Generative Capability

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Generative Capability Sunny Li Sun

Abstract—How do firms leverage existing innovations for the next round of innovations? We develop a novel concept of generative capability in product innovation to address this question. Generative capability applies the mechanisms of iteration and rapid knowledge integration to guide innovation activities. Based on panel data from 2001 to 2009 of 56 344 manufacturing firm-year observations in China, we find that institutional complexity and internationalization are positively related to generative capability, that state-owned enterprises (SOEs) have lower levels of generative capability than do firms with other ownership structures, and that generative capability improves firms’ performance significantly. We conclude with a discussion of the implications of generative capability for theoretical development and point to a number of fruitful areas for future research. Index Terms—Generativity, institutional complexity, iteration, product innovation. [There’s a] momentum you enjoy based on common understanding and common learning as you move from one project to the next. You really benefit from all of the struggles, all of the challenges from one project, to help enable the next. –Jonathan Lve, Chief design officer of Apple1

I. INTRODUCTION OW does a firm’s next product innovation benefit from its current innovation? How can a firm capture the value of current innovations to build on future innovations? Although absorptive capacity and disruptive technology have been widely discussed in product innovation literature [1], [2], innovation continuity and generativity within or across product generations seems to have been largely ignored. Scholars in other areas of study have recognized the importance of generativity in creating new ideas, new language developments, new architectural patterns, or new algorithm solutions [3]–[6]. For example, in the field of human behavior theory, Epstein [7, 763] suggests that

H

Manuscript received March 29, 2017; revised February 24, 2018 and April 14, 2018; accepted May 20, 2018. The work of B. Zou was supported by the China Natural Science Foundation under Grant 71672049 and Grant 71202159. This paper was presented in part at the International Association for Chinese Management Research Conference, Hangzhou, China, Jun. 2016, and in part at the IEEE Technology and Engineering Management Conference, Santa Clara, CA, USA, Jun. 2017. Review of this manuscript was arranged by Department Editor N. Joglekar. (Corresponding author: Bo Zou.) S. L. Sun is with the Robert J. Manning School of Business, University of Massachusetts Lowell, Lowell, MA 01854 USA (e-mail:,[email protected]). B. Zou is with the School of Management, Harbin Institute of Technology, Harbin 150001, China (e-mail:,[email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TEM.2018.2841803 1 Guglielmo C. 2016. Jony Ive talks about putting the Apple ‘touch’ on the MacBook Pro, CNet, Nov. 27, https://www.cnet.com/special-reports/jony-ivetalks-about-putting-the-apple-touch-on-the-macbook-pro/

and Bo Zou

“novel behavior is the result of an orderly and dynamic competition among previously established behaviors, during which old behaviors blend or become interconnected in new ways.” In another study of innovation in the mechanical industry over a period of one hundred years, Usher [8] finds that a widely used innovation relied on continually revisiting original novel insights. Inspired by multidisciplinary work on generativity, we develop a novel concept of generative capability in the field of product innovation. By integrating the concepts of generative emergence [9], innovation butterfly [10], multistage product generation [11], [12], generative appropriability [13], and iteration in product development [14], we identify two important mechanisms of generative capacity: iteration and rapid knowledge integration. We then examine these two mechanisms in the context of China, the world’s largest emerging economy. We argue that institutional complexity can push firms to improve these two mechanisms of product innovation. However, state-owned enterprises (SOEs) apply these two mechanisms weakly because of their rigidly formal and centralized organizational structure. Internationalization, especially outward foreign direct investment (OFDI), can improve a firms’ generative capability. We further examine how a firm’s productivity could benefit from its generative capability. A complete theoretical framework is shown in Fig. 1. We then examine our hypotheses in a large sample: 56 344 manufacturing firm-year observations from the Annual Census on Industrial Enterprises (ACIE) Database (2001–2009). The empirical findings lend significant support to our five hypotheses. Our study contributes to the extensive literature on innovation. First, we develop a new concept of generative capability. Second, by emphasizing innovative continuity and generativity both within a single generation and across multiple generations of a product, we help to clarify the learning process with respect to knowledge reconfiguration and integration. Third, this study demonstrates Chinese firms’ institutional complexity, ownership structure, and their impact on generative capability. It aids in the understanding of emerging innovative methods in China. Fourth, this study carries significant implications for managers and policy makers who are engaged in innovation. II. CONCEPT DEVELOPMENT: GENERATIVE CAPABILITY The concept of generativity has been initiated in many fields of research. Human behavioral theory argues that novel behaviors emerge from old behaviors that are blended or interconnected in new ways [7]. In the field of artificial intelligence, the genetic algorithm is a similar optimization of search

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Fig. 1.

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Theoretical framework.

techniques based on the principles of genetics and natural selection [6]. Genetic algorithms work with a set of possible solutions to a given problem. Each solution is assigned a “fitness score” according to how well suited the solution is to the given problem. Highly fit solutions are given opportunities to “reproduce” by “cross-breeding” them with others. This produces new solutions, known as “offspring.” A whole new set of solutions can thus be produced by selecting the best solutions from the current “generation” and “mating” them [15, 58]. In addition to the fields mentioned above, the idea of generativity has also been introduced in the areas of personality development [5], language development [4], architectural patterns [3], social events [16], digital artifacts [17], computer design [18], emergence [9], commons-based peer production [19], internet technology [20], and strategic partnership [21]. In the field of information systems, Avital and Te’eni [22] point out that because nowadays computer systems are expected to enhance people’s creativity, information system designs should pay greater attention to two related design considerations—“generative capacity” and “generative fit.” They summarize earlier research on generativity, writing that “generativity refers to a capacity for rejuvenation, a capacity to produce infinite possibilities or configurations, a capacity to challenge the status quo and think out-of-the-box, a capacity to reconstruct social reality and consequent action, and a capacity to revitalize our epistemic stance” [22, 349]. Although generativity and its related thoughts affect creativity and the development of new knowledge “in a substantial and robust sense” [23, 393], the literature on innovation has not paid sufficient attention to this research stream. Having reviewed the literature on innovation, we find three concepts related to generative capability. The first such concept is product generation. Ironically, the classic textbooks on innovation generally ignore the relationship between earlier product generations and the current generation, e.g., [24], although product life-cycle theory is well understood from the perspectives both of the user and of innovation diffusion [25]. Iansiti [12] first identifies how continuity over product

generations can help research and development (R&D) to proceed in the shortest time and at the lowest cost. He refers to the integration of the entire R&D process, as opposed to just shooting projects down a pipeline, as a “system focus.” The system focus approach emphasizes that a firm can gain the knowledge over several generations by discovering and capturing knowledge about the interactions between new research in the lab and the company’s existing product and manufacturing systems. A firm can also adapt and develop new product designs nonlinearly with feedback loops from discrete and sequential stages [10], [11]. For instance, Prahalad [26] shows the evolution of the combination stove, an innovative product in India. Bergek et al. [27] find that Toyota gradually applied its hybrid technology from the Prius I (in 1997) to the luxury Lexus line, the mass market Camry (in 2006), and the cost-sensitive subcompact Yaris (in 2012). The second concept is generative appropriability. Ahuja et al. [13] distinguish two forms of appropriability: primary and generative. Generative appropriability refers to “a firm’s effectiveness in capturing the greatest share of future inventions spawned by its existing inventions” [13, 248]. They point out that the outcomes of a firm’s generative appropriability are likely to depend on access to and utilization of the inventive knowledge of the firm. Unfortunately, to the best of our knowledge, no empirical studies exist to support their concept. The third concept related to generativity is iteration, which is used to explore the process of strategy-making and product development. Based on case studies, Noda and Bower [28] demonstrate that strategy-making is an iterative process of resource allocation. Eisenhardt and Tabrizi [14] further propose that iteration improves the odds of success of product development and accelerates development as well, particularly when the development path is unpredictable. Anderson Jr. and Joglekar [10] suggest that innovation butterfly involves multiple feedback loops. Taking these three related concepts together, we find that product generation, generative appropriability, and iteration share some common characteristics: the new product generation/version can evolve on the basis of the original gen-

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Fig. 2. Generative capability across multiple product generations. ti : the production generation i.

eration/version, even if the product in the original generation/version is markedly inferior. Integrating these related concepts, we build the concept of generative capability in product innovation and define it as a firm’s high-level routine that reconfigures knowledge embodied within the product generation iteratively, and that rebundles external and internal knowledge rapidly across product generations, to develop the nextgeneration product. Fig. 2 depicts the concept of generative capability inside of the organization. The concept of generative capability proposed in this study is positioned in the field of product innovation, which aims to address the question of how firms leverage existing innovations for the next round of innovations. Prior literature has focused mainly on the achievement of profit from existing product innovations. However, “the most valuable application of a given invention is often not the application for which it was originally conceived” [13, 249]. Generative capability emphasizes continuity and interconnections across product generations. By doing so, it enriches the literature on product innovation by contributing to the understanding of innovative continuity and generativity within a single product generation and across multiple product generations. In other words, our generative-capability framework provides a new explanation for the effect of a firm’s innovation on performance in the long run. In the field of management, one of the concepts that is similar to generative capability is absorptive capacity, which is “the ability of a firm to recognize the value of new, external information, assimilate it, and apply it to commercial ends” [20, 128]. We address the following differences. 1) The starting point of absorptive capacity is the acquisition of outside knowledge. However, with generative capability, the starting point is the utilization of inside knowledge. In particular, the knowledge embodied within the product generation. 2) Absorptive capacity can experience “lockout” [20, 136]— once a firm terminates the new investment in its absorptive

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capacity, it may cease to exploit new information. However, generative capability can become the seed of future concepts and ideas [13]. 3) Generative capability and absorptive capacity supplement each other. Absorptive capacity emphasizes a firm’s ability to exploit the external knowledge [2], such as during the process of internationalization; it can accumulate abundant knowledge stocks and thus enhance a firm’s generative capability. We treat generative capability in product innovation as a highlevel (second-order) routine that guides, monitors, directs, selects, and maintains ordinary innovation activities [30]–[33]. Given repeated, patterned innovation activities, generative capability can be a “learning-to-learn” capability that will produce advantages in a strongly innovation-driven competition [34, 143]. Based on the emergence theory, this high-level capability “transcends but includes” its components [9] and then introduces something fundamentally new. Therefore, generative capability has the properties of qualitative novelty and nonreducibility. It could reveal “the constructed-but-spontaneous nature of emergence” [9, 117, 30]. The qualitative novelty of generative capability comes from two important mechanisms. The first is the mechanism of iteration, which creates multiple options within a single generation of a product. This means that managers cannot only sense and configure more opportunities for innovation by accelerating product design and build in understanding through trial and error [14], but can also seize new opportunities emerging from iteration and the transfer of knowledge gained thereby across different product generations [12], [31], [33]. The second is the mechanism of rapid knowledge integration. In the dynamics of product generations, this means learning how to reveal errors in previous product generations and add organizational knowledge in multiple rapid cycles across product generations [23]. By rebundling external and internal knowledge within a system of interconnected activities, managers can discover the errors in previous generations of a product, improve learning by doing, and transfer tacit knowledge through double-loop learning across product generations [10], [11], [35]. For example, Tesla, an innovative electric car maker, states in its IPO file: “We have designed our product development process to rapidly react to data collected from our vehicles and the direct interaction with our customers at our company-owned stores, which we believe will enable us to rapidly introduce new vehicles and features.”2 Applying these mechanisms under different scenarios, we first elaborate the hypotheses on the antecedents of generative capability in the following section. We further argue that a firm with strong generative capability can achieve the best product innovations in the shortest time with the lowest cost [12], [14]. We then develop hypotheses on the consequences of generative capability (see Fig. 1 for a complete diagram).

2 Tesla Motors, Inc. 2010, Form S-1 Registration Statement http://www. sec.gov/Archives/edgar/data/1318605/000119312510017054/ds1.htm italics are emphasized by the authors.

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III. HYPOTHESIS DEVELOPMENT A. Innovation Strategy Under Institutional Complexity Firms sometimes find themselves facing multiple formal or informal institutions in which a set of rules may be in mutual conflict or incompatible [36]–[38]. In addition, these rules, principles, or forces “ebb and flow in wavelike patterns as societal expectations evolve, coalesce, and dissipate” [39, 1722]. Such a dynamic of pluralistic rules, principles, or forces is called institutional complexity [36], [40]. For example, there are multiple dimensions of market-based reform in China. On the basis of a variety of institutions [41], [42], Fan et al. [43] classify five dimensions of marketization: 1) business-government interfaces; 2) development of private firms; 3) product market development; 4) factor market development; and 5) development of rule of law and legal intermediaries. If there are changes in these five dimensions every year, they cannot be mutually consistent or compatible [44], [45]. Such complexity can make it difficult for a firm to interpret environmental reality, understand rules, and engage in appropriate behavior [46], thus affecting the firm’s innovation strategy and generative capability. Child and Rodrigues [47] suggest that firms can engage with environmental complexity in two ways: cognitively and relationally. We then argue that institutional complexity can positively impact generative capability in two ways—cognitive reconstruction and relationship building. Cognitive reconstruction, which focuses on managers’ perceptions of institutional complexity when making innovationrelated decisions, can affect the iteration mechanism of generative capability. A high degree of institutional complexity creates ambiguity and conflicting expectations for managers [48]. They cannot identify the cause-and-effect linkages between possible alternative options and potential outcomes [49], [50], so they either apply the iteration strategy to product innovation in order to breed more opportunities for a “generative fit” [22, 349] or accelerate the innovation process with the existing stock of knowledge and technology integration to build more flexibility [12], [14]. Therefore, firms will be more likely to improve generative capability in order to reduce the impact of institutional complexity [47]. Relationship building focuses on how firms deal with the uncertainties of institutional complexity through cooperation with innovation partners. Child and Rodrigues [47] indicate that “complexity mediation”—which emphasizes reliance on external parties—is one way to deal with environment complexity. The innovation process involves multiple cooperative partners. However, institutional complexity can make the relationship difficult to build and sustain [51]. For example, in some regions of China, market-based reforms in one dimension promote the development of a product or factor market, but in another dimension, legal reforms lag far behind the reform agenda [45]. This conflict or noncomplementarity in institutional changes under different dimensions can damage a firm’s cooperative relationship with its partners. Therefore, a firm should accelerate knowledge iteration among shared resources and unshared resources within existing alliance portfolios and networks [52], [53], or accelerate knowledge integration continued updates on different partnerships to generate novel products [54].

Otherwise, the firm cannot exploit the benefits and capture the value from partners under the institutional complexity. These generative partnerships within and across its boundaries, as one kind of generative capability, could be a good strategic choice to deal with the uncertainties that come with institutional complexity [21]. Therefore, we hypothesize the following. H1: An increase in institutional complexity is positively associated with a firm’s generative capacity in new product development.

B. Ownership Effect: SOEs Since firms with different types of ownership have different organizational structures and domains [55], which produces heterogeneity in knowledge utilization and acquisition [56], they have different levels of generative capability. We argue that the generative capability of Chinese SOEs is lower than that of enterprises with other types of ownership in two ways. First, in regards to the iteration mechanism, Chinese SOEs are characterized by a high degree of formalization and centralization in organizational structures [57], which can harm generative capability. Formalization refers to the rigid written rules and procedures [57], which lead to less openness and flexibility and makes it more difficult for firms to acquire and utilize knowledge through informal communications [58]. A centralized organization, in which decisions are made only at the level of the firm as a whole, is also generally considered to hinder knowledge utilization [59]. This kind of organization structure hurts a firm’s generative capability in iteration among multiple options within the product generations [13]. With respect to the domain, compared to other types of ownership, Chinese SOEs are focused more on their domestic market, which leads to limited international scope, a narrower product line, and slow updating of technology [60], [61]. Although Chinese SOEs can access more external resources in both product markets and factor markets and gain more regulatory support than firms with other types of ownership, their formality narrows the iteration options and constrains the development of generative capability. Second, in the rapid integration mechanism, SOEs tend to respond slowly to feedback, with X-inefficiency, which focuses on productive efficiency and minimizing costs rather than allocative efficiency [62], thus hindering generative capability. In the second mechanism of generative capability, a decentralized structure disperses decision-making authority throughout the innovative process [11], [63], which enables employees at every level to engage in customer analysis and form feedback loops, thus accelerating the process of learning and adaptation [35], [64]. In contrast, as mentioned above, the organizational structure of Chinese SOEs hinders knowledge utilization and acquisition and integrates external and internal knowledge slowly, thereby impeding the improvement of their generative capability. Therefore, we hypothesize the following. H2: SOEs engaged in new product development are negatively related to a firm’s generative capabilities.

C. Internationalization Numerous studies in the literature show that firms are exposed to and then assimilate different types of knowledge when they

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move into foreign markets [65], [66]. In this section, we focus on the effect of internationalization, such as OFDI, on firms’ generative capability. With respect to the iteration mechanism, internationalization increases the option pool of innovation opportunities. According to the asset-seeking perspective, OFDI has been adopted to allow multinational corporations to tap into strategic resources in a foreign market, such as market intelligence, technological knowhow, management expertise, etc. [67]. Many OFDIs from China are driven by the search for these strategic resources [68], [69]. As a result, Chinese firms can often acquire and exploit different types of advanced knowledge through OFDI that helps firms address established problems and explore multiple potential solutions in iteration, thereby increasing generative capability. With respect to the rapid integration mechanism, among firms with subsidiaries in foreign countries, internationalization broadens a firm’s knowledge base and scope, offers a more effective channel for the transfer of tacit knowledge to Chinese firms from leading companies, causes a firm to move upward in the global value chain, provides learning opportunities, and allows it to absorb external novel innovations [70], [71]. In addition, internationalized firms are able to leverage the advantage of multiple modes, such as greenfields, joint ventures, or cross-border mergers and acquisitions to enhance the novelty of next-generation products [72]. Therefore, we hypothesize the following. H3: An increase in a firm’s internationalization is positively related to a firm’s generative capability in new product development.

D. Impact of Generative Capability on a Firm’s Performance The viewpoint that innovation is positively related to a firm’s performance has been widely accepted in the literature [73]. In particular, we argue that generative capability in product innovation can increase a firm’s effectiveness, especially in total factor productivity (TFP), which captures the outcome produced from a fixed set of inputs that include labor, capital, and materials [74]. First, generative capability can increase labor productivity. “Upstream transfer of information provides not only continuity from generation to generation but also continual learning about the impact of new technology on the complex product capacities of an organization” [12, 144]. This learning increases labor’s human capital and organizational knowledge. Crespi et al. [75, 1] point out that “increases in knowledge, in turn, are typically ascribed to the following three main sources: 1) investment in new knowledge within the firm (e.g., R&D); 2) use of existing knowledge from the firm (e.g., from past discoveries or knowledge-sharing with other divisions of the firm); and 3) use of knowledge from outside the firm.” Generative capability integrates external and internal knowledge rapidly and improves the labor productivity. Second, generative capability can increase the return on capital investment. Iteration in product innovation can accelerate the adaptation of organizational processes by focusing on frequency and stage-gates; minimizing the length of the product development cycle; and reducing the waste of resources on related activities, changes, and mistakes [11], [14], [76]. When a

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firm can launch its product earlier than its competitor can, it can realize a higher profit margin and increase the return on capital. Third, generative capability could increase manufacturing efficiency. Because generative capability emphasizes continuity across product generations, it can reduce handling and inventory costs, improve the quality of raw materials provided by suppliers, and reduce assembly errors [76]. For example, in its IPO file, Tesla states that “we are designing the Model S to have an adaptable platform architecture and common electric powertrain in order to allow us to efficiently create other electric vehicles, which may include a sport utility vehicle, commercial van, or a coupe.”3 This common platform helps Tesla introduce subsequent vehicles and technologies with greater cost efficiency. Therefore, generative capability improves manufacturing quality, reduces delays, and accelerates the launch of new products. In the end, generative capability increases a firm’s efficiency in output. Therefore, we hypothesize the following. H4: A firm’s generative capability in new product development is positively related to a firm’s performance.

E. Impact of Generative Capability on the Firm’s Performance of SOEs The positive effect of generative capability on TFP in SOEs is probably weaker than in firms with other ownership types. There are a number of possible reasons for this. First, the positive effect of generative capability on labor productivity may be constrained in SOEs because of organization formalization and centralization [57]; SOEs benefit less from knowledge acquisition and utilization. Second, the positive effect of generative capability on capital output may be lower in SOEs, since the rate of change in SOEs is slow, and their product development cycles are usually longer than in other types of firms. Third, the positive effect of generative capability on material productivity is reduced in SOEs because of X-inefficiency in resource allocation [62]. Therefore, we hypothesize the following. H5: The positive association of generative capability in SOEs engaged in new product development with the firm’s performance is smaller than the association in firms with other types of ownership.

IV. METHODS A. Empirical Setting, Data, and Sample To examine our hypotheses as they related to China, we decided on the period 2001–2009, since during that time Chinese firms’ innovative capacity and TFP improved greatly, moving upward in the global value chain, especially after China’s entry into the World Trade Organization in 2000 [29], [54], [71]; innovation activities were strongly affected by institutional changes in market-based reforms [77], the legal environment [45], and types of ownership [56]; and many Chinese firms began to explore the overseas market through export, international joint ventures (IJVs), or foreign direct investment (FDI) [61], [78]. We collected data from the ACIE database (2001–2009). The National Bureau of Statistics of China (NBSC) conducts the 3 Similarly, Uber spent 11–12 months for one generation of product design. Its R&D manager claimed: “In 3.5 years we have rewritten our dispatch system three times.” Helft, M. 2016. Uber’s bold move. Forbes, Dec. 30, pp.70.

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census annually for all Chinese manufacturing firms, including SOEs, privately owned enterprises, and IJVs, with annual revenues greater than 5 million RMB (about US$303,000) [79]. According to Chinese regulations on statistics, all Chinese manufacturing firms are required to report related information to the NBSC and participate in the annual census. Therefore, the data based on these censuses are the most comprehensive and accurate business description of Chinese manufacturing firms. Scholars have applied this dataset to study collaboration and productivity [79], organizational forms [55], spillover [80], and product innovation [56]. We obtained the market-based reform index from the National Economic Research Institute (NERI). The NERI builds multiple indexes to describe the progress over time of marketization across different provinces in China [43]; these are based on provincial-level data from the NBSC or the annual surveys conducted by NERI itself. We also collected data on the internationalization of firms from the Directory of Chinese Outward FDI firms (DCOFDI), published by the Ministry of Commerce of the People’s Republic of China (MOFCOM). This dataset contains the data of Chinese firms’ OFDI. We first combined these two datasets, that from the census and that on internationalization, based on firm name and address, and then combine the third NERI market-based reform index based on the province. After combining these three datasets, we excluded observations for which values were missing. The final sample includes nine years of firm-level data points for 2001–2009 with an unbalanced panel structure. After lagging the variables, we obtained the final sample. It included 7398 firms or 56 344 firm-year observations. In a post hoc robustness test, we collected R&D data from the ACIE database. The NBSC had not disclosed data on the amount of R&D for some years, which led to some missing values and reduced our sample size to 10 498 firm-year observations. We conducted t-tests on firm size, sales, and generative capability, and firm’s performance variables between these two samples. The results show that there is no significant difference between the two samples. B. Dependent Variables

Yt = TFPt ∗ F (Kt , Lt , Mt ) where Yt is the output and Fis a function of observable inputs capital Kt , labor Lt , and intermediate materials Mt , and a factor-neutral shifter TFPt . With a natural logarithm conversion LnYt = α0 + β1 LnKt + β2 LnL + β3 LnMt + ωt , the estimated logged TFP is, thus, the sum of a constant term and resid ˆ + ω t . TFP captures multifactor producual: LnAt = tf pt = α tivity variation. It is a better measure of innovative performance than other measures, such as return on equity (ROE, applicable only in the shareholder dimension), return on assets (ROA, applicable only in the total asset dimension), and return on sales (ROS, applicable only in the sales dimension), since innovation can have an impact on output across production units at the system level, including labor, capital, and materials [74]. C. Independent Variables

1) Generative Capability: Ahuja et al. [13, 252] suggest using “the proportion of inventions/products spawned by the focal firm’s inventions that are created by the focal firm” to measure generative appropriability. We calculate generative capability based on the following formula: Generative Capability  =

processes.” In addition, new products usually are approved by the local government. This definition of new products is consistent with that used in the prior research [56], [81]. Our measurement of generative capability is a second-order construction, different from a first-order construction—i.e., the ratio of new product value to total sales—on product innovation, such as that used by Zhou and Li [56]. We believe that our approach captures not only the speed and effectiveness of product innovation, but also “how much more could have been possible by recognizing and exploiting follow-on inventions” [13, 266], the key construct of our theoretical argument. In addition, our approach captures the pattern of innovation activities at a high level of capacity [30], [31]. The advantage of this second-order construct is that our approach is comparable across industries. It is not affected by different economic scales [56]. The distribution of the ratio of a new product value to total sales, as the first-order construction, is between 0.02 and 0.78. Therefore, we have not found the outliers or extreme values in the range of generative capability as a second-order variable. 2) Firm’s Performance: We use TFP to capture production efficiency. TFP is the outcome generated from a fixed set of inputs, based on the following Cobb–Douglas production function [74]:

New Pro duct Sales t+1 Total Sales t+1

 New

−

New Pro duct Sales t Total Sales t

Pro duct Sales t Total Sales t



 .

“New products,” according to the NBSC’s definition, are those that are “new to the market [and] that either adopt completely new scientific principles, technologies, or designs; or are substantially improved in comparison with existing products in terms of performance and functionality, through significant changes in structure, materials, design, or manufacturing

1) Institutional Complexity: Institutional complexity can be measured by multiple conflicting and complementary forces or logics under a pluralistic environment [36], [39]. The NERI has developed multiple indexes to describe market-based reforms across different provinces in China over time [43]. The indexes have received significant attention in the management literature [82]–[84]. To assess the conflicting and complementary forces, we first detected the rate of change of different dimensions of market-based reform, including the following: 1) business-government interfaces; 2) the development of private firms; 3) the development of product markets; 4) the development of factor markets; 5) the development of market and legal intermediaries [43]. We utilized the following equation to obtain the changes in each of these five dimensions, which are related to all five

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dimensions among 31 provinces:

Capacity: the ratio of R&D investment to total revenues [2]. Finally, we also controlled Year Dummies at the year level.

ΔReform Dimension Indexi,j ΔRi,j =  ΔReform Dimension Indexi,j where i is the dimension of the subindex change in one year, and j is the jth province. Second, we applied an entropy approach to capture the heterogeneity of rate of change in five dimensions. This approach is able to express the multiple conflicting logics that can make it difficult for a firm to adapt to these different institutional demands [85]. Third, we took the inverse of the entropy value to capture the level of institutional complexity, as shown in the following: Institutional Complexity   = Max − ΔRi, j ∗ In

1 ΔR i, j

.

2) SOE: SOE refers to state control and is measured by a dummy variable (1 and 0), where 1 indicates that the firm is controlled by state or governmental authorities, and 0 otherwise [61]. 3) Internationalization: We used a dummy measure to capture the focal firm’s overseas direct investment from the DCOFDI. If the focal firm has an investment in the focal year, this variable equals 1; otherwise, it equals 0. D. Control Variables We controlled several important variables at the industry level that may influence a firm’s capacity. To capture the industrial heterogeneity and diversity of the task environment, we applied the reversed Herfindahl Index of sales for each firm in the focal industry and focal year as a proxy for Industrial Complexity [86], [87]. For Industry Dummies, we followed the NBSC’s industry classification standard to give every industry a dummy value. We further controlled other important variables at the regional level, including Regional GDP per Capita: the natural logarithm of GDP per capita; Regional FDI Investment: the amount of annual FDI flow received by the focal province, adjusted by local GDP [45]; and Regional Dummies. China has 22 provinces, 4 municipalities, and 5 autonomous regions with diverse formal and informal institutional dimensions [88]. We further controlled other important variables at the firm level, including Firm Age: the natural logarithm of difference between a given year and focal firm’s startup year. We further added Firm Age Square to test routine’s obsolescence effect [89] for Firm Size: the natural logarithm of the focal firm’s total number of employees; New Product Ratio: the ratio of new product value to total sales [56]; and Capital Intensity: the ratio of capital expenses to total sales. A firm’s generative capability can be affected by its resource allocation and strategic investment based on capital intensity [26]; Export Intensity: the ratio of a firm’s export sales to its total sales in a given year [29]; Capitalization: the ratio of equity value to total liability outstanding, which depends on financing and investment decisions, can constrain a firm’s innovation [90]; and Absorptive

E. Estimation Strategy The effect of spatial analysis on innovation has been widely researched by management scholars [91], [92]. However, Doh and Hahn [93] found that while scholars use spatial statistics in 29 articles published between 1996 and 2006 in the Strategic Management Journal, most of them have not applied a spatial method appropriate to fully capture the effect of geography. Doh and Hahn [93] suggest that econometrics models should address potential issues of spatial dependence, which may violate statistical assumptions regarding the independence among observations in cross-sectional data. For example, if firm I is located in city J, its innovation and capacity development may be affected by similar firm K’s decision to locate in adjoining city L because the two firms share similar institutions and value chains and the same talent pool [71]. Because it is unsuited to dealing with potential spatial dependence issues with respect to innovation, a traditional regression model could generate Type I errors and increase unreliable evaluations [94]. To address this issue, we first used firm headquarters’ postal codes to identify Chinese firms’ locations and city nearest to them. We then tested the model at two different levels, the city level and the provincial level, through Moran’s I with the command “spatwmat” and “spatgsa” in Stata, which can assess spatial dependence issues [93]. We find that spatial dependence exists at the city level (the null hypothesis of no spatial dependence can be rejected), but is weak at the provincial level (the null hypothesis of no spatial dependence is not rejected) [93]. It suggests that in terms of innovation and development capabilities, Chinese firms are affected by other firms in nearby cities, but are not significantly affected by other firms in adjacent provinces. In other words, Chinese firms in the same province share similar cultures, regulations, industry structures, and value chains. This result is consistent with previous findings that institutions at the provincial level can be a significant explanatory variable in China [83], [84], [88], [95], [96]. Therefore, a multilevel analysis using provincial-level data may be better than an analysis using city-level data to address the spatial dependence issue. We then apply a random coefficient model and process our data according to a two-level hierarchical structure [97], [98]. The low level is the firm level, and the high level is the provincial level. The data on the firm level is nested within that of the provincial level. Such a data structure allows us to apply crosslevel analysis, whereby we can identify the effect of institutional complexity at the provincial level on generative capability at the firm level. We further calculate intraclass correlation (ICC) and find that ICC to be 0.113, suggesting that 11.3% of the variation in generative capability can be explained by intraprovincial differences. Therefore, the random coefficient model is appropriate in cross-level analysis in our case [99]. Finally, we applied the random coefficient model in a regression with the “xtmixed” command in Stata V.13. In this mixed model, the regression coefficients of the low-level variables (mostly firm-level) are regressed on the high-level

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TABLE I DESCRIPTIVE STATISTICS AND CORRELATION MATRIX

Correlations above |.12| and significant at the .05 level are in italicized bold typeface.

variables such as institutional complexity, regional GDP per capita, and regional FDI investments [100]. To show the direction of cause and effect, we used the data of generative capability and firm’s performance in the period of t + 1 as a dependent variable and other variables in the period of t. In the firm’s performance regression, we further controlled the firm’s performance in the period of t − 1. This can raise the autocorrelated error in the equations. We further calculated Durbin’s h and found that the autocorrelated error was not an issue after passing the Durbin–Watson statistic [101]. V. FINDINGS A. Results Table I shows our descriptive statistics and correlations. The average firm age is 10.66 years (before log); the average new product ratio is 3.7%, and 8.1% of observations are of SOEs. We find that regional GDP per capita has high correlations (above |.12|) with regional FDI Investment, firm age, firm size, SOE, and new product ratio. We then conducted variance inflation factor (VIF) analysis in model 3. All VIFs are below the recommended level of 10. We believe that multicollinearity is not an issue to bias the estimated model [102]. Table II reports the multilevel regression results of mixed models on generative capabilityt+1 . Table III reports the multilevel regression results of mixed models on firm’s performancet+1 . As the baseline model, model 1 in Table II only includes the control variables. Model 6 in Table III includes all control variables and three main variables, since all these variables can affect the firm’s performance. H1 suggests that institutional complexity increases a firm’s generative capability. Model 2 in Table II includes the main independent variable, institutional complexity, to examine this hypothesis. The results show that the coefficient of institutional complexity is significantly positive (β = 0.0688; p < 0.001). Therefore, H1 receives strong support. H2 suggests that SOEs have relative lower generative capability than do firms with other ownership structures. We included

the SOE dummy variable in model 3 to examine this hypothesis. The coefficient of SOE is negative and statistically significant (β = –0.0587, p < 0.001). It suggests that SOEs have 5.87% lower generative capability than do firms with different ownership structures. Therefore, H2 is supported. H3 further suggests that internationalization increases firms’ generative capability. We therefore added internationalization into model 4 and found that its coefficient is significantly positive (β = 0.146; p < 0.01). Therefore, H3 also receives strong support. One new overseas investment could increase a firm’s generative capability by 14.6%. Comparing models 2, 3, and 4 with model 1, we find that the fit of each model, as measured by the Wald chi-square, consistently increases, suggesting that every model gains more significant explanatory power for dependent variable generative capabilityt+1 . We use a likelihood ratio test to compare the model fit between models 2, 3, and 4, and model 1. The chi-square and p-value indicate that three main variables significantly improved the model’s fit. To examine H4, on the relationship between generative capability and firm’s performance (TFP), we then added generative capability in model 7 of Table III. The result shows that its coefficient is significantly positive (β = 0.191; p < 0.01). Therefore, H4 receives support. To examine the moderating effect of H5 on the relationship between generative capability and a firm’s TFP, we then add interaction term generative capability∗ SOE in model 8. The result shows that this interaction’s coefficient is significantly negative (β = –0.00329; p < 0.10). Therefore, H5 receives weak support. Since TFP captures input–output production efficiency, even though generative capability’s coefficient is small, its impact on firm’s output has economic significance under our large sample. B. Post Hoc Robustness Check We use other firm’s performance measures, such as profitability, return to asset (ROA), return to equity (ROE), or return to invested capital. The results still support our main hypotheses. We also believe that the absorptive capacity, as an important

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TABLE II RESULTS OF MULTILEVEL MIXED MODELS ON GENERATIVE CAPABILITY t + 1

Standard errors in parentheses, Year Dummy, Region Dummy, Industry Dummy are controlled, but not report here. +p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001 i: the scale of focal variable is adjusted by 1000 to receive the observable coefficients in two decimal places.

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TABLE III RESULTS OF MULTILEVEL MIXED MODELS ON FIRM’S PERFORMANCEt + 1

Standard errors in parentheses; Year Dummy, Region Dummy, and Industry Dummy are controlled but are not reported here. + p < 0.1, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. i: the scale of focal variable is adjusted by 100 to receive the observable coefficients in two decimal places.

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variable, can affect the generative capability. However, because R&D data are missing from some years of the ACIE database, we have to calculate the ratio of R&D investment to total revenues as absorptive capacity [2] and added it to model 5 of Table II in a post hoc robustness check. The final sample size is reduced from 56 344 to 10 500. However, the coefficients of the three main variables did not change direction.4 The coefficients of institutional complexity and SOE maintain their significant levels. VI. DISCUSSION AND CONCLUSION A. Contributions Our study makes several contributions to the relevant literature and offers some insights to practitioners. First, we develop a new concept of generative capability with a novel approach. Broadly speaking, as a higher level capability, generative capability belongs to one kind of dynamic capability [30], [96]. Like the dynamic capabilities of sensing, seizing, and reconfiguring [31], generative capability can be broken down into the following three subabilities. 1) When firms are developing the current generation of their product, they try to sense opportunities and threats through scanning, searching, and exploring across technologies and markets [103]. 2) After iterating multiple options, firms decide on the direction of development of the next generation of the product so as to seize these opportunities. 3) To enhance the effectiveness of subsequent-generation products, firms need to use knowledge embodied in the existing product iteratively and to reconfigure both external and internal knowledge rapidly. By proposing the concept of generative capability and revealing its mechanisms, this study enriches the literature of dynamic capabilities. Second, this study also contributes to the understanding of the learning process during the development of product innovations. The extant literature on product innovation highlights the importance of external searching [54], [104], and a few of them also suggests that old and familiar knowledge can increase the reliability and predictability of an innovation [105]. We further argue that reconfiguring and integrating knowledge embodied in earlier products is essential for a firm’s ability to develop subsequent products. Our empirical results show that iterative learning can happen in different environments (institutional complexity or industrial complexity), under different types of ownership (SOEs), and with different strategic behaviors (internationalization). Third, this study aids in the understanding of the emerging methods of innovation in China. We explore the interplay between institutional complexity and generative capability in the Chinese context. Market reform involves market-oriented change across a set of institutional dimensions [42], [83], [88]. Our results find that institutional complexity affects firms’ generative capability from the perspectives of both cognitive reconstruction and relationship building (H1). In addition, our 4 Absorptive Capacity only contributes 0.0004 of R squared change in a regular regression model.

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empirical results show that Chinese SOEs have lower generative capability than do firms with other ownership structures (H2), and that generative capability improves the firm’s performance in SOEs less than it does in firms with other ownership structures (H4). We argue that the formalized and centralized organizational structure and the limited domain of SOEs are the main reasons for these results. Finally, this study has significant practical implications. Managers should build generative capability through a “fail fast and often” and “love for problem solving” approach [4: 148]. They could make a new generation of product generativity by applying the principles of leverage, adaptability, ease of mastery, accessibility, and transferability [4], [13], [20]. In line with our empirical results, we further suggest that managers can enhance their firm’s generative capability through internationalization and enhancing absorptive capacity. In particular, for SOEs, managers should draw attention to building a decentralized organizational structure and empowering employers in innovation. B. Limitations and Future Directions Our study’s limitations reveal opportunities for future research. First, our measurement of generative capability is not flawless. Our approach is inspired by the notion of generative appropriability defined by Ahuja et al. [13], taking the measurement period for one generation of product development as one year. However, the time required to develop a new generation of a product may be less or more than one year. Firms that have adopted agile portfolio planning and execution [3], for example, could develop a new product generation in a shorter amount of time. Other approaches, such as patent citations, may further justify generative appropriability [105].5 Second, the development of a firms’ generative capability is a process of adaptation, so case studies need to be carried out to identify generative capability’s iteration and rapid integration mechanisms in a longitudinal setting. For example, a deep case study can help us understand how iteration evolves from single-loop learning and knowledge rebinding in double-loop learning [35]. Third, the study sample consists only of manufacturing companies in China, which limits the generalizability of its results to the other industries and countries. Actually, not only do manufacturing companies need leverage existing innovations for the next round of innovations which are based on generative capability, but so do in service industries. Future research can use the data of multiple industries to verify the theoretical framework proposed in this paper to enhance the generalizability of generative capability. At the same time, a comparative study of firms’ generative capability under different state settings also needs to be carried out, for instance, China and the U.S., could reveal more nuances in product innovation in different institutions and innovative infrastructures. In addition, our data could not differentiate between the incremental innovations and radical innovations in the product profile. These two kinds of innovation 5 Unfortunately, the ACIE database has not included a firm’s patent and its citation data.

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could have different mechanisms on iteration and knowledge integration. Fourth, our results carry some interesting findings. For example, Table II suggests that the firm’s age has a significant positive effect on generative capability, but firm’s age square has a significant negative effect. It suggests that a firm’s generative capability outcome becomes obsolete because of rigid routines [89]. We also find that capitalization has a significantly negative effect on generative capability (β = – 0.533; p < 0.01) in model 1 of Table II. It indicates that a firm’s generative capability could be constrained by its financing capability. Our empirical results suggest that while OFDI is positively related to generative capability, export orientation is negatively related to generative capability (β = –0.0916; p < 0.001) in model 1 of Table II. Although export orientation can bring new knowledge to the focal firm, just as internationalization through OFDI does, export firms, such as an original equipment manufacturer, do not engage in product innovation iteratively and rapidly, the two main mechanisms of generative capability. Our results show that export orientation can improve firm productivity (see Table III), but does not benefit generative capability (see Table II). Is this phenomenon limited to the Chinese context? Future research could examine this interesting cause-and-effect relationship. Finally, the concept of generative capability can inspire many new avenues of innovation research. We link generative capability with emergence and complexity theory on innovation butterfly [9], [10]. Generative capability could be “a constructive and co-creative process in which the drive to create is interdependent with the building blocks that are available and at hand” [9: 117]. How could this high-level routine be guided by simple rules or structural principles [30], [106]? Whether this routine could become rigid so as to be obsolete to innovation [89]? What percentage of exploration and exploitation should product innovation iteration and rapid integration require? How does one maintain balance and ambidexterity in the development of generative capability [64]? What kind of relationship exists between generative capability and disruptive innovation? Although a product in the early stages of disruptive innovation is always inferior, generative capability could play a major role in continuing to improve it and deliver breakthrough innovations [1]. How does a firm’s generative capability evolve by learning in a longitudinal setting? The answers to these questions in a multidisciplinary framework can advance the key concepts, such as dynamic capability, in the innovation and strategy literature [31], [32]. While these questions could lead to many new avenues of research, this study seeks to fill a gap in the literature with regards to how a firm can capture the value of an existing innovation to build its next innovation. This paper is among the first to contribute to the development of the new concept of generative capability and breaks new ground in understanding product innovation. ACKNOWLEDGMENT The authors would like to thank S. Roberto, Y. Chen, and the conference participants for their helpful comments. They would

also like to thank the editor N. Joglekar and two reviewers for their excellent guidance.

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Sunny Li Sun received the Ph.D. degree in organization, strategy, and international business from the University of Texas at Dallas, Richardson, TX, USA, in 2010. He is an Associate Professor of entrepreneurship and innovation with the University of Massachusetts Lowell, Lowell, MA, USA. His research interests cover entrepreneurship, corporate governance, venture capital, network, and institution. He has published 40 papers in the Strategic Management Journal, Organization Science, the Journal of International Business Studies, Entrepreneurship Theory and Practice, the Journal of Business Ethics, the Journal of Management Studies, Academy of Management Perspective, the Journal of World Business, Industrial Marketing Management, and the Journal of International Marketing, and other English journals. He has four papers listed as “highly cited papers” (in the top 1% of its academic field) based on Thomson Reuters’ Essential Science Indicators. Before joining the academia, he had 11 years’ industrial experience in new venture creating, financing, and consulting. Dr. Sun is the Editor of special issues for the Asia Pacific Journal of Management and the Journal of Product Innovation Management.

Bo Zou received the Ph.D. degree in management from the Harbin Institute of Technology, Harbin, China, in 2009. He is an Associate Professor with the Harbin Institute of Technology. His research interests cover innovation, entrepreneurship, and knowledge management. He has published 53 papers in English and Chinese management journals. Dr. Zou’s had one paper selected by the Annual Meeting of Academy of Management as the best papers in the program 2018.