a positive team environment, and the cultivation of digital trust enhances organizational identification. These factors collectively improve the efficiency of knowledge integration during the routine replication process, accelerating the organization’s ability to identify, evaluate, and validate opportunities. Aligned with Nambisan’s [23] view that “digital teams are crucial for opportunity creation in digital startups”, our study shows that leaders’ digital skills and the establishment and maintenance of digital teams enable organizations to effectively leverage digital technologies. This capability strengthens the process of identifying and developing diverse entrepreneurial opportunities during routine replication. In summary, under the influence of digital leadership, organizations actively learn from and adopt best general routines and flexible routines from partner organizations. This optimization of structure and processes significantly accelerates the iteration of entrepreneurial opportunities in a digital context.

5.2. Theoretical Contributions

The present research findings offer significant theoretical contributions. First, this research explores the continuous strategy of opportunity iteration in digital startups, integrating network capability into the opportunity iteration framework from an ambidexterity perspective. It reveals the driving factors of opportunity iteration in a digital context through the lens of optimal distinction theory. Previous studies have addressed the definition, characteristics, applicable scenarios, and development processes of opportunity iteration, analyzing its influencing factors from customer orientation and strategic learning perspectives. However, they have overlooked the differentiated resource allocation behaviors of network ambidextrous capabilities from the optimal distinction perspective. This study examines the relationship between digital leadership and network ambidexterity through the upper echelons theory. We refine network ambidextrous capabilities into two dimensions: exploitation and exploration, reflecting different organizational strategies in pursuing legitimacy and differentiation. Network exploitation capability maintains consistency with external entities by preserving existing relationships, thereby gaining legitimacy. The resources obtained through this trust and support facilitate continuous opportunity iteration [39] within the relational organization of entrepreneurial ecosystems. Conversely, network exploration capability fosters differentiation by establishing new relationships to acquire heterogeneous resources [9], thus becoming a critical driver for organizational opportunity iteration. Responding to calls in the field for detailed analysis, this research deconstructs the key resource provision behaviors of network exploitation and exploration capabilities in relation to opportunity iteration. These findings offer new theoretical insights into the mechanisms driving opportunity iteration, interpreting network ambidextrous capabilities from the perspective of optimal distinctiveness theory for the first time and expanding its application within opportunity iteration theory. Additionally, since opportunity iteration is a continuation of opportunity creation, this study enriches the constructivist view of opportunities and deepens the understanding of the dynamic process of opportunity construction [1].

Secondly, from the perspective of capability, we categorize routine replication into two dimensions: general and flexible. By integrating the dynamic processes of gradual and transformative routine replication, we clarify the different integration paths of explicit and tacit knowledge in the realization of opportunity iteration. This advances our understanding of the multi-path co-evolution mechanism in the opportunity iteration process. Existing studies on routine replication primarily focus on role activation, situational dependence, and expectation gaps, neglecting the influence of the digital context and corporate network capability on routine replication and its critical role in opportunity iteration. Our research integrates the differentiated processes of routines replication in various contexts, exploring the mediating role of conventional and flexible routine replication between network ambidextrous capabilities and opportunity iteration. General routines replication, by imitating and adapting clear and stable rules or behavior patterns, seeks to achieve incremental capability improvements for the continuous enhancement of entrepreneurial opportunities in digital startups. In contrast, flexible routines replication deconstructs diverse and variable implicit rules or behavior patterns to achieve radical capability improvements, driving the renewal and iteration of entrepreneurial opportunities. The findings illuminate the mechanisms between dual network ambidextrous capabilities and opportunity iteration, providing new insights into the paths through which differentiated routine replication facilitates opportunity iteration.

Thirdly, this study analyzes the contingent role of digital leadership from the perspective of upper echelon theory, enriching our understanding of the boundaries of digital leadership in a digital context and expanding its research scenarios. Digital leadership influences organizational behavior by identifying digital opportunities, integrating internal and external resources, promoting organizational change, and replicating routines. This, in turn, fosters innovation and development within enterprises. This also enhances the understanding of the contingent conditions for routine replication in a digital environment. Given the uncertainty and rapid changes in digital technology, startups must quickly identify appropriate product and service positioning [95]. Effective digital leadership can boost organizational adaptability and innovation, ensuring the survival and success of enterprises in digital transformation [96]. Therefore, digital leadership is a critical contextual factor for the development of startups in a digital environment. While existing research has explored the impact of digital leadership on organizational innovation, it has overlooked its role in acquiring routine knowledge. This study combines micro-level individual and organizational factors to verify the moderating role of digital leadership between routine replication and opportunity iteration, enriching our understanding of the boundary role of routine replication in the startup development process and extending the impact of digital leadership on opportunity iteration.

5.3. Managerial Implications

First, digital startups should prioritize iterative development of opportunities and enhance their network ambidextrous capabilities to facilitate this process. Digital startups often lack resources, making it difficult for them to seize opportunities in rapidly changing environments. Network ambidextrous capabilities offer an effective mechanism for digital startups to identify and leverage new opportunities in a rapidly changing technological environment. This capability allows companies to explore new opportunities while continuing to utilize existing technologies and resources, leading to sustained innovation and growth. Startups should establish flexible organizational structures and cultures to enhance adaptability and effectively leverage current network relationships for business optimization and efficiency improvements. Additionally, firms should expand their network relationships through partnerships, alliances, and open innovation platforms to acquire external resources and knowledge, accelerating the innovation process and technological iteration. For example, the Chinese company Pinduoduo based in Shanghai, China initially leveraged the WeChat(8.0.50) platform developed by Tencent in Shenzhen, China, for promotion and user acquisition, fully utilizing Tencent’s social network resources. Later, Pinduoduo established extensive network relationships by collaborating with various suppliers, logistics companies, and local governments. These partnerships helped Pinduoduo optimize supply chain management, enhance delivery efficiency, and further improve user experience.

Second, companies should emphasize the replication of routines to enhance iterative innovation. By leveraging data and digital technologies to drive decision-making, companies can objectively evaluate the effectiveness of existing routines through customer feedback and market analysis. Replicating external or existing best practices helps identify new opportunity spaces. Moreover, fostering a learning culture that encourages employees to acquire new skills, share knowledge, and adopt best practices from existing business processes and models is crucial. This approach not only helps team members understand when and where to apply past experiences but also promotes creative collisions and innovative thinking within the team, fostering the development of iterative opportunities. For example, TikTok(30.3.0) enhances user experience and engagement by analyzing user viewing and interaction data, and promptly adjusting its recommendation algorithm accordingly.

Finally, digital startups should focus on cultivating digital leadership among their leaders. Digital leadership not only influences corporate culture and innovation capabilities but also determines whether a company can stand out in a competitive market. Companies should encourage their leadership teams and employees to participate in online courses, workshops, and regular knowledge-sharing sessions to stay updated with the latest digital technologies and trends. Promoting the use of data analytics tools ensures decision-making is based on accurate and timely data. Open discussions about data insights and business metrics within teams can enhance transparency and engagement, fostering a data-centric decision-making culture. Furthermore, respecting for diverse perspectives and backgrounds and promoting cross-functional team collaboration helps break down information silos, enhancing overall digital capabilities and fostering an open and inclusive corporate environment. For example, Huawei regularly conducts online training sessions and seminars worldwide to keep employees and leadership informed about the latest technological trends and market dynamics. Huawei University offers a range of professional courses covering cutting-edge technologies such as 5G, artificial intelligence, and cloud computing.

5.4. Research Limitations and Future Directions

As an exploratory study, this research has several limitations. First, the study uses cross-sectional data from China rather than panel data. Given the rapid evolution of digital technologies and the changing environments of startups, future research could conduct longitudinal studies in other countries with mature enterprises to assess how ambidextrous network capabilities, routine replication, digital leadership, and opportunity iteration influence each other over time. Second, while this study controls for the significant impact of technological environment openness on the dependent variable, future research could explore the relationship between technological environment openness and opportunity iteration in greater depth. Third, this study considers digital leadership as a boundary condition for routine replication. Subsequent research could further investigate the impact of organizational digital leadership on routine replication and the mechanisms between them.

Author Contributions

Conceptualization, F.J. and X.Y.; methodology, F.J. and X.Y.; software, F.J.; validation, F.J. and J.L.; formal analysis, F.J. and J.L.; investigation, F.J. and X.Y.; resources, J.L.; data curation, F.J.; writing—original draft preparation, F.J.; writing—review and editing, F.J. and X.Y.; visualization, J.L.; supervision, J.L.; project administration, J.L. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The data supporting the findings of this study are available from the author, Fang Jiang, upon reasonable request.

Acknowledgments

We also thank the anonymous reviewers for providing valuable comments on the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Theoretical Model.

Figure 2. The moderating effect of digital leadership on replication of general routines and opportunity iteration.

Figure 3. The moderating effect of digital leadership on replication of flexible routines and opportunity iteration.

Table 1

Reliability and validity test results.

Variable	Factor Loading	Cronbach’s a	CR	KMO	AVE	1	2	3	4	5	6	7
1. Network exploitation capability	0.715~0.769	0.925	0.9	0.95	0.554	0.744
2. Network exploration capability	0.697~0.761	0.892	0.9	0.917	0.541	0.507	0.712
3. Replication of general routines	0.740~0.771	0.87	0.9	0.878	0.572	0.535	0.706	0.756
4. Replication of flexible routines.	0.715~0.756	0.857	0.9	0.869	0.546	0.517	0.509	0.6	0.719
5. Opportunity Iteration	0.741~0.782	0.875	0.9	0.879	0.585	0.591	0.619	0.69	0.633	0.765
6. Digital Leadership	0.725~0.779	0.873	0.9	0.878	0.579	0.596	0.603	0.61	0.608	0.613	0.76
7. Technological environment openness	0.736~0.785	0.872	0.9	0.869	0.577	0.575	0.558	0.622	0.531	0.53	0.66	0.76

Note: The bold diagonal part in the table is the square root of the variable AVE.

Table 2

Variable descriptive statistics and correlation analysis.

Variables:	Experience	Size	Industry	TEO	NEIC	NERC	RGR	RFR	DL	OI	VIF
Experience	1										1.248
Size	0.379 **	1									1.235
Industry	0.14	−0.031	1								1.019
TEO	0.151 **	0.18 **	−0.008	1							1.412
NEIC	0.324 **	0.3 **	0.085	0.386 **	1						1.542
NERC	0.073	0.068	−0.043	0.357 **	0.337 **	1					1.391
RGR	0.05	0.127 *	−0.031	0.383 **	0.341 **	0.429 **	1				1.432
RFR	0.097	0.082	−0.04	0.34 **	0.347 **	0.322 **	0.364 **	1			1.327
DL	0.121 *	0.135 **	0.012	0.393 **	0.374 **	0.359 **	0.345 **	0.363 **	1		1.386
OI	0.12 *	0.107 *	−0.032	0.317 **	0.37 **	0.366 **	0.395 **	0.38 **	0.344 **	1
Mean	0.6	2.82	2.19	4.4231	4.3016	4.3948	4.3909	4.4022	4.3349	4.2796
Standard Deviation	0.491	1.282	0.891	1.30163	1.26787	1.26562	1.28185	1.26534	1.30814	1.30878

Note: *, **, respectively represent p < 0.05, p < 0.01.

Table 3

Hierarchical regression analysis results.

Variable	OI									RGR		RFR
	M1	M2	M3	M4	M5	M6	M7	M8	M9	M10	M11	M12	M13
Control variable
Experience	0.065	0.001	0.059	0.076	0.051	0.024	0.049	0.046	0.053	−0.034	−0.087	0.045	0.04
Size	0.027	−0.023	0.028	0.004	0.025	−0.031	0.027	−0.044	0.004	0.071	0.03	0.005	0.006
Industry	−0.03	−0.057	−0.018	−0.022	−0.029	−0.044	−0.02	−0.037	−0.028	−0.025	−0.047	−0.002	0.007
TEO	0.302 ***	0.204 ***	0.2 ***	0.182 ***	0.201 ***	0.125	0.137 **	0.058	0.063	0.375 ***	0.294 ***	0.332 ***	0.251 ***
Independent variable
NEIC		0.303 ***				0.235 ***		0.151 **			0.251 ***
NERC			0.287 ***				0.229 ***		0.164 **				0.23 ***
Mediator variable
RGR				0.32 ***		0.268 ***		0.242 ***
RFR					0.305 ***		0.253 ***		0.205 ***
Moderator variable
DL								0.142 **	0.16 **
Interaction term
RGR × DL								0.176 ***
RFR × DL									0.211 ***
R2	0.107	0.176	0.179	0.194	0.189	0.234	0.233	0.277	0.288	0.152	0.199	0.118	0.164
Adj. R2	0.098	0.165	0.168	0.183	0.178	0.221	0.22	0.261	0.272	0.143	0.188	0.108	0.152
F	11.039	15.636	15.993	17.67	17.099	18.548	18.454	17.39	18.352	16.442	18.193	12.227	14.319

Note: M = model; **, ***, respectively, represent p < 0.01, p < 0.001.

Table 4

Test results of moderated mediation effect.

Indirect Effect		Coefficient	Standard Error	95% Confidence Interval
				Lower Limit	Upper Limit
Replication of general routines	Low (−1 s.d)	0.0192	0.0176	−0.126	0.0567
	Medium	0.0627	0.0189	0.0286	0.103
	High (+1 s.d)	0.1062	0.0282	0.0547	0.1644
	Criterion for Determining Moderated Mediation Effects	0.0332	0.0106	0.0141	0.0555
replication of flexible routines	Low (−1 s.d)	0.0006	0.0169	−0.0342	0.0329
	Medium	0.0487	0.0168	0.0199	0.0847
	High (+1 s.d)	0.0968	0.0277	0.0485	0.1559
	Criterion for Determining Moderated Mediation Effects	0.0367	0.012	0.0164	0.0634

Table 5

Robustness test of regression analysis: adding control variables.

Variable	OI									RGR		RFR
	M1	M2	M3	M4	M5	M6	M7	M8	M9	M10	M11	M12	M13
Control variable
Experience	0.053	0.02	0.048	0.08	0.056	0.049	0.051	0.069	0.063	−0.083	−0.109	−0.008	−0.012
Size	0.012	−0.003	0.014	0.007	0.028	−0.005	0.027	−0.02	0.013	0.016	0.004	−0.052	−0.05
Industry	−0.056	−0.063	−0.045	−0.039	−0.05	−0.047	−0.043	−0.044	−0.044	−0.054	−0.06	−0.019	−0.011
TEO	0.29	0.198 ***	0.187 ***	0.174 ***	0.19	0.119 *	0.125 *	0.051	0.054	0.365	0.294 ***	0.329	0.247 ***
age	0.038	−0.041	0.037	−0.001	0	−0.058	0.006	−0.049	−0.016	0.124	0.063	0.126	0.125
attributes	−0.07	−0.03	−0.075	−0.052	−0.065	−0.023	−0.07	−0.033	−0.055	−0.055	−0.025	−0.017	−0.02
Independent variable
NEIC		0.304 ***				0.241 ***		0.153			0.236 ***
NERC			0.289 ***				0.231 ***		0.165 **				0.23 ***
Mediator variable
RGR				0.317 ***		0.27 ***		0.243 ***
RFR					0.303		0.25 ***		0.205 ***
Moderator variable
DL								0.144 **	0.159 **
Interaction term
RGR × DL								0.175 ***
RFR × DL									0.208 ***
R2	0.113	0.177	0.185	0.197	0.193	0.235	0.237	0.279	0.291	0.162	0.201	0.125	0.171
Adj. R2	0.098	0.162	0.169	0.181	0.177	0.219	0.22	0.259	0.271	0.149	0.186	0.111	0.155
F	7.72	11.213	11.814	12.741	12.436	13.972	14.095	13.96	14.787	11.799	13.114	8.707	10.75

Note: M = model; *, **, *** respectively represent p < 0.05, p < 0.01, p < 0.001.

Table 6

Robustness test of moderated mediation: adding control variables.

Indirect Effect		Coefficient	Standard Error	95% Confidence Interval
				Lower Limit	Upper Limit
Replication of general routines	Low (−1 s.d)	0.0184	0.0168	−0.105	0.0554
	Medium	0.0591	0.019	0.0256	0.0986
	High (+1 s.d)	0.0999	0.0285	0.0479	0.1583
	Criterion for Determining Moderated Mediation Effects	0.0311	0.0104	0.0123	0.0524
Replication of flexible routines	Low (−1 s.d)	0.0013	0.0164	−0.0329	0.0333
	Medium	0.0487	0.0165	0.0202	0.0833
	High (+1 s.d)	0.0961	0.0279	0.0463	0.1541
	Criterion for Determining Moderated Mediation Effects	0.0362	0.0121	0.0153	0.062

References

1. Wood, M.S.; Mckinley, W. After the venture: The reproduction and destruction of entrepreneurial opportunity. Strateg. Entrep. J.; 2017; 11, pp. 18-35. [DOI: https://dx.doi.org/10.1002/sej.1238]

2. Liu, Z.Y.; Li, B.; Zhuang, X.H. Research on iterative mechanism of entrepreneurial opportunity in start-up enterprises. Stud. Sci. Sci.; 2019; 37, pp. 500-516.

3. Zhang, H.; Hu, L.; Kim, Y. How Does Iteration of Entrepreneurial Opportunities in User Enterprises Affect Entrepreneurial Performance? A Dual Case Study Based on Dual Strategic Orientations. Systems; 2023; 11, 459. [DOI: https://dx.doi.org/10.3390/systems11090459]

4. Sadreddin, A.; Chan, Y.E. Pathways to developing information technology-enabled capabilities in born-digital new ventures. Int. J. Inf. Manag.; 2023; 68, 102572. [DOI: https://dx.doi.org/10.1016/j.ijinfomgt.2022.102572]

5. Nambisan, S. Digital entrepreneurship: Toward a digital technology perspective of entrepreneurship. Entrep. Theory Pract.; 2017; 41, pp. 1029-1055. [DOI: https://dx.doi.org/10.1111/etap.12254]

6. Zhang, H.J.; Hu, L.Y.; Gu, Y.Z. Research on the impact of user experience on entrepreneurial opportunity iteration—An exploratory case study based on Xiaomi. Stud. Sci. Sci.; 2022; 40, pp. 2035-2044.

7. Guo, R.P.; Han, M.Y.; Li, S.M. Customer orientation, entrepreneurial learning, and opportunity iteration in digital new ventures. Stud. Sci. Sci.; 2023; 41, pp. 1661-1670.

8. Ge, B.; Wang, Q.; Yao, M. From ideas to entrepreneurial opportunity: A study on AI. Syst. Res. Behav. Sci.; 2022; 39, pp. 618-632. [DOI: https://dx.doi.org/10.1002/sres.2874]

9. Faroque, A.R.; Morrish, S.C.; Kuivalainen, O.; Sundqvist, S.; Torkkeli, L. Microfoundations of network exploration and exploitation capabilities in international opportunity recognition. Int. Bus. Rev.; 2021; 30, 101767. [DOI: https://dx.doi.org/10.1016/j.ibusrev.2020.101767]

10. Gupta, A.; Hoopes, D.G.; Knott, A.M. Redesigning routines for replication. Strateg. Manag. J.; 2015; 36, pp. 851-871. [DOI: https://dx.doi.org/10.1002/smj.2254]

11. Chen, Y.L.; Gao, C. Study on the Replication Mechanism of Routines in Organizations Based on Organization Ambidexterity Competence. China Ind. Econ.; 2014; 28, pp. 147-159.

12. Winter, S.G.; Szulanski, G.; Ringov, D.; Jensen, R.J. Reproducing knowledge: Inaccurate replication and failure in franchise organizations. Organ. Sci.; 2012; 23, pp. 672-685. [DOI: https://dx.doi.org/10.1287/orsc.1110.0663]

13. Brewer, M.B. The social self: On being the same and different at the same time. Pers. Soc. Psychol. Bul.; 1991; 17, pp. 475-482. [DOI: https://dx.doi.org/10.1177/0146167291175001]

14. Ardichvili, A.; Cardozo, R.; Ray, S. A theory of entrepreneurial opportunity identification and development. J. Bus. Ventur.; 2003; 18, pp. 105-123. [DOI: https://dx.doi.org/10.1016/S0883-9026(01)00068-4]

15. Wang, T.; Lin, X.; Sheng, F. Digital leadership and exploratory innovation: From the dual perspectives of strategic orientation and organizational culture. Front. Psychol.; 2022; 13, 902693. [DOI: https://dx.doi.org/10.3389/fpsyg.2022.902693]

16. Neely Jr, B.H.; Lovelace, J.B.; Cowen, A.P.; Hiller, N.J. Metacritiques of upper echelons theory: Verdicts and recommendations for future research. J. Manag.; 2020; 46, pp. 1029-1062. [DOI: https://dx.doi.org/10.1177/0149206320908640]

17. Fenwick, M.; McCahery, J.A.; Vermeulen, E.P. Will the world ever be the same after COVID-19? Two lessons from the first global crisis of a digital age. Eur. Bus. Organ. Law. Rev.; 2021; 22, pp. 125-145. [DOI: https://dx.doi.org/10.1007/s40804-020-00194-9]

18. Yao, Q.; Tang, H.; Liu, Y.; Boadu, F. The penetration effect of digital leadership on digital transformation: The role of digital strategy consensus and diversity types. J. Enterp. Inf.; 2024; 37, pp. 903-927. [DOI: https://dx.doi.org/10.1108/JEIM-09-2022-0350]

19. Autio, E. Digitalisation, Ecosystems, Entrepreneurship and Policy. 2017; Available online: https://tietokayttoon.fi/documents/1927382/2116852/20_2017_Digitalisation%2C+ecosystems%2C+entrepreneurship+and+policy/6b383210-70de-491f-b0df-38de52699458?version=1.0 (accessed on 18 August 2024).

20. Legner, C.; Eymann, T.; Hess, T.; Matt, C.; Böhmann, T.; Drews, P.; Ahlemann, F. Digitalization: Opportunity and challenge for the business and information systems engineering community. Bus. Inf. Syst. Eng.; 2017; 59, pp. 301-308. [DOI: https://dx.doi.org/10.1007/s12599-017-0484-2]

21. Davidson, E.; Vaast, E. Digital entrepreneurship and its sociomaterial enactment. Proceedings of the 2010 43rd Hawaii International Conference on System Sciences; Honolulu, HI, USA, 5–8 January 2010; pp. 1-10.

22. Dutot, V.; Van Horne, C. Digital entrepreneurship intention in a developed vs. emerging country: An exploratory study in France and the UAE. Transnatl. Corp. Rev.; 2015; 7, pp. 79-96. [DOI: https://dx.doi.org/10.5148/tncr.2015.7105]

23. Nambisan, S.; Lyytinen, K.; Majchrzak, A.; Song, M. Digital innovation management. MIS Q.; 2017; 41, pp. 223-238. [DOI: https://dx.doi.org/10.25300/MISQ/2017/41:1.03]

24. Zhu, X.M.; Liu, Y.; Chen, H.T. Digital entrepreneurship: Research on its elements and core generation mechanism. FEM; 2020; 42, pp. 19-35. [DOI: https://dx.doi.org/10.1017/jfm.2020.752]

25. Yu, J.; Meng, Q.S.; Zhang, Y.; Jin, J. Digital entrepreneurship: The future directions of entrepreneurship theory and practice in the digital era. Stud. Sci. Sci.; 2018; 36, pp. 1801-1808.

26. Kahre, C.; Hoffmann, D.; Ahlemann, F. Beyond business-IT alignment-digital business strategies as a paradigmatic shift: A review and research agenda. Proceedings of the Hawaii International Conference on System Sciences; Waikoloa Beach, HI, USA, 4–7 January 2017; pp. 4706-4715.

27. Vial, G. Understanding digital transformation: A review and a research agenda. J. Strateg. Inf. Syst.; 2021; pp. 13-66. [DOI: https://dx.doi.org/10.1016/j.jsis.2019.01.003]

28. Guo, R.P.; Gong, R.; Lu, P. Strategic Learning, Organizational Agility, and Opportunity Iteration: An Empirical Study Based on Digital Startups. FEM Foreign Econ. Manag.; 2024; 46, pp. 22-37.

29. Broekhuizen, T.L.; Broekhuis, M.; Gijsenberg, M.J.; Wieringa, J.E. Introduction to the special issue–Digital business models: A multi-disciplinary and multi-stakeholder perspective. J. Bus. Res.; 2021; 122, pp. 847-852. [DOI: https://dx.doi.org/10.1016/j.jbusres.2020.04.014]

30. Adler, P.S.; Kwon, S.W. Social capital: Prospects for a new concept. Acad. Manag. Rev.; 2002; 27, pp. 17-40. [DOI: https://dx.doi.org/10.2307/4134367]

31. Naudé, P.; Sutton-Brady, C. Relationships and networks as examined in Industrial Marketing Management. Ind. Mark. Manag.; 2019; 79, pp. 27-35. [DOI: https://dx.doi.org/10.1016/j.indmarman.2019.03.006]

32. Gronum, S.; Verreynne, M.L.; Kastelle, T. The role of networks in small and medium-sized enterprise innovation and firm performance. J. Small Bus. Manag.; 2012; 50, pp. 257-282. [DOI: https://dx.doi.org/10.1111/j.1540-627X.2012.00353.x]

33. Yamakawa, Y.; Yang, H.; Lin, Z.J. Exploration versus exploitation in alliance portfolio: Performance implications of organizational, strategic, and environmental fit. Res. Policy; 2011; 40, pp. 287-296. [DOI: https://dx.doi.org/10.1016/j.respol.2010.10.006]

34. Felicetti, A.M.; Corvello, V.; Ammirato, S. Digital innovation in entrepreneurial firms: A systematic literature review. Rev. Manag. Sci.; 2024; 18, pp. 315-362. [DOI: https://dx.doi.org/10.1007/s11846-023-00638-9]

35. Svensson, C.; Udesen, J.; Webb, J. Alliances in financial ecosystems: A source of organizational legitimacy for fintech startups and incumbents. Technol. Innov. Manag.; 2019; 9, pp. 20-32. [DOI: https://dx.doi.org/10.22215/timreview/1209]

36. Bembom, M.; Schwens, C. The role of networks in early internationalizing firms: A systematic review and future research agenda. Eur. Manag. J.; 2018; 36, pp. 679-694. [DOI: https://dx.doi.org/10.1016/j.emj.2018.03.003]

37. Zhao, E.Y.; Fisher, G.; Lounsbury, M.; Miller, D. Optimal distinctiveness: Broadening the interface between institutional theory and strategic management. Strateg. Manag. J.; 2017; 38, pp. 93-113. [DOI: https://dx.doi.org/10.1002/smj.2589]

38. Ma, H.; Guo, H.; Shen, R. Organisational regulatory legitimacy, entrepreneurial orientation, and SME innovation: An optimal distinctiveness perspective. Technol. Anal. Strateg. Manag.; 2019; 31, pp. 833-847. [DOI: https://dx.doi.org/10.1080/09537325.2018.1555320]

39. Spigel, B. The relational organization of entrepreneurial ecosystems. Entrep. Theory Pract.; 2017; 41, pp. 49-72. [DOI: https://dx.doi.org/10.1111/etap.12167]

40. Feldman, M.S.; Pentland, B.T. Reconceptualizing organizational routines as a source of flexibility and change. Adm. Sci. Q.; 2003; 48, pp. 94-118. [DOI: https://dx.doi.org/10.2307/3556620]

41. Kreuzer, T.; Lindenthal, A.K.; Oberländer, A.M.; Röglinger, M. The effects of digital technology on opportunity recognition. Bus. Inf. Syst. Eng.; 2022; 64, pp. 47-67. [DOI: https://dx.doi.org/10.1007/s12599-021-00733-9]

42. Alvarez, S.A.; Barney, J.B. Entrepreneurial opportunities and poverty alleviation. Entrep. Theory Pract.; 2014; 38, pp. 159-184. [DOI: https://dx.doi.org/10.1111/etap.12078]

43. Katz, M.L.; Shapiro, C. Network externalities, competition, and compatibility. Am. Econ. Rev.; 1985; 75, pp. 424-440.

44. Mubarak, M.F.; Petraite, M. Industry 4.0 technologies, digital trust and technological orientation: What matters in open innovation?. Technol. Forecast. Soc. Chang.; 2020; 161, 120332. [DOI: https://dx.doi.org/10.1016/j.techfore.2020.120332]

45. Steininger, D.M. Linking information systems and entrepreneurship: A review and agenda for IT-associated and digital entrepreneurship research. Inf. Syst. J.; 2019; 29, pp. 363-407. [DOI: https://dx.doi.org/10.1111/isj.12206]

46. Hofacker, C.; Golgeci, I.; Pillai, K.G.; Gligor, D.M. Digital marketing and business-to-business relationships: A close look at the interface and a roadmap for the future. Eur. J. Mark.; 2020; 54, pp. 1161-1179. [DOI: https://dx.doi.org/10.1108/EJM-04-2020-0247]

47. Guo, H.; Li, Y.; Li, Y.H. Impact of Innovation Strategy and Political Strategy on the Performance of Digital New Ventures from Optimal Distinctiveness Perspective. R. D Manag.; 2021; 33, pp. 12-26.

48. Annarelli, A.; Battistella, C.; Nonino, F.; Parida, V.; Pessot, E. Literature review on digitalization capabilities: Co-citation analysis of antecedents, conceptualization and consequences. Technol. Forecast. Soc. Chang.; 2021; 166, 120635. [DOI: https://dx.doi.org/10.1016/j.techfore.2021.120635]

49. Venkataraman, S.; Sarasvathy, S.D.; Dew, N.; Forster, W.R. Reflections on the 2010 AMR decade award: Whither the promise? Moving forward with entrepreneurship as a science of the artificial. Acad. Manag. Rev.; 2012; 37, pp. 21-33. [DOI: https://dx.doi.org/10.5465/armr.2011.0079]

50. Bessant, J.; Öberg, C.; Trifilova, A. Framing problems in radical innovation. Ind. Mark. Manag.; 2014; 43, pp. 1284-1292. [DOI: https://dx.doi.org/10.1016/j.indmarman.2014.09.003]

51. Spee, P.; Jarzabkowski, P.; Smets, M. The influence of routine interdependence and skillful accomplishment on the coordination of standardizing and customizing. Organ. Sci.; 2016; 27, pp. 759-781. [DOI: https://dx.doi.org/10.1287/orsc.2016.1050]

52. Heimeriks, K.H.; Schijven, M.; Gates, S. Manifestations of higher-order routines: The underlying mechanisms of deliberate learning in the context of postacquisition integration. Acad. Manag. J.; 2012; 55, pp. 703-726. [DOI: https://dx.doi.org/10.5465/amj.2009.0572]

53. Bresman, H. Changing routines: A process model of vicarious group learning in pharmaceutical RD. Acad. Manag. J.; 2013; 56, pp. 35-61. [DOI: https://dx.doi.org/10.5465/amj.2010.0725]

54. Sutcliffe, K.M.; McNamara, G. Controlling decision-making practice in organizations. Organ. Sci.; 2001; 12, pp. 484-501. [DOI: https://dx.doi.org/10.1287/orsc.12.4.484.10634]

55. Parmigiani, A.; Howard-Grenville, J. Routines revisited: Exploring the capabilities and practice perspectives. Acad. Manag. Ann.; 2011; 5, pp. 413-453. [DOI: https://dx.doi.org/10.5465/19416520.2011.589143]

56. Szulanski, G.; Jensen, R.J. Overcoming stickiness: An empirical investigation of the role of the template in the replication of organizational routines. MDE Manag. Decis. Econ.; 2004; 25, pp. 347-363. [DOI: https://dx.doi.org/10.1002/mde.1195]

57. Wei, L.; Dang, X.H.; Li, L.X. The Impact of Routines Replication and Inter-organizational Dependence on Innovation Catalyst: A Moderated Mediating Model. Manag. Rev.; 2023; 35, 131.

58. Wei, L.; Dang, X.H. Impact of routine replication on bootleg innovation: The moderating effect of network closure and knowledge base. Sci. Res. Manag.; 2020; 41, 30.

59. Ruggles, R. The state of the notion: Knowledge management in practice. Calif. Manag. Rev.; 1998; 40, pp. 80-89. [DOI: https://dx.doi.org/10.2307/41165944]

60. Nahapiet, J.; Ghoshal, S. Social capital, intellectual capital, and the organizational advantage. Acad. Manag. Rev.; 1998; 23, pp. 242-266. [DOI: https://dx.doi.org/10.2307/259373]

61. Friesl, M.; Larty, J. Replication of routines in organizations: Existing literature and new perspectives. Int. J. Manag.; 2013; 15, pp. 106-122. [DOI: https://dx.doi.org/10.1111/j.1468-2370.2012.00340.x]

62. Jin, Y.; Shao, Y.F.; Wu, Y.B. Routine replication and breakthrough innovation: The moderating role of knowledge power. Technol. Anal. Strateg. Manag.; 2021; 33, pp. 426-438. [DOI: https://dx.doi.org/10.1080/09537325.2020.1818716]

63. Segarra-Ciprés, M.; Bou-Llusar, J.C. External knowledge search for innovation: The role of firms’ innovation strategy and industry context. J. Knowl. Manag.; 2018; 22, pp. 280-298. [DOI: https://dx.doi.org/10.1108/JKM-03-2017-0090]

64. Benner, M.J.; Tushman, M.L. Exploitation, Exploration, and Process Management: The Productivity Dilemma Revisited. Acad. Manag. Rev.; 2003; 28, pp. 238-256. [DOI: https://dx.doi.org/10.2307/30040711]

65. Winter, S.G.; Szulanski, G. Replication as strategy. Organ. Sci.; 2001; 12, pp. 730-743. [DOI: https://dx.doi.org/10.1287/orsc.12.6.730.10084]

66. Faroque, A.R.; Torkkeli, L.; Sultana, H.; Rahman, M. Network exploration and exploitation capabilities and foreign market knowledge: The enabling and disenabling boundary conditions for international performance. Ind. Mark. Manag.; 2022; 101, pp. 258-271. [DOI: https://dx.doi.org/10.1016/j.indmarman.2021.12.013]

67. Liu, J.D.; Wu, H.M. Routine Replication and Environmental Munificence Mediation of Human Capital and Knowledge Transfer. Sci. Technol. Prog. Policy; 2023; 40, pp. 141-150.

68. Khan, S.H.; Majid, A.; Yasir, M.; Javed, A. Social capital and business model innovation in SMEs: Do organizational learning capabilities and entrepreneurial orientation really matter?. Eur. J. Innov. Manag.; 2021; 24, pp. 191-212. [DOI: https://dx.doi.org/10.1108/EJIM-04-2020-0143]

69. Alvarez, S.A.; Barney, J.B.; McBride, R.; Wuebker, R. Realism in the study of entrepreneurship. Acad. Manag. Rev.; 2014; 39, pp. 227-231. [DOI: https://dx.doi.org/10.5465/amr.2013.0244]

70. El Sawy, P.; Kraemmergaad, H.; Amsinck, A. Vinther How LEGO built the foundations and enterprise capabilities for digital leadership. MIS Q. Exec.; 2016; 15, pp. 141-166.

71. Yücebalkan, B.; Eryılmaz, B.; Özlü, K.; Keskin, Y.B.; Yücetürk, C. Digital leadership in the context of digitalization and digital transformations. Curr. Acad. Stud. Soc. Sci.; 2018; 1, pp. 489-505.

72. Waal, B.D.; van Outvorst, F.; and Ravesteyn, P. Digital leadership: The objective-subjective dichotomy of technology revisited. Proceedings of the 12th European Conference on Management, Leadership and Governance ECMLG; Bucharest, Romania, 10–11 November 2016; pp. 52-61.

73. Li, W.; Liu, K.; Belitski, M.; Ghobadian, A.; O’Regan, N. e-Leadership through strategic alignment: An empirical study of small-and medium-sized enterprises in the digital age. J. Inf. Technol.; 2016; 31, pp. 185-206. [DOI: https://dx.doi.org/10.1057/jit.2016.10]

74. Singh, A.; Klarner, P.; Hess, T. How do chief digital officers pursue digital transformation activities? The role of organization design parameters. Long. Range Plan.; 2020; 53, 101890. [DOI: https://dx.doi.org/10.1016/j.lrp.2019.07.001]

75. Lane, J.; Leonardi, P.; Contractor, N.; DeChurch, L. Teams in the Digital Workplace: Technology’s Role for Communication, Collaboration, and Performance. Small Group Res.; 2024; 55, pp. 139-183. [DOI: https://dx.doi.org/10.1177/10464964231200015]

76. Fatima, T.; Masood, A. Impact of digital leadership on open innovation: A moderating serial mediation model. J. Knowl. Manag.; 2024; 28, pp. 161-180. [DOI: https://dx.doi.org/10.1108/JKM-11-2022-0872]

77. Berman, S.; Korsten, P. Leading in the connected era. Strategy Leadersh.; 2014; 42, pp. 37-46. [DOI: https://dx.doi.org/10.1108/SL-10-2013-0078]

78. Lynn Pulley, M.; Sessa, V.I. E-leadership: Tackling complex challenges. Ind. Commer. Train.; 2001; 33, pp. 225-230. [DOI: https://dx.doi.org/10.1108/00197850110405379]

79. Benitez, J.; Arenas, A.; Castillo, A.; Esteves, J. Impact of digital leadership capability on innovation performance: The role of platform digitization capability. Inf. Manag.; 2022; 59, 103590. [DOI: https://dx.doi.org/10.1016/j.im.2022.103590]

80. Rudito, P.; Sinaga, M.F. Digital Mastery: Building Digital Leadership to Win the Era of Disruption; Gramedia Main Library: Jakarta, Indonesia, 2017.

81. Zimmerman, M.A.; Zeitz, G.J. Beyond survival: Achieving new venture growth by building legitimacy. Acad. Manag. Rev.; 2002; 27, pp. 414-431. [DOI: https://dx.doi.org/10.2307/4134387]

82. Walter, A.; Auer, M.; Ritter, T. The impact of network capabilities and entrepreneurial orientation on university spin-off performance. J. Bus. Ventur.; 2006; 21, pp. 541-567. [DOI: https://dx.doi.org/10.1016/j.jbusvent.2005.02.005]

83. Mitrega, M.; Forkmann, S.; Ramos, C.; Henneberg, S.C. Networking capability in business relationships—Concept and scale development. Ind. Mark. Manag.; 2012; 41, pp. 739-751. [DOI: https://dx.doi.org/10.1016/j.indmarman.2012.06.002]

84. Parida, V.; Lahti, T.; Wincent, J. Exploration and exploitation and firm performance variability: A study of ambidexterity in entrepreneurial firms. Int. Entrep. Manag. J.; 2016; 12, pp. 1147-1164. [DOI: https://dx.doi.org/10.1007/s11365-016-0387-6]

85. Avolio, B.J.; Sosik, J.J.; Kahai, S.S.; Baker, B. E-leadership: Re-examining transformations in leadership source and transmission. Leadersh. Q.; 2014; 25, pp. 105-131. [DOI: https://dx.doi.org/10.1016/j.leaqua.2013.11.003]

86. Van Wart, M.; Roman, A.; Wang, X.; Liu, C. Operationalizing the definition of e-leadership: Identifying the elements of e-leadership. Int. Rev. Adm. Sci.; 2019; 85, pp. 80-97. [DOI: https://dx.doi.org/10.1177/0020852316681446]

87. Politis, D. The process of entrepreneurial learning: A conceptual framework. Entrep. Theory Pract.; 2005; 29, pp. 399-424. [DOI: https://dx.doi.org/10.1111/j.1540-6520.2005.00091.x]

88. Petruzzelli, A.M.; Ardito, L.; Savino, T. Maturity of knowledge inputs and innovation value: The moderating effect of firm age and size. J. Bus. Res.; 2018; 86, pp. 190-201. [DOI: https://dx.doi.org/10.1016/j.jbusres.2018.02.009]

89. He, Z.L.; Wong, P.K. Exploration vs. exploitation: An empirical test of the ambidexterity hypothesis. Organ. Sci.; 2004; 15, pp. 481-494. [DOI: https://dx.doi.org/10.1287/orsc.1040.0078]

90. Guo, H.; Wang, C.; Su, Z.; Wang, D. Technology push or market pull? Strategic orientation in business model design and digital start-up performance. J. Prod. Innov. Manag.; 2020; 37, pp. 352-372. [DOI: https://dx.doi.org/10.1111/jpim.12526]

91. Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci.; 2015; 43, pp. 115-135. [DOI: https://dx.doi.org/10.1007/s11747-014-0403-8]

92. Baron, R.M.; Kenny, D.A. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J. Personal. Soc. Psychol.; 1986; 51, 1173. [DOI: https://dx.doi.org/10.1037/0022-3514.51.6.1173]

93. Shrout, P.E.; Bolger, N. Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychol. Methods; 2002; 7, 422. [DOI: https://dx.doi.org/10.1037/1082-989X.7.4.422]

94. Shi, X.; Su, L.; Cui, A.P. A meta-analytic study on exploration and exploitation. J. Bus. Ind. Mark.; 2020; 35, pp. 97-115. [DOI: https://dx.doi.org/10.1108/JBIM-03-2019-0119]

95. Giardino, C.; Bajwa, S.S.; Wang, X.; Abrahamsson, P. Key challenges in early-stage software startups. Proceedings of the Agile Processes in Software Engineering and Extreme Programming: 16th International Conference, XP 2015; Helsinki, Finland, 25–29 May 2015; Springer International Publishing: Cham, Switzerland, 2015; Proceedings 16 pp. 52-63.

96. Prommer, L.; Tiberius, V.; Kraus, S. Exploring the future of startup leadership development. J. Bus. Ventur. Insights; 2020; 14, e00200. [DOI: https://dx.doi.org/10.1016/j.jbvi.2020.e00200]