How Does the Digital Ecosystem Foster New Quality Productive Forces? A Dynamic QCA of Sustainable Development Pathways in China

Abstract

The digital ecosystem is playing an increasingly pivotal role in shaping the sustainable development of new quality productive forces (NQPFs), enabling cross-regional and cross-temporal optimization of production factors and efficiency enhancement. To uncover the complex mechanisms underpinning this process, this study constructs a “Wuli–Shili–Renli” (WSR) framework to analyze how digital ecosystems drive NQPF development. Using panel data from 30 Chinese provinces (2019–2022), we employed dynamic qualitative comparative analysis (QCA) to explore the causal configurations and temporal–spatial dynamics of this relationship. The key findings include: (1) the development of NQPFs necessitates the interaction among multiple objects, events, and individuals, and no single condition can be considered a necessary condition; (2) there are four distinct configuration pathways for high NQPF development: application-oriented comprehensive WSR-driven, innovation-oriented WR-driven, talent-supported WS-driven, and infrastructure-supported WS-driven; (3) in terms of the temporal dimension, no significant temporal effect is observed; (4) spatial heterogeneity is pronounced, with less developed regions facing constraints in leveraging digital ecosystems for NQPF growth. By disentangling the multidimensional roles of digital ecosystems in promoting sustainable productivity, this research provides empirical evidence and practical strategies for regional policymakers to optimize digital infrastructure, capitalize on local strengths, and bridge development gaps, ultimately fostering inclusive and efficient new quality productive forces aligned with global sustainability goals.

1. Introduction

In the new era of innovation-driven global economic transformation [1], China is actively navigating through a profound structural transformation by cultivating new quality productive forces (NQPFs), a strategic concept enshrined in the Communique of the Third Plenary Session of the 20th Central Committee of the Communist Party of China [2]. NQPFs, characterized by high-tech integration, efficiency, and sustainability, represent a paradigm shift from traditional productivity models, emphasizing technological breakthroughs [3], optimized factor allocation [4], and industrial transformation [5]. At the heart of this transition lies the digital ecosystem—a dynamic nexus of technologies, institutions, and human capital—that acts as a catalyst for reshaping production relationships, resource utilization, and regional economic upgrading [6]. Against this backdrop, the digital ecosystem has emerged as a forward-looking paradigm for redefining productivity, increasingly recognized as a pivotal driver of regional economic transformation and industrial upgrading [7]. It not only embodies an innovative overhaul of socioeconomic operational models, but also serves as a critical lever to reconfigure labor–capital relationships, production tools, and resource allocation mechanisms—collectively reshaping the entire production relationship framework. This systemic transformation injects robust momentum into China’s structural economic transition, aligning with global trends where digitization acts as a catalyst for redefining value chains and institutional architectures.

While scholars worldwide have made significant strides in researching NQPFs, the critical relationship between digital ecosystems and this emerging productivity paradigm remains largely uncharted in contemporary academic discourse. Existing research diverges into three main streams. First, traditional regression analysis has been used to examine single-factor effects on NQPF development, with studies identifying digital economy growth [8] and digital infrastructure improvement [9] as the key drivers. Second, theoretical discourses analyze how digital ecosystem elements influence NQPFs, highlighting digital infrastructure’s role in providing technological support, innovative ecosystems, and resource allocation efficiency [10]. Third, panel data configurational analyses have deepened our understanding of complex causal relationships between digital ecology elements and agricultural NQPF development, revealing that effective synergy among digital ecosystem components is a necessary condition for cultivating advanced agricultural productivity [11].

To summarize, current research on digital ecosystem-enabled NQPFs has three limitations. First, most studies rely on traditional regression analysis, which often overlooks interactions among multiple factors and focuses narrowly on single-factor direct effects, leaving gaps in understanding how digital ecosystem components collectively influence NQPFs. Second, although some studies adopt configurational methods, they either employ oversimplified dichotomous logic or confine analysis to narrow socioeconomic–ecological subsystems, lacking a unified framework to comprehensively capture digital ecosystem antecedents and limit multi-perspective strategy exploration. Third, while dynamic QCA has been applied in agricultural productivity research [12], its scope has been restricted to institutional factors, neglecting NQPFs’ broader conceptual boundaries and limiting cross-cultural generalizability.

The Wuli–Shili–Renli (WSR) systems methodology, designed to address complexity and systemic challenges [13], exhibits inherent compatibility with the digital ecosystem’s multidimensional complexity. This congruence positions WSR as a robust framework for theorizing how digital ecosystems drive NQPF growth. Grounded in this methodology, we developed a configurational model to investigate NQPF-influencing factors within digital ecosystems. Using dynamic QCA on China’s provincial panel data (2019–2022), this study addresses three core questions: (1) Does any core element constitute a necessary condition for enhancing NQPF development? (2) Do temporal effects characterize the influence of mechanisms? (3) What spatial heterogeneities exist in developmental pathways across regions, and what latent factors underlie such disparities?

The study’s key contributions are threefold. First, it pioneers integrating WSR systems thinking into digital ecosystem research, establishing an innovative configurational framework that synthesizes methodology and context. Second, it advances methodological practice by applying dynamic QCA to analyze regional productivity patterns, overcoming conventional QCA’s limitations in processing longitudinal data analysis and enabling deeper exploration of configurational evolution in productivity enhancement. Third, the findings offer novel empirical insights and methodological references for designing context-sensitive regional strategies in the digital era.

2. Literature Review and Theoretical Framework

2.1. Digital Ecosystem and NQPFs

A digital ecosystem is a comprehensive system driven by digital technology [14], which integrates multiple dimensions such as technology, human resources, organization, and culture and effectively enhances economic vitality and productivity [15]. The Digital Ecology Index evaluation system has been constructed, and with the help of dynamic big data, the digital ecological status of 31 provinces in mainland China has been comprehensively evaluated using the dimensions of digital infrastructure, data resources, policy environment, and digital talents [14]. Building on this framework, the study further analyzed the index framework in depth from the perspective of economic input–output, further refined into three major aspects of digital foundation, digital capacity, and digital application [16]. By maximizing the effect of data elements in the digital ecosystem, it aims to promote the transformation and upgrading of traditional industries, stimulate new vitality of economic growth, and accelerate the vigorous development of NQPFs [17].

Moreover, digital ecosystems have nurtured new forms of labor, labor objects, and labor materials [18]. Considering these advancements, prioritizing digital cluster development, promoting inter-regional collaboration, and prioritizing scientific and technological breakthroughs are critical [19]. Specific strategies include accelerating digital infrastructure upgrades, strengthening joint research on core technologies to ensure seamless data element circulation [20], and enhancing the adoption of digital innovation outcomes to drive industrial transformation toward high-end and intelligent sectors [21]. These measures foster synergistic development between digital ecosystems and NQPFs. Under new-era development trends, constructing productive relationships aligned with digital institutions, resources, and capabilities is essential for rapid NQPF advancement [22]. Leveraging digital platforms’ pivotal mediating role in data exchange can accelerate the conversion of data into NQPFs and inject fresh momentum into NQPF development [23]. Given the intrinsic link between the digital economy and NQPFs, proactive technological and institutional innovation, coupled with the cultivation of emerging industries, will drive substantial productivity leaps [24].

Overall, while NQPF research is gaining traction, theoretical discussions on its influencing factors dominate, whereas empirical studies on causal pathways remain scarce—particularly regarding the synergistic effects of multiple antecedents within digital ecosystems. NQPFs exhibit complex systems science characteristics (e.g., nonlinearity, uncertainty), necessitating systematic methodological integration to study their determinants. Accordingly, this paper employs the WSR methodology to synthesize multilevel influencing factors from a digital ecosystem perspective, using dynamic fuzzy-set QCA to achieve a comprehensive understanding of NQPF formation mechanisms.

2.2. WSR Systems Methodology

Wuli (physics), Shili (methodology), Renli (humanity)—the WSR systems methodology was jointly proposed by Chinese scholar Gu [13] and British scholar Zhu. Rooted in the Eastern systems theory while integrating Western systematic approaches, it represents an integrated framework that combines qualitative and quantitative analysis. This methodology emphasizes holistic thinking through the interdependent interactions and constraints among three dimensions: Wuli, Shili, and Renli [25]. It can not only study variable impacts on outcomes but also identify antecedent conditions leading to specific results. For example, Hu et al. [26] constructed a WSR framework to explore drivers of regional entrepreneurship quality, while Zhang et al. [27] utilized its architecture to investigate the antecedents and mechanisms influencing entrepreneurial ecosystem resilience. Crucially, the WSR methodology highlights human agency, stressing that complex system studies must incorporate human initiative as an integral component for structured analysis [13].

The WSR methodology continues to evolve, and its integration into the study of NQPFs represents a mutually beneficial endeavor. Although over a quarter-century has passed since its inception, the methodology requires ongoing refinement to adapt to emerging challenges [28]. Concurrently, as an emerging academic domain, NQPF research gains from this integration: it enriches the WSR methodology’s application portfolio while accessing novel analytical resources. Practices in complex system governance have demonstrated that the Wuli, Shili, and Renli dimensions of the WSR framework do not operate in isolation; instead, they act synergistically to generate collective effects. Configurational analysis serves as an effective tool to clarify how such factors complexly influence outcomes [27].

In summary, this paper employs the WSR methodology as a framework to investigate the multiple factors influencing NQPFs. Given the significant disparities in economic development and resource endowments across Chinese provinces [29], the methodology’s advantages in addressing complex, dynamic issues become particularly pronounced. It enables flexible shifts in analytical focus across the Wuli, Shili, and Renli dimensions according to contextual needs [30]. By adopting a configurational approach, this study systematically examines how antecedent conditions across these three dimensions interactively shape and promote NQPF development.

2.3. Theoretical Framework

NQPFs constitute a complex organic system with specific structures, carriers, and functions [31]. Ensuring collaborative synergy among its elements is critical for development and governance [32]. This involves systemic entities (Wu), operational logics (Shi), and collaborative–competitive dynamics among actors (Ren)—aspects that align with the WSR methodology’s application scope. Building on prior research, this paper analyzes NQPF-influencing factors across the Wuli, Shili, and Renli dimensions.

2.3.1. Wuli (Physics) Dimension

The Wuli dimension focuses on objective, structural elements in systemic evolution [13], with its essence lying in analyzing questions of “what is”. In our digital ecosystem framework, Wuli factors denote preconditions for regional digital development, primarily digital infrastructure and digital security. These form the foundational prerequisites for NQPF growth: infrastructure provides the technological base, while security ensures operational stability.

Digital infrastructure lays a solid foundation for advancing NQPFs by accelerating digital technological innovation, fostering information technology industry growth, and deepening digital industrialization implementation, thereby significantly driving NQPFs’ qualitative leap [33]. This robust foundation not only encompasses hardware facility optimization but also incorporates continuous refinement and iteration of software technologies, providing comprehensive support for innovative activities in NQPFs. Furthermore, digital security mechanisms and governance frameworks for digital industries play a pivotal role in shaping innovation-driven NQPFs [34]. These mechanisms ensure data integrity and confidentiality during innovation processes, thereby creating a robust safeguard for practical applications of emerging technologies [35].

2.3.2. Shili (Methodology) Dimension

The Shili dimension centers on the principles and objective laws derived from practical activities, which provide epistemological and operational guidance for human engagement with the world [36]. Within our digital ecosystem research framework, Shili factors specifically denote operationalizable elements systematically implemented during regional digital ecosystem development. Digital applications, as tangible manifestations of digital advancement, not only embody the realization of digital ecosystem value [16] but also serve as the primary catalyst for propelling NQPF evolution. Therefore, this study focuses on digital applications as the core Shili factor.

Emerging within the digital economy era characterized by ubiquitous technological adoption and escalating application demands [37], NQPFs fundamentally stem from the deepening integration of digital applications. These applications, empowered by advanced data processing capabilities and intelligent decision support systems, drive revolutionary transformations in production paradigms [38]. Crucially, NQPFs represent a quantum leap beyond traditional productive forces, achieved through pervasive penetration and synergistic convergence of digital applications across conventional industries. This convergence accelerates industrial digital transformation, enables intelligent upgrading, spawns emerging industrial forms, and unlocks novel economic growth drivers.

2.3.3. Renli (Humanity) Dimension

The Renli dimension addresses principles of human conduct [39], underscoring the pivotal role of human agency and subjective initiative in practical engagements [40]. Within our analytical framework, Renli factors converge on digital talent and digital innovation—interdependent and mutually reinforcing elements. Digital talent, functioning as proactive participants, strategic decision-makers, and environmental co-creators, constitutes the linchpin of developmental processes [41], while digital innovation persistently acts as the transformative force reshaping operational landscapes [42]. This dynamic synergy collectively propels domain-specific advancement.

Firstly, digital talent universally serves as the critical enabler for NQPF enhancement, forming the foundational pillar for unlocking productivity potential in digital economies [43]. Through mastery of cutting-edge digital technologies and execution of innovative capacities, digital professionals facilitate both the actualization of NQPFs and structural optimization of industrial systems, effectively addressing the growing demand for specialized competencies [44]. Secondly, digital innovation, the emergent driving force of digital economic development, diversifies labor object typologies and configurations, epistemologically marking the transitional phase from conventional to neo-labor paradigms in NQPF evolution [45]. As the core propulsion mechanism, digital innovation orchestrates fundamental reconfiguration of production factors, deep-seated transformation of industrial architectures, and high-quality economic progression [46].

In conclusion, this study constructs the configuration model depicted in Figure 1 based on the WSR methodology, systematically elucidating how five antecedent conditions across the WSR’s tripartite dimensions interactively coalesce to synergistically determine NQPF development trajectories.

3. Methodology

3.1. Research Method

This study adopts the fsQCA method to investigate the causal and intricate mechanisms of the digital ecosystem in relation to NQPF development. The justifications are as follows. First, digital ecosystems are generally complex systems, and NQPF development is the outcome of the joint efforts of multiple entities and elements. It is often challenging to account for the mechanism with a single independent factor. The QCA method, grounded in Boolean algebra and set theory, explores the impact of combinations of multiple condition variables on the outcomes [47]. This is suitable for the complex systems problem in this study. Second, the QCA method is more adaptable to the requirements of study samples. It can be applied not only to small and large samples but also to medium-sized samples ranging from 10 or 15 to 50 cases. This study was carried out in 30 Chinese provinces, and the sample size was in line with the method. Third, since none of the condition variables in this study can be regarded as binary, this paper utilizes the QCA method to deal with the problem of set membership degrees. Finally, the traditional QCA method frequently uses cross-sectional data, which overlook the sequence and dynamics of conditions. Thus, this study employs the dynamic QCA method, proposed by Castro and Ariño [48] and supported by the R language package (RStudio 2024.12.1—563 Windows Version), to explore how various entities and elements in the digital ecosystem influence NQPF development over time.

Nevertheless, the QCA method assesses the necessity of condition variables from a qualitative perspective. To overcome this shortcoming, this paper employs the R programming language (R 4.4.3) to implement the necessary condition analysis (NCA) for exploring the necessary relationships [49]. The NCA method does not merely identify if conditions are necessary; it also gauges the bottleneck levels these conditions must reach to have an impact on the outcome. Consequently, this paper applies the NCA method to explore the necessary relationship between multiple conditions within the digital ecosystem and NQPF development. Subsequently, the fsQCA method is utilized to verify the robustness of the results of the necessity analysis.

Considering the temporal dimension of panel data, this study employs dynamic QCA to reduce the number of condition variables and mitigate the risk of overfitting through aggregating annual data. Furthermore, by integrating a priori screening of the necessary conditions with NCA, it narrows the analytical space of QCA and further enhances the parsimony of the model.

3.2. Research Samples and Data Collection

The present study focuses on the period from 2019 to 2022 and selects 30 provincial-level regions in mainland China as case study samples (excluding Hong Kong, Macao, Taiwan, and Tibet). The data sources are primarily derived from two components. (1) The measurement of the digital ecosystem predominantly references the 2020–2023 Digital Ecosystem Index [16] series reports. These reports provide comprehensive and in-depth assessments of provincial digital ecosystems across China, offering robust data support for this research. (2) The measurement of NQPFs primarily utilizes data from annual editions of China Industrial Statistical Yearbook (https://www.cnki.net/), China Environmental Statistical Yearbook (https://www.stats.gov.cn/?from=screen), China Energy Statistical Yearbook (https://www.stats.gov.cn/?from=screen), and provincial statistical yearbooks. To preserve the integrity of the sample dataset while addressing limited missing values in raw data, missing values were supplemented through linear interpolation.

The selection of 2019–2022 as the study period is strategically significant for three reasons. First, this timeframe captures the accelerated phase of China’s digital infrastructure deployment under the New Infrastructure national strategy. Second, it encompasses the complete pandemic cycle, allowing observation of the digital ecosystem resilience. Third, 2019 marks the institutionalization of provincial digital economy statistical systems in China, ensuring data comparability across regions.

3.3. Measurement and Calibration

3.3.1. Outcome Variable: NQPF Development

This study adopts the methodological framework proposed by Wang et al. [50], analyzing the subject through three dimensions: laborers, labor objects, and production materials. In the laborers dimension, the assessment incorporates the following indicators: average educational attainment, composition of higher education enrollment, human capital structure of the workforce, income per capita, GDP per capita, proportion of tertiary industry employment, and entrepreneurial activity intensity. In the labor objects dimension, evaluation criteria include the share of strategic emerging industries, forest coverage rate, industrial robot density, environmental protection investment intensity, pollutant emission volume, and industrial waste treatment efficiency. In the production materials dimension, measurement encompasses such indicators as the digital infrastructure development index, traditional infrastructure quality, total energy consumption, patent ownership per capita, renewable energy consumption ratio, R&D expenditure intensity, enterprise digitalization level, digital economy maturity.

3.3.2. Condition Variables

The condition variables are presented in

Table 1. Variable measurements and description.

Condition Variable	Measurement
Digital infrastructure	Calculated by averaging the infrastructure, digital resources, and policy environment indicators	Infrastructure: combines the New Infrastructure Competitiveness Index, the Yunqi Index, and the Rural Digital Infrastructure Index using weighted scores
		Digital resources: integrates the Data Circulation Index and the Open Data Index through weighted evaluation
		Policy environment: assessed using the Digital Policy Index
Digital security	Assessed using the Cybersecurity Ecosystem Development Index
Digital application	Calculated by averaging the Digital Government, Digital Economy, and Digital Society indices	Digital government: weighted composite of the Online Government Service Capability, Rural Governance Digitalization, and Smart Environmental Protection indices
		Digital economy: weighted aggregation of the AI Industry Development, Big Data Industry Development, Digital Industry Electricity Consumption, Digital Economy Investor Confidence, Enterprise Digital Transformation, and Rural Economy Digitalization indices
		Digital society: weighted combination of the Digital Life, Digital Inclusive Finance, Social Dispute Search, and Rural Life Digitalization indices
Digital talent	Weighted composite of the Digital Human Capital Index and the AI Developer Index
Digital innovation	Quantified using the Digital Patent Index

. This study employs five condition variables with the following quantification framework: all variables are standardized on a 0–100 scale; higher scores reflect enhanced performance of ecological components. This measurement follows Wang et al. [14].

3.3.3. Calibration

The data for variables must be calibrated to transform regular data into fuzzy-set data, ranging from 0 to 1, before the QCA analysis. The three calibration points of full membership, the crossover point, and full non-membership are 95%, 50%, and 5% [51]. To avoid cases with an affiliation of 0.5 losing their analytical validity, 0.001 was added to them, according to the existing research [47,52]. The calibration results and descriptive statistics are shown in

Table 2. Calibration results and descriptive statistics.

Variables		Calibration			Descriptive Statistics
		Full Membership	Crossover	Full Non-Membership	Mean	Min	Max	SD
Outcome variable	NQPF	0.484	0.217	0.129	0.255	0.120	0.550	0.108
Condition variables	Digital infrastructure	83.269	34.606	14.734	38.706	10.470	87.190	21.141
	Digital security	57.084	21.921	12.078	26.628	10.000	100.000	17.512
	Digital application	76.617	38.164	19.573	42.595	14.980	92.480	17.881
	Digital talent	85.253	29.386	11.706	35.974	10.000	100.000	22.241
	Digital innovation	70.784	17.999	10.577	24.521	10.000	100.000	19.612

.

4. Results

4.1. Necessity Analysis of Single Conditions

4.1.1. NCA Results

In this research, ceiling regression (CR) and ceiling envelopment (CE) were employed to develop functions. These functions were then analyzed to derive the effect size and the results of the Monte Carlo simulation replacement test, as presented in

Table 3. NCA results.

Condition Variable	Method	Accuracy	Effect Size (d)	p-Value 1
Digital infrastructure	CR	95.6%	0.021	0.001
	CE	100%	0.025	0.000
Digital security	CR	100%	0.007	0.097
	CE	100%	0.013	0.037
Digital application	CR	95.6%	0.031	0.000
	CE	100%	0.024	0.001
Digital talent	CR	91.1%	0.122	0.000
	CE	100%	0.024	0.006
Digital innovation	CR	100%	0.008	0.028
	CE	100%	0.017	0.007

Note: ¹ p-value: permutation test, number of resamplings = 10,000.

. According to the criterion, if the effect size (d) exceeds 0.1 and the p-value is significant, it can be affirmed that the condition variable is a necessary condition for the outcome variable [53]. The findings indicate that, except for digital talent, the effect sizes (d) of all the other condition variables are less than 0.1. As a result, these variables do not form a necessary condition for high NQPF development.

As shown in Figure 2, plotting the X–Y scatter plot of digital talent and the outcome variable reveals that nearly one-third of the case points lie above the diagonal. This indicates that the condition variable failed to pass the necessity test and does not constitute a necessary condition for NQPF development [51].

The bottleneck level within the framework of NCA was analyzed, and the resulting heatmap is depicted in Figure 3. The bottleneck level is visually conveyed through a color scale. Specifically, when the color is closer to yellow, the value is lower; when it is closer to green, the value is higher; and when the color is white, there is no bottleneck level for that particular condition.

The analysis of the bottleneck levels revealed the following requirements for high NQPF development. To achieve 20% high NQPF development, 1.0% of digital talent is required. For 90% high NQPF development, 5.0% digital application and 2.0% digital talent are needed. When aiming for 100% high NQPF development, it necessitates 64.3% digital infrastructure, 65% digital security, 95% digital application, 55% digital talent, and 84.4% digital innovation.

4.1.2. QCA Results

In this paper, the necessity analysis of QCA was utilized to conduct a robustness test on the necessary results. The determination criterion was set as follows: when the consistency level is over 0.9, it implies that the condition serves as a necessary condition for the occurrence of the outcome [52].

Table 4. Analysis of necessary conditions.

Condition Variable	High NQPF Development				Non-High NQPF Development
	Aggregated Consistency	Aggregated Coverage	Inter-Group Consistency Distance	Intra-Group Consistency Distance	Aggregated Consistency	Aggregated Coverage	Inter-Group Consistency Distance	Intra-Group Consistency Distance
Digital infrastructure	0.791	0.854	0.099	0.362	0.461	0.522	0.241	0.506
~Digital infrastructure	0.557	0.496	0.068	0.500	0.871	0.814	0.104	0.219
Digital security	0.817	0.877	0.093	0.282	0.505	0.569	0.277	0.581
~Digital security	0.599	0.536	0.041	0.443	0.891	0.836	0.074	0.155
Digital application	0.854	0.853	0.085	0.299	0.511	0.536	0.233	0.489
~Digital application	0.535	0.511	0.131	0.518	0.860	0.861	0.110	0.230
Digital talent	0.818	0.859	0.159	0.305	0.507	0.559	0.162	0.339
~Digital talent	0.579	0.529	0.214	0.472	0.872	0.834	0.148	0.311
Digital innovation	0.788	0.902	0.107	0.316	0.462	0.555	0.457	0.960
~Digital innovation	0.611	0.520	0.055	0.437	0.918	0.819	0.104	0.219

presents the necessity of five antecedent variables. The analyses of necessary conditions for high NQPF development disclose that the consistency of each condition is under 0.9. Regarding non-high NQPF development, apart from low-level digital innovation (where the consistency > 0.9 and the coverage > 0.5), all the other conditions do not reach the 0.9 consistency threshold.

As depicted in Figure 4, when plotting the X–Y scatter plot of low-level digital innovation and the outcome variable, it was observed that nearly one-third of the case points were located above the diagonal, with the majority concentrated near the right Y-axis. This suggests that the condition variable failed the necessity test and does not form a necessary condition for non-high NQPF development [51].

In addition, in dynamic QCA, the reliability of aggregated consistency levels is evaluated by means of inter-group consistency distance. When the inter-group consistency distance is less than 0.2, it indicates that the aggregated consistency remains relatively stable, thereby demonstrating the precision of measurement results [54]. Causal combination analysis with potential inter-group effects is presented in

Table 5. Inter-group data with consistency distance greater than 0.2.

Cause-and-Effect Combinations			Year
			2019	2020	2021	2022
Situation 1	Digital infrastructure and non-high NQPF development	Inter-group consistency	0.554	0.494	0.331	0.483
		Inter-group coverage	0.542	0.509	0.491	0.539
Situation 2	Digital security and non-high NQPF development	Inter-group consistency	0.411	0.680	0.464	0.478
		Inter-group coverage	0.498	0.612	0.583	0.570
Situation 3	Digital application and non-high NQPF development	Inter-group consistency	0.666	0.482	0.455	0.458
		Inter-group coverage	0.567	0.531	0.525	0.515
Situation 4	~Digital talent and high NQPF development	Inter-group consistency	0.676	0.629	0.549	0.441
		Inter-group coverage	0.569	0.583	0.460	0.484
Situation 5	Digital innovation and non-high NQPF development	Inter-group consistency	0.738	0.398	0.370	0.367
		Inter-group coverage	0.646	0.500	0.523	0.511

. Given that the inter-group consistency of the following variables exceeds 0.2, further overall consistency tests were conducted for these variables. Table 5 shows that although their inter-group consistency distances exceed 0.2, the annual inter-group consistency from 2019 to 2022 remained below 0.9. This implies that these variables failed to meet the requirement of being necessary conditions for the outcome variable each year.

The results show that a single condition variable, similar to NCA results, cannot lead to high NQPF development.

4.2. Sufficiency Analysis of Conditional Groupings

The goal of group QCA is to identify the sets of antecedent conditions that lead to the existence of results, that is, which combinations are sufficient conditions that can lead to high or non-high NQPF development. When performing configuration analysis, thresholds are set to distinguish whether the configuration meets conformance requirements. Referring to the setting criteria of most of the existing literature [55], the study followed the recommendations of Du et al. [56] by setting the raw consistency threshold at 0.8 and the case frequency threshold at 1. Raw consistency score ≥ 0.8 indicates that over 80% of cases within the configuration support the causal relationship “condition combination → high NQPF”, aligning with QCA’s rigorous definition of “sufficient conditions”. To avoid potential subset relationships between solutions and their negations in both outcome and non-outcome conditions, the PRI (proportional reduction in inconsistency) consistency threshold was maintained above the recommended minimum of 0.5, with this research adopting a threshold of 0.6 [57]. The PRI threshold ≥ 0.5 indicates that this criterion filters out “non-specific” configurations (i.e., combinations that show no significant difference between outcome presence and absence). Drawing on the analytical framework for complex systems proposed by Du et al. [56], it guarantees solution specificity. The analysis identified four distinct configurations associated with high NQPF development, encompassing 120 case observations. Detailed configuration analysis results are presented in

Table 6. Configuration of high NQPF development.

Condition Variable	High NQPF Development
	S1	S2	S3	S4
Digital infrastructure	⏺	⏺		•
Digital security		•	⏺	⏺
Digital application	⏺		⏺	⏺
Digital talent	⏺	⏺	•
Digital innovation		⏺	•	•
Consistency	0.946	0.976	0.964	0.978
Raw coverage	0.705	0.643	0.677	0.655
Unique coverage	0.067	0.005	0.040	0.018
Inter-group consistency distance	0.047	0.027	0.038	0.027
Intra-group consistency distance	0.138	0.098	0.109	0.092
Overall coverage	0.767
Overall consistency	0.938

Note: ⏺ = core causal condition (present); • = peripheral condition (present).

.

4.2.1. Aggregated Results

There are four configurations to produce high NQPF development, with an overall consistency of 0.938 and an overall coverage of 0.767, as shown in Table 5. Furthermore, all individual configurations demonstrated consistency-adjusted distances below the threshold of 0.2 for both inter-group and intra-group comparisons. This empirical evidence substantially corroborates the high precision of aggregate consistency measures and reinforces the validity of the analytical framework employed in this study [58].

(1)

Application-Oriented Comprehensive WSR-Driven Configuration

Configuration S1 illustrates that high NQPF development can be achieved through a synergistic framework prioritizing three core dimensions: digital infrastructure, digital application ecosystems, and digital talent cultivation. This pathway accounts for 70.5% of the case coverage, with a unique coverage of 6.7% after accounting for overlapping configurations. The case of Shanghai Municipality (from 2019 to 2022) exemplifies this configuration. Local legislative initiatives, including the Provisional Measures for Public Data Openness and Shanghai Data Regulations, established institutional safeguards for data market development. Operational deployment of an AI public computing service platform provided robust computational support and data acquisition infrastructure. Concurrently, the phased optimization of the One-Network Unified Governance system accelerated deep integration of digital technologies with real economy sectors, catalyzing emerging industries and innovative business models. Strategic talent initiatives—encompassing identification, training, and dissemination of digital transformation expertise—further solidified Shanghai’s position as a national hub for cultivating research-oriented digital professionals, thereby generating sustained momentum for NQPFs.

(2)

Innovation-Oriented WR-Driven Configuration

The analytical results reveal that Configuration S2 achieves high NQPF development through a quadripartite framework integrating digital infrastructure, innovation ecosystems, and talent cultivation, augmented by cybersecurity reinforcement. This pathway accounts for 64.3% of case coverage, with a unique explanatory coverage of 0.5% after controlling for configuration overlaps. The Shandong Province case (from 2019 to 2020) exemplifies this model. Accelerated deployment of intelligent infrastructure significantly enhanced the digital integration level of converged infrastructure systems. A hierarchical cybersecurity governance architecture spanning provincial–municipal–county administrations ensured rigorous implementation of security protocols and technological safeguards. Notably, the establishment of cross-jurisdictional cybersecurity liaison networks and multiple technical assurance platforms strengthened systemic risk mitigation capacities. Policy innovations proved instrumental, particularly the 2019 Digital Shandong Implementation Plan and Guidelines for 5G Industry Development, which strategically prioritized new infrastructure initiatives. Institutionally, the province pioneered a professional certification system for cybersecurity engineers. Targeted talent development was achieved through national cybersecurity awareness campaigns and competitive platforms such as the Mount Tai Cup Cybersecurity Challenge and Intercollegiate Cybersecurity Skills Competitions, effectively identifying and cultivating technical expertise. These coordinated measures collectively established Shandong as a frontier in digital economy transformation through secure technological empowerment.

(3)

Talent-Supported WS-Driven Configuration

Empirical evidence indicates that Configuration S3 achieves high NQPF development through a dual-core framework prioritizing cybersecurity governance and digital application ecosystems, synergistically reinforced by innovation acceleration and talent cultivation. This pathway demonstrates 67.7% empirical coverage across case samples, with a unique explanatory contribution of 4% after controlling for inter-configurational overlaps. The Jiangsu Province case (from 2020 to 2022) exemplifies this model. Strategic implementation of national legislative mandates, including the Data Security Law and the Personal Information Protection Law, was operationalized through 23 provincial-level regulatory measures and compliance monitoring systems. Notably, the 2022 National Finals of the Digital Economy Innovation Competition attracted 458 emerging technology projects, catalyzing a 28% year-on-year increase in provincial tech startup registrations. Concurrently, Jiangsu pioneered cross-regional digital currency innovations through the Yangtze River Delta Integration Demonstration Zone. The 2022 digital RMB pilot program established 12 scenario-specific applications spanning cross-border trade, smart logistics, and green finance, supported by interprovincial task force coordination. Institutional breakthroughs emerged in human capital development: the provincial Digital Skills Enhancement Initiative institutionalized a tiered certification system for “digital craftsmen”, integrating technical and vocational advancement pathways. This dual-track approach yielded a 41% growth in certified hybrid talent (technical professional) within two years, directly supporting advanced manufacturing digitization.

(4)

Infrastructure-Supported WS-Driven Configuration

Configuration S4 indicates that with digital security and digital applications as the core conditions, supplemented by digital innovation and digital infrastructure, high NQPF development can be generated. This path covers 65.5% of the sample cases, and after excluding the overlapping parts with other configurations, the coverage rate is 1.8%. A typical case is Anhui Province (from 2019 to 2020). By 2020, Anhui Province had successfully established a provincial-level government cloud and those of its 16 subordinate municipal levels. Up to this point, the e-government extranet system that comprehensively covers the provincial, municipal, county, township, and village levels had been basically established, and the intensive benefits of government cloud construction had been initially demonstrated, providing a data element foundation for the development of new and high NQPF development. Departments such as the Market Supervision Administration of Anhui Province have convened several meetings to emphasize the importance of network security work, which has enhanced the awareness of prevention, safeguarded the “main position” of ideology, and maintained political security in the online space. Disciplinary and professional competitions, such as the Digital Media Innovation Design Competition for College Students, have promoted the innovative application of digital technologies in fields such as cultural and creative industries and contributed to the improvement of digital innovation capabilities of college students. The provincial government has continuously advanced its integrated government service capabilities and has ranked among the leading national positions for four consecutive years.

4.2.2. Inter-Group Results

Although traditional fuzzy-set qualitative comparative analysis (fsQCA) does not directly handle time series, dynamic QCA indirectly captures temporal dynamics by comparing solution structures across different years to identify stable causal configurations. To thoroughly examine whether temporal effects exist across the configuration pathways, this study constructed inter-group consistency trend diagrams (Figure 5). Analytical results demonstrate that all four configurations maintained consistency distances below the 0.1 threshold throughout the observation period. Notably, during 2019–2022, their consistency values consistently exceeded the predefined 0.75 benchmark. These findings empirically confirm the absence of significant temporal effects in any configuration.

During the data preprocessing stage, a test for temporal autocorrelation was conducted on the data panel (Durbin–Watson statistic = 2.178, close to 2, indicating no significant autocorrelation). As a result, lagged variables were not introduced in the QCA. If future research identifies significant autocorrelation, the model could be extended by incorporating first-order lagged condition variables.

The Wooldridge test was then applied to the model residuals to detect first-order within-group autocorrelation (

Table 7. Wooldridge test results.

Variable	Coefficient	Std. Error	t-Statistic	p-Value	95% Confidence Interval
					Lower bound	Upper bound
const	−0.0019	0.002	−0.830	0.409	−0.006	0.003
resid_lag	0.0998	0.104	0.959	0.340	−0.107	0.307

). This analysis was conducted using IBM SPSS Statistics 26. The test results showed that the coefficient of the lagged residual term was 0.0998 (t = 0.959, p = 0.340). Since the p-value exceeded 0.05, we could not reject the null hypothesis of no first-order within-group autocorrelation, suggesting that the panel data exhibited no significant first-order serial correlation under the current model specification. Additionally, an autocorrelation plot of the residuals (Figure 6) was generated to visually examine autocorrelation at different lag orders.

4.2.3. Intra-Group Results

To further investigate the spatial effects of high green development configurations, this study discusses the within-consistency adjusted distance. The results of the within-consistency adjusted distance are shown in Table 6. Within-consistency adjusted distances for configurations S1 and S3 are 0.138 and 0.109, respectively, higher than the threshold value of 0.1, and thus may have a significant spatial effect. QCA has not yet developed a measure specifically for regional differences in the groupings. Therefore, we used the KW test to explore whether there are significant differences in the coverage distribution of each path in the eastern, central, and western regions. This analysis was conducted using IBM SPSS Statistics 26. The results of the Kruskal–Wallis H test are shown in

Table 8. Kruskal–Wallis test results.

Configuration	SD	Statistics	p	Cohen’s f
Configuration S1	0.250	7.178	0.028 **	0.575
Configuration S2	0.266	8.426	0.015 **	0.640
Configuration S3	0.255	8.062	0.018 **	0.620
Configuration S4	0.260	8.868	0.012 **	0.664

Note: ** p < 0.05.

. The results show that there are significant regional differences (p < 0.05) in configurations, so further analysis of the pairwise comparisons is necessary.

To further explore potential regional influences on configurational effectiveness, this study analyzed intra-group consistency trends (Figure 7). The findings reveal notable regional differences in configuration performance, with some areas showing consistency levels below the 0.75 benchmark. A case in point is Guizhou Province, where Configurations S2, S3, and S4 all achieved high consistency scores of 0.817, while S1 underperformed at 0.515. This pattern suggests that while multiple configurational pathways can lead to high NQPF development, their effectiveness varies significantly by regional context. The divergence in performance highlights the importance of considering local conditions when implementing digital transformation strategies, as certain configurations may be more suitable for specific regional characteristics than others. [59] These insights provide valuable guidance for policymakers seeking to optimize productivity outcomes through tailored approaches.

As shown in

Table 9. Mean regional coverage.

Regional	S1	S2	S3	S4
Eastern China	0.774	0.745	0.705	0.734
Central China	0.648	0.639	0.593	0.618
Western China	0.477	0.400	0.387	0.387

, all the configurations demonstrate their highest coverage in eastern regions, reflecting the relatively weaker digital ecosystem in central and western areas compared to the east. However, while the conditional configurations are predominantly distributed in eastern regions, economically developed central and western provinces such as Hubei and Chongqing also exhibit relatively high coverage. This pattern indicates that through targeted development of their digital ecosystems, these regions can achieve—and in some cases surpass—eastern regions in terms of NQPF development.

The findings suggest that while regional disparities in digital infrastructure exist, strategic investments and policy support can enable central and western regions to overcome initial disadvantages and compete at the highest levels of productivity development. This underscores the importance of region-specific digital transformation strategies that account for local conditions and leverage unique competitive advantages.

4.2.4. Configuration of Non-High NQPF Development

As presented in

Table 10. Configuration of non-high NQPF development.

Condition Variable	Non-High NQPF Development
	NS1	NS2	NS3	NS4	NS5	NS6
Digital infrastructure		⊗	⊗	•	⊗	⊗
Digital security				᳁	᳁	᳁
Digital application	⊗	⊗		⊗	⊗	•
Digital talent	᳁	᳁	᳁
Digital innovation	⊗		⊗	⊗	•	⊗
Consistency	0.904	0.900	0.899	0.932	0.967	0.960
Raw coverage	0.778	0.786	0.751	0.401	0.447	0.386
Unique coverage	0.016	0.030	0.011	0.009	0.006	0.008
Inter-group consistency distance	0.055	0.041	0.052	0.036	0.027	0.019
Intra-group consistency distance	0.144	0.144	0.144	0.127	0.081	0.098
Overall coverage	0.882
Overall consistency	0.871

Note: • = peripheral condition (present); ⊗ = core causal condition (absent); ᳁ = peripheral condition (absent).

, six distinct configuration paths were identified as failing to achieve high NQPF development. Configurations NS1, NS2, and NS3 demonstrate that regions struggle to develop advanced productive forces when all ecosystem elements underperform. Meanwhile, Configurations NS4, NS5, and NS6 reveal that even with strong individual components, such as robust digital infrastructure, abundant digital talent, or vibrant digital innovation, the absence of complementary ecosystem factors still results in suboptimal productivity outcomes.

4.3. Robust Analysis

To ensure the reliability of our findings, this study implemented rigorous robustness checks based on established methodological principles in qualitative comparative analysis. Following Schneider and Wagemann’s [51] theoretical framework, which posits that research results demonstrating stable subset relationships across different consistency thresholds can be considered robust, we conducted comprehensive sensitivity analyses.

To test the robustness of the results, the original consistency threshold was raised from 0.8 to 0.85, and the results of the configurations remained essentially unchanged (

Table 11. Raising the original consistency threshold from 0.80 to 0.85.

Condition Variable	High NQPF Development Original Consistency Threshold = 0.80				High NQPF Development Original Consistency Threshold = 0.85
	S1	S2	S3	S4	S1	S2	S3	S4
Digital infrastructure	⏺	⏺		•	⏺	⏺		•
Digital security		•	⏺	⏺		•	⏺	⏺
Digital application	⏺		⏺	⏺	⏺		⏺	⏺
Digital talent	⏺	⏺	•		⏺	⏺	•
Digital innovation		⏺	•	•		⏺	•	•
Consistency	0.946	0.976	0.964	0.978	0.946	0.976	0.964	0.978
Raw coverage	0.705	0.643	0.677	0.655	0.705	0.643	0.677	0.655
Unique coverage	0.067	0.005	0.040	0.018	0.067	0.005	0.040	0.018
Inter-group consistency distance	0.047	0.027	0.038	0.027	0.047	0.027	0.038	0.027
Intra-group consistency distance	0.138	0.098	0.109	0.092	0.138	0.098	0.109	0.092
Overall coverage	0.767				0.767
Overall consistency	0.938				0.938

Note: ⏺ = core causal condition (present); • = peripheral condition (present).

); the PRI threshold was raised from 0.6 to 0.65, and the results of the configurations remained essentially unchanged (

Table 12. Raising the PRI threshold from 0.60 to 0.65.

Condition Variable	High NQPF Development PRI Threshold = 0.60				High NQPF Development PRI Threshold = 0.65
	S1	S2	S3	S4	S1	S2	S3	S4
Digital infrastructure	⏺	⏺		•	⏺	⏺		•
Digital security		•	⏺	⏺		•	⏺	⏺
Digital application	⏺		⏺	⏺	⏺		⏺	⏺
Digital talent	⏺	⏺	•		⏺	⏺	•
Digital innovation		⏺	•	•		⏺	•	•
Consistency	0.946	0.976	0.964	0.978	0.946	0.976	0.964	0.978
Raw coverage	0.705	0.643	0.677	0.655	0.705	0.643	0.677	0.655
Unique coverage	0.067	0.005	0.040	0.018	0.067	0.005	0.040	0.018
Inter-group consistency distance	0.047	0.027	0.038	0.027	0.047	0.027	0.038	0.027
Intra-group consistency distance	0.138	0.098	0.109	0.092	0.138	0.098	0.109	0.092
Overall coverage	0.767				0.767
Overall consistency	0.938				0.938

Note: ⏺ = core causal condition (present); • = peripheral condition (present).

). Therefore, the results of this study are robust.

5. Conclusions and Implications

5.1. Conclusions

This study employs dynamic QCA methodology to analyze panel data from 30 Chinese provinces between 2019 and 2022. Guided by the WSR (Wuli–Shili–Renli) theoretical framework, we systematically investigate how five variables across the physics, methodology, and humanity dimensions interact synergistically to drive the development of NQPFs. In response to the three core research questions raised in the Introduction, this study provides the following clear answers:

• Does any core element constitute a necessary condition for enhancing NQPF development? Necessity analysis via NCA and QCA confirms that none of the five conditions—digital infrastructure, digital security, digital application, digital talent, and digital innovation—serves as a single necessary condition.
• Do temporal effects characterize the influence of mechanisms? Overall consistency did not exhibit a significant temporal effect. This indicates that the mechanism through which digital ecosystems drive NQPFs exhibits cross-period stability, unaffected by short-term policy shocks or technological disruptions.
• What spatial heterogeneities exist in developmental pathways across regions, and what latent factors underlie such disparities? Kruskal–Wallis H tests (Table 8) demonstrate pronounced regional disparities. Intra-group consistency trends (Figure 6) reveal notable regional differences in configuration performance. All the configurations demonstrate their highest coverage in eastern regions (Table 9). These differences correlate with regional variations in digital infrastructure maturity, innovation ecosystem completeness, and talent agglomeration, underscoring the need for context-specific strategies.

Four distinct configurational pathways for high NQPF development were identified, each reflecting unique combinations of WSR dimensions and conditional synergies:

• Application-oriented comprehensive WSR-driven configuration (S1). This pathway centers on the triad of digital infrastructure (Wuli), digital applications (Shili), and digital talent (Renli), where technological adoption and human capital act as joint drivers. By integrating foundational infrastructure (e.g., Shanghai’s AI computing platforms) with user-centric applications (e.g., One-Network Governance systems) and skilled labor, it transforms technical potential into tangible productivity gains. Unlike single-factor studies, this configuration demonstrates that infrastructure alone cannot drive NQPFs; its impact is amplified through seamless alignment with talent capabilities and real-world application scenarios.
• Innovation-oriented WR-driven configuration (S2). Anchored in digital infrastructure (Wuli), digital innovation (Renli), and digital talent (Renli), with digital security (Wuli) as a critical enabler, this pathway prioritizes risk-managed innovation. Such regions as Shandong leverage policy-supported infrastructure upgrades (e.g., 5G networks) alongside cybersecurity frameworks to foster an ecosystem where talent-driven creativity (e.g., Mount Tai Cup competitions) and secure data flows coexist. Here, digital security is not a standalone requirement but a complementary condition that unlocks innovation’s full potential, illustrating the interdependence of the Wuli and Renli dimensions.
• Talent-supported WS-driven configuration (S3). This pathway centers on a Wuli (security)–Shili (application) core engine, complemented by peripheral Renli (talent/innovation) to form a closed “security–compliance–application–adoption–talent–adaptation” loop. For instance, Jiangsu ensures secure data environments through regulatory measures, activates cross-border trade and green finance applications via digital RMB pilots, and cultivates industry-compatible talent through the Digital Craftsman initiative. Unlike comprehensive WSR paths requiring balanced development across all dimensions, S3 demonstrates that moderate Renli endowments (talent/innovation) can catalytically amplify a strong security–application core, making it viable for regions with medium talent reserves to achieve productivity gains through scenario-driven innovation and regulatory compliance.
• Infrastructure-supported WS-driven configuration (S4). This pathway relies on a Wuli (security)–Shili (application) core, supported by peripheral Wuli (infrastructure) and Renli (innovation) to create an “infrastructure–foundation–security–escort–application–penetration” progression. For example, Anhui strengthens the foundation of digital infrastructure (consolidates digital infrastructure) through province-wide government cloud platforms, advances One-Network Governance applications under secure network environments, and stimulates digital technology adoption in cultural industries via regional innovation competitions. Unlike S3’s talent-focused supplementation, S4 leverages infrastructure scale effects, making it suitable for regions with nascent infrastructure but moderate talent resources to enhance productivity through secure, application-driven incremental improvements.

These pathways collectively demonstrate that high NQPF development arises from configurational equivalence—different combinations of the WSR dimensions can achieve equivalent outcomes based on regional endowments. While prior studies often isolated the roles of infrastructure, talent, or innovation, this research reveals their synergistic logic.

5.2. Implications

Based on the above research conclusions, this paper proposes the following practical implications:

• For regions characterized by the application-oriented comprehensive WSR-driven configuration (S1) (e.g., Shanghai, Guangdong), efforts should focus on deepening the integration of digital application scenarios and talent cultivation. Building industry-level digital platforms (e.g., industrial Internet, smart city hubs) can accelerate technology translation, while refining “data elements × talent” collaboration mechanisms creates a closed-loop ecosystem of “infrastructure foundation–application implementation–talent value” conversion.
• Regions with potential for the innovation-oriented comprehensive WSR-driven configuration (S2) (e.g., Shandong, Zhejiang) need to synchronize upgrades to digital innovation ecosystems and security systems. Policymakers should adopt a portfolio of “innovation tolerance–security sandbox–achievement transformation”, strengthening R&D in frontier technologies such as blockchain and AI while constructing a hierarchical digital security governance framework to balance innovative vitality and systemic stability.
• Areas at the talent-assisted WS-driven configuration stage (S3) (e.g., Jiangsu, Hubei) must prioritize digital talent agglomeration, complemented by policies enabling secure data circulation and application empowerment. Initiatives such as establishing digital economy industrial parks and talent-specific funds can attract “technology management” composite talents, while breaking down institutional barriers to data sharing unleashes the multiplicative effect of data elements on productivity.
• Midwestern regions relying on the infrastructure-assisted WS-driven configuration (S4) (e.g., Anhui, Chongqing) should first consolidate their digital infrastructure foundation, focusing on new infrastructure such as 5G base stations and computing power centers. Simultaneously advancing digital security compliance systems and localized application innovation creates a pathway of “infrastructure gap-filling–security capacity-building–application scenario incubation” for leapfrog NQPF development.

While this study focuses on the Chinese context, its conclusions engage in a dialogue with international experiences. For example, Silicon Valley’s digital ecosystem in the United States is characterized by the concentration of innovative talent + active venture capital + rapid technological transformation, sharing similar logic with the innovation-oriented path (S2) identified in this study. However, it faces challenges such as insufficient participation of small and medium-sized enterprises (SMEs) due to data monopolies. The European Union’s “digital sovereignty” model, constructed through such initiatives as the Digital Compass, emphasizes cross-border data flow and sustainable development—similar to China’s focus on regional collaboration in the East Data, West Computing project. Yet, the digital divide among the EU member states (e.g., lagging infrastructure in Central and Eastern Europe) constrains overall effectiveness. South Korea’s government-led infrastructure investment under the Digital New Deal (e.g., world-leading 5G base station density) aligns with this study’s infrastructure-supported path (S4), but its talent structure—skewed toward technical R&D—lacks digital–industrial hybrid professionals, leading to lower-than-expected efficiency in transforming innovation into practice.

In contrast, China’s digital ecosystem uniquely combines government coordination and market dynamism: it integrates top-level design such as New Infrastructure with grassroots innovation from internet enterprises (e.g., Shanghai’s One Network, One Platform governance system). Future research could further compare the evolutionary paths of digital ecosystems under different institutional environments to provide a more universal theoretical framework for global sustainable productivity transformation.

5.3. Limitations and Prospects

This study has the following limitations. On the one hand, the QCA methodology focuses on qualitative analysis. As data acquisition becomes easier and relevant indicators become more comprehensive in the future, it is necessary to integrate QCA with other quantitative research methods to conduct in-depth investigations and further explore the various antecedent conditions influencing the level of NQPF development. On the other hand, limited by the number of cases and the level of detail, this paper identifies five key influencing factors based on the WSR methodology. Future research could consider expanding the condition variables or integrating with the technology–organization–environment (TOE) framework to explore the influencing factors of NQPF development from different perspectives, thereby enabling a more comprehensive analysis of the potential interactive relationships among various conditions.