hansen_2002_knowledge networks explaining effective knowledge sharing in multiunit

7/27/2019 HANSEN_2002_Knowledge Networks Explaining Effective Knowledge Sharing in Multiunit

1/18

Knowledge Networks: Explaining Effective Knowledge Sharing in Multiunit

Companies

Morten T. Hansen

Organization Science, Vol. 13, No. 3, Knowledge, Knowing, and Organizations. (May - Jun.,2002), pp. 232-248.

Stable URL:

http://links.jstor.org/sici?sici=1047-7039%28200205%2F06%2913%3A3%3C232%3AKNEEKS%3E2.0.CO%3B2-4

Organization Science is currently published by INFORMS.

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available athttp://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtainedprior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content inthe JSTOR archive only for your personal, non-commercial use.

Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained athttp://www.jstor.org/journals/informs.html.

Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printedpage of such transmission.

The JSTOR Archive is a trusted digital repository providing for long-term preservation and access to leading academicjournals and scholarly literature from around the world. The Archive is supported by libraries, scholarly societies, publishers,and foundations. It is an initiative of JSTOR, a not-for-profit organization with a mission to help the scholarly community takeadvantage of advances in technology. For more information regarding JSTOR, please contact [email protected].

http://www.jstor.orgFri Jul 20 10:48:57 2007
http://links.jstor.org/sici?sici=1047-7039%28200205%2F06%2913%3A3%3C232%3AKNEEKS%3E2.0.CO%3B2-4http://www.jstor.org/about/terms.htmlhttp://www.jstor.org/journals/informs.htmlhttp://www.jstor.org/journals/informs.htmlhttp://www.jstor.org/about/terms.htmlhttp://links.jstor.org/sici?sici=1047-7039%28200205%2F06%2913%3A3%3C232%3AKNEEKS%3E2.0.CO%3B2-4


2/18

Knowledge Networks: Explaining Effective Knowledge Sharing in Multiunit Com panies Morten T. HansenHaward Business School, Morgan Hall, Soldiers Field Park, Boston, Massachusetts 02163

mhansen @ hbs.edu

AbstractThis paper introduces the concept of knowledge networks toexplain why som e business units are able to benefit from knowl-edge residing in other parts of the company while others arenot. The core premise of this concept is that a proper under-standing of effective interunit knowledge sharing in a multiunitfirm requires a joint consideration of relatedness in knowledgecontent amo ng business units and the network of lateral inter-unit relations that enables task units to access related knowl-edge. Results from a study of 120 new product developmentprojects in 41 business units of a large multiunit electronicscompany showed that project teams obtained more existingknowledge from other units and completed their projects fasterto the extent that they had short interunit network paths to unitsthat possessed related knowledge. In contrast, neither networkconnections nor extent of related knowledge alone explainedthe amoun t of knowledge obtained an d project completion time.The results also showed a contingent effect of having directinterunit relations in knowledge networks: While establisheddirect relations mitigated problems of transferring noncodifiedknowledge, they w ere harmful when the knowledge to be trans-ferred was codified, because they were less needed but stillinvolved maintenance costs. These findings suggest that re-search on know ledge transfers and synergies in multiunit firmsshould pursue new perspectives that combine the concepts ofnetwork connections and relatedness in knowledge content.(Kn owle dge Network s; Multlurzit Firms; O rganization C apabilities; Syn-e r g y; K n o ~ , l e d g e a n a ge m e n t)

Wh y are so me business units able to benefit from knowl-edge residing in other parts of the company while othersare not? Both strategic managem ent and organization the-ory scholars have extensively researched this question,but differences in focus between the various approacheshave left us with an incomplete understanding of whatcauses knowledge sharing to occur and be beneficialacross business units in multiunit firms. In one line ofresearch, scholars have focused on similarity in knowl-edge content among business units, arguing that a firm

ORG ANIZA TIONCIENCE,O 2002 INFORMSVol. 13,No. 3, May-June 2002, pp. 232-248

and its business units perform better to the extent thatunits possess related competencies that can be used bymultiple units (e.g., Rume lt 1974, Markides and W illiamson1994, Farjoun 1998). While this knowledge content viewhas demonstrated the importance of relatedness in skillbase, it does not shed much light on the integrative mech-anisms that would allow one business unit to obtainknowledge from another (Ramanujam and Varadarajan1989, Hill 1994). When sharing mechanisms are consid-ered in this research, it is often assum ed that the corporatecenter is able to identify and realize synergies arisingfrom similarity in knowledge content among businessunits, but this assumption is typically not tested empiri-cally and excludes a consideration of lateral interunit re-lations (Chandler 1994, Markides and Williamson 1994,Farjoun 1998).In other lines of research, in contrast, scholars havedemonstrated the importance of having lateral linkagesamong organization subunits for effective knowledgesharing to occur. Research has shown that a subunit'sinformation processing capacity is enhanced by lateralinterunit integration mechanisms (e.g., Galbraith 1973,1994; Egelhoff 1993; Gupta and Govindarajan 2000),product innovation knowledge flows more efficientlythrough established relationships spanning subunitboundaries (Tushman 1977, Ghoshal and Bartlett 1988,Nobel and Birkinshaw 1998, Hansen 1999), and bestpractices are transferred more easily when a positive ex-isting relationship exists between the two parties to atransfer (Szulanski 1996). These lines of research on link-ages have, however, not incorporated opportunities forknowledge sharing based on commonality in knowledgecontent among subunits, but has taken this aspect asgiven.Yet the existence of both related knowledge in thefirm-i.e., expertise in the firm's business units that canbe useful for tasks performed in a focal business unit-and a set of established linkages among business unitsseems necessary for interunit knowledge sharing to occur

1047-7039/02/1303/0232/$05.001526-5455 electronic IS SN


3/18

MORTEN T . HANSEN Knowledge Networks

and be effective. In this paper, I consider both dimensio nsand develop the concept of task-specific knowledge net-works, which com prise not only those business units thathave related knowledge for a focal task unit, but also theestablished direct and indirect interunit relations con-necting this subset of business units. I define establishedinterunit relations as regularly occurring informal con-tacts between groups of people from different businessunits in a firm, and I assume that task units will be ableto use these relations to search for and access knowledgeresiding in other business units.I make two main arguments. First, with respect to in -direct relations (i.e., connections through intermediaries),I argue that task teams in focal business units with shortpath lengths in a knowledge network (i.e., few interme-diaries are needed to connect with other units) are likelyto obtain more kno wledge from other business units andperform better than those with long path lengths becauseof search benefits accruing to business units with shortpath lengths. Long path lengths, in contrast, lead to in-formation distortion in the knowledge network, makingsearch for useful knowledge more difficult. Second, I ar -gue that a focal unit's direct established relations in aknowledge network are a two-edged sword: While theyprovide imm ediate access to other business units that pos-sess related know ledge, they are also costly to m aintain.They are, therefore, most effective when they help teamssolve difficult transfer problems, as when the knowledgeto be transferred is noncodified (Szulanski 1996, Hansen1999 ). Wh en there is no transfer problem, they are likelyto be harmful for task-unit effectiveness because of theirmaintenance costs.This knowledge network model seeks to advance ourunderstanding of knowledge sharing in multiunit com-panies in several ways. First, by integrating the conceptsof related knowledge and lateral network connections thatenable knowledge sharing, the model seeks to extend ex-tant research that has addressed on ly one of these aspects.Second, while extant research on knowledge transferstends to focus on direct relations (i.e., the dyadic linkbetween a recipient and a source unit of knowledge), Ialso consider the larger organization context of indirectrelations, which are conduits for information about op-portunities for know ledge sharing (cf. Ghoshal and Bartlett1990 ). This app roach enables a richer understanding ofsearch processes for knowledge use in multiunit firms.Third, while scholars often consider the positive effectsof network relations on knowledge sharing, I also con-sider maintenance costs of networks by incorporating thistime com mitmen t in analyzing the impact of interunit net-work relations on knowledge-sharing effectiveness inmultiunit firms.

Knowledge Networks in M ultiunit FirmsTh e joint consideration of related know ledge and lateralinterunit relations of a knowledge network is illustratedin Figure 1 for a new product development team, whichis the unit of analysis in this paper. Diagram l a illustratesa network of relations among all business units in a firm,but does not partition these units into those that have re-lated knowledge for the focal new product developmentteam, A (i.e., a pure network consideration). Diagram Ib,in contrast, partitions the business units in the firm intothose that have related knowledge for the focal productdevelopment team (A) and those that have not, but thereis no consideration of the network among the units (i.e.,

Figure 1 Illustration of a Project-Specific Knowledge Net-work for Project A

la . Network among business units (no related know ledge)

lb . Related knowledge between business units (no network)

00 0

Has related knowledge for A0 Does not have related knowledge for A

lc . Project-specific knowledge network (network and related knowledge)Knowledge netwo k

Has related knowledge for A0 Does not have related knowledge for A

ORGANIZATIONCIENCE NO^. 13, NO. 3, May-June 2002 23 3


4/18

MORTEN T. HANSEN Knowledge Networks

a pure related knowledge consid eration). Diagra m 1c il-lustrates a project-specific knowledge network: Businessunits are partitioned into those that have related knowl-edge for the focal product developm ent team (A), and thecomplete set of network of relations among them are in-cluded, including both direct and indirect relations (i.e.,intermediary links connecting the focal unit with othersin the knowledge network). Both the indirect and directrelations affect the extent to which a focal product de-velopment team is able to obtain knowledge from otherbusiness units and use it to perform better.Effects of Indirect Relations in Knowledge NetworksA product development team's direct and indirect inter-unit relations in its knowledge network affect the effec-tiveness of its search for useful knowledge by being im-portant conduits for information about opportunities-the existence, whereabouts, and relevance of substantiveknowledge residing in other business units. While busi-ness units in the network may not be able to pass onproduct-specific knowledge directly, as such knowledgeoften requires direct interaction with the source to be ex-tracted, a focal team that hears about opportunitiesthrough the network can contact the source directly toobtain the knowledge. Such knowledge, as defined here,includes product-specific technical know-how, knowl-edge about technologies and markets, as well as knowl-edge embodied in existing solutions, such as already de-veloped hardware and software.Although direct relations in the knowledge networkprovide im med iate access and hence are especially usefulfor a focal team inquiring about opportunities, indirectrelations a re beneficial as well, because information abo utopportunities is likely to be passed on by intermediaryunits and eventually reach the focal team, provided thatbusiness units in the knowledge network are reachable.'The idea that intermediaries pass on messages and thatthey help forge connections has been well supported incomm unications and social network research. Studies in-vestigating the "sma ll-wo rld phenomenon demonstratedthat the path length (i.e., the minimum number of inter-mediaries) needed to con nect two strangers from differentstates in the United S tates was remarkably short and con-sisted of about five to seven intermediaries (Milgram1967, Kochen 1989, Watts 1999). Early work on inno-vation research showed that new product developmentteams benefited from having a gatekeeper or boundaryspanner, that is, a person who scans and interprets theteam 's environment and then passes on information to therest of the team (Allen 1977 , Katz and Tushm an 1979 ).In social network research, Granovetter (1973) showedthat intermediary persons who are weakly tied to a focal

person are uniquely placed to pass on information aboutnew job opportunities because they are mo re likely thanstrongly tied conn ections to possess nonredundant infor-mation. Th e comm on thread in these lines of work is thatindirect relations are pervasive conduits for information.Intermediaries help forge connections and pass on mes-sages that bridge two otherwise disconnected actors.Howev er, indirect interunit relations may not be perfectconduits of information about opportunities. As infor-mation gets passed on ac ross people from different units,there is likely to be some degree of imperfect transmis-sion of the message about opportunities for knowledgeuse. In particular, when information about opportunitieshas to be passed on through many intermediaries (i.e.,through long paths, cf. Freeman 1979), it is likely to be-com e distorted (Bartlett 1932, March and Simon 1958).People wh o exchange such information are prone to mis-understanding each other, forgetting details, failing tomention all that they know to others, filtering, or delib-erately withholding aspects of what they know (Collinsand Guetzkow 1964, Huber and Daft 1987, Gilovich1991 ). Th e distortion may be unintentional or deliberate(07 Re illy 1978). Huber (1982) relates a dramatic exam -ple, originally provided by Miller (1972), of a mistakemade during the Vietnam War. The chain of messageswas as follows: The order from headquarters to the bri-gade was "on no occasion m ust hamlets be burned down,"the brigade radioed the battalion "do not burn dow n anyhamlets unless you are absolutely convinced that the VietCong are in them;" the battalion radioed the infantry com -pany at the scene "if you think there are any Viet Congin the hamlet, bum it down;" the company commanderordered his troops "burn down that hamlet." Thus, themore intermediaries needed, the higher the chances ofsuch distortion, and hence the less precise is the infor-mation that is passed on (Miller 1972, Huber 1 982).The implication of receiving imprecise information inthis context is that a project team cannot easily focus ona few opportunities that are especially relevant, but mustinstead check a number of imprecise leads to verifywhether they are relevant for the team, resulting in a mo reelaborate interunit search process that takes time. Fo r ex-am ple, a project manager in my study told m e that he hadbeen told by a third party in the company about a groupof engineers in another unit who were supposed to havesome useful technical know-how, but when he was ableto reach them after trying for a while, it turned out thatthe know-how was not relevant for the project. Such fruit-less searches not only take time , but also cause delays inthe project to the extent that the needed know ledge inputholds up the completion of other parts of the project.Because of the problem of information distortion when

ORGANIZATION 2002CIENCE NO^. 13, NO . 3, May-June34


5/18


relying on intermediary units, a focal team is likely tobenefit from short path lengths in the knowledge network(i.e., few intermediaries required to connect a team in afocal unit with other units). Short path lengths enable theteam to know about precisely described opportunities in-volving related knowledge and allow it to discard infor-mation abo ut irrelevant opportunities. The team can thenfocus on opportunities with a high degree of realizationpotential and can quickly contact people in these unitsand begin working with them to extract and incorporatetheir knowledge into the focal project. Thus, less time isspent evaluating and pu rsuing opportunities, reducing ef-forts devoted to problem istic search, including search ef-forts that establish that no useful opportunities exist(Cyert and March 1992 ). Team s with short path lengthsare thus more likely than team s with long path lengths tohear about m ore opportunities that overall yield more use-ful knowledge, to the extent that opportunities are notredundant to one another. All else equal, this benefitshould reduce a focal team's time to complete the project.The arguments can be summarized in two hypotheses.

HY POT HES IS1 . The shorter a team 's path lengths inthe knowledge network, the more knowledge obtainedfrom other business units by the team.HYPOTHESIS. The shorter a team 's path lengths inthe knowledge network, the shorter the project comple-tion time.

Effects of Direct Relations in Knowledge NetworksThe shortest possible path length is to have an establisheddirect relation to all other business units in a knowledgenetwork. Such a network position does not require anyintermediary units and should remove the informationdistortion caused by using intermediaries. However, un-like indirect relations, which are maintained by interme-diary business un its, direct interunit relations need to b emaintained by people in the focal business unit, possiblyincluding focal team members, and require their own setof activities that take time. In the company I studied, forexample, product developers spent time outside of theirprojects traveling to oth er business units on a regular ba-sis to discuss technology developments, market oppor-tunities, and their respective product development pro-grams. Such interunit network maintenance can be adistraction from completing specific project tasks: Timespent on m aintaining direct contacts is time not spent oncompleting project-related tasks.Although direct interunit relations involve maintenancecosts, they also provide a benefit in certain situations:Established direct relations between a focal team and an-other business unit may be helpful when the team iden-tifies knowledge that requires effort to be m oved fro m the

ORGANIZATION 2002CIENCE NO^. 13, NO . 3, May-June

source unit and incorporated into the project. For exam-ple, in a number of projects in my sample, team mem berswere frequently able to obtain software code from engi-neers in other business units, but som etimes the engineerswho w rote the code needed to explain it and help the teamto incorporate the code into the new project. Receivingsuch help was often much easier when the team and theengineers providing the code knew each other before-hand. T his likely positive aspect of direct relations needsto be compared with their maintenance costs.Direct relations are especially helpful when a team isexperiencing transfer difficulties-i.e., spend ing signifi-cant time extracting, moving, and incorporating knowl-edge from other subunits-because the knowledge is non-codified, which is defined as knowledge that is difficultto adequately articulate in writing (Zander and Kogut1995 , Hansen 1999). Relying on established direct rela-tions may ease the difficulties of transferring noncodifiedknowledge, because the team and people in the directlytied un it have most likely worked with each other beforeand have thus established some heuristics for workingtogether, reducing the time it takes to explain the know l-edge and understand one another (Uzzi 1997 , Hansen1999). When a focal team experiences significant transferdifficulties because of noncodified knowledge, having es-tablished d irect relations to related bu siness units is likelyto reduce the amount of time spent transferring knowl-edge , which may o ffset the costs of maintaining such re-lations and shortening project com pletion time. In partic-ular, having a number of direct relations in a knowledgenetwork increases the likelihood that a team will be ableto use on e of them in transferring noncodified kno wledge.Thu s, while indirect relations are beneficial to the extentthat they serve as intermediaries that provide a focal unitwith nonredundant information, direct relations are ben-eficial to transferring noncodified knowledge, implyingthat the benefit of having intermediaries supplying non-redundant information is relative (cf. Burt 19 92).In contrast, this transfer benefit of direct relations isless important when a focal team can easily extract andincorporate the knowledge that was identified in anothersubunit, as when that knowledge is highly codified. Inthese situations, direct interunit relations are not usefulfor transfer, but they still carry maintenance costs, whichtake time away from the completion of the project to theextent that team mem bers do not have slack resources thatcan be devoted to maintaining these relationships. Themore such relations that are maintained by a focal unit,the higher the maintenance costs, and the more time istaken away from com pleting a project. The arguments canbe summarized as follows:

HYPOTHESIS ~ .he higher a team's number of direct

235


6/18


relations in the knowledge network, the shorter the proj-ect completion time w hen the knowledge to be transferredis noncodijied.HYPOTHESIS3 ~ .he higher a tea m's number of directrelations in the knowledge netwo rk, the longer the projectcompletion time when the knowledge to be transferred iscodijied.

Data and M ethodsSettingI tested the knowledge network model in a large, multi-divisional and m ultinational electronics company (here-after called "the Company"). I negotiated access to thecompany through three senior corporate R&D managersand initially visited 14 divisions where I con ducted open-ended interviews with 5 0 project engineers and managersto better understand the context, and to develop surveyinstruments. The company, which has annual sales ofmore than $5 billion, is involved in developing, manu-facturing, and selling a range of industrial and consumerelectronics products and systems, and is structured into41 fairly autonomous operating divisions that are respon-sible for product development, manufacturing, and sales.By focusing on these divisions, I was able to compareunits that occupy the same formal position in the Com-pany, thereby controlling for a potential source of varia-tion in formal structure. They all had the same formalstatus as a business unit with profit-and-loss responsibil-ity, all had a general manager, and none of the divisionsreported to another division. In addition to interunit re-lations, there were a few other integrative mechanismsacross divisions, notably divisionwide conferences andelectronic knowledge m anagement systems, but initial in-terviews revealed that these did not vary much am ong thedivisions.Selecting Product Development ProjectsI used two surveys: a network survey administered to theR&D managers in the 41 divisions and a survey for theproject managers of the product development projects in-cluded in this study. In selecting projects, I first createda list of all projects that the divisions had undertaken dur-ing the three-year period prior to the time of data collec-tion. I then exclude d very sm all projects (i .e., those withless than two project engineers) and projects that had notyet moved from the investigation to the developmentphase and were therefore hard to track. I also excludedidiosyncratic projects that had no meaningful start andend (e.g., special ongoing customer projects). Includingonly success fully complete d projects may lead to an over-representation of successful projects, biasing the results.

I therefore included both canceled projects and projectsstill in progress. After having removed too-small, pre-mature, and idiosyncratic projects, I ended up with a listof 147 projects. The project managers of 120 of thesereturned their surv ey, yielding a response rate of 85% . Ofthe 120 projects, 22 were still in progress at the time ofdata collection, four had been canceled, and 54 reporteda significant transfer event involving another division.Specifying Project-Spe cific Knowledge Networks

Identifying Related Subunits. Together with the threecorporate R&D managers, I developed a list of 22 tech-nical competencies that constituted related knowledge ar-eas (see Appendix 1 for the list of technical competen-c i e ~ ) . ~asked the R&D managers in the divisions toindicate up to four specific competencies of their divi-sions on this list and to add any if they thought the listwas incomplete. The three corporate R& D m anagers re-viewed the responses to verify whether it made sense togroup those divisions that had reported the same com-petence. The project managers of the 120 projects werethen asked to indicate what technical competencies thespecific project required and were presented with thesame list that was presented to the divisional R&D man-agers. Thus, for a given project, a number of divisionshad a competence that matched the requirements listedby the project manager (see Appendix 1 for the distri-bution of projects per competence). For example, a pro-ject manager indicated that his project required technicalcompetencies in three areas: distributed measurement,communication system monitoring, and optics. Twelvediferent divisions had at least one of these technical com-petencies and thus constituted the knowledg e network forthis particular project.

Specifying Interunit Relations. A gro up of engineers ina division typically maintained an informal regular con-tact with a group of engineers in another division, and aproject team would use such contacts to access other di-visions. These relationships were common knowledge inthat most product developers seemed to know about theirexistence and how to use them, and I was told in prelim-inary interviews that a m ain responsibility of a division'smanagers was to provide these contacts for his or herproject teams, should the need arise. I therefore assumedthat at least one member of a project team would knowabout the divisional-level contacts and that the teammembers could access these contacts if they wanted to.Because of the importance of these interdivisional con-tacts in the company, I chose to focus on these types ofcontacts.Following previous research, I used a key informant to

ORGANIZATIONCIENCENOI.3,NO. 3, May-June 200236


7/18


obtain information on interdivisional relations (Knokeand Kuk linski 198 2, Marsden 1990 ). I considered the di-visional R& D managers to be the most appropriate in-formants because they were "in the thick of things" in theR& D department in their division. Th e R& D manager ineach of the 4 1 divisions received a questionnaire asking,"Over the past two years, are there any divisions fromwhom your division regularly sought technical andlormarket-related input?" Th e question was followed by alist of the 41 divisions included in the study, allowingrespondents to indicate whether they had a tie to any onthe list, leading to a complete network where everybodywas asked whether a tie existed with everybody else(Marsden 1990). Because I asked everybody to indicatewhether a tie existed with each of the other 4 0 divisions,I avoided a potential bias resulting from having to asksomeone to ascertain whether ties exist among others(Krackhardt and kl d uf f 1999) .To validate the responses, I employed the cross-validation method used by K rackhardt (1990) by askingth e R&D managers who comes to them for input. Anactual tie exists when both divisions agree that one com esto the other for input. I then sent an e-mail to all of theR& D manage rs, asking them about the ones about whichthere was no joint agreement. On the basis of their re-sponses, I included some of these suspect ties and ex-cluded others.

Merging Network and Project Data. I constructedproject-specific knowledge networks by including all re-lations among divisions possessing related knowledge fora given project. For exam ple, for the aforementioned proj-ect for which there w ere 12 related divisions, I includedall relations amo ng these 12 divisions, and this networkconstituted the project-specific knowledge network. Toconstruct these project-specific networks, I merged theproject data with the divisional network data by assigninga division 's netw ork relations to its projects. Thus, inter-divisional ties became the equivalent of interdivisionalproject ties. It is important to record the values on thenetwork variables prior to the start of a project becausemy theoretical argumen ts assume that a project team usesestablished preexisting interunit ties to search for andtransfer knowledge. Following the approach of Burt(1992) and Podolny and Baron (1997), I handled this is-sue by measuring the interdivisional network relationsover several years by only assigning network ties thatexisted prior to the start of the project. This proceduregenerated time-varying network data from informationthat the respondents could recall.The potential bias in this approach is that it may ex-clude so me relations that existed prior to a project's start

ORGANIZATIONCIENCE NO^. 13,NO. 3, May-June 2002

but that ceased to exist by the time the R& D managerscompleted the survey. This problem can be partially con-trolled for. This potential bias should be m ore of a prob-lem for projects in divisions in which relations com e andgo than in divisions with long-lasting relations. If a di-vision's relations are long lasting, then it is less likelythat there were some relations that ceased to exist be-tween the time just prior to the project's start and the timeof surveying. T o control for this potential bias, I entereda control variable for the average age of direct relationsto related subunits (Ag e relations).Dependent Variables

Project Completion Time. To assess project task per-formance, I measured project completion time as thenumbe r of months from the start of concept developmentto the time of m arket introduction for a given project (ortime to the end of the study period or cancellation forongoing and canceled projects, respectively). I definedstarting time as the month when a dedicated personstarted working part or full time on the project, whichtypically coincided with the time an accou nt was openedfor the project. I defined the end date as the date on w hichthe product was released to shipment, which is a formalmilestone date in this company because it signifies thatthe product is ready to be manufactured and shipped ona regular basis. These definitions turned out to be veryclear and provided few problems in specifying the startand completion times, which were 14.8 months on aver-age for completed projects.Scholars have proposed two alternative measures ofcompletion time. First, completion time can be m easuredas the extent to w hich the project is finished on schedule(e.g., Ancona and Caldwell 1992 ). Th e assumption in thisschedule m easure is that inherent project differences areaccounted for in the original schedule, but also that every-body sets equally ambitious schedules, which was mostlikely not true in this company, where individual projectmanagers set their own targets. A second approach is togroup projects according to some similarity measure andthen take a project's deviation from the mean completiontime of the group (Eisenhardt and Tabrizi 1995 ). Th eproblem with this approach is that the mean deviationrelies on a clear similarity measure that was not easy toattain in this setting. Given that these two alternativemethods seemed problematic, I chose to use the numberof months a s the dependent v ariable and then add project-specific variables to control for inherent differences be-tween the projects.

Amount Acquired Knowledge. During field interviewsI was told that the most common knowledge that project

237


8/18


teams received from other divisions took the form of tech-nical solutions embodied in already developed softwarecode and hardware co mpo nents. There were two types of"ware" bein g used in the projects-standard input to theproducts being m ade (e.g., componen ts that were used innearly all oscilloscopes being manufactured), and warethat helped solve a d hoc problem s that were unique to agiven project (i.e., technical know-how that had been em-bodied in software code or hardware). While the formerwas typically handled within divisions, the latter was typ-ically obtained through interdivisional network contacts.Because my theoretical analysis focuses on knowledgethat was obtained to solve ad hoc problems for a project,I chose to focus on software and hardware that the focalteam obtained from other divisions to solve emergingproblems. With a few exceptions, most of the ware ob-tained from other divisions was of this kind.4 During pre-tests, project managers thought they could indicate theamount of ware obtained from other divisions fairly ac-curately. The project manager was asked to indicate thepercentage of all the project's software and hardware thatcame from o ther divisions in the company (see Appendix1 for the specific question). To construct the dependentvariable, I computed the fraction of ware (ranging fromzero to one) that cam e from other divisions (Amount ac -quired knowledge).Wh ile engineers also obtained other types of know ledgefrom other divisions, such as informal technical advice notembo died in either software or hardware, these were moredifficult to quantify, and I therefore did n ot develo p a sepa-rate dependen t variable for these types. Howev er, I did askthe project manager to indicate the extent to which theteam had obtained such knowledge, and this measure cor-related 0.7 with the chosen dependent variable. It is thuslikely that my measure of amount acquired knowledge isa proxy for more informal types of knowledge obtainedthrough the network in this setting.Independent Variables

Path Lengths in a Knowledge Network. I relied on g e-odesics to compute the distances in the network. A geo-desic is the shortest path length (i.e., the one w ith fewestintermediaries) between a focal division and another di-vision in a knowledge network (Wasserman and Faust1994). How ever, the measure is complicated because sev-eral of the project-specific knowledge networks were dis-connected in that some divisions did not have a tie withother divisions in the knowledge network. I handled thisproblem by creating a control variable that indicates thefraction of related divisions that were reachable in aknowledge network (Reach). This variable takes on a

value of zero if no d ivisions w ere reachable (i.e ., therewere no paths connecting the divisions) and a value ofone if all divisions in the project-specific know ledge net-work w ere reachable (the mean value for this variable is0.85).I used the measure of closeness centrality to measurepath lengths in the network (Freeman 1979 ). Closenesswas measured as (Wasserman and Faust 1994)

where d (ni, nj) is the geodesics linking d ivisions n i and nj.Summing over all reachable related divisions excludingthe focal one (g - l) , this gives division n,'s total close-ness score. This measure is standardized, so that a divi-sion has th e sh ortest path leng th (i.e ., is closest) to relateddivisions when the index is one and the longest pathlength when the index is near zero (Close related). Thesemeasures were computed in UCINET IV (Borgatti et al.1992).

Direct Relations with Divisions in a Knowledge Net-work. Because direct relations were asymmetric in thenetwork in the Company, I distinguished between directrelations in which the focal team went to other divisionsfor advice (i.e., advice-seeking relations) and direct re-lations in wh ich other divisions went to the focal on e foradvice (i.e., advice-giving relations). Each type of rela-tions implies different costs. Advice-seeking relationsneed to be maintained, while advice-giving relations re-quire time helping others. I coded the number of directadvice-seeking relations to related divisions by countingthe number of preexisting divisional ties to divisions thathad related know ledge fo r a project and then assign ed thatvalue to the focal project (Outdegree related). I thencoded the number of direct advice-giving relations to re-lated divisions by counting the number of preexisting di-visional ties in which a related division reportedly wentto the focal division for advice on a regular basis (Inde-gree related).To control for the possibility that these variables aresimply an indication of the division's overall number ofdirect relations, I also included similar measures for directrelations outside a project's knowledge network. I sub-tracted the num ber of related advice-seeking ties from thetotal numb er of direct advice-seeking relations for the fo-cal division to arrive at the unrelated advice-seeking ties(Outdegree unrelated). I subtracted the number of relatedadvice-giving ties from the total number of advice-givingties to compute the number of unrelated advice-givingties (Indegree unrelated).Finally, I included a measure of the strength of related

ORGANIZATIONCIENCE NO^. 13 , NO . 3, May-June 200238


9/18


advice-seeking ties. Previous research has shown thatweak ties may facilitate search but impede the transfer ofcomplex k nowledge (Hansen 1999). Although the theoryin this paper do es not pertain to the effects of tie weaknesson interunit kno wledge transfers, I wanted to control forthe possible effect of tie weakness. Tie weakness wascompu ted by asking the R& D divisional managers to in-dicate on a seven-point scale how frequently people intheir division talked to people in the other division andhow close their working relationship was (see Appendix1 for the specific questions). I took the average frequencyand closeness for related advice-seeking ties to comp utethe measure (Strength related).

Noncodijed Knowledge. I constructed a three-itemscale of noncodification (see Appendix 1 for the specificitems) and asked the project manager to indicate the levelof codification of the knowledge that the project teamreceived from o ther divisions (No nco dije d). This vari-able was then interacted with the number of relatedadvice-seeking relations to test the hypothesis (Noncod-i j e d X outdegree related).

Alternative Explanations. I included variables to testfor the possibility that eith er short path lengths or relatedknow ledge (but not both) exp lains the am ount of acquiredknowledge and product development time. First, I in -cluded an overall closeness centrality measure by usingthe above equations for the closeness centrality measure,using the en tire set of 41 division s as the relevant network(Close all) . To mak e this analysis com parable to the restof the analysis, I also included a variable indicating thetotal number of direct advice-seeking relations (Outde-gree al l). If the estimate for the general closeness measureis positive and significant, then the network argumentabout the importance of close positions (irrespective ofkno wled ge relatedness ) is plausible. T o capture the extentof related knowledge available to a project team, I in-cluded a variable measuring the number of related divi-sions (No. related units). If this measure of the extent ofrelated knowledge in the Company is positive and sig-nificant, then the argument about the importance of re-lated knowledge (irrespective of network relations) isplausible.Control Variables

Betweenness Centrality. Because the closeness central-ity measu res may be correlated with othe r centrality mea-sures that attempt to capture other causal mechanism s, Iincluded a measure of betweenness centrality, which isoften used to measure a focal actor's brokering positionin the network (Freeman 1979, Brass and Burkhardt1992, Burt 1992). Divisions with high betweenness may

ORGANIZATION 2002CIENCE NO^. 13,NO. 3, May-June

be in a powerful position where they can control the flowof information between two other units, thus using thisbenefit to ob tain favors from others, such as help in trans-ferring knowledge. To control for this power-orientedbenefit of central positions, I included a measure of be-tweenness centrality (W asserman and Faust 1 994)

where g,, is the number of geodisics linking division j an dk, and gj, (n,) is the number of geod isics linking divisionj and k that involve the focal division i. The m easure is asum of the probabilities that the focal division will fallon the geo desics linking all pairs of related divisio ns. Th emeasure is standardized as follows:

where the den om inator is the numb er of pairs of divisionsnot including the focal division i. This measure rangesfrom zero to one, where on e is the maxim um related be-tweenness among related divisions (Between related).

Projec t Attribute Controls. To mak e the projects com-parable, I controlled for several project-specific factors. Icontrolled for the extent to which the project used soft-ware and h ardware existing within its division. This mea-sure controls to some extent for a project team's moti-vation to conduct searches through the interunit network.The team should be less motivated to the extent that itcan use existing ware inside its own division. Projectmanagers were asked to indicate the percentage of allsoftware and hardw are in the project that they reused orleveraged fro m their own division (Own existing w are).

I used the log of estimated dollar costs at the start ofthe project to control for size and scope differences be-tw een th e projects ( B ~ d ~ e t ) . ~n my field interviews withproject m anagers, I was also told that estimated costs cap-ture inherent differences in technical complexity amongthe projects (the more complex the technology, the moreengineering hours billed to the project). I used the budgetfigure to avoid an interaction between final costs and thedependent variable. High final costs may reflect longcompletion time because of more engineering hoursbilled to the project.

I also coded whether a project-specific patent was ap-plied for, to measure degree of innovation (Patent), andwhether the project team developed a produ ct or a system(Product). More innovative projects presumably take

239


10/18


longer to complete. The product-systems distinction wasentered as a variable to control for possible differencesbetween these two categories with respect to cross-divisional knowledge use. Each variable was coded as adummy variable, where a value of one indicates a patentand a product, respectively.Finally, because strictly personal relations spanningsubunits may be used by team members to obtain knowl-edge, I entered a control measure that was obtained froma third survey that was se nt to all engineers on the projectsin the sample (see Hansen et al. 2001). Engineers wereasked to indicate the number of advice-seeking relationsthat they personally had to people in other divisions. Ithen summed these relations for a team (excluding con-tact names m entioned m ore than once) to arrive at a team-level measure of direct interpersonal relations spanningsubunits (Personal relation^).^

Statistical ApproachBecause 66 projects did not report any knowledge usefrom other divisions, the dependent variable "amount ac-quired knowledge" was set to zero for these projects. Be-cause of this large number of observations with a valueof zero, a least squares regression model was inappropri-ate, and I employed a tobit model, using maximum like-lihood estimation (Maddala 1983, Greene 1993).In addition, the statistical analysis of completion timewas com plicated by the fact that 22 of the 120 projectswere still ongoing at the time of data collection. The da-taset, therefore, includes right-censored cases (Tum a andHannan 1984). Furthermore, four projects were canceled.Because the dataset contains right-censored data, ordi-nary least squares regression analysis cannot be employed(Tuma and Hannan 1984), but the problem of right cen-soring can be dealt with by using a hazard rate model. Inthis approach, a project enters the risk set from the timeit was started and leaves the risk set when it is completedor canceled. The instantaneous transition rate-the de-pend ent variable-is a me asure of the likelihood of aproject either completing or terminating at time t, con-ditional on it not having completed or terminated beforet. The higher the transition rate, the more likely the pro-ject will be completed faster. The hazard rate model takesthe following form :

where r(t), is the completion rate of project j, t is projecttime in the risk set, and r(t)T is the completion rate in-cluding the effects of all of the control variables in themode l. The effects of the independent variables are spec-ified in the expon ential bracket; a is a vector of estimatedcoefficients, and C is a vector of independent variables.

I used the piecewise exponential specification as im-plemented in the statistical program TDA, because I didnot want to make any assumption about duration depen-dence that would require a specific parametric distribu-tion. I controlled for duration dependence, however, be-cause the survivor plot revealed a nonmonotonic curve(cf. Tu ma and H annan 1984). Th e plot revealed severaltransition phases occurring at 10, 12, 15, 18, and 21months and I therefore entered six time-period variablesthat reflect the time distribution of events. The transitionrate is assumed to be constant within these periods, andcovariates are assumed not to vary across time periods(Blossfeld and Rohwer 1995).Because multiple projects belong to a division, it ispossible that project-specific observations are non-independent because they vary w ith divisional attributes.I therefore chose a fixed effect specification and entered26 dummy variables, one for each division (except one)that had a project in the sample (G reene 1993). Thesetake on a value of one for projects belonging to the di-vision, and zero otherwise. Because the variables for thealternative explanations do not vary with divisional at-tributes, I could not use this fixed effect specification andomitted the dummy variables for those models.

ResultsDescriptiv e statistics are reported in Ta ble 1, and resultspertaining to the amount of acquired know ledge and proj-ect completion rate are presented in Tables 2 and 3, re-spectively. Models 1 and 2 in Tables 2 and 3 present theresults for the alternative exp lanations that general close-ness centrality (i.e., path length) o r knowledge relatedness(but not both com bined) explains the extent of knowledgeobtained and product development time. None of thesevariables are significant in these models. Project teams indivisions with short path lengths in the entire network didnot acquire more know ledge (i.e., software and hardw are)from other divisions and were not completed faster. Inaddition, project teams for which many other divisionshad related knowledge available did not acquire moresoftware and hardware from other divisions and were notcompleted faster. These results show that neither the ex-tent of related kno wledge that is available in the Companynor a beneficial netwo rk position consisting of short pathlengths in the entir e network is a sufficient factor explain-ing the amount of interunit knowledge sharing and prod-uct development time.The independent variables predicting the extent ofknowledge acquired from other divisions are entered inModels 3 and 4 in Table 2. Th e main effect for the "closerelated" variable is positive and significant. Projects in

ORGANIZATIONCIENCENOI.3, NO. 3, May-June 200240


11/18

Table 1 Descriptive StatisticsItem Var~ables Mean S. D Mln Max 1 2 3 4 5

1. Amount of acqu~red knowledge** 0.083 0.157 0.000 0 900

2. Own existing ware 0.450 0.307 0.000 1.000 -0.418

3. Budget 6.735 1.061 4.500 10 719 0.012 -0 2574 Patent 0.200 0.402 0.000 1.000 -0.037 0 137 0.2825 Product 0.750 0.435 0.000 1 000 -0.095 0.1 10 -0.044 - 0.0966 Age relations 2.873 1.041 1.000 5 000 0.090 - 0 083 0.037 0.159 0 0487 Personal

relations 1.618 2 186 0 000 9.000 -0.1 16 0 123 0.272 0 402 0.0468. Strength

related 4.299 1.275 1167 7.000 0.103 -0166 -0 08 5 -01 97 0.1429. Outdegree

unrelated 2 317 1.824 0 000 8 000 0.062 0 184 0 115 0.337 -0 05810, lndegree

related 3.550 2.503 0.000 11.000 0.069 0.080 0 230 0 065 0.09711 lndegree

unrelated 2.725 2.843 0.000 14.000 -01 37 0.132 0.083 -00 69 010012 Number of

related un~ts* 0.000 1.000 -2.337 1.857 0 112 -0.094 0.233 -0 018 -0.05013. Outdegree

related' 0.000 1.000 - 1.376 2 386 0 205 -0.036 0 368 0 245 -0.04214 Between

related 00870.1 08 0 0650 0015 0.111-0.103 0.190 0.08815. Close related' 0.000 1.000 -2 754 2 271 0 033 0.100 -0.078 0.003 0 16416. Reach 0.856 0.237 0 000 1.000 0.061 -00 17 0 118 -0.044 0 08617. Noncod~f~ed0.000 1.000 -0 74 2.73 -0.1 17 0 121 0 020 -0 124 -0 05818. NoncodifiedX

Outdegreerelated 0 272 2.162 -7.803 8.758 0.160 0 035 0.283 0 181 -0 026

19 Outdegree all 8.608 3.892 1 000 16 000 0 015 0.222 0 154 0.212 0.1 1020. Close all 0.400 0 097 0 227 0.597 -0.040 0.170 -0.027 0 105 0.054

N = 120* Standardized variables (mean set to 0; S.D set to 1)** Dependent var~able


12/18

MORTEN T. HANSEN KnowledgeNetworks

Table 2 Results from T obit Model Estimating the Amo unt of Acquired Software and H ardware from Other DivisionsVariables Mode l 1 Model 2 Model 3 Model 4Control VariablesOwn existing wareBudgetPatentProductAge relationsPersonal relationsAlternative ExplanationsNumber of related unitsOutdegree allClose allKnow ledge Network Controlslndegree relatedlndegree unrelatedOutdegre e relatedOutdegree unrelatedStrength relatedBetween relatedReachHypothes ized EffectClose relatedFixed Effects? No No Yes YesLog likelihood -3 3 1Chi square (d.f.)Num ber of Observa tions 120Note. Number of observations with dependent variable value of 0 is 66. Coefficients for dummy variables in Models 3 an d 4 and other dummycontrols in all models are not shown.* pi .1 : **p< 0.05: ***pi.01Two-tailed tests for variables; standard errors in parantheses.+Com pared with Model 1 + +com pare d with Model 3divisions with a high degree of closeness centrality (i.e., lations) and transfer difficulty variable (i.e., noncodifiedshort path lengths) in their respective knowledge network knowledge) is entered in Model 5 in Table 3. When thiswere able to acquire more knowledge from other divi- interaction effect is added to the model, the main effectsions. This result supports Hypothesis 1. for outdegree to related divisions becomes significant andThe results for the independent variables predicting negative, while the coefficient for the interaction variableproject completion time are included in Models 4 and 5 including outdegree and noncodified knowledge is posi-in Table 3. The main effect of the "close-related" variable tive. These results can be interpreted as follows:is positive and significant. That is, projects whose divi-sions have a high degree of closeness centrality (i.e., short completion rate = exp [- 1.230*outdegreepath lengths) in their respective knowledge network were + outdegree*(0.237*Noncodified)].likely to be completed more quickly than those with alow degree of closeness centrality (a positive hazard rate The net effect remains negative (i.e., the completion rateabove one indicates faster completion). This result sup- is below one, which means slower project completionports Hypothesis 2. time) even for high degrees of noncodification (e.g., whenThe interaction effect for the outdegree (i.e., direct re- the noncodified variable takes on a value of two, which242 ORGAN IZATIONCIENCE/VOI. 3, NO. 3, May-June 2002


13/18


Table 3 Results for Hazard Rate Analysis of Project Completion TimeVariables Model 1 Model 2 Model 3 Model 4 Model 5Control Variables Own existing ware Budget Patent Product Age relations Persona l relations Amount of acquired know ledge Noncodification Alternative Explanations Number of related units Outdegree all Close all Know ledge Network Controls lndegree related lndegree unrelated Outdegree unrelated Strength related Between related Reach Hypothesized Effects Close related 0.373** 1.79) 0.718** 0.346) Outdegree related -0 683 (0.713) - 1.230* 0.720) Nonco difiedXOutdeg ree related 0.237** 0.107) Fixed Effect Model? No No Yes Yes Yes Log likelihood -640.1 - 639.8 -613.8 61 1.4 -607.2Chi square (d. f.) 0.6 (1)+ 4.8*( I ) + + 13.2** 2)+ +Number of observations 120 120 120 120 120Note. Coefficients for divisional dummy variables in Models 3 through 6 and time pe riods and other dum my controls in all models are notshownTwo-tailed tests for variables. Standard errors in parentheses.+Compared with model 1; + +compa red with Model 3.* p< 0.1 ; * *p< 0.05;***p< 0.01

is two standard deviation from its mean, the completion the higher the number of related divisions that come torate is still below one). Thus, having direct relations to the focal division for advice, the slower the completionother divisions in the project's knowledge network miti- time of the focal project. My interpretation for this effectgated the difficulties in transferring noncodified knowl- is that focal team members spend time helping others whoedge, but the net effect of having these direct relations come to the focal division for advice, leading to pro-led to longer project completion time, likely because of longed completion time of the focal project. Second, proj-the maintenance costs involved in keeping them. These ect teams that obtained high levels of knowledge fromresults lend partial support to Hypothesis 3a and full sup- other divisions (i.e., the first dependent variable) com-port to Hypothesis 3b. pleted their projects faster than those that did not. Thus,In addition, the results in Model 5 in Table 3 reveal a controlling for network relations, the use of existingfew other interesting findings. First, there is a significant knowledge from other divisions led to higher degrees ofnegative effect for the indegree-related variable. That is, effectiveness as measured by completion time.

ORGANIZATIONCIENCE NO^. 13, NO. 3, May-June 2002


14/18


Discussion and ConclusionTh e main finding in this study is that a joint c onsiderationof related knowledge and lateral network relations isneeded to explain the extent and benefits of interunitknowledge sharing in multiunit firms. Projects in divi-sions with short network path lengths to other divisionsthat possessed related knowledge obtained more knowl-edge from other divisions and were completed faster,likely because of the search benefits accruing to projectteams with this network position. In contrast, neither theextent of available related knowled ge in the Company no rthe path length in the entire network explained the amoun tof knowledge obtained from other divisions and projectcompletion time.LimitationsThe study was limited to lateral relations between divi-sions and excluded othe r means of obtaining knowledge,potentially biasing the results. As mentioned in the meth-ods section, howe ver, the other existing interunit integra-tive mechanisms did not vary much across the divisionsand were therefore held fairly constant in the empiricaltest. Another bias m ay exist because so me divisions werelocated in the sam e geographical area, while others wereisolated geographically. Thus, some project teams mayhave been located in divisions that were in close physicalproximity w ith clusters of other divisions, perhaps m ak-ing it easier to interact and obtain useful knowledge.However, this variation among divisions would havebeen captured by the divisional dummy variables, whichcontrolled for divisional attributes.Another limitation is that the network mo del presentedhere treats the network variables as exoge nous, but theymay be an outcom e of other variables, such as team dem-ographic variables. This possibility raises the issue ofwhether any omitted variables create a spurious associa-tion between the m ain network variables (closeness cen-trality and outdegree) and project completion time. Forexam ple, team mem bers with long tenure may have largernetworks and be more skilled at completing projectsquickly. To check for this possibility, I ran several othermodels where I included average team tenure and age(and standard deviation for team tenu re), but these vari-ables did not alter the results, so I excluded them.Finally, because this study focused on one company,the results may not generalize to a diverse group of com-panies. The company that I studied is most likely morenetworked than are many multiunit firms, especially whenit is compared with holding-type companies in whichbusiness units tend to operate independently of one an-other. This bias may, however, be in a conservative di-rection. It is likely that network search for related knowl-edge will be more difficult in a network with fewer

interunit relations because teams will, on average, havelonger path le ngths. If interunit network sea rch for relatedknowledge is problematic in a relatively dense interunitnetwork, then it is also likely to occur in a com pany witha less dense network.ImplicationsWh ile these limitations imply that some caution is neededto interpret the findings, the study provides new insightsinto the challenges of sharing knowledge across businessunits in a multiunit firm. Although a prior paper re po ~t edresults based on some of the same data (Hansen 1999),this study extends well beyond those findings and theo-retical framew ork in two im portant ways. First, the earlierstudy did not consider the possibility that divisions maypossess different competencies and hence provide differ-ent levels of utility to teams that seek their knowledge.As the results reported here demonstrate, network rela-tions have different performance implications, dependingon whether they tie a team to units that possess relatedknowledge. Second, the earlier study did not include afull analysis of indirect relations but was limited to a di-vision's direct contacts and the ties between those con-tacts (i.e., maximum path lengths of one intermediary).The finding that long path lengths impede search becausethey lead to information distortion could not have beenobtained in the first study. Because most prior researchon know ledge transfers (including Hansen 1999) has fo-cused on direct relations only, this paper makes a contri-bution to that line of research by empirically testing theimplications of indirect relations on a task unit's abilityto obtain and benefit from knowledge transfers.This study also builds on other studies that have at-tempted to empirically capture how noncodified or tacitknowledge is transferred across organization boundaries.The scale that I developed in a prior paper (Hansen 1999)and used here com pleme nts other scales, notably two (seeAppendix 2 for a comparison of the questions in thesethree scales). It is similar to the one used by Szulanski(1996), who measured knowledge dimensions in thetransfer of best practice, and to the one used by Zanderand Kogut (1995), who measured knowledge aspects ofmanufacturing processes that were transferred across or-ganization units. A few differences notwithstanding, thethree scales are very similar. Their empirical validationsuggests that subseq uent research can usefully apply boththe construct of noncodified knowledge and the existingmeasurements.The most important implication of this study for exist-ing research is the suggestion that scholars adopt a newway of analyzing knowledge synergies in multiunit firms.In their extensive review of de cades of em pirical researchon the value of firm diversification, Ramanujam andVaradarajan (1989 ) concluded that "we still do not know

ORGA NIZATIONCIEN CEN OI.3, NO . 3, May-June 200244


15/18


why synergy in diversification is so elusive to obtain."They suggested that research refocus on internal organi-zation structures and processes that mediate the link be-tween the firm's overall diversity status and its perfor-mance. This paper has attempted to uncover some of theinternal organization network mechanisms that explainthe extent and effectiveness of knowledge synergies. Inparticular, the results suggest that subsequent research onthis topic needs to be altered in three ways.First, existing research needs to be more integrative byconsidering both relatedness in skill bases among busi-ness units and interunit integrative mechanisms. Researchon relatedness in knowledge can benefit by incorporatinglateral interunit sharing mechanisms in addition to thecurrent focus on the relatedness co nstruct, thus attaininga better understanding of how synergies are realized. Thisapproach requires that researchers collect network dataon companies; it is not sufficient to collect only archivaldata on the types and degrees of relatedness in multiunitfirms. Likewise, research on lateral integrative mecha-nisms, including network research, can benefit by consid-ering the knowledge content, such as technical compe-tencies, that is obtainable through networks.Th e second implication for research on knowledge syn-ergies in multiunit firms is the need to incorporate con-cepts that include indirect relations am ong business units.Extant research on interunit knowledge sharing hasmainly focused on "nodal" (i.e., the attributes of the sub-unit only) or dyadic (i.e., the relations between two partiesto a transfer) interunit relations but not on network po-sitions characterized by both direct and indirect interunitrelations (for an exception, see Nohria and Ghoshal1997). As demonstrated by the empirical study in thispaper, a unit's entire path length in a knowledge networkaffects its ability to access useful knowledge in the firmand improve task performance. While my study focusedon path lengths, subsequent studies can address attributesof intermediary units that facilitate the routing of mes-sages (cf. Huber and Daft 1987).A third implication is that research on knowledge syn-ergies in multiunit firms also needs to consider the costsand drawbacks of having integrative interunit mecha-nisms. While direct interunit relations enable task unitsto access and transfer knowledge, they also carry costs inthe form of time spent building and maintaining them andefforts required to h elp other units. Unless research doesnot distinguish between costs and benefits of having di-rect interunit relations, it is unlikely to find robust resultsfor the net effects of lateral integrative mechanisms onperformance. Future research thus needs to more care-fully specify conditions under which lateral integrative

ORGANIZATIONCIENCE NO^. 13 , NO. 3, May-June 2002

mechanisms will have negative or positive performanceimplications.In conclusion, by incorporating the dual dimension ofrelatedness in knowledge content and network relationsand the issues of indirect ties and cost considerations,subsequent research on knowledge synergies and trans-fers in multiunit companies is likely to provide new in-sights into the question of why k nowledge sharing in mul-tiunit firms leads to performance imp rovement.Appendix 1. List of Techn ical Competencies andSurvey QuestionsTechnical Competencies:

Number of Number ofDivision s per Projects per

Technical Competence Competence Competence(1) Digital signal processing 12 37(2) Analog signal processing 10 MHz 5 27(4) Quartz/cesium resonance 1 4(5) Optics 4 12(6) Mechanical measurement 4 9(7) Distributed measurement 5 8(8) Compo nent test technology 3 17(9) Real-time software 15 48

(10) IC design 3 16(1 1) Mea surem ent integration 9 20(1 2) Analog to digital con version 4 15(13) Fault diagnostics 2 1(14) High-speed digital design 10 27(15) RF measurement 10 27(16) Comm unication system monitoring 7 17(17) Test system architecture 10 19(1 8) High-power design 3 5(19) Software engineering 7 13(20) Device physics 1 1(21) Special user interface 4 1(22) Protocol test 2 4Average 4.9 2.9Survey Questions:

Amount Acquired Knowledge (Dependent Variable). This variableincludes software and hardware that were defined as follows in thesurvey: Software included firmware and flow and structure charts;hardware included electronic, electrical, and mechanical parts. Projectmanagers were asked, "Of all the software (hardware) that was neededin the product, what were the following breakdowns?' They were giventhe following six categories and aske d to allocate 100 points amongthem: (1) already developed ware from own division, (2) new waredeveloped in own division, (3) already developed ware from other di-visions, (4) new w are developed in other divisions, (5) already devel-oped ware from outside of the company, and (6) new ware developedoutside the company. The project manager was also asked to indicatethe project's percentage split between software and hardware, and Iused this information to take a weighted sum of (3) and (4), which Idivided by 100.

245


16/18

MORTEN T . HANSEN Knowledge Networks

Noncodijied Know ledge. Project managers answered three questionsbased on seven-point scales: (1) "How well documented was theknowledg e that your team leveraged from this division? Consider allthe knowledge." Categories were: 0 = It was very well documented,3 = It was somewhat well documented, 6 = It was not well docu-mented. (2) "Was all of this knowledge sufficiently explained to yourteam in writing (in code co mments, written reports, manuals, e-mails,faxes. etc.)?' Categories were: 0 = All of it was, 3 = Half of it was,6 = None of it was. (3 ) "What type of knowledge came from thisdivision?" Categories were: 0 = Mainly reports, manuals, documents,self-explanatory software, etc., 3 = Half know-how and half reports/documents, 6 = Mainly personal practical know-how, tricks of thetrade. The average of the three scores represents noncodified knowl-edge (Cronbach ' s Alpha = 0.81).

Interdivisional Tie Strength to Related Divisions. Divisional R&Dmanagers answered two questions on a seven-point scale: (1) "Howfrequently do (did) people in your division interact with this division(on average over the past two years)?" Categories were: 0 = once aday , 1 = twice a week, 2 = once a week, 3 = twice a month, 4 =once a month, 5 = once every second month, 6 = once every 3months. (2 ) "How close is (was) the working relationship between yourdivision and this division?" Categories were: 0 = "Very close, prac-tically like being in the same work group," 3 = "Somewhat close, likediscussing and solving issues together," 6 = "Distant, like an arm's-length delivery of the input." The two scores were reversed, the scaleset to one through seven (zero for no ties), and the average of the tworepresents tie strength.

Appendix 2. Comparison of Three Measures for Codified KnowledgeHansen 1999 and This Study Szulanski 1996 Zander and Kogut 1995

Measure s (Noncodification) (No Causal Ambiguity) (Codifiability)Similar i tems to this study *Ho w well docum ented was the

knowledg e that your teamleveraged from this division?( 0 = It was very welldocumented , 6 = It was not welldocumented)*Wa s all of this knowledgesufficiently explained to your teamin writing (in code co mmentswritten reports, manuals, e-mails,faxes, etc.)? (0 = All of it was, 6= None of it was)*Wh at type of knowledge camefrom this division? (0 = Mainlyreports, manuals, documents, self-explanatory software, etc., 6 =Mainly personal practical know-how, tricks of the trade)

Less similar items to this study

(scale with five answers possible:Yes!; yes, but; no opinion; no, notreally; no!)*Th ere is a precise list of theskills, resources, and prerequisitesnecessary for successfullyperforming the practice

Useful manuals for the practiceare available*Existingwork manuals andoperating procedures describeprecisely what people working inthe practice actually doOperating procedures for the

practice are available

*Large parts of our manufacturingcontrol are embodied in standardtype software that we modified forour needs*Large parts of our manufacturingcontrol are embodied in softwaredeveloped within our companyexclusively for our use*Extensive documentationdescribing critical parts of themanufacturing process exist in ourcompany

*T he limits of the practice are A useful manual describing ourfully specified manufacturing process can be*With the practice we know why a writ tengiven action results in a givenoutcome*W hen a problem surfaced for thepractice, the precise reasons forfailure could not be articulatedeven after the event (reversed)*It is well known how thecomponents of that list interact toproduce practice's output.Cronbach 's Alpha for scale 0 .81 0.86 0.678

Source. Szulanski 1996, causal ambiguity sc ale in Appendix 2 . Zander and Kogut 1995, codifiability scale in Append ix 1.

246 ORGANIZATIONCIENCE NO^. 13, NO . 3, May-June 2002


17/18


Endnotesonre reachable units do not have any relations in the knowledge net-work and are isolates, malung it more difficult for focal teams in otherunits to know about opportunities for knowledge sharing in those units.2 ~ h eist reveals that the technical com petencies are at a fairly detailedlevel, because they are mainly in one industry, but interviews indicatedthat this was the most appropriate level of detail to study: A moreabstract level would mask important differences in technical compe-tencies among divisions, whereas a more detailed level would forcedifferences among divisions even where commonalities existed.'ln addition to these contacts, there were a few regularly occurringlicensing agreements between divisions and some enduring cross-divisional groups that were focused around a specific technology area.I included these two types of fairly informal relations so as not to biasthe network information.41n a few instances, divisions did obtain standardized software and hard-ware components from other divisions and had entered into cross-divisiona l licensing agre emen ts that specified transfer prices for these trans-actions. To remove this potential bias from my measure of interdivisionalsoftware and hardware for ad hoc problem solving, I created a dummyvariable that took on a value of one if the focal division had an inter-divisional licensing agreement, and zero otherwise. Because this variabledid not alter any of the results, I excluded it from the main results.'~ ec a u se nformation on estimated costs was missing for 19 projects,I imputed the values for these projects through a regression analysisestimating the log of estimated project costs at the start of the project.The results of this imputation are not reported.6 ~ h i snformation was m issing for 62 projects whose response rate forthe individual network survey was below 50%. I therefore set thepersonal-relation variable to zero for these projects and entered adummy variable that took a value of one if the information was missing ,and a zero otherwise.ReferencesAllen, T. 1977. Managing the Flow of Technology. Massachusetts In-stitute of Technology Press, Cambridge, MA.Ancona, D., D. Caldwell. 1992. Bridging the boundary: External ac-

tivity and performance in organizational teams. Admin. Sci.Quart. 37(4) 634-665.

Bartlett, F. C. 1932 . Remembering, A Study in Experimental and SocialPsychology. Cambridge U niversity Press, London, U.K . and NewYork.

Blossfeld, H. P., G. Rohwer. 1 995. Techniques of Event History Mod-eling: New Approaches to Causal Analysis. Lawrence ErlbaumAssociates, Mahwah, NJ.

~ ~S, P., M , G. E ~ ~~ ~ ~ ~ ~ i ~~ , ,~L. C , F ~ ~1992, ~TJCINETIV, ver-sion 1.00.Analytic Technologies, Columbia, SC.

Brass, D. J., M . E. Burkhardt. 1992. Centrality and power in organi-zations. N. Nohria and R. G . Eccles, eds. Networks and Organi-zarions: Structure, Form and Action. HBS Press, Boston, MA,191-215.

Burt, R. 1992. Structural Holes: The Social Structure of Competition.Harvard University Press, Cambridge, MA .

Chandler, A. 1994. The functions of the HQ unit in the multibusinessfirm. R. Rumelt, D. Schendel, and D. Teece, eds. FundamentalIssues in Strategy. Harvard Business School Press, Boston, MA ,323-360.

ORGANIZATION 13 , NO. 3, May-June 2002C I ENC ENOI .

Collins, B. E., H. A . Guetzkow. 1964. A Social Psychology of GroupProcesses for D ecision Making. Wiley, New York.

Cyert, R., J. G. March. 1992. A Behavioral Theory of the Firm, 2n ded. Blackwell B usiness, Cambridge, MA.

Egelhoff, W. 1993. Information processing theory and the multina-tional corporation. S. Ghoshal and D. E. Westney, eds. Organi-zation Theory and The Multinational Corporation. St. Martin'sPress, New York, 182-210.

Eisenhardt, K., B. Tabrizi. 1995. Accelerating adaptive processes:Product innovation in the global computer industry. Admin. Sci.Quart. 40(1) 84-1 10.

Farjoun , M. 19 98. The ind epend ent and joint effects of the skills andphysical bases of relatedness in diversification. Strategic Man-agement J. 19( 7) 61 1-630.

Freeman, L. 1979. Centrality in social networks: Conceptual clarifi-cation. Social Networks l ( 3 ) 215-234.

Galbraith, J. 1973. Designing Complex Organizations. Addison-Wesley, Reading, MA.

1994. Competing with Flexible Lateral Organizations, 2nd ed.Addison-Wesley, Reading, MA.

Ghoshal, S., C. Bartlett. 1988. Creation, adoption, and diffusion ofinnova tions by subsidiaries of multinational corporatio ns. Inter-nat. Bus. Stud. 19( 3) 365-388.-- 1990. The multinational corporation as an interorganiza-tional network. Acad. Management Rev. 15 603-625.

Gilovich, T. 1991. How We Know What Isn't So: The Fallibilin. ofHuman Reason in Everyday Life. Free Press, New York.

Granovetter, M. 1973. The strength of weak ties. Amer. J. Soc. 6(6)1360-1380.

Greene, W. H. 1993. Econometric A nalysis, 2nd ed. Macmillan Pub-lishing, New York.

Gupta, A. K., V. Govindarajan. 2000. K nowledge flows within multi-national corporations. Strategic Managem ent J. 21(4) 473-496.

Hansen, M . T. 1999. The search-transfer problem: The role of w eakties in sharing know ledge across organization subunits. Admin.Sci. Quart. 44(1) 82-1 11.

-, J. M. Podolny, J. Pfeffer. 2001. So many ties, so little time: Atask contingency perspective on corporate social capital. Res. inthe Sociology of Organ. 1 8 21-57.

Hill, C. 1994. Diversification and economic performance: Bringingstructure and corporate management back into the picture. R.Rumelt , D. Schendel , and D. Teece, eds. Fundamental Issues inStrategy. Harvard Business School Press, Boston, MA, 297-3LL .

Huber, G . 1982. Organizational information systems: Determinants of~ ~ , their performance and behavior. Management Sci. 28(2) 135-155. -R. Daft. 1987. The information environments of organizations.F. Jablin, L. L. Putnam, K. H. Roberts, and L. W. Porter, eds.Handbook of Organization Communication. Sage, Beverly Hills,CA, 130-163.Katz, R., M. Tushman. 1979. Cc nm unic ation Patterns, project perfor-mance, and task characteristics: An em pirical evaluation and in-tegration in an R&D setting. Management Sci. 23(2) 139-162.

Knoke, D., J. Kuklinski. 1982. Network Analysis. Sage, Newbury Park,CA.

Kochen, M., ed. 1989. The Small World. Ablex, Norwood, NJ.

24 7


18/18

MORTEN T . HANSEN Knowledge NetworksKrackhardt, D. 1990. Assessing the political lan dscape : Structure, cog-

nition, and power in organizations.Adm in. Sci. Quart. 35(2) 342-369.

-, Martin K ilduff. 1999. Whether close or fa r: Social distance ef-fects on perceived balance in friendship networks. J. Personalit3:and Soc. Psych. 76(5) 770-782.

Maddala, G. S. 1983. Limited-Dependent and Qualitative Variables inEconometrics. Cambridge University Press, New York.

March, J. G., H. A. Simon. 1958. Organizations. Wiley, New York.Markides, C., P. Williamson. 1994. Related diversification, core com-

petencies and corporate performance. Strategic Management J.15(Su mm er Special Issue) 149-1 67.

Marsden, P. 1990. Network data and measurement. J. Blake, ed.AnnualReview of Sociology, vol. 16. Annual Reviews, Inc., Palo Alto,C A , 4 3 5 4 6 3 .

Milgram, S. 1967. The sm all world problem. Psych. Today 2(1) 60-67 .

Miller, J . G. 1972. Living systems: The organization. Behavioral Sci.17(1 ) 1-8.

Nobel, R., J. B irkinshaw. 1998. Innovation in multinational corpora-tions: Control and communication patterns in international R&Doperations. Strategic M anagement J. 1 9 (5 ) 4 7 9 4 9 6 .

Nohria, N., S. Ghoshal. 1997. The Differentiated Network: OrganizingMultinational Corporations for Value Creation. Jossey-Bass,Inc., San Francisco, CA.

O'Reilly, C. 1978 . The intentional distortion of information in orga-Accepted by Bruce Kogut.

nization communication: A laboratory and field approach. HumanRelations 31(2) 173-193.

Podolny, J., J. Baro n. 19 97. Resources and relationships: So cial net-works and mobility in the workplace. Amer. Soc. Rev. 62(5) 673-693.

Ramanujam, V ., P. Varadarajan. 1989. Research on corporate diver-sification: A synthesis. Strategic Managem ent J. 10(6) 524-551.

Rumelt, R. 1974. Strategy, Structure, and Economic Performance. Har-vard University Press, Boston, MA.Szulanski, G. 1996. Exploring internal stickiness: Impediments to the

transfer of best practice within the firm. Strategic M anagement J.17(Winter Special Issue) 2 7 4 3 .

Tuma, N., M. Hannan. 1984. Social Dynamics: Models and Methods.Academic Press, New York.

Tushman, M. 1977. Special boundary roles in the innovation process.Admin. Sci. Quart. 22(4) 587-605.

Uzzi, B. 1997. Social structure and competition in interfirm networks:The paradox of embededness. Admin. Sci. Quart. 42(1) 35-67.

Wasserman, S., K. Faust. 1994. Social Network Analysis: Methods andApplications. Cambridge University Press, Cam bridge, MA .

Watts, D. 1999. Small Worlds: The Dynamics of Networks BetweenOrder and Randomness. Princeton University Press, Princeton,NJ .

Zander, U., B. Kogut, 1995. Knowledge and the speed of transfer andimitation o f organiz ational capabilities: A n empirical test. Organ.Sci. 6(1) 76-92.

ORGANIZATION 2002CIENCE NO^. 13 , NO.3 , May-June

hansen_2002_knowledge networks explaining effective knowledge sharing in multiunit

Documents