clustering and imitation in innovation strategy toward an incumbent-entrant dynamics
DESCRIPTION
Clustering and Imitation in Innovation Strategy Toward an Incumbent-Entrant DynamicsTRANSCRIPT
1
Clustering and Imitation in Innovation Strategy: Toward an Incumbent-Entrant Dynamics
YIYI SU School of Economics and Management
Tongji University 2007, Zonghe Building, Tongji University
Shanghai 200092, China Telephone/Fax: 86-21-6598-6119
Email: [email protected]
CHANGHUI ZHOU Guanghua School of Management
Peking University Beijing 100871, China
Telephone/Fax: 86-10-6275- 5089 Email: [email protected]
2
Clustering and Imitation in Innovation Strategy: Toward an Incumbent-Entrant
Dynamics
Abstract
In an emerging market, the lack of the intermediary mechanisms, constraints in
knowledge flow, and high transaction costs in acquiring intellectual assets bring about
the problem of institutional voids in intellectual asset market, which impedes
organizational learning and firm innovation. Under this circumstance, industrial
cluster functions as institutional substitute, i.e., the clustered firms mimetically learn
from other clustered firms in innovation strategy. Based upon Beijing Zhongguancun
Science Park, we found that entrants tend to imitate incumbents’ innovation strategy
within an industrial cluster and the imitation effect is moderated by cluster density and
cluster variability.
Key words: firm innovation; emerging market-based industrial cluster; imitation
3
Introduction
Innovation strategy plays a crucial role in organizational adaptation and survival.
In general, R&D and innovation indicate a substantial input of capital, human, and
management cognitive resources for the development of absorptive capacity (Cohen
and Levinthal, 1989; Kor, 2006) and long-term competitive advantage (Dierickx and
Cool, 1989); it is also conceived as an innovative search behavior through which
organizations evolve and adapt (Greve, 2003a). Despite of its strategic importance,
firms in an emerging market usually find themselves in an embarrassing position in
making innovation strategy: on one hand, innovation is a key element for firms to gain
competitive edge; on the other hand, expensive R&D investment may put their limited
resource and legitimacy under pressure. Especially, in the context of an emerging
market, where the lack of efficient institutional intermediaries brings about
institutional voids problem in the intellectual asset market, firms will face substantial
institutional uncertainty in making innovation strategy and their learning and
innovation mechanism is largely impeded.
Drawing insights from the existing literature on imitation (see Lieberman and
Asaba (2006) for a recent review), this paper proposes, under institutional voids,
imitation can be an alternative learning mechanism for firms in an emerging market.
From institutional perspective, firms in face of environmental uncertainty will
naturally seek to reduce uncertainty by imitation; such mimetic isomorphism process
can partially alleviate legitimacy constraints of newly founded establishments
(DiMaggio and Powell, 1983; Meyer and Rowan, 1977). From learning perspective,
firms tend to draw inferences from the behavior of other firms when their own
experience provides inadequate guidance (Cyert and March, 1963; Levitt and March,
1988; March, 1991). From information cascade theory, firms follow the patterns of the
“fashion leader”, which is perceived to have superior information (Bikhchandani,
Hirshleifer, and Welch, 1992, 1998). From game-theoretical perspective, in
“winner-takes-all” situations, rival firms tend to adopt similar innovation strategy to
maintain relative competitive position (Cockburn and Henderson, 1994; Dasgupta and
Stigliz, 1980). Different focuses and rationales as they have, all the theoretical
perspectives suggest that imitation poses a viable strategy for firms in an emerging
market.
This paper attempts to explore imitation process in the context of an emerging
4
market-based industrial cluster. Our first goal, then, is to test whether firms in an
emerging market mimetically adopt innovation strategy under institutional voids. Our
second goal is to examine the moderating role of external information distribution on
imitative behavior. Literature has long recognized the vital role played by information
in imitation and learning process (Haunschild and Miner, 1997; Lieberman and Asaba,
2006). Researchers made endeavors to explore the potential factors contributing to
information condition, say, information quality and quantity, concerning imitation,
and resultant imitative behavior, such as attributes of the reference group (Haunschild
and Miner, 1997; Haveman, 1993), inter-firm linkage (Greve, 1998a; Haunschild,
1993), network structures (Abrahamson and Rosenkopf, 1997). Following this line of
research, we identify cluster density and cluster variability as the determinants of
information environment surrounding imitators, and empirically examine their
moderating effects on imitation of innovation strategy.
We test these relationships by looking into firm’s R&D investment strategy in the
largest Chinese technology park, Beijing Zhongguancun Science Park (Zhongguancun
Science Park, hereafter), from 2001 to 2003. The Zhongguancun Science Park is
characterized by geographic agglomeration of small- and medium-sized firms,
incubation of innovation, and dynamic institutional environments, constituting a
natural laboratory for entrepreneurship and innovation research (Tan, 2005).
Furthermore, geographical proximity makes the Zhongguancun Science Park a
confluence of information and knowledge, and facilitates the diffusion of
organizational practices. Specifically, we attempt to investigate firms’ imitation
process through the incumbent and entrant dynamics. We argue, in the emerging
market-based industrial cluster, the entrants tend to mimetically learn from the
incumbents in innovation strategy. In this sense, industrial cluster functions as
institutional substitutes. We further predict that characteristics of the geographic
industries influence information condition in the imitation process, and thus, shape
their perception of the reference group, and imitative behavior. Set in this specific
context, our study extends the recent developments in imitation research (Baum, Li,
and Usher, 2000; Lieberman and Asaba, 2006), and echoes the persistent interests in
industry clusters and agglomeration economies (Audretsch and Feldman 1996;
Marshall, 1920; Porter, 1998).
In the following sections, we will firstly go over relevant research streams in the
literatures, paying specially attention to research on imitation, institutional voids, and
5
information process view of organizations, and develop a set of testable hypotheses
concerning imitation. Then we will briefly introduce our research setting, Beijing
Zhongguancun Science Park, and empirically test our theoretical framework. In the
last section, we will summarize and analyze the main founding of the paper and
discuss both the limitations and contributions of the research.
Theoretical Background
Imitation as a Viable Strategy
There is a fairly well-developed literature concerning interorganizational
imitation in organizational and management research. While early work in this
domain elaborated imitation solely from economics, sociology, or psychological
perspective, recent research has integrated and contrasted various theories of imitation,
and specified the conditions to discriminate different theories. Gimeno et al. (2005)
demonstrated that the drivers for clustering can be classified into (1) externalities
among the strategic actions of organizations, (2) competitive reactions among
organizations, and (3) noncompetitive referential process. Lieberman and Asaba (2006)
proposed a two-part typology of imitation theories, viz., information-based theories
and rivalry-based theories: the former emphasize the information value from imitation,
while the latter underline competition mitigation via imitation.
In the following, we selectively review the research streams on imitation, paying
special attention to institutional theory, organizational learning theory, information
cascade theory, and rivalry-based theory of imitation, and then look into pertinent
discussions about innovation strategy. Since our study does not aim to conduct a
comprehensive survey, the literature search is not exhaustive and focuses on those
most relevant to our research topic.
Institutional theory. Institutional theorists argued that organizations imitate other
organizations in pursuit of legitimacy or for taken-for-granted practices (DiMaggio
and Powell, 1983; Meyer and Rowan, 1977). Imitation can be seen as a natural
response to environmental uncertainty; organizations facing high uncertainty will seek
to reduce uncertainty by copying other organizations’ action, i.e., mimetic
isomorphism (DiMaggio and Powell, 1983). The central focus of imitation in
institutional theory is legitimacy, rather than efficiency. Empirically, Deephouse (1996)
further found a positive relationship between strategic isomorphism and legitimacy in
banking industry.
6
Organizational learning theory. Organizational learning theorists argued that
learning from other organizations can be seen an exploratory learning mode and is
more likely to be used when organizations’ own experience provides inadequate
guidance (Cyert and March, 1963; Levitt and March, 1988; March, 1991). Empirically,
Henisz and Delios (2001) found that firms lacking experience in the host country tend
to imitate plant location decision of industry peers. Compared with institutional
isomorphism, imitation in organizational learning theory embodies both technical and
social values (Haunschild and Miner, 1997).
Information cascade theory. Information cascade theory is an economics
version of imitation theory and explicitly articulates the information aspects in
imitation (Bikhchandani, Hirshleifer, and Welch, 1992, 1998). In this model, the
behavior of the first actor is based upon his private information, and conveys
information to his followers; as information accumulates, the followers imitate others’
behavior regardless of their own information. This model has generally been applied
in FDI and financial market. In contrast with institutional isomorphism, imitation in
information cascade is less enduring, since new information often reveres imitation
process (Lieberman and Asaba, 2006).
Rivalry-based theory of imitation. While the above theories of imitation,
explicitly or implicitly, emphasize on the information value in imitation, rivalry-based
theory regards competitive reaction as the driver for imitation (Gimeno et al., 2005;
Lieberman and Asaba, 2006). When one firm takes competitive move to improve its
position at the expense of the others, its rivalries tend to make “retaliation or efforts to
counter the move” (Porter, 1980). As for the respondents, imitation is a rational
behavior to signal decisiveness to maintain position without escalating rivalries (Chen
and Miller, 1994). This line of research can be further classified according to their
imitation motivation: to mitigate competition, e.g., multi-market contact, and to
minimize risk, e.g., FDI, R&D (Lieberman and Asaba, 2006).
Different theories of imitation are not mutually exclusive; their mechanism can
simultaneously work, with one dominating over another at a given time (Lieberman
and Asaba, 2006). Academic discussion about imitation in R&D inputs is confined to
rivalry-based theory (Cockburn and Henderson, 1994; Dasgupta and Stigliz, 1980).
According to Dasgupta and Stigliz (1980), R&D investment “is not a case of a single
firm making a single decision, but rather a case in which several firms make a
complex of decisions” (p.267). In “winner-take-all” situations, competition in R&D
7
becomes a Prisoner’s Dilemma, and rival firms may imitate other firms’ research
strategy to maintain their competitive position, leading to over-investment in research.
Yet, relevant empirical evidence is still lacking. Cockburn and Henderson (1994)
found that research investment is only weakly correlated across firms in
pharmaceutical industry. Additional work is needed to model dynamic competition in
R&D.
Furthermore, we do see some opportunities to apply other imitation theories in
R&D investment research (Lieberman and Asaba, 2006). Especially, when
institutional uncertainty confounds the predicted outcome of R&D investment, when
firms cannot rely on experiential learning, or when firms need to derive legitimacy
from R&D activities, imitation of R&D investment strategy is not merely a response
to competition, but a self-adjusted information-processing process.
Institutional Voids and Organizational Learning
Both in the fields of economics and sociology, institutional theory emphasizes
institutional influences on organizational structures and processes (Aoki, 1990;
DiMaggio and Powell, 1983; Granovetter, 1984; North, 1990; Powell and DiMaggio,
1991). New institutional economics examines the interaction between institutions and
firms due to market imperfections (Harriss, Hunter and Lewis, 1995). Specifically,
one critical dimension of institutions, specialized intermediaries, plays a significant
part in organizational structure and performance implications. Such intermediaries can
partially solve the transaction and information costs in transactions and therefore
reduce the transaction costs in labor, product or financial markets. From transaction
cost economics (Coase, 1937; Williamson, 1975, 1985), the optimal scope of a firm is
the function of transaction costs and extent of specialized intermediation.
The development and maturity of the specialized intermediaries varies across
different institutional environments. In the institutional context of developed countries,
the specialized intermediaries are well developed and can efficiently bring down
transaction costs. On the contrary, in emerging markets, there exist severe problems of
market failure. Take financial market for example, the financial market in an emerging
market faces substantial challenges: lacking efficient and adequate disclosure system
and weak corporate governance, not well-developed financial intermediation system
(e.g., financial analysts, mutual funds, investment bank, venture capitalists, and
financial press), distorted governance regulation and incomplete legal systems. All
these challenges result in high transaction costs for firms in emerging markets. Such
8
problems also take place in product and labor markets.
Khanna and Palepu (1997) characterized the specific institutional environment of
emerging markets as institutional voids. The authors illustrated the impact of
institutional voids on organizational structure and diversification strategy through the
case of diversification business group. Their main argument is, under institutional
voids in emerging markets, diversification business group functions as specialized
intermediation, which bridges the individual firms and the incomplete markets. Such
group can use its broad scope to smooth out income flows in individual business units
and reduce potential risks; it can also provide the channels of internal financing and
relieve the financing problems in emerging markets. Therefore, in the specific context
of emerging markets, diversified business group contributes to value creation,
although the benefits will decrease as the institutions or specialized intermediaries
gradually develop.
In retrospective of the previous literature, we found out that research on
institutional voids mainly focuses on financial or labor market. We argue that the
institutional voids problem can also occur in intellectual asset market in emerging
markets. The lack of intermediaries (e.g., industry association, underdeveloped
technological personnel market) constrains the information flow and technology
spillover and impedes the organizational learning process. Subsequently, we will
adopt an information process view of organization and look into the role of
information in organizational learning and imitation process.
The Role of Information in Imitation Process
Information-processing view of organizations posits that organizations need
quality information to improve decision making and to deal with the uncertainty
stemming from environmental turbulence and dynamism (Galbraith, 1973). From this
perspective, imitation can be conceptualized as an information-processing process,
where firms acquire knowledge based upon the observation of other firms, distribute
information across organizations, interpret the information towards a better
understanding, and eventually decide to incorporate it into current routines (Huber,
1991; March and Simon, 1958). Even in rivalry-based theories of imitation, where
acquiring information is not a major concern, information structure of the game is still
a precondition of competitive behavior, for example, stochastic racing models of
R&D are built on a strong assumption that information is available to actors in the
game (e.g., Reinganum, 1982).
9
Literature has seen persistent attempts to explore how imitative behavior is
contingent upon the information potentially flowing through the imitators (Lieberman
and Asaba, 2006). Some studies show that characteristics (e.g., profitability, largeness)
of the reference group convey information about value, legitimacy, efficiency in
imitation, and lead to “trait-based imitation” (Haunschild and Miner, 1997; Haveman,
1993). Some studies treat certain inter-firm linkage as the information sharing
mechanism in imitation process, such as interlocking directors (Haunschild, 1993),
market contact (Greve, 1998a).
Social network research also sheds light on the information component of
imitative behavior. Relevant studies demonstrate that social network channels
information to potential adopters, and therefore have effects on diffusion of
organizational practice and innovation (Abrahamson and Rosenkoft, 1997;
Granovetter, 1985). For example, Abrahamson and Rosenkoft (1997) adopted a
simulation approach to model the effects of idiosyncratic social networks on
innovation diffusion by disseminating information concerning innovation to network
participants. The social network logics can be further extended to other environmental
contexts. Take geographical industries for example. Conceiving a geographical
industry as an institutional field with interconnected organizational constituencies
(DiMaggio and Powell, 1983), we predict that the structure of geographic industry
may well condition information distribution and imitative behavior within it.
Hypotheses Development
Institutional Voids Framework and Imitation: Industrial Cluster as the
Institutional Substitute
As we discussed above, the institutions in emerging markets, especially, the
specialized intermediation, are not well developed, bringing about the institutional
voids in intellectual asset market and impeding organizational learning and firm
innovation. In this situation, new entrants in an industrial cluster usually face
substantial difficulty deciding upon the appropriate level of R&D investments:
long-term oriented R&D should be balanced with the current high rate of failure;
direct experience is lacking to provide adequate guidance; insufficient legitimacy may
make their behavior or strategy absurd; even worse, institutional voids in emerging
markets obscure the potential cost and benefit of R&D investments.
Drawing upon recent developments in imitation research (Abrahamson and
10
Rosenkopf, 1993, 1997; Gimeno et al., 2005; Lieberman and Asaba, 2006), we argue
that entrants in an emerging market-based industrial cluster tend to follow the strategy
of certain reference groups at least for four reasons: (1) to overcome the liability of
newness via legitimacy building, (2) to vicariously learn to innovate in absence of
experiential experience, (3) to acquire relevant information dealing with
environmental uncertainty, and (4) to gain or maintain competitive positive relative
their rivals. In this sense, industrial cluster functions as institutional substitute by
setting the reference groups in organizational learning and imitation. The critical role
of industrial cluster in organizational learning has been elaborated in previous
literatures. For example, Frost and Zhou (2000) identified firms’ immediate
geographic milieu as the source of learning. Research on industrial cluster pointed out
that geographic agglomeration facilitates imitation and learning among the
organizations (Tan, 2006; Pouder and St. John, 1996).
So how do entrants in an industrial cluster choose their reference group? In
previous literature, the judgment of reference groups is based on the similarity in
industry (Porac and Thomas, 1994), geographic location (Baum et al., 2000), strategy
(Fiegenbaum and Thomas, 1995) and others. Oftentimes, scholars adopt multiple
criteria in defining reference groups for specific research settings. In examination of
mimetic entry into foreign markets, Xia, Tan and Tan (2008) relied on similarity
judgments regarding industry, geographic location, and country origin, and identified
as reference groups industry peers in the home country and in the host country. Such
similarity judgments in strategy formulation proffer a simplified decision-making
mechanism to model the external environments (Farjoun and Lai, 1997).
In our research setting, we suggest that entrants in an emerging market-based
industrial cluster tend to resort to a unique set of reference groups: incumbents in the
geographic industry. As for the new entrants in an industrial cluster, they face a
substantial dilemma in making innovation strategy: the entrants are unfamiliar with
the specific institutional environment of industrial cluster, while the institutional voids
problem inhibits the transmission of information and knowledge. Under this
circumstance, the experiences of incumbents within the industrial cluster seem
particularly valuable, because the incumbents usually have better knowledge about the
specific industrial cluster and innovation strategy within it. Therefore, the incumbents
proffer reliable role models for the new entrants in imitation and learning. By
mimetically learning from the incumbents, new entrants can partially alleviate the
11
decision-making problems resulting from environmental uncertainty and institutional
voids. Furthermore, geographic proximity facilitates formal and informal information
sharing, making geographic industry peers more observable for imitation than other
types of industry peers (Greve, 1998a; Tan, 2005). Therefore, we hypothesize that:
Hypothesis 1a. Within an emerging market-based industrial cluster, the entrant is more likely to undertake R&D strategy, when the proportion of incumbents that undertake R&D strategy is higher. Moreover, extant research argues that an organization tends to model after
organizations with certain traits (e.g., salience, ease of observation, and similarity),
which confer both technical and legitimacy values (Haunschild and Miner, 1997;
Haveman, 1993; Greve, 1998a). Following this logic, we formulate our hypotheses by
defining different reference groups in incumbents and linking them to entrants’
mimetic behavior. Therefore,
Hypothesis 1b. Within an emerging market-based industrial cluster, the entrant is more likely to undertake R&D strategy, when the proportion of similar incumbents that undertake R&D strategy is higher. Hypothesis 1c. Within an emerging market-based industrial cluster, the entrant is more likely to undertake R&D strategy when the proportion of salient incumbents that undertake R&D strategy is higher.
The Moderating Effect of Cluster Density
Literature has long documented the external economies that geographic
concentration produces (Baum and Haveman, 1997; Marshall, 1920; Graitson, 1982).
Marshall (1920) was the first to describe the benefits for firms within industrial
districts and proposed three agglomeration economies: interorganizational knowledge
spillovers, specialized labor and intermediary inputs. In the context of geography,
economies of agglomeration was further elaborated in terms of (1) shared
infrastructure available to firms that locate close to each other, (2) information
externalities about demand or the feasibility of production at a particular location that
are available to the prospective entrants who observe established firms operating there
profitable, and (3) reduction of consumer search costs (e.g., Graitson, 1982). Past
research has found that firms locate close to other organizations for information
consideration (e.g., Baum and Haveman, 1997).
In this paper, we focus on the information externalities of geographic
12
concentration and explore how cluster density moderates the imitation process by
determining the quantity of information flow. From information-processing view of
imitation, one premise of imitative behavior is that an imitator can get access to the
information about the role models. Sufficient information flow can call attention to
the prevalence of innovation or organizational behavior, increase the perceived value
of imitation, and facilitate information analysis and interpretation. In other words,
firms having sufficient information are more likely to copy or vicariously learn from
other firms’ behaviors.
New entrants in a highly agglomerated industry are usually exposed to an
information-rich environment. Firstly, new entrants in high-density industries can gain
first-hand information via personal observation and communication (Greve, 1998a).
Secondly, their industry peers can act as a conduit to disseminate information about
incumbents. Thirdly, frequent job mobility of the workforce assists the diffusion of
information (Tan, 2006). Furthermore, conceptualizing geographic industry as a
network, we argue that cluster density may be a proxy for network size, which is
positive related to innovation diffusion (Abrahamson and Rosenkopf, 1997).
Therefore, we argue that information externalities stemming from a high-density
environment drive new entrants to imitate incumbents’ innovation strategy during
institutional transition.
Hypothesis 2. Within an emerging market-based industrial cluster, the relationship between the proportion of incumbents adopting innovation strategy and the likelihood of a new entrant adopting innovation strategy is strengthened by cluster density.
The Moderating Effect of Cluster Variability
By cluster variability, we mean the extent to which innovation pattern varies with
respective to the reference group. We argue that not only the prevalence of innovation
strategy but also the overall strategy profile of reference group exerts influences on
new entrants’ R&D strategy. Great strategic variability in the reference group may
reduce the accountability and reliability of the prevailing strategy perceived by the
new entrants. Also, it increases complexity in the processing of information, and
therefore may negatively moderate the imitation of innovation strategy.
Here, we replicate Koput’s (1997) model of innovation search in our research
setting. Imagine a simplified scenario: organizations in the reference group are so
13
different in innovation behavior that each of them represents a completely different
role model. In this situation, new entrants will face two problems in their information
processing process: (1) an absorptive capacity problem—too many role models for the
firm to learn and choose among, and (2) an attention-allocation problem—there are so
many role models, few of these role models are taken seriously. Hence, great strategic
variability within the referent groups may distract new entrants from finding or paying
attention to the dominant strategy. It is also consistent with information overload
argument that information overload hinders effective interpretation (Huber, 1991).
In face of information complexity, organizations might leap into a biased model
of the objective world to simplify the evaluation and to reduce cognitive strains (e.g.,
Bruner, 1957; March and Simon, 1958). We can find clues in the following argument.
Presented with a complex stimulus, the subject perceives in it what it is ready to perceive; the more complex or ambiguous the stimulus, the more perception will be determine by what is already “in” the subject and the less by that is in the stimulus (Bruner 1957, pp. 132-133) In our research setting, one natural response to a high level of cluster variability
might be that “incumbents differ in innovation strategy and all they survive; therefore,
it does not matter much for survival and performance”. In this situation, new entrants
may have less incentive to learn from incumbents’ innovations. Therefore, we predict
that cluster variability will decrease the imitation of innovation strategy by the new
entrants.
Hypothesis 3. Within an emerging market-based industrial cluster, the relationship between the proportion of incumbents adopting innovation strategy and the likelihood of a new entrant adopting innovation strategy is weakened by cluster variability of incumbent.
Research Setting: Beijing Zhongguancun Science Park
The Zhongguancun Science Park originated from the Zhongguancun electronic
marketplace in the early 1980s and is the largest technology park in China. Up to
2004, there were 13957 firms in operation with 557,000 employees. In 2004, total
income of the Zhongguancun firms reached 369.22 billion RMB (about 46.15 billion
U.S. dollars), with a growth rate of 16.7%.
Within the Zhongguancun Science Park, small- and medium- sized enterprises
cluster together with extensive inter-firm linkages; highly concentrated scientific and
14
technological institutions foster strong academic-and-industry links; the government
issued a series of preferential policies on taxes, loans, and others to promote regional
development. Since different organizational constituencies interact with each other in
these industry clusters, the science park can be conceived as an integrated geographic
system within which firms are no longer kept isolated, their strategic decision being
determined not only by firm-specific capabilities or an independent assessment of the
environment, but by the behavior of other firms within the region (Gimeno, Hoskisson,
Beal and Wan 2005). Tan (2006) identified three mechanisms to account for regional
knowledge/information sharing, viz., formal ties, informal information network, and
job mobility. Given the overwhelming role played by the government in the
Zhongguancun Science Park, we add the fourth mechanism – the government, which
disseminates information for its own economic and political purposes.
Another distinctive feature of the Zhongguancun Science Park is that it has
undergone fundamental institutional transitions since inception. Tan (2006) classified
the evolutionary path of the Zhongguancun Science Park into four major stages: (1)
institutional innovation (early 1980s-late1980s), (2) technological innovation (late
1980s to early 1990s), (3) market innovation (early 1990s to late 1990s), and (4)
transition and reorientation (1998 to early 21st century). This dynamic nature of the
institutional environment brings about substantial ambiguity surrounding firms’
long-term strategic planning and motivates mimetic behavior.
During the time period covered in this study, the Zhongguancun Science Park
was confronted with stagnation and reorientation. A number of intertwining factors
hinder the technological progress within the science park, e.g., diseconomies of
agglomeration, insufficient venture capital, strategic rigidity of the existing firms (Cao,
2004; Tan, 2005). Among these inhibiting factors, lack of entrepreneurship and
underinvestment in R&D is especially essential, as both entrepreneurship and R&D
investments provides motivation and energy for technological innovation. Therefore,
exploring new entrant strategy in R&D in this setting has not only theoretical
implications, but also practical implications.
Methods
Data and Sample
We collected data from a unique database, the Zhongguancun database, provided
by Administrative Committee of Zhongguancun Science Park of Beijing Municipal
15
Government (ACZSP, hereafter). The Zhongguancun database recodes detailed
information about every high-tech corporations certificated by ACZSP. The database
contains basic information (ownership type, time of entry) and financial reports for
the period 1998 to 2003, and firm technological activities for the period 2000 to 2003.
Because ACZSP certificates high-tech corporations to give preferential treatments
(e.g., tax deduction), firms have the incentive to apply for the high-tech certification
as they enter the science park. The certificated firms are also required to submit the
yearly reports to ACZSP. The Zhongguancun database is compiled based on high-tech
certification and yearly reports. In total, the database contains 31274 company-years
for 2000-2003.
We operationalized an entrant in year t as the firms that are not included in our
database in year t-1, and appear in our database for the first time in year t; and used
the entrant’s R&D activities in year t+1 as the dependent variable, which reflects a
one-year lag design. We operationalized an incumbent in year t as the ones that have
existed in year t-2 and are still in operation in year t; and used their R&D data in year
t to generate independent variables, imitation. For example, we counted Firm A in
2000 as an incumbent if the firm had been found in our database in 1998. In other
words, an incumbent entered the science park at least two years earlier than an entrant.
The two-year design is an outcome of the time frame of the database, 1998-2003. It
may seem short but most Zhongguancun firms are young (firms’ average age is 3.61
in our database) and two-year Zhongguancun experience is especially significant for
the young firms. Based on the operational definitions, we identified 10552 incumbents
for 2000-2002 and 5575 entrant for 2001-2003.
Besides, we used three sampling criteria: 1) the Zhongguancun firms that were
not in normal operation were excluded; 2) industries in which the number of the firms
was less than 5 for any year through 2000-2002 were excluded; and 3) food and
retailing industries are excluded for they are not conventional high-tech industry.
Because some of our independent and control variables are industry factors, our
research design was cross-industry, instead of single-industry. We used two-digit
industry code of Industrial Classification for National Economic Activities (GB/T
4754—2002), which was issued by the National Bureau of Statistics, to create
industry-based variables and to conduct sampling procedures.
The final sample included independent and control variables for the period
2000-2002 and entrants’ R&D strategy for the period 2001-2003, yielding 4472
16
observations across 20 high-tech industries. Table 1 shows the distribution of critical
variables over industries in our final sample. We can see that the average R&D
intensities are highest in instrumental machinery, research service and telecom service.
The lowest average R&D intensities are in chemistry, other machinery, and
environmental management. The proportions of group-affiliated firms are generally
low across different industries, ranging form 0.03 to 0.2. The aggregate number of
entrants from 2001-2003 is highest in software industry (1251), followed by
professional service (650) and computers and communications equipments (626). The
lowest numbers of entrants are found in petroleum (13), other machinery (25), and
mining (28).
------------------------------------
Insert Table 1 about here
------------------------------------
All the aforementioned evidence showed that industries in the science park have
experienced unbalanced development in the period 2001-2003. To note, Table 1 only
provides us interindustry distribution concerning entrants, which might not represent
the general industrial R&D patterns.
Measures
Dependent variable. The dependent variable, an entrant’ R&D strategy, was a
dummy variable. We observed whether an entrant was in the top quartile for R&D
intensity of all Zhongguancun firms, irrespective of industry. R&D intensity was
measured as R&D spending divided by the number of employees. We normalized
R&D spending by employment, rather than by sales because more than half of the
entrants (2627) in our final sample were newly founded firms and employment can be
more reliable than sales for these new founders.
When the top quartiles were used as cutoffs, the thresholds for R&D strategy are
6.79, 15.14, 20, and 28.57 for year 2000, 2001, 2002 and 2003. We coded R&D
strategy as one if the firm’s R&D intensity exceeded the current-year threshold, and
zero otherwise. We used a dummy variable rather than continuous R&D intensity, for
the former, to do or not to do, can better capture firms’ strategic orientation in R&D
activities. As a similar example, Haveman (1993) employed a 5-percent threshold (5
percent of firms’ asset) to define whether a firm entered into a new market.
To validate the measure, we experimented with different cut-off points for R&D
strategy, e.g., whether an entrants’ R&D intensity was in the top 50% of the
17
Zhongguancun firms. Also, we alternatively used the continuous variable, R&D
intensity, as the dependent variable to run the regressions for the robustness check.
Imitation. Hypothesis 1a -1c predict that incumbents’ R&D patterns pose role
models for the entrants in making innovation strategy. To test Hypothesis 1a-1c, we
developed five measurements to capture the prevalence of R&D strategy in different
reference groups of incumbents: general, size-localized, ownership-localized,
profitable and large. The first reference group is the general incumbents, the second
and third reference groups refer to similar incumbents, and the last two reference
groups refer to salient incumbents.
As for imitation (general incumbents), we calculated the proportion of
incumbents that undertake R&D strategy, i.e., whose R&D intensity were in the top
quartile of all Zhongguancun firms in the two-digit industry in a particular year.
By size-localized incumbents, we meant incumbents that had similar size
compared to the entrant. Size here was measured in terms of firm employment, the
number of employees the firm has. The notion “size-localized” comes from ecology
literature (Hannan and Freeman, 1977; Haveman, 1993), indicating that firms’
interaction tend to be localized along a size gradient and that organizations compete
only with other organizations within some range of their own sizes. Consistent with
previous literature (Haveman, 1993), we set the size window for an entrant is (.5S,
1.5S), where S represents the size (employment) of the entrant, and measured
imitation (size-localized) as the proportion of incumbents that undertake R&D
strategy within the size-localized window of the entrant.
By ownership-localized incumbents, we meant that incumbents that have the
same ownership type, state-controlled or non-state-controlled, tend to have similar
institutional constraints and resource endowments in transitional economies (Nee,
1992). Analogous with size-localized argument, we postulate that interaction tend to
be localized within the same ownership type. We measured imitation
(ownership-localized) as the proportion of incumbents that undertake R&D strategy
within the ownership-localized window of the entrants, i.e., having the same
ownership type as the entrant, in the two-digit industry. In other words, the
ownership-localized for (non)state-controlled entrant is the proportion of incumbents
that undertake R&D strategy in the (non)state-controlled incumbents in the two-digit
industry.
As for imitation (profitable) and imitation (large), we calculated the proportions
18
of incumbents that undertake R&D strategy in the most profitable and largest
incumbents in the two-digit industry, i.e., in the industrial top quartile for profitability
(ROA) and size (employment) of the industry in a particular year.
We developed a number of alternative measures of imitation variables for
robustness checks, e.g., using alternative profitability (ROS) and size measure (assets),
measuring the profitable and large incumbents using the top quartile cutoffs based on
all Zhongguancun firms.
To investigate the moderating effects of the structural factors of industrial cluster
in imitation process, we create interactive terms for the moderating effects, i.e.,
imitation X cluster density and imitation X cluster variability. Before multiplication,
we adopt mean-centering approach to partially alleviate the potential problem of
multicollinearity (Haunschild and Miner, 1997; Li and Atuahene-Gima, 2001).
Cluster density. We measure cluster density as the natural logarithm of the
number of firms in the two-digit industry of the science park.
Cluster variability. We created five cluster variability variables to capture the
extent to which R&D intensity varied in five reference groups of incumbents, namely,
cluster variability (general), cluster variability (size-localized), cluster variability
(ownership-localized), cluster variability (profitable), and cluster variability (large).
Because deviation or standard deviation of R&D intensity will be inflated by
some very large values, we measured cluster variability using a Herfindahl index,
which has been widely used in strategy research, e.g., diversification (Acar and
Sankaran, 1999). Firstly, we calculated 0.2, 0.4, 0.6, and 0.8 quantiles for incumbents’
R&D intensity: 0, 0, 1.5, 9.34 in 2000, 0.13, 2.5, 7.11, 18.29 in 2001, and 0.61, 4.48,
11.11, 27.78 in 2002. We then used the quantiles to classify incumbents’ R&D
intensities into five categories each year (exc. three categories in 2000), and assigned
1-5 to each categories from the smallest to the largest. Finally, we calculated cluster
variability for different reference groups in incumbents using the following
formula:5
2
1
1 ii
p=
−� , where p is the percentage of certain types of incumbents in each
categories. For example, for cluster variability (size-localized), we calculated the
Herfindahl index of R&D intensity in all the incumbents that fall into the entrant’s
size window in the two-digit industry.
Control variables. Three control variables were included in our regression
models: firms size (natural logarithm of the number of employees), age (in years),
19
state-control (dummy variable: 1=state-controlled, 0=otherwise), business group
affiliation (dummy variable: 1 if the entrant was affiliated to a business group and 0 if
it was not.) and performance feedback variables (firm performance-cluster aspiration
(<0) and firm performance-cluster aspiration (>0)). Besides, we created and included
industry dummies at the two-digit level, as well as year dummies in regressions. Table
2 briefly summarized the definitions of variable.
------------------------------------
Insert Table 2 about here
------------------------------------
Statistical Model
As the dependent variable, the entrant’ R&D strategy, is binary, we employed
pooled logit regression to predict the likelihood of an entrant to undertake R&D
strategy. Panel analyses cannot be applied because we identified an entrant by a
moving window. As for Hypothesis 2-3, we used hierarchical moderated regression
analyses to model the moderating effects. Additional robustness checks (not reported,
to save space) were also conducted, e.g., using R&D intensity as a continuous variable,
alternative measures for profitability and size, alternative cutoffs to code dummy
variables.
Results
Table 3 presents means, standard deviations, minimums, maximums, and
pairwise correlations for the independent and dependent variables. The table shows
some relatively high correlations, which need clarifications in two aspects. Firstly, the
high correlations among the five measures of imitation variables are quite
understandable, reflecting some basic industrial trends. As Haveman (1993), we
included in separate models the R&D variables for general, size-localized,
ownership-localized, profitable and large incumbents. Secondly, the high correlations
between imitation and cluster density deserve our special attention. The following
measures were took to diagnose and relieve the potential problem of multicollinearity:
i) we mean-centered the two sets of variables for creating interactive terms, ii) we
calculated their variance inflation factors (VIFs) using OLS regressions and all VIFs
were well below 10, and iii) we found that the regression estimates were stable and
log-likelihoods consistently increased after introducing the relevant variables into our
hierarchical models (Haunschild and Miner, 1997).
------------------------------------
20
Insert Table 3 about here
------------------------------------
Table 4 shows the results of pooled of logit regressions. As in Table 4, the
models marked by “a” includes the main effects of imitation, cluster density and
cluster variability; the models marked by “b” includes both main effects and
interactive terms. The numbers, 1-5 in Table 4 represents imitation variable in specific
reference groups of incumbent firms: 1 for general incumbents, 2 for size-localized
incumbents, 3 for ownership-localized incumbents, 4 for profitable incumbents, and 5
for large incumbents. For example, in Model 1b imitation means the innovation
patterns of general incumbents, i.e., the prevalence of innovation strategy in
incumbents; in the Model 2b imitation means the innovation patterns of size-localized
incumbents,
------------------------------------------
Insert Table 4 about here
------------------------------------------
Hypothesis 1a-1c predict that when innovation strategy is more prevalent in
general, similar and salient incumbent firms, an entrant is more likely to undertake
R&D strategy. As we can see in Table 4, the coefficients of imitation are positive and
significant across the models. Therefore, Hypothesis 1a - 1c are supported.
Hypothesis 2 posits a positive moderating effect of cluster density on entrants’
mimetic behavior. This hypothesis was tested by including the interactive term,
imitation X cluster density. It receives partial support in Model 1b and 2b, with
positive signs and significance levels in the ranges of p<0.10 and p<0.05. Therefore,
Hypothesis 2 is partially supported.
Hypothesis 3, predicting a negative moderating effect of cluster variability of the
reference groups, was tested by including the interactive term, imitation X cluster
variability. The coefficients of the interactive terms are negative and significant in the
ranges p<0.1 and p<0.05. Hypothesis 3 hence received consistent and strong support.
Discussions and Implications
This paper contributes to extant literature about innovation strategy among
Chinese firms in three ways. First, we conceptualize innovation strategizing in China
as a imitation process that Chinese firms tend to imitate other firms’ innovation
21
strategy. It is deeply rooted in the Chinese context: institutional voids and firms’
inexperience in innovation activities. The perspective reveals an important
decision-making process, imitation, in Chinese firms’ innovation strategy.
Conceptualizing innovation itself as a learning technology, this perspective also
echoes with the organizational learning arguments on ecologies of learning and
learning to learn (Heimer, 1985; Levitt and March, 1988). Second, identifying the
moderating effects of informational factor further contributes to our understanding
about how imitation unfolds in the context China. Thirdly, we test our theoretical
framework in an interesting setting: an emerging market-based science park. It
demonstrates interorganizational learning through incumbent-entrant dynamics:
entrants learn from the incumbent within the high-tech cluster, while the entrant’s
strategy in itself is imitated by the later follower.
Our results on Hypothesis 1a-1c depict positive relationships between the
prevalence of innovation strategy in incumbents and the likelihood of an entrant to
undertake innovation. The coefficients vary in their magnitude and significance levels
in the ranges of p<0.5 and p<0.01. Concerning firms’ cognitive categorization process
(e.g., Porac, Thomas, Wilson, Paton and Kanfer, 1995), it is possible that some
characteristics is more likely to be used than others in identifying the role models
among incumbents.
Also partially supported is the positive relationship between cluster density and
new entrants’ imitation of innovation strategy in Hypothesis 2. The coefficients are
significant for general and similar-sized reference group. One possible explanation for
the weak results is that the quantity of information pertaining to cluster density might
go to the other extreme and bring about information overload problem (Huber, 1991).
The strong finding on Hypothesis 3 confirms our prediction about the negative
relationship between strategic variation in the reference group and entrants’ mimetic
behavior. It suggests that information complexity may impede new entrants to learn
from the incumbents.
The results should be interpreted within the limits of the study. The first has to do
with different types of innovation strategy. The analysis of R&D expenditure in our
paper cannot be readily explored to other innovation strategy (e.g., product innovation
strategy). The second has to do with the specific research setting. The science park in
China has some peculiar features, e.g., the clustering of high-tech corporations.
Therefore, the finding in our paper might not be generalizable to other less technology
22
intensive context. For example, substantial affiliated corporations in our sample are
research centers. While the centers has the mandate given by business group to learn
from other Zhongguancun firms, it is still unclear that affiliated corporations in other
districts have such learning propensity.
In conclusion, this paper explores imitation of innovation strategy in the context
of an emerging market-based industrial cluster. Our results show that entrants under
institutional voids tend to mimetically learn from incumbents’ innovation strategy and
that mimetic behavior is determined both by informational conditions of the industrial
clusters, as proxied by the cluster density and cluster variability.
23
REFERENCES
Abrahamson E., Rosenkof L., 1997, “Social network effects on the extent of innovation diffusion: A computer simulation”, Organization Science, 8, 289-309.
Acar W., Sankaran K., 1999, “The myth of the unique decomposability: Specializing the Herfindal and entropy measures?”, Strategic Management Journal, 20(10), 969-975.
Aoki M., 1990, “Toward an economic model of the Japanese firm”, Journal of Economic Literature, 28, 1-27.
Audretsch DB., Feldman MP., 1996, “R&D spillovers and the geography of innovation and production”, American Economic Review, 86(3), 630-640.
Baum J., Haveman, H., 1997, “Love thy neighbor? Differentiation and agglomeration in the Manhattan Hotel industry, 1898-1990”, Administrative Science Quarterly, 42(2), 304-339.
Baum JAC., Li SX., Usher JM., 2000, “Making the next move: How experiential and vicarious learning shape the locations of chains’ acquisition”, Administrative Science Quarterly, 45(4), 766-801.
Bikhchandani S., Hirshleifer D., Welch I., 1992, “A theory of fads, fashion, custom, cultural change as information cascades”, Journal of Political Economy, 100, 992-1026.
Bikhchandani S., Hirshleifer D., Welch I., 1998, “Learning from the behavior of others: Comformity, fads, and informational cascades”, Journal of Economic Perspectives, 12, 151-170.
Bruner JS., 1957, “On perceptual readiness”, Psychological Review, 64, 132-133.
Cao C., 2004, “Zhongguancun and China’s high-tech parks in transition”, Asian Survey, 5, 647-668.
Chen MJ., Miller D., 1994, “Competitive attack, retaliation and performance: An expectancy-valence framework”, Strategic Management Journal, 15, 85-102.
Coase R., 1937, “The nature of the firm”, Economica, 4, 386-405.
Cockburn I., Henderson R., 1994, “Racing to invest? The dynamics of competition in ethical drug discovery”, Journal of Economics and Management Strategy, 3, 481-519.
Cohen WM., Levinthal DA., 1990, “Absorptive capacity: A new perspective on learning and innovation”, Administrative Science Quarterly, 35, 128-152.
Cyert RM., March JG., 1963, A Behavioral Theory of the Firm, Prentice-Hall, Englewood Cliffs, NJ.
Dasgupta GF., Stigliz JE., 1980, Industrial structure and the nature of innovative
24
activity, Economic Journal, 90, 266-293.
Deephouse DL., 1996. “Does isomorphism legitimate?”, Academy of Management Journal, 394, 1024-1039.
Dierickx I., Cool K., 1989, Asset accumulation and sustainability of competitive advantage, Management Science, 35, 1504-1511.
DiMaggio PJ., Powell WW., 1983, “The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields”, American Sociological Review, 48, 147-160.
Farjoun M., Lai L., 1997, “Similarity judgments in strategic formulation: Role, Process and implications”, Strategic Management Journal, 18(4), 255-273.
Fiegenbaum A., Thomas H., 1995, “Strategic groups as reference groups: Theory, modeling and empirical examination of industry and competitive strategy”, Strategic Management Journal, 16, 461-576.
Frost T., Zhou C., 2000, “The geography of foreign R&D within a host country: An evolutionary perspective on location-technology selection by multinationals”, International Studies of Management & Organization, 30: 10-43.
Galbraith J., 1973, Strategies of Organization Design, Addison-Wesley, Reading, MA.
Gimeno J., Hoskisson RE., Beal BD., Wan WP., 2005, “Explaining the clustering of international expansion moves: A critical test in the U.S. telecommunications industry”, Academy of Management Journal, 48, 297-319.
Graitson D., 1982, “Spatial competition a la Hotelling: A selective survey”, Journal of Industrial Economics, 31, 13-25.
Granovetter M., 1984, “Business groups”, In N.J. Smelser & R. Swedborg (Eds.), Handbook of Economic Sociology: 453-475. Princeton, NJ: Princeton University Press: New York: Russell Sage Foundation.
Greve HR., 2003a, “A behavioral theory of R&D expenditures and innovations: Evidence from shipbuilding”, Academy of Management Journal, 46, 685-702.
Hannan MT., Freeman J., 1977, “The population ecology of organizations”, American Journal of Sociology, 82(5), 929-964.
Harriss J., Hunter J., Lewis CM., 1995, The New Institutional Economics and Third World Development, London, Routledge.
Haunschild PR., 1993, “Interorganizational imitation: The impact of interlocks on corporate acquisition activity”, Administrative Science Quarterly, 38, 564-592.
Haunschild PR., Miner AS., 1997, “Modes of interorganizational imitation: The effects of outcome salience and uncertainty”, Administrative Science Quarterly, 42, 472-500.
25
Haveman HA., 1993, “Follow the leader: Mimetic isomorphism and entry into new markets”, Administrative Science Quarterly, 38, 593-627.
Heimer CA., 1985, “Allocating information costs in a negotiated information order: Interorganizational constraints on decision making in Norwegian oil insurance”, Administrative Science Quarterly, 30, 395-417.
Henisz WJ., Delios A., 2001, “Uncertainty, imitation, and plant location: Japanese multinational corporations, 1990-1996”, Administrative Science Quarterly, 46, 443-475.
Huber GP., 1991, “Organizational learning: The contributing processes and the literature”, Organizational Science, 2(1), 88-115.
Khanna T., Palepu K., 1997, “The future of business groups in emerging markets: Long-run evidence from Chile”, Academy of Management Journal, 43(3), 268-285.
Koput KW., 1997. “A chaotic model of innovative search: Some answers, many questions”, Organizational Science, 8(5), 528-542.
Kor YY., 2006, “Direct and interaction effects of top management team and board compositions on R&D investment strategy”, Strategic Management Journal, 27, 1081-1099.
Levitt B., March, JG., 1988, “Organizational learning”, Annual Review of Sociology, 14, 319-340.
Levitt B., March, JG., 1988, “Organizational learning”, Annual Review of Sociology, 14, 319-340.
Lieberman MB., Asaba S., 2006, “Why do firms imitate each other?”, Academy of Management Review, 31(2), 366-385.
March JG., 1991, “Exploration and exploitation in organizational learning”, Organization Science, 2, 71-87.
March JG., Simon HA., 1958, Organizations, New York, John Wiley.
Marshall A., 1920, Principles of Economics, 8th edition, Macmillan, London
Meyer J., Rowan B., 1977, “Institutionalized organizations: Formal structure as myth and ceremony”, American Journal of Sociology, 83, 340-363.
North DN., 1990, Institutions, Institutional Change and Economic Performance, New York, Cambridge University Press.
Porac JH., Thomas H., 1994, “Cognitive categorization and subjective rivalry among retailers in a small city”, Journal of Applied Psychology, 79(1), 54-66.
Porac JH., Thomas H., 1994, “Cognitive categorization and subjective rivalry among retailers in a small city”, Journal of Applied Psychology, 79(1), 54-66.
26
Porter ME., 1998, On Competition, Harvard Business School Press, Boston.
Pouder R., St. John CH., 1996, “Hot spots and blind spots: Geographical clusters of firms and innovation”, Academy of Management Review, 21, 1192-1225.
Powell WW., DiMaggio PJ., 1991, The New Institutionalism in Organizational Analysis, University of Chicago Press: Chicago, IL.
Reinganum JF., 1982, “A dynamic game of R and D: Patent protection and competitive behavior”, Econometrica, 50(3), 671-689.
Tan J., 2006, “Growth of industry clusters and innovation: Lessons from Beijing Zhongguancun Science Park”, Journal of Business Venturing, 21, 827-850.
Williamson O., 1985, The economic institutions of capitalism, New York, Free Press.
Williamson, O., 1975, Markets and Hierarchies, New York, Free Press.
Xia J., Tan J., Tan D., 2008, “Mimetic entry and bandwagon effect: The rise and decline of international equity joint venture in China”, Strategic Management Journal, 29, 195-217.
27
Table 1 Basic Information about Late Entrants by Industry (2001-2003)
Industry Average R&D/employees
Proportion of the affiliated
Number of new entrants
Agriculture 20.02 0.06 112 Mining 40.73 0.04 28 Petroleum 33.85 0.08 13 Chemistry 13.24 0.10 125 Medicine 25.33 0.06 190 Metals 14.70 0.05 132 General machinery 23.03 0.05 157 Specialized machinery 23.23 0.07 263 Transport machinery 28.92 0.05 39 Electrical machinery 23.75 0.07 103 Computers & communications equip. 38.14 0.09 626 Instrumental machinery 82.98 0.07 263 Other machinery 13.75 0.08 25 Telecom service 62.02 0.09 133 Computer service 30.64 0.03 413 Software service 32.33 0.05 1251 Research service 68.79 0.07 75 Profession service 21.93 0.04 650 Scientific service 23.14 0.07 138 Environmental management 14.49 0.03 38
Sources: Administrative Committee of Zhongguancun Science Park.
28
Table 2 Variable Specification
Variable Specification Innovation Strategy Dummy variable: 1=R&D/employment in the top quartile of all
Zhongguancun firms, 0=otherwise Imitation (General Incumbent)
Proportion of incumbents that undertake R&D strategy the two-digit industry
Imitation (Size-localized)
Proportion of size-localized incumbents that undertake R&D strategy the two-digit industry
Imitation (Ownership-localized)
Proportion of ownership-localized incumbents that undertake Innovation strategy the two-digit industry
Imitation (Profitable Incumbent)
Proportion of profitable incumbents that undertake Innovation strategy the two-digit industry
Imitation (Large Incumbent)
Proportion of large incumbents that undertake Innovation strategy the two-digit industry
Group Affiliation Dummy variable: 1=group-affiliated, 0=not Cluster Density (log) Natural logarithm of the number of firms in the two-digit industry Cluster Variability (General Incumbent)
The Herfindahl index of R&D intensity for incumbents in the two-digit industry
Cluster Variability (Size-localized)
The Herfindahl index of R&D intensity for size-localized incumbents in the two-digit industry
Cluster Variability (Ownership-localized)
The Herfindahl index of R&D intensity for ownership-localized incumbents window in the two-digit industry
Cluster Variability (Profitable Incumbent)
The Herfindahl index of R&D intensity for the most profitable incumbents in the two-digit industry
Cluster Variability (Large Incumbent)
The Herfindahl index of R&D intensity for the largest incumbents in the two-digit industry
Performance-cluster aspiration (>0)
Performance minus industry average if performance > social aspiration, and 0 if performance<social aspiration
Performance-cluster aspiration (<0)
0 if performance > industry average, and performance minus social aspiration if performance<social aspiration
Employees (log) Natural logarithm of the number of employees Age Current year minus year of founding State-control Dummy variable: 1=state-controlled, 0=not
29
Table 3 Means, Standard Deviations, Minimums, Maximums, and Pairwise Correlations Variable Mean S.D. Min. Max 1 2 3 4 5 6 7
1 The Entrant’s Innovation Strategy 0.30 0.46 0.00 1.00 1 2 Imitation (General Incumbent) 0.27 0.08 0.09 0.60 0.10* 1 3 Imitation (Size-localized) 0.26 0.11 0.00 1.00 0.11* 0.62* 1 4 Imitation (Ownership-localized) 0.27 0.09 0.00 0.83 0.09* 0.88* 0.55* 1 5 Imitation (Profitable Incumbent) 0.37 0.12 0.00 0.67 0.09* 0.87* 0.53* 0.79* 1 6 Imitation (Large Incumbent) 0.33 0.13 0.00 0.67 0.11* 0.86* 0.52* 0.78* 0.81* 1 7 Business Group Affiliation 0.06 0.23 0.00 1.00 0.06* -0.01 0.06* 0.02 -0.01 0.00 1 8 Cluster Density (log) 6.29 1.01 2.48 7.57 0.04* 0.56* 0.33* 0.51* 0.55* 0.53* -0.04* 9 Cluster Variability (General Incumbent) 0.76 0.06 0.58 0.85 -0.06* -0.15* -0.15* -0.14* -0.18* -0.16* -0.01 10 Cluster Variability (Size-localized) 0.73 0.11 0.00 0.88 -0.02 0.05* 0.08* 0.05* 0.02 0.04* -0.01 11 Cluster Variability (Ownership-localized) 0.76 0.06 0.44 0.85 -0.05* -0.10* -0.11* -0.07* -0.12* -0.13* -0.01 12 Cluster Variability (Profitable Incumbent) 0.73 0.06 0.00 0.84 -0.06* -0.24* -0.18* -0.23* -0.13* -0.19* 0.00 13 Cluster Variability (Large Incumbent) 0.73 0.06 0.00 0.84 -0.06* -0.22* -0.16* -0.17* -0.08* -0.21* -0.01 14 Performance-cluster aspiration (>0) 0.07 0.18 0.00 6.34 0.07* 0.09* 0.09* 0.08* 0.07* 0.08* -0.02 15 Performance-cluster aspiration (<0) -0.15 0.79 -35.85 0.00 -0.04* -0.04* -0.02 -0.04* -0.04* -0.03* 0.03* 16 Employees (log) 2.81 1.04 0.00 8.16 0.07* -0.03* 0.36* 0.02 -0.01 -0.01 0.22* 17 Age 1.17 2.43 0.00 20.00 -0.06* -0.10* 0.01 -0.07* -0.09* -0.08* 0.07* 18 State-controlled 0.15 0.36 0.00 1.00 0.01 -0.11* 0.04* 0.00 -0.08* -0.10* 0.31*
Note: * p<0.05.
30
Table 3 (Continued) Variable 8 9 10 11 12 13 14 15 16 17 18 8 Cluster Density (log) 1 9 Cluster Variability (General Incumbent) -0.01 1
10 Cluster Variability (Size-localized) 0.24* 0.57* 1 11 Cluster Variability (Ownership-localized) 0.06* 0.95* 0.55* 1 12 Cluster Variability (Profitable Incumbent) 0.08* 0.67* 0.46* 0.65* 1 13 Cluster Variability (Large Incumbent) 0.25* 0.58* 0.46* 0.59* 0.80* 1 14 Performance-cluster aspiration (>0) 0.04* 0.01 0.03* 0.01 -0.02 -0.02 1 15 Performance-cluster aspiration (<0) -0.05* 0.00 -0.02 0.00 0.01 0.00 0.07* 1 16 Employees (log) -0.03* -0.10* 0.05* -0.11* -0.07* -0.04* 0.10* 0.01 1 17 Age -0.06* 0.01 -0.02 -0.01 0.04* 0.04* 0.00 0.03 0.19* 1 18 State-controlled -0.06* -0.02 -0.03* -0.05* 0.03* 0.04* 0.01 0.05* 0.30* 0.17* 1
Note: *p<0.05.
31
Table 4 Results of Logit Regression Analysis General Size-localized Ownership-localized Profitable Large 1a 1b 2a 2b 3a 3b 4a 4b 5a 5b
Imitation 1.87* (0.71) 2.16**
(0.72) 1.27** (0.39) 1.54**
(0.41) 1.22* (0.54) 1.17*
(0.54) 1.10* (0.46) 1.63**
(0.52) 1.47** (0.45) 1.69**
(0.47) Imitation X Cluster Density
0.86† (0.51) 0.65*
(0.29) 0.55 (0.43) 0.51
(0.36) 0.43 (0.32)
Imitation X Cluster Variability
-20.33* (8.49) -11.88*
(5.08) -12.63† (6.88) -16.62*
(5.89) -12.20* (5.28)
Cluster Density (Log)
0.00 (0.08) -0.06**
(0.02) 0.01 (0.08) -0.06**
(0.02) -0.01 (0.08) -0.06**
(0.02) -0.04 (0.08) -0.06**
(0.02) -0.06 (0.08) -0.06**
(0.02) Cluster Variability -1.86
(1.31) -0.07 (0.10) -2.43†
(1.28) -0.05 (0.10) -1.89
(1.33) -0.11 (0.10) -1.74
(1.34) -0.07 (0.10) -0.99
(1.38) -0.06 (0.10)
Employees (Log) 0.13** (0.04) 0.52**
(0.14) 0.08† (0.04) 0.53**
(0.14) 0.13** (0.04) 0.53**
(0.14) 0.13** (0.04) 0.53**
(0.14) 0.13** (0.04) 0.52**
(0.14) Age -0.06**
(0.02) 0.78** (0.20) -0.06**
(0.02) 0.78** (0.21) -0.06**
(0.02) 0.79** (0.20) -0.06**
(0.02) 0.79** (0.20) -0.06**
(0.02) 0.77** (0.20)
State-controlled -0.07 (0.10) -0.14*
(0.06) -0.06 (0.10) -0.15*
(0.06) -0.10 (0.10) -0.14*
(0.06) -0.08 (0.10) -0.14*
(0.06) -0.07 (0.10) -0.14*
(0.06) Group Affiliation 0.53**
(0.14) -0.04 (0.08) 0.53**
(0.14) -0.02 (0.08) 0.53**
(0.14) -0.02 (0.08) 0.53**
(0.14) -0.05 (0.08) 0.52**
(0.14) -0.05 (0.08)
Performance-cluster aspiration (>0)
0.78** (0.20) -0.25
(1.51) 0.78** (0.21) -1.01
(1.37) 0.78** (0.20) -0.85
(1.49) 0.79** (0.20) 0.12
(1.59) 0.76** (0.20) 0.03
(1.52) Performance-cluster aspiration (<0)
-0.14* (0.06) 0.12**
(0.04) -0.15* (0.06) 0.05
(0.04) -0.14* (0.06) 0.12**
(0.04) -0.14* (0.06) 0.12**
(0.04) -0.14* (0.06) 0.12**
(0.04) Log-likelihood -2651.8 -2648.0 -2633.3 -2628.0 -2652.8 -2650.6 -2652.6 -2648.2 -2649.9 -2646.9 Chi2 171.4** 179.1** 173.6** 184.1** 169.5** 174.0** 170.0** 178.6** 175.3** 181.19**
Note: 1. †p<0.10;*p<0.05;**p<0.01.