How Important are Entrepreneurial Social Capital and Knowledge Structure in New Venture Innovation?

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1 How Important are Entrepreneural Socal Captal and Knowledge Structure n New Venture Innovaton? Yang Xu Penn State Unversty, New Kensngton Drawng upon the resource-based vew and knowledge-based theory, ths research nvestgated the roles of entrepreneural socal captal and knowledge structure n the process of new venture nnovaton. A questonnare survey was conducted on a sample of 1000 new ventures n U.S. The analyss results show that an entrepreneur s socal captal nfluences ther knowledge structure of new producton development, and ultmately mpacts new venture nnovaton. By ncorporatng the entrepreneur s knowledge structure wth ther socal captal n the context of new venture nnovaton, ths research attempted to develop a theory of acton whch connects ndvdual dfferences wth socal structure. INTRODUCTION Innovaton s the drvng force of economc growth; however, there s stll much confuson over how to make t happen. Drawng upon the resource-based vew (Barney, 1991; Peteraf, 1993) and knowledgebased theory (Grant, 1996), ths research attempts to nvestgate the roles of entrepreneural socal captal and knowledge structure n the process of new venture nnovaton. Two related ssues are addressed. Frst, how does an entrepreneur s socal captal nfluence ther knowledge structure concernng new product development? Next, to what extent does the entrepreneur s knowledge structure nfluence new venture nnovaton? Entrepreneural frms are rsk takng, proactve and nnovatve (Barrnger & Bluedorn, 1999), and the entrepreneur plays a crtcal role n the new venture success (Hall & Hofer, 1993; Herron, 1990; Shane & Venkataraman, 2000; R. W. Stuart & Abett, 1990). Entrepreneur s socal network and knowledge structure are crtcal factors n new venture nnovaton. In a complex and uncertan envronment, the entrepreneur s socal network and knowledge structure change through adaptaton and learnng. Socal captal s defned as networks of relatonshps and assets located n these networks (Batjargal, 2003; Bourdeu, 1985; Coleman, 1988). Ths study focuses on network content the characterstcs or attrbutes of the members embedded n the entrepreneur s socal network. Network dversty descrbes the extent to whch each member s attrbutes are dfferent from other members. Knowledge structure s defned as mental templates that entrepreneurs mpose on an nformaton doman new product development n ths research to gve t form and meanng (Lyles & Schwenk, 1992; Walsh, 1995). Two knowledge structure characterstcs are relevant to nnovaton actvtes: complexty and centralty. Complexty reflects the level of dfferentaton and ntegraton n an actor s knowledge structure (Walsh, 1995). Centralty reflects the level of focus and herarchy n an actor s knowledge structure (Eden et al., 1992). Complexty measures the entrepreneur s nformaton-processng capablty of capturng a broad collecton of envronmental, strategc and organzatonal concepts. Journal of Management Polcy and Practce vol. 12(5)

2 Centralty measures the entrepreneur s tendency to centralze a strategy frame around a few core concepts. Ths study focuses on these two characterstcs of knowledge structure. Drawng upon the representatve works on nnovaton, socal captal and organzaton learnng, next I derved a seres of hypotheses lnkng entrepreneural socal captal, knowledge structure, and new venture nnovaton, followed by the methodology and data analyss, and ended wth the dscusson. HYPOTHESES DEVELOPMENT Socal network has sgnfcant mpacts on outcomes such as the performance of new ventures (Baum, Calabrese, & Slverman, 2000; T. E. Stuart, 2000), organzatonal learnng (Anand & Khanna, 2000; Kale, Sngh, & Perlmutter, 2000; Kraatz, 1998; Olver, 2001), and nnovaton (Powell, Koput, & Smth- Doerr, 1996; Shan, Walker, & Kogut, 1994). In a dverse network, knowledge and nformaton are dspersed among the actors embedded n the network. When knowledge s broadly dstrbuted, the entrepreneur obtans non-redundant nformaton, knowledge, and resources from varous socal and busness relatons. Ths nformaton dversty enhances ther comprehenson of a busness context or phenomenon from multple perspectves. Ther knowledge structures become more complex durng ths process of outsourcng cogntve tasks to dverse nformaton connectons. However, dverse nformaton and knowledge assmlated from dfferent busness tes may create nformaton overload. Through gradual learnng over tme, the entrepreneur s more lkely to develop a herarchcal knowledge structure to process these dverse nformaton and knowledge. Ths herarchcal knowledge structure entals a clear dstncton between the core concepts and the perpheral concepts (Carley & Palmqust, 1992). Consequently, a dverse socal captal enhances the entrepreneur s knowledge structure centralty. In a dverse network, entrepreneurs tend to centralze ther strategy frame around a few core concepts n order to process effcently a broad collecton of envronmental, strategc and organzatonal concepts. Smultaneously, thnkng drves strategy makng. Networkng s a key strategc acton and a process of an ndvdual s nteractng wth the envronment. The entrepreneur s knowledge structure nfluences ther networkng process. An entrepreneur actvely establshes tes through whch nformaton and ad flow (Baron & Markman, 2003; Shane & Stuart, 2002). Those wth a more complex mental model are able to collect more nformaton from the envronment and process the nformaton. In complex stuatons, the more complex the cogntve structure s, the more accurate the percepton s, and the more effectve the behavor s (Bartunek, Gordon, & Weathersby, 1983). However, nformaton overload may hnder the actor s ablty to make effectve decsons. Entrepreneurs wth a more centralzed knowledge structure are more effcent to establsh a dverse socal network because they are able to dfferentate the key busness tes from the other busness tes for the perpheral factors. Hypothess 1a: The dversty of an entrepreneur s socal captal s assocated postvely wth ther knowledge structure complexty. Hypothess 1b: The dversty of an entrepreneur s socal captal s assocated postvely wth ther knowledge structure centralty. The process of new venture nnovaton s nfluenced by the entrepreneur s knowledge structure through whch they acqure, store, transform, and use nformaton. As a mental template, the entrepreneur s knowledge structure gudes ther strategc actons (Walsh, 1995). Durng the process of nteractng wth a technology, actors mental templates help them construct dfferent nterpretatons of the technology (Bjker, Pnch, & Hughes, 1990; Bloomfeld, 1986; Woolgar, 1981). A techncal system s operated by dfferent socal systems such that ultmately the desgn of work depends on human choces about how best to optmze the ft between the techncal and socal systems (Thompson, 1967). Human understandngs and mental models nfluence the ways n whch the technologes functon. There s no objectve real organzaton or technology ndependent of the mental templates of the people nvolved 12 Journal of Management Polcy and Practce vol. 12(5) 2011

3 (Weck, 1979). Actors mental model plays an mportant role n decsons about technologcal nnovaton (Swan, 1995, 1997). In new venture nnovaton, the entrepreneur s knowledge structure nfluences the process of recevng nformaton, seekng reference ponts, and establshng work routnes. Entrepreneurs collectvely construct organzed knowledge about an nformaton envronment new product development n ths study that enables nterpretaton and acton n that envronment. They contnuously assmlate nformaton and knowledge from the others they nteract wth. As learnng depends on expermentaton and feedback, learnng opportuntes tend to be local to prevous knowledge (Teece, Psano, & Shuen, 1997). Entrepreneurs wth a more dverse knowledge structure wll create and select deas wth greater novelty than those wth access to a more narrow pool of knowledge. Meanwhle, entrepreneurs wth complex knowledge structure are more alert to varous types of nformaton and other external nfluences. They are payng more attenton to the dfferent reference frms, and are less constraned by the stable routnes. Dverse reference ponts and flexble work routnes drve the entrepreneur to select the most creatve deas that wll be advanced nto nnovatons n new areas. At the same tme, the entrepreneur wth a more centralzed knowledge structure selectvely receves certan nformaton and knowledge relevant to the core concepts and perpheral concepts. Ths effcency enhances the actor s ablty to assmlate more nformaton and knowledge to explore new productve resources. Meanwhle, snce the entrepreneur can dfferentate core factors from perpheral factors, ths herarchcal knowledge structure enables them to mtate dverse reference companes for dfferent factors. Furthermore, ths nformaton-processng effcency enhances ther ablty of notcng the envronmental changes and beng less constraned by the exstng organzatonal routnes. Hypothess 2a: The entrepreneur s knowledge structure complexty s assocated postvely wth the new venture s degree of nnovatveness. Hypothess 2b: The entrepreneur s knowledge structure centralty s assocated postvely wth the new venture s degree of nnovatveness. METHODOLOGY Sample and Survey Desgn A survey was conducted on a sample of 1000 new ventures n U.S. Partcpants were recruted drectly by sendng out the paper questonnares addressng the owner of the frm followed up wth telephone calls. Owner s contact nformaton was obtaned from the Hoover s Company Database. I employed four crtera when developng the malng lst. Frst, I selected new ventures from multple technology ndustres to ncrease the fndngs generalzablty. These ndustres are chemcals, computer hardware and software, consumer products manufacturng, electroncs, ndustral manufacturng, pharmaceutcals, telecommuncatons equpment. Second, I restrcted the remanng sample to frms 10 years of age or younger n ths study. Dfferent age ranges have been used n the prevous lterature, such as 12 (Covn, Slevn, & Covn, 1990), 8 (McDougall, 1989; S. A. Zahra, 1996), and 6 years (Shaker A Zahra, Ireland, & Htt, 2000). By the age of fve, many startups have become extnct f they have faled to buld strong market postons; meanwhle, older companes (up to the age of 12) have survved the lablty of newness but have not become establshed frms (Bantel, 1998). Gven the need to nclude enough samples n the mal survey, I used only frms 10 years of age or younger. Thrd, the potental sample was requred to be an ndependent busness, rather than a subsdary, a dvson of another frm, or a unt of a conglomerate. Otherwse, the startup s socal captal and nnovaton s attrbuted to decsons made by the parent frm rather than the entrepreneur. Fourth and fnally, only frms wth fewer than 500 employees were allowed to be n the sample. The orgnal questonnare was revsed based on the feedback from ndustry experts and two plot tests. To encourage partcpaton and vald responses, the ntroducton scrpt of the questonnare emphaszed the potental benefts of ths project to the entrepreneurs themselves. If any respondent request, I would send them an analyss report about comparng hs/her knowledge structure and socal Journal of Management Polcy and Practce vol. 12(5)

4 captal wth those of others as well as ther effects on new venture nnovaton. Approxmately 1000 technology U.S. new ventures were contacted durng the perod between August 2006 and January frms could not be reached, n spte of checkng ther address data. As a result, 849 frms were reached by mal. Among these 849 frms, 89 completed the full sx-page questonnare. Among the 89 respondents that completed the questonnare, 70 are the founders of ther busnesses. Varables Operatonalzaton Followng prevous lterature (Auto, Sapenza, & Almeda, 2000; Esenhardt & Schoonhoven, 1990; Smth, Collns, & Clark, 2005), I developed a two-tem scale to measure the degree of nnovatveness. The respondents were asked to ndcate ther degree of agreement wth the followng statements usng a fve-pont Lkert scale rangng from 1 to 5 (Scale 1 5: Strongly Agree Strongly Dsagree): (1) We used all exstng knowledge to buld the frst product, servce or technology. (2) We syntheszed exstng knowledge to produce our frst product, servce or technology. The cronbach alpha supported the nternal consstency valdty of ths two-tem scale. Socal captal s measured by the poston generator method. Ths methodology captures occupatonal or postonal characterstcs of network alters, and enables one to collect data on strong and weak tes smultaneously (Ln, 2001). The poston generator methodology has been used frutfully n the socal scence studes (Batjargal, 2003; Bellveau, O'Relly, & Wade, 1996; Ln & Dumn, 1986) because t allows the respondents to summarze ther socal contacts n each occupaton, and report the te strength smultaneously. Ths method s theoretcally meanngful because occupaton plays an mportant role n modern socetes. A person s occupaton ndcates ther socal resources. People who know others n a wde varety of occupatons can access the broader range of varous resources. Researchers can measure the network dversty wth the poston generator by countng up the number of dfferent knds of occupatons n whch a person knows someone. Usng ths methodology, I measured the entrepreneur s access to occupatonal postons through socal relatonshps. These socal tes are crtcal for them to seek advce, obtan fundng, establsh cooperatve relatonshps, and promote ther products or servces. A table was presented n whch 18 types of occupatons are lsted n rows, and three types of te strength (Relatves, Frends, Acquantances) are placed n columns (Ln & Dumn, 1986; Ln, Fu, & Hsung, 2001). The respondents were asked to ndcate how many people were n each cell. I developed the followng eghteen types of occupatons based on the research of numerous prevous studes (Batjargal, 2003; Bellveau, et al., 1996; Cooke & Wlls, 1999; Dakhl & Clercq, 2004; Erckson, 2004; Ln & Dumn, 1986; Ln, et al., 2001; Van der Gaag & Snjders, 2004): (1) Professonals n unverstes, research nsttutes and government labs; (2) Professonals n trade assocatons and ndustry assocatons; (3) Managers of large banks, venture captal frms or other fnancal nsttutons; (4) Other staff members of large banks, venture captal frms or other fnancal nsttutons; (5) Managers of medum and small banks, venture captal frms or other fnancal nsttutons; (6) Other staff members of medum and small banks, venture captal frms or other fnancal nsttutons; (7) Owners or managers of large frms n your own ndustry; (8) Other staff members of large frms n your own ndustry; (9) Owners or managers of medum and small frms n your own ndustry; (10) Other staff members of medum and small frms n your own ndustry; (11) Owners or managers of large frms n dfferent ndustres; (12) Other staff members of large frms n dfferent ndustres; (13) Owners or managers of medum and small frms n dfferent ndustres; (14) Other staff members of medum and small frms n dfferent ndustres; (15) Hgh-rank offcal n local governments; (16) Mddle- and low-rank offcal n local governments; (17) Hgh-rank offcal n mnstres and agences; (18) Mddle- and low-rank offcal n mnstres and agences. Next I used the Entropy ndex (Hosksson, Hll, & Km, 1993; Jacquemn & Berry, 1979; Palepu, 1985) to measure the dversty of socal captal. The dversty of socal captal measures the degree to whch an egocentrc network contans alters from dverse occupatons. The entropy measure measures the degree of dsperson of busness tes n varous occupatons by multplyng a weght varable ln( 1/ p ). 14 Journal of Management Polcy and Practce vol. 12(5) 2011

5 Entropy Measure [ p ln(1/ p )] where P s defned as the proporton of occupaton s busness contacts n all busness tes and ln( 1/ p ) s the weght for each occupaton. Knowledge structure s measured by usng the causal mappng methodology (Axelrod, 1976). Causal maps are representatons of ndvduals (or groups) belefs about causal relatons. To construct a causal map, the frst step s to develop a pool of constructs by conductng a revew of relevant lterature. In the second step, have each subject select a fxed number of constructs by dentfyng tems from a constant pool of constructs. Fnally, construct the causal map of each ndvdual subject by havng them assess the nfluence of each of ther selected constructs on ther other selected constructs. In ths study, to mprove the valdty of knowledge structure measures and expedte the mappng process, the questonnare asked the respondents to construct the causal relatons between dentfed concepts drectly. From a lst of concepts generated from the nnovaton lterature, each respondent selected the concepts they thnk mportant for new venture nnovatons, and establshed the causal relatonshp between these concepts. Ths s an effcent and effectve way to capture each respondent s mental map of new venture nnovaton. I nput each causal map matrx nto the UCINET software (Borgatt, Everett, & Freeman, 2002) to compute the centralty and complexty measures of entrepreneural knowledge structures. Bult on the prevous lterature (Bemans, 1991; Cooper, 1984; Powell, Koput, Bowe, & Smth- Doerr, 2002; Powell, et al., 1996; Sapenza, 1992; Saxenan, 1994; Shan, et al., 1994; Slater & Narver, 1999; Todtlng & Kaufmann, 2002; Tyler & Gnyawal, 2002), I generated the followng concepts: (1) Antcpate customers needs; (2) Buldng market share; (3) Encourage customer retenton; (4) Approprate response to target market growth projectons; (5) Product bulds on frms technologcal competences; (6) Coordnaton of desgn specfcatons wth operatons; (7) Parallel development efforts across dvsons; (8) Satsfy customers needs; (9) Compettors nnovaton actvtes; (10) Compettors cost advantage; (11) Speed of compettor response; (12) Antcpate compettors moves; (13) Consstent nvestment n R&D; (14) Exstng capabltes to develop new products/servces; (15) Potental to patent new products/desgns; (16) Coordnaton between manufacturng and R&D; (17) Flow of market nformaton between unts; (18) Venture captal nvolvement; and (19) Jont research and development wth busness partners and/or research nsttutes. Complexty of the mental model s measured by the densty of a causal map. The densty of a causal map refers to the rato of causal lnks to the total number of constructs n the causal map (Eden, Ackermann, & Cropper, 1992). A hgher rato ndcates that the entrepreneur s causal map s densely connected and supposes a hgher level of complexty. lnks C complexty constructs I used the establshed measure of centralty (Eden, et al., 1992) to calculate the degree centralty of each chosen concept and gves the overall causal map centralzaton. Centralty of each concept n the causal map was measured by addng the total number of concepts to whch a specfc concept n the map s lnked ether drectly or ndrectly. Each successve layer of concepts was assgned a dmnshng weght. The centralty of a concept s the weghted average length of all the total paths that lnk t to other concepts n the map. The centralty of the causal map s the centralty of the most central concept mnus the centralty of all other concepts n the map scaled by the total number of possble lnks between the concepts n the map (Borgatt, et al., 2002; Freeman, 1979). C C Centralty Centralty n [ C max [ C ( p ) Centralty 1 n 1 n 1 Centralty ( p*) C a( p, p ) Centralty ( p*) C k Centralty ( p )] ( p )] where a p, p ) = 1 f and only f p and p are connected by a lne; 0 otherwse k ( k Journal of Management Polcy and Practce vol. 12(5)

6 C Centralty ( p*) = largest value of p ) C for any concept n the map, and D ( = the maxmum possble sum of dfferences n pont centralty for a map of n concepts. max n 1 [ C Centralty ( p*) C Control Varables In the questonnare, respondents were asked to report age, gender, level of educaton, level of nvolvement n socal actvtes, length of workng experence, level of ownershp, and startup experence. Numerous prevous studes have shown that these factors play sgnfcant roles n new venture nnovaton. Among all these varables, the level of nvolvement n socal actvtes s noteworthy. Snce the respondents were asked to report ther busness tes, t s essental to control ther level of partcpaton n socal actvtes at the aggregate level. They are asked to ndcate the level of nvolvement (mnmal, regular and heavy) for seven types of organzaton/club/group (Professonal assocaton, Trade assocaton, Alumn assocaton, Athletc club, Poltcal party, Relgous group, and Other). In addton, the questonnare asked the respondents to report the ndustry, sze, and hstory of the frm as well as the proporton of R&D expendture n ts annual sales. The followng questons are asked n the survey to obtan these control varables: (1) When was your company founded? (2) Number of current employees; (3) On average, how much s nvested annually by your company n R&D as a percentage of sales? RESULTS AND IMPLICATIONS TABLE 1 PEARSON CORRELATIONS a Centralty ( p )] Mean s.d Degree of nnovatveness Knowledge structure centralty Knowledge structure complexty *** 4 Socal captal dversty ** 5 Frm sze Frm hstory R&D nvestment Partcpaton n socal actvtes ** Age *** Gender * 11 Educaton *.315** * * Workng experence *** Industry dummy *.254* ** ** Ownershp * * Startup experence Dummy * * a N =70. p <.1 (two-taled) * p <.05 (two-taled) ** p <.01 (two-taled) *** p <.001 (two-taled) I used general least squares modelng to analyze the data. The model I used to test hypotheses 1a and 1b s represented by the followng equaton: y x where t y s the knowledge structure property of respondent ; x s a vector of characterstcs of new venture, ncludng the ndependent varables and 16 Journal of Management Polcy and Practce vol. 12(5) 2011

7 control varables; and s an error term. The model I used to test hypotheses 2a and 2b s represented by the followng equaton: y x t where y s the degree of nnovatveness of new venture ; x s a vector of characterstcs of new venture, ncludng the ndependent varables and control varables; and s an error term. Table 1 presents the descrptve statstcs and correlaton matrx of all the varables. It s noteworthy that, even wthout controllng any other effects, the correlaton between socal captal dversty and knowledge structure complexty s sgnfcant at.01 level. Knowledge structure complexty and centralty s strongly correlated, whch means an actor wth more complex knowledge structure s more lkely to develop the ablty of dfferentatng the core factors from the perpheral factors. TABLE 2 EFFECT OF SOCIAL CAPITAL DIVERSITY ON KNOWLEDGE STRUCTURE a Knowledge Structure Complexty Knowledge Structure Centralty Model 1 Model 2 Model 1 Model 2 (Constant) (.844) (.746) (.169) (.161) Frm sze (.002) (.002) (.000) (.000) Frm hstory (.017) (.015) (.003) (.003) R&D nvestment (.041) (.038) (.008) (.008) Partcpaton n socal actvtes (.044) (.044) (.009) (.010) Age (.111) (.099) (.022) (.021) Gender (.310) (.280) (.062) (.061) Educaton (.047) (.042) (.009) (.009) Workng experence (.010) (.009) (.002) (.002) Industry dummy * (.239) (.219) (.048) (.047) Ownershp (.031) (.029) (.006) (.006) Startup experence dummy (.206) (.184) (.041) (.040) Socal captal dversty.638***.086* (.179) (.039) R F * * a The table gves parameter estmates; the standard error s below each parameter estmate n parentheses. N=55 p <.1 (two-taled) * p <.05 (two-taled) ** p <.01 (two-taled) *** p <.001 (two-taled) Journal of Management Polcy and Practce vol. 12(5)

8 Table 2 and 3 present the results from the least squares regresson analyss. The models n Table 2 ndcate a sgnfcant postve relatonshp between socal captal dversty and knowledge structure complexty, so s between socal captal dversty and knowledge structure centralty. Frm ndustry and entrepreneur s educaton level are margnally sgnfcant. Ths regresson analyss supports Hypothess 1a and 1b. The models n Table 3 ndcate a weak negatve relatonshp between knowledge structure complexty and degree of nnovatveness, so s between knowledge structure centralty and degree of nnovatveness. R&D nvestment, age, ndustry dummy, and ownershp are also sgnfcant. TABLE 3 EFFECTS OF KNOWLEDGE STRUCTURE ON NEW VENTURE INNOVATION a Model 1 Model 2 Model 3 Model 4 Constant (1.115) (1.073) (1.099) (1.076) Frm sze (.003) (.003) (.003) (.003) Frm hstory (.023) (.023) (.023) (.023) R&D nvestment.138*.151**.152**.157** (.057) (.055) (.057) (.055) Partcpaton n socal actvtes (.059) (.058) (.058) (.059) Age.347*.330*.315*.315* (.154) (.147) (.152) (.149) Gender (.407) (.391) (.415) (.403) Educaton (.061) (.059) (.060) (.059) Experence (.014) (.013) (.014) (.014) Industry dummy -.767* -.759* -.671* -.734* (.317) (.289) (.315) (.292) Ownershp * * (.044) (.040) (.044) (.041) Startup experence dummy (.276) (.253) (.271) (.254) Knowledge structure complexty (.200) (.307) Knowledge structure centralty (1.032) (1.453) R F * * a The table gves parameter estmates; the standard error s below each parameter estmate n parentheses. 63<= N <=66 p <.1 (two-taled) * p <.05 (two-taled) ** p <.01 (two-taled) *** p <.001 (two-taled) 18 Journal of Management Polcy and Practce vol. 12(5) 2011

9 The analyss results are consstent wth Hypothess 1a and 1b. An entrepreneur s socal captal nfluences ther knowledge structures of new venture nnovaton. However, a negatve, nstead of postve, relatonshp between knowledge structure complexty and centralty and degree of nnovatveness was found sgnfcant. The possble explanaton s that some respondents frms are operatng n the tradtonal manufacturng ndustres. Although technologes are nvolved n the manufacturng process, these new ventures had to explore new knowledge domans to produce ther frst product, servce or technology n these mature ndustres. Conversely, n the emergng hgh-technology ndustres, new ventures can survve by leveragng exstng knowledge and technologes. Both the correlaton matrx and Table 3 provded evdence that ndustry dummy s negatvely correlated wth degree of nnovatveness. The mplcaton s that hgh-technology startups tend to rely on exstng knowledge bases to develop product, servces, or technologes; whereas new ventures n the tradtonal manufacturng ndustres tend to explore new knowledge domans to develop ther busnesses. Moreover, knowledge structure complexty s postvely correlated wth ndustry dummy. In the hgh-technology ndustres, entrepreneurs tend to have a more complex mental model. The combned effect of these two correlatons sgnfcantly nfluences the analyss results of ths study. Furthermore, because the respondent s the sole data source for both ndependent varables and dependent varable, common method varance (Avolo, Yammarno, & Bass, 1991; Podsakoff & Organ, 1986) could ntroduce spurous correlaton between the varables. Future research could address ths ssue by focusng on a sngle ndustry or use objectve measures of frm nnovatveness. DISCUSSION Contrbutons Ths research extends several sets of lterature organzaton theory and new venture nnovaton, as does organzatonal research methodologes. Entrepreneurs socal captal nfluences ther knowledge structure of new product development. In a dverse socal network, actors knowledge structure tends to be more complex, and more centralzed. Ths fndng hghlghts the mportance of entrepreneurs socal networkng actvtes. When uncertanty s hgh, entrepreneurs should turn to dfferent contacts to seek advce, establsh cooperatve relatonshps, and obtan fundng. These busness contacts not only provde external resources to the entrepreneurs, but also nfluence postvely ther nternal knowledge structure. Ths renforcng effect of socal captal helps the formaton of nnovaton networks. Ths fndng also mples that the networkng actvtes between dfferent ndustres and regons could beneft the partcpants knowledge structures. Moreover, ths research contrbutes to a rcher understandng of the sources and process of new venture nnovaton. The analyss results suggest that entrepreneur s knowledge structure appear to nfluence the nnovaton process. By ncorporatng the entrepreneur s knowledge structure wth ther socal captal n the context of new venture nnovaton, ths paper attempts to develop a theory of acton whch connects ndvdual nterests wth socal structure (Poole & Vandeven, 1989). In addton, emprcal measures of latent constructs, such as entrepreneur s knowledge structure characterstcs, were developed. Ths emprcal study mproves on pror research by ncludng measures of knowledge structure that are comparable across entrepreneurs. Lmtatons and Drectons for Future Research Despte the contrbutons ths research s expected to make, several unanswered questons reman, provdng mportant drectons for future research. Frst, there mght be an nteracton between actors knowledge structures and socal captal n the context of new venture nnovaton. Future research could examne ths nteractve effect. Second, f technology startups could be traced over tme, a longtudnal study could examne the dynamc nnovaton process. Thrd, future research could conduct comparatve research across ndustres as well as across countres. Fourth, hgh-technology regons play a leadng role n technologcal nnovatons. Future research could target the technology startups n these regons such as the Slcon Valley of Calforna and Research Trangle Park of North Carolna. Regonal compettveness Journal of Management Polcy and Practce vol. 12(5)

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