Clustering and Imitation in Innovation Strategy: Toward an Incumbent-Entrant  Dynamics
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  • 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: suyiyi@tongji.edu.cn CHANGHUI ZHOU Guanghua School of Management Peking University Beijing 100871, China Telephone/Fax: 86-10-6275- 5089 Email: czhou@gsm.pku.edu.cn 1
  • 2. Clustering and Imitation in Innovation Strategy: Toward an Incumbent-Entrant Dynamics Abstract In an emerging market, the lack of the intermediary mechanisms, constraints inknowledge flow, and high transaction costs in acquiring intellectual assets bring aboutthe problem of institutional voids in intellectual asset market, which impedesorganizational learning and firm innovation. Under this circumstance, industrialcluster functions as institutional substitute, i.e., the clustered firms mimetically learnfrom other clustered firms in innovation strategy. Based upon Beijing ZhongguancunScience Park, we found that entrants tend to imitate incumbents’ innovation strategywithin an industrial cluster and the imitation effect is moderated by cluster density andcluster variability.Key words: firm innovation; emerging market-based industrial cluster; imitation 2
  • 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, andmanagement cognitive resources for the development of absorptive capacity (Cohenand Levinthal, 1989; Kor, 2006) and long-term competitive advantage (Dierickx andCool, 1989); it is also conceived as an innovative search behavior through whichorganizations evolve and adapt (Greve, 2003a). Despite of its strategic importance,firms in an emerging market usually find themselves in an embarrassing position inmaking innovation strategy: on one hand, innovation is a key element for firms to gaincompetitive edge; on the other hand, expensive R&D investment may put their limitedresource and legitimacy under pressure. Especially, in the context of an emergingmarket, where the lack of efficient institutional intermediaries brings aboutinstitutional voids problem in the intellectual asset market, firms will face substantialinstitutional uncertainty in making innovation strategy and their learning andinnovation mechanism is largely impeded. Drawing insights from the existing literature on imitation (see Lieberman andAsaba (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 willnaturally seek to reduce uncertainty by imitation; such mimetic isomorphism processcan 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 ownexperience 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 tomaintain relative competitive position (Cockburn and Henderson, 1994; Dasgupta andStigliz, 1980). Different focuses and rationales as they have, all the theoreticalperspectives suggest that imitation poses a viable strategy for firms in an emergingmarket. This paper attempts to explore imitation process in the context of an emerging 3
  • 4. market-based industrial cluster. Our first goal, then, is to test whether firms in anemerging market mimetically adopt innovation strategy under institutional voids. Oursecond goal is to examine the moderating role of external information distribution onimitative behavior. Literature has long recognized the vital role played by informationin imitation and learning process (Haunschild and Miner, 1997; Lieberman and Asaba,2006). Researchers made endeavors to explore the potential factors contributing toinformation condition, say, information quality and quantity, concerning imitation,and resultant imitative behavior, such as attributes of the reference group (Haunschildand Miner, 1997; Haveman, 1993), inter-firm linkage (Greve, 1998a; Haunschild,1993), network structures (Abrahamson and Rosenkopf, 1997). Following this line ofresearch, we identify cluster density and cluster variability as the determinants ofinformation environment surrounding imitators, and empirically examine theirmoderating effects on imitation of innovation strategy. We test these relationships by looking into firm’s R&D investment strategy in thelargest Chinese technology park, Beijing Zhongguancun Science Park (ZhongguancunScience Park, hereafter), from 2001 to 2003. The Zhongguancun Science Park ischaracterized by geographic agglomeration of small- and medium-sized firms,incubation of innovation, and dynamic institutional environments, constituting anatural laboratory for entrepreneurship and innovation research (Tan, 2005).Furthermore, geographical proximity makes the Zhongguancun Science Park aconfluence of information and knowledge, and facilitates the diffusion oforganizational practices. Specifically, we attempt to investigate firms’ imitationprocess through the incumbent and entrant dynamics. We argue, in the emergingmarket-based industrial cluster, the entrants tend to mimetically learn from theincumbents in innovation strategy. In this sense, industrial cluster functions asinstitutional substitutes. We further predict that characteristics of the geographicindustries influence information condition in the imitation process, and thus, shapetheir perception of the reference group, and imitative behavior. Set in this specificcontext, our study extends the recent developments in imitation research (Baum, Li,and Usher, 2000; Lieberman and Asaba, 2006), and echoes the persistent interests inindustry 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 theliteratures, paying specially attention to research on imitation, institutional voids, and 4
  • 5. information process view of organizations, and develop a set of testable hypothesesconcerning imitation. Then we will briefly introduce our research setting, BeijingZhongguancun Science Park, and empirically test our theoretical framework. In thelast section, we will summarize and analyze the main founding of the paper anddiscuss both the limitations and contributions of the research. Theoretical BackgroundImitation as a Viable Strategy There is a fairly well-developed literature concerning interorganizationalimitation in organizational and management research. While early work in thisdomain elaborated imitation solely from economics, sociology, or psychologicalperspective, 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) externalitiesamong the strategic actions of organizations, (2) competitive reactions amongorganizations, and (3) noncompetitive referential process. Lieberman and Asaba (2006)proposed a two-part typology of imitation theories, viz., information-based theoriesand 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, payingspecial attention to institutional theory, organizational learning theory, informationcascade theory, and rivalry-based theory of imitation, and then look into pertinentdiscussions about innovation strategy. Since our study does not aim to conduct acomprehensive survey, the literature search is not exhaustive and focuses on thosemost relevant to our research topic. Institutional theory. Institutional theorists argued that organizations imitate otherorganizations in pursuit of legitimacy or for taken-for-granted practices (DiMaggioand Powell, 1983; Meyer and Rowan, 1977). Imitation can be seen as a naturalresponse to environmental uncertainty; organizations facing high uncertainty will seekto reduce uncertainty by copying other organizations’ action, i.e., mimeticisomorphism (DiMaggio and Powell, 1983). The central focus of imitation ininstitutional theory is legitimacy, rather than efficiency. Empirically, Deephouse (1996)further found a positive relationship between strategic isomorphism and legitimacy inbanking industry. 5
  • 6. Organizational learning theory. Organizational learning theorists argued thatlearning from other organizations can be seen an exploratory learning mode and ismore likely to be used when organizations’ own experience provides inadequateguidance (Cyert and March, 1963; Levitt and March, 1988; March, 1991). Empirically,Henisz and Delios (2001) found that firms lacking experience in the host country tendto imitate plant location decision of industry peers. Compared with institutionalisomorphism, imitation in organizational learning theory embodies both technical andsocial values (Haunschild and Miner, 1997). Information cascade theory. Information cascade theory is an economicsversion of imitation theory and explicitly articulates the information aspects inimitation (Bikhchandani, Hirshleifer, and Welch, 1992, 1998). In this model, thebehavior of the first actor is based upon his private information, and conveysinformation to his followers; as information accumulates, the followers imitate others’behavior regardless of their own information. This model has generally been appliedin FDI and financial market. In contrast with institutional isomorphism, imitation ininformation cascade is less enduring, since new information often reveres imitationprocess (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-basedtheory regards competitive reaction as the driver for imitation (Gimeno et al., 2005;Lieberman and Asaba, 2006). When one firm takes competitive move to improve itsposition at the expense of the others, its rivalries tend to make “retaliation or efforts tocounter the move” (Porter, 1980). As for the respondents, imitation is a rationalbehavior to signal decisiveness to maintain position without escalating rivalries (Chenand Miller, 1994). This line of research can be further classified according to theirimitation motivation: to mitigate competition, e.g., multi-market contact, and tominimize risk, e.g., FDI, R&D (Lieberman and Asaba, 2006). Different theories of imitation are not mutually exclusive; their mechanism cansimultaneously work, with one dominating over another at a given time (Liebermanand Asaba, 2006). Academic discussion about imitation in R&D inputs is confined torivalry-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 singlefirm making a single decision, but rather a case in which several firms make acomplex of decisions” (p.267). In “winner-take-all” situations, competition in R&D 6
  • 7. becomes a Prisoner’s Dilemma, and rival firms may imitate other firms’ researchstrategy 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 inpharmaceutical industry. Additional work is needed to model dynamic competition inR&D. Furthermore, we do see some opportunities to apply other imitation theories inR&D investment research (Lieberman and Asaba, 2006). Especially, wheninstitutional uncertainty confounds the predicted outcome of R&D investment, whenfirms cannot rely on experiential learning, or when firms need to derive legitimacyfrom R&D activities, imitation of R&D investment strategy is not merely a responseto competition, but a self-adjusted information-processing process.Institutional Voids and Organizational Learning Both in the fields of economics and sociology, institutional theory emphasizesinstitutional 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 andfirms due to market imperfections (Harriss, Hunter and Lewis, 1995). Specifically,one critical dimension of institutions, specialized intermediaries, plays a significantpart in organizational structure and performance implications. Such intermediaries canpartially solve the transaction and information costs in transactions and thereforereduce the transaction costs in labor, product or financial markets. From transactioncost economics (Coase, 1937; Williamson, 1975, 1985), the optimal scope of a firm isthe function of transaction costs and extent of specialized intermediation. The development and maturity of the specialized intermediaries varies acrossdifferent institutional environments. In the institutional context of developed countries,the specialized intermediaries are well developed and can efficiently bring downtransaction costs. On the contrary, in emerging markets, there exist severe problems ofmarket failure. Take financial market for example, the financial market in an emergingmarket faces substantial challenges: lacking efficient and adequate disclosure systemand weak corporate governance, not well-developed financial intermediation system(e.g., financial analysts, mutual funds, investment bank, venture capitalists, andfinancial press), distorted governance regulation and incomplete legal systems. Allthese challenges result in high transaction costs for firms in emerging markets. Such 7
  • 8. problems also take place in product and labor markets. Khanna and Palepu (1997) characterized the specific institutional environment ofemerging markets as institutional voids. The authors illustrated the impact ofinstitutional voids on organizational structure and diversification strategy through thecase of diversification business group. Their main argument is, under institutionalvoids in emerging markets, diversification business group functions as specializedintermediation, which bridges the individual firms and the incomplete markets. Suchgroup can use its broad scope to smooth out income flows in individual business unitsand reduce potential risks; it can also provide the channels of internal financing andrelieve the financing problems in emerging markets. Therefore, in the specific contextof emerging markets, diversified business group contributes to value creation,although the benefits will decrease as the institutions or specialized intermediariesgradually develop. In retrospective of the previous literature, we found out that research oninstitutional voids mainly focuses on financial or labor market. We argue that theinstitutional voids problem can also occur in intellectual asset market in emergingmarkets. The lack of intermediaries (e.g., industry association, underdevelopedtechnological personnel market) constrains the information flow and technologyspillover and impedes the organizational learning process. Subsequently, we willadopt an information process view of organization and look into the role ofinformation in organizational learning and imitation process.The Role of Information in Imitation Process Information-processing view of organizations posits that organizations needquality information to improve decision making and to deal with the uncertaintystemming from environmental turbulence and dynamism (Galbraith, 1973). From thisperspective, imitation can be conceptualized as an information-processing process,where firms acquire knowledge based upon the observation of other firms, distributeinformation across organizations, interpret the information towards a betterunderstanding, and eventually decide to incorporate it into current routines (Huber,1991; March and Simon, 1958). Even in rivalry-based theories of imitation, whereacquiring information is not a major concern, information structure of the game is stilla precondition of competitive behavior, for example, stochastic racing models ofR&D are built on a strong assumption that information is available to actors in thegame (e.g., Reinganum, 1982). 8
  • 9. Literature has seen persistent attempts to explore how imitative behavior iscontingent upon the information potentially flowing through the imitators (Liebermanand Asaba, 2006). Some studies show that characteristics (e.g., profitability, largeness)of the reference group convey information about value, legitimacy, efficiency inimitation, and lead to “trait-based imitation” (Haunschild and Miner, 1997; Haveman,1993). Some studies treat certain inter-firm linkage as the information sharingmechanism in imitation process, such as interlocking directors (Haunschild, 1993),market contact (Greve, 1998a). Social network research also sheds light on the information component ofimitative behavior. Relevant studies demonstrate that social network channelsinformation to potential adopters, and therefore have effects on diffusion oforganizational practice and innovation (Abrahamson and Rosenkoft, 1997;Granovetter, 1985). For example, Abrahamson and Rosenkoft (1997) adopted asimulation approach to model the effects of idiosyncratic social networks oninnovation diffusion by disseminating information concerning innovation to networkparticipants. The social network logics can be further extended to other environmentalcontexts. Take geographical industries for example. Conceiving a geographicalindustry as an institutional field with interconnected organizational constituencies(DiMaggio and Powell, 1983), we predict that the structure of geographic industrymay well condition information distribution and imitative behavior within it. Hypotheses DevelopmentInstitutional Voids Framework and Imitation: Industrial Cluster as theInstitutional Substitute As we discussed above, the institutions in emerging markets, especially, thespecialized intermediation, are not well developed, bringing about the institutionalvoids in intellectual asset market and impeding organizational learning and firminnovation. In this situation, new entrants in an industrial cluster usually facesubstantial 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 maymake their behavior or strategy absurd; even worse, institutional voids in emergingmarkets obscure the potential cost and benefit of R&D investments. Drawing upon recent developments in imitation research (Abrahamson and 9
  • 10. Rosenkopf, 1993, 1997; Gimeno et al., 2005; Lieberman and Asaba, 2006), we arguethat entrants in an emerging market-based industrial cluster tend to follow the strategyof certain reference groups at least for four reasons: (1) to overcome the liability ofnewness via legitimacy building, (2) to vicariously learn to innovate in absence ofexperiential experience, (3) to acquire relevant information dealing withenvironmental uncertainty, and (4) to gain or maintain competitive positive relativetheir rivals. In this sense, industrial cluster functions as institutional substitute bysetting the reference groups in organizational learning and imitation. The critical roleof industrial cluster in organizational learning has been elaborated in previousliteratures. For example, Frost and Zhou (2000) identified firms’ immediategeographic milieu as the source of learning. Research on industrial cluster pointed outthat geographic agglomeration facilitates imitation and learning among theorganizations (Tan, 2006; Pouder and St. John, 1996). So how do entrants in an industrial cluster choose their reference group? Inprevious literature, the judgment of reference groups is based on the similarity inindustry (Porac and Thomas, 1994), geographic location (Baum et al., 2000), strategy(Fiegenbaum and Thomas, 1995) and others. Oftentimes, scholars adopt multiplecriteria in defining reference groups for specific research settings. In examination ofmimetic entry into foreign markets, Xia, Tan and Tan (2008) relied on similarityjudgments regarding industry, geographic location, and country origin, and identifiedas reference groups industry peers in the home country and in the host country. Suchsimilarity judgments in strategy formulation proffer a simplified decision-makingmechanism to model the external environments (Farjoun and Lai, 1997). In our research setting, we suggest that entrants in an emerging market-basedindustrial cluster tend to resort to a unique set of reference groups: incumbents in thegeographic industry. As for the new entrants in an industrial cluster, they face asubstantial dilemma in making innovation strategy: the entrants are unfamiliar withthe specific institutional environment of industrial cluster, while the institutional voidsproblem inhibits the transmission of information and knowledge. Under thiscircumstance, the experiences of incumbents within the industrial cluster seemparticularly valuable, because the incumbents usually have better knowledge about thespecific industrial cluster and innovation strategy within it. Therefore, the incumbentsproffer reliable role models for the new entrants in imitation and learning. Bymimetically learning from the incumbents, new entrants can partially alleviate the 10
  • 11. decision-making problems resulting from environmental uncertainty and institutionalvoids. Furthermore, geographic proximity facilitates formal and informal informationsharing, making geographic industry peers more observable for imitation than othertypes 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 afterorganizations 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 bydefining 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 geographicconcentration produces (Baum and Haveman, 1997; Marshall, 1920; Graitson, 1982).Marshall (1920) was the first to describe the benefits for firms within industrialdistricts and proposed three agglomeration economies: interorganizational knowledgespillovers, specialized labor and intermediary inputs. In the context of geography,economies of agglomeration was further elaborated in terms of (1) sharedinfrastructure available to firms that locate close to each other, (2) informationexternalities about demand or the feasibility of production at a particular location thatare available to the prospective entrants who observe established firms operating thereprofitable, and (3) reduction of consumer search costs (e.g., Graitson, 1982). Pastresearch has found that firms locate close to other organizations for informationconsideration (e.g., Baum and Haveman, 1997). In this paper, we focus on the information externalities of geographic 11
  • 12. concentration and explore how cluster density moderates the imitation process bydetermining the quantity of information flow. From information-processing view ofimitation, one premise of imitative behavior is that an imitator can get access to theinformation about the role models. Sufficient information flow can call attention tothe prevalence of innovation or organizational behavior, increase the perceived valueof imitation, and facilitate information analysis and interpretation. In other words,firms having sufficient information are more likely to copy or vicariously learn fromother firms’ behaviors. New entrants in a highly agglomerated industry are usually exposed to aninformation-rich environment. Firstly, new entrants in high-density industries can gainfirst-hand information via personal observation and communication (Greve, 1998a).Secondly, their industry peers can act as a conduit to disseminate information aboutincumbents. Thirdly, frequent job mobility of the workforce assists the diffusion ofinformation (Tan, 2006). Furthermore, conceptualizing geographic industry as anetwork, we argue that cluster density may be a proxy for network size, which ispositive related to innovation diffusion (Abrahamson and Rosenkopf, 1997). Therefore, we argue that information externalities stemming from a high-densityenvironment drive new entrants to imitate incumbents’ innovation strategy duringinstitutional 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 withrespective to the reference group. We argue that not only the prevalence of innovationstrategy but also the overall strategy profile of reference group exerts influences onnew entrants’ R&D strategy. Great strategic variability in the reference group mayreduce the accountability and reliability of the prevailing strategy perceived by thenew entrants. Also, it increases complexity in the processing of information, andtherefore may negatively moderate the imitation of innovation strategy. Here, we replicate Koput’s (1997) model of innovation search in our researchsetting. Imagine a simplified scenario: organizations in the reference group are so 12
  • 13. different in innovation behavior that each of them represents a completely differentrole model. In this situation, new entrants will face two problems in their informationprocessing process: (1) an absorptive capacity problem—too many role models for thefirm to learn and choose among, and (2) an attention-allocation problem—there are somany role models, few of these role models are taken seriously. Hence, great strategicvariability within the referent groups may distract new entrants from finding or payingattention to the dominant strategy. It is also consistent with information overloadargument that information overload hinders effective interpretation (Huber, 1991). In face of information complexity, organizations might leap into a biased modelof 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 variabilitymight 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 entrantsmay have less incentive to learn from incumbents’ innovations. Therefore, we predictthat cluster variability will decrease the imitation of innovation strategy by the newentrants. 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 electronicmarketplace in the early 1980s and is the largest technology park in China. Up to2004, there were 13957 firms in operation with 557,000 employees. In 2004, totalincome of the Zhongguancun firms reached 369.22 billion RMB (about 46.15 billionU.S. dollars), with a growth rate of 16.7%. Within the Zhongguancun Science Park, small- and medium- sized enterprisescluster together with extensive inter-firm linkages; highly concentrated scientific and 13
  • 14. technological institutions foster strong academic-and-industry links; the governmentissued a series of preferential policies on taxes, loans, and others to promote regionaldevelopment. Since different organizational constituencies interact with each other inthese industry clusters, the science park can be conceived as an integrated geographicsystem within which firms are no longer kept isolated, their strategic decision beingdetermined not only by firm-specific capabilities or an independent assessment of theenvironment, but by the behavior of other firms within the region (Gimeno, Hoskisson,Beal and Wan 2005). Tan (2006) identified three mechanisms to account for regionalknowledge/information sharing, viz., formal ties, informal information network, andjob mobility. Given the overwhelming role played by the government in theZhongguancun Science Park, we add the fourth mechanism – the government, whichdisseminates information for its own economic and political purposes. Another distinctive feature of the Zhongguancun Science Park is that it hasundergone fundamental institutional transitions since inception. Tan (2006) classifiedthe evolutionary path of the Zhongguancun Science Park into four major stages: (1)institutional innovation (early 1980s-late1980s), (2) technological innovation (late1980s 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 theinstitutional 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 Parkwas confronted with stagnation and reorientation. A number of intertwining factorshinder the technological progress within the science park, e.g., diseconomies ofagglomeration, insufficient venture capital, strategic rigidity of the existing firms (Cao,2004; Tan, 2005). Among these inhibiting factors, lack of entrepreneurship andunderinvestment in R&D is especially essential, as both entrepreneurship and R&Dinvestments provides motivation and energy for technological innovation. Therefore,exploring new entrant strategy in R&D in this setting has not only theoreticalimplications, but also practical implications. MethodsData and Sample We collected data from a unique database, the Zhongguancun database, providedby Administrative Committee of Zhongguancun Science Park of Beijing Municipal 14
  • 15. Government (ACZSP, hereafter). The Zhongguancun database recodes detailedinformation about every high-tech corporations certificated by ACZSP. The databasecontains basic information (ownership type, time of entry) and financial reports forthe 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 certificationas they enter the science park. The certificated firms are also required to submit theyearly reports to ACZSP. The Zhongguancun database is compiled based on high-techcertification and yearly reports. In total, the database contains 31274 company-yearsfor 2000-2003. We operationalized an entrant in year t as the firms that are not included in ourdatabase in year t-1, and appear in our database for the first time in year t; and usedthe entrant’s R&D activities in year t+1 as the dependent variable, which reflects aone-year lag design. We operationalized an incumbent in year t as the ones that haveexisted in year t-2 and are still in operation in year t; and used their R&D data in yeart to generate independent variables, imitation. For example, we counted Firm A in2000 as an incumbent if the firm had been found in our database in 1998. In otherwords, 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. Itmay seem short but most Zhongguancun firms are young (firms’ average age is 3.61in our database) and two-year Zhongguancun experience is especially significant forthe young firms. Based on the operational definitions, we identified 10552 incumbentsfor 2000-2002 and 5575 entrant for 2001-2003. Besides, we used three sampling criteria: 1) the Zhongguancun firms that werenot in normal operation were excluded; 2) industries in which the number of the firmswas less than 5 for any year through 2000-2002 were excluded; and 3) food andretailing industries are excluded for they are not conventional high-tech industry.Because some of our independent and control variables are industry factors, ourresearch design was cross-industry, instead of single-industry. We used two-digitindustry code of Industrial Classification for National Economic Activities (GB/T4754—2002), which was issued by the National Bureau of Statistics, to createindustry-based variables and to conduct sampling procedures. The final sample included independent and control variables for the period2000-2002 and entrants’ R&D strategy for the period 2001-2003, yielding 4472 15
  • 16. observations across 20 high-tech industries. Table 1 shows the distribution of criticalvariables over industries in our final sample. We can see that the average R&Dintensities are highest in instrumental machinery, research service and telecom service.The lowest average R&D intensities are in chemistry, other machinery, andenvironmental management. The proportions of group-affiliated firms are generallylow across different industries, ranging form 0.03 to 0.2. The aggregate number ofentrants from 2001-2003 is highest in software industry (1251), followed byprofessional service (650) and computers and communications equipments (626). Thelowest numbers of entrants are found in petroleum (13), other machinery (25), andmining (28). ------------------------------------ Insert Table 1 about here ------------------------------------ All the aforementioned evidence showed that industries in the science park haveexperienced unbalanced development in the period 2001-2003. To note, Table 1 onlyprovides us interindustry distribution concerning entrants, which might not representthe general industrial R&D patterns. Measures Dependent variable. The dependent variable, an entrant’ R&D strategy, was adummy variable. We observed whether an entrant was in the top quartile for R&Dintensity of all Zhongguancun firms, irrespective of industry. R&D intensity wasmeasured as R&D spending divided by the number of employees. We normalizedR&D spending by employment, rather than by sales because more than half of theentrants (2627) in our final sample were newly founded firms and employment can bemore reliable than sales for these new founders. When the top quartiles were used as cutoffs, the thresholds for R&D strategy are6.79, 15.14, 20, and 28.57 for year 2000, 2001, 2002 and 2003. We coded R&Dstrategy as one if the firm’s R&D intensity exceeded the current-year threshold, andzero otherwise. We used a dummy variable rather than continuous R&D intensity, forthe former, to do or not to do, can better capture firms’ strategic orientation in R&Dactivities. As a similar example, Haveman (1993) employed a 5-percent threshold (5percent 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&Dstrategy, e.g., whether an entrants’ R&D intensity was in the top 50% of the 16
  • 17. Zhongguancun firms. Also, we alternatively used the continuous variable, R&Dintensity, as the dependent variable to run the regressions for the robustness check. Imitation. Hypothesis 1a -1c predict that incumbents’ R&D patterns pose rolemodels for the entrants in making innovation strategy. To test Hypothesis 1a-1c, wedeveloped five measurements to capture the prevalence of R&D strategy in differentreference groups of incumbents: general, size-localized, ownership-localized,profitable and large. The first reference group is the general incumbents, the secondand third reference groups refer to similar incumbents, and the last two referencegroups refer to salient incumbents. As for imitation (general incumbents), we calculated the proportion ofincumbents that undertake R&D strategy, i.e., whose R&D intensity were in the topquartile of all Zhongguancun firms in the two-digit industry in a particular year. By size-localized incumbents, we meant incumbents that had similar sizecompared to the entrant. Size here was measured in terms of firm employment, thenumber of employees the firm has. The notion “size-localized” comes from ecologyliterature (Hannan and Freeman, 1977; Haveman, 1993), indicating that firms’interaction tend to be localized along a size gradient and that organizations competeonly with other organizations within some range of their own sizes. Consistent withprevious 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 measuredimitation (size-localized) as the proportion of incumbents that undertake R&Dstrategy within the size-localized window of the entrant. By ownership-localized incumbents, we meant that incumbents that have thesame ownership type, state-controlled or non-state-controlled, tend to have similarinstitutional constraints and resource endowments in transitional economies (Nee,1992). Analogous with size-localized argument, we postulate that interaction tend tobe localized within the same ownership type. We measured imitation(ownership-localized) as the proportion of incumbents that undertake R&D strategywithin the ownership-localized window of the entrants, i.e., having the sameownership type as the entrant, in the two-digit industry. In other words, theownership-localized for (non)state-controlled entrant is the proportion of incumbentsthat undertake R&D strategy in the (non)state-controlled incumbents in the two-digitindustry. As for imitation (profitable) and imitation (large), we calculated the proportions 17
  • 18. of incumbents that undertake R&D strategy in the most profitable and largestincumbents 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 forrobustness checks, e.g., using alternative profitability (ROS) and size measure (assets),measuring the profitable and large incumbents using the top quartile cutoffs based onall Zhongguancun firms. To investigate the moderating effects of the structural factors of industrial clusterin 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 ofmulticollinearity (Haunschild and Miner, 1997; Li and Atuahene-Gima, 2001). Cluster density. We measure cluster density as the natural logarithm of thenumber of firms in the two-digit industry of the science park. Cluster variability. We created five cluster variability variables to capture theextent 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 bysome very large values, we measured cluster variability using a Herfindahl index,which has been widely used in strategy research, e.g., diversification (Acar andSankaran, 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&Dintensities into five categories each year (exc. three categories in 2000), and assigned1-5 to each categories from the smallest to the largest. Finally, we calculated clustervariability for different reference groups in incumbents using the following 5formula: 1 − pi2 , where p is the percentage of certain types of incumbents in each i =1categories. For example, for cluster variability (size-localized), we calculated theHerfindahl index of R&D intensity in all the incumbents that fall into the entrant’ssize window in the two-digit industry. Control variables. Three control variables were included in our regressionmodels: firms size (natural logarithm of the number of employees), age (in years), 18
  • 19. state-control (dummy variable: 1=state-controlled, 0=otherwise), business groupaffiliation (dummy variable: 1 if the entrant was affiliated to a business group and 0 ifit was not.) and performance feedback variables (firm performance-cluster aspiration(<0) and firm performance-cluster aspiration (>0)). Besides, we created and includedindustry dummies at the two-digit level, as well as year dummies in regressions. Table2 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 employedpooled logit regression to predict the likelihood of an entrant to undertake R&Dstrategy. Panel analyses cannot be applied because we identified an entrant by amoving window. As for Hypothesis 2-3, we used hierarchical moderated regressionanalyses 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 dummyvariables. Results Table 3 presents means, standard deviations, minimums, maximums, andpairwise correlations for the independent and dependent variables. The table showssome relatively high correlations, which need clarifications in two aspects. Firstly, thehigh correlations among the five measures of imitation variables are quiteunderstandable, reflecting some basic industrial trends. As Haveman (1993), weincluded in separate models the R&D variables for general, size-localized,ownership-localized, profitable and large incumbents. Secondly, the high correlationsbetween imitation and cluster density deserve our special attention. The followingmeasures 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) wecalculated their variance inflation factors (VIFs) using OLS regressions and all VIFswere well below 10, and iii) we found that the regression estimates were stable andlog-likelihoods consistently increased after introducing the relevant variables into ourhierarchical models (Haunschild and Miner, 1997). ------------------------------------ 19
  • 20. Insert Table 3 about here ------------------------------------ Table 4 shows the results of pooled of logit regressions. As in Table 4, themodels marked by “a” includes the main effects of imitation, cluster density andcluster variability; the models marked by “b” includes both main effects andinteractive terms. The numbers, 1-5 in Table 4 represents imitation variable in specificreference groups of incumbent firms: 1 for general incumbents, 2 for size-localizedincumbents, 3 for ownership-localized incumbents, 4 for profitable incumbents, and 5for large incumbents. For example, in Model 1b imitation means the innovationpatterns of general incumbents, i.e., the prevalence of innovation strategy inincumbents; in the Model 2b imitation means the innovation patterns of size-localizedincumbents, ------------------------------------------ Insert Table 4 about here ------------------------------------------ Hypothesis 1a-1c predict that when innovation strategy is more prevalent ingeneral, similar and salient incumbent firms, an entrant is more likely to undertakeR&D strategy. As we can see in Table 4, the coefficients of imitation are positive andsignificant 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, withpositive 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 thereference groups, was tested by including the interactive term, imitation X clustervariability. The coefficients of the interactive terms are negative and significant in theranges 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 amongChinese firms in three ways. First, we conceptualize innovation strategizing in Chinaas a imitation process that Chinese firms tend to imitate other firms’ innovation 20
  • 21. strategy. It is deeply rooted in the Chinese context: institutional voids and firms’inexperience in innovation activities. The perspective reveals an importantdecision-making process, imitation, in Chinese firms’ innovation strategy.Conceptualizing innovation itself as a learning technology, this perspective alsoechoes with the organizational learning arguments on ecologies of learning andlearning to learn (Heimer, 1985; Levitt and March, 1988). Second, identifying themoderating effects of informational factor further contributes to our understandingabout how imitation unfolds in the context China. Thirdly, we test our theoreticalframework in an interesting setting: an emerging market-based science park. Itdemonstrates interorganizational learning through incumbent-entrant dynamics:entrants learn from the incumbent within the high-tech cluster, while the entrant’sstrategy in itself is imitated by the later follower. Our results on Hypothesis 1a-1c depict positive relationships between theprevalence of innovation strategy in incumbents and the likelihood of an entrant toundertake innovation. The coefficients vary in their magnitude and significance levelsin 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 somecharacteristics is more likely to be used than others in identifying the role modelsamong incumbents. Also partially supported is the positive relationship between cluster density andnew entrants’ imitation of innovation strategy in Hypothesis 2. The coefficients aresignificant for general and similar-sized reference group. One possible explanation forthe weak results is that the quantity of information pertaining to cluster density mightgo to the other extreme and bring about information overload problem (Huber, 1991). The strong finding on Hypothesis 3 confirms our prediction about the negativerelationship between strategic variation in the reference group and entrants’ mimeticbehavior. It suggests that information complexity may impede new entrants to learnfrom the incumbents. The results should be interpreted within the limits of the study. The first has to dowith different types of innovation strategy. The analysis of R&D expenditure in ourpaper cannot be readily explored to other innovation strategy (e.g., product innovationstrategy). The second has to do with the specific research setting. The science park inChina 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 21
  • 22. intensive context. For example, substantial affiliated corporations in our sample areresearch centers. While the centers has the mandate given by business group to learnfrom other Zhongguancun firms, it is still unclear that affiliated corporations in otherdistricts have such learning propensity. In conclusion, this paper explores imitation of innovation strategy in the contextof an emerging market-based industrial cluster. Our results show that entrants underinstitutional voids tend to mimetically learn from incumbents’ innovation strategy andthat mimetic behavior is determined both by informational conditions of the industrialclusters, as proxied by the cluster density and cluster variability. 22
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  • 27. Table 1 Basic Information about Late Entrants by Industry (2001-2003) Average Proportion of Number of Industry R&D/employees the affiliated 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 38Sources: Administrative Committee of Zhongguancun Science Park. 27
  • 28. Table 2 Variable Specification Variable SpecificationInnovation Strategy Dummy variable: 1=R&D/employment in the top quartile of all Zhongguancun firms, 0=otherwiseImitation (General Proportion of incumbents that undertake R&D strategy the two-digitIncumbent) industryImitation Proportion of size-localized incumbents that undertake R&D strategy(Size-localized) the two-digit industryImitation Proportion of ownership-localized incumbents that undertake(Ownership-localized) Innovation strategy the two-digit industryImitation (Profitable Proportion of profitable incumbents that undertake InnovationIncumbent) strategy the two-digit industryImitation (Large Proportion of large incumbents that undertake Innovation strategy theIncumbent) two-digit industryGroup Affiliation Dummy variable: 1=group-affiliated, 0=notCluster Density (log) Natural logarithm of the number of firms in the two-digit industryCluster Variability The Herfindahl index of R&D intensity for incumbents in the(General Incumbent) two-digit industryCluster Variability The Herfindahl index of R&D intensity for size-localized incumbents(Size-localized) in the two-digit industryCluster Variability The Herfindahl index of R&D intensity for ownership-localized(Ownership-localized) incumbents window in the two-digit industryCluster Variability The Herfindahl index of R&D intensity for the most profitable(Profitable Incumbent) incumbents in the two-digit industryCluster Variability The Herfindahl index of R&D intensity for the largest incumbents in(Large Incumbent) the two-digit industryPerformance-cluster Performance minus industry average if performance > socialaspiration (>0) aspiration, and 0 if performance<social aspirationPerformance-cluster 0 if performance > industry average, and performance minus socialaspiration (<0) aspiration if performance<social aspirationEmployees (log) Natural logarithm of the number of employeesAge Current year minus year of foundingState-control Dummy variable: 1=state-controlled, 0=not 28
  • 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. 29
  • 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* 1Note: *p<0.05. 30
  • 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* 2.16** 1.27** 1.54** 1.22* 1.17* 1.10* 1.63** 1.47** 1.69** (0.71) (0.72) (0.39) (0.41) (0.54) (0.54) (0.46) (0.52) (0.45) (0.47) Imitation X Cluster 0.86† 0.65* 0.55 0.51 0.43 Density (0.51) (0.29) (0.43) (0.36) (0.32) Imitation X Cluster -20.33* -11.88* -12.63† -16.62* -12.20* Variability (8.49) (5.08) (6.88) (5.89) (5.28) Cluster Density 0.00 -0.06** 0.01 -0.06** -0.01 -0.06** -0.04 -0.06** -0.06 -0.06** (Log) (0.08) (0.02) (0.08) (0.02) (0.08) (0.02) (0.08) (0.02) (0.08) (0.02) Cluster Variability -1.86 -0.07 -2.43† -0.05 -1.89 -0.11 -1.74 -0.07 -0.99 -0.06 (1.31) (0.10) (1.28) (0.10) (1.33) (0.10) (1.34) (0.10) (1.38) (0.10) Employees (Log) 0.13** 0.52** 0.08† 0.53** 0.13** 0.53** 0.13** 0.53** 0.13** 0.52** (0.04) (0.14) (0.04) (0.14) (0.04) (0.14) (0.04) (0.14) (0.04) (0.14) Age -0.06** 0.78** -0.06** 0.78** -0.06** 0.79** -0.06** 0.79** -0.06** 0.77** (0.02) (0.20) (0.02) (0.21) (0.02) (0.20) (0.02) (0.20) (0.02) (0.20) State-controlled -0.07 -0.14* -0.06 -0.15* -0.10 -0.14* -0.08 -0.14* -0.07 -0.14* (0.10) (0.06) (0.10) (0.06) (0.10) (0.06) (0.10) (0.06) (0.10) (0.06) Group Affiliation 0.53** -0.04 0.53** -0.02 0.53** -0.02 0.53** -0.05 0.52** -0.05 (0.14) (0.08) (0.14) (0.08) (0.14) (0.08) (0.14) (0.08) (0.14) (0.08) Performance-cluster 0.78** -0.25 0.78** -1.01 0.78** -0.85 0.79** 0.12 0.76** 0.03 aspiration (>0) (0.20) (1.51) (0.21) (1.37) (0.20) (1.49) (0.20) (1.59) (0.20) (1.52) Performance-cluster -0.14* 0.12** -0.15* 0.05 -0.14* 0.12** -0.14* 0.12** -0.14* 0.12** aspiration (<0) (0.06) (0.04) (0.06) (0.04) (0.06) (0.04) (0.06) (0.04) (0.06) (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. 31