How Innovation Ecosystems Drive A Resilient Economy: A Panel Data Fuzzy-Set QCA Approach

2.1 Innovation Ecosystem Components

In the context of an innovation ecosystem, symbiosis describes how a network of different actors share mutual innovation resources and promote co-value activities to improve their innovation capabilities and achieve profitability (Lombardi & Laybourn, 2012). Drawing inspiration from biological systems, the ecosystem represents a network of actors that “co-evolve their capabilities and roles and tend to align with directions set by one or more central companies” (McIntyre & Srinivasan, 2017). An innovation ecosystem emphasizes the importance of embedded and symbiotic innovation collaborations as central to value creation. (Adner, 2017; Pushpananthan & Elmquist, 2022). It comprises two distinct sectors: the market-driven commercial economy and the knowledge economy driven by foundational research (Klimas & Czakon, 2022). The actors in this binary framework typically include firms, universities, and research institutions.

However, this structure falls short in addressing the role of innovation resources (Sirmon et al., 2011) and the environment (García-Pozo et al., 2018). In addition, scholars have recognized the barriers between universities and industry (U-I) in terms of cultural, institutional, regulatory, and geographical differences (O'Dwyer et al., 2023; Villani et al., 2017). Pushpananthan and Elmquist (2022) proposed that an innovation ecosystem's interactions are usually centered around a technology platform. Hence, we incorporate “innovation platforms” into the three-layered structure to remove obstacles between the U-I actors. The most common forms of innovation platforms are technology business incubators (TBIs), university research parks, and mass maker spaces.

Due to the rising costs of R&D, individual firms increasingly struggle to sustain their creativity and innovation in isolation. Innovation networks have increasingly garnered interest from scholars, practitioners, and policymakers alike (Freytag & Young, 2014; Goerzen, 2007). Collaborative innovation networks refer to a firm's interactions with a range of partners, including universities and research institutions (Najafi-Tavani et al., 2018). The term reflects the essence of innovation ecosystems: the interactions and linkages among various actors (Arenal et al., 2020). Therefore, the symbiotic structure must be extended to include innovation networks, forming a comprehensive structure consisting of innovation actors, platforms, networks, resources, and the environment.

2.2 Economic Resilience

The recent surge in unpredictable shocks and acute crises has prompted increasing scholarly interest in economic resilience (Trippl et al., 2022). Economic resilience, within this context, denotes the capacity of a regional economy to resist, or recover from shocks (Martin & Gardiner, 2019; Martin & Sunley, 2015). More specifically, economic resilience is a complex and multidimensional performance measure of the regional economy that includes resistance (the degree of sensitivity of reaction), recovery (the speed of return to pre-shock levels), re-orientation (the extent to adapt to the economy), and renewal (the extent to renew its growth path) (Martin & Sunley, 2015).

The varying rates of recovery of regional economies from uncertain and unexpected events (e.g., financial crisis, the COVID-19 pandemic) have raised interesting questions about the factors influencing economic resilience (Bamweyana et al., 2020; Bristow & Healy, 2014). Since the publication of Schumpeter (1942), innovation has been considered a critical factor in facilitating economies to adapt industrial and technological structures, thereby aiding their recovery from recessions (Bristow & Healy, 2014; Wolfe & Gertler, 2016).

Despite the expanding body of literature on economic resilience, there is no generally accepted method of how it should be measured (Martin & Gardiner, 2019). In this paper, we construct a composite index from the perspectives of resistance, recovery, re-orientation, and renewal.

2.3 Theoretical Framework

The effects of symbiotic innovation components on economic resilience are interacted and interconnected.

At the core of innovation ecosystems are the innovation actors, which include industry, firms, and universities (Jacobides et al., 2018). These diverse, cross-sectoral actors collaborate to create and capture value from cooperative activities (Florin & Schmidt, 2011). Despite potential divergences and conflicts in their economic and social goals (Oskam et al., 2021), such tensions can often be resolved through mechanisms, such as shared knowledge and open communication mechanisms (Ritala et al., 2013). To this end, a shared value proposition enables collaborative actors to resist and recover from economic fluctuations.

Innovation platforms (e.g., productivity promotion centers, university science parks, and TBIs) are designed to overcome the conflicting U-I goals (Alexander & Martin, 2013). These platforms are established to reduce geographical, social, cognitive, and organizational distances among U-I actors (Villani et al., 2017). Serving as intermediaries, they mitigate innovation risk costs, facilitate knowledge and technology transfer, and enhance economic recovery capabilities.

Innovation networks, which are similar to the industry-university-research collaborative models (Etzkowitz & Leydesdorff, 2000), emphasize cooperation and collaboration among innovation actors and platforms (Rouyre et al., 2024). Thus, technology emerges as a necessary prerequisite for regional economic development, but it is only through innovation networks that technology is transformed into the firms’ competitiveness, thereby contributing to economic prosperity (Rutten & Boekema, 2007). Moreover, organizational learning (Argyris & Schön, 1978) can improve innovation efficiency and effectiveness through cross-regional and cross-organizational collaboration networks, optimizing the orchestration of innovation resources and strengthening economic resilience to recover from shocks.

Innovation resources are heterogeneous and mainly include human, financial, and technical resources (Sirmon et al., 2011). The resource-based view states that innovation resources are the basis for innovation actors to generate high-quality performance and achieve sustainable growth (Barney, 1991). Effective resource allocation and orchestration are crucial to maximizing the role of innovation resources. The emergence of an innovation ecosystem enables the integration of scattered resources into a unified whole, optimizing the allocation of various resources. Heterogeneous and integrated innovation resources can buffer economic shocks, enhance reorganization capabilities, and prevent economic collapse.

The innovation environment, encompassing openness, culture, education, digital finance, and the business environment, has an indirect impact on innovation capacity and efficiency. It is widely accepted that the innovation environment has become a pivotal reform implemented by governments to foster economic prosperity in developing and emerging economies (Chadee & Roxas, 2013). Economic success is attributed in part to the strategic role of an enabling environment. An inclusive and tolerant environment empowers innovators to break free from path dependency, create breakthroughs, and contribute to economic recovery amidst periods of economic turbulence.

2.4 Research Propositions

As illustrated above, these five components overlap and intersect with each other, indicating the mixed effects of these components on economic resilience. To provide a more nuanced comprehension of the influence of innovation ecosystems on resilience, Figure 1 presents the configuration framework, delineating the interplay among these components.

OPEN IN VIEWER

Figure 1.

The Configuration Framework of Economic Resilience.

A fundamental idea in configurational analysis is equifinality, wherein different combinations of the antecedent conditions exhibit equal effectiveness (Fiss, 2011). In essence, a resilient economy can equally be described by alternative combinations of innovation ecosystem components. In turn, the five innovation ecosystem components are essential casual conditions to understand economic resilience, and can be combined in a variety of configurations to describe a resilient economy. Based on this, we propose the following propositions:

Causal asymmetry is another important principle in configuration analysis, indicating that the presence or absence of an innovation component depends on its interaction with others for a given outcome (i.e., a resilient economy) to occur (Fiss, 2011). Thus, the following propositions are presented:

3.1 Data Collection

This study encompasses data from 31 Chinese provinces and municipalities, excluding Hong Kong, Macau, and Taiwan due to data availability constraints. To account for potential time-lag effects, the data for economic resilience spans from 2017 to 2022, while the data for innovation ecosystem components is from 2015 to 2020.

3.2 Data Pre-Processing

FsQCA can be used to recognize complex causality and multiple interactions (Ragin, 1999; Rihoux & Ragin, 2009). Economic resilience is complex and is influenced by multidimensional and overlapping innovation ecosystem components. Thus, the use of fsQCA complements the current research in economic resilience, providing a more detailed understanding of how these components combine to establish configurations that promote a resilient economy.

However, conventional fsQCA cannot process panel data, thereby restricting analyses to cross-sectional and temporal data (Garcia-Castro & Ariño, 2016). To address this limitation, this study uses the panel data fsQCA methodology to explore the effect of innovation ecosystem components on economic resilience across regions and over time. The concepts of pooled consistency (POCONS), between consistency (BECONS), and within consistency (WICONS) are crucial to understanding the time and individual effects. Specifically, POCONS indicates the overall consistency observed in the regions; BECONS assesses the cross-sectional consistency for each year; and WICONS measures the longitudinal consistency for each region over time (Garcia-Castro & Ariño, 2016).

According to Garcia-Castro and Ariño (Garcia-Castro & Ariño, 2016), BECONS (WICONS) adj-distances are recommended to confirm the stability of these consistencies over time and across different regions. The smaller the distance, the more stable the BECONS (WICONS) are over time and across regions. Using Monte Carlo simulations, the BECONS (WICONS) adj-distance threshold of 0.2 has been validated (Garcia-Castro & Ariño, 2016).

4.1 Calibration

It is necessary to calibrate all measures into fuzzy sets with values ranging from 0 to 1, representing membership (Ragin, 1999; Schneider & Wagemann, 2010). We define a 95% quantile, a 50% quantile, and a 5% quantile, utilizing these values as the three thresholds for full-set membership, intermediate-set membership, and full-set non-membership, respectively. As recommended by Fiss (2011), a constant of 0.001 is added to cases that exactly match the 0.5 calibration point. The calibration and the descriptive statistics for the ER and the innovation components are shown in Table 3.

Before conducting the panel data fsQCA analysis, a single-condition necessity analysis is performed to determine if the symbiotic innovation ecosystem components are necessary for a resilient economy. The antecedent condition can be viewed as necessary for a resilient economy if the consistency exceeds 0.9 (Cabrera-Flores et al., 2020; Fiss, 2011). The results of the necessity analysis are presented in Table 4.

In the necessity test for the factors leading to a resilient economy, the POCONS value for IA, IN, IR, and IE are all below 0.9, and their corresponding BECONS adj-distances are less than 0.2, indicating that none of these four antecedent conditions are necessary for ER. However, it is worth investigating the necessity of ∼IP, as its BECONS adj-distance (0.226) exceeds the 0.2 threshold. Figure 2 illustrates the trend of BECONS of ∼IP from 2017 to 2022. Since the BECONS of ∼IP are all less than 0.9, we can conclude that ∼IP is not a necessary condition.

OPEN IN VIEWER

Figure 2.

BECONS of ∼IP in the Period of 2017–2022.

WICONS adj-distances are higher than 0.2, indicating that the presence of stratification and cross-regional effects. It is found that IA, IR, and IE are deemed necessary, especially in the eastern and western economic zones of China. One plausible interpretation is that the eastern zone, being economically developed, naturally exhibits advanced IA, IR, and IE. With the implementation of the grand western development strategy, both central and local governments have actively promoted economic growth in the western regions through a series of innovation-centric policies. As a result, the spillover effects of innovation have become apparent, highlighting the increasingly crucial roles of IA, IR, and IE in these regions. Due to space constraints, the analysis related to ∼ER is not presented here.

To identify the configurations that lead to a resilient economy, the sufficient conditions were analyzed. When constructing the truth table, three thresholds were established: the frequency threshold, the consistency threshold, and the PRI threshold. Since the number of our cases is 31, the frequency threshold was set at 1. We opted for a consistency threshold of 0.75. Configurations with PRI values below 0.5 indicate severe inconsistency (Fiss, 2011), thus the PRI consistency threshold was set at 0.75.

Running the R software produced three types of solutions: complex, intermediate, and parsimonious solutions. The core and peripheral conditions can be obtained from the intermediate and parsimonious solutions (Ragin, 1999; Rihoux & Ragin, 2009).

In Table 5, three configurations emerge as strong determinants of economic resilience. The overall solution consistency is 0.964, indicating the significance level of all configurations as a whole. The overall solution coverage is 0.764, suggesting that a substantial proportion of the outcome (i.e., a resilient economy) is covered by the three solutions. The BECONS and WICONS adj-distances are below 0.2, indicating that the three solutions are sufficient in fostering economic resilience over time and across regions.

We also identified four configurations that lead to ∼ER. The overall solution consistency for theses configurations is 0.979, with a coverage of 0.667. The BECONS (WICONS) adj-distances indicated that the solutions have robust explanatory power.

In this paper, we conducted robustness tests using various methods, including adjusting case frequency and consistency thresholds. We first increased the consistency threshold from 0.75 to 0.9 and raised the frequency threshold from 1 to 2. The results revealed that the three solutions were still supported, with the overall solution consistency and coverage unchanged from those presented in Table 5.

Subsequently, as suggested by Garcia-Castro and Ariño (Garcia-Castro & Ariño, 2016), we applied the POCONS, BECONS, and WICONS measures as robustness checks. The BECONS (WICONS) adj-distances for all configurations were found to be below the 0.2 threshold in Table 5, indicating the robustness of configurations across time and regions.

5.1 Analysis of Configurations Leading to ER

Solutions 1–3 reveal the combinations of innovation ecosystem components conducive to a resilient economy. In detail, when innovation resources and environment (core conditions) are present, a resilient economy may be achieved through their interaction with either (i) innovation actors and platforms, with the absence of innovation networks (solution 1), or (ii) innovation actors and networks, with the absence of platforms (solution 2), or (iii) innovation platforms and networks, with the absence of innovation actors (solution 3). In essence, these configurations reveal two main equivalent driving paths: “platform-dominated” (solutions 1, 3) and “network-dominated” (solution 2).

All three propositions are supported by the findings. First, the existence of multiple configurations leading to robust economic resilience suggests equifinality (Proposition 1). Second, the findings show configurations of a resilient economy where one condition (e.g., innovation networks, innovation platforms) may either be present or absent based on its interaction with other conditions, demonstrating causal asymmetry (Proposition 2). Third, there is evidence of substitution between casual conditions within configurations that lead to strong economic resilience (Proposition 3). For example, in solutions 1 and 3, innovation networks and actors exhibit mutually substitutability; while in solutions 2 and 3, the innovation actors and platforms are interchangeable.

5.2 Configurations Analysis from the Perspective of Time Effect

In the three configurations, there are no apparent differences or heterogeneity observed over time in terms of their BECONS adj-distances (0.018, 0.024, and 0.027). A further examination of the evolution of BECONS from 2017 to 2022, as illustrated in Figure 3, shows an upward trend, indicating the importance of the platform- and network-dominated configurations in driving a resilient economy in this period.

OPEN IN VIEWER

Figure 3.

BECONS of Configurations from 2017 to 2022.

Figure 3 also provides a new insight, revealing higher consistency peaks in the year 2020. This surge can be attributed to the breakout of the COVID-19 epidemic, which has caused a critical health concerns and triggered severe socio-economic crises worldwide. In response, the Chinese government, enterprises, and university research institutions have remained steadfast in their commitment to investing in and coordinating resources for innovation platforms and networks.

For instance, in Jiangsu province, significant breakthroughs were made in 29 major S&T innovation platform projects focusing on information technology and new materials by mid-2022. This includes the establishment of over 200 joint innovation centers by leading enterprises, the transfer and transformation of more than 6,200 technological achievements to the market, and the incubation of over 1,200 S&T enterprises. As a result, Jiangsu's economic resilience increased from 0.578 to 0.676 in the post-epidemic era.

The WICONS adj-distances of the three configurations are all below the 0.2 threshold, suggesting no significant differences across regions. A more detailed analysis in terms of the WICOV is required. Table 6 presents the average WICOV of the three configurations categorized by economic zones. The results show Configuration 1 and 3 have higher average WICOV (0.695 and 0.682) in the eastern economic zone, while Configuration 2 exhibits a higher average WICOV in the central economic zone (0.721). This suggests a trend where the innovation platform-dominated configuration is favored in the eastern economic zone, while the network-dominated configuration is more commonly found in the central economic zone.

The eastern regions have made significant progress in the development of innovation platforms. They have introduced and implemented a series of incentive policies aimed at encouraging leading firms to establish R&D centers and open innovation platforms. As a result, three international S&T innovation platforms have been successfully established in Beijing, Shanghai, and Guangdong. Additionally, the central regions have capitalized on the advantages offered by the Yangtze River Economic Belt, forming collaborative networks with other national regions. For example, Hubei province, strategically positioned within the central zone, has consistently ranked among the top four provinces in terms of collaborated S&T papers with other provinces.

6.1 Discussion

This study was inspired by recent theoretical and empirical research highlighting the significant effect of innovation ecosystems on economic resilience. However, previous studies have not thoroughly investigated the interactive play within the innovation ecosystem on economic resilience, diverting scholastic focus away from considering the temporal and cross-sectional effects, especially in the face of ongoing economic challenges. Therefore, we adopted a symbiotic theory perspective and identified five innovation ecosystem components to investigate how these antecedent conditions combined to determine economic resilience over time and across regions using the panel data fsQCA methodology.

First, one of the most crucial findings of this study is the evident significance of innovation platforms, emerging as a core condition in two out of three solutions. Innovation platforms contribute to a resilient economy in combination with innovation resources and environment, both of which are present as core conditions (Solutions 1, 3). These solutions emphasize the importance of open innovation platforms, highlighting their indispensable role alongside abundant innovation resources and an inclusive innovation environment, which are crucial in determining the effectiveness and efficiency of the economic resilience.

Second, another significant result relates to the combination of innovation actors and networks, serving as core conditions in Solution 2. Unlike the platforms-dominated configuration, this solution leading to strong economic resilience places more emphasis on innovation networks. However, a resilient economy cannot be attained just by innovation networks; rather, it must be complemented by innovation resources and environment. Accordingly, two different innovation development models are proposed: innovation platform-dominated (Solutions 1, 3) and innovation network-dominated (Solution 2).

Third, another noteworthy finding from the panel data fsQCA methodology is that there are no significant differences or heterogeneity over time and across regions in the three configurations, as indicated by their BECONS (WICONS) adj-distances. However, a more detailed analysis suggests that the innovation platform-dominated configuration is favored in the eastern economic zone, while the network-dominated configuration is more commonly found in the central economic zone. Additionally, since the COVID-19 pandemic, innovation platforms and networks have become increasingly important in stimulating economic resilience.

6.2 Theoretical Implications

This study contributes to the innovation ecosystem literature by introducing a multi-layered framework that includes innovation actors, platforms, networks, resources, and the environment. By adopting the panel data fsQCA methodology, this study sheds light on how these components interact and combine to form configurations that influence economic resilience. Hence, this study offers a deeper comprehension of specific patterns of innovation ecosystem components that contribute to a resilient economy over time and across regions. These configurations can be summarized as innovation platform-dominated and innovation network-dominated.

In addition, this study extends the application of panel data fsQCA to the fields of innovation ecosystem and economic resilience. Given the evolving nature of innovation ecosystems and economic resilience, it is critical to consider the interconnected links between these components. Therefore, the panel data fsQCA, based on a configurational perspective, demonstrates the significance of explaining complex causal patterns between innovation ecosystems and economic resilience, simultaneously taking the temporal and cross-sectional effects into account.

6.3 Management Implications

The findings of this study underscore the considerable importance of innovation platforms and networks in fostering economic resilience. As such, policymakers must introduce and implement incentive policies aimed at establishing hierarchical, cooperative, and interactive platforms and networks at the corporate, industrial, and regional levels. Leading firms and university research institutions should take proactive steps in constructing various cross-regional and cross-sectional innovation platforms. Emerging innovation startups should leverage their comparative advantages to actively engage in open innovation platforms. These measures will contribute to the formation of collaborative innovation networks, enhancing innovation efficiency and boosting the resilience of the local economy on a broader scale.

The results can be particularly significant for policymakers, providing insights into alternative combinations of innovation ecosystem components. Knowing which components are more crucial than others and which combinations of components better clarify economic resilience can, in particular, assist policymakers and stakeholders in creating more effective and efficient innovation development strategies. For the eastern economic zone, policymakers are advised to focus on policies that encourage innovation platform development; while in the central regions, adopting a collaborative innovation development model and seeking innovative cooperation from developed regions to expand and optimize innovation networks may be more beneficial.

Additionally, this study identified the core conditions of innovation resources and environment to explain strong economic resilience. Given that economic resilience is significantly determined by the inclusivity of the innovation environment, governments should establish e-government systems, streamline administrative procedures, and introduce business-supporting policies to foster a favorable and open environment for enterprises. In terms of innovation resources, simply processing resources is not enough; effective resource management and asset orchestration are essential. Businesses should structure their innovation resource portfolio, integrate these resources into capabilities, and leverage these configurations to create competitive advantages.