Research paper

HOW KNOWLEDGE AFFECTS RADICAL INNOVATION: KNOWLEDGE BASE, MARKET KNOWLEDGE ACQUISITION, AND INTERNAL KNOWLEDGE SHARING Author(s): KEVIN ZHENG ZHOU and CAROLINE BINGXIN LI Source: Strategic Management Journal, Vol. 33, No. 9 (September 2012), pp. 1090-1102 Published by: Wiley Stable URL: https://www.jstor.org/stable/23261319 Accessed: 29-11-2018 18:43 UTC

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Strategic Management Journal Strut. Mgmt. J., 33: 1090-1102 (2012)

Published online EarlyView in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/smj.l959

Received 29 May 2009; Final revision received 20 January 2012

RESEARCH NOTES AND COMMENTARIES

HOW KNOWLEDGE AFFECTS RADICAL INNOVATION:

KNOWLEDGE BASE, MARKET KNOWLEDGE ACQUISITION, AND INTERNAL KNOWLEDGE SHARING

\ KEVIN ZHENG ZHOU1* and CAROLINE BINGXIN LI2 School of Business, University of Hong Kong, Hong Kong Daniels College of Business, University of Denver, Denver, Colorado, U.S.A.

This paper examines how existing knowledge base (i.e., knowledge breadth and depth) interacts with knowledge integration mechanisms (i.e., external market knowledge acquisition and internal knowledge sharing) to affect radical innovation. Survey data from high technology companies in China demonstrate that the effects of knowledge breadth and depth are contingent on market knowledge acquisition and knowledge sharing in opposite ways. In particular, a firm with a broad knowledge base is more likely to achieve radical innovation in the presence of internal knowledge sharing rather than market knowledge acquisition. In contrast, a firm with a deep knowledge base is more capable of developing radical innovation through market knowledge acquisition rather than internal knowledge sharing. Copyright © 2012 John Wiley & Sons, Ltd.

INTRODUCTION

Radical innovation is the novel, unique, or state of-the-art technological advance in a product cat egory that significantly alters the consumption patterns in a market (Abernathy and Utterback, 1978; Gatignon et al., 2002). As radical innova tion reshapes the competitive landscape and cre ates new market opportunities, various approaches have been proposed to identify its drivers (e.g., Chandy and Tellis, 1998; Smith and Tushman, 2005), among which the knowledge-based view

Keywords: knowledge-based view; knowledge breadth; knowledge depth; radical innovation; China * Correspondence to: Kevin Zheng Zhou, School of Business, University of Hong Kong, Pokfulam, Hong Kong. E-mail: kevinzhou@business.hku.hk

(KBV) has recently gained prominence. The basic premise of the KBV is that new product cre ativity is primarily a function of the firm's abil ity to manage, maintain, and create knowledge (Grant, 1996). Whereas early KBV studies tend to focus on how knowledge affects innovation in general (e.g., Bierly and Chakrabarti, 1996; DeCarolis and Deeds, 1999), more recent devel opments assert that a firm's knowledge base rep resents its most unique resource for radical innova tion development (e.g., Hill and Rothaermel, 2003; Miller, Fern, and Cardinal, 2007; Subramaniam and Youndt, 2005; Zhou and Wu, 2010).

However, extant literature offers conflicting views regarding how a firms' existing knowledge base, namely, its knowledge breadth and depth, affects radical innovation. For example, Taylor

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Research Notes and Commentaries 1091

and Greve (2006) suggest that firms with diverse knowledge domains are more likely to gener ate cutting-edge ideas and novel combinations of knowledge components. A broad knowledge base with varied, accumulated observations and cues facilitates understanding of new information and potential changes, which enhances the firm's abil ity to detect remote technological or market oppor tunities for its radical innovation (Chesbrough, 2003). In contrast, Laursen and Salter (2006) posit that though diverse knowledge may stimulate a variety of ideas, without sufficient synthesis and utilization efforts, those ideas will just touch on shallow surfaces rather than drill down to the

essence of an emerging breakthrough, which pro motes incremental but not radical innovation.

Regarding the role of knowledge depth, Zahra and George (2002) believe that in-depth knowl edge in a specific industrial field is essential for radical innovation because it facilitates the effec

tive realization of substantial new ideas. Many firms produce promising novel ideas but fail in the midst of implementation because they lack sufficient expertise to resolve complex or unusual problems (Katz and Du Preez, 2008). In contrast, Tripsas and Gavetti (2000) warn that deep knowl edge in a specialized field may generate cogni tive inertia, which constrains the firm to its cur rent market segment or established technology for minor improvement (Levinthal and March, 1993) but deteriorates its ability to pioneer using emerg ing technologies (Christensen and Bower, 1996).

To address these inconsistencies, we propose that the effect of a firm's existing knowledge base must be considered together with its exter nal and internal knowledge integration mecha nisms. As Ahuja and Katila (2001) indicate, many knowledge sources necessary to trigger radical innovation can be found only outside the firm, with customers, competitors, and suppliers (see also Laursen and Salter, 2006). Others empha size that knowledge sharing may nurture interunit cooperation and mutual learning, stir up existing knowledge repositories, and stimulate new ideas for radical innovations (e.g., Tsai, 2001; Zander and Solvell, 2000). Therefore, we examine how external market knowledge acquisition and inter nal knowledge sharing may condition the effect of the knowledge base on radical innovation. Because knowledge breadth reflects the horizontal dimen sion, whereas knowledge depth captures the ver tical dimension of knowledge, we posit that their

effects on radical innovation are likely moderated by market knowledge acquisition and knowledge sharing in opposite ways.

We test our propositions with two studies of high technology firms in China. As China becomes inte grated into the global economy, competition has intensified substantially, forcing both local and for eign firms to develop radical innovations to survive and succeed (Atuahene-Gima, 2005; Zhang and Li, 2010; Zhou, Yim, and Tse, 2005). In 2009, China filed 7,946 international patent applications, up 29.7 percent from the previous year, such that it ranks first in terms of growth and fifth in total number of international patent filings (World Intel lectual Property Organization, 2010). The complex and dynamic nature of the transitional Chinese market makes it a rich context to test the impact of knowledge on radical innovation.

THEORY AND HYPOTHESES

According to the KBV, a firm's existing knowl edge base delimits its scope and capacity to com prehend and apply novel knowledge to radical innovations (Hill and Rothaermel, 2003). Knowl edge breadth and depth are two distinct dimensions of a knowledge base that reveal both the structure and content of the knowledge a firm holds. Knowl edge breadth refers to the extent to which the firm's knowledge repository contains distinct and multi ple domains; knowledge depth refers to the level of sophistication and complexity of knowledge in key fields (Bierly and Chakrabarti, 1996). The breadth attribute captures the horizontal dimen sion of knowledge and heterogeneous knowledge content, whereas the depth attribute reflects a ver tical dimension and unique, complex, within-field knowledge content (De Luca and Atuahene-Gima, 2007). Verona (1999) further proposes that a knowl

edge base alone may not be sufficient for suc cessful product development; the firm also must use knowledge integration mechanisms to cap ture, interpret, and deploy its knowledge resources. In particular, market knowledge acquisition is an external integration mechanism that facilitates the absorption of critical knowledge from external market sources (Laursen and Salter, 2006). Knowl edge sharing entails an internal integration mecha nism, involving the dissemination and synthesis of individually and organizationally held knowledge

Copyright © 2012 John Wiley & Sons, Ltd. Strut. Mgmt. J., 33: 1090-1102 (2012) DOI: 10.1002/smj

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1092 K.Z. Zhou and C. B. Li

through established processes and routines (Kale and Singh, 2007; Verona, 1999). Such external and internal knowledge integration mechanisms likely affect how firms fully utilize the potential of their knowledge bases.

Knowledge breadth and radical innovation

To develop radical innovations, firms must fulfill two requirements: generating breakthrough ideas that enable firms to discover nascent technologies and real opportunities hidden within miscellaneous information; and implementing the breakthrough ideas into commercial technologies through resource synthesis and utilization (Hill and Rothaermel, 2003; Zahra and George, 2002).

In line with this logic, we posit that a firm with broad knowledge may benefit more from knowl edge sharing than from market knowledge acqui sition in developing radical innovation. A firm with broad knowledge has accumulated know-how across a variety of disciplines and heterogeneous market domains through its extensive knowledge exploration (Prabhu, Chandy, and Ellis, 2005). Because the firm already has information about heterogeneous market segments, the marginal ben efits of additional market knowledge acquisition for generating breakthrough ideas decline. The inflow of overlapping market information likely brings in ideas for minor refinement or extension of existing knowledge, but not the discovery of breakthrough ideas for radical innovation.

In contrast, knowledge sharing offers the poten tial for new, truly innovative combinations of knowledge by evoking a 'kaleidoscopic thinking' (Kanter, 1988): when a firm's knowledge base comprises diverse domains, the firm needs a good 'shake' to create a new perspective on its exist ing pieces of knowledge. Just like a kaleidoscope, the same fragments form completely new patterns when shaken. Knowledge sharing provides such a shaking process, through which the firm can con nect and integrate its broad knowledge across dis parate fields in unforeseen and unusual patterns to generate breakthrough ideas for radical innovation (Zahra and George, 2002).

For the idea implementation stage, additional information acquired from the external market can lead to information overload for a firm with broad

knowledge. Because a firm's cognitive attention is a limited resource, working on too many ideas may cause insufficient attention to any individual idea

(Laursen and Salter, 2006). Moreover, the com plexity of managing a variety of knowledge and their relationships makes it difficult to utilize diver sified know-how. Without a sufficient understand

ing and full utilization of acquired knowledge, incremental improvement and refinement, but not the development of true breakthroughs, is more likely (Katz and Du Preez, 2008).

In contrast, knowledge sharing involves the hor izontal integration of disparate knowledge, and a broad knowledge base provides diverse knowl edge interfaces among functional units. Through increased interactions and knowledge exchange, individual members of different functions recog nize how others' know-how bears on their own

work and how to synthesize it to serve the com mon goal of radical innovation creation (Schulz, 2001). The resulting communication of best prac tices across functional units enhances the firm's

ability to commercialize ideas into radical innova tions.

Hypothesis 1: A firm with a broad knowledge base benefits more from knowledge sharing than from market knowledge acquisition for fostering its radical innovation.

Knowledge depth and radical innovation

We posit that a firm with knowledge depth likely benefits more from market knowledge acquisi tion than from knowledge sharing. First, a firm with a deep knowledge base has accumulated thorough experience and know-how about exist ing technologies and markets. Internal knowledge sharing can further synthesize individually held know-how and help construct a deeper and more refined understanding of its existing knowledge (Kale and Singh, 2007; Tsai, 2001). However, such refined expertise likely prompts more incremental improvement that yields immediate and foresee able returns but not rule-breaking ideas for radical innovation. As Christensen and Bower (1996) doc ument, when a firm becomes deeply entrenched with existing markets, it tends to focus on incre mental innovations that are favored by its existing customers, but forgo explorations of new ideas for emerging markets.

Market knowledge acquisition instead helps expand the scope of information search beyond existing customers or markets. The infusion of new

Copyright © 2012 John Wiley & Sons, Ltd. Strat. Mgmt. J., 33: 1090-1102 (2012) DOI: 10.1002/smj

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Research Notes and Commentaries 1093

information from emerging markets likely gener ates new ideas for radical innovations (Laursen and Salter, 2006). Moreover, by integrating knowledge from potential markets into its deep understanding of current segments, the firm may detect future market trends and invest accordingly to explore them.

Second, when a firm has developed deep knowl edge and core competencies, in the form of tech nical or professional expertise, it tends to engage in activities in its existing, specialized domains (Christensen, 2006). By activating the integration and use of best practices within the firm, knowl edge sharing accentuates its self-reinforcing cycle of competence. As a result, the firm develops increasingly efficient processes and routines that sustain its current focus but become inertia in

exploring potential opportunities in other domains (Tripsas and Gavetti, 2000). To overcome such organizational inertia, firms must actively learn from external market players. Market knowledge acquisition provides access to diverse knowledge domains, such as from competitors or suppliers, distinct modes of reasoning, and varied problem solving approaches (Ahuja and Lampert, 2001). Exposure to these different approaches pushes the firm to take a renewed look at cause-and-effect

understanding, question existing cognitive struc tures, and uncover current competence deficiencies (Subramaniam and Youndt, 2005). As a result, firms can update and renew their organizational processes and routines to implement ideas from emerging technological and market domains. Thus,

Hypothesis 2: A firm with a deep knowledge base benefits more from market knowledge acquisi tion than from knowledge sharing for fostering its radical innovations.

STUDY 1

Sample and data collection

We randomly selected a sample of 500 high tech nology companies located in the Yangtze River Delta in China from a list provided by a market ing research company. The Yangtze River Delta, which contains the Shanghai, Jiangsu, and Zhe jiang provinces, is the most developed area in China and contributed 21.4 percent of the coun try's gross domestic product in 2009. Many multi national corporations, such as Microsoft, Intel, GE,

and Dow, have set up research and development (R&D) centers in this region (Einhorn, 2006). The output of the high technology sector in this area represents 34.2 percent of the national total. We selected Chinese managers (local or Chinese over seas returnees) as key informants, because foreign firms rely on these managers for business opera tions in China. Our field interviews also reveal that

they are highly familiar with their firms' knowl edge use and product innovation.

We prepared the questionnaire in English, then had two independent translators translate the instru ment into Chinese, following a back-translation process. To ensure content and face validity, we conducted five in-depth interviews with senior marketing managers who had at least three years' business experience in high technology sectors. In accordance with their responses, we revised a few questionnaire items to enhance clarity. We then conducted a pretest with 20 senior managers. Based on their feedback, we finalized the instru ment. The final survey was conducted in Chinese (i.e., Mandarin).

For the final survey, we selected two key infor mants in each firm: one senior manager (e.g., chief executive officer, vice president, general manager) and one middle manager (marketing, sales, R&D department manager). We administered the ques tionnaires onsite. Trained interviewers scheduled

appointments with the two key informants, pre sented the questionnaires to them separately, and then collected the questionnaires after their com pletion. We also asked the interviewers to collect business cards from the interviewed managers to ensure the interviews were performed and to facil itate future contacts. This procedure is critical in emerging economies to ensure valid, high quality data (Zhang and Li, 2010; Zhou and Wu, 2010).

The final sample consists of 177 firms (354 informants) from various high technology indus tries (information technology 31.6%; electronic 23.7%, mechanical and electric equipment 20.9%; new pharmaceuticals and bioengineering 12.4%; semiconductor design 11.3%), for an effective response rate of 35.4 percent. Of the participating firms, 31.6 percent are international joint ventures (IJVs), 32.8 percent are wholly owned foreign enterprises (WOFs), and 35.6 percent are domestic companies.1 The comparison of participating and

1 The high proportion of IJVs and WOFs reflects the competi tion status of high technology industries in China, which has

Copyright © 2012 John Wiley & Sons, Ltd. Strut. Mgmt. J., 33: 1090-1102 (2012) DOI: 10.1002/smj

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1094 K.Z. Zhou and C. B. Li

nonparticipating firms indicates no significant dif ferences in terms of firm age, number of employ ees, or sales, which suggests no notable response bias.

To minimize potential common method bias, we obtained information from different sources

in each firm. The senior managers answered sur vey questions about the characteristics of existing knowledge (i.e., knowledge depth and breadth). Middle managers provided information at the oper ational level, such as market knowledge acquisi tion, knowledge sharing, and radical innovation.2 To ensure the informants were appropriate and reli able, we assessed their knowledge about the survey questions and their experience with the firm and industry. The means for the senior and middle managers' knowledge levels were 6.20 and 5.88, respectively (1 = 'little knowledge,' 7 = 'great deal of knowledge'), highly comparable to pre vious studies (e.g., De Luca and Atuahene-Gima, 2007; Moorman and Miner, 1997).

Measures

We develop our measure of knowledge breadth on the basis of Bierly and Chakrabarti (1996) and Moorman and Miner (1997); it assesses diversi fication in a firm's knowledge of customer port folios, market segments, and technological back ground (see Appendix A). Following Moorman and Miner (1997) and Prabhu et al. (2005), we develop a measure of knowledge depth that indi cates the thoroughness of a firm's knowledge and technical expertise within its specialized fields. The measure of knowledge sharing is adapted from Schulz (2001) and captures the extent of knowl edge flow among individuals, decision makers, and

become a red hot battlefield for international companies. For example, in infonnation technology and electronics industries in Shanghai, IJVs and WOFs take up 55.4 percent of the number of enterprises and 92.3 percent of industrial output in 2008 (Shang hai Industrial Development Report, 2009). In the semiconductor industry, foreign investment accounts for over 80 percent of total investment. And among the top 20 semiconductor packaging and testing companies in China, 13 are WOFs, five are IJVs, and only two are domestic firms (China Electronic Components Association, 2010). 2 We used Harman's one-factor test to check for the presence of common method bias (Podsakoff and Organ, 1986). We sub jected all measurement items to a factor analysis; the solution accounts for 66.21 percent of the total variance, and the first fac tor accounts for 23.3 percent. Because no single factor accounts for a majority of the variance, common method bias is unlikely to be a major concern.

units within the firm. Market knowledge acqui sition is measured by three items adapted from Tsang (2002); it captures the amount and extent of knowledge the firm has acquired from external parties, such as customers, competitors, and sup pliers. We adapt a measure of radical innovation from Atuahene-Gima (2005), Chandy and Tellis (1998), and Zhou et al. (2005), which reflects the degree of technological advances and performance revenue involved in radical innovation.3

Control variables: At the firm level, we con trol for size, ownership, and prior performance. We measure firm size as the logarithm of the number of employees. Two dummy variables (IJV and WOF) indicate ownership, with domestic firms as a base line. Market share in the previous year indicates firm performance. We also control for environ mental variables: competitive intensity, technolog ical turbulence, and market growth (Zhou et al., 2005). Because the sample consists of companies in five industries, we code four industry dummy variables, with information technology as the base line. Finally, we use middle manager tenure ('years working in the company') to control for potential respondent effects.

Construct validity: We test the construct validity using confirmatory factor analysis (see Appendix A). All items load significantly on their expected constructs (p < 0.01). The fit indexes show that the overall model provides satisfactory fit to the data. Furthermore, the composite reliability of all constructs exceeds the 0.70 benchmark, and all average variances extracted (AVE) are greater than 0.50. These measures demonstrate adequate con vergent validity and reliability (Fornell and Lar cker, 1981). To assess discriminant validity, we run a series of chi-square difference tests for all constructs in pairs to determine whether the uncon strained model is significantly better than the con strained model. All the chi-square differences are

3 To validate the perceptual measure of radical innovation, we collected additional data by using the same measure through tele phone interviews, and obtained 56 responses from 28 firms (two middle managers from each). The Spearman-Brown test of inter class correlation shows that the interrater reliability between the two managers is 0.79 for radical innovation, well above the 0.60 benchmark. This finding exhibits satisfactory consistency and supports our key informant approach. In addition, we asked the middle managers to indicate 'the number of radical innovations introduced by your firm in the last three years,' and obtained 42 responses. We conducted a correlation analysis between this objective item and the perceptual measure. The result shows strong consistency (r = 0.63, p < 0.001), which further validates our subjective measure of radical innovation.

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Research Notes and Commentaries 1095

highly significant, indicating discriminant valid ity (e.g., knowledge breadth versus knowledge depth: Ax2 (1) = 169.31, p = 0.000). Moreover, the AVE of each construct is much higher than its highest shared variance with other constructs, which again supports discriminant validity. In Table 1, we report the descriptive statistics and correlations among the variables.

Analysis and results

A moderated regression analysis is appropriate for testing the interaction effects (Aiken and West, 1991). Following Hamilton and Nickerson (2003), we employ a two-stage regression model, which can correct for potential endogeneity. In stage one, we regress knowledge breadth and depth against market knowledge acquisition and knowl edge sharing, respectively, to obtain residuals free of the influence of these variables. We then use

the residuals as indicators of knowledge breath and depth. In stage two, we first regress radi cal innovation against the control variables, mar ket knowledge acquisition, and knowledge sharing in Model 1, Table 2. Then we add the residu als of knowledge breadth and knowledge depth (see Model 2), the interaction between the resid uals of breadth and market knowledge acquisi tion (Model 3), the interaction between the resid ual of breadth and knowledge sharing (Model 4), the interaction between the residual of depth and marketing knowledge acquisition (Model 5), and the interaction between the residual of depth and knowledge sharing (Model 6), sequentially. Finally, we include all variables and interaction terms in Model 7 (Table 2). To minimize possi ble colinearity between the main and interaction effects, we mean-center all pertinent independent variables and create the interaction terms by mul tiplying them (Aiken and West, 1991). The largest variance inflation factor is 1.82, well below the 10.0 benchmark, which indicates multicolinearity is not an issue in our analysis.

The results of the stage one estimation show that knowledge breadth is positively associated with market knowledge acquisition (b = 0.31, p < 0.01) but negatively related to knowledge sharing (b = —0.14, p < 0.10). Knowledge depth relates positively to both market knowledge acquisition (b = 0.38, p < 0.01) and knowledge sharing (b = 0.27, p < 0.01). These results confirm that it is

suitable to use the two-stage model to correct for the potential endogeneity problem.

With Hypothesis 1, we consider whether a firm with knowledge breath benefits more from knowl edge sharing than from market knowledge acqui sition. The results indicate that the interaction

between knowledge breadth and knowledge shar ing relates positively to radical innovation (M4: b = 0.12, p < 0.10; M7: b = 0.19, p < 0.01). In contrast, the interaction between knowledge breadth and market knowledge acquisition is neg atively associated with radical innovation (M3: b = -0.16, p < 0.05; M7: b = -0.20, p < 0.01). These findings lend support to Hypothesis 1.

For Hypothesis 2, we find that the interaction of knowledge depth and market knowledge acquisi tion is positively associated with radical innovation (M5: b = 0.15, p < 0.05; M7: b = 0.17, p < 0.05). In contrast, the interaction of knowledge depth and knowledge sharing is negatively related to radical innovation (M6: b = —0.11, p < 0.10; M7: b = —0.19, p < 0.01). That is, a firm with knowledge depth gains more from the presence of market knowledge acquisition than from knowl edge sharing, in support of Hypothesis 2.

STUDY 2

One of the major limitations of Study 1 is its cross-sectional design, which restricts our ability to assess causal relationships. The measures of rad ical innovation and knowledge breadth/depth also need refinement. The sample is also limited to the Yangtze River Delta. To address these limitations, we conducted Study 2 with a longitudinal design and updated measures in the three most developed areas in China, namely, the Yangtze River Delta, Beijing district, and Guangdong-based Pearl River Delta.

Using a similar approach, we conducted onsite interviews with managers of high technology com panies. We first collected information of knowl edge breadth/depth, knowledge sharing, and mar ket knowledge acquisition. Six months later, we contacted the same managers by telephone and collected information of radical innovation. We

successfully obtained complete responses from 68 firms. Further analysis shows that the infor mants exhibit sufficient knowledge about the sur vey questions and industry practice (knowledge level mean — 6.19).

Copyright © 2012 John Wiley & Sons, Ltd. Strat. Mgmt. J., 33: 1090-1102 (2012) DOI: 10.1002/smj

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1096 K.Z. Zhou and C. B. Li

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DOI: 10.1002/smj

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Research Notes and Commentaries 1097

Table 2. Standardized regression estimates (Study 1)

Radical innovation

Predictors

Knowledge breadth (breadth) Knowledge depth (depth) Market knowledge acquisition (MKA) Knowledge sharing (KS) Interactions Breadth x MKA Breadth x KS

Depth x MKA Depth x KS Control variables

Technology turbulence Competitive intensity Market growth Firm size

International joint venture Wholly owned foreign firm Market share

Manager tenure Electronic

Semiconductor design Pharmaceutical and bioengineering Mechanical and electric equipment F

Adjusted R2

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

0.34** 0.35** 0.30" 0.32** 0.32" 0.24" 0.17** 0.18" 0.18" 0.16* 0.18" 0.20**

0.27" 0.33" 0.33" 0.30** 0.31" 0.31" 0.30** 0.08 0.10 0.12 0.10 0.10 0.11 0.13t

-0.16* -0.20"

0.12t 0.19** 0.15* 0.17*

—0.1 If -0.19"

0.11 0.14t 0.15* 0.16* 0.14t 0.14f 0.13t 0.31** 0.11 0.16* 0.10 0.10 0.11 0.17* 0.10 0.07 0.04 0.08 0.09 0.07 0.10

0.04 -0.08 -0.10 -0.08 -0.08 -0.09 -O.llf 0.02 -0.02 0.01 -0.03 -0.03 -0.02 -0.01 0.06 -0.05 -0.03 -0.05 -0.07 -0.04 -0.03 0.30** -0.28" -0.26** -0.28" -0.30" -0.27" -0.28" 0.10 0.07 0.10 0.08 0.06 0.06 0.09 0.04 0.07 0.08 0.07 0.06 0.06 0.06 0.04 0.11 0.14* 0.10 0.10 0.12f 0.14* 0.08 0.08 0.07 0.07 0.06 0.08 0.05 0.04 0.10 0.10 0.09 0.09 0.10 0.10 5.57** 8.03" 8.02** 7 ~jg** 7.84" 7.80" 8.31" 0.27 0.39 0.41 0.40 0.40 0.40 0.46

Notes: The variance inflation factors range from 1.10 to 1.82. Values of knowledge breadth and depth are residuals (Y-YprediCted) from the Stage 1 estimation. ** p < 0.01. * p < 0.05. tp < 0.10 (two-tailed, sample size = 177)

The new scale of radical innovation is developed based on Gatignon et al. (2002) and Smith and Tushman (2005), and the new measure of knowl edge breadth/depth is adapted from De Luca and Athuaene-Gima (2007). As the Appendix shows, the measures in Study 2 possess satisfactory con vergent and discriminant validity. We report the descriptive statistics of the variables and their cor relations in Table 1.

Overall, the results of Study 2 are highly consis tent with the findings from Study 1, despite the fact that some effects become marginally significant due to the small sample size (see Table 3). Con sistent with Hypothesis 1, the interaction between knowledge breadth and knowledge sharing is posi tively associated with radical innovation (b = 0.26, p < 0.05), whereas the interaction effect between breadth and market knowledge acquisition is nega tive (b = —0.32, p < 0.01). In line with Hypothe sis 2, the interaction of knowledge depth with mar ket knowledge acquisition is positively associated

with radical innovation (b = 0.19, p < 0.10), but its interaction with knowledge sharing is nega tively, though not significantly, related to radical innovation (b = —0.13, p < 0.10).

DISCUSSION

This paper contributes to research on knowledge management and radical innovation in several ways. First, whereas previous research highlights the important role of knowledge in product innova tion (e.g., Miller et al„ 2007; Prabhu et ai, 2005), conflicting views exist about whether knowledge breadth and depth actually benefit radical innova tions (e.g., Tripsas and Gavetti, 2000; Zahra and George 2002). Our paper advances this line of inquiry by proposing that the roles of knowledge breadth and depth critically depend on external and internal knowledge integration mechanisms. Our

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1098 K.Z. Zhou and C. B. Li

Table 3. Standardized regression estimates (Study 2)

Radical innovation

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model "

Predictors

Knowledge breadth (breadth) 0.38" 0.38" 0.34** 0.38** 0.39** 0.33" Knowledge depth (depth) 0.26* 0.26* 0.32" 0.27* 0.27* 0.28" Market knowledge acquisition (MKA) 0.12 0.31* 0.21* 0.31" 0.37" 0.32" 0.28* Knowledge sharing (KS) 0.09 0.14 0.17 0.13 0.18 0.16 0.20f Interactions Breadth x MKA -0.28* -0.32** Breadth x KS 0.17t 0.26* Depth x MKA 0.13f 0.19t Depth x KS -0.09 -0.13 Control variables

Technology turbulence 0.23 0.19 0.17 0.20 0.16 0.20 0.12 Competitive intensity 0.32** 0.11 0.21* 0.12 0.10 0.09 0.25*

Market growth -0.07 -0.04 -0.08 -0.02 -0.02 -0.04 -0.01 Firm size -0.08 -0.15 —0.20+ -0.15 -0.16 -0.17 -0.18

International joint venture -0.12 -0.11 -0.08 -0.12 -0.14 -0.13 -0.09

Wholly owned foreign firm 0.06 0.05 0.10 0.04 0.02 0.03 0.07 Market share -0.31* -0.32** -0.28" -0.33" -0.36" -0.32** -0.36"

Manager tenure 0.12 0.16 0.21* 0.22* 0.12 0.19t 0.21* Electronic 0.04 -0.03 0.01 -0.05 -0.04 —0.02 -0.03

Semiconductor design -0.09 0.04 0.09 0.03 0.03 0.03 0.09 Pharmaceutical and bioengineering 0.04 -0.03 -0.01 -0.04 -0.03 -0.03 -0.01 Mechanical and electric equipment -0.07 -0.02 -0.04 -0.02 -0.02 -0.02 -0.07 F 2.84** 5.76** 6.21** 5.89** 5.46** 5.45** 6.08"

Adjusted R2 0.28 0.54 0.58 0.56 0.54 0.54 0.61

Notes: The variance inflation factors range from 1.33 to 2.63. Values of knowledge breadth and depth are residuals (Y-Ypreacted) from the Stage 1 estimation. ** p < 0.01. * p < 0.05. +p < 0.10 (two-tailed, sample size = 68)

findings indicate that a firm with a broad knowl edge base is more capable of developing radical innovations in the presence of internal knowl edge sharing rather than external-focused mar ket knowledge acquisition; a firm with a deep knowledge base is better able to achieve radical innovation through enhanced market knowledge acquisition rather than internal knowledge sharing. These results suggest the importance of fit between the existing knowledge base and the way a firm integrates its knowledge. Extending previous con ceptual works (Verona, 1999; Zahra and George, 2002), our findings provide a more nuanced under standing of how knowledge base and knowledge integration mechanisms jointly affect radical inno vation.

Second, a large body of innovation literature emphasizes that the creation of radical innova tion always favors externally oriented explorers who actively acquire information from outside (e.g., Afuah 1998; Laursen and Salter, 2006).

Our findings warn that this premise is only par tially correct, because acquired market knowledge complements deep knowledge but substitutes for broad knowledge in radical innovation develop ment.4 That is, for a firm with diverse knowl edge, additional market knowledge acquisition is

4 To reveal more insights, we considered boundary conditions of such effects by comparing large versus small and established versus new companies in Study 1. The results of these subgroup analyses show that the negative interaction between knowledge breadth and market knowledge acquisition is stronger for small firms than large firms (t = —3.39, p < 0.01) and for new ven tures than established firms (t = —2.81, p < 0.01). That is, small or new ventures with diverse knowledge are less likely than large or established firms to develop radical innovations if they focus too much on market knowledge acquisition. Because small or new ventures have relatively limited resources, experiment ing with too many ideas is counterproductive. In contrast, the strength of the positive interaction between knowledge depth and market knowledge acquisition does not differ significantly across small and large firms (t = 1.01, p > 0.10), but it is greater for new than for established ventures (t = 3.17, p < 0.01). There fore. new ventures with deep knowledge in specific fields benefit more if they can externally integrate and rejuvenate knowledge flows from the marketplace.

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Research Notes and Commentaries 1099

counterproductive. This insight is also consistent with Miller et al.'s (2007) argument that the use of interdivisional knowledge has a stronger effect on an invention's impact than does the use of knowledge outside of the firm. Related, previ ous literature highlights knowledge sharing as an important means for facilitating product innova tion (e.g., Grant, 1996; Kale and Singh, 2007). Our findings suggest that this proposition works for firms with diverse knowledge, such that knowl edge sharing bridges functional units within the organization and fosters new ways to combine and use disparate knowledge to achieve unique product advances. However, for firms with deep knowledge in specific fields, knowledge sharing reinforces existing expertise and operational rou tines, which may lead to inertia and prevent radical innovation.

Attracted by China's huge market size and low cost knowledge workers, foreign high technology companies have been rushing into China and now take up a large portion of market, which intensifies the competition and pushes local players to pursue radical innovations proactively. Our findings pro vide managers with direct implications about how to manage knowledge resources for radical innova tion. First, managers must examine the knowledge base they already have and identify whether its nature, content, and embedded advantages reflect depth or breadth. Then, managers should adjust their knowledge integration mechanisms to fit with their firms' existing knowledge base. To maxi mize the benefits from accumulated knowledge resources and enhance radical innovations, we sug gest that a firm with a broad knowledge base strengthen its knowledge sharing processes and routines; a firm with a deep knowledge base should take concerted efforts to build and refine the pro cesses associated with acquiring and integrating external market intelligence.

This paper also has some limitations that future work can address. First, though we employ two studies to corroborate our findings and adopt a lagged measure of radical innovation in Study 2, the cross-sectional nature of Study 1 and the small sample size of Study 2 limit our ability to fully test causality. Additional research should adopt a lon gitudinal approach and a large sample to examine these causal relationships. Second, our measure of radical innovation is based on managers' percep tions. Whereas the measure has been used in prior studies (e.g., Atuahene-Gima, 2005; Chandy and

Tellis, 1998; Gatignon et al., 2002), it may involve a psychological bias, such that managers tend to overestimate their abilities. Given that our interac

tion hypotheses are well supported, it is unlikely that respondents have an 'interaction-based the ory' in their minds that enable systematical bias in their response to get these results (Aiken and West, 1991; see also Zhang and Li, 2010), which reduces this concern. Nevertheless, further research should obtain objective measures, such as patents, to cor roborate our findings. Third, radical innovation could be either competence enhancing or compe tence destroying (Gatignon et al, 2002). It would be interesting to examine the effect of knowledge on competence-anchored characteristics of prod uct innovation. Fourth, our paper focuses on the feature of knowledge base and finds that a firm with a deep knowledge base benefits from mar ket knowledge acquisition. Given that some firms are better at acquiring external knowledge than others, what capabilities would enable firms to benefit more from external knowledge acquisition? Is it alliance capability (Kale, Dyer, and Singh, 2002), governance capability (Mayer and Salomon, 2006), or relational capability (Li, Poppo, and Zhou, 2010)? As knowledge acquisition activities increasingly take place through alliances and out sourcing, understanding how and why some firms can better utilize their acquired knowledge from their partners represents an intriguing avenue for further research.

ACKNOWLEDGEMENTS

The authors thank the two anonymous reviewers and Associate Editor Professor Jiatao Li for their

insightful and constructive comments on earlier versions. This study was supported by the General Research Fund from the Research Grants Council, Hong Kong SAR Government (Project no. HKU 75901 IB).

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Research Notes and Commentaries 1101

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1102 K.Z. Zhou and C. B. Li

APPENDIX: MEASUREMENT ITEMS AND VALIDITY ASSESSMENT3

A. Study 1 Model fit: RMSEA = 0.066, CFI = 0.95, IFI = 0.95, NNFI = 0.93. SFL

Knowledge breadth CR = 0.80, AVE = 0.57, HSV = 0.38 1. We possess market information from a diversified customer portfolio. 0.91 2. We have accumulated knowledge of multiple market segments. 0.76 3. Our R&D expertise consists of knowledge from a variety of background. 0.56 Knowledge depth CR = 0.84, AVE = 0.57, HSV = 0.25 1. We are highly familiar with this industry. 0.87 2. We have acquired a great deal of experience about this industry. 0.76 3. The knowledge of our firm in this industry is thorough. 0.72 4. We have in-depth knowledge about the technology in this industry. 0.66 Market knowledge acquisition CR = 0.84, AVE = 0.64, HSV = 0.25 Our company has processes 1. for continuously collecting information from customers. 0.85 2. for continuously collecting information about competitor activities. 0.89 3. for continuously collecting information from our suppliers. 0.63 Knowledge sharing CR = 0.85, AVE = 0.65, HSV = 0.10 Our company has processes 1. for sharing information effectively throughout the organization. 0.81 2. for sharing information between all parties involved in the decisions. 0.87 3. for transferring organizational knowledge to individuals (such as employee training programs). 0.74 Radical innovation CR = 0.79, AVE = 0.56, HSV = 0.38 1. Our company frequently introduces products that are radically different from existing products. 0.84 2. Compared to your major competitor, our company introduced more radical product innovations in the last 0.69

three years. 3. Percent of total sales from radical innovations introduced in the last three years. (Less than 1%, 1-5%, 0.70

6%—10%, 11%—15%, 16%—20%, 21%—25%, over 25%)

B. Study 2 Model fit: RMSEA = 0.091, CFI = 0.91, IFI = 0.92, NNFI = 0.90 Knowledge breadth CR = 0.84, AVE = 0.65, HSV = 0.27 1. We possess market information from a diversified and wide-ranging customer portfolio. 0.83 2. We have accumulated knowledge of multiple market segments. 0.92 3. Our R&D expertise consists of technical knowledge from a variety of background. 0.64 Knowledge depth CR = 0.78, AVE = 0.55, HSV = 0.42 1. We have thorough understanding and experience of current customers. 0.93 2. We have accumulated in-depth knowledge of the key market segment that we focus on. 0.66 3. Our R&D experts have thorough technical knowledge and skills within our specialized domain. 0.60 Market knowledge acquisition CR = 0.81, AVE = 0.60, HSV = 0.12 Our company has processes 1. for continuously collecting information from customers. 0.76 2. for continuously collecting information about competitor activities. 0.93 3. for continuously collecting information from our suppliers. 0.60 Knowledge sharing CR = 0.88, AVE = 0.72, HSV = 0.06 Our company has processes 1. for sharing information effectively throughout the organization. 0.79 2. for sharing information between all parties involved in the decisions. 0.94 3. for transferring organizational knowledge to individuals (such as employee training programs). 0.80 Radical innovation CR = 0.78, AVE = 0.54, HSV = 0.42 We introduced innovation that

1. involves a fundamentally major improvement over the previous technology. 0.75 2. leads to products that are difficult to replace with substitute using older technology. 0.66 3. brings in substantial transformation in consumption patterns in the market. 0.79

Notes: SFL = standardized factor loading; CR = composite reliability; AVE = average variance extracted, and HSV = highest shared variance with other constructs. RMSEA = root mean squared error of approximation, IFI = incremental fit index, CFI = comparative fit index, and NNFI = nonnormed fit index. a Items are measured with seven-point Likert scales (1 = 'strongly disagree,' 7 = 'strongly agree').

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