Multi-Dimensional Pathways of Digitally-Empowered New-Quality Productive Forces in Enterprises: A Configurational Analysis Based on Resource Orchestration Theory

Abstract

In order to cope with the multimodal changes led by the digital era, enterprises urgently need to promote the construction of new-quality productive forces (NQPFs) through digital transformation. NQPFs take digital technology empowerment as the core driving force and emphasize the dynamic matching and synergy between the new-quality elements (digital infrastructure, digital talents, data resources, and diversified ecology) and the new-quality capabilities (digital dynamic capabilities) so as to unleash the innovation potentials of different production modes. Based on resource orchestration theory, this study constructs a “resource-capability-value creation” framework for digital empowerment (D-RCV) and employs fuzzy set qualitative comparative analysis (fsQCA) to examine 205 enterprise samples. Results reveal that enhanced innovation performance stems from digital empowerment at both resource and capability levels, generating three configurational paths: collaborative symbiosis, resource optimization, and data-driven approaches. These paths emerge through the interaction of resources and capabilities under different conditions. This study contributes by proposing a digital empowerment framework and exploring multiple pathway choices for new-quality productivity development. The findings provide theoretical insights for enterprise innovation research and practical guidance for innovation management strategies.

1. Introduction

As the current wave of global technological revolution and industrial transformation deepens, innovation-driven development has emerged as a strategic pathway for achieving high-quality economic and social progress. In building a distinctly Chinese approach to modernization, the Chinese government has explicitly positioned high-quality development as a fundamental modernization requirement and emphasized innovation’s strategic role in supporting economic and social transformation. Consequently, the concept of “NewQuality Productive Forces (NQPF)” has been introduced, highlighting the pivotal role of original and disruptive technological innovation in promoting high-quality economic and social development [1,2]. In response to these new developmental imperatives, data elements, as transformative agents of new-quality elements (NQEs), have facilitated the reconstruction of traditional productive factors and organizational transformations and have emerged as the primary catalyst for advancing NQPFs [3]. By integrating human, machine, data, and physical resources, the new-quality productive model forms a “human–machine-digital” synergy system [4,5], which is essential for achieving competitive advantage and sustainable development [2]. As market entities responsible for constructing NQEs, enterprises must reconfigure their value creation logic amid the urgency and complexity of transformation and upgrading [6]. Building NQEs requires enterprises to achieve elemental and systemic synergy across dimensions of internal specialization, horizontal collaboration, and vertical coordination, placing higher demands on enterprises’ dynamic adaptive capabilities [5,7]. However, many SMEs experience digital divides due to inadequate assessment of their needs and capabilities during transformation processes [8]. Given this complex and evolving environment, understanding how enterprises can construct NQPF systems through digitalization has become a critical concern for both academic and practical communities.

New-quality productive forces (NQPFs), emerging from the deep integration of scientific and technological innovation with economic development, have transcended the constraints of traditional productive factors, forming an advanced productive system driven by digital technology, supported by novel production elements, and enhanced by digital dynamic capabilities [9]. In the digital era, understanding the mechanisms and implementation paths for constructing NQPF has become a prominent topic in academic and practical circles. However, current research—mainly grounded in resource-based and dynamic capability perspectives—presents notable limitations [1,2,10]. Initially, existing research insufficiently explores the mechanisms for constructing NQPF systems, with limited understanding of how elements and capabilities function within these systems [3]. From a resource-based view, digital technologies transform traditional production inputs, facilitating qualitative shifts and producing novel element forms such as data, computing power-based digital infrastructure (DI), data resources (DRs), digital talent (DT), and digital ecological relationships (DERs) [11,12]. These elements exhibit distinctive fluidity and context-dependent characteristics during innovation processes [13]. However, examining the impact of these factors on innovation performance from a single-factor perspective overlooks the systemic nature of NQEs. The “Solow paradox” demonstrates that innovation performance improvements require systemic restructuring and coordinated factor allocation [14,15,16]. Dynamic capability theory addresses the limitations of traditional static perspectives, with digital dynamic capabilities representing the core manifestation of new-quality capabilities (NQCs) within NQPF systems [17,18]. In response to external uncertainties, enterprises are compelled to actively engage in digital sensing and knowledge integration, thereby updating digital and non-digital resources to achieve comprehensive restructuring [19]. From a systems theory perspective, NQPFs are realized through the organic integration of substantive elements, embedded factors, and operational components to unlock production efficiency and potential [20]. Therefore, examining the impact of NQPFs on innovation performance from a singular perspective neglects their systemic characteristics, thereby limiting our ability to fully elucidate the underlying mechanisms through which these forces operate.

Secondly, existing studies insufficiently examine the combinatorial effects of the interaction between new-quality elements (NQEs) and new-quality capabilities (NQCs). Although recent research has begun to focus on the synergistic integration of these two components [21], the specific mechanisms underlying this synergistic effect and the pathways through which it influences corporate innovation remain inadequately explored and empirically unvalidated. Resource orchestration theory offers a robust theoretical framework for understanding the dynamic interplay between resources and capabilities [22], providing guidance on how enterprises can strategically leverage NQEs and NQCs to achieve optimal configurations. Furthermore, effective value creation necessitates alignment between a firm’s capability advantages and its resource portfolio [23]. Given the scarcity of NQEs and the inherent constraints in developing new-quality digital capabilities, the pathways for enhancing firm innovation performance may vary considerably based on factors including resource endowments, organizational architecture, investment intensity, and strategic orientations. Addressing these research gaps, this study conceptualizes corporate innovation performance as the outcome of multifactorial combinations and employs resource orchestration theory to explicate the complex effects of the interactive relationship between NQEs and NQCs on corporate innovation performance.

2. Literature Review and Theoretical Framework

2.1. From Traditional Resource-Capability View to Digital Orchestration: Theoretical Evolution and Gaps

2.1.1. Contributions and Limitations of the Traditional RCV Framework

Current research on the antecedents of corporate innovation performance predominantly focuses on the Resource-Based View (RBV) and the Dynamic Capabilities View (DCV) [24]. These perspectives have led past studies to concentrate on the role of resources and capabilities in driving innovation, forming the foundation of the resource–capability–value (RCV) framework. The RCV framework emphasizes the strategic allocation of resources [10,25], organizational capability development, and dynamic environmental adaptation [23,24,26,27]. The RBV emphasizes that a firm’s sustained competitive advantage derives from its proactive acquisition, accumulation, and continued possession of unique resources [10], including technology, knowledge, experience, and talent [28]. Firms must reconfigure and recombine resources to optimize value creation processes [29]. This evolution is depicted in the hierarchical structure shown in Figure 1. The DCV further develops this notion by emphasizing a firm’s ability to perceive opportunities, capture value, and reconfigure resources in rapidly changing environments [23,29], offering an essential theoretical basis for understanding adaptive innovation. These frameworks have substantially advanced the understanding of how firms achieve innovation advantages through distinctive resources and capabilities, forming the dominant theoretical paradigms in strategic management and innovation research.

However, the traditional RCV framework exhibits two key limitations in explaining corporate innovation within digital contexts. The first limitation concerns the static ownership perspective. Traditional theories overemphasize static resource and capability possession, assuming firms automatically convert unique assets into competitive advantages [10,24,30]. This perspective overlooks managerial orchestration processes that transform potential into actual value, neglecting dynamic mechanisms of resource allocation, integration, and utilization. The second limitation involves linear thinking constraints. Traditional theories adopt linear additive models, treating firm performance as a simple function of individual resources and capabilities [31,32]. Such models cannot adequately capture multi-element configurations’ nonlinear effects and emergent properties. Especially in complex innovation contexts, the relationships among resources and capabilities—such as complementarity, substitutability, and synergy—are intricate and cannot be fully explained by traditional linear models [33]. Researchers need to adopt an extended framework that effectively captures the logic of value creation in the digital era.

2.1.2. Digital Challenges and Resource Orchestration Theory

Digital technologies have fundamentally restructured firm resource-capability relationships, creating new value creation logics and competitive dynamics [18]. Under the NQPF model, the source of a firm’s competitive advantage has shifted from traditional resource endowments to the systematic orchestration of digital resources and the synergistic empowerment of digital dynamic capabilities. The most prominent feature of this transition is the close symbiotic relationship between NQEs and NQCs, characterized by creating a unique “resource-capability” bundle [34], which drives the transformation of production paradigms toward NQPFs. NQEs—including infrastructure, data, technologies, and talent—exhibit fluidity, network effects, replicability, and decreasing marginal costs [23,35,36]. These characteristics contrast sharply with traditional resource scarcity and exclusivity. Simultaneously, NQCs exhibit dynamic features such as real-time responsiveness, reconfigurability, and cross-boundary integration [37,38], requiring firms to possess enhanced environmental sensing and rapid response abilities [18]. NQEs must align with corresponding capabilities to unlock their potential value. For example, vast amounts of accumulated data within firms cannot be transformed into tangible business value without corresponding data mining, analysis, and application capabilities [39]. Such alignment demands sophistication and dynamism far beyond traditional contexts, necessitating that firms possess greater dynamic adjustment capabilities. Moreover, the enhancement of digital capabilities will foster deeper exploration of the value of digital resources [21], while abundant digital resources provide practical scenarios and data support for the further development of digital capabilities [33]. This co-evolutionary process renders traditional static and linear models inadequate for explaining the complex dynamics of innovation within firms in digital contexts.

While academic attention increasingly focuses on digitalization’s innovation impact, existing research predominantly examines individual elements such as digital technologies and data resources [3,18,40,41]. Such research reveals significant theoretical and methodological gaps. Firstly, from a theoretical standpoint, an integrated and systematic research framework for new-quality resource-capability configuration is lacking. Existing studies have not incorporated the digital elements and capabilities involved in firms’ daily business operations into a unified framework to explore the synergistic effects of different combinations of these elements. Moreover, they inadequately leverage resource orchestration theory for explaining complex configurations. Resource orchestration theory emphasizes managerial roles in structuring, bundling, and utilizing resources, providing superior theoretical foundations for digital configurations [22]. Secondly, research on NQEs and NQCs is still in its infancy, with limited findings often presenting conflicting outcomes. Digital technologies and capabilities contribute to improvements in firm output and reductions in management costs. However, isolated digital initiatives inadequately address long-term innovation requirements [42]. Unfocused configurations create “digital capability traps” where technological debt and capability rigidity hinder innovation [43]. This paradox generates high investment with low returns, potentially causing diminishing marginal returns or negative impacts [44]. These conflicting findings highlight digitalization’s complexity and context dependence, suggesting that multiple equivalent pathways may exist to achieve high performance.

Based on the aforementioned analysis, this study systematically fills the research gap in both theoretical construction and empirical methodology by introducing the perspectives of resource orchestration theory and configurational research. Resource orchestration theory addresses traditional RCV limitations through three mechanisms. Firstly, it transforms resource value realization from passive ownership to active managerial orchestration [45]; secondly, to overcome the constraints of linear thinking, it emphasizes the nonlinear synergistic effects between resources and capabilities, capturing emergent characteristics arising from the interaction of multiple elements through the construction of configuration bundles [29]; thirdly, it supports digital resource-capability symbiosis models that explain bidirectional empowerment dynamics [46]. Accordingly, this study develops the D-RCV framework, conceptualizing NQPF as a configurational orchestration of digital resources and capability bundles. It explores how the combination and configuration of NQEs (digital infrastructure, data resources, digital talent, and ecological relationships) and NQCs (digital perception capability, digital utilization capability, and digital reconfiguration capability) in the context of digital empowerment lead to value creation, systematically identifying and validating the existence of multiple equivalent pathways. The study examines how firms develop NQPFs through resource orchestration and identifies equivalent configurations that enhance innovation performance, offering guidance for digital transformation decisions.

2.2. Resource Orchestration Theory-Based D-RCV Framework Construction

Forming NQPFs is a symbiotic process between digital resources and dynamic capabilities, where substantial elements, attachment, and operational factors interact to form an integrated whole [20]. Among these, digital infrastructure, digital talent, data resources, and diverse ecological relationships serve as substantial elements that form the foundation of production. Digital technologies serve as embedded factors, permeating production processes and enabling traditional-to-new element transformation; digital dynamic capabilities function as operational factors, optimizing element configuration through sensing, utilization, and reconfiguration. Unlike traditional paradigms, this study applies resource orchestration theory to explain how firms leverage NQE and NQC for value creation in complex environments. These elements demonstrate interdependent, co-evolutionary relationships in digital contexts. From configurational perspectives, this study examines how digital infrastructure, digital talent, data resources, diverse ecological relationships, and dynamic capabilities create synergistic innovation effects through orchestration processes of structuring, bundling, and leveraging. As shown in Figure 2, the research framework proposes that different compositions within the NQPF system have positive effects on corporate innovation performance.

2.2.1. The Digital Evolution of New-Quality Productive Factors: Digital Resources and Innovation Performance

NQEs constitute the core orchestration objects in the D-RCV framework, requiring systematic configuration and management during digital transformation. These digital resources are not isolated but form an organic resource ecosystem through complex interrelationships, providing multi-level and multi-dimensional support for corporate innovation.

Digital infrastructure (DI), as the foundational support for NQEs, provides foundational support for digital transformation through hardware (servers, computing systems, mobile devices) and software (applications, platforms, systems) components [47]. Digital infrastructure investment creates technological systems supporting innovation activities [48,49,50]. On the one hand, infrastructure investment enables extensive data collection from operational processes, expanding storage capacity, and establishing digital platform foundations [48]. On the other hand, applying digital infrastructure facilitates the strengthening of knowledge, technology, and equipment processing and transmission capabilities by linking intelligent devices and application scenarios. This promotes the sharing of internal and external information and innovation resources, compressing the spatial and temporal distances of innovation collaboration and knowledge diffusion to some extent [50]. It reduces uncertainties and risks caused by information asymmetry, enhancing firms’ innovation success probability.

Data resources (DRs), facilitated by new technological infrastructures such as big data, cloud computing, and blockchain, enable the collection and processing of data. These resources are critical strategic assets for developing innovative products, services, and processes [51]. As a core element, firms integrate data resources with traditional production factors, forming unique and hard-to-replicate combinations of production elements within the organization, thereby providing conditions for value creation [3,52]. Data resources exhibit inherent value, scarcity, shareability, and non-depletable characteristics [41]. Their virtual nature breaks time and space constraints, enabling the establishment of digital industry clusters and collaborative sharing relationships more efficiently through strategic information, technology, and resource flows [53]. This enables access to heterogeneous knowledge and technologies, establishing foundations for innovation investment. Moreover, the rapid iteration of data resources accelerates updates to internal innovation technologies, organizational structures, and management practices, enhancing firms’ agility in supply chain management and market decision-making to respond to external changes [54]. Ultimately, this leads to innovations in process flows and product quality, driving firms to achieve higher innovation performance [41].

Digital talent (DT), as a core dynamic element within the digital resource bundle, refers to individuals within firms who possess and apply digital technology knowledge and skills, emphasizing their understanding of digital tools, methodologies, and digital culture [55]. In the era of big data and information explosion, human capital functions as the primary conduit for tacit knowledge flows and organizational knowledge processing [56,57]. With vast amounts of digital information, digitally competent professionals establish efficient data resource sharing channels, enabling sophisticated processing that meaningfully influences production processes and enhances product and service innovation [58]. Furthermore, digital technologies empower innovative talent to rapidly identify previously undetectable data, breaking down barriers to the diffusion of tacit knowledge to some extent [59]. Enhanced data transparency mitigates information silo challenges, accelerating knowledge absorption and fostering innovation-oriented behaviors [60].

Diverse ecological relationships (DERs) encompass interactive relationships between firms and stakeholders, including customers, suppliers, partners, institutions, and governments. Diverse ecological relationships are cultivated through information exchange, resource collaboration, and strategic partnerships [27]. Ecosystem diversity reflects organizational partners’ heterogeneous capabilities and strategic orientations [61]. As digital technologies dissolve inter-organizational boundaries, network effects amplify exponentially, providing organizations with enhanced access to heterogeneous knowledge repositories [62]. Organizations effectively mitigate resource constraints through strategic engagement with diverse external stakeholders, catalyzing innovation processes [27]. Additionally, considering the complexity and instability of interactions between multiple stakeholders, which can lead to innovation risks, firms proficient in utilizing digital technologies can often secure a favorable position in relational networks. This strategic positioning enables organizations to rapidly penetrate specialized market segments while enhancing their competitive capabilities and innovation performance outcomes.

2.2.2. The Digital Evolution of New-Quality Productive Capabilities: Digital Dynamic Capabilities and Innovation Performance

NQCs represent the core capabilities within the D-RCV framework, enabling organizations to perceive market dynamics, integrate resources, and strategically reconfigure operations. As operational factors in NQPF systems, these capabilities optimize substantive element allocation through digital sensing, utilization, and reconfiguration dimensions [37].

Digital perception capability (DPC) encompasses advanced dynamic competencies related to organizational digital intelligence and strategic scenario planning, enabling organizations to identify emerging technological developments and competitive market trends, thereby establishing foundational conditions for innovation initiatives [37]. On one hand, firms with strong digital sensing capabilities can rapidly acquire key information regarding policy directions, market dynamics, and digital R&D from external environments using digital tools and components, such as big data, the Internet of Things (IoT), and mobile internet [63]. Organizations can subsequently integrate external market intelligence with their internal digitalization maturity levels to formulate strategic digital enhancement initiatives, thereby strengthening their adaptive capacity for responding to environmental fluctuations while creating strategic opportunities for innovation advancement. On the other hand, through digital means like business intelligence analysis, firms with higher digital sensing capabilities can detect market demand preferences and identify viable business opportunities. This enhanced capability consequently empowers organizations to formulate more strategically informed innovation decisions, augmenting their knowledge assimilation capacity and strategic market intelligence [39].

Digital utilization capability (DUC), as the execution core of the digital dynamic capability bundle, drives transformative value creation through strategic resource integration and process optimization [64]. Firstly, by embedding the perceived and acquired information into various business processes and technology types, firms can fully unlock the innovative value of data elements, catalyzing profound transformations in digital productivity and innovation capabilities [52]. Secondly, organizations can dynamically reconfigure existing resource combinations by leveraging real-time analytical insights derived from big data ecosystems, thereby enhancing innovation efficiency through optimized resource deployment and minimized resource redundancy [40,65]. Additionally, based on the pervasiveness and synergy of digital technologies [18], firms can effectively connect business units through digital platforms and online channels, improving communication and coordination efficiency across organizational levels and departments. These integrated digital management enhancements facilitate the reduction in operational barriers encountered throughout innovation processes while concurrently mitigating innovation failure risks [14].

Digital reconfiguration capability (DRC), as the transformative driver within the digital dynamic capability bundle, enables organizations to dynamically adjust development pathways through strategic model transformation and systematic upgrades [66]. Drawing upon existing technological intelligence and emerging market dynamics, organizations that strategically leverage digital analytical tools to systematically reassess and reconfigure internal and external resource portfolios frequently discover the latent innovation potential embedded within existing assets while simultaneously expanding the strategic applications of resource components. This systematic resource reconfiguration approach enables organizations to circumvent the constraints of resource rigidity and path dependency while generating continuous innovation momentum for sustainable performance enhancement [38,67]. Moreover, firms with strong digital reconfiguration capability can modularize and creatively reorganize existing data assets, business processes, and organizational structures, achieving strategic updates in innovation activities and driving significant breakthroughs in innovation performance [37]. Furthermore, based on digital restructuring and intelligent upgrades, firms can strategically optimize their existing business layout and product services and, leveraging digital insights, expand new R&D capabilities and niche markets, thereby catalyzing a comprehensive improvement in innovation output [68].

3. Research Design

3.1. Research Methods

This study explores how configurational coordination of digital resources and capabilities within NQPF systems enhances enterprise innovation performance [33]. These elements create emergent value through nonlinear interactions and multiple equifinal pathways. Fuzzy-set Qualitative Comparative Analysis (fsQCA), proposed by Ragin (2009), addresses multiple concurrent causal relationships [31]. Compared with traditional research methods, structural equation modeling is constrained by the assumption of symmetry and thus struggles to capture the emergent effects and equivalent pathways of multi-element collaborative configurations. While single-case analysis explores internal mechanisms, it lacks cross-case comparative capability and cannot systematically identify multiple equifinal configuration patterns. fsQCA provides superior analytical capability for addressing this study’s configurational research questions. Firstly, fsQCA avoids the assumption of variable independence inherent in traditional regression methods, and the configuration analysis approach aligns well with the system emergence of digital transformation; the improvement of enterprise innovation performance results from the interactions among various digitally empowered new-quality resources and capabilities. Secondly, fsQCA does not assume symmetric relationships between NQEs and innovation performance, enabling the identification of multiple equifinal paths that reflect diverse innovation achievement approaches. Thirdly, through necessity and sufficiency analysis, fsQCA can effectively distinguish between core conditions that play a key role in the configuration and peripheral conditions that serve as auxiliary factors, enabling in-depth analysis of the specific roles of each element in different configurations.

3.2. Sample Selection and Data Sources

This study targeted enterprises in electronics and communications, biopharmaceuticals, logistics and transportation, manufacturing industries, and aerospace sectors across key economic regions, including Beijing, Shanghai, Zhejiang, Jiangsu, Anhui, Guangdong, and Liaoning. The research specifically focused on organizations possessing substantial digital resource capabilities and demonstrating advanced digital technology competencies. To establish questionnaire reliability and validity, the research team distributed online survey instruments to senior executives and R&D department managers employing snowball sampling methodology. Drawing from 52 pilot questionnaires, the research team subsequently refined the measurement items. To ensure organizational maturity and innovation capacity, this study selected enterprises with operational histories exceeding five years and demonstrated substantial innovation performance capabilities.

The research team conducted the formal survey through the Credamo platform. To ensure data accuracy and sample heterogeneity, the researchers implemented a two-phase data collection strategy: Phase I spanning 20 September 2023 to 2 October 2023, and Phase II covering 15 April 2024 to 29 April 2024. The survey yielded 516 completed questionnaires, achieving a response rate of 81.40%. The research team subsequently conducted manual validation of all returned questionnaires, systematically excluding responses exhibiting single-option patterns or extensive missing data, ultimately retaining 302 valid responses, representing a validity rate of 71.90%. To ensure an authentic representation of enterprise digital transformation implementation, this study incorporated enterprise classification variables within the questionnaire design to examine the transformation characteristics and developmental differences across small and medium enterprises (SMEs) at various implementation stages, thereby capturing the comprehensive digital transformation landscape of SMEs. Drawing upon established digital transformation maturity frameworks [69,70], the classification integrates two core assessment dimensions: enabling factors (strategy, leadership, culture, capabilities, processes, personnel) and technological factors (manufacturing technologies, control systems, digital infrastructure, product-service innovations). The research categorized SME digital transformation maturity into five distinct classifications: “preliminary awareness without implementation,” “initial awareness with nascent implementation,” “experimental implementation across selective business domains with demonstrable results,” “comprehensive large-scale digital transformation completion,” and “enterprise-wide digitalization as operational standard.” Among the collected responses, 147 questionnaires represented the “preliminary awareness without implementation” category. The research team excluded these responses, establishing a final valid sample of 205 questionnaires.

Table 1. Sample description.

Characteristics		Frequency	Percent of Sample
Ages	1–3	42	20.6%
	3–5	63	30.8%
	5–10	63	30.8%
	>10	36	17.8%
Number of founders	1	23	11.1%
	2	70	34.4%
	3	76	37.2%
	>3	36	17.4%
Enterprise size	0–10	25	12.1%
	11–50	30	14.4%
	51–100	57	27.7%
	101–500	47	23.1%
	>500	47	22.7%
Nature of Company	State-owned Enterprises	41	20.0%
	Private enterprise	82	39.9%
	Foreign-funded Enterprises	45	21.9%
	Sino-foreign Joint Venture	37	18.2%
Digital transformation phase	Initial period	81	39.3%
	Growing period	78	38.1%
	Maturity period	46	22.5%

provides comprehensive sample demographic characteristics.

3.3. Variable Measurement

This research derived measurement items for all constructs from psychometrically validated instruments employed in established domestic and international scholarship. The research team implemented a rigorous back-translation protocol and subsequently adapted the instruments to align with the specific contextual requirements of this investigation. This study utilized seven-point Likert-type scales for all construct measurements, where response anchors ranged from “1”, representing “strongly disagree”, to “7”, representing “strongly agree.”

Table 2. Reliability and validity of analysis results.

Factors	Items	Factor Loadings	Cronbach’s α	KMO	AVE	CR
DI	DI1	0.757	0.888	0.837	0.662	0.887
	DI2	0.788
	DI3	0.864
	DI4	0.841
DT	DT1	0.862	0.908	0.887	0.671	0.91
	DT2	0.863
	DT3	0.685
	DT4	0.794
	DT5	0.875
DR	DR1	0.734	0.944	0.956	0.593	0.945
	DR2	0.853
	DR3	0.569
	DR4	0.748
	DR5	0.842
	DR6	0.826
	DR7	0.809
	DR8	0.812
	DR9	0.825
	DR10	0.758
	DR11	0.627
	DR12	0.783
DER	DER1	0.698	0.887	0.81	0.67	0.89
	DER2	0.887
	DER3	0.825
	DER4	0.852
DPC	DSC1	0.842	0.894	0.885	0.633	0.896
	DSC2	0.82
	DSC3	0.827
	DSC4	0.791
	DSC5	0.689
DUC	DUC1	0.833	0.91	0.856	0.671	0.91
	DUC2	0.851
	DUC3	0.863
	DUC4	0.834
	DUC5	0.706
DRC	DRC1	0.805	0.908	0.899	0.628	0.909
	DRC2	0.712
	DRC3	0.871
	DRC4	0.675
	DRC5	0.835
	DRC6	0.837
IP	IP1	0.694	0.892	0.863	0.593	0.945
	IP2	0.816
	IP3	0.868
	IP4	0.74
	IP5	0.826

Note: Digital infrastructure—DI; Digital talent—DT; Data resource—DR; Diverse ecological relationships—DER; Digital perception capability—DPC; Digital utilization capability—DUC; Digital reconfiguration capability—DRC.

presents the comprehensive measurement specifications and corresponding literature sources.

Outcome Variables—Innovation Performance. Following Bell (2005) and Ritter and Gemünden (2004), IP is measured using five items assessing enterprises’ comparative advantages in new product introduction, technology application, and development success rates relative to industry peers [71,72].

Antecedent Variables: (1) New-Quality Elements. Digital infrastructure is measured through four items adapted from Li et al. (2023), evaluating digital equipment effectiveness and innovation support capabilities [73]. Digital talent is assessed using five items from AL-Khatib (2022), measuring employees’ technical proficiency and creativity under digital technology support [74]. Data resources employ twelve items from Suoniemi (2020), capturing three dimensions: technical infrastructure, analytical capabilities, and organizational support [75]. Diverse ecological relationship is measured via four items from Leeuw et al. (2014), assessing digital technology-enabled alliance capabilities with diverse partners [62]. (2) New-Quality Capabilities. Based on the dynamic capabilities framework adapted for digital contexts, digital dynamic capabilities are conceptualized as three dimensions measured using items from Teece (2007) [67]. Digital perception capability (five items) assesses the enterprises’ abilities to identify valuable data sources and market changes. Digital utilization capability (five items) measures capabilities to leverage digital information for market positioning and process optimization. Digital reconfiguration capability (six items) evaluates abilities to decompose transformation processes and achieve stakeholder collaboration.

3.4. Validity and Reliability Assessment

This study employed SPSS 26.0 and AMOS 24.0 to assess the reliability and validity of measurement instruments across all constructs. Table 2 presents the comprehensive analytical results, demonstrating that all constructs satisfied reliability thresholds.

Following the methodological standards established by Hair et al. (2021) and Bagozzi and Yi (1988), Cronbach’s α coefficients and composite reliability values must exceed 0.7 [76,77], while average variance extracted (AVE) values must surpass 0.5, consistent with the criteria established by Fornell and Larcker (1981) [78]. Regarding reliability assessment, all constructs demonstrated Cronbach’s α coefficients and composite reliability (CR) values exceeding 0.85, indicating robust internal consistency among measurement items and establishing superior scale reliability. Concerning validity assessment, all measurement items exhibited factor loadings exceeding 0.6, achieved a minimum cumulative variance contribution rate of 62.51%, and demonstrated average variance extracted (AVE) values surpassing 0.5. These results collectively indicate robust convergent validity and discriminant validity across all measurement instruments.

4. Data Analysis

4.1. Variable Calibration

This investigation implemented direct calibration procedures to transform condition and outcome variables, converting raw measurement data into fuzzy-set membership scores ranging from 0 to 1 utilizing the calibration function within fsQCA 4.0 software. Following established calibration protocols, this research established calibration thresholds for the crossover points of digital infrastructure, digital talent, data resources, diverse ecological relationships, digital perception capability, digital utilization capability, and digital reconfiguration capability at 4.0, with full membership thresholds set at 6.0 and complete non-membership thresholds anchored at 2.0 [79]. Given the seven-point Likert-type scale implementation, the original dataset contained substantial integer clustering. Adhering to established methodological protocols, this study augmented crossover point values by 0.001 [80].

4.2. Necessary Condition Analysis

Table 3. Necessity analysis of a single factor.

Condition Variable		High Innovation Performance		Non-High Innovation Performance
		Consistency	Coverage	Consistency	Coverage
New-quality elements	DI	0.825	0.849	0.742	0.283
	~DI	0.303	0.761	0.603	0.560
	DT	0.801	0.886	0.622	0.255
	~DT	0.326	0.700	0.722	0.573
	DR	0.859	0.859	0.736	0.272
	~DR	0.272	0.736	0.618	0.619
	DER	0.720	0.870	0.637	0.285
	~DER	0.408	0.752	0.710	0.484
New-quality capabilities	DPC	0.873	0.872	0.683	0.253
	~DPC	0.252	0.682	0.653	0.655
	DUC	0.874	0.873	0.676	0.250
	~DUC	0.250	0.676	0.657	0.659
	DRC	0.874	0.878	0.674	0.251
	~DRC	0.255	0.678	0.673	0.644

Note:The tilde symbol (~) denotes the absence of a condition. Digital infrastructure—DI; Digital talent—DT; Data resource—DR; Diverse ecological relationships—DER; Digital perception capability—DPC; Digital utilization capability—DUC; Digital reconfiguration capability—DRC.

depicts the relationships between the necessity of the seven conditions (digital infrastructure, digital talent, data resource, diverse ecological relationship, digital perception capability, digital utilization capability, and digital reconfiguration capability) and the outcome (innovation performance). Prior to executing configuration analysis, this investigation assessed whether necessary conditions exist for achieving superior corporate innovation performance. The consistency coefficients across all antecedent conditions fall below the 0.9 threshold, demonstrating that no individual antecedent factor constitutes a necessary condition for enhancing corporate innovation performance.

Consequently, this analysis reveals that enterprise NQEs and NQCs must operate synergistically to facilitate sustainable improvements in corporate innovation performance.

4.3. Analysis of Sufficient Conditions

Based on the methodological design, this study employs fuzzy set Qualitative Comparative Analysis (fsQCA) to analyze new-quality resource and capability configurations and innovation performance relationships. Following Fiss (2011)’s analytical standards, fsQCA 3.0 software generates three solution types: complex, parsimonious, and intermediate [81]. Comparative analysis of nested relationships identifies core conditions (appearing in both parsimonious and intermediate solutions) and peripheral conditions (appearing only in intermediate solutions). Moreover, configurations are theoretically classified based on patterns of core conditions, grouping those with similar combinations into the same theoretical type. This approach identifies essential characteristics and mechanisms across configuration patterns.

Acknowledging the significance of individual case representation, this research established analytical parameters with case frequency thresholds set at 1, Proportional Reduction in Inconsistency (PRI) thresholds at 0.75, and consistency thresholds at 0.8. The QCA analysis generated complex, parsimonious, and intermediate solutions.

Table 4. Conditional configurations of high and non-high innovation performance.

Conditional Variable	High Innovation Performance						Non-High Innovation Performance
	S1	S2a	S2b	S2c	S3a	S3b	NS1
DI	●	●	●	●		⊗	●
DT	⊗	●	●	●	●	●	⊗
DR			●	●	●	●	●
DER	●	⊗	●	⊗	●	●	⊗
DPC	●	●	●	●	●	⊗	⊗
DUC	●	●	●	⊗	●	●	⊗
DRC	●	●		⊗	●	⊗	⊗
raw coverage	0.198	0.266	0.509	0.103	0.559	0.090	0.317
unique coverage	0.056	0.106	0.003	0.003	0.049	0.004	0.317
consistency	0.894	0.958	0.972	0.978	0.965	0.966	0.876
Solution coverage	0.753						0.317
Solution consistency	0.948						0.876

Note: ● and ● = the presence of a causal condition; ⊗ and ⊗ = the absence of a causal condition; black circles (● and ⊗) = the core condition; blue circles (● and ⊗) = the peripheral condition. Blank spaces indicate “don’t care”. Digital infrastructure—DI; Digital talent—DT; Data resource—DR; Diverse ecological relationships—DER; Digital perception capability—DPC; Digital utilization capability—DUC; Digital reconfiguration capability—DRC.

presents the results of the configurational analysis for corporate innovation performance. The notation system employs ● to indicate condition presence, ⊗ to denote condition absence, and blank cells to represent configurational irrelevance. Symbol color differentiates core conditions from peripheral conditions, with black symbols representing core conditions and blue symbols representing peripheral conditions in each pathway. Through a systematic analysis of core condition combinations, six resource-capability configuration patterns (S1, S2a, S2b, S2c, S3a, S3b) that promote superior innovation performance in a digital context are identified. Based on core condition similarities, these configurations are categorized into three distinct types. S1 represents the synergy-oriented new-quality productive mode, compensating for internal digital talent deficiencies through external ecosystem synergy and digital dynamic capabilities. S2a, S2b, and S2c form the resource-optimization-oriented mode, sharing core conditions (digital infrastructure, digital talent, and digital perception capability) while differing in peripheral conditions (data resources, diverse ecological relationships, digital utilization capability, and digital reconfiguration). These reflect internal resource optimization paths across different development stages and strategic priorities. S3a and S3b form the data-driven mode, sharing core conditions (data resource, diverse ecological relationship, digital talent, and digital utilization capability) while differing in digital infrastructure investment and capability configuration, indicating diverse data-driven realization paths [81].

4.4. Sensitivity Analysis

Robustness tests were conducted for high innovation performance configurations by adjusting consistency thresholds, case frequency, and PRI consistency levels [31,81,82]. Following the established QCA protocol, three comprehensive robustness tests were performed to verify the stability of the configurations identified. The detailed results of these robustness tests are presented in

Table 5. Results of robustness tests for high IP.

Conditional Variables	High IP (the Consistency Threshold Increased from 0.8 to 0.85)						High IP (the Case Frequency Threshold Increased from 1 to 2)				High IP (the PRI Consistency Threshold Increased from 0.75 to 0.8)
	S1	S2a	S2b	S2c	S3a	S3b	S1	S2a	S2b	S3	S1	S2a	S2b	S2c	S3
DI	●	●	●	●		⊗	●	●	●		●	●	●	●
DT	⊗	●	●	●	●	●	⊗	●	●	●	⊗	●	●	●	●
DR			●	●	●	●		●	●	●	⊗		●	●	●
DER	●	⊗	●	⊗	●	●	●		●	●	●	⊗	●	⊗	●
DPC	●	●	●	●	●	⊗	●	●	●	●	●	●	●	●	●
DUC	●	●	●	⊗	●	●	●	●	●	●	●	●	●	⊗	●
DRC	●	●		⊗	●	⊗	●	●		●	●	●		⊗	●
raw coverage	0.198	0.266	0.509	0.103	0.559	0.090	0.198	0.510	0.628	0.559	0.266	0.509	0.559	0.103	0.135
unique coverage	0.056	0.106	0.003	0.003	0.049	0.004	0.059	0.004	0.123	0.054	0.106	0.004	0.054	0.003	0.023
consistency	0.894	0.958	0.972	0.978	0.965	0.966	0.894	0.972	0.970	0.965	0.958	0.972	0.965	0.978	0.935
Solution coverage	0.753					0.740					0.716
Solution consistency	0.948					0.950					0.959

Note: ● and ● = the presence of a causal condition; ⊗ and ⊗ = the absence of a causal condition; black circles (● and ⊗) = the core condition; blue circles (● and ⊗) = the peripheral condition. Blank spaces indicate “don’t care”. Digital infrastructure—DI; Digital talent—DT; Data resource—DR; Diverse ecological relationships—DER; Digital perception capability—DPC; Digital utilization capability—DUC; Digital reconfiguration capability—DRC.

. In the first test, the consistency threshold was increased from 0.8 to 0.85, while case frequency remained at 1 and PRI consistency remained at 0.75. The configuration produced by this test was identical to the one before the adjustment, with consistency and coverage levels similar to the original. All six original configurations (S1, S2a, S2b, S2c, S3a, and S3b) maintained structural integrity under elevated consistency requirements. In the second test, case frequency was adjusted from 1 to 2, while consistency threshold and PRI consistency remained at 0.8 and 0.75, respectively. This adjustment eliminated configuration S2c, while the remaining five configurations (S1, S2a, S2b, S3a, and S3b) remained largely unchanged. S2c exclusion minimally affected overall solution coverage, decreasing from 0.753 to 0.740. In the third test, the PRI consistency threshold was increased from 0.75 to 0.8, while the consistency threshold remained at 0.8 and the case frequency remained at 1. This test eliminated configuration S3b, while the remaining five configurations maintained stability, with overall solution coverage decreasing from 0.753 to 0.716. The excluded configurations (S2c and S3b) demonstrated substantial overlap with other configurations within their respective clusters, confirming the robustness of our findings.

Robustness tests were conducted for non-high innovation performance configurations by adjusting consistency thresholds and case frequency [82,83]. As detailed in

Table 6. Results of robustness tests for non-high IP.

Conditional Variables	Non-High IP (the Consistency Threshold Increased from 0.8 to 0.85)	Non-High IP (the Case Frequency Threshold Increased from 1 to 2)
	NS1
DI	●	●
DT	⊗	⊗
DR	●	●
DER	⊗	⊗
DPC	⊗	⊗
DUC	⊗	⊗
DRC	⊗
raw coverage	0.317	0.317
unique coverage	0.317	0.317
consistency	0.876	0.876
Solution coverage	0.317	0.317
Solution consistency	0.876	0.876

Note: ● = the presence of a causal condition; ⊗ and ⊗ = the absence of a causal condition; black circles (● and ⊗) = the core condition; blue circles (⊗) = the peripheral condition. Blank spaces indicate “don’t care”. Digital infra-structure—DI; Digital talent—DT; Data resource—DR; Diverse ecological relationships—DER; Digital perception capability—DPC; Digital utilization capability—DUC; Digital reconfiguration capability—DRC.

, in the first test, the consistency threshold was increased from 0.8 to 0.85, while case frequency remained at 1 and PRI consistency remained at 0.75. The configurations produced remained identical to those before adjustment, with unchanged consistency and coverage levels. In the second test, the case frequency was adjusted from 1 to 2, while the consistency threshold remained at 0.8 and the PRI consistency remained at 0.75. This adjustment caused slight configurational changes, while consistency and coverage levels remained unchanged. The stability of core non-high innovation performance configurations demonstrates minimal sensitivity to frequency threshold modifications. Therefore, these robustness tests demonstrate that non-high innovation performance configurations exhibit high stability, providing methodological confidence in configurational asymmetry findings and reinforcing the theoretical validity of our D-RCV framework for explaining innovation performance variations.

5. Result

5.1. Digital Resource-Capability Configurations for High Innovation Performance

The results presented in Table 4 demonstrate that the consistency levels of five configurational pathways for enhancing enterprise innovation performance exceed 0.85, with an overall consistency level of 0.948, which surpasses the established threshold of 0.8. The overall solution coverage reaches 0.753, indicating that the configurational effects are significant and demonstrate substantial explanatory power. Integrating the theoretical framework and empirical findings of this investigation, three principal innovation performance enhancement pathways emerge: the collaborative symbiosis-oriented new-quality productive mode (S1) characterized by the synergy of diverse ecological relationship and digital dynamic capabilities, the resource-optimization-oriented new-quality productive mode (S2) centered on the integration of data resource and digital infrastructure, and the data-driven-oriented new-quality productive mode (S3) propelled by data resource utilization.

Configuration 1 is Collaborative Symbiotic New-Quality Productive Mode. Configuration S1 suggests that a combination of NQEs and NQCs, where “DI•DE•DPC•DYC” are core conditions and “~DT•DRC” are peripheral conditions, can effectively drive enterprises to achieve high innovation performance. Configuration S1 suggests that enterprises confronting the escalating strategic significance of NQE, those experiencing environmental pressures from accelerated technological product cycles, and organizations constrained by limited innovative talent and weak independent research and development (R&D) capabilities can compensate for innovation resource deficiencies by constructing diversified ecological systems and cultivating digital dynamic capabilities [61]. From an absorptive capacity theory perspective, Configuration S1 embodies the innovation logic of compensating for limited “internal knowledge creation capabilities” by enhancing “external knowledge acquisition capabilities” [84]. In the knowledge acquisition stage, diversified ecological systems provide transitioning firms with access to heterogeneous technologies and knowledge resources, enabling them to address environmental uncertainty arising from technological change intensification and secure competitive advantages in market competition [85,86]. Additionally, enterprises possessing acute environmental awareness and forward-looking planning capabilities can identify digital opportunities and innovation pathways aligned with their strategic objectives, continuously optimize market positioning, and adjust organizational structures and business processes [87], thereby effectively promoting innovation performance enhancement. In the knowledge utilization stage, digital reconfiguration capability, as a peripheral condition, plays a key adaptive regulatory role, helping enterprises adjust their internal organizational structures and business processes based on the characteristics of external knowledge [37,88]. In the knowledge transformation stage, digital infrastructure investment and deployment have fundamentally transformed enterprise production and operational modes. Machine intelligence augments individual employee capabilities in organizational decision-making participation, reshapes production relationships, enhances the absorption and utilization of externally introduced innovative knowledge, and mitigates talent shortage constraints to some extent [48,50].This collaborative symbiosis model particularly suits small and medium-sized enterprises (SMEs) in traditional manufacturing, local service enterprises, and resource-based companies. Such enterprises often face talent attraction and technology accumulation challenges but maintain stable cooperative relationships and market positions within specific industry ecosystems. For example, traditional manufacturing SMEs, despite struggling to recruit top AI engineers or data analysts, can access advanced digital technology support through collaborations with industrial Internet platforms and intelligent manufacturing solution providers. This configurational pathway accounts for approximately 19.8% of high innovation performance cases, of which approximately 5.6% can be attributed exclusively to this pathway.

Configuration 2 is Resource-Optimized New-Quality Productive Mode. Configuration S2 emphasizes “DI•DT•DPC” as core conditions. Compared to the S1 path, this model places greater focus on the optimization of internal new-quality resources, highlighting the central role of digital talent in driving innovation. Research demonstrates that digital talent, as a critical element in advancing NQFP, significantly influences innovation output quality and quantity through knowledge and skill proficiency levels [59]. During human–machine interaction processes, employees integrate their knowledge and experience into intelligent algorithms and devices, thereby enhancing machine intelligence levels; simultaneously, machines assume routine task execution, enabling employees to engage in more creative and challenging work [89]. In this process, digital infrastructure provides technical support, while digital perception capability enables enterprises to swiftly capture market changes, technological advancements, and customer demands, collectively forming the foundation for independent innovation capabilities. S2a, S2b, and S2c represent different combinations of peripheral conditions reflecting distinct resource-capability strategies: S2a features “DUC•DRC•~DE” as peripheral conditions. This pathway’s core characteristic involves achieving disruptive innovation under technological blockade constraints and limited ecological cooperation through “inward-focused” and “capability internalization” strategies, ensuring technological sovereignty. Under harsh technological environments, enterprises must rely on strong internal talent teams supported by digital utilization capability to address core technological challenges, overcome critical bottlenecks, and leverage digital reconfiguration capability for technological system iteration and upgrading [33]. This pathway primarily applies to high-end equipment manufacturing fields, such as aerospace engines and photolithography machines—industries with ultra-high technological barriers requiring high autonomy in technological decisions, R&D directions, and industrialization paths. S2b, which features “DR•DE•~DRC” as peripheral conditions, reflects the evolution of innovation from a closed model to a dynamic, networked, multi-party collaboration model, based on ecosystem theory [90]. Enterprises in this pathway heavily rely on modular technology environments and build tightly knit innovation ecosystems with suppliers through patent cross-licensing, providing structured channels for knowledge acquisition, transformation, and utilization [23,43]. Strong internal digital talent teams actively identify, select, and integrate external ecosystem resources [56], while digital utilization capability ensures effective integration of diverse supplier ecosystem data to optimize technological modular configurations. This pathway applies to industries requiring deep supply chain collaboration, such as electric vehicles and consumer electronics, explaining approximately 50.9% of high-performance cases. S2c emphasizes agility and flexibility, focusing on optimizing operational efficiency through precise data application. By leveraging digital resources and infrastructure synergistically, this pathway reduces the need for high-level digital capabilities, enabling enterprises to quickly perceive market information and establish production connections, ensuring lean management with low inventory and high efficiency. This path explains about 26.6% of high-performance cases, primarily applied in large-scale manufacturing.

Configuration 3 is Data-Driven New-Quality Productive Mode. Configuration S3 focuses on “DR•DE•DT•DUC” as core conditions. S3a and S3b differ in peripheral conditions such as digital infrastructure, digital perception capability, and digital reconfiguration capability, but both emphasize data’s key role in NQPF formation. The core conditions constitute data-driven innovation infrastructure: digital talent serves as the core entity for realizing data value, data resource functions as core innovation production factors, diverse ecological relationships provide collaborative networks for data acquisition and sharing, and digital utilization capability ensures enterprises fully exploit data potential. Empowered by digital technologies, emerging technologies such as artificial intelligence and cloud computing can realize intelligent decision-making and algorithmic learning compared with traditional IT systems [40], which demonstrate high dependence on massive and high-quality data supplies from enterprises, and data quality directly impacts prediction result validity and accuracy [52], thereby facilitating the development of “human–machine-digital-object” multifaceted productivity models. The differentiation between S3a and S3b configurations may stem from varying enterprise understandings of innovation strategies. S3a enterprises invest more extensively in digital NQE, possess stronger internal development and new technology adaptation capabilities, focus on internal technological breakthroughs and business model innovation, and utilize data resources to drive higher-order digital perception capability and digital reconfiguration capability, thereby forming data-driven productivity systems. This configuration’s unique mechanism creates a “perception-analysis-reconstruction” closed-loop innovation process, not merely using data to improve existing business but redefining business possibilities based on data. This pathway applies to data-intensive industries, such as financial services and modern distribution industries, explaining 55.9% of high-performance cases. S3b enterprises invest comparatively less in digital infrastructure, wherein data resources become the critical factor for obtaining competitive advantages. This type of market competitiveness derives from strategic partnerships that facilitate data sharing to acquire external technical support or market opportunities. This configuration reduces innovation barriers and costs through data sharing and ecosystem development and is more suitable for digital service providers and e-commerce platforms, explaining only 9% of high-performance cases.

5.2. Digital Resource-Capability Configurations Generating Non-High Innovation Performance

To examine causal asymmetry, this investigation analyzes the resource-capability combinations that generate non-high innovation performance and identifies one configuration that consistently yields such performance outcomes. Configuration NS1 features “DI•~DT•DR•~DE•~DPC•~DUC” as core conditions, with “~DRC” as a peripheral condition. This configuration represents enterprises possessing hardware assets such as data resources and digital infrastructure but lacking supporting soft capabilities, including digital talent, diverse ecological relationships, and digital perception capability. The effectiveness of their data resource remains limited, constraining innovation performance and creating the “silo trap” phenomenon in digital investment. This finding highlights the systemic nature of constructing NQPF: digital transformation transcends mere technological accumulation, requiring coordinated development across multiple dimensions, including talent, technology, ecology, and capabilities. This finding confirms the theoretical proposition initially advanced in this investigation: when enterprises lack specific innovation experience accumulation, do not possess corresponding digital talent, and cannot expand their innovation cooperation networks, investing in digital resources without strategic consideration cannot effectively enhance their innovation performance in the digital era.

6. Discussion

6.1. Research Findings

Drawing upon the “D-RCV” framework and resource orchestration theory, this investigation reveals that digital transformation and empowerment constitute critical determinants in advancing NQPFs and that the interaction and dynamic alignment of NQEs and NQCs under digital empowerment conditions represents the fundamental mechanism for enterprise innovation and development. Further examination of this framework identifies three distinctive pathways for enhancing enterprise innovation performance: multiple ecological synergies, resource-capability integration, and data-driven approaches. Enterprises can accomplish innovation breakthroughs through these three differentiated pathways, contingent upon specific environmental conditions and contextual factors.

Firstly, grounded in digital empowerment and resource orchestration theory, this study employs fsQCA to reveal complex causal pathways whereby new-quality productive elements and capabilities synergistically influence enterprise innovation performance under digitalization. Conditional analysis indicates that no NQEs and NQCs are necessary for high innovation performance. The findings demonstrate that data resource transformation, diversified ecosystem development, and digital talent cultivation broadly impact innovation capability enhancement in the contemporary era. Unlike traditional regression models yielding linear conclusions, this study identifies three new-quality productive models for achieving high innovation performance: collaborative symbiosis, resource optimization, and data-driven approaches. The temporal-spatial and resource endowment heterogeneity across enterprise types implies diverse innovation performance generation mechanisms.

Secondly, qualitative comparative analysis of the three high-performance configurations reveals that enterprises with varying technological intensity levels differ significantly in strategic investment approaches, particularly regarding digital infrastructure adoption and capability development priorities. Technology-intensive enterprises should prioritize compatible digital capabilities alongside talent acquisition, infrastructure upgrading, and data resource utilization to accelerate digital operations and innovation integration. Moreover, digital transformation processes eliminate geographical constraints on information sharing, reduce information asymmetries among stakeholders, and accelerate innovation cycles. Consequently, enterprises inevitably integrate into supply chain networks and establish diverse partnerships with upstream and downstream stakeholders. These ecosystem relationships provide access to heterogeneous knowledge while expanding demands for digital perception, acquisition, and transformation capabilities. Finally, this study identifies one configuration yielding non-high innovation performance. Under limited digital transformation capabilities, enterprises cannot achieve the anticipated innovation levels despite substantial digital asset investments. Therefore, enterprises should develop differentiated and feasible pathways based on their digital resource endowments and capability configurations. This study provides theoretical foundations for understanding enterprise digital transformation and innovation enhancement within new-quality productive systems while offering practical guidance for context-specific innovation strategies.

6.2. Theoretical Implications

The principal theoretical contribution of this investigation is to examine the internal mechanisms of enterprise innovation and development from the new-quality productivity perspective, informed by digital development characteristics in the knowledge economy era; develop the “D-RCV” analytical framework for digital empowerment; and elucidate systematic construction pathways of NQEs and NQCs, thereby advancing new-quality productivity theoretical understanding. This investigation extends the conceptual foundations of productivity theory [20]. On this basis, this study further explores the interaction between digitally empowered productive elements and capabilities and the effective configuration paths for improving innovation performance under their interplay. Specifically, the theoretical contributions of this investigation are primarily manifested in the following dimensions:

Firstly, this investigation innovatively deconstructs the theoretical conceptualization of NQPF within digitalization contexts from the new-quality productive system perspective. The investigation conceptualizes NQPF as an integrated system characterized by dynamic interactions between NQEs (digital infrastructure, digital talent, data resource, and diverse ecological relationship) and NQCs (digital perception capability, digital utilization capability, and digital reconfiguration capability) with digital technology functioning as an enabler, thereby revealing the multidimensional structural characteristics and multifaceted operational attributes of NQEs within digital transformation contexts [20,68]. Consequently, this investigation not only advances theoretical understanding of NQPFs within management scholarship but also provides practical theoretical guidance for enterprises to strategically orchestrate NQEs and NQCs in digital transformation initiatives.

Moreover, grounded in resource orchestration theory and digital empowerment theory, this study constructs a resource–capability–value creation (D-RCV) theoretical model tailored to digital scenarios, thereby extending the boundaries of research on resource allocation and capability coordination. Existing studies primarily focus on either resource acquisition or capability development from a singular perspective, overlooking the complex synergy between resource and capability elements in terms of structural integration, capability conversion, and value amplification in digital contexts [1,33,38]. This study extends resource orchestration theory within digital contexts by integrating dynamic capabilities and resource-based theories. It provides strategic guidance for enterprises to systematically configure and coordinate NQEs and NQCs for value maximization. Anchored in resource orchestration and digital empowerment theory, this study develops the D-RCV model of “digital resources–capability synergy–value creation” to systematically analyze the integration logic, dynamic alignment, and interactive effectiveness of various NQEs and NQCs under digital environments. This not only responds to the research initiative on the “transformation of new-quality productive models” but also deepens the understanding of how resource–capability coupling driven by digital empowerment enhances enterprise innovation performance.

Finally, this research reveals multiple pathways for NQPF construction based on configurational approaches and elucidates the intrinsic mechanisms underlying the integration of NQEs and NQCs with innovation performance, drawing upon resource orchestration theory. This investigation employs fsQCA to identify three new-quality productivity construction modes: collaborative symbiosis, resource optimization, and data-driven approaches. This analysis aims to demonstrate the coupling effects and influence pathways of digitally driven dual-structured new-quality productivity on enterprise innovation performance enhancement. The collaborative symbiosis-oriented production configuration led by multiple ecological relationships (Configuration S1) demonstrates that when enterprises possess sufficient production resources and substantial digitalization capabilities, diversified external resource acquisition channels can compensate for digital talent deficiencies [89]. The resource optimization-oriented production configurations led by internal resources (S2a, S2b, S2c) demonstrate that when enterprises possess core resources such as digital infrastructure and digital talent, they can effectively enhance enterprise knowledge and technology absorption and achieve superior innovation performance [49]. The data-driven production configurations dominated by data resources (S3a, S3b) demonstrate that for light-asset enterprises with relatively limited digital infrastructure development, strategic utilization of data resources can substantially compensate for resource constraints [75]. Even when enterprise digital facilities are relatively outdated, organizations can still leverage data resources to construct “human–machine-digital-material” multidimensional synergistic intelligence systems, thereby achieving superior enterprise innovation performance.

6.3. Managerial Implications

Based on digital empowerment, resource orchestration theory, and fsQCA configuration results, this study reveals multiple practical pathways for building NQPF (collaborative symbiosis, resource optimization, and data-driven approaches), the following managerial implications are proposed. Moving beyond traditional linear approaches, the research identifies six distinct configuration paths across three categories, offering systematic guidance for firms building new-quality productivity through digital transformation. These findings are particularly valuable for firms undergoing digital transformation amid rapid technological change and complex market environments.

Firstly, managers should abandon traditional, singular thinking in digital transformation and focus on a systematic strategy for configuring digital resources and capabilities. This study demonstrates that effective combinations and collaborative configurations of digital elements drive innovation performance enhancement. For instance, the S1 configuration leverages synergies among ecological relationships, digital infrastructure, and digital sensing capabilities, while the S2 configuration highlights digital talent and infrastructure centrality. Managers should design digital element combinations aligned with organizational contexts and strategic objectives, rather than blindly pursuing uniform enhancement of all digital capabilities. For example, within the S1 configuration, companies can compensate for internal technological capability deficits through external ecological cooperation and knowledge acquisition. Conversely, the S2 configuration requires strengthening internal capabilities through increased investments in digital talent and technology platforms. More importantly, different digital elements can complement or substitute for each other within specific configurations.

Secondly, enterprises should select configuration models for NQPFs that align with their industry characteristics, developmental stages, and resource endowments [21,29]. This study demonstrates that S1 collaborative symbiosis, S2 resource optimization, and S3 data-driven configurations correspond to distinct business contexts and strategic objectives. Traditional manufacturing SMEs, regional service enterprises, and resource-based companies confronting technology accumulation deficits and talent acquisition constraints should prioritize the S1 configuration. These enterprises should focus on building diverse ecological relationships and strengthening digital perception capability to compensate for internal knowledge creation deficits through external knowledge acquisition. In contrast, companies with strong resource integration capabilities and a solid technological foundation, especially high-end manufacturing and technology-intensive firms, should consider the S2 resource optimization configuration. These enterprises would benefit from prioritizing digital talent development and internal capability enhancement to achieve technological autonomy and innovation advancement. For industries with high risks of technological blockade, such as aerospace engines and photolithography machines, companies should prioritize the S2a capability-internalization strategy, constructing a complete internal R&D system and multi-level digital talent teams to ensure the autonomy and control of core technologies [91]. In highly modular industries, such as electric vehicles and consumer electronics, companies can adopt the S2b ecological collaboration strategy, achieving modular innovation through deep digital collaboration with suppliers, improving production efficiency, and reducing costs [92]. For sectors demanding high efficiency and flexibility, such as logistics, the S3 configuration proves crucial. This is particularly relevant in data-intensive industries like fintech and e-commerce, where enterprises should develop data-driven innovation models by investing in data analysis platforms and AI capabilities to enhance data perception, analysis, and business restructuring. Moreover, SMEs can leverage privacy-preserving technologies such as federated learning for ecosystem-wide data collaboration, achieving value sharing and cost control while protecting data privacy [93].

Thirdly, enterprises should adjust their NQPF configurations dynamically based on developmental stages, market dynamics, and strategic objectives [92,94]. The effectiveness of different configuration paths will vary depending on internal and external conditions. Startups facing resource and capability limitations should adopt the S1 collaborative symbiosis configuration. This approach enables ecosystem embedding to access external resources while reducing independent R&D risks and costs. As companies grow, they can gradually transition to the S2 resource optimization configuration, strengthening internal digital capabilities and enhancing technological autonomy and innovation leadership. Mature enterprises should dynamically select between S2 and S3 configurations or explore hybrid models based on strategic positioning and market opportunities. Furthermore, environmental shifts, such as technological blockades or intensifying competition, influence configuration effectiveness. For instance, enterprises should emphasize the S2a capability-internalization configuration when confronting geopolitical risks or technological blockades to enhance technological autonomy. Conversely, when market demands shift rapidly or competition intensifies, S1 collaborative symbiosis or S3 data-driven configurations prove more suitable for improving market responsiveness and innovation agility.

Fourthly, enterprises and policymakers should establish a performance evaluation system based on configuration-oriented approaches, avoiding a “one-size-fits-all” evaluation method. Traditional digital performance evaluations often overlook the differences in innovation mechanisms and value creation paths among different configuration models. This study suggests governments and enterprises provide customized support based on industry-specific and regional configuration requirements. Leading enterprises should proactively build open ecosystems, attract complementary and peripheral firms, and foster synergistic innovation effects [95]. For example, traditional manufacturing clusters primarily utilizing S1 configurations require policies focused on platform construction, ecological collaboration, and enhanced cooperation mechanisms. High-tech industries dominated by S2 configurations benefit from policies emphasizing talent attraction, R&D support, and intellectual property protection. Digital economy demonstration zones where S3 configurations prevail should prioritize data governance, privacy protection, and data element market development.

6.4. Limitations and Future Studies

This investigation acknowledges certain limitations and provides directions for future research. Initially, due to data availability constraints, this investigation conducts static analysis based on cross-sectional data, thereby overlooking digital transformation dynamics. The emphasis on digitally enabled NQEs and NQCs may vary across transformation stages—a factor considered in the discussion but not empirically validated. Consequently, future researchers can collect longitudinal data to analyze the complex impacts of new-quality resources and capabilities on innovation performance during dynamic transformation processes. Furthermore, while fsQCA employs integrated quantitative and qualitative analysis to reveal multiple configurational forms, conducting in-depth individual case analysis remains challenging, representing a methodological limitation of fsQCA. Future researchers can further investigate diverse productive configurations to enhance enterprise innovation capabilities and elucidate the evolutionary process of digital resource-capability alignment in promoting innovation performance. Thirdly, data were primarily collected through self-reported questionnaires, which may introduce subjectivity bias. In particular, self-assessments of digital capabilities and innovation performance are susceptible to cognitive distortion and social desirability effects. Future studies could enhance objectivity and validity by incorporating third-party data, industry evaluations, or publicly available financial reports for triangulation and cross-verification. Fourthly, although this study categorized enterprises by digital transformation stages, the classification criteria were largely based on subjective judgment and empirical inference, which could result in misclassification. Future research could apply more rigorous methods, such as validated maturity models or quantitative indicators, to assess digitalization levels with greater precision, thereby improving the robustness and credibility of findings. Finally, considering the specificity of digitalization contexts, environmental factors’ influence on enterprise innovation performance should not be underestimated. Investigating how environmental factors affect the processes and effectiveness of enterprise NQPF construction under diverse market conditions will provide critical perspectives for understanding practical applications and challenges of digitalization across varied economic contexts.