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Citation: Lipparini, A., Lorenzoni, G. & Ferriani, S. (2014). From core to periphery and back: A study on the deliberate shaping of knowledge flows in interfirm dyads and networks. Strategic Management Journal, 35(4), pp. 578-595. doi: 10.1002/smj.2110

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Strategic Management JournalStrat. Mgmt. J., 35: 578–595 (2014)

Published online EarlyView 21 March 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/smj.2110

Received 24 July 2010 ; Final revision received 31 July 2012

RESEARCH NOTES AND COMMENTARIES

FROM CORE TO PERIPHERY AND BACK: A STUDY ONTHE DELIBERATE SHAPING OF KNOWLEDGE FLOWSIN INTERFIRM DYADS AND NETWORKS

ANDREA LIPPARINI,1* GIANNI LORENZONI,1 and SIMONE FERRIANI1,2

1 Department of Management, University of Bologna, Bologna, Italy2 Cass Business School, City University London, London, U.K.

We study 892 Italian motorcycle industry projects carried out via 184 different buyer–supplierand supplier-supplier relationships to provide evidence on the knowledge dynamics occurring indyads and networks and to understand the underexplored but important (perhaps even dominant)leading role that some firms play in the evolution of networks and interfirm learning processes. Wedevelop a multiphase model which, from a multilevel perspective addressing different relationalsubsets, suggests how firms can best organize to generate and exchange knowledge efficiently.We argue that extant theoretical perspectives can profitably draw on our findings to strengthentheir dynamic components and help them explain the widely diffused ‘exploring through partner’strategies more effectively. Copyright 2013 John Wiley & Sons, Ltd.

INTRODUCTION

Both executives and academics have recently iden-tified knowledge as one of the most importantfactors contributing to firms’ ability to achieve sus-tainable competitive advantage (Dyer and Singh,1998; Dyer and Nobeoka, 2000; Zhao, Anand, andMitchell, 2005). At the same time, a growing bodyof research has come to consider networks as anappropriate unit of analysis (Powell, Koput, andSmith-Doerr, 1996; Kogut, 2000) and to exam-ine the question of where a firm should drawits boundaries (Parmigiani and Mitchell, 2009)on the understanding that ‘where boundaries are

Keywords: knowledge exchange and creation; interfirmnetworks; core and peripheral firms; knowledge-enhanc-ing practices; motorcycle industry*Correspondence to: Andrea Lipparini, Department of Manage-ment, University of Bologna, Via Capo di Lucca, 34 – Bologna,Italy. E-mail: [email protected]

drawn affects the value placed on knowledge andits usefulness’ (Argote, McEvily, and Reagans,2003: 574). This work has shown how knowl-edge and other critical resources increasingly spanfirm boundaries so that the advantage of a sin-gle firm is often linked to those of the networkof ties in which it is embedded (Dyer and Singh,1998). In particular, research rooted in innova-tion literature (Powell et al., 1996; Zhao et al.,2005) and in buyer–supplier relationships (Kotabe,Martin, and Domoto, 2003) has emphasized theincreasing importance of networking, alliances andidiosyncratic interfirm relations (Martin, Mitchell,and Swaminathan, 1995), and the advantages ofadopting a cooperative mode for knowledge cre-ation (Mesquita, Anand, and Brush, 2008). Strate-gic literature’s focus on knowledge creation andmanagement has been expanding in unprecedentedfashion, so that the key knowledge-based ques-tions companies face are not only how to organizethemselves to exploit their already developed

© 2013 The Authors. Strategic Management Journal published by John Wiley & Sons Ltd.This is an open access article under the terms of the Creative Commons Attribution License, which permits use,distribution and reproduction in any medium, provided the original work is properly cited.

Research Notes and Commentaries 579

knowledge or capabilities, but also ‘how to orga-nize to efficiently generate knowledge and capa-bility’ (Nickerson and Zenger, 2004: 617).

Our multiple case, inductive study (Eisenhardt,1989) attempts to answer the above question.We present a multiphase model of interfirmknowledge creation and exchange across dyadsand networks and discuss the important (per-haps even dominant) role that buying firmsplay in the evolution of networks and of inter-firm learning processes, which has hitherto beenunderexplored in the strategic literature. Weposit that networks become viable organizationalforms for sharing and generating valuable knowl-edge when a set of purposefully implementedknowledge-enhancing practices—which we defineas distinctive patterns of action and interactionbetween core firms—‘buyers’—and peripheralfirms—‘suppliers’—that enable the exchange,recombination, and co-creation of specializedknowledge—allow firms to leverage knowledgelocated beyond their organizational boundaries.1 Inparticular, we focus on a total of 892 projects asso-ciated with the design and/or manufacture of com-ponents for motorbikes and/or scooters in the Ital-ian two-wheeler industry. These projects involved17 leading motorcycle manufacturers—core buyerfirms who were responsible for setting up andsubsequently ‘orchestrating’ dyads and networks(Dhanaraj and Parkhe, 2006)—and their (periph-eral) first-tier suppliers.

CONCEPTUAL BACKGROUND

Although prominent firm perspectives have con-tributed greatly to explain how firms achieve sus-tainable competitive advantage, they still overlookthe fact that, increasingly, even unique resourcesspan firm boundaries and can be selectively trad-able through a network of firms (Kogut and Zan-der, 1992; Gulati, 1999). Even contributions thathave significantly extended resource-based reason-ing (Barney, 1991; Peteraf, 1993) and are rooted inknowledge and capability-based views in strategy(Kogut and Zander 1992; Conner and Prahalad,

1 Throughout the article, we make use of the core peripherypattern to convey the particular situation in which core firms,which possess prominence gained through relational skills andtheir central position in network structures, leverage theirposition to harness the knowledge dispersed among peripheralfirms.

1996; Teece, Pisano, and Shuen, 1997; Felin andHesterly, 2007) share a main focus on intrafirmtransfers of existing knowledge (Hansen 1999; Tsai2001; Reagans and McEvily, 2003; Levin andCross, 2004) and are less well equipped to analyzeknowledge transfer and creation across fully inde-pendent entities (which have become significantlymore common over a wide range of industries,from automobiles to semiconductors) (Nickersonand Zenger, 2004; Hansen, Mors, and Løvas,2005; Mesquita et al., 2008). A growing numberof empirical studies rooted in innovation literature(Powell et al., 1996; Zhao et al., 2005), and espe-cially those adopting a relational perspective (Dyerand Singh, 1998), explicitly consider the collabo-rative network, rather than the single firm, as thelocus for learning and innovation. In particular,research on buyer–supplier networks in the auto-mobile industry (Dyer, 1996; Osborn and Hage-doorn, 1997; Argote et al., 2003) has shown howthe extensive involvement of suppliers in productand process development has given lead assem-blers faster product development cycles, lowercosts, better end product quality (Womack, Jones,and Roos, 1990; Dyer and Nobeoka, 2000), andfaster problem solving (Takeishi, 2001). The ideathat networks are critical for knowledge creationand exchange is corroborated by studies conductedfrom knowledge transfer perspectives (Tsai, 2001;Reagans and McEvily, 2003; Inkpen and Tsang,2005). Systematic evidence from this researchstream indicates that networks with substantialknowledge transfer mechanisms between man-ufacturers and suppliers out-innovate relationalsets with less well-developed knowledge-sharingroutines.

Following Kogut (2000), we see networks as theoutcomes of generative rules of knowledge coordi-nation, which constitute capabilities (e.g., speedyresponse to change, resource orchestration, etc.)generating relational rents, which can be subject toprivate appropriation. Our study focuses on mul-tiple transactions and on those deliberate and pur-poseful actions designed to facilitate knowledgegeneration and mobility across networks’ bound-aries, which are central to their ability to co-innovate and so co-create value (Zhao et al., 2005).While most studies focus on ties with genericallydefined content, we disentangle the intertempo-ral processes of knowledge creation and exchangebetween organizations and adopt a multilevel per-spective that addresses three different subsets

Strat. Mgmt. J., 35: 578–595 (2014)DOI: 10.1002/smj

© 2013 The Authors. Strategic Management Journal published by John Wiley & Sons Ltd.

580 A. Lipparini, G. Lorenzoni, and S. Ferriani

of relationships—R&D, operations, and other up-and downstream activities. We also investigatelearning processes and knowledge-related activi-ties involving both sides of the dyad and considerthem as both knowledge transmitters and recipi-ents. Although several studies have included bilat-eral (buyer and supplier) analyses (e.g., Zanderand Kogut, 1995; Takeishi, 2001; Kotabe et al.,2003), they have considered knowledge flows onlyas unidirectional—one unit providing and onereceiving—and such perspectives clearly limit ourunderstanding of cases where firms both provideand receive knowledge. Finally, we show how theeffective creation and extraction of network valuehinges on the purposeful enactment by core firmsof practices designed to lead and facilitate knowl-edge sharing and deployment.

METHODS

Research design, setting, and sampleconstruction

Our inductive, multiple case study (Eisenhardt,1989) examines the Italian powered two-wheelerindustry, which we deemed appropriate for sev-eral reasons. First, product architecture: motorcy-cles and scooters2 are made of dozens of mod-ules and subassemblies whose development andmanufacture requires diverse and specific know-how and expertise (Helfat, Lipparini, and Verona,2011). Second, the level of product innovationinvolved is appropriate: manufacturers are con-tinually challenged by the introduction of newmaterials—plastics, titanium, carbon fibers, lightalloys, etc.—into frames and components, aswell as the need to meet changing environ-mental regulations (Helfat et al., 2011). Third,the intense industrial process innovations, whichrequire manufacturers to adopt state-of-the-artequipment and R&D practices and to base theirmanufacturing activities on leading-edge technolo-gies (Gavetti, 2001). Fourth, the size of Italy’sdomestic market—the largest in Europe in termsof powered two-wheeler production (nearly 55%),registrations (26.7%), and numbers of motorcy-cles in use (26%)—and its competitive position as

2 A scooter is a small-engined, two-wheeled motor vehicle withcertain design characteristics such as a step-through frame, smallwheels, and an automatic transmission.

Europe’s leading exporter, headed only by Japanand Taiwan in world rankings (ACEM - Associa-tion des Constructeurs Europeens de Motocycles,2010).

Our study included core motorcycle firms(assemblers) that sold products in Italy (regard-less of their country of origin) and peripheralfirms (suppliers) localized in Italy that special-ized in the design and/or manufacture of industrycomponents. Both of these types of firms workwithin a network structure of non-equity, long-term cooperative buyer/supplier relationships. Weselected core firms on the basis of lists sup-plied by the Italian Ministry of Transportation(from www.trasporti.gov.it) and the trade associa-tion (www.ancma.it), and we used Italian marketsales performance figures for 2007, the most recentyear for which comprehensive statistics were avail-able. We contacted representatives at the full list of25 core firms specializing in scooter and/or motor-cycle design and/or production, and 17—coveringnearly 85 percent of Italian market sales—agreedto participate. In selecting peripheral firms, wefirst used business publications to identify relevantgroups of two-wheeler components and modules,each including multiple, often specialized, sub-components (thus, ‘body’ includes frame, suspen-sion, forks, etc.; ‘brakes’ include drums, cylinders,pads, shoes, etc.). Working from publicly avail-able sources (e.g., motorcycle and spare parts cata-logs, national and international exhibition businessdirectories, online bikers’ communities, etc.), weidentified a list of 43 component suppliers. Check-ing this list with our full core firm group, weidentified 19 suppliers who each had links withat least seven core firms: 13 of them agreed toparticipate and supplied details about their ties tocore firms.

Data collection and analyses

To avoid restrictive theoretical or empiricallenses (Santos and Eisenhardt, 2005), we definedknowledge broadly, as encompassing both easilycodifiable and transmittable information and com-plex and more difficult to codify know-how. Theinteraction patterns we noted implied the existenceof ties between two entities, which we define asvoluntary collaborative arrangements of strategicsignificance between independent organizations,based on written contracts and aimed at sharingtacit and explicit knowledge. Our study employed




several data sources: quantitative and qualitativedata from semi-structured interviews with exec-utives from both tie partners; archival data (pressreleases, annual reports, corporate documents,etc.); direct observations, including visits to theR&D units, plants, and headquarters of nearlyall core firms and every peripheral firm; ande-mails, phone calls, and follow-up interviews. Inparticular, we conducted 81 face-to-face on-siteinterviews (totaling nearly 170 hours) over a28-month period in 2007, 2008, and 2010 with 52different executives from both core and peripheralfirms. Respondents were selected according toseveral criteria (Yin, 1994): long tenure in thecompany; direct involvement in knowledge-relateddecisions; and functional and hierarchical variety.This use of multiple internal sources helpedmitigate the potential biases of any individualrespondent (Kumar, Stern, and Anderson, 1993).3

We began our interviews at the core firms.Each consisted of three main parts: backgroundinformation on the firm and its business strategy;description of key events in the formation andevolution of the firm’s ties (if any) with our samplesuppliers; and direct questions about knowledgeexchange/creation, including details of develop-ment projects for new scooter/motorcycle com-ponents core firms had with each listed supplier.The first interview round (2007 to 2008) focusedon projects from 2004 to 2007, and the secondround (2010) focused on 2008 to 2009 projects. Wequeried the types of knowledge exchanged witheach supplier and the main direction of knowl-edge flows, which might be simple—mostly out-bound flows of information and know-how towardsuppliers or return flows to core firms—or moreadvanced—bidirectional and intertwined interac-tions that might contribute to the cogeneration ofknowledge. Lastly, we asked core firms about thepractices they had implemented to support suchknowledge flows and any that facilitated flowsbetween their suppliers. We used interview tech-niques such as ‘event tracking’ to gain accurateinformation (Eisenhardt, 1989) and repeated theprocess later with peripheral firms. We triangulatedour interview data with observation and archivaldata to improve its accuracy and completeness,combining contemporary and retrospective data

3 The composition of our sample firms, their product ranges, andthe position of each company’s interviewees is available onlineSee Appendix S1.

to improve both external and internal validity(Leonard-Barton, 1990).

We selected key peripheral firm informants onthe same criteria as for core firms and followedthe same basic interview structure so that theresponses complemented our core firm data. Weasked supplier executives to report about the spe-cific projects identified by the core firms—aboutthe levels of relevant information and know-howthey had received from their buyers, the knowl-edge they had returned to them embedded incomponents or services, and about bidirectionalflows and joint knowledge creation. They alsoreported on knowledge flows between them andother peripheral firms promoted by a commoncore firm. In all, we collected data on 892 projectsenacted via 184 dyads connecting 17 core firmsand 13 peripheral firms: more specifically, on776 projects carried out via 163 buyer–supplierties and 116 stemming from 21 supplier-supplierrelationships. All these collaborations (58 ofwhich involved racing) were based on writtencontracts and (as our interview analysis revealed)could be categorized into one of three broad func-tional areas: R&D, operations, and other up- anddownstream activities, which we identified as theindustry’s focal organizational areas for knowledgeexchange and creation. Figure 1 highlights thewhole pattern of ties between core and peripheralfirms (solid lines), as well as between peripheralfirms (dotted lines). Table 1 highlights the mainactivities carried out by the core firms and thecomponents/groups supplied by peripheral firms.

Our data analyses were structured accordingto established procedures for theory buildingfrom inductive research, working recursivelybetween multiple cases and theory (Eisenhardt,1989; Yin, 1994). We included all the interviewsand archival and observational data in a reportof approximately 550 pages (including quotes),beginning with in-depth analyses, synthesizingthe data for each project and dyad into individualcase histories, and noting knowledge flows, theirdominant directions, and the practices underlyingthem. We then moved to cross-case analyses,which produced consistent patterns and commonthemes (Eisenhardt and Graebner, 2007): initiallywe compared varied pairs of cases (e.g., specificknowledge transfer occurrences seen from thecore and peripheral firms’ perspectives) and addedother interfirm knowledge-enhancing practicesas patterns emerge to identify more robust




P13P2

P1

nb=7ns=3

nb=14ns=4

P12 P3

P11P4

P5

P10

P9 P6

P8 P7

= tie between core firms and peripheral firms= tie between peripheral firms

nb = number of ties with core firmsns = number of ties with other peripheral firms

nb=9ns=3

nb=17ns=7

nb=11ns=5

nb=13ns=4

nb=16ns=4

nb=12ns=3

nb=10ns=5

nb=9ns=0

nb=16ns=0

nb=13ns=0

nb=16ns=4

YamahaAprilia

Betamotor

CagivaSuzuki

BMW

MV Agusta

Piaggio

Moto Guzzi

Mbk

Malaguti KymcoKTM

H-D

Honda

Ducati

Husqvarna

Figure 1. Ties between Italian top two-wheeler buyers (or ‘core firms’) and key component suppliers(or ‘peripheral firms’ - P). N = 184.

causal and temporal relationships (Eisenhardt,1989).

We structured our analysis of knowledge-enhancing practices using a ‘temporal bracketing’strategy (Langley, 1999), organizing the data intosuccessive ‘phases,’ defined so that there is con-tinuity in the activities within each phase but dis-continuities at their frontiers (Langley and Truax,1994). As Langley (1999: 703) notes, ‘many tem-poral processes can be decomposed in this way,at least partly, without presuming any progressivedevelopmental logic.’ Our data analysis allowed usto develop a four-phase conceptual model of howcore firms deliberately shaped knowledge prac-tices to stimulate interfirm knowledge exchangeand creation, at both the dyad and network levels.Our firm informants helped us identify and validatethe four phases and the knowledge flows involved.

THE SHAPING OF KNOWLEDGEEXCHANGE AND CREATION IN DYADSAND NETWORK: A FOUR-PHASEMODEL

The interfirm exchange and co-creation of knowl-edge in the Italian motorcycle industry network weinvestigated can be portrayed in four phases. InPhase 1, knowledge flows are mainly from core toperipheral firm, the former channeling informationand know-how to a number of selected suppliers.In Phase 2, knowledge flows reverse this direc-tion, moving mainly to core firms from peripherals,who return some of the knowledge they origi-nally received in more refined forms and transferssome of their own proprietary knowledge to thecore firms. Once knowledge transfer channels havebeen established, Phase 3 sees bidirectional flows,




Table 1. Core and peripheral firms (activities andcomponents/groups supplied)

Core firms Activities

Aprilia Scooter and motorcycle design andproduction

Betamotor Motorcycle design and productionBMW Motorcycle design and productionCagiva Motorcycle design and productionDucati Motorcycle design and productionHarley-Davidson Motorcycle design and productionHonda Motor Scooter and motorcycle design and

productionHusqvarna Motorcycle design and productionKTM Motorcycle design and productionKymco Scooter design and productionMalaguti Scooter design and productionMBK Scooter design and productionMoto Guzzi Motorcycle design and productionMV Agusta Scooter design and productionPiaggio Motorcycle design and productionSuzuki Scooter and motorcycle design and

productionYamaha Scooter and motorcycle design and

production

Peripheral firms Specialized components/groups

P1 Electronic ignition and batteryrecharging system

P2 Electronic carburetors/oil pumps/throttle bodies

P3 BrakesP4 WheelsP5 Steel and light alloy precision

frameworksP6 Forks - shock absorbersP7 Front suspensionsP8 Shock absorbers - fuel pumps and valvesP9 Plastic - rubberP10 Turn indicators - lightsP11 Tail lights - indicators - reflectorsP12 Light alloy cast components and

integral wheelsP13 Design and development

with core and peripheral firms exchanging knowl-edge simultaneously, making it difficult to identifya predominant direction. In Phase 4, knowledgeflows between peripheral firms that are linked tothe same core firm, and learning extends fromdyads to the network.

These phases do not represent ‘stages’ in thesense of being a predictable and sequential processand do not imply a progressive life cycle logic asfound in many normative change theories. Specifi-cally, they allow us to constitute comparative units

of analysis to explore theoretical ideas (Langley,1999)—and are especially useful in illustratinghow the actions in one period affect those under-taken in the next. Figure 2 illustrates the temporalstructuring of the different phases, showing how anexample core firm (Honda Italia) maintains a port-folio of ties with different peripheral firms. Eachcore firm will have such a portfolio of ongoingprojects with each supplier, which may be at differ-ent phases and may relate to R&D, operations, orupstream/downstream activities alone, or to themall. This example case shows Honda’s portfolio ofeight projects (related to the many versions of itsscooters and bikes) in which it collaborates withP12 (a steel alloy body supplier) and their differentlearning phases. Thus, Project A (the heavyweightCBR1000RR bike entirely designed and manufac-tured in Italy) entered Phase 1 in Q1, 2003 andPhase 2 in Q2, 2004, and has been in Phase 3since Q2, 2006. Project B (developing and manu-facturing frames for top-selling scooters, e.g., theSH300) spent more than two years in Phase 1, hadbeen through Phase 2 (with inbound flows overlap-ping the Phase 1 outbound flows by a considerablemargin), and entered Phase 3 in Q1, 2009. At thetime of the survey, it was expected to enter Phase4 in Q2, 2012, as the peripheral firm P12 startscollaborations with two other suppliers to developa frame for next-generation scooters. Project C(developing steel alloy frame for scooters like theForesight 250) has been in Phase 1 since Q1,2003: Honda Japan had already developed defini-tive engineering details, and suppliers were invitedto exploit Honda’s innovation to add value forboth partners. But Phase 4-type information andknow-how flows have been taking place betweenthe frame maker and partners supplying brakingsystems and wheels (three highly interdependentcomponents) since Q2, 2008. At the time of thesurvey, Project C was expected to enter Phase 2in Q3, 2012, with inbound knowledge flows fromP12 constituting the platform for Honda engineers’future design activities.

Phase 1—Knowledge flows from core firms toperipheral firms

Core firms are channeling knowledge (informationand know-how) to peripheral firms in differentforms and at different codification levels, leadingto spiraling interactions between tacit and explicitknowledge (Nonaka, 1994). Table 2 reports some




Q1-2003 PHASE1Q4-2004

Q2-2006Overlapping ofknowledge flows

CBR1000RR

Q2-2004PHASE

2

PHASE3

Q4-2006

Overlapping ofknowledge flows

Pro

ject

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pher

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irm

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Overlapping ofknowledge flows PHASE

4

PHASE1

Q1-2003

Q2-2008

PHASE2

Pro

ject

C

Foresight250

PHASE4


ExpectedQ3-2012

Figure 2. Example of knowledge flows between core firm (Honda Italia) and peripheral firm (P12-steel alloy framesfor bikes and scooters).

practices that shape outbound flows and the mainbenefits (peripheral) recipients perceive from thoseflows. With respect to R&D (Table 2, Phase 1), in301 projects, we found core firms sharing infor-mation about development cycles with periph-eral firms to ease prototype development andgain time/cost efficiencies. Core firms transferredinformation and techniques on product conceptsand assembly designs to suppliers in nearly one-third of the projects we studied, and they also(to a lesser extent) shared detailed blueprintsand offered specific training about product con-cepts and design-for-assembly logics and tech-niques. Other core firm practices we observedincluded: preparing manuals outlining practicesand rules for R&D interactions, supporting pur-chasing costs of R&D tools, and licensing pro-prietary technologies to peripheral firms. Trans-ferring tacit knowledge effectively requires exten-sive personal contact and trust and the supplier

developing codification processes to transform itinto explicit knowledge. At the operations level(Table 3, Phase 1), practices supporting outboundknowledge flows included: sharing detailed pro-duction schedules with suppliers, checkup visits totheir company and plant, organizing and deliveringtraining about production methods and workingpractices (such as just-in-time and lean productionprinciples) to align and synchronize manufacturingand assembly processes. Core firms also arrangedtraining on statistical quality control methods andquality performance indicators to assist suppli-ers with quality assurance and quality certifica-tion, as well as on ICT and enterprise resourceplanning (ERP) platforms, inventory managementtechniques, and plant management principles. Anumber of practices covered other upstream anddownstream activities (Table 4, Phase 1). Forinstance, core firms revealed proprietary informa-tion about market forecasts and trends to their




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es12

315

.957

.7C

ost

redu

ctio

n-

time

savi

ngs

Sim

ulat

ion-

driv

ende

sign

/Off

-the

-she

lfm

echa

nica

lsi

mul

atio

nso

ftw

are

112

14.4

52.6

Red

uctio

nof

engi

neer

ing

cost

s-

mor

eef

fect

ive

desi

gnan

alys

esD

evel

opm

ent

ofun

ique

tech

nica

lso

lutio

nsfo

rra

cing

activ

ities

9111

.742

.7E

nabl

ing

the

tech

nolo

gy’s

pote

ntia

l-

lear

ning

byra

cing

effe

cts




Tabl

e2.

Con

tinue

d

%on

%on

Phas

e3

Prac

tices

aim

edat

shap

ing

bidi

rect

iona

lkn

owle

dge

Nto

tal

proj

.in

Mai

nbe

nefit

s(a

spe

rcei

ved

bypa

rtie

sin

ady

ad)*

flow

s(b

etw

een

core

and

peri

pher

alfir

ms)

proj

ects

Phas

e3

(N=

776)

(N=

201)

Stru

ctur

edm

eetin

gson

aw

eekl

yba

sis

143

18.4

71.1

Iden

tifica

tion

ofho

wto

fixpr

oble

ms

-fa

stre

spon

seIm

plem

enta

tion

ofco

mpo

nent

san

dm

odul

esco

desi

gnan

dco

-eng

inee

ring

activ

ities

132

17.0

65.7

Cos

tre

duct

ion

-tim

esa

ving

s

Gue

sten

gine

ers

atth

ecu

stom

er’s

and

the

supp

lier’

spl

ant

116

14.9

57.7

Shar

ing

ofca

pab.

inco

ncur

rent

deve

lop.

-le

arni

ngfr

omot

hers

Join

tte

sts

ona

regu

lar

basi

s11

514

.857

.2Q

ualit

yim

prov

emen

t-

impr

oved

relia

bilit

yJo

int

trai

ning

onva

lue

engi

neer

ing/

valu

ean

alys

isac

tiviti

es98

12.6

48.8

Unl

eash

edte

chno

logy

’spo

tent

ial

-tim

esa

ving

sR

egul

arco

nfro

ntat

ion

onho

wto

sync

hron

ize

and

upda

teC

AD

mod

els

9412

.146

.8U

pdat

ing

ofto

ols

-tim

esa

ving

s

Buy

er–

supp

lier

tria

lce

nter

sfo

rra

cing

expe

rien

ces

658.

432

.3Sh

apin

gof

know

ledg

ead

vanc

emen

ts-

fast

resp

onse

%on

Phas

e4

Prac

tices

aim

edat

shap

ing

know

ledg

eN

tota

lPo

tent

ial

bene

fits

(exp

ecte

dby

core

firm

s)*

flow

sbe

twee

npe

riph

eral

firm

spr

ojec

ts(N

=11

6)

Tra

inin

gpr

ovid

edby

core

firm

sto

impr

ove

first

-tie

rsu

pplie

rs’

team

-wor

king

skill

s69

59.5

-D

evel

opm

ent

ofre

latio

nal

capa

bilit

ies

-hi

gher

effe

ctiv

enes

sT

rans

fer

ofm

etho

ds/te

chni

ques

tost

imul

ate

code

sign

activ

ities

amon

gfir

sttie

rs58

50.0

-Sh

arin

gof

prac

tices

-hi

gher

effic

ienc

yan

def

fect

iven

ess

Reg

ular

sem

inar

san

dw

orks

hops

onR

&D

’sor

gani

zing

prin

cipl

esat

the

netw

ork

leve

l36

31.0

-E

ffec

tive

know

.ge

nera

tion,

tran

sfer

,an

dac

cess

-tim

esa

ving

sA

ssis

tanc

ein

the

iden

tifica

tion

ofse

cond

tiers

with

com

plem

enta

ryR

&D

capa

bilit

ies

2925

.0-

Acc

ess

toun

ique

R&

Dca

pabi

litie

s-

netw

ork

effe

ctiv

enes

s

Not

e:D

ots

refe

rto

core

firm

s/bu

yers

(cir

cle)

and

peri

pher

alfir

ms/

supp

liers

(tri

angl

es).

Arr

ows

conn

ectin

gdo

tshi

ghlig

htth

em

ain

dire

ctio

nalit

yof

know

ledg

eflo

ws.

*O

nly

the

two

mos

thi

ghly

cite

dbe

nefit

sfr

omfie

ldin

terv

iew

sar

ere

port

ed.




Tabl

e3.

The

know

ledg

eflo

ws-

enha

ncin

gpr

actic

esre

late

dto

oper

atio

ns.

N=

776

for

Phas

es1,

2,an

d3;

N=

116

for

Phas

e4.

%on

%on

Phas

e1

Prac

tices

aim

edat

shap

ing

know

ledg

eN

tota

lpr

oj.

inM

ain

bene

fits

(as

perc

eive

dby

peri

pher

alfir

ms)

*flo

ws

from

core

firm

sto

peri

pher

alfir

ms

proj

ects

Phas

e1

(N=

776)

(N=

362)

Shar

ing

ofde

taile

dpr

oduc

tion

sche

dulin

gpr

ogra

ms

324

41.8

89.5

Tim

esa

ving

s-

impr

oved

plan

ning

capa

bilit

ies

Com

pany

and

plan

tvi

sits

for

chec

kup

309

39.8

85.4

Cos

tef

ficie

ncie

s-

fast

reco

very

Tra

inin

gon

prod

uctio

nm

etho

dsan

dw

orki

ngpr

actic

es(e

.g.,

just

-in-

time

and

lean

prod

uctio

n)25

432

.770

.2C

ost

and

time

savi

ngs

-in

crea

sed

prod

uctiv

ity

Con

sulta

ncy

rela

ted

toqu

ality

assu

ranc

e19

625

.354

.1H

igh

relia

bilit

y-

cost

savi

ngs

Tra

inin

gre

late

dto

ICT

and

ER

Ppl

atfo

rms

168

21.6

46.4

Tim

eef

ficie

ncie

s-

impr

oved

com

mun

icat

ion

Supp

ort

prov

ided

for

qual

ityce

rtifi

catio

n15

620

.143

.1H

igh

relia

bilit

y-

impa

cton

supp

liers

’re

puta

tion

Tech

nica

lsu

ppor

tin

buyi

ngeq

uipm

ent/m

achi

nery

132

17.0

36.5

Cos

tsa

ving

s-

high

relia

bilit

yat

the

proc

ess

leve

lT

rain

ing

onin

vent

ory

man

agem

ent

tech

niqu

esan

dpr

inci

ples

ofpl

ant

man

agem

ent

127

16.4

35.1

Cos

tef

ficie

ncie

s-

was

tere

duct

ions

%on

%on

Phas

e2

Prac

tices

aim

edat

shap

ing

know

ledg

eflo

ws

from

Nto

tal

proj

.in

Mai

nbe

nefit

s(a

spe

rcei

ved

byco

refir

ms)

*pe

riph

eral

firm

sto

core

firm

spr

ojec

tsPh

ase

2(N

=77

6)(N

=21

3)

Inve

stm

ent

onhi

ghly

spec

ializ

edfin

ishi

ngpr

oces

ses

and

trea

tmen

t16

421

.177

.0H

igh

qual

ity-

cost

savi

ngs

Del

iver

ing

ofup

date

dpr

oduc

tion

sche

dulin

gpr

ogra

ms

147

18.9

69.0

Qui

ckra

mp-

upto

requ

ired

outp

utle

vel

-hi

ghfle

xibi

lity

Inve

stm

ent

onth

ede

velo

pmen

tof

prod

uctio

nqu

ality

capa

bilit

ies

121

15.6

56.8

Aug

men

ted

prod

uctiv

ityan

dqu

ality

-co

stsa

ving

sPr

even

tive

mai

nten

ance

ofth

em

achi

nes

118

15.2

55.4

Cos

tan

dtim

esa

ving

s-

high

qual

ityD

edic

ated

tool

man

ufac

turi

ng/C

NC

mac

hine

s/C

AD

-CA

Mst

atio

ns/fl

exib

lem

fg.

stat

ions

109

14.0

51.2

Flex

ibili

tyan

dre

liabi

lity

-tim

ean

dco

stef

ficie

ncy

Hom

olog

atio

ntr

ials

rela

tive

topr

oduc

tssu

pplie

dby

the

cust

omer

9712

.545

.5H

igh

qual

ity-

relia

bilit

y

Stat

ic/d

ynam

icpr

oduc

tlia

bilit

yan

dm

ater

ial

com

posi

tion

test

sin

the

man

yst

ages

ofpr

oduc

tion

9612

.445

.1H

igh

conf

orm

ityan

dqu

ality

-in

crea

sed

prod

uct

dura

bilit

yIn

vest

men

tin

robo

tized

wor

kun

its/la

ser

cutti

ngst

atio

ns/d

ie-c

astin

gfo

undr

y83

10.7

39.0

Tim

eef

ficie

ncy

-qu

ality

impr

ovem

ents




Tabl

e3.

Con

tinue

d

%on

%on

Phas

e3

Prac

tices

aim

edat

shap

ing

bidi

rect

iona

lkn

owle

dge

Nto

tal

proj

.in

Mai

nbe

nefit

s(a

spe

rcei

ved

bybo

thpa

rtie

sin

ady

ad)*

flow

s(b

etw

een

core

and

peri

pher

alfir

ms)

proj

ects

Phas

e3

(N=

776)

(N=

201)

Mon

thly

mee

tings

for

aco

nsta

ntas

sess

men

tof

the

pull

logi

c(i

nre

spon

seto

dem

and)

155

20.0

77.1

Alig

nmen

t-

low

erco

ordi

natio

nco

sts

Join

tas

sess

men

tof

prin

cipl

esan

dte

chni

ques

ofle

anm

anuf

actu

ring

and

valu

est

ream

map

ping

143

18.4

71.1

Hig

hef

ficie

ncy

ofco

stan

dtim

e-

disc

over

yof

oppo

rtun

ities

Join

top

timiz

atio

nof

war

ehou

sest

ock

volu

mes

,jo

int

orde

rsfo

rpu

rcha

sing

mat

eria

ls12

816

.563

.7C

ost

savi

ngs

-re

duct

ion

ofop

port

unity

cost

s

Join

tin

vest

men

tfo

rth

eim

plem

enta

tion

ofin

tegr

ated

ente

rpri

sere

sour

cepl

anni

ng(E

RP)

122

15.7

60.7

Sche

dulin

gan

dm

onito

ring

ofpr

oduc

tion

-hi

ghef

ficie

ncy

Act

ivat

ion

ofqu

ality

circ

les

tore

duce

defe

cts,

scra

ps,

and

was

te10

113

.050

.2C

ost

savi

ngs

-de

velo

pmen

tof

reco

very

capa

bilit

ies

Join

tin

itiat

ives

for

fast

erde

liver

yof

spar

epa

rts,

impr

ovin

gth

equ

ality

ofre

pair

ing

serv

ices

9011

.644

.8T

ime

effic

ienc

y-

impa

cton

repu

tatio

n

Con

stan

tco

nfro

ntat

ion

onst

atis

tical

qual

ityco

ntro

lm

etho

dsan

dqu

ality

perf

orm

ance

indi

cato

rs85

11.0

42.3

Cos

tsa

ving

s-

high

conf

orm

ity

Initi

ativ

esto

supp

ort

the

full

inte

grat

ion

oflo

gist

icpr

oces

ses

ofbo

thpa

rtie

s85

11.0

42.3

Cos

tan

dtim

eef

ficie

ncy

-fa

stre

spon

ses

%on

Phas

e4

Prac

tices

aim

edat

shap

ing

know

ledg

eflo

ws

betw

een

Nto

tal

Pote

ntia

lbe

nefit

s(e

xpec

ted

byco

refir

ms)

*pe

riph

eral

firm

spr

ojec

ts(N

=11

6)

Initi

ativ

es(e

.g.,

wor

ksho

psan

dco

achi

ng)

toex

tend

effe

ctiv

eSC

Man

dm

fg.

prac

tices

toth

ew

hole

netw

ork

4942

.2-

Net

wor

kef

ficie

ncy

and

effe

ctiv

enes

s-

high

prod

uctiv

ity

Tra

nsfe

rof

met

hods

/tech

niqu

esto

stim

ulat

eco

ordi

natio

nof

mfg

.ac

tiviti

esam

ong

first

tiers

3933

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Impr

ovem

ents

ofm

anuf

actu

ring

capa

bilit

ies

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twor

kef

ficie

ncy

Tra

inin

gon

the

diff

usio

nof

plan

ning

tool

s(e

.g.,

soft

war

esy

stem

s)fr

omin

itial

orde

rto

deliv

ery

2723

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Impr

oved

plan

ning

capa

city

-be

tter

plan

tut

iliza

tion

Ass

ista

nce

bybu

yer

firm

sin

the

initi

alst

ages

ofth

eir

rela

tions

hip

with

othe

rsu

pplie

rs22

19.0

-In

crea

sed

netw

orki

ngca

paci

ty-

netw

ork

resp

onsi

vene

ss

Not

e:D

ots

refe

rto

core

firm

s/bu

yers

(cir

cle)

and

peri

pher

alfir

ms/

supp

liers

(tri

angl

es).

Arr

ows

conn

ectin

gdo

tshi

ghlig

htth

em

ain

dire

ctio

nalit

yof

know

ledg

eflo

ws.

*O

nly

the

two

mos

thi

ghly

cite

dbe

nefit

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omfie

ldin

terv

iew

sar

ere

port

ed.




Tabl

e4.

The

know

ledg

eflo

ws-

enha

ncin

gpr

actic

esre

late

dto

upst

ream

and

dow

nstr

eam

activ

ities

.N

=77

6fo

rPh

ases

1,2,

and

3;N

=11

6fo

rPh

ase

4

%on

%on

Phas

e1

Prac

tices

aim

edat

shap

ing

know

ledg

eflo

ws

Nto

tal

proj

.in

Mai

nbe

nefit

s(a

spe

rcei

ved

bype

riph

eral

firm

s)fr

omco

refir

ms

tope

riph

eral

firm

spr

ojec

tsPh

ase

1(N

=77

6)(N

=36

2)

Man

ager

ial

supp

ort

prov

ided

tosu

pplie

rsfo

rm

arke

tfo

reca

sts

and

mar

ket

tren

ds15

920

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prov

edpl

anni

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paci

ties

-op

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iona

lef

fect

iven

ess

Tra

inin

gon

the

brea

kdow

nof

cost

s(s

ugge

stin

gw

ays

that

supp

liers

can

seek

tore

duce

cost

s)13

317

.136

.7C

ost

savi

ngs

-op

port

uniti

esfo

rne

win

vest

men

tsT

rain

ing

supp

liers

onm

anag

emen

t/str

ateg

yto

ols

insu

ppor

tof

the

dyad

120

15.5

33.1

Acc

urat

ese

izin

gof

mar

kets

-as

sess

men

tof

reso

urce

sFi

nanc

ial

supp

ort

toth

esu

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r’s

proc

urem

ent

ofto

ols

and

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pmen

t11

815

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ost

effic

ienc

ies

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port

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vest

men

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omm

unic

atio

nin

itiat

ive

infa

vor

tosu

pplie

rsto

enla

rge

thei

rsp

are

mar

ket

109

14.0

30.1

Bet

ter

asse

ssm

ent

ofm

arke

ts-

addi

tiona

lre

sour

ces

Priv

ilege

dac

cess

tore

sour

ces

(e.g

.,ta

lent

edem

ploy

ees)

via

bene

fit-r

ich

ties

102

13.1

28.2

Impr

oved

capa

bilit

yba

se-

acce

ssto

dist

inct

ive

capa

b.Su

ppor

tin

the

prep

arat

ion

ofan

expo

rtpl

ans

(for

thos

esu

pplie

rsw

how

ant

toex

port

over

seas

)98

12.6

27.1

Stra

tegi

cef

fect

iven

ess

-im

prov

edm

anag

eria

lfo

resi

ght

Tra

inin

gon

mar

ketin

gan

dre

latio

nal

capa

bilit

ies

toen

larg

eth

esu

pplie

r’s

cust

omer

base

8511

.023

.5R

educ

edde

pend

ence

byco

refir

ms

-le

arni

ngfr

omot

hers

%on

%on

Phas

e2

Prac

tices

aim

edat

shap

ing

know

ledg

eflo

ws

from

Nto

tal

proj

.in

Mai

nbe

nefit

s(a

spe

rcei

ved

byco

refir

ms)

*pe

riph

eral

firm

sto

core

firm

spr

ojec

tsPh

ase

2(N

=77

6)(N

=21

3)

Det

aile

dbr

eakd

own

ofpr

ices

and

tran

spar

ency

ofth

eva

riou

sof

feri

ngs

131

16.9

61.5

Impr

oved

capa

city

inva

lue

anal

ysis

-tr

ust

cons

olid

atio

nSu

ppor

tin

the

prep

arat

ion

ofte

chni

cal

spec

ifica

tions

and

docu

men

tsfo

rfin

alus

ers

129

16.6

60.6

Incr

ease

dre

liabi

lity

-im

pact

onco

refir

m’s

imag

eSh

arin

gof

info

rmat

ion

rela

tives

toco

mpl

emen

tary

sour

ces

ofco

mpo

nent

san

dte

chno

logy

105

13.5

49.3

Effi

cien

cyin

crea

ses

-as

sesm

ent

ofsu

pply

mar

kets

Seiz

ing

oppo

rtun

ities

tocu

stom

ers

(e.g

.,lo

wer

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peripheral firms, thus improving their planningcapacities and operational effectiveness, and alsogave training on cost breakdowns and on manage-ment and strategy tools to help suppliers improvetheir efficiency, even providing financial supportto help them buy tools and equipment. Core firmsalso established communication initiatives aimedat helping suppliers find new markets to increasetheir abilities to identify and enter new markets(often related to racing events) to help improvetheir financial stability: shaping joint visions andseizing market opportunities allowed these part-ners to develop joint dynamic capabilities.

Phase 2—Knowledge flows from peripheralfirms to core firms

Peripheral firms relying simply on markets toaccess specialized assets and acquire technologyare likely to see their capabilities deteriorate(Teece, 1986) and to remain stuck in passive,dependent postures (Rosenberg, 1982). Periph-erals that purposefully supply core firms withreturn knowledge turn their relationships intopartnerships, making it harder for core firms toswitch to other suppliers. In terms of inboundR&D knowledge (Table 2, Phase 2), our studydepicts peripheral firms as providing special-ized knowledge and technical information: bysharing proprietary technology and know-how,including alerting their partners about advances instate-of-the-art technologies and proposing designalternatives to allow them to fulfill their customers’unique demands more completely. Other practicesincluded: organizing training sessions to developdesign-sharing capabilities, developing rapid pro-totyping techniques, and using simulation-drivendesign to give core partners with significant engi-neering cost benefits and more effective designanalysis. At the operations level (Table 3, Phase2), suppliers shaped their knowledge flows to corefirms by investing in highly specialized finishingprocesses and treatments, a practice we observedin nearly one-fifth of the projects and which bene-fitted a wide range of activities including productand process feasibility, surfacing, structural anal-ysis, and prototype engineering. Peripheral firmsalso transferred knowledge embedded in updatedproduction scheduling programs, made consider-able investments in developing quality productioncapabilities, and offered preventive maintenanceof core firms’ production machinery—flows that

were enabled by their possession of dedicatedmanufacturing tools, CNC machines, CAD-CAMworking stations, and flexible manufacturing sys-tems. Such inbound flows also extended to otherupstream and downstream activities (Table 4,Phase 2). Peripheral firms provided core firmswith detailed price breakdowns and supportedthe preparation of technical specifications anddocuments for final users, building core firms’reliability and reputation. Their practices extendedto sharing information about complementarysources of components and technology, whichmay seem to be counterintuitive behavior, butwas aimed at reinforcing network potential andeffectiveness and supporting core firms’ reactionsto market opportunities, e.g., by speedy responseto changing vehicle emissions regulations.

Phase 3—Simultaneous, bidirectionalknowledge flows between core and peripheralfirms

In Phase 3, firms reciprocally exchange knowledgeand cocreate ‘collaborative knowledge’ (Simonin,1997), a prerequisite for virtuous relational learn-ing. In terms of R&D at this phase (Table 2,Phase 3), design engineers on both sides of thedyads recognized the value of working together onprojects to share expertise, foster problem solving,and improve the efficiency and quality of prod-uct development: the interaction inherent in theseknowledge-enhancing practices nourished virtu-ous cycles of reciprocal learning. In the projectswe studied, we observed structured meetings toexchange information and know-how to fix prob-lems rapidly, as well as recurrent codesign andco-engineering activities at component and mod-ule levels, joint testing, and the systematic paral-lel updating of partners’ CAD models to reducedesign times and costs, allowing both parties torapidly assess concept, development, and total leadtimes via interfirm ties. Hosting partners’ engi-neering teams supported simultaneous knowledgeexchanges, and our data also revealed a signif-icant number of relational platforms (or ‘cus-tomer trial centers,’ as one core company labeledthem) where teams of engineers from core andperipheral firms interacted to solve specific prob-lems. Joint participation in racing also enhancedboth parties’ knowledge and supported their R&Dactivities, as many innovations developed for rac-ing bikes were later featured in retail production




models. At the operations level (Table 3, Phase 3),monthly meetings were arranged in one-fifth of theprojects we studied, allowing them to constantlyassess the ‘pull’ logic, increasing core/peripheryalignment and time efficiency and lowering coordi-nation costs. We also noted how parties interactedto jointly assess lean manufacturing principles andtechniques and employed value stream mappingto raise their cost and time efficiency and theireffectiveness in attaining manufacturing goals.Other practices supporting bidirectional knowl-edge flows included: synchronizing purchasingorders and optimizing warehouse stock volumes,jointly implementing integrated enterprise resourceplanning (ERP), and using ‘quality circle’ initia-tives to cut defects and waste. In the ‘other activ-ities’ sphere (Table 4, Phase 3), we saw evidenceof joint assessment of end user profiles to improveresponse effectiveness and participation in fund-raising projects to increase resource availability.Coevolutionary alignment took the form of theperiodic updating of communication and informa-tion flows needed to respond quickly to environ-mental jolts or to competitors’ moves.

Phase 4—Knowledge flows between peripheralfirms

In the fourth model phase, we observed knowl-edge flowing between different peripheral firms,whose connection had been promoted and sup-ported by a common core firm. Largely unexploredby strategic literature, this guided evolution oflearning processes from dyads to networks yieldssignificant insight into how core firms connectalters to gain informational (Burt, 2000) and otherdifficult-to-replicate network-specific advantages.Redeploying knowledge dispersed across collabo-rative networks requires that core firms possess (ordevelop) learning and teaching capabilities acrossorganizational boundaries, as this targeted coordi-nation of knowledge mobility is central to a net-work’s ability to co-create value and innovation(Zhao et al., 2005). As for R&D (Table 2, Phase4), we observed knowledge exchange and cre-ation between peripheral firms being accomplishedby: training suppliers on improving team workingskills for greater effectiveness across dyads andnetworks, transferring methods and techniques tostimulate co-design activities among first-tier sup-pliers, and organizing workshops and seminars onR&D organizing principles at the network level.

Core firms supported such intersupplier knowledgeflows by identifying second-tier suppliers withcomplementary R&D capabilities. At the opera-tions level (Table 3, Phase 4), core firms extendedeffective supply chain and manufacturing activitiesacross their networks by organizing workshops andcoaching sessions, transferring methods and tech-niques to stimulate coordination among first-tiersuppliers, and providing training to diffuse plan-ning tools covering a range of activities from initialorders to delivery. To a lesser extent, we also sawcore firms play active roles in helping peripheralsmanage the initial stages of their interrelationships,such as helping develop joint manufacturing plans.In other areas (Table 4, Phase 4), core firms orga-nized sessions between peripheral firms on marketforecasts and identified synergies across differ-ent alliances. Core firms’ ability to lead by shar-ing strategy also hinged on them sharing lessonslearned, databases, and potential partner profilesto create further opportunities for their suppli-ers. Core firms ran coaching sessions to reinforcethe network’s goal alignment and helped supplierselaborate ‘network contracts’ to give them uniqueidentities in fund-raising applications.4

DISCUSSION AND CONCLUSION

We present a structured framework that takes amultilevel perspective to address different rela-tional subsets, depicting knowledge flows at dyadand network levels. We found core firms thattook charge of the processes of interfirm learn-ing from firms to dyads, and from dyads to net-works, and of knowledge-enhancing practices allneeded to nurture the transfer, recombination andcreation of specialized knowledge. Such practicesare favoring the affirmation of higher-order orga-nizing principles (Grant, 1996), the idiosyncraticlearning processes (Kotabe et al., 2003), and theinterfirm knowledge-sharing routines (Dyer andSingh, 1998). The network we studied combinedthe advantages of common identity and lan-guage normally associated with hierarchical forms(Kogut and Zander, 1992; Conner and Prahalad,1996) with the learning incentives that typicallyoccur in dyads and networks. Having multiple

4 Key quotes from our interviews across the four phases areavailable online See Appendix S2.




actors involved in defining the rules governingcooperation decisions (Kogut, 2000) led the net-work to develop stronger shared identity and lan-guage, lowering the costs of communication andknowledge exchange, establishing rules and prin-ciples for coordination, and influencing the direc-tion(s) of both searching and learning. The wholenetwork, thus, becomes more expert at captur-ing opportunities, developing new products, andextracting technical and organizational capabili-ties both from current ties and from dormant orunexpected contacts (Kane, 2010). In our setting,the threat of opportunism appears to have beenmore than outweighed by the advantages of: learn-ing from other partners (Lorenzoni and Lipparini,1999), including developing joint ‘interaction rou-tines’ (Nelson and Winter, 1982), making iteasier for firms to modify existing behaviorpatterns as needed and improving transactiveknowledge—about who knows what—to betterenable information transfer (Reagans, Argote, andBrooks, 2005). These incentives help firms per-ceive networks as ‘safe places’ where ideas andknowledge can be exchanged, but with reducedrisk of knowledge spillovers to competitors whoshare the same partners (Lester and Piore, 2004).

Our findings suggest that all else being equal,the most successful learning processes will accruein dyads and networks where both sources andrecipients possess the requisite knowledge trans-fer capacity. A company’s capacity to effectivelydeploy knowledge is only as strong as the weakestpartner in its innovation value chain, so unilat-eral flows of knowledge to third parties do notguarantee the sustainability of any advantage. Wecontribute to explaining why some dyads and net-works succeed in learning faster than their com-petitors (Dyer and Nobeoka, 2000), enabling theirpartner firms to achieve their strategic objectives(Zollo, Reuer, and Singh, 2002). Our study alsocontributes to clarifying the role of firm capabil-ities in alliances and, in particular, the capabil-ities they need when entering alliances. Extantcontributions have carefully examined alliance(Kale, Dyer, and Singh, 2002), relational (Loren-zoni and Lipparini, 1999) or boundary-bridging(Takeishi, 2001) and orchestration capabilities(Dhanaraj and Parkhe, 2006), and we add tothis list the capacity for transferring and creat-ing knowledge (Smith, Collins, and Clark, 2005)which we show is needed by all parties indyads and networks. Future research could refine

and build on this study in several directions:it could empirically determine which networkstructure dimensions are most likely to influ-ence project outcomes, as well as examine therelationship between project phases, knowledge-enhancing activities, and outcomes. Equally inter-esting would be to study whether informal, distant,or infrequent relationships—rather than strongrecurring ties among a small number of buyers andsuppliers—lead to more or less efficient knowl-edge sharing (Hansen et al., 2005). It would alsobe interesting to consider ‘negative ties’ (Labiancaand Brass, 2006), given that many of the corefirms in our sample are competitors. As to this, itcould be interesting to examine how competitiverelationships between core firms might influence,for instance, choices about forming ties with spe-cific peripheral firms that are already tied to rivals(Gimeno, 2004), therefore gaining a deeper under-standing of both the ‘structure of cooperation’(Ahuja, 2000) and the structure of competition.

ACKNOWLEDGEMENTS

We received valuable insights from Will Mitchelland from two anonymous reviewers. We thankJon Morgan of Paraphrase for expert copyeditingand Lois Gast for effective management of thismanuscript. We express our appreciation to allthe firms, respondents, and interviewees whoparticipated in this study. We acknowledge supportfrom the Strategy & Organization Center at theUniversity of Bologna. The authors alone areresponsible for errors and omissions.

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SUPPORTING INFORMATION

Additional supporting information may be foundin the online version of this article:

Appendix S1. List of core and peripheral firms(product mix and company’s interviewees)Appendix S2. Typical quotes illustrating patternsof interaction in the Italian motorcycle industry(excerpts from the interviews)



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