Knowledge‐based dynamic capabilities and innovation in networked environments

Knowledge-based dynamic capabilitiesand innovation in networked environments

Suli Zheng, Wei Zhang, Xiaobo Wu and Jian Du

Abstract

Purpose – The purpose of this paper is to clarify the concept of dynamic capabilities from theknowledge-based perspective and investigate the mechanisms of dynamic capabilities on innovationperformance in networked environments.

Design/methodology/approach – This paper designed a seven-point Likert questionnaire measuringthe dynamic capabilities, innovation performance and network embeddedness and a sample of 218Chinese manufacturing firms were surveyed. Structural equation modeling method was used tostatistically test the theoretical hypothesis.

Findings – Significant relationships were found between dynamic capabilities and innovationperformance and knowledge combination capability played a mediating role in this relationship. For thelinks between network embeddedness and dynamic capabilities, knowledge acquisition capability wasaffected mainly by relational embeddedness and the diversity of network and joint problem solvingcontributed much to knowledge combination capability.

Research limitations/implications – This paper deepened the understanding on dynamic capabilitiesand the mechanism between network embeddedness, knowledge-based dynamic capabilities andinnovation performance. In the future, the construct of knowledge-based dynamic capabilities calls formore examination and verification.

Originality/value – Drawing on the literature of dynamic capabilities framework, knowledge-based viewand the network model, this study extends the literature of dynamic capabilities and its link withinnovation performance in networked environments. Using survey data and structural equationmodeling, this study offers rich evidence on the contribution of dynamic capabilities on innovationperformance and the antecedents of dynamic capabilities.

Keywords Dynamic capabilities, Knowledge-based view, Innovation, Network embeddedness,Information networks, China

Paper type Research paper

1. Introduction

Dynamic capability is one of the most important constructs in strategic management in

recent decades (Teece et al., 1997). The dynamic capability framework is based on, but

different from, the resource-based view in that it contributes to this field by focusing on how

firms renew their resource-based competitive advantage dynamically. The literature on this

topic has grown rapidly since dynamic capability was first introduced, but substantial gaps

remain. On the one hand, many theoretical works emerged and deepened the

understanding of this concept (Eisenhardt and Martin, 2000; Winter, 2003; Teece, 2007).

On the other hand, little empirical research on dynamic capabilities has been taken out even

though 14 years have passed. This paper proposes that such absence of empirical

evidence is the result of two reasons. First, the concept of dynamic capability is still vague

and inconsistent. Especially, what is the object that dynamic capabilities act on, resources,

capabilities or knowledge? Second, the construct of dynamic capabilities is in need of

operationalization. As the literature has not provided a measurable construct, testable

propositions can hardly be put forward and tested.

DOI 10.1108/13673271111179352 VOL. 15 NO. 6 2011, pp. 1035-1051, Q Emerald Group Publishing Limited, ISSN 1367-3270 j JOURNAL OF KNOWLEDGE MANAGEMENT j PAGE 1035

Suli Zheng is Assistant

Professor at the School of

Economics and

Management, China Jiliang

University, Hangzhou,

China. Wei Zhang is a

Senior Manager at Xizi

United Holding Corporation

Hangzhou, China.

Xiaobo Wu is Professor and

Vice Dean and Jian Du is

Associate Professor, both at

the School of Management,

Zhejiang University,

Hangzhou, China.

The authors gratefullyacknowledge support for thisresearch from the NationalNatural Science Foundation ofChina (project nos 71002103,70902059 and 70910107021)and Soft Science ProjectZhejiang Science TechnologyDepartment (projectno. 2010C25013). All opinionsexpressed as well as errors andomissions are entirely theauthors’.

Received June 2011Revised July 2011Accepted July 2011

The central research question of this paper is: ‘‘How do knowledge-based dynamic

capabilities contribute to firm’s innovation performance?’’ The authors theorized and

examined this central question in three steps. As a foundational issue, the first step was to

clarify the meaning and construct of dynamic capabilities from knowledge-based view. In

the second step, the authors modeled and investigated the relationship of knowledge-based

dynamic capabilities and innovation performance. The proposition of this relationship is not

as straight and evident as people used to suppose and significant mediating effects exist.

Finally, this paper examined whether network embeddedness influenced knowledge-based

dynamic capability, and thus further worked on innovation. Taken together, these three

sub-questions helped us to understand the contributions of dynamic capabilities to

innovation performance, as well as to understand the antecedents of dynamic capabilities

that may enhance or weaken it in networked environments.

This paper is built on the work of dynamic capabilities, knowledge-based view and the

networked innovation model. It contributes to the literature in the following three ways. First,

this paper extends previous research on dynamic capabilities by conceptualizing dynamic

capabilities as knowledge-based and consisting of acquiring, generating and combing

knowledge resources. It further theorizes that there is a systematic structure between

knowledge acquisition, generation, and combination and innovation performance. Second,

the empirical work, based on 218 survey samples, validates the vital role of dynamic

capabilities on firm innovation performance. Third, this paper suggests that network

embeddedness is an important antecedent of dynamic capabilities in more and more

networked environments; managers need to pay more attention to their alliance network and

make adjustment according to their capability and performance.

The following paper is organized into four parts. First, the authors developed the

knowledge-based dynamic capabilities construct drawing on the knowledge-based view

and dynamic capabilities perspectives. A theoretical model linking dynamic capabilities with

innovation and network enbeddedness was put forward in the second section. In the third

section, statistical analysis based on a survey of 218 Chinese manufacturing firms was taken

out. The method of analysis is structural equation modeling with AMOS 7.0. Discussions and

implications were given in the last section.

2. Literature review and construct development

A. From dynamic capabilities to knowledge-based dynamic capabilities

In a fast-changing world, firms must explore, adapt to and exploit changes in their

business environments. Teece et al. (1997) introduced the dynamic capabilities

framework to explore firm behavior in turbulent environment and innovation-based

competition. In their pioneering work, dynamic capabilities are defined as ‘‘the ability to

integrate, build, and reconfigure internal and external competences to address rapidly

changing environments’’ (Teece et al., 1997). This framework is illuminating in that it

addresses the problem of how to gain competitive advantage in this fast-changing world.

However, the concept received several critiques afterwards. First, the resource base of

dynamic capabilities, or in other words the object of dynamic capabilities, was not clear.

As Teece has put, the resource base to be changed by dynamic capabilities includes

tangible, intangible, and human assets as well as capabilities the organization owns,

controls, or has access to (Teece et al., 1997; Helfat et al., 2007). It is obvious that

capability was also considered as a kind of resource in the general sense. This implies

that dynamic capabilities can modify or extend dynamic capabilities themselves and thus

will form an infinite loop. Second, it is claimed that the framework was conceptually vague

and tautological, short of mechanisms by which resources and capabilities actually

contribute to competitive advantage. Put it in another way, the performance consequence

of dynamic capabilities was uncertain.

Since then, the emphasis of strategic management shifted to the ability to change and

quickly develop new organizational capabilities, and subsequent work refined and

expanded the original definition of dynamic capabilities. Eisenhardt and Martin (2000)

PAGE 1036 j JOURNAL OF KNOWLEDGE MANAGEMENTj VOL. 15 NO. 6 2011

proposed that dynamic capabilities are ‘‘a set of specific and identifiable processes

integrate, re-configurate and gain/release of resource’’. In this conception, dynamic

capabilities take the form of organizational processes and have significant commonalities

across firms. In their opinion, dynamic capabilities are processes that can operate in both

dynamic environments and environment that are not experiencing rapid change. Zollo and

Winter (2002) explored dynamic capabilities with evolutionary ideas, defining dynamic

capability as ‘‘a learned and stable pattern of collective activity’’ to modify their operational

processes and improve their effectiveness. In their framework, learning mechanisms such

as knowledge-related activities are prominent driver of the evolution of dynamic

capabilities.

The above-mentioned research constitutes the foundation of dynamic capability research,

lots of literature has sprung up during the recent years. However, this concept has been

used to explain a variety of organizational questions, but yielded little concrete or solid

conclusions. One reason is that although this definition can be explained theoretically, the

operationalization and empirical validation of this construct is still a great challenge. The

research on dynamic capabilities to date has been largely theoretical or case-based. To

investigate how well or poorly dynamic capabilities perform, a clear conceptualization of

dynamic capabilities was required. Then further research can be taken out to translate such

a conceptualization into empirical metrics. To address these problems, Helfat et al. (2007)

suggested that researchers canmeasure dynamic capabilities with performance yardsticks,

such as quality per unit cost and survival, growth, value creation and competitive advantage.

The problem with performance yardstick is that although the dynamic capability framework

deems resource as a source of competitive advantage, the link between capabilities and

competitive advantage always needs to be tested rather than a perfect equal. In Teece’s

recent works he tried to refine dynamic capabilities as the capabilities that enable business

enterprises to create, deploy, and protect the intangible assets that support superior and

long business performance (Teece, 2007, 2009). This definition is different from the 1997

version in that the object of dynamic capability is those intangible assets, such as

knowledge, which will probably promote the research work in this field.

In fact, the framework of dynamic capabilities overlaps a lot with absorptive capacity (Cohen

and Levinthal, 1990) – another influential construct in strategic management that was

developed in parallel since 1990. Introduced by Cohen and Levinthal (1990), a firm’s

absorptive capacity refers to its ‘‘ability to identify, assimilate, and exploit knowledge from

the environments’’. Substantial extensions were made, such as relative absorptive capacity

(Lane and Lubatkin, 1998), potential absorptive capacity and realized absorptive capacity

(Zahra and George, 2002). It should be noted that these extensions of absorptive capacity

have added valuable new insights but the essence of absorptive capacity remain relatively

stable. The key idea of this concept remains as a firm’s ability to acquire knowledge from its

external environments and absorptive capacity is in essence a special kind of dynamic

capability (Zahra and George, 2002; Lane et al., 2006).

The above literature in these two areas converged toward the same idea-the

knowledge-related dynamic capabilities, which may promote our understanding and the

validation of dynamic capabilities (Easterby-Smith and Antonacopoulou, 2006;

Easterby-Smith and Prieto, 2008). According to the knowledge-based view, organizations

are knowledge-bearing entities, the fundamental function of the firm is to integrate and use

knowledge (Grant, 1996). Although Grant mentioned the role of relational networks on

accessing explicit knowledge, the scope of knowledge integration/combination is mainly

restricted within the organizational boundary and external knowledge is foreign to the

combination framework. This paper synthesized these different streams of work and put

forward the knowledge-based dynamic capabilities framework, which was defined as the

ability to acquire, generate and combine knowledge resources to sense, explore and

address environment dynamics. The underlying process of knowledge-based dynamic

capabilities consists of knowledge related activities of both internal knowledge and external

knowledge embedded in alliances and networks.

VOL. 15 NO. 6 2011 j JOURNAL OF KNOWLEDGE MANAGEMENTj PAGE 1037

B. The construct of knowledge-based dynamic capabilities

As mentioned above, dynamic capabilities are the ability to acquire, generate and combine

knowledge resources to sense, explore and address environment dynamics. In this

framework, knowledge is consistent with the traditional definition and includes various kinds

of knowledge, such as explicit and tacit knowledge, information and know-how,

technological, management and marketing knowledge. Three sub-capabilities –

knowledge acquisition capabilities (KAC), knowledge generation capabilities (KGC), and

knowledge combination capabilities (KCC) – represent three dimensions of

knowledge-based dynamic capabilities and they build upon each other to produce the

integrate dynamic capabilities of a firm (illustrated in Figure 1). Although these components

influence one another to a great extent, we will first discuss them separately for analytical

clarity and explain their internal structure in detail in the hypothesis development section.

Following Eisenhardt and Martin (2000), the authors argue that although dynamic

capabilities have some commonalities across different firms, they are idiosyncratic in the

specific ways firms pursue, develop and employ them. Hence, in spite of the fact that all

firms can develop dynamic capabilities, their level and form of dynamic capabilities can be

quite different and leading to distinctive organizational performance.

KAC. Knowledge is the principle productive resource of the firm. Considering firm boundary,

knowledge can be categorized into internal accumulated/generated knowledge and

external knowledge (Cohen and Levinthal, 1990). As the technological and market

environments become more and more dynamic, external knowledge come to the center of

stage. Scholars apply the concept of absorptive capacity (Cohen and Levinthal, 1990) to

address such issues. In fact, absorptive capacity consists of a series of processes handling

external knowledge-acquire, assimilate, transform, and exploit external knowledge, being

dynamic in their nature and distinct with one another. To deep our understanding, this paper

separates KAC out as the first component of knowledge-based dynamic capabilities,

knowledge acquisition means the firm’s ability to identify and acquire useful external

knowledge. In fact, the knowledge of organization exist in two different forms: explicit

knowledge and tacit knowledge, the latter draws much attention due to its limited

transferability (Kogut and Zander, 1992). Hence, efforts spend on knowledge acquisition

activities encompass creative searching and strategic sense-making and were greatly

influenced by the manager’s logic pattern and behavior (Pandza and Thorpe, 2009).

KGC. The second component of knowledge-based dynamic capabilities is knowledge

generating capabilities in the knowledge-based dynamic capability framework. Firms exist

as repositories of knowledge and one attribute that differentiates one organization from the

Figure 1 Theoretical model

Diversity

Non-redundancy

Trust

Joint problem solving

Commitment

Structural em

beddednessR

elational em

beddedness

Knowledge based dynamic capabilities

KAC

KCC

KGCInnovation

performance

H1a (+)

H1b (+)

H1c (+)

H2b (+) H2a (+)

H2c (+)

H3a (+)

H3b (+)H4a (+)

H6a (+)

H6b (+)

H6c (+)

H5a (+)

H5b (+)

H5c (+)

H4b (-)


others is its knowledge generating capabilities. Generation denotes a firm’s ability to

develop and refine the activities and processes that facilitate creating/generating new

knowledge. The underlying processes include internal R&D, SECI process proposed by

Nonaka (1994, 2005), and knowledge creation through external venturing (Wadhwa and

Kotha, 2006). The KGC are especially stressed by those catching-up economies in recent

decades. As Rosenberg (1982) pointed out very early that ‘‘reliance on borrowed

technology (by developing countries) perpetuates a posture of dependency and passivity’’,

catching-up firms without knowledge generating capabilities have experienced the

‘‘acquisition-lag out-acquisition’’ in many later comer economies (Rosenberg, 1982). As a

result, the generation of knowledge becomes the focus of firms, especially catching up

firms.

KCC. The third dimension of knowledge-based dynamic capabilities is knowledge

combination capability. Combination capability is the firm’s ability to integrate and apply

internal and external knowledge. Sometimes, combination gives birth to total new

knowledge. Kogut and Zander (1992) suggest that firms learn new skills by recombining

their current capabilities. New knowledge, such as knowledge in the process of innovation,

is produced by combining new knowledge with existing knowledge or experimenting new

applications of existing knowledge. It means that, combinative capability can be applied

both within and out of the firm boundary. Van Den Bosch et al. (1999) distinguished three

subtypes of combinative capabilities: systems capabilities, coordination capabilities, and

socialization capabilities. For the purpose of simplicity, the authors do not include this

distinction here and they synthesize this progress in the construct measurement section.

The interaction between three dimensions. As discussed above, the three dimensions do not

work alone without each other. They tend to develop cumulatively, be path dependent, and

build on each other to form integrate dynamic capabilities of the firm. Acquisition of new

knowledge requires a certain amount of knowledge stocks, at the same time, it will influence

the subsequent knowledge creation process. Knowledge combination refers to the process

of bringing together and mixing different kinds of old knowledge or old and new knowledge.

Hence, knowledge acquisition and generation form the important antecedents of

combination. This paper will discuss the internal structure of this construct in-depth in the

next section.

C. Network embeddedness and innovation

Since put forward by Schumpeter, innovation persistently attracted the attention of both

economists and managers. In turbulent environments, innovation becomes central in the

field of strategic management as its vital role in gaining and maintaining competitive

advantage. How to enhance innovation capability and improve innovation performance is

the focus of many scholars. In recent years, more and more researchers come to agree that

knowledge is the most critical input to innovation process and the ability to exploit and

explore knowledge thus becomes a critical component of competitive advantages. Much

work has been done to examine those separate knowledge management processes and

their effects on innovation, however, an integrate framework clarifying the internal

relationship of these processes and their influence on performance as a whole is needed.

In recent years, alliances and networks have become an integral part of a firm’s business

environments. Alliances are formal or informal arrangements of firms that enable firms to

gain and exchange resources or to engage in shared goals (Jarillo, 1988; Gulati, 1998).

Firms are embedded in networks of such strategic relationships which will provide

informational benefit through different mechanisms. The rapid proliferation of strategic

networks invoked the study on various kinds of networks, some studies focused on the

effects of network resources on general performance (Gulati, 1999; Hoffmann, 2007; Lavie,

2007) and innovation (Hagedoorn and Schakenraad, 1994; Kotabe and Swan, 1995), other

researchers have further explained the effect of network resource on capabilities acquisition

(McEvily and Zaheer, 1999; McEvily and Marcus, 2005). A fundamental question can be

drawn from these researches, that is, if dynamic capabilities were ‘‘higher-order

capabilities’’ that governing operational capabilities change, this effect of network


resources on first-order capabilities and performance must occur through its influence on

dynamic capabilities first. However, little research has investigated how alliance networks

influence the dynamic capabilities and this gap inspires the authors to investigate the

relationship between network embeddedness and dynamic capabilities.

3. Hypothesis development

As Powell et al. (1996) pointed out that the rapid growth of various alliances and networks

has changed the modes of innovation and ‘‘the locus of innovation’’ shifted to the networks of

learning. In this context, knowledge becomes the most critical input to innovation process,

hence, the ability to exploit and explore knowledge turns to be a critical component of

competitive advantages (Kogut, 2000). As defined in the above section, this paper

conceptualizes dynamic capabilities as a series of knowledge-based capabilities. On one

hand, these activities will improve innovation performance; on the other hand, external

networks may have substantial influences on these capabilities. Figure 1 illustrates the

relationship between a firm’s knowledge-based dynamic capabilities and innovation

performance and the role of network embeddedness in the process of knowledge-based

dynamic capabilities development. Through their relationships with network partners in the

environments, firms acquire or combine new knowledge that will greatly facilitate their

innovation activities and hence promote their innovation performance.

A. The link between knowledge-based dynamic capabilities and innovation

Nowadays, companies must utilize not only internal resources but also external resources to

tackle the ever-changing environments. Dynamic capabilities emphasize a firm’s constant

pursuit to acquire, generate and combine/reconfigure their resource bases. Of these

resources, knowledge resource gets more and more attention in this knowledge economy

era. Knowledge-based dynamic capabilities enable the firm to renew their knowledge base

continually and thus able to address the changing environments (Ambrosini and Bowman,

2009). Hence, by governing the change rate of knowledge, dynamic capabilities become

the ‘‘ultimate’’ organizational capabilities that are conductive to long-term performance.

Hence, this paper puts forward the following hypotheses:

H1a. KAC are positively related to innovation performance.

H1b. KGC are positively related to innovation performance.

H1c. KCC are positively related to innovation performance.

As the paper put earlier, there is an internal structure between knowledge acquisition,

knowledge generation and knowledge combination. Though knowledge acquisition

capability and knowledge generation capability are important antecedents of innovation,

the authors propose that knowledge combination capability contributes much more to

innovation activities and performance. Knowledge combination transform exist internal and

external knowledge to novel knowledge through new ways of configuration. Knowledge

acquisition capability and knowledge generation capability consist of the preconditions for

knowledge combination. They provide the knowledge foundation to be combined and

effective knowledge acquisition and generation enlarged the knowledge base to be

combined, which will result in more rapid and effective innovations.

Taken together, the link between knowledge acquisition capability, knowledge generation

capability and innovation performance is indirect, the first two processes provide the raw

material to be synthesized. Knowledge combination capability contributes directly to

innovation as combination provides a locale for different knowledge to interact and

experiment with new ways of configuration. In addition to affecting knowledge combination

capability, knowledge acquisition capability contributes to knowledge generation capability

as previous research has established, hence, this causal relation is also included in our

model. Hence, this model supposes that:

H2a. The relationship between KAC and innovation is mediated by KGC such that KAC

are positively related to KGC.


H2b. The relationship between KAC and innovation is mediated by KCC such that KAC

are positively related to KCC.

H2c. The relationship between KGC and innovation is mediated by KCC such that

KGC are positively related to KCC.

B. The antecedents of dynamic capabilities: network embeddedness

According to the literature, alliances and networks could improve the focal firm’s capabilities

by providing potential information and resource advantage. As a result, the characteristics

and structure of networks will influence the firm’s level of dynamic capabilities. In order to

investigate such effect, the authors introduced network embeddedness as the antecedents

of dynamic capabilities. Network embeddeded is a multi-dimensional concept that can be

defined from social, technical, relational and structural perspectives. This paper focused on

two dimensions-structural embeddedness and relational embeddedness. Structural

embeddedness analyzes the structure of the integrate network system and pays special

attention to the benefits it draws from the relative position in the network. Relational

embeddedness underscores the characteristics of direct ties which will promote deep and

extensive knowledge exchange.

Structural embeddedness. Diversity. Successful innovation requires different kinds of

knowledge. Firms can augment their knowledge by accessing to and assimilating relevant

knowledge and resources of their partners. As innovation process becomes more and more

open and interactive (Chesbrough, 2003), innovators must have the ability to grasp various

knowledge existed in their partner networks. The knowledge of their suppliers, customers,

competitors and so on are all important and they are complementary with each other in their

nature. When the network partners are more diverse, the focal firm will have more potential to

get needed knowledge and innovative use of knowledge. The use of different knowledge

sources will also facilitate the combination of knowledge and enable the focal firm to

complete the innovation more successfully. Based on these literatures, this paper argues

that the nodal heterogeneity in the form of partner diversity will facilitate knowledge-based

dynamic capabilities:

H3a. Network diversity is positively associated with the KAC of firm.

H4a. Network diversity is positively associated with the KCC of firm.

Nonredundancy. Granovetter (1985) emphasized the role of weak ties and pointed out that

firms are more likely to get novelty knowledge through weak ties. The philosophy is that

knowledge base between acquaintances may overlap greatly, leading to a high

redundancy. Conversely, weak ties may transmit knowledge from totally different fields

and inspire new ideas. Burt (1992) joined this argument and pointed out that the literature

should shift their focus from the strength of a tie to the overall structure of the network. The

existence of structural holes will decrease the frequency of interaction between firms and

their collaborating partners and hence increase the information richness of the knowledge

that the focal firm could acquire. All these arguments recognize that the potential of

information advantage is largely determined by nonredundancy. Nonredundancy will

improve the opportunity of acquiring new knowledge. However, nonredundancy implies

diversity of knowledge and large volume of new knowledge which will bring a great

challenge to knowledge combination. As opinions and behaviors are more heterogeneous

between sparse network collaborators, more obstacles will appear during the combination.

Hence, this paper expects that:

H3b. Network nonredundancy will positively influence the KAC of firm.

H4b. Network nonredundancy will negatively influence the KCC of firm.

Relational embeddedness. Trust between organizations is the state that firms feel confident

about their partners and have positive expectations about the actions of their partners

(McEvily and Marcus, 2005). Alliance partners may not share their information and


knowledge until they know that this knowledge will be used in the interest of their common

goals. This confidence will reduce uncertainty and hence makes firms more open with their

network partners. When partners have developed a certain level of trust, they are not only

more likely to share and exchange information with each other, but also have more

confidence in the information they get from this partner (Das and Teng, 1998). Trust often

triggers reciprocal behaviors in that partners are more likely to work cooperatively when trust

is high and thus provide the potential of knowledge combination. Hence, two hypotheses are

proposed:

H5a. Trust will positively influence the KAC of firm.

H6a. Trust will positively influence the KCC of firm.

Joint problem solving means that the network partners share the responsibility to maintain

the cooperation and to tackle the problems they meet during their cooperation (Heide,

1994). These activities are important domains of cooperation and certain pattern of

problems solving and mutual adaptation are developed over time. During this process,

partners not only share explicit knowledge, but also understand their partners’ more tacit

knowledge and further promote knowledge exchange and assimilation (McEvily and

Marcus, 2005). Combination of knowledge requires innovative use and configuration of

knowledge, during joint problem solving process the focal firm can access and make use of

external knowledge, even mixed them with internal knowledge. Hence, joint problem solving

is a platform for firms to experiment different kinds of knowledge integration. The effect of

joint problem solving on dynamic capabilities is predicted as follows:

H5b. Joint problem solving will positively influence the KAC of firm.

H6b. Joint problem solving will positively influence the KCC of firm.

Commitment. The sustaining and success of alliances or partnerships lie greatly on the

existence and intensity of commitments. Interorganizational commitment means that a

partner believes that the interorganizational relationship is very important and they are willing

to exert greatest efforts to maintain it (Morgan and Hunt, 1994). Commitment among

partners is also a decisive factor to achieve their shared goals. This attachment binds the

alliance partners more tightly and they become more and more dependent on each other as

time goes on. Commitment from both firms increases the level of knowledge sharing and

transferring. When partners are committed to the relationship, partners will more open to

disclose their knowledge pools. Moreover, partners are more likely to work together and help

each other to achieve the shared goals through knowledge combination. Hence,

commitment will positively contribute to knowledge-based dynamic capabilities:

H5c. Commitment will positively influence the KAC of firm.

H6c. Commitment will positively influence the KCC of firm.

4. Methodology

A. Research setting

The propositions were tested in the context of China, the largest manufacturing center in the

world, producing 18.6 percent of the world’s industrial goods in 2009 (data from Financial

Times). As one of the most important emerging economies, the business environment is

highly dynamic in China and innovation is the key strategic aim at both the national and firm

level. Under such condition, knowledge-based capabilities play a vital role in innovation and

market competition. The empirical work was based on a survey taken out during July 2008 to

January 2009 in Yangtze River Delta region, one of the most advanced industrial regions in

China. Our population consisted of manufacturing firms of various sizes, age, who has

participated in alliance with customer, supplier, or other kinds of partnerships during the

recent years.


B. Research design and data collection

The questionnaire was developed through extensive literature research and fieldwork. The

first draft of the questionnaire was developed based on the literature, and then this version

was adapted according to the opinions of three kinds of experts: the authors consulted the

content validity with two professors majoring in knowledge management, and then another

professor good at survey design was asked to examine the whole structure of the

questionnaire. After that, the survey was sent out to a subset of the target firms which were

randomly chosen and some of the instruments were modified to be better understood based

on their suggestions. After these adaptations, the questionnaire was ready to be sent out.

Then the survey was complemented by the persons most familiar with knowledge related

activities and alliance activities in each firm (chief technical officer, chief marketing officer,

vice president).

Data were collected through two different ways:

1. Face-to-face investigation. As this study is an important part of national funded research

project, the authors conducted a lot of field investigations and collected the survey.

2. E-mail. A manufacturing manager database consisting of managers’ information from the

Yangzi delta region was formed in NIIM research center of Zhejiang University, the

authors sent the electronic questionnaire to the managers in this database through e-mail.

Altogether, 512 questionnaires were sent out and responses from 229 firms were received,

with a response rate of 44.7 percent. Of these firms 11 were excluded as they did not provide

clear and integrate information. Hence, in the following parts, the hypotheses were tested

with the remaining 218 firms’ sample.

C. Operational measures

Dependent variable: innovation performance. This paper used four proxies to reflect the

innovation performance of various firms: number of new products, share of turnover with new

products, the speed of new product development and commercialization, the ratio of

successful product innovation.

Knowledge-based dynamic capabilities. Parallel instruments were developed to measure

the three components of knowledge-based dynamic capabilities, as shown in Table I.

Knowledge acquisition is a five-item instrument measured by a scale of Lane et al. (2001),

Huber (1991), and Nooteboom (2000). The instrument captures the degree to which the

focal firm could acquire technological, marketing, managerial, manufacturing and other

relevant knowledge from its partners. Knowledge generation capability has the same

structure as knowledge acquisition capability and the instrument includes five items asking

for the degree to which the firm could generate new technological, marketing, managerial,

manufacturing and other relevant knowledge endogenously. Drawing on the work of Grant

(1996), Kogut and Zander (1992), and Van Den Bosch et al. (1999) for knowledge

combination capability, knowledge combination capability is measured with six items as

shown in Table I.

Independent variables: network embeddedness. Following the work of McEvily and

Zaheer (1999) and McEvily and Marcus (2005), network embeddedness is defined as

a two-dimensional construct: structural embeddedness and relational embeddedness.

Structural embeddedness is measured with terms of nonredundancy and diversity,

and relational embeddedness is made up of trust, joint problem solving and

commitment.

Nonredundancy. This item is operationalized as an ego-centered network measure following

the work of McEvily and Zaheer (1999). The respondents were requested to write down the

five most important external relational partners and to evaluate if there is interaction between

each pair of partners. Based on the matrix of these five partners, the redundancy score can

be calculated using the following formula:

Nonredundancy ¼ number of potential ties2 number of actual tiesð Þ=number of partners:


Hence, nonredundancy is a ratio and the range for this ratio is between zero and two when

the number of partners is five. Smaller ratios represent low nonredundancy and the

existence of interactions between the focal firm’s partners will decrease the level of

nonredundancy.

Diversity. The network may consist of various kinds of relationships and this paper includes

only the firm’s relationships with those partners that they had direct experience, such as

customers, suppliers, trade associations, and even competitors and authorities. This item

was constructed based on the work of Baum et al. (2000) and was defined as a number

computed by the following formula.

Diversity ¼ 12X

i the number of alliance with ith type of partner=total number of alliance� �2h i

=

total number of alliances:

The range for this number is between zero and one and increase in this number indicates an

increase in diversity of alliances partners.

Trust. Four measures were used to capture interorganizational trust based on the work of

McEvily and Marcus (2005), including:

1. our main partner negotiates fairly with us;

2. our main partner does not mislead us;

3. our main partner keeps its words; and

4. our main partner is reliable.

Joint problem solving. Drawing on the work of McEvily and Marcus (2005), joint problem

solving were measured using three items:

Table I Measurement instruments and validity of knowledge-based dynamic capabilities

Construct Measurement itemsInternal consistency

reliability (a)Standard

coefficients CR

Knowledge acquisition Our firm could acquire technological knowledge 0.911 0.867capability Our firm could acquire marketing knowledge 0.830 13.362

Our firm could acquire managerial knowledge 0.817 13.014Our firm could acquire manufacturing and processknowledge 0.790 12.289Our firm could acquire other knowledge andexpertise 0.773

Knowledge generation Our firm could create technological knowledge 0.929 0.817 11.218capability Our firm could create marketing knowledge 0.906 12.800

Our firm could create managerial knowledge 0.875 12.233Our firm could create knowledge 0.828Our firm could create technological knowledge 0.808 12.310

Knowledge combinationcapability

Our firm could combine internal and externalknowledge 0.938 0.891 14.425Our firm could integrate knowledge from differentsegments, team and individuals 0.864 13.683Our firm could combine knowledge in differenttechnological or market fields 0.798 12.493Our firm could combine new knowledge with originalknowledge pool 0.879 12.309Our firm could adapt the internal structure andprocess to combine knowledge effectively 0.851 13.359Our firm could coordinate internal and externalnetworks to combine knowledge effectively 0.841 13.115

Notes: X 2 ¼ 120:026; x 2=df ¼ 1:188; df ¼ 101; CFI ¼ 0:991; TLI ¼ 0:990; RMSEA ¼ 0:035


1. our main partner work with us to overcome difficulties;

2. we are jointly responsible with our main partners for getting things done; and

3. we work with our main partner to help solve each other’s problem.

Commitment. Three items were included in this measure based on the work of Morgan and

Hunt (1994):

1. we are very committed to this relationship;

2. we make our maximum effort to maintain this relationship; and

3. this relationship is something my firm intends to maintain indefinitely.

Control variables. Size was included as control variable in this study since firm size may have

significant influence on firm innovation behavior and results (Stock et al., 2002; Shefer and

Frenkel, 2005). Large firms may have access to more or better resources than smaller firms

and benefit from scale economy, while smaller firms may be more entrepreneurial and have

more flexibility. Firm size was measured as the average annual revenue of the latest two

years.

5. Results and analysis

A. Construct validity and reliability

Before estimating the model, it is important to judge the reliability and validity of the

constructs. Validity is the extent to which the measure actually measures what the construct

is indicating for. As the measure for innovation performance and network embeddedness

are drawn from established studies, this paper focuses on the measures of the newly

developed construct of knowledge-based dynamic capabilities. The reliability of the

knowledge-based dynamic capabilities was examined with Cronbach’s alpha, and all scales

have reliabilities greater than the recommended 0.90 level (shown in Table I), suggesting a

high reliability. As the questionnaire is carefully designed based on in-depth literature review

and field interview, the content validity of this construct can be justified. Confirmative factor

analysis was used to evaluate the convergent validity and discriminant validity and the result

was shown in Table I. The factors loaded perfectly on the items as expected and all

indicators were above 0.77 and statistically significant. This result indicates that both the

convergent validity and discriminant validity of these measures are good enough to do the

following analysis.

B. Hypothesis testing

The hypotheses were tested using the method of structural equation modeling with the

software of AMOS 7.0. Structural equation modeling was an appropriate method as it allows

us to estimate the complex relationship between network embeddedness, dynamic

capabilities and innovation at the same time, especially the mediation effects within the three

components of dynamic capabilities. Moreover, it can accommodate the measurement error

of survey data.

The result for the structural equation model is shown in Figure 2. The path coefficients and

their significance of each hypothesis were reported. Several different indices were also

provided to determine the overall fit of the estimated model. Overall, the value of x 2 (478 df)

is 860.755 and the value for x 2/df is 1.801. CFI and TLI indices are 0.927 and 0.920 (all

above 0.9), and the value of RMSEA is 0.076 (below 0.08), these indices show that the

estimated model has a reasonable fit with the data.

H1. As the model predicted, the positive relationship between dynamic capabilities and

innovation performance are well supported. The relationship between knowledge

combination capability and innovation performance is highly positive and significant

(g ¼ 20:629, t ¼ 3:881, p ¼ 0:000), the positive link between knowledge acquisition

capability and innovation performance and knowledge generation capability are supported

too, although the coefficients and significance are lower than that of knowledge combination


capability (KAC: g ¼ 20:331, t ¼ 2:202, p ¼ 0:045, KGC: g ¼ 20:251, t ¼ 0:2:378,

p ¼ 0:017).

H2. The second hypotheses predicted the mediation effects within the three components of

knowledge-based dynamic capabilities. The mediation effects are largely supported by the

data. The relationship between knowledge acquisition capability and knowledge generation

capability is significant and positive (g ¼ 20:708, t ¼ 6:787, p ¼ 0:000), the link between

knowledge generation capability and knowledge combination capability is positive and

significant too (g ¼ 20:584, t ¼ 10:173, p ¼ 0:000). The relationship between knowledge

acquisition capability and knowledge combination capability is positive at a 0.059 level

(g ¼ 20:492, t ¼ 1:888).

H3. The hypothesized relationship between structural embeddedness and knowledge

acquisition capability is not supported. Neither diversity (g ¼ 0:683, t ¼ 1:030, p ¼ 0:303)

nor nonredundancy (g ¼ 0:006, t ¼ 0:108, p ¼ 0:914) showed any significant influence on

knowledge acquisition capability.

H4. The predicted relationship between structural embeddedness and knowledge

combination capability is partly supported. The results show that the relationships

between diversity and knowledge combination capability are positive and statistically

significant (g ¼ 2:677, t ¼ 3:377, p ¼ 0:000). However, out of our expectation the

relationship between nonredundancy and knowledge combination capability is positive

too, though not very statistically significant (g ¼ 20:117, t ¼ 21:824, p ¼ 0:068).

H5. The model states that relational embeddedness positively associated with knowledge

acquisition capability and our result supported this hypothesis. As predicted, the link

between trust and knowledge acquisition capability is positive and significant (g ¼ 20:378,

t ¼ 28:606, p ¼ 0:000), the relationship of joint problem solving and knowledge acquisition

capability is positive and significant too (g ¼ 20:136, t ¼ 23:146, p ¼ 0:002), commitment

Figure 2 Structural equation model

Note: *p < 0.05; **p < 0.01; ***p < 0.001, t-value in parentheses

Model statistics χ2 df

χ2/df

860.755478

1.801

CFI TLI

RMSEA

0.927 0.920 0.076

取Y1 Y2 Y3 Y4

KAC

KGC

Y11 Y12 Y13 Y14 Y15

KCC

Y16

Y17 Y18 Y19 Y20

IP

SIZE

deta1

Y5

1

1

Joint problemsolving

1

X2

X1

X3

X4

1

X5

X6

X7

1

X8

X9

X10

Trust

Diversity

Non-redundancy

Y6 Y7 Y8 Y9 Y10

1

γ51 = 0.683 (1.030)

Commit-ment

γ52 = 2.677 (3.377)***

γ41 = 0.006 (0.108)

γ22 = 0.318 (5.082) ***

γ42 = -0.117 (-1.824)

γ11 = 0.378 (8.606)***

γ12 = 0.005 (0.048) γ21 = 0.136

(0.146)*

γ31 = 0.147 (2.693) **

β13 = 0.492 (1.888)

β12 = 0.708 (6.787) ***

β23 = 0.584 (10.173) ***

δ11 = 0.331 (2.002)*

δ21 = 0.251 (2.378)*

δ31 = 0.629 (3.881)***

η = 0.101 (2.477)**

γ32 = 0.161 (2.291) *


contributes to knowledge acquisition capability positively (g ¼ 20:147, t ¼ 2:693,

p ¼ 0:007).

H6. The positive relationship between relational embeddedness and knowledge

combination is largely supported. The result reported here indicates that positive

relationships exist between joint problem solving and knowledge combination capability

(g ¼ 20:318, t ¼ 5:082, p ¼ 0:000) and between commitment and knowledge combination

capability (g ¼ 20:161, t ¼ 2:291, p ¼ 0:022). However, the relationship between trust and

knowledge combination capability is not supported, the coefficient is very low and the

significance is much larger than critical level (g ¼ 20:005, t ¼ 0:048, p ¼ 0:962).

C. Robustness of the results

Some additional analyses were taken out to test the robustness of the above results. In

particular, the authors examined whether the mediation effects did exist. First, direct paths

between network embeddedness and innovation performance were added. The coefficients

for the additional paths are insignificant and the overall fit of the model is not improved.

Second, to test the mediation effects within the dynamic capabilities, three mediation paths

– the path linking knowledge acquisition capability and knowledge generation capability to

knowledge combination capability and the path between knowledge acquisition capability

and knowledge generation capability – were removed. The direct path from knowledge

acquisition capability is significant at 0.1 level and the link between knowledge generation

capability remain insignificant, however, the overall fit indices get worse. These models offer

consistent evidence to the theoretical model.

From the theoretical model to the best model. In this section, insignificant paths were

trimmed off step by step, each time the path with the lowest CR value was deleted. Finally,

the result gave us a model in which all the coefficients were significant (p , 0:05), as shown

in Figure 3. Control variable was also included in the best model although the coefficient was

not significant. The authors compared this model with the hypothesized model and

observed that most significant paths were still significant and the difference is that some

marginally significant paths became insignificant and at the same time the explanative

power of significant paths was enlarged. Hence, the best model gave us more concise

structure between the independent and dependent variables.

Figure 3 The best model

Model statisticsχ2df

χ2/df

866.452485

1.786

CFITLI

RMSEA

0.9280.9210.071

Innovation p e r f o r m a n c eNon-redundancy

DiversityTrust

Joint PS

Commitment

KAC

KGCKCC

VOL.15 NO.6 2011jJOURNAL OF KNOWLEDGEMANAGEMENTjPAGE 1047

6. Discussion and conclusions

The literature agrees that dynamic capabilities are critical for innovation and competitive

advantage, yet it is not clear how it contributes to innovation. This study extends and

deepens our understanding of dynamic capabilities and its link with innovation performance

in networked environments.

The first contribution of this study is that the authors proposed a concise construct of

dynamic capabilities and validated it through empirical studies. Although 14 years have

passed since dynamic capabilities were first introduced, much confusion still remained in

this field. By reducing the subject of dynamic capabilities from the general resource to

knowledge resource, this study developed a convergent construct that can be empirically

tested. Conceptualize dynamic capabilities as a series of processes handling knowledge

resources and aiming at addressing dynamic environments, their attributes and

constitutions were clarified. Additionally this conception made it possible to identify and

measure dynamic capabilities practically and also facilitate us to examine the relationship

between dynamic capabilities and innovation performance empirically.

This paper offered rich evidence on the contribution of dynamic capabilities on innovation

performance. Whereas most extant work demonstrates the contribution of dynamic

capabilities theoretically, this paper put forwards a set of propositions linking dynamic

capabilities to innovation performance systematically. The empirical study with 218 samples

of Chinese manufacturing firms provided strong support for this effect. Further, intrinsic

structure within the three dimensions was discovered. Our results reported that knowledge

combination capability promotes innovation performance directly and mediates the process

between knowledge acquisition, knowledge generation and innovation. In other words,

knowledge acquisition capability and knowledge generation capability are important

preconditions of knowledge combination and contribute to innovation performance

indirectly. These findings echoed the research of Kogut and Zander (1992) that firms’

innovations are products of its combinative capabilities.

This study revealed that network embeddedness was an important antecedent of dynamic

capabilities. The results showed that knowledge acquisition capability and knowledge

combination capability were greatly influenced by network embeddedness. The results of

our empirical study indicate that relational embeddedness exhibits a greater influence on

the knowledge-based dynamic capabilities. Knowledge acquisition capability is mainly

influenced by trust, joint problems solving and commitment, while knowledge combination is

mainly driven by joint problem solving and commitments. An interesting result was that

nonredundancy has no significant links with dynamic capabilities which is in controversy

with the literature. A possible explanation is that the advantage of structural embeddedness

is their ability to provide relatively new knowledge while the acquisition and combination of

knowledge concerns mainly with in-depth knowledge exchange activities which will go on in

a continuous period.

7. Limitations and direction for future research

While this research deepened our understanding on dynamic capabilities and the

mechanism between network embeddedness, knowledge-based dynamic capabilities and

innovation performance, further questions emerged in this study. First, the construct of

knowledge-based dynamic capabilities calls for more examination and verification. The

conceptualization and operationalization of dynamic capabilities in this paper will provide a

starting point for future empirical studies based on large samples and different context and

much work still needs to be done to yield a mature construct. Second, manufacturing is a

broad context which includes both high-tech sectors such as chip manufactures as well as

relative stable industries such as the steel industry, however, this paper does not examine

the differences between different industries. Further studies may overcome this problem by

adding control variables or restrict their investigations to specific kinds of industry. Third, the

literature has recognized the evolutionary nature of dynamic capabilities, there is a

possibility that firms will reconstruct their organizational environments deliberately to sustain


competitive advantage. In other words, managers may adapt their alliance networks

dynamically. Hence, feedbacks between network environments, dynamic capabilities and

innovation performance link the cycle of these constructs. In the future, scholars could

investigate these relationships either though case studies or large scale empirical data.

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About the authors

Suli Zheng is an Assistant Professor of China Jiliang University; her research interests areinnovation and strategy management. She has published several papers on innovationmanagement and knowledge management. She is now leading a NSFC project on globalmanufacturing networks and innovation. Suli Zheng is the corresponding author and can becontacted at: [email protected]

Wei Zhang is a Senior Manager in Xizi United Holding Corporation; he holds a PhD degree inManagement Science and Engineering. His major research interest was R&D managementand knowledge management.

Xiaobo Wu is Professor and Vice Dean of School of Management, Zhejiang University, Chinaand the Director of National Institute for Innovation Management (NIIM). His researchinterests are in technological innovation, global manufacturing and business strategy.

Jian Du is an Associate Professor of the School of Management, Zhejiang University; hermajor interests are FDI and innovation management.


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Knowledge‐based dynamic capabilities and innovation in networked environments