External learning, market dynamics, and radical innovation: Evidence from China's high-tech firms

Journal of Business Research 65 (2012) 1226–1233

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Journal of Business Research

External learning, market dynamics, and radical innovation: Evidence from China's high-tech firms Yongchuan Bao a,⁎, Xiaoyun Chen b, Kevin Zheng Zhou c a b c

Department of Marketing, Sawyer Business School, Suffolk University, 8 Ashburton Place, Boston, MA 02108, United States Department of Marketing, Faculty of Business Administration, University of Macau, Av. Padre Tomás Pereira, Macau, China School of Business, the University of Hong Kong, Pokfulam, Hong Kong, China

a r t i c l e

i n f o

Article history: Received 2 December 2010 Accepted 3 June 2011 Available online 2 July 2011 Keywords: External learning Technical learning Administrative learning Radical innovation Technological turbulence Competitive intensity

a b s t r a c t Based on a dichotomy of knowledge content, this study examines how two types of external learning (i.e. technical and administrative learning) affect radical innovation, and assesses how such effects are conditional on two types of market dynamics (i.e. technological turbulence and competitive intensity). A survey of 183 high-tech firms in China shows that both technical and administrative learning facilitate development of radical innovation. Further, technological turbulence reduces the effect of technical learning but enhances the effect of administrative learning on radical innovation, whereas competitive intensity enhances the effect of technical learning but reduces the effect of administrative learning on radical innovation. © 2011 Elsevier Inc. All rights reserved.

1. Introduction Radical innovation refers to a new breed of technology that departs from the evolutionary path of existing technologies and provides substantially greater customer benefits than existing technologies (Chandy and Tellis, 1998; Sorescu, Chandy, and Prabhu, 2003). With the potential to escalate a new entrant into a prominent position and destroy dominant firms, radical innovation fosters the growth and renewal of firms and economies of countries (Tellis, Prabhu, and Chandy, 2009). Radical innovation development is therefore enticing numerous research efforts. Research has examined a variety of drivers of radical innovation, among which organizational learning has received prominent attention in recent literature (e.g. Baker and Sinkula, 2005; Morgan and Berthon, 2008; Zhou, Yim, and Tse, 2005). For example, studies in strategic alliances and network research emphasize that learning from partner firms or relational ties represents a critical source for innovations (e.g. Ahuja, 2000; Chesbrough, 2008; Grant and BadenFuller, 2004; Powell, Koput, and Smith-Doerr, 1996; Thomas and Hans Georg, 2004). Despite the great interest in this literature, few studies have paid attention to the role of the content of learning in radical innovation development. Prior studies tend to conceptualize learning as a culture or value embedded in the fabrics of organizational activities (Baker and Sinkula, 1999; Sinkula, Baker, and Noordewier, 1997). However, the ⁎ Corresponding author. Tel.: + 1 617 3051933; fax: + 1 617 9735382. E-mail addresses: [email protected] (Y. Bao), [email protected] (X. Chen), [email protected] (K.Z. Zhou). 0148-2963/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusres.2011.06.036

content of learning is also important, since an innovation can never be materialized without the input and proper functioning of knowledge (Capon and Glazer, 1987; O'Conner and DeMartino, 2006). Based on a classification scheme on knowledge (Damanpour and Evan, 1984; Han, Kim, and Srivastava, 1998; Zhou, Tse, and Li, 2006), we dichotomize external learning into two types: learning technical knowledge vs. learning administrative knowledge from other companies, and examine how technical and administrative learning influence radical innovation. Second, most extant literature has taken a general view that learning is beneficial to innovation (Baker and Sinkula, 1999, 2005; Hurley and Hult, 1998; Morgan and Berthon, 2008; Zhou et al., 2005); yet few studies examine under what conditions learning is more or less effective (Gnyawali and Stewart, 2003). According to the contingency theory, the effectiveness of a strategy depends on the fit between the strategy and the business environment (Zajac, Kraatz, and Bresser, 2000). Thus, the effects of external learning likely vary across different environmental conditions. Uncovering such contingent forces that either circumscribe or amplify the effects of learning is necessary to enrich understanding and development of a contingent view of learning. This study aims to address these two research gaps. It examines the effects of two types of external learning (i.e. technical learning and administrative learning) on radical innovation, and the contingent role of two fundamental market dynamics (i.e. technological turbulence and competitive intensity) (see Fig. 1). We propose that although both technical and administrative learning facilitate development of radical innovation, their effects are contingent on technological turbulence and competitive intensity in opposite ways. In particular, we find that technological turbulence reduces the effect of technical learning but

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H1 + Technical Learning

H3a+

H4a-

Technological Turbulence Administrative Learning

Competitive Intensity

H3b+

Radical Innovation

H4bH2 +

Fig. 1. Conceptual Model.

enhances the effect of administrative learning on radical innovation, whereas competitive intensity enhances the effect of technical learning but reduces the effect of administrative learning on radical innovation. 2. Conceptual framework 2.1. External learning perspective Organizational learning takes place either within or beyond the boundary of organizations (Rosenkopf and Nerkar, 2001). In the internal locus of learning, firms learn from past experience or leverage internal knowledge. In boundary-spanning learning, firms search for knowledge generated by other organizations and integrate it with own knowledge or capabilities to build innovations. As compared to internal learning, external learning opens up gateways to new knowledge that departs from existing organizational memory, thus reducing the possibility of transformation from competency into rigidity (Leonard-Barton, 1992). Furthermore, given that inter-organizational networks provide munificent knowledge and novel ideas (Laursen and Salter, 2006; Powell et al., 1996), external learning increases the opportunities of innovation. Consistent with the literature on organizational learning (Baker and Sinkula, 1999; Huber, 1991; Slater and Narver, 1995), we define external learning as a focal firm's acquisition, processing, and integration of knowledge from other companies. Knowledge can be classified into two types: technical and administrative (Damanpour and Evan, 1984; Han et al., 1998; Zhou et al., 2006). Technical knowledge refers to the product and production technologies or the technical systems of an organization, whereas administrative knowledge refers to firms' management systems and practices, such as reward system and internal competition mechanism (Chandy and Tellis, 1998; Han et al., 1998). This classification scheme of knowledge forms a basis for different focuses of external learning. In particular, technical learning refers to learning technical knowledge from other companies. For example, Japanese firms are famous for their innovation capability based on learning of the technologies invented by U.S. enterprises (Hamel, Doz, and Prahalad, 1989). Administrative learning refers to learning management systems and practices from other companies. For instance, General Electric (GE) sent representatives to some rival electronic components firms, such as Hewlett-Packard and Xerox, to learn about their business operations (Fortune, 1991). 2.2. External learning and radical innovation Prior studies identify three critical conditions for successful development of radical innovation. First, companies need to diversify the technology base or scientific expertise (Cardinal, 2001). QuintanaGarcia and Benavides-Velasco (2008) demonstrate that technological diversification has a strong impact on exploratory innovation, because it fuels cross-fertilization between different technology fields and consequently mitigates core rigidity. Second, radical innovation is premised on cannibalization of prior investments in existing competencies (Chandy

and Tellis, 1998) and identification of new market opportunities (Day, 1994; O'Connor and Rice, 2001). Therefore, it requires a paradigm shift in organizational mental models, which govern the allocation of resources and the recognition of market opportunities (Baker and Sinkula, 2005). Third, the initiative of radical innovation evokes radical changes in organizational routines and consequently causes resistance from affected members within an organization (Bao, 2009; Hannan and Freeman, 1984). Accordingly, development of radical innovation behooves legitimization of radical changes (Dougherty and Heller, 1994). External learning creates favorable conditions that satisfy these requirements for radical innovation. Specifically, learning external technical knowledge fosters diversification in the knowledge input (Chang and Cho, 2008; Lichtenthaler, 2010) and hence increases the chance of radical innovation. The positive relationship between this type of learning and radical innovation receives strong support in the literature. For instance, Gilsing, Nooteboom, Vanhaverbeke, Duysters, and Oord (2008) suggest that learning from companies with dissimilar technical know-how offers opportunities for the novel integration of complementary knowledge resources. Further, Wuyts, Dutta, and Stremersch (2004) explicitly show that exchange of technological knowledge between organizations enhance firms' ability to introduce radical innovation. Therefore, H1. Technical learning has a positive effect on radical innovation. Learning administrative knowledge can trigger a shift in organizational mindset and facilitate legitimization of organizational changes. Through learning successful management practices from other companies, a firm is able to identify deficiencies or even mistakes in its current management routines or mindset (Ghoshal and Westney, 1991). The dissatisfaction with the status quo catapults an “unlearning” process, in which firms change views or assumptions about the business world and refresh their mindsets (Baker and Sinkula, 1999; Birkinshaw and Mol, 2006; Slater and Narver, 1995). For example, based on learning from rivals, GE realized a mistake in management practice and accordingly switched the emphasis from business outcome to process (Fortune, 1991). Legitimization of organizational changes works through implementation of institutional programs (Dougherty and Heller, 1994). However, firms' own management systems and processes, which are designed for existing products, may constitute part of the forces of resistance to innovations. By tapping external sources for new management practices, an innovating firm is better able to legitimize changes and reduce innovation resistance (Birkinshaw and Mol, 2006; Zhou et al., 2006). Thus, we predict that H2. Administrative learning has a positive effect on radical innovation. 2.3. Contingent effects of market dynamics According to the contingency theory, the effectiveness of corporate strategies depends on the fit between the strategies themselves and

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the characteristics or requirements of business environments (Zajac et al., 2000). The dependence of firm strategy on environmental forces is especially pronounced for external learning, given that the knowledge learned originates from external sources. In the following sections, we examine how technological turbulence and competitive intensity, two representative indicators of market dynamics (Luo and Hassan, 2009; Zhou et al., 2005), moderate the effects of technical and administrative learning. Technological turbulence refers to the rate of technological advances in an industry (Zhou et al., 2005). Prior studies show that technological turbulence creates opportunities for breakthrough innovations and forces firms to accelerate the rate of innovations in order to avoid lagging behind rivals (Jaworski and Kohli, 1993; Zhou et al., 2005). Consistent with this perspective, we argue that a rapid pace of change in technologies leads to a high level of technological munificence in the external environment and expands the scope of technical knowledge available for a learning firm. As a result, the external technical knowledge base is increasingly diversified (Lichtenthaler, 2010). Technological diversification has been shown to enhance firm capability of combining and recombining existing knowledge with new components of knowledge and increase the likelihood of radical innovation (Quintana-Garcia and Benavides-Velasco, 2008). As additional discrete technical knowledge pieces are available, integration and reconfiguration of radical linkages among complementary knowledge sources become more likely for firms that engage in external knowledge acquisition (Gilsing et al., 2008). Therefore, H3a. The effect of technical learning on radical innovation is stronger when technological turbulence is high than when it is low. A firm can also build radical innovation on the basis of internal knowledge resources (Chandy and Tellis, 1998; Tellis et al., 2009). The need for internal knowledge may be heightened in a technologically turbulent environment, because a rapid pace of technological change creates uncertainty that may destroy a focal firm's technological expertise or core competency (Anderson and Tushman, 1990; Weiss and Heide, 1993). In order to insulate core competence from the threat of external technological changes, a firm may have to switch resource allocation from acquisition of external technical knowledge to internal R&D. For example, fearing the potential threat imposed by the explosive growth of the Web to its Window operating systems, Microsoft developed its own web browser (Internet Explorer) instead of licensing it from Netscape (Computer Reseller News, 1995). To develop radical innovation within the constraints of internal resources, a company needs to set up a flexible and efficient management system to deal with the high uncertainty and complexity of a radical innovation project (Chandy and Tellis, 1998; O'Conner and DeMartino, 2006). The old management systems designed for existing technology base are most likely inadequate for such innovative venture. Learning from other companies' successful management ideas and practices may help solve this problem. Further, it facilitates paradigm shift in organizational mindsets and legitimization of changes in organizational systems (Birkinshaw and Mol, 2006; Dougherty and Heller, 1994). Thus, H3b. The effect of administrative learning on radical innovation is stronger when technological turbulence is high than when it is low. Competitive intensity refers to the degree to which competitors engage in competitive activities, such as altering marketing mix instruments, to obtain competitive advantages (Song and Perry, 2009; Zhou and Li, 2010). In the presence of competitive pressures, companies tend to narrow their focus on established markets and pay close attention to the needs of existing customers (Christensen, 1997). The “tyranny of the served market” or an emphasis on protecting the incumbent turf prevents a firm from altering its view of the business environment, even though the environment changes continuously

(Hamel and Prahalad, 1994). As a result, firms are often unable to respond effectively to the emergence of new technologies (Tripsas and Gavetti, 2000). Instead, they may stick to dominant organizational routines that increase the reliance on existing resources but preclude development of new competences (Gilbert, 2005). This kind of organizational inertia or rigidity is accentuated when firms face threats from the environment (Staw, Sandelands, and Dutton, 1981). Industrial competition represents a common form of environmental threats to firm survival and business performance. As a result, firms in intense competition are reluctant to experiment with new technologies, but focus on leveraging existing technology base to achieve competitive edge (Christensen, 1997; Gilbert, 2005). Therefore, whereas technical learning occurs in a competitive environment, the newly acquired technical knowledge may not be transformed to radical innovation due to organizational inertia. Hence, H4a. The effect of technical learning on radical innovations is weaker when competitive intensity is high than when it is low. When competition intensifies, firms are forced to invest in both defensive and offensive marketing strategies, such as relationship marketing, price wars, and massive promotion campaigns (Bridges and Freytag, 2009; Zhou et al., 2005). However, implementation of these strategies and tactics requires administrative knowledge. Although administrative learning helps a firm achieve this goal, the competitive activities and marketing programs are primarily designed to either attract new customers or retain existing customers (Voss and Voss, 2008). Thus, the administrative knowledge acquired may not be readily used as valuable inputs in the innovation process. In other words, in an intensely competitive environment, while administrative learning improves the effectiveness of marketing management, it carried limited value for promoting the innovation initiatives. Also, as described above, intense competition as a source of environmental threats drives firms to maintain established organizational routines that facilitate the exploitation of existing competence (Gilbert, 2005; Staw et al., 1981). Consequently, this type of organizational inertia hinders the development of radical innovation (Gilbert, 2005). Therefore, H4b. The effect of administrative learning on radical innovations is weaker when competitive intensity is high than when it is low.

3. Research methodology 3.1. Sampling and data collection This study conducts the empirical test in China's high-tech sectors, due to the following considerations. First, the dramatic social and economic changes in China's during the past decades force firms to learn actively from others in order to develop innovation skills and increase the odds of business success (Liu, Luo, and Shi, 2003; Zhou et al., 2005). Second, because high-tech firms in China often lack necessary resources or experience, learning from others is an efficient shortcut to narrow the competence gap between Chinese firms and firms from developed countries (Atuahene-Gima, 2005). The sample frame consists of firms from a wide range of high-tech industries, including information technology, software development, biotechnology, and electronics product development. These firms are located in the High Technology Experimental Zones of Beijing, Shanghai, and Shenzhen, the three most developed high-tech industrial zones in the country. The administrative offices of these industrial zones offer the sample frame, from which this study randomly selects 200 firms in each of the three cities. Of the 600 firms contacted, 250 agree to participate. Then, the authors contact these firms to identify the key informants. The qualified respondents include R&D directors, project managers, and product managers.

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Consistent with prior studies, this study employs the method of onsite interviews for the survey (Zhou et al., 2005). During the interviews, we explain the meanings of all key constructs and use examples to illustrate. More than 70% of the interviews take place at the respondents' offices. Each interview lasts about 30 min on average. In total, the study collects 198 responses. Fifteen responses are removed from the analysis due to lack of knowledge on the subject matter. The final sample consists of 183 high-tech firms, with a response rate of 30.5% (183 out of 600 firms). 3.2. Measures All measures are prepared in English, translated into Chinese, and then back-translated into English to ensure accuracy. Based on a pretest with 15 Chinese managers in high-tech industries, the authors revise some items to ensure the face validity and meaningfulness of measures in the research context of China. All measurement items, shown in the Appendix, employ the 7-point Likert scale (1 — strongly disagree to 7 — strongly agree). The measures of external learning (technical learning and administrative learning) are developed based on the works of Huber (1991). The measurement items assess a firm's commitment to learning from other companies the technical and administrative knowledge, and the degree to which a firm acquires, processes, and integrates these two types of knowledge. Measures of technological turbulence and competitive intensity are adapted from Jaworski and Kohli (1993). The items for technological turbulence capture the degree of technological change in an industry, while the items for competitive intensity appraise the level of competition and the severity of price undercut. Radical innovation refers to a new breed of technology that deviates from the existing technological evolution trajectory of a firm or an industry and offers substantially new benefits to customers (Chandy and Tellis, 1998). Atuahene-Gima (2005) adapts the measurement items of radical innovation from the seminal work of Chandy and Tellis (1998) and applies to a survey among high-tech companies in China. Because our survey was also carried out in China's high-tech industries, we adopt the same measurement items. The first two items reflect a firm's absolute and comparative levels of radical product introduction in the past three years, respectively. The other two items indicate the objective information of the percentage of sales coming from radical innovation and number of radical innovation; however, these two items receive too many missing values and are thus dropped from the measurement model. To corroborate our measure, we test the correlation of the radical innovation scale with the two objective measurement items of radical innovation, using the subset of sample firms that provide data of the objective measures (we thank a reviewer for this suggestion). The results (correlation with percentage of sales from radical innovation = 0.67, p b 0.001; correlation with number of radical innovation = .86, p b 0.001) provide support to our measure of radical innovation. Following Zhou et al. (2006), this study includes such control variables as industry sectors, firm size, firm age, firm type, and R&D expenditures. The authors use dummy variables to represent their specific industry sectors: information technology, software development, biotechnology, and electronics product development. The authors use the logarithm of years that a firm existed in an industry to represent firm age and the logarithm of the number of employees to indicate firm size. Firm type is a dummy variable, with 1 denoting domestic companies and 0 representing foreign companies or international joint ventures. R&D expenditure is measured by the percentage of sales volume devoted to R&D activities. 3.3. Measurement validity Following Anderson and Gerbing (1988), this study adopts a twostep approach to examine the reliability and validity of the measures.

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First, this study runs exploratory factor analysis (principal component analysis with varimax rotation) for the two types of organizational learning, environmental factors, and radical innovation. The analysis generates five factors as expected and the eigenvalue for each factor is larger than one. All items exhibit high loadings on their construct (all above 0.7), as theoretically predicted, and there are no substantial cross-loadings (all below 0.3). The results support the distinction of these constructs. Second, this study assesses the unidimensionality of the measures with a confirmatory factor analysis (CFA) using AMOS (see the Appendix). The overall model fits well the data: χ(122) = 215.34, p = .000; CFI = .95, IFI = .95, RMSEA = .06, and Tucker–Lewis index = .94. All factor loadings for the underlying constructs are significant (p b .01). In addition, the composite reliabilities of all main constructs are above the .60 benchmark (Bagozzi and Yi, 1988). The average variances extracted (AVE) exceed the .50 cutoff point (Fornell and Larcker, 1981), except for competitive intensity (.46). These results demonstrate both convergent validity and reliability of the latent variables. To examine the discriminant validity of the latent variables, this study runs pair-wise chi-square difference tests for all constructs, using both a constrained and an unconstrained model. In each test, the constrained model fit is significantly worse than that of the unconstrained model, in support of discriminant validity (Anderson and Gerbing, 1988). Moreover, AVE of each construct exceeds the squared correlations between latent variable and every other one, which provides further support of discriminant validity (Fornell and Larcker, 1981). 3.4. Common method variance To minimize common method variance (CMV), this study takes both ex ante and ex post approaches. Ex ante, following Podsakoff, MacKenzie, Lee, and Podsakoff (2003) suggestion, the authors use control variables to separate the measurement of the independent, dependent variables and moderators, thus reducing the respondent's motivation to use his or her prior responses to answer subsequent questions. Ex post, the study conducts Harman's single-factor test (Podsakoff and Organ, 1986). A factor analysis of the dependent and independent variables yields a factor solution that accounts for 77.0% of the total variance, and the first factor only accounts for 20.8% of the variance. Because a single-factor solution does not emerge and the first factor does not explain the majority of the variance, common method bias does not pose a serious threat to the validity of research findings in this study. 4. Analysis and results This study employs the hierarchical moderated regression to test the hypotheses. With the unidimensionality of the measures established, this study uses the composite scores of each construct in the analysis (Table 1). To mitigate potential threats of multicollinearity, this paper mean-centers the independent and the moderating variables (Aiken and West, 1991). The largest variance inflation factor is 2.79, well below the benchmark of 10.0. Therefore, multicollinearity is not a concern in our analysis. In the hierarchical approach, Model 1 includes only the industry sector controls, Model 2 adds other control variables, Model 3 adds the main effects, and Model 4 adds the interaction effects. The summarized results in Table 2 reveal that the R 2 value increases significantly for Models 3 and 4 and that Model 4 explains 43% of the total variance in radical innovation. As shown in Table 2, both technical learning (β = .36, p b .001) and administrative learning (β = .19, p b .01) exert positive effects on radical innovation, in support of H1 and H2. Furthermore, technological turbulence significantly reduces the positive effect of technical learning (β = −.17, p b .05), contrary to the prediction of H3a, but increases the

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Table 1 Means, standard deviations, and correlations of the constructs.

1. Technical learning 2. Administrative learning 3. Technological turbulence 4. Competitive intensity 5. Radical innovation 6. Firm age 7. Firm type 8. R&D expense 9. Firm size Mean S. D.

1.

2.

1.00 .52⁎⁎ .41⁎⁎ .17⁎ .52⁎⁎ −.10 −.01 .07 −.03 5.21 1.05

3

1.00 .32⁎⁎ .09 .42⁎⁎ −.13 −.11 .01 −.03 4.86 1.16

4

1.00 .44⁎⁎ .38⁎⁎ .03 .04 .20⁎ .10 5.27 1.14

1.00 .03 .19⁎ .14 .19⁎ .22⁎⁎ 5.46 .98

5

1.00 .12 .05 .15⁎ .12 4.91 1.43

6

7

1.00 .01 .34⁎⁎ .48⁎⁎

1.00 .14 .18⁎

2.16 .91

1.53 .50

8

9

1.00 .84⁎⁎ 1.97 .94

1.00 2.65 .99

Notes: N = 183. ⁎⁎ p b .01. ⁎ p b .05.

positive effect of administrative learning (β = .24, p b .01), in support of H3b. In contrast, competitive intensity marginally increases the positive effect of technical learning marginally (β = .12, p b .1), contrary to the prediction of H4a, but reduces the positive effect of administrative learning (β = −.16, p b .05), in support of H4b. In addition, environmental factors directly influence radical innovation, as shown in Table 2. When technological turbulence is high, firms are more likely to develop radical innovations; in contrast, high levels of competitive intensity reduce the firm's intention to develop radical innovations. This study also conducts slope analysis (Aiken and West, 1991) to illustrate the interactions between external learning and market dynamics. The analysis provides further support to the moderated regression analysis (Fig. 2). 5. Discussion This study makes two major contributions to the literature. First, it adds to the learning literature by differentiating between two types of learning: technical and administrative. In contrast to the conventional view that learning from others is equivalent to imitation and consequently causes a downward spiral in the societal inventory of innovations (Huber, 1991; Levinthal and March, 1993), our research shows that both technical and administrative learning positively Table 2 Standardized regression coefficient estimates. Radical innovation Control variables Industry sectors Information technology Software development Biotechnology Electronics product development Firm age Firm type R&D expense Firm size Independent variables Technical learning (TL) Administrative learning (AL) Technological turbulence (TT) Competitive intensity (CI) Interactions TT × TL TT × AL CI × TL CI × AL R-square R-square change ***pb .001. **p b .01. *p b .05.

+

p b .10 (two-tailed).

Model 1

Model 2

Model 3

Model 4

.05 −.03 −.05 .03

.07 −.01 −.02 .08 .11 .03 .19 −.07

.09 −.01 .00 .09 .17* .05 .04 .06

.08 .00 .01 .09 .15* .06 −.02 .10

.37*** .19** .25*** −.21**

.36*** .19** .26** −.19*

.40*** .35***

−.17* .24** .12+ −.16* .43*** .03***

.01

.05 .04

affect radical innovation. The implication is that learning external knowledge does not necessarily lead to “copying and pasting” of knowledge generated by other companies; instead, it is a viable strategy that firms can leverage to build radical innovation. This insight provides additional support to the theoretical arguments in current literature that inter-firm learning in strategic alliance or networks facilitates innovations (e.g. Ahuja, 2000; Grant and BadenFuller, 2004; Thomas and Hans Georg, 2004). It is also consistent with the open innovation paradigm. Studies adopting this perspective emphasize the role of external resources and knowledge in innovation development (Chesbrough, 2008) and suggest that the abundant knowledge residing in the external environment provides a wide range of novel ideas and innovation opportunities (Laursen and Salter, 2006; Powell et al., 1996). Paralleling this view, the current research shows that, by tapping into the external knowledge, a firm is able to generate radical innovation. Second, this study enriches the contingent view of learning by showing the significant influence of market dynamics (i.e. technological turbulence and competitive intensity). Specifically, we find that, technological turbulence reduces the effect of technical learning but enhances the effect of administrative learning on radical innovation; competitive intensity strengthens the effect of technical learning but weakens the effect of administrative learning on radical innovation. The remarkable contrast indicates that market dynamics either amplifies or mitigates the effects of organizational learning, depending on the learning contents. The implications are that if research neglects the environmental forces in examining the learninginnovation relationships, it may yield inaccurate estimation of the learning effects. Conceptually, these results suggest a refinement of the dominant view in current literature that organizational learning benefits firms unconditionally, and in doing so, uncover the boundary conditions of the learning effects for innovations. In the meantime, they respond to the call for research attention to the contingency of organizational learning (Gnyawali and Stewart, 2003). Surprisingly, it contradicts our prediction that technological turbulence exerts a negative moderating effect while competitive intensity has a positive moderating effect on technical learning. A plausible factor that drives the negative effect of technological turbulence is the time sensitivity of technological knowledge in a turbulent environment. Weiss and Heide (1993) posit that fast changes in the technological environment render product information “time sensitive” — that is, information obtained now becomes less valuable in the future as new technologies offering higher benefits are introduced rapidly. Moorman and Miner (1997) concur that environmental turbulence may reduce the value of prior learning. Given that a fast pace of technological change can cause the acquired knowledge to lose value, technological turbulence may actually mitigate the diversity of knowledge that is useful for radical innovation, even

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Technological Turbulence

a) Technical learning (H3a) high technological turbulence

b) Administrative learning (H3b)

low technological turbulence

high technological turbulence

low technological turbulence

1.5

Radical innovation

Radical innovation

1.5 1 0.5 0 -0.5 -1

1 0.5 0 -0.5 -1 -1.5

-1.5 -2

-1

0

1

2

-2

Technical learning

-1

0

1

2

Administrative learning

Competitive Intensity

c) Technical learning (H4a) high competitive intensity

d) Administrative learning (H4b)

low competitive intensity

high competitive intensity

low competitive intensity

1.5

Radical innovation

Radical innovation

1.5 1 0.5 0 -0.5 -1 -1.5

1 0.5 0 -0.5 -1 -1.5

-2

-1

0

1

2

-2

Technical learning

-1

0

1

2

Administrative learning Fig. 2. Simple Slope Analyses.

though it generates a plethora of technical knowledge over time, and consequently reduces the effect of technical learning. For the positive moderating effect of competitive intensity, we reason that it may be due to the unique role of radical innovation in helping a firm outrun rivals in competitive moves. That is, radical innovation offers novel functionalities and distinct customer benefits, which are difficult to imitate, vis-à-vis cutthroat price war and other similar marketing programs. In other words, development of radical innovation provides a more effective approach to sustainable competitive advantage (Jaworski and Kohli, 1993; Zhou et al., 2005). To counteract competitive pressures, therefore, a firm may have a high incentive to search for useful technologies to innovate (Song and Perry, 2009). The external search increases the diversity of the technical knowledge base (Laursen and Salter, 2006), and consequently enhances the effect of technical learning on radical innovation. It also warrants emphasis that, although the main effects of market dynamics are not formally examined in the current study, they turn out to be significant in the regression model. The results are consistent with Zhou et al.'s research (2005). Their study finds that technological turbulence exerts a positive effect on both technology-based and market-based breakthrough innovation while competitive intensity imposes a negative effect on market-based breakthrough innovation. The consistent findings across different studies indicate a robust influence of market dynamics on breakthrough or radical innovation. This study also offers some important practical insights for managers. To enhance radical innovation competence, a firm must actively acquire external knowledge, including both technical and

administrative knowledge. Although the conventional focus of learning is on technical knowledge, a common component of technological products, administrative knowledge can also lead to radical innovation. One useful strategy of learning might be to establish a knowledgesharing network that consists of companies with both diverse technology bases and innovative management skills. On the other hand, management of organizational learning for radical innovation requires firms to be flexible in adjusting the content of learning in different environmental conditions. Market dynamics cause the variation in the value of acquired knowledge and impose changes on the information need of a learning firm. The best strategy of external learning seems to strike a fit between the knowledge content of learning and the idiosyncratic characteristics of market dynamics. Specifically, when the market is characterized by rapid (stable) technological development, a firm may concentrate on learning management (technical) knowledge; in contrast, when the competition is (not) intensive, knowledge acquisition may focus on the technical (administrative) content. This study contains some limitations. First, the research findings are based on cross-sectional data. Given that the effects of organizational learning may take time to materialize (Zhou et al., 2006), longitudinal studies are necessary to enhance understanding of the long-term effects of organizational learning. Second, external learning as a firm strategy can also be used for imitation purpose. This study does not discriminate the learning effects between innovation and imitation. Future research is encouraged to examine how different types of external learning influence product innovation and imitation

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differentially. Third, this study excludes from inquiry the effects of external learning on financial or market performance of radical innovation. Radical innovation involves high risks (Sorescu et al., 2003; Zhou et al., 2005) and thus does not guarantee business success. Learning of technological or administrative knowledge that proves effective in other companies' innovation activities may reduce such risks. Hence, it is plausible that radical innovation derived from external learning may have a higher likelihood of success than based on internal R&D. Future studies can extend the current research efforts by examining the performance implications of external learning for radical innovation.

Acknowledgment We thank Paul Adler for his helpful comments, a reviewer and Mario Krenn for their constructive suggestions.

Appendix. Measurement items and validity assessment

Loading Technical learning (CA = .91; CR = .88; AVE = .60) With regard to technological expertise and product development or design, our company Actively acquires information from other companies on new products .87 development or new technology innovation. Systematically processes and analyzes other companies' ways to .96 develop new products to upgrade our techniques. Periodically integrates new technology information of other 1.00a companies in our product development. Often learns from other companies on technology innovation or .94 product development (e.g., new function or design). Often analyzes other companies' products and technologies to .89 improve our own. Administrative learning (CA = .91; CR = .85; AVE = .59) With regard to managerial techniques about how to manage the company, our company Actively acquires information from other companies on their .87 management system or organizational design. Systematically processes and analyzes other companies' managerial 1.00a techniques. .93 Periodically integrates information from other companies on their way of managerial operation as a benchmark in our management practices. Often learn from other companies about their management .99 practices to improve our own. Technological turbulence (CA = .89; CR = .83; AVE = .55) 1. The technology in our industry is changing rapidly. .96 2. A large number of new product ideas have been made possible 1.00a through technological breakthroughs in our industry. 3. There have been major technological developments in our .80 industry. 4. The technological changes in this industry are frequent. .86 Competitive intensity (CA = .75; CR = .68; AVE = .46) Competition in our industry is cutthroat. .63 One hears of a new competitive move very frequently. 1.00a Price competition is a hall mark of our industry. .70 Radical Innovation (CA = .92; CR = .84; AVE = .73) This firm frequently has introduced radical new products into new .98 markets in the last 3 years. Compared with our major competitor, this firm has introduced more 1.00a radical new products in the last three years. Percentage of total sales from radical products introduced by our * company in the last three years (b5%, 5–10%, 11–15%, 16–20%, N 20%). Number of radical products introduced by the firm in the last three * years. Goodness-of-fit: χ2(122) = 215.34, p = .000; CFI = .95, IFI = .95, TLI = .94; RMSEA = .06 Notes: CA = Cronbach's alpha, CR = composite reliability, AVE = average variance extracted. a Fixed factor loading, * item deleted because of missing values.

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