Internal innovation or external innovation? An organizational context-based analysis in China

Journal of High Technology Management Research 24 (2013) 118–129

Contents lists available at ScienceDirect

Journal of High Technology Management Research

Internal innovation or external innovation? An organizational context-based analysis in China AiHua Wu a,⁎, JingQin Su b, Haiwei Wang c a b c

School of Business, Ludong University, Yantai, China Faculty of Management and Economics, Dalian University of Technology, Dalian, China School of Business, Jilin University, Changchun, China

a r t i c l e

i n f o

Available online 2 October 2013 Keywords: Information structure Human capital Innovation pattern Moderator

a b s t r a c t The purpose of this paper is to explore how enterprises choose innovation pattern from the perspective of internal organization context. This article distinguishes between internal innovation and external innovation, such that human capital and information structure are the core factors affecting firm's choice decision for internal or external innovation. Based on a survey in China, this study analyzes the relationships among specific human capital, information structure and innovation pattern. Further we also examine the moderating effect of cooperative motivation including R&D motivation, technical learning motivation and strategy motivation. The results suggest that with the degree of specific human capital increase, enterprises tend to choose internal innovation pattern and with the information structure more dispersed/horizontal, enterprises more tend to choose internal innovation pattern. What's more, motives related to research and development, and technology learning are two relatively significant moderators in the relationships among specific human capital, information structure and innovation pattern. © 2013 Elsevier Inc. All rights reserved.

1. Introduction Innovation contributes positively to business performance (Daniel & Raquel, 2011), and it is a critical factor in explaining firms' competitiveness, particularly in knowledge-intensive industries (García-Muiña, Pelechano-Barahona, & Navas-López, 2009). In the past, most industrial firms focus on internal innovation, whereas external technology exploitation (Lichtenthaler, 2010) and external learning open up gateways to new knowledge that departs from existing organizational memory (Bao, Chen, & Zhou, 2012). Concurrent with the open innovation approach (Chesbrough, 2003), a rather voluminous literature has emerged that examines the relationship between internal and external strategies (Hagedoorn & Wang, 2012; Mata & Woerter, 2013). At the heart of the literature is the discussion of the complementarity or substitutability between internal and external R&D strategies for managing innovation (Hagedoorn & Wang, 2012), and a small but growing literature has started investigating the impact of these strategies upon innovation outcomes and performance (Cassiman & Veugelers, 2006; Lokshin, Belderbos, & Carree, 2008). Surprisingly little is known about what causes firms to choose among the various modes of innovation (Hull & Covin, 2010). Only a few studies (Hitt, Hoskisson, Johnson, & Moesel, 1996; Hoskisson & Busenitz, 2002; Poot, Faems, & Vanhaverbeke, 2009) have considered both internal innovation and external innovation simultaneously. Steensma and Fairbank (1999) examine a number of contingencies that may influence a firm's choice of governance mode for the procurement of external technical know-how. Other studies emphasize that these modes should be put in a firm's contexts for a better managerial understanding (Dabić, Daim, Aralica, & Bayraktaroglu, 2012). Thus, the organizational context has been identified by researchers as an important consideration in the study of innovation. According to William and Elizabeth (2010), most of a firm's R&D activity, which is the foundation and core of innovation activities, can be explained by time-invariant factors which are believed relate to internal and specific characteristics such as the firm's managerial ⁎ Corresponding author. Tel.: +86 5356676517. E-mail address: [email protected] (A. Wu). 1047-8310/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.hitech.2013.09.006

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dimensions, competitive strategy and how it communicated with employees. Souitaris (2002) investigates the ‘importance’ and ‘awareness’ of firm-specific competencies as antecedents of technological innovation, and develops a literature-based portfolio model including 17 established innovation-determining factors, relating to the firm's technical, market, human resource and organizational competencies. Wu (2008) examines how firm-level corporate governance may give rise to firm innovativeness. As a fact, innovation has also been defined as the most knowledge-intensive organizational process, which depends on the individual members and the collective knowledge of the firm (Adamides & Karacapilidis, 2006), and the collective knowledge of the firm is related to the communication of individual members, which can be defined as information structure. In addition, most innovation studies underscore that person-to-person communication is a critical variable for innovation (Poolton & Barclay, 1998). Thus we argue that specific human capital and information structure are two main organization factors influencing the innovation pattern. Despite that there are some literatures about specific human capital, information structure and innovation pattern respectively, few studies have paid attention to the relationships among them. This paper contributes to innovation management theory and opens innovation by examining the effects of two organizational contexts (i.e. specific human capital and information structure) on innovation mode, and the moderating effect of three cooperative motivations (i.e. research and development motivation, technical learning motivation and strategy motivation) on the impact of human capital and information structure on innovation mode (Fig. 1). The rest of the paper is structured as follows. The next section reviews technological innovation and transaction cost economics relative to innovation pattern and information structure. The subsequent section theoretically analyzes the relationships among them, and the moderation effect of cooperative motivation, and put forward 8 research hypotheses. Then the paper describes the research design, including the data base and the methodology used in searching for innovation modes and other variables. The empirical results to identify innovation modes and other variables are presented. Finally, the article presents the main results and draws some conclusions and implications, limitations and directions for further research.

2. Theory and hypotheses There are many studies about innovation mode from innovation management theory (Bruce & Abdelouahid, 2008; Morten, Bjorn, Edward, & Bengt, 2007). The innovation pattern in this paper is the organizational mode about innovation activities, which is relative to transaction cost, resource-based and firm boundary theory. From the perspective of resource-based theory, organizational modes for technological collaboration include acquisition, merger, licensing, minority equity, joint ventures, joint R&D, R&D contract, alliance, networking and outsourcing (Chiesa & Manzini, 1998). Based on firm boundary (Frida, Elsebeth, & Pedersen, 2012), the innovation pattern includes internal innovation and external innovation (Fig. 2). Internal innovation pattern is that the activities of R&D are inside firm boundaries, including internal R&D department and mergers/ acquisitions. With the development of capital markets, enterprises can substantively control the subsidiary by controlling the stock, so the innovative development undertaken by subsidiaries is also within the scope of internal innovation mode. Internal innovation mode is a traditional mode, the core technology in this pattern coming from the firm's technology accumulation and breakthroughs. However, external innovation pattern is that the activities of R&D are outside boundaries of the firm. The forms of external innovation mode are more abundant than that of internal innovation mode. Judging from the current development, the forms include technology outsourcing, joint innovation between enterprises, joint innovation between enterprises and scientific research units. As for information structure, Aoki (1986) considers that it includes horizontal information structure and vertical information structure. Vertical information structure refers to the subordinate relationship through the division of internal power and responsibility. For example, in American companies information is transferred between superior and subordinates, and decisions are made by superiors based on professional knowledge. In contrast, in Japanese firms, workers' jobs are not specified in detail and workers rotate among various jobs, so they are gradually familiar with the whole work process and become capable of coping with unexpected emergencies, thus forming horizontal information structure. When a company is reengineered to a horizontal structure, all employees throughout the organization working on a particular process (such as claim handling or order fulfillment) have easy access to one another so they can communicate and coordinate with

Fig. 1. Conceptual framework.

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Fig. 2. Innovation patterns.

each other, which may increase the social capital of the firm. Social capital appears as a result of collaboration and interaction among those people who share their ideas (Subramaniam & Youndt, 2005; Wright, Dunford, & Snell, 2001). This horizontal structural approach is largely a response to the profound changes that have occurred in the workplace and the business environment over the past fifteen to twenty years (Daft, 2007). The last key organization context factor influencing innovation pattern is human capital, which reflects the knowledge and skills of an organization's members that is valuable for the organization and cannot easily be copied and imitated by others, thus constituting a source of competitive advantage (Behrens, Patzelt, Schweizer, & Bürger, 2012). General human capital, such as literacy or basic computer skills, is freely transferable because it is useful to several employers (Becker, 1964). Specific human capital is related to the definition of asset specificity. Williamson (1975, 1985, 1986) argued that transaction-specific assets were non-redeployable physical and human investments that were specialized and unique to a task. Firm-specific human capital investment describes the skills, experiences, and knowledge that are useful only to a single firm. The value of the skills will decrease differently with the different specific degrees when the staff is leaving the enterprise, in other words, when the specific degree is stronger, the degree of value decrease is more, the value of the skills to outside enterprises is less, and it is the same on the contrary. As a fact, employees undertake some firm specific investment more or less in their work. 2.1. Specific human capital, information structure and innovation pattern 2.1.1. Information structure and innovation pattern In the horizontal information structure, firms tend to get information through employees. As the decision-making power is deconcentrated in employees, employees need mutual understanding of their job responsibilities and content within the enterprise to establish a stronger relationship of social capital and networks. At this context, social capital can easily be formed, as Subramaniam and Youndt (2005) state, given that innovation is fundamentally a collaborative effort; social capital assumes a key role in generating innovations. Many researchers suggest that knowledge sharing (social capital) influences the firm innovativeness as it supports creativity and inspires new knowledge and ideas (Aragón-Correa, García-Morales, & Crodón-Pozo, 2007; Carmona-Lavado, Cuevas-Rodríguez, & Cabello-Medina, 2010). So in the horizontal information structure, enterprises can easily make R&D, new product applications, and all other activities inside of firm boundaries, thus forming internal innovation pattern. However in vertical information structure, since the same type of information is mainly transferred between the upper and lower levels, employees of different positions do not require mutual understanding. As managers are in power of the main decision-making, employees do not need a lot of decision-making in their work; therefore, employees do not need more understanding to internal strategy, operating rules, processes and culture. Thus, we consider that the social capital can't be easily formed in this kind of information communication. In the vertical information structure, enterprises and employees may not have enough ability to innovate, so the internal innovation is difficult to launch and the external resources for innovation are used, which is an external innovation pattern. To sum up, with the information structure more dispersed/horizontal, enterprises more tend to choose internal innovation pattern. In other words, a firm with horizontal information structure may choose internal innovation; otherwise, a firm with vertical information structure may choose external innovation. Based on the above arguments, we hypothesize as follows: H1. Information structure has a positive effect on innovation pattern.

2.1.2. Specific human capital and innovation pattern Specific human capital is beneficial for trust develops in the firm. Harris and Helfat (2007) argue that “repeated communication between individuals can help to build and strengthen relational ties…. As a result, individuals may come to have greater trust….” Similarly, Lawler, Thye, and Yoon (2000) reason that “… frequent social exchange results in positive

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emotions that solidify and strengthen the person-to-group bond….”. Innovation is a social process and specific human capital is the basis of all innovation processes in the firm, which provides the main source for developing new ideas and knowledge (Snell & Dean, 1992). To develop internal technological innovations, the firm relies on a set of different capabilities, such as technological or market capabilities (Lettl, 2007). Strong empirical evidence suggests that diverse forms of trust in particular contribute more than any other explanatory variable in determining innovation and the radical nature of innovation (Chou, 2006). On the contrary, low trust can discourage innovation (Knack & Keefer, 1997). A relationship based on trust would enable firms to improve their capabilities for innovativeness. So, we argue that firms with specific human capital tend to adopt internal innovation. In contrast, general human capital is crucial for removing an organization's technology boundaries, increasing their capacity to absorb and deploy external different knowledge domains (Subramaniam & Youndt, 2005). General human capital may question established organizational routines; hence, this type of human capital becomes critical to pushing the firm to its technological borders and constitutes the best incentive towards obtaining new knowledge and achieving innovations, which induces external innovation. To sum up, with the degree of specific human capital increase, enterprises tend to choose internal innovation pattern. In other words, a firm with specific human capital may choose internal innovation; otherwise, a firm with general human capital may choose external innovation. Based on the above arguments, we hypothesize as follows: H2. Specific human capital has a positive effect on innovation pattern.

2.2. Moderation effect of cooperative motivation The motives can be derived from open innovation, innovation collaboration (Van de Vrande, de Jong, Vanhaverbeke, & de Rochemont, 2009) and strategic alliances studies (Sambasivan, Siew-Phaik, Mohamed, & Leong, 2013). Hagedoorn (1993) summarizes three motives for interfirm technology cooperation: motives related to basic and applied research and some general characteristics of technological development, motives related to concrete innovation processes and motives related to market access and search for opportunities. Sambasivan et al. (2013) review the various theories used in explaining the strategic alliances: transaction cost theory (TCT), resource-based theory (RBT), contingency theory (CT), social exchange theory (SET), and personal relationship theory (PRT). Based on the literatures, we divide technical cooperation motivation into three aspects: R&D motivation, technical learning motivation, and strategic motivation. The main purpose of R&D motivation is to increase the firm's innovation ability and reduce innovation time-span. Richard, William, Esther, and Antonio (2011) consider that technological innovation capabilities include seven capabilities: learning capability, R&D capability, resource allocation capability, manufacturing capability, marketing capability, organizational capability, and strategic planning capability, they find that external expert organizations affect only the firm's R&D and resource allocation capabilities. Thus, from resource-based view (RBV) of a firm (Barney, 1991), when R&D motivation is stronger, for enterprises with horizontal information structure, as high frequency of intercoordination and interactive communication, which makes knowledge and information more likely to be fully shared and used, the resources and capabilities in the enterprises can be fully accumulated, thus enterprises are more likely to choose internal innovation. Therefore, we posit: H3a. The stronger the R&D motivation, the greater the positive relationship between information structure and internal innovation. The dynamic capabilities framework (Teece, Pisano, & Shuen, 1997), extending the resource-based view, stresses the importance of tangible and intangible “specific asset positions” in shaping firm resources (Voudouris, Lioukas, Iatrelli, & Caloghirou, 2012). Under this perspective, firms invest in particular types of technology resources and learn how to use them over time by developing asset specific skills and accompanying routines (Cohen & Levinthal, 1990). So, for enterprises with high degree of human capital specificity, when R&D motivation is stronger, enterprises more tend to choose internal innovation. Therefore, we posit: H3b. The stronger the R&D motivation, the greater the positive relationship between degree of human capital specificity and internal innovation. The main purpose of technical learning is to capture of partner's tacit knowledge of technology. For this kind of experiential knowledge and skill, which is generally difficult transmitted through language and words, it is necessary for enterprises to strengthen communication and reciprocal interaction with the outside to gain themselves tacit knowledge, technique and ability. Internal innovation pattern requires enterprises to place many innovation activities within the boundary of them, so technical learning motivation decreases the tendency of the choice of internal innovation pattern. Therefore, we hypothesize that: H4a. The stronger the technical learning motivation, the weaker the positive relationship between information structure and internal innovation. H4b. The stronger the technical learning motivation, the weaker the positive relationship between degree of human capital specificity and internal innovation. Strategic motivation mainly includes expansion of product range, new products and markets, internationalization, globalization and entry to foreign markets, influencing the market structure, leading technological innovation opportunity and controlling cooperating partners, etc. For enterprises with stronger strategy motivation, they have more tendencies to adopt

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external innovation pattern to achieve strategic objective. As described by Rosenfeld (1996) and Hagedoorn, Albert, and Vonortas (2000), not only multinational firms but also small and medium-sized firms are establishing more and tighter relationships with other companies in order to achieve economies of scale, market strength, or to exploit new opportunities (de Faria, Lima, & Santos, 2010). Tether (2002) also finds that firms that engage in R&D and that are attempting to introduce higher level innovations, i.e. ‘new to the market’ rather than ‘new to the firm’ innovations ― are much more likely to engage in co-operative arrangements for innovation. Thus, we argue that stronger strategy motivation attenuates the positive effect of information structure and specific human capital on internal innovation. Based on the above arguments, we posit: H5a. The stronger the strategy motivation, the weaker the positive relationship between information structure and internal innovation. H5b. The stronger the strategy motivation, the weaker the positive relationship between degree of human capital specificity and internal innovation. 3. Research design 3.1. Sample and data collection The sampling population consists of 380 firms located in coastal regions which are the most developed in China (Table 2). First, the firms' general managers were contacted by telephone and explained the aim of the study to them. Of the 380 firms contacted, 232 agreed to participate in the survey study. Then the general managers most knowledgeable about the organization's employees, culture, financial situation and operations were asked to fill out the surveys. After qualifying the respondents, everyone was informed that his/her responses would remain anonymous and would not be linked to them individually, nor to their companies or products. This was done to assure anonymity, thus increasing the motivation of informants to cooperate without fear of potential reprisals. In addition, the respondents were assured that there were no right and wrong answers and they should answer questions as honestly and forthrightly as possible. Further, the study developed a cover story to make it appear that the measurement of the predictor variable was not connected with or related to the measures of the criterion variable. These procedures reduced the evaluation apprehension and made the subjects less likely to edit their responses to be more socially desirable, lenient, and consistent with how they think the researchers wanted them to respond (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Because of data screening, 26 out of 232 surveys were discarded. Table 1 shows the sample characteristics. 3.2. Measures To test the above hypotheses, multi-item scales adopted or developed from prior studies for the measurement of the constructs were used. Informants rated all items on 5-point scales ranging from ‘strongly disagree’ (1) to ‘strongly agree’ (5). The measures used Table 1 General situation of the sample. Type

Measurement

Number of firms (N = 206)

Percentage (%)

Form of ownership

State-owned firms Private firms Joint ventures and foreign-funded firms Below 100 staffs 100–1000 staffs 1000–3000 staffs 3000–10,000 staffs Above 10,000 staffs Infant stage Growth stage Maturity stage Decline stage Bottom 15% Bottom 30%–15% Middle Top 15%–30% Top 15% Jilin Liaoning Shandong Jiangsu Zhejiang Guangdong Beijing Shanghai

82 98 26 26 39 56 56 29 16 85 82 23 16 10 29 62 88 21 33 34 18 23 20 26 31

39.7 47.6 12.7 12.7 19.0 27.0 27.0 14.3 7.9 41.3 39.7 11.1 7.9 4.8 14.3 30.2 42.9 10.3 15.9 16.7 8.7 11.1 9.5 12.7 15.1

Firm size

Product development stage

Industrial position

Distribution area

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Table 2 KMOs, Cronbach alpha coefficients and total variance explained. Construct

KMO

Cronbach alpha coefficient

Total variance explained (%)

Specific human capital Information structure R&D motivation Technical learning motivation Strategy motivation Internal innovation External innovation

0.777 0.665 0.500 0.500 0.513 0.713 0.745

0.809 0.715 0.711 0.705 0.704 0.819 0.807

57.627 60.638 75.255 72.613 58.823 73.843 64.189

for the constructs in the model, mostly derived from previous research but some developed for this study. Appendix A contains the questionnaire. A brief summary of the measures follows. The independent variables include information structure and specific human capital. This study develops a four-item measure of information structure on the basis of the theoretical work of Aoki (1986). The measure of specific human capital uses items adopted from Lepak and Snell (2002). The moderation variable (cooperative motivation) was measured using an eight-item scale adapted and modified from Hagedoorn (1993). The dependent variable is innovation pattern, including internal innovation and external innovation. Internal innovation contains three items and external innovation contains four items. Although not the focus of our study, some variables were included as controls because they were shown to affect key variables in our study. Previous research suggested that firm size, product development stage and industrial position can have significant influence on firm innovation and performance (Weiner & Mahoney, 1981). So in this paper they are control variables. 3.3. Common method variance To minimize common method variance (CMV), this study takes both ex ante and ex post approaches. Ex ante, following Podsakoff et al. (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 & Organ, 1986). A factor analysis of the dependent and independent variables yields a factor solution that accounts for 69.0% of the total variance, and the first factor only accounts for 22.7% 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 Table 2 shows KMO, Cronbach alpha coefficient and total variance explained for the constructs, and Appendix A shows factor loading for items. Table 3 and Appendix A suggest that data from survey have good reliability and validity, which is suitable for the need of this study. Correlation analysis is a method of analyzing relation and reporting degree of relevance among variables. Table 3 demonstrates there is some correlation between variables. To test our hypotheses, consistent with the literature, we performed a series of multiple linear regression models and structural equation modeling (SEM) (Ali, Halit, & John, 2009). SEM enables us to test several multiple regression equations at the same time and is therefore a very useful tool for testing overall model fit with a lower degree of measurement error (Richard et al., 2011). In order to test Hypotheses H1 and H2, we performed a structural equation modeling analysis.1 In the model analysis, maximum likelihood solution was used for interpretation. Multiple indices of fit including IFI, CFI, and cmin/df were used to specify the overall model fit. The IFI and CFI values were over 0.9 and that of cmin/df was below 3, indicating a good degree of model fit. An RMSEA value of less than 0.7 indicates an adequate degree of model fit. Table 4 demonstrates the relationships among information structure, specific human capital and innovation pattern. The results indicate that specific human capital and information structure both have positive relation with internal innovation (β = 0.427, p b 0.01; β = 0.448, p b 0.01), and negative relation with external innovation (β = − 0.359, p b 0.01; β = − 0.361, p b 0.01), supporting Hypotheses H1 and H2. The results show that information structure and human capital specificity have obvious effects on innovation pattern choice in Chinese companies. Different types of information structure and human capital respectively correspond to different innovation patterns. Vertical information structure and low specific human capital are consistent with external innovation, and vice versa. 1 As the sample examined in this study was relatively small, partial least squares modeling (PLS-SEM) could have been used (Falk & Miller, 1992). However, studies in the prior literature have continued to question the use of PLS-SEM and it is a method that is not commonly used in the field of general management (Rouse & Corbitt, 2008). PLS analysis also requires a relatively large sample (Marcoulides & Saunders, 2006). In view of this, we preferred to adopt the wellestablished and widely used SEM methodology (Rouse & Corbitt, 2008). The survey data for this study were acceptable for SEM and the study adopted fit indexes (e.g. CFI and RMSEA) that are least affected by sample size (Fan, Thompson, & Wang, 1999). It is acknowledged, however, that the small sample examined here represents a limitation of this study.

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Table 3 Correlations and descriptive statistics. Variables

Mean

S.D.

1

2

3

4

5

6

7

8

9

10

Firm size Product development stage Industrial position Specific human capital Information structure R&D motivation Technical learning motivation Strategy motivation Internal motivation External motivation

3.11 2.54 3.95 3.19 3.60 3.63 3.45 3.30 3.50 3.14

1.25 0.80 1.22 0.57 0.65 0.74 0.67 0.34 0.77 0.79

1.00 0.17 .37⁎⁎ 0.25 0.03 .37⁎⁎ −0.01 −0.10 −0.04 0.02

1.00 0.13 0.11 0.06 −0.06 0.11 −0.16 0.00 0.17

1.00 0.14 0.10 0.02 −.01 −.02 0.00 0.12

1.00 0.26 0.00 .32⁎ 0.23 .56⁎⁎ −.61⁎⁎

1.00 −0.20 .25⁎ 0.15 .65⁎⁎ −.59⁎⁎

1.00 −0.22 −0.18 −0.12 .43⁎⁎

1.00 0.21 0.19 −.31⁎

1.00 0.19 −0.23

1.00 −.70⁎⁎

1.00

⁎ p b .05 (2-tailed). ⁎⁎ p b .01 (2-tailed).

Regarding Hypotheses H3a and H3b, the moderator role of R&D motivation, consistent with the literature, a moderated multiple hierarchical regression analysis was used (Irwin & McClelland, 2001). Table 5 shows the results for the moderated hierarchical regression analysis. In the hierarchical approach, Model 1 includes the control variables, Model 2 adds the main effects, and Model 3 adds the interaction effects. To avoid multicollinearity, all the interaction terms were mean-centered before performing the linear regression model as suggested by Aiken and West (1991). The summarized results in Table 5 reveal that the R2 value increases significantly for Models 2 and 3 and that Model 3 explains 85.2% of the total variance in internal innovation. Table 5 concludes that there is positive moderation effect of R&D motivation on the relationship between information structure and internal innovation (β = 0.70, p b 0.05), and that there is negative moderation effect of technical learning motivation on the relationship between internal innovation and specific human capital (β = − 0.67, p b 0.05), while the remaining four supposed moderation effects have no significance statistically. So H3a and H4b are tested while H3b, H4a, H5a and H5b are rejected. For a firm with high R&D motivation, it would build horizontal information structure, which can encourage internal communication and information sharing, thus creating an atmosphere for internal innovation, so the stronger the R&D motivation, the positive relationship between horizontal information structure and internal innovation is greater. When a firm has strong technical learning motivation, it will strengthen ties and exchange with various organizations, putting into a lot of resources. Accordingly, the resources the firm put into itself for internal innovation has to be reduced, thus will lead to the insufficiency force of internal innovation. So the stronger the technical learning motivation, the positive relationship between specific human capital and internal innovation is weaker. This study also conducts slope analysis (Aiken & West, 1991) to illustrate the interactions between external learning and market dynamics. To better understand moderation effect of R&D motivation and technical learning motivation, Figs. 3 and 4 demonstrate the significant moderated relationships. In addition, the results do not test the moderation effect of strategy motivation, which is mainly because when a firm has both high degree of specific human capital and horizontal information structure, whether it wants to influence market structure, lead innovation or control cooperating partners, there is not much motivation for knowledge sharing, technology transfer or synergism. In contrast, the firm has to protect its proprietary technology. Internal innovation exactly meets this above, which makes the firm not choose internal innovation pattern.

Table 4 Path model. Hypothesis

Path

Path value

Hypothesis 1

Information structure → internal innovation Information structure → external innovation Specific human capital → internal innovation Specific human capital → external innovation Firm size → internal innovation Firm size → external innovation Product development stage → internal innovation Product development stage → external innovation Industrial position → internal innovation Industrial position → external innovation

0.477⁎⁎⁎ −0.351⁎⁎⁎ 0.331⁎⁎⁎ −0.477⁎⁎⁎

Hypothesis 2 Control variables

Fit indices: CFI = .91, IFI = .92, RMSEA = .06, χ2(21) = 43.26, χ2/df = 2.06. ⁎ p b .1. ⁎⁎ p b .05. ⁎⁎⁎ p b .01.

−0.10 0.07 −0.04 0.21⁎⁎ −0.05 0.18⁎

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Table 5 Results of main and moderation effect analysis. Internal innovation

Control variables Firm size Product development stage Industrial position

Model 1

Model 2

Model 3

Model 1

−0.057 −0.043 −0.010

−0.042 −0.036 −0.089

−0.062 −0.048 −0.022

−0.950 0.139 0.018

0.430⁎⁎⁎ 0.461⁎⁎⁎ 0.189 −0.031 0.050

0.589⁎⁎⁎ 0.294⁎⁎ 0.265⁎⁎ 0.137 −0.015

0.677 0.617

−0.672⁎⁎ 0.703⁎⁎ −2.356 0.950 0.336 −1.060 0.852 0.175

Main effects Specific human capital Information structure R&D motivation Technical learning motivation Strategy motivation Interactions Specific human capital × R&D motivation Information structure × R&D motivation Specific human capital × technical learning motivation Information structure × technical learning motivation Specific human capital × strategy motivation Information structure × strategy motivation R2 ΔR2

External innovation

0.060

Model 2 0.100 0.218⁎⁎ 0.134 −0.346⁎⁎⁎ −0.362⁎⁎⁎ 0.198⁎ −0.082 0.000

0.070

0.812 0.742

⁎ p b .1. ⁎⁎ p b .05. ⁎⁎⁎ p b .01.

5. Discussions and future research This paper studies technological innovation mode choice from the perspective of enterprise internal organization context. We found that information structure and human capital specificity had obvious effects on innovation pattern choice in Chinese companies. Vertical information structure and low specific human capital (general human capital) are consistent with external innovation, and vice versa. There are few literatures relating to the three topics simultaneously, this study is an explorative research. Thus, in general, it adds to the knowledge in the field of technology and innovation management by empirically demonstrating the relationships among specific human capital and information structure as well as innovation mode. Our research expands the understanding of open innovation, which is a largely debated issue in innovation and technology management literature (Mattia, Alberto, Davide, Federico, & Vittorio, 2011). The classification of internal/external innovation and open innovation is different in dividing basis. As far as the organizational modes are concerned, it is necessary to distinguish two dimensions of the open innovation paradigm, namely inbound and outbound open innovation (Chesbrough, Vanhaverbeke, & West, 2006). Mattia et al. (2011) mention the wide spread organizational modes for inbound and outbound open innovation. From the innovation practice, we discuss internal innovation and external innovation as shown in Fig. 2, which makes it easy to differentiate them. In addition, the study also analyzes the interaction effects between different cooperation motives in specific human capital and innovation mode, information structure and innovation mode. The findings conclude that the motives related to research and development, and technology learning are two relatively significant moderators in the relationships among specific human capital, information structure and innovation pattern, the strategy motivation isn't a significant moderator. Three important managerial implications follow the empirical results. First, a firm should evaluate its degree of specific human capital properly to choose right innovation pattern. Scholars have argued that firm-specific human capital is an important source of competitive advantage because it is often tacit, context-specific, and path-dependent, and thus valuable and inimitable (Eisenhardt & Schoonhoven, 1990; Hatch & Dyer, 2004). The results suggest that enterprises with specific human capital tend to adopt internal innovation pattern, and general human capital adopt external pattern. Generally speaking, the higher the degree of specific human capital, the better the firm should adopt internal innovation pattern. Some measures should be taken for a firm to guide human capital to make specific investment such as corporate culture and staff training. When a firm has low degree of specific human capital, it is appropriate for it to adopt external innovation pattern which includes purchasing developed and applied technology and so on to reduce governance cost and production cost. Second, a firm should evaluate its information structure. The departments have to communicate and coordinate with each other in a firm. In reality, the coordination ways of different enterprises are also different. Even for the same thing, the ways of accessing, transferring and using the information in different enterprises are vastly different. In a firm with horizontal information structure, there is an environment in favor of knowledge sharing and communication among departments, which can induce the firm independent innovation. In this case, the firm is more willing to go on internal innovation to develop its specialized techniques that can give the firm competitive advantage. So in this kind of internal environment, the firm has more tendencies to

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A. Wu et al. / Journal of High Technology Management Research 24 (2013) 118–129 High R & D motivation

Low R & D motivation

Internal innovation Information structure Fig. 3. Moderation effect of R&D motivation.

put innovation activities within its own boundary, especially when the firm has proprietary technology, instead of putting outside of the firm. Third, a firm should define its own cooperative motivation to choose right innovation pattern. When a firm has a horizontal information structure, R&D motivation will strengthen positive relation between degree of horizontal information structure and internal innovation. When a firm has a high degree of specific human capital, driving force of the external exchange and learning will decrease, so technical learning motivation will weaken positive relation between degree of specific human capital and internal innovation. Matching different cooperative motivations with specific human capital and information structure properly is necessary to choose right innovation pattern. This study is subject to several limitations that suggest some directions for further research. Firstly, Due to the restrictions of data, the study of innovation mode choice is only limited in small range from the perspective of transaction cost theory. In addition to the nature of data, the generalizability of sampling is another limitation of this study. This study was conducted in a specific national context of Chinese firms. It is important to note that one should be cautious when generalizing the results to different cultural contexts. Further, this study has some limitations in respect to measures, which may overlap with existing constructs in other theoretical traditions. Secondly, the analysis is exploratory and the numbers of research hypotheses are still simple, and the relative problems such as the influence mechanism of information structure, specific human capital to innovation mode selection, and the relationships among them are for further research. For example, topics about the relationships between specific human capital and innovation ability, information structure and social capital are interesting, further persuasive efforts to understand them are worthwhile. What's more, the effects of specific human capital and information structure to different innovation patterns for incremental or radical innovation maybe different. For example, García-Muiña et al. (2009) find that accumulating knowledge using internal

Low technical learning motivation

High technical learning motivation

Internal innovation Specific human capital Fig. 4. Moderation effect of technical learning motivation.

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sources and not codifying it significantly improves the firm's capacity to develop radical innovations. Thus, the role of two kinds of technological innovation (incremental and radical) is also an interesting topic for future research. Finally, there are a lot of factors affecting the choice of innovation. We study it from the perspective of internal organization context, however, at the firm level, variables such as enterprise culture, R&D capability, and company strategy as well as emotional capability (Ali et al., 2009) may also determine the innovation pattern. At a macro level, as we know from previous studies, industry and governmental industrial policies are also major factors influencing innovation. For example, Salavisa, Sousa, and Fontes (2012) compare the difference of molecular biotechnology and software for telecommunications, and they consider that the sectoral differences affect the type, the sources and the modes of access to resources necessary for innovation and therefore firms' networking behavior. Breznitz (2007) utilizes the development of the Israeli industry to empirically explore the argument of the horizontal technology policies (HTP) framework on the impact of neutral science and technology policies on industrial development. A promising avenue for future research could be a systematic investigation of the variables that have determined the observed organizational modes for innovation. In addition, the relation between internal/external innovation and firm performance may be a particularly fruitful avenue for further research. Acknowledgments The authors would like to thank the anonymous reviewers for the helpful comments and suggestions on the earlier versions of this paper. This research work was supported in part by the National Natural Science Foundation of China (NSFC) under the Grant Nos. 71203083 and 71033002 and Chinese Ministry of Education under the Grant No. 11YJC630222. Appendix A. Measurement items and validity assessment Construct

Items

Loading

Specific human capital

1. It is hard to get most of employees in job market for our firm. 2. Employees are hard to be replaced in our firm. 3. It is hard for our competitors to get their employees. 4. Employees are considered best in our industry. 5. Employees have unique value for our firm. 6. It is difficult for our competitors to imitate and copy their employees. 7. Employees can meet needs of our firm. 8. Employees can make a difference compared to our competitors. 1. There is a lot of inter-coordination between management and staffs in our firm. 2. There is a lot of flexible and exceptional task in our work. 3. Technical environment of our firm is not constant. 4. Our firm encourages communication and mutual learning between staffs. 1. We cooperate with our partner to increase our innovation ability. 2. We cooperate with our partner to reduce innovation time-span. 1. We cooperate with our partner because there is technological complementarity in our firms. 2. We cooperate with our partner to lead technological innovation. 1. We cooperate with our partner to influence market structure. 2. We cooperate with our partner to control it. 3. We cooperate with our partner because the financial resources in our firm are often not sufficient, to share the high costs and risks of R&D in our firm. 4. We cooperate with our partner to compete with other firms. 1. The innovation in our firm is realized by internal development department. 2. The innovation in our firm is realized by mergers. 3. The innovation in our firm is realized by acquisitions. 1. The innovation in our firm is realized by market transaction. 2. The innovation in our firm is realized by joint innovation with other enterprises. 3. The innovation in our firm is realized by joint innovation with scientific research units. 4. The innovation in our firm is realized by joint venture.

0.706 0.766 0.708 0.706 0.750 0.721 0.791 0.710 0.705 0.749 0.711 0.700 0.867 0.867 0.852 0.852 −0.704 0.776 0.724

Information structure

R&D motivation Technical learning Motivation Strategy motivation

Internal innovation

External innovation

0.707 0.879 0.864 0.835 0.772 0.838 0.731 0.857

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