Qian, Lihong, Rajshree Agarwal and Glenn Hoetker, Configuration of Value Chain Activities

20 Pages·2011·0.65 MB·English

Published online ahead of print September 28, 2011
OrganizationScience
ArticlesinAdvance,pp.1–20
issn1047-7039(cid:151)eissn1526-5455 http://dx.doi.org/10.1287/orsc.1110.0682
©2011INFORMS
eg.
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o
ots. Configuration of Value Chain Activities:
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manf The Effect of Pre-Entry Capabilities, Transaction Hazards,
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filns and Industry Evolution on Decisions to Internalize
eo
hsi
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mi
ers.er
bp Lihong Qian
rio
sct SchoolofBusinessAdministration,PortlandStateUniversity,Portland,Oregon97207,
by [email protected]
uc
soli Rajshree Agarwal
op
t DepartmentofManagementandOrganization,RobertH.SmithSchoolofBusiness,UniversityofMaryland,CollegePark,Maryland20742,
s
blethi [email protected]
ailang Glenn Hoetker
vdi DepartmentofManagement,W.P.CareySchoolofBusiness,ArizonaStateUniversity,Tempe,Arizona85287,
aar [email protected]
eg
de
ar
ms We integrate insights from organizational capabilities, organizational economics, and industry evolution to examine
n
so industry entrants’ boundary choices about value chain activities and test hypotheses in 1978–2009 data from a
histi sample of U.S. bioethanol producers. We find support for our predictions that transaction hazards, decomposed as either
ce
hiu enduring or transient over the stages of industry evolution, are positively associated with the choice to internalize value
wq chain activities. Pre-entry experience in an activity increases the likelihood of its internalization and reduces the effect of
n,ny enduring transaction hazards on the internalization choice. Importantly, we also distinguish between firm- and founder-
oa
sid levelpre-entrycapabilities(thatis,thecapabilitiesoffirmsversusthoseoffounders).Diversifyingentrantswithfirm-level
ern integrative capabilities are more likely to internalize value chain activities than start-ups, and this effect persists over the
ve
s industry’slifecycle.Therelationshipbetweenpre-entryexperiencewithanactivityandthelikelihoodofinternalizingitis
e
c e alsostrongerfordiversifyingentrants.Findingsimproveunderstandingoftherelationshipsamongcapabilitydevelopment,
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a a boundarychoice,andindustryevolution.
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Ad Pl Keywords: pre-entryexperience;valuechainactivity;governancechoice;industryevolution
n e. History: PublishedonlineinArticlesinAdvance.
si sit
cle r’s
Arti utho Introduction industry-specific bundles of resources and capabilities
sa Whether entry into an industry is undertaken by a diver- after entry is path dependent (Dierickx and Cool 1989),
thihe sifying entrant or a start-up,1 a critical strategic ques- with the initial design setting the trajectory for long-
togt tion is the configuration of value chain activities, which term capability development (Helfat and Peteraf 2003).
htn include procurement, production, marketing and dis- Decisions made at entry are likely to create differences
gdi
riu tribution, and technology development (Porter 1985). in competitive advantages not just then but also over
pycl
on Firms vary in the extent to which they choose to con- time. Given the strategic importance of initial design of
ci
se, duct these activities within organizational boundaries. valuechainactivities,weexaminethefollowingresearch
oldbsit Given trade-offs between incurring resource commit- question: What factors determine how a firm configures
he ments for internalization and transaction hazards for value chain activities when entering a new industry?
w
S
Mr external sourcing, organizational choices about value Specifically,whatarethemainandmoderatingeffectsof
e
Rh chain activities are of strategic importance to entrants transactionhazardsandpre-entryorganizationalcapabil-
Oot
F that may lack experiential knowledge, either within or ities on the choice between internalization and external
Ny
Ian outside the focal industry context. development of a value chain activity?
ht: n Balancing the trade-offs involved in optimal orga- We draw on the organizational economics, organiza-
g o nizational choices about value chain activities at the tional capabilities, and industry evolution literatures to
ri d
y e time of entry is critical for several reasons. First, to address the above question. In doing so, we build on an
op ost ensure efficient production, a firm needs to design a insightful set of studies that have started to integrate the
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system of activities that accounts for both complemen- three bodies of research. In organizational economics, a
tarities and substitution effects (Milgrom and Roberts dynamic view of transaction costs enables examining a
1995, Porter 1991). Second, the firm’s development of firm’s boundary decisions while incorporating both firm
1
Qian, Agarwal, and Hoetker: Configuration of Value Chain Activities
2 Organization Science, Articles in Advance, pp. 1–20, ©2011 INFORMS
evolution (Argyres and Liebeskind 1999) and industry their founders’ pre-entry experience (Agarwal et al.
evolution (Argyres and Bigelow 2007, 2010; Bigelow 2004, Helfat and Lieberman 2002, Klepper 2002). A
eg.
bor and Argyres 2008; Jacobides and Winter 2005). Simi- dichotomous variable may not capture the rich hetero-
ots. larly, in the organizational capabilities literature, schol- geneity in the types of experience an entrant brings into
nm
yor ars have begun to examine how relational capabilities the new industry. Both diversifying and start-up entrants
manf (Hoetker2005)orproductivecapabilities(Jacobidesand (through founder(s)) may have had experience in one or
e@i Hitt 2005, Leiblein and Miller 2003, Walker and Weber more value chain activities. Diversifying entrants may
filns 1984) explain boundary choices. Empirical evidence also be heterogeneous regarding specific experience in
eo
hsi from these two research streams shows that firm and and capability for an activity. Furthermore, diversify-
Ts
mi industry evolution both matter and that a firm’s capabil- ing entrants may possess integrative firm-level capabil-
ers.er ities also play an important role in its boundary choices. ities that start-ups lack (Chen et al. 2011, Helfat and
ribop No study has, however, to the best of our knowledge, Campo-Rembado 2010). To the best of our knowledge,
sct examined all three sets of factors together. nostudyhassystematicallycompareddifferencesinpre-
by
uc The strategic importance of value chain organiza- entry experience in an activity and in where that experi-
ospoli tion for industry entrants and the potentially fruitful ence resides—whether in an individual founder (i.e., at
bletthis tnhiezoatrieotnicaallicnatpegabrailtiitoineso,faonrgdaniinzdautisotnryaleecvoonluotmioincs,liotregraa-- tlhevee“l”fo).uOnduerrslteuvdeyl”a)dodrreinssfiesrmthrisougtainpe.s (i.e., at the “firm
a
ailng ture motivate the current study. The make-or-buy logic Building on these theoretical perspectives, we derive
vdi from transaction costs theory (Coase 1937; Williamson
ar the main and moderating effects of transaction hazards
ega 1975, 1985) is clearly applicable; substantial transac- and pre-entry experience on a firm’s decisions about
madre tion costs cause firms to internalize an activity, other configuring value chain activities. We hypothesize that
s things being equal. Transaction hazards, however, may
n transactionhazards,bothtransientandenduring,arepos-
hisstio be either transient or enduring. Stigler (1951) argued itivelyrelatedtointernalizationofanactivity,asarepre-
ce that as industries mature, markets become increasingly
hiu entry experiences at the activity and firm levels. Impor-
wq efficient. Thus, as industries evolve, reductions in tech- tantly, we predict that the influences of organizational
n,ny nological and demand uncertainty and in asset speci- capabilities on boundary choices vary over time, given
oa ficity can lower market frictions. Furthermore, to the
sid the transience of some transaction hazards. We further
veren extent that capabilities and resources affect the above predictthatpre-entryactivityexperienceatthefirmlevel
s ceterisparibusassumptionregardingtheeffectoftransac-
e ratherthanfounderlevelwillresultinahigherlikelihood
c e tion costs on internalization, insights from the resource-
Advan Pleas bfealsted19a8p4p)roaancdh t(hBeardnyenyam19ic91c,aPpeanbriloistiees19v5ie9w, W(Herenlefar-t opfosiintitveernlyalimzaotidoenraatensd tthheat rperlea-teionntrsyhiapctbiveittwyeeexnpeerniednucre-
ing transaction hazards and decisions to internalize. We
n e. et al. 2007, Teece et al. 1997) are relevant. Indeed,
si sit acknowledging that firms’ capabilities and transaction tested our hypotheses in the evolving U.S. bioethanol
rticle hor’s coonstthsetseendphtoenionmteretnwaincaenanbdecthoamtpthleempeernstpaeryc,tiLvaensgfolociussainndg ionfdtuhsetmry.from 1978 to 2009 and found support for most
A ut Foss(1999)calledformoreintegrativeefforts.Similarly, Ourstudymakescontributionstoeachtheoreticalper-
sa
hie Williamson(1999,p.1103)rephrasedtheoriginalmake- spective it draws upon. First, it extends the transaction
th costliteraturebeyondthefewstudiesthathaveexamined
ot or-buyquestionas,“HowshouldfirmA—whichhaspre-
tg governance choice in start-ups and small firms (Bigelow
htn existingstrengthsandweaknesses(corecompetenceand
gdi disabilities)—organizeX?”Anunresolvedquestionhere and Argyres 2008) by answering the (rephrased) call
riu
pycl is, what types of firm capabilities may intertwine with by Williamson (1999): Given their particular resources
coin transaction characteristics, and in what way? and capabilities at founding, how should a firm orga-
oldsbsite, incAepltthioonugahndthreesuelvtoinlugtifiornmohfavfiermbeecnapthaebilfioticeisoffrothme ntriizbeutevsalutoe ecfhfaoirntsatcotivjoitiyn Xth?e Soregcaonnidz,attihoinsalstcuadpyabcilointy-
he
w dynamic resource-based framework (Helfat and Peteraf and the transaction cost theories (Argyres 1996, Hoetker
S
Mer 2003), not all firms entering an industry are start-ups. 2005, Jacobides and Winter 2005, Leiblein and Miller
ORoth Helfat and Lieberman (2002), drawing from the indus- 2003, Madhok 2002, Walker and Weber 1984) to reveal
F try evolution literature (Agarwal et al. 2002, Carroll how a firm’s initial bundle of resources and capabilities
Ny
Ian et al. 1996, Klepper and Simons 2000), recommended may be the foundation for capability development and
:
ht n that scholars use entry into a focal industry as a clear governance choice strategies after entry into an indus-
rig do demarcation point to examine how pre-entry experi- try. Third, our study decomposes this initial bundle into
opy oste eonpcmeenmt,ayanadfffiercmt spuebrsfeoqrmueanntcec.hToihciess,recsaepaarcbhil,ithyowdeevveelr-, aecnttiivailtye-ffeacntds.fiTrmhu-sleivteelncraicphaebsiltihtieescotonssthruocwt othfepirred-eifnfterry-
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has largely measured pre-entry experience as a dichoto- experiencedevelopedintheindustryevolutionliterature,
mous, firm-level variable (i.e., diversifying entrants ver- further enabling inquiries into the effect of pre-entry
sus start-ups), even though start-ups may benefit from experience on postentry strategies, complementing the
Qian,Agarwal,andHoetker: ConfigurationofValueChainActivities
OrganizationScience,ArticlesinAdvance,pp.1–20,©2011INFORMS 3
often-studied relationships between pre-entry experience and Lieberman 2002, Klepper 2002), because founders
and firm performance or survival. convey resources (e.g., human capital, networks, status)
eg.
bor It is our hope that, taken together, these contribu- and capabilities (e.g., marketing know-how, technologi-
ots. tions position our study as the first of further inquiries cal know-how). Here, we decompose a firm’s pre-entry
nm
yor about the coevolving relationships among governance experience into activity-level pre-entry experience (for
manf choice, capability development, and industry evolu- both diversifying entrants and start-ups) and firm-level
@i
e tion. Our framework highlights the need for an inte- integrative capabilities (for diversifying entrants only).
filns grative approach to theory development, emphasizing We define a firm’s pre-entry activity-level experience
eo
hsi that organizational economics, organizational capabil- as experience accrued by the firm, or the founder(s)
Ts
mi ity, and industry evolution explanations work in tandem of a start-up, that is related to a particular value chain
ers.er rather than in isolation. Just as industry evolution activity and gained prior to entry in the focal industry.
bp
rio reshapes boundary choices by reshaping governance Pre-entry activity-level experience is thus the posses-
sct costs, so do pre-entry experience and capabilities. Fur- sion of resources and capabilities related to that value
by
suolic thermore, although transaction cost economics focuses chain activity. For example, in our empirical setting, the
op onafirm’sabilityto“manageacrossactivities”(totrans- bioethanol industry, a pipeline company would have rel-
t
blethis afocctuesxeschoannmgeasn),againndgaoprgaartnicizualtairoancatlivcitayp,atbhielyitibeosththseeoermy einvafnetedpsreto-ecnktrpyroecxupreermieenncte. iInn fcuoenltrdaisstt,ribautfiaornmebrutwnhoot
a
ailng toyieldsimilarpredictionsaboutboundarychoices.This is engaged in bioethanol production would likely have
vdi
ar parallel leads to an intriguing speculation that the capa- experience in feedstock procurement but not in mar-
a
deeg bility for managing an activity and that for governing it keting and distribution of bioethanol. We note that the
mar are less distinct theoretically and empirically than they construct of activity-level pre-entry experience places
s
son are commonly considered to be. diversifiers and start-ups on common ground, given both
histi founders’ and firms’ prior capabilities.
ce
hiu Nonetheless, the distinction between diversifying
wq Hypothesis Development entrants and start-ups is germane, because there may be
n,ny Becauseweintegrateseveraltheoreticalperspectives,we systematic differences in how activity-level capabilities
oa
sid begin with an overview of the constructs derived from affect boundary choices if these capabilities reside in
veren each theory and their potential interrelationships. Simi- founders (for start-ups) rather than in firms (for diver-
s
e lar to Porter (1985), we describe a firm as engaging in sifiers). Theoretically, this relates to the presence of
c e
an as various value chain activities that complement its core established firm-level routines or integrative capabili-
dv Ple production activity and in particular differentiate four ties, which Helfat and Raubitschek (2000) defined as
A
activities: raw material procurement, marketing and dis- knowledge of how to integrate activities, capabilities,
n e.
si sit tribution for primary products, marketing and distribu- and products in one or more vertical chains. Helfat and
ticle or’s taicotniviftoyr creoqpuroirdeusctdsi,ffaenrednttecshetnsoloofgyredseovuerlcoepsmaenndt.cEaapcah- Cinatemgpraot-iRveemcabpaadboili(t2ie0s10i)n heingahblliignhgtecdomthmeurnoilceatoiofnsuancdh
r h
A ut bilities and thus can be regarded as a sub-bundle of coordination across stages of value chain activities. By
hisea resources and capabilities. An underlying assumption is virtueofhavingexistedinanotherindustrypriortoentry
th that differences in needed resources and capabilities are into a new one, diversifying entrants possess integrative
ot
tg much larger between than within activities, thus permit- capabilities relatively lacking in start-ups (Chen et al.
htn
gdi ting “activity-level” decomposition of capabilities. 2011).Wedefinefirm-levelorintegrativepre-entrycapa-
pyriclu Among various organizational capabilities, pre-entry bilities as present in diversifying entrants.
coin experience is experience in related industries gained Following Capron and Mitchell (2009), we define the
se, before entry into a focal industry. The term is com- boundary choice for a value chain activity as the deci-
oldbsit prehensive, including both functional and dynamic sion to undertake it either through internal development
he
w capabilities and both general and specialized resources (within firm boundaries) or external development (part-
S
Mr (Helfat and Lieberman 2002). Researchers have often nering with another firm through joint venture, licensing
e
Rh bundled these together, largely assuming that start-ups or strategic alliances, or outsourcing). External sourcing
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F lack pre-entry experience and have fewer resources of a value chain activity exposes a firm to transaction
Ny
Ian and capabilities than diversifying entrants (Helfat and hazards, defined as the costs incurred if an activity is
ht: n Lieberman 2002, Teece 1986). For example, as Teece carried out externally. Furthermore, transaction hazards
g o (1986)pointedout,alackofcomplementaryassets(e.g., can be of two types: enduring and transient.
ri d
y e a marketing capability) puts start-ups at a disadvan-
op ost tage, even though they may have superior technologi- Transaction Costs and Economies of Scale:
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cal capabilities. However, other researchers have noted Organizational Economics Hypotheses
that start-ups can have pre-entry experience in the form When it comes to vertical integration in an industry
of founder knowledge (see Agarwal et al. 2004, Helfat evolution context, Williamson (1975) and Stigler (1951)
Qian,Agarwal,andHoetker: ConfigurationofValueChainActivities
4 OrganizationScience,ArticlesinAdvance,pp.1–20,©2011INFORMS
differ about the relative impact of production costs they mature, resulting in higher transaction costs. Fur-
and transaction costs. Stigler (1951) regarded scale thermore, variations in the transaction hazards related
eg.
bor economies to be an independent source of produc- to value chain activities may persist. For instance, a
ots. tion cost differentials and developed an industry life- bioethanol firm needs to transact to procure feedstock
nm
yor cycle theory of vertical integration. In this theory, as often as production requires, and the transactions are
manf firms in young industries tend to be vertically inte- subject to price fluctuations in the commodity market.
@i
e grated, but as industries grow, specialized suppliers Neitherthefrequencyoftransactionsnorthepriceuncer-
filns can be sustained by a larger scale of industry output. tainty is correlated with industry life stage. We note
eo
hsi Williamson(1975)argued,however,thatproductioncost thatthistypeofenvironmentaluncertaintycontrastswith
Ts
mi differentials derived from scale economies are not suffi- technological uncertainty, which is likely to decrease as
ers.er cient for vertical integration, as buyers can also achieve an industry evolves.
bp
rio economies of scale by producing in-house and selling In all, as an industry evolves, transaction costs result-
sct extras to customers, absent transaction costs. Put differ- ing from transient factors may decrease, but transaction
by
uc ently, it is high transaction cost, rather than low produc- costs resulting from enduring factors remain. Put dif-
soli
op tion cost, that leads to vertical integration (Riordan and ferently, although the influence of transient transaction
t
blethis WiTllhiaemsesotwno19v8i5e)w.s about vertical integration over an cfoorstasg(eiv.ge.n,aascsteivtistpyemciafiycivtyarcyhaansginesd)uostnryboaugneds,arthyecihnoflicue-
a
ailng industry’slifecyclecanbesynthesizedviaadecomposed ence of enduring transaction costs (e.g., uncertainty and
vdi
ar view of transaction costs, however. Asset specificity, transaction frequency) is independent of industry evolu-
a
eg uncertainty, and transaction frequency are the primary tion. Given this temporal dimension of transaction costs,
de
mar drivers of transaction costs (Williamson 1975). Some of the timing of entry into the industry is an important
s
n these sources may be more enduring than others. The determinant of the magnitude of transaction hazards an
so
histi Stiglerian view highlights transient transaction hazards, entrantexperiences.Therefore,wemaintainthatboththe
ce
hiu which studies of industry evolution have associated with Williamsonian transaction costs view and the Stiglerian
wq asset specificity and technological uncertainty (Agarwal view can be valid in the context of industry evolution,
n,ny et al. 2002, Argyres and Bigelow 2010, Gort and Klep- and we put forward two baseline hypotheses.
oa
versiend pinedru1s9tr8y2)p.rKomnoowtelesdtagnedaacrcduizmautiloantioonfatencdhsnpoillologvyersspeinciafin- Hypothesis 1A (H1A). An entrant is more likely to
s internalize a value chain activity if the enduring trans-
e cations (Abernathy and Utterback 1978, Gort and Klep-
c e action hazards of carrying out that value chain activity
dvan Pleas pasesret19s8p2e)c,ifithciutsy,readnudcipnogtetnhteialteecchonnoolomgiiccahlouldn-cueprtsaitnhtayt, are high.
A drive high transaction costs (Argyres and Bigelow 2010, Hypothesis 1B (H1B). A late entrant is less likely to
n e.
si sit Hoetker 2004, Williamson 1975). Although asset speci- internalizeavaluechainactivitybecausetransienttrans-
ticle or’s fispceitcyifinceitvyersuvcahnisahsesphcyosmicpalletceolylo,caantidonasapnedctstheofleaasrsne-t action hazards are lower later in the industry life cycle.
r h
A ut ing costs engaged in switching partners remain signifi- Pre-Entry Experience: Organizational
sa
hie cantevenforhigherstandardizedproducts(Klein1988), Capability Hypotheses
th its relative importance declines over time. For instance, The resource-based theory argues that firms pursue
ot
tg both technological uncertainty and asset specificity of growth based on extant resources and capabilities
htn
gdi thefeedstockprocurementactivityhavedecreasedasthe (Wernerfelt 1984, Chang 1995). Extended use of re-
riu
pycl bioethanol industry has evolved. Along with standard- sources and capabilities provides economies of scope
coin ization of various value chain activities, specialized sup- (Teece 1980)2 and, thus, production cost advantages
se, pliers have emerged. Stigler’s (1951) view is therefore for entrants in the focal industry who possess the rel-
oldbsit conceptually aligned with the idea of transient transac- evant resources and capabilities. In general, scholars
he
w tion costs, in that a decrease in uncertainties and asset have documented that pre-entry experience provides rel-
S
Mr specificity facilitates economies of scale. evant resources and complementary assets, resulting in
e
Rh However, other sources of costs, particularly as they a performance and survival advantage, and such pre-
Oot
F relate to other types of uncertainty (e.g., demand and entry experience can exist at both the firm level (Carroll
Ny
Ian environmental,systemictechnologyshocks)andtransac- et al. 1996, Helfat and Lieberman 2002, Klepper 2004,
:
ht n tion frequency may endure throughout an industry’s life. Klepper and Simons 2000, Mitchell 1991) and the indi-
g o Some industries and some activities may exhibit con- vidualfounderlevel(Agarwaletal.2004,Phillips2002).
ri d
y e sistently higher transaction hazards than others, regard- Complementing the literature on diversification in cor-
op ost less of life-cycle stage. Helfat and Campo-Rembado porate strategy regarding what industries or markets
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(2010) discussed how some industries (characterized by a firm should enter (Chatterjee and Wernerfelt 1991,
multiple overlapping markets) require more coordina- Montgomery and Hariharan 1991, Montgomery and
tion and communication than other industries even as Wernerfelt 1988, Silverman 1999), industry evolution
Qian,Agarwal,andHoetker: ConfigurationofValueChainActivities
OrganizationScience,ArticlesinAdvance,pp.1–20,©2011INFORMS 5
scholars have compared entrants’ pre-entry experience vertically integrated and specialized firms. Helfat and
(Bayus and Agarwal 2007, Carroll et al. 1996, Klepper Campo-Rembado(2010)foundthatfirmsmaychooseto
eg.
bor and Simons 2000). These two theoretical perspectives remain vertically integrated in anticipation of new sys-
ots. conveyonecommonmessage:therelatednessofapoten- temic changes in either their own or other industries,
nm
yor tial entrant’s resources and capabilities to those required and Chen et al. (2011) found that diversifying entrants
manf by an industry is a driver of industry entry (Helfat and navigate systemic change better than start-ups. Like sys-
@i
e Lieberman 2002). temic innovation (Helfat and Campo-Rembado 2010),
filns In addition, Helfat and Eisenhardt (2003) extended entryintoadifferentindustryrepresentssystemicchange
eo
hsi the concept of economies of scope to include a tempo- for a firm and calls for integrative capabilities (Chen
Ts
mi ral dimension: a firm achieves intertemporal economies et al. 2011). Yet relative to diversifying entrants, start-
ers.er of scope by transferring resources and capabilities from ups may experience higher costs for integration of value
bp
rio existing uses that may become obsolete to emerging chain activities, particularly in the absence of founder
sct and profitable new opportunities. Thus, whether firms pre-entry capability in the activities. Given the greater
by
suolic with pre-entry experience pursue existing or intertem- levelsofpre-entryintegrativecapabilitiesofdiversifying
op poral economies of scope, production costs are likely entrants, we predict the following.
t
blethis teoxpbeerielonwcee.r for them than for entrants who lack such Hypothesis 2B (H2B). Adiversifyingentrantismore
a
ailng Although governance choice is not a central topic of likelytointernalizeavaluechainactivitythanastart-up.
vdi
ar either the resource-based or the industry evolution liter- We now turn to an important distinction that relates
a
deeg ature, the formula for economies of scope suggests an to where pre-entry activity experience resides. Hypoth-
mar integration strategy. To achieve economies of scope, a esis 2A concerns the relatedness of pre-entry activity
s
son firm may choose to integrate two businesses within its experience and not whether such experience is equally
histi boundaries. The same line of reasoning can be applied transferable to a new industry for both start-ups and
ce
hiu to value chain activities. If an entrant has experience in diversifying entrants. Similarly, Hypothesis 2B focuses
wq operations (either at the firm level or founder level) that on the existence of an entrant’s firm-level integrative
on,any use resources and capabilities similar to those required capabilities but not potential interactions with pre-entry
sid by a value chain activity in the new industry, then this activity-level capabilities. We now argue that a firm’s
veren entrant is likely to have a cost advantage over entrants integrative capabilities positively moderate the relation-
s
ce e without such experience and is likely to carry out that ship between pre-entry experience in an activity and the
an as activity itself. Therefore, we predict the following. decision to internalize that activity.
v e
Ad Pl Hypothesis 2A (H2A). An entrant is more likely to A diversifying entrant’s integrative capabilities should
n e. internalizeavaluechainactivityifitpossessespre-entry help it transfer activity experience. Start-up founders
si sit experience related to that activity. with experience in an activity may possess the rele-
ticle or’s Asecondimportantclassofcapabilitiesrelatestointe- vreasnoturkcneoswalneddgcea,pabbuitlitfiierms -floervethleroaubtiilniteystothcaotncdoumctbitnhee
r h
A ut gration of activities via communication and coordina- activity in-house are still needed. In contrast, even if
sa
hie tion across the components of a system or across value activity experience acquired in related industries needs
otth chain activities at different stages of production (Chen to be reconfigured to meet the new industry’s require-
httng et al. 2011, Helfat and Campo-Rembado 2010, Helfat ments, diversifying entrants have already incurred much
gdi and Raubitschek 2000, Henderson 1994, Parmigiani and of the necessary cost (Helfat and Campo-Rembado
riu
pycl Mitchell2009).Suchintegrativecapabilities,whichexist 2010), whereas start-ups have to incur these costs anew.
coin at the firm level, are important for product innovation A diversifying entrant’s integrative capabilities should
oldsbsite, (eHt eallf.at20a1n1d),Raanudbitfsocrherekdu2c0i0n0g),thfoercfiormmmgurnoiwcatthio(nChanend astlasort-eunpabcalen,itbetocaluesveerfiargme-sleuvcehlerxopuetirnieenscaerebemttoerrethhoanlisa-
he
Sw coordination costs of conducting a transaction in-house tic and encompassing than knowledge residing in the
Mer (Poppo and Zenger 1998). mind of a founder that needs to be translated to the firm
Rh Diversifying industry entrants have more integrative
Oot level. This idea is consistent with the finding by Chen
F capabilities than start-ups. Development of these capa-
Ny etal.(2011)thatdiversifyingfirmscandealwith“grow-
Ian bilities, however, is costly (Helfat and Campo-Rembado ing pains” better than start-ups. Thus, we predict the
:
ht n 2010). Firms may choose to vertically integrate or dein- following.
g o tegrate as an industry evolves but bear significant costs
yri ed for such flexibility. The cost of setting up a verti- Hypothesis 2C (H2C). The positive relationship be-
op ost cally integrated governance structure can be particularly tween a firm’s likelihood of internalizing a value chain
C p
high for specialized firms and start-ups (Leiblein and activity and the possession of pre-entry experience in
Miller 2003). As a result, there is likely to be a ten- that activity is stronger for diversifying entrants than
dency to maintain the initial boundary choice for both for start-ups.
Qian,Agarwal,andHoetker: ConfigurationofValueChainActivities
6 OrganizationScience,ArticlesinAdvance,pp.1–20,©2011INFORMS
Organizational Economics and Organizational time increase the efficiency of organizing via the market
Capabilities: An Integration Exercise and reduce the advantage of leveraging integrative expe-
eg.
bor rience to conduct activities in-house.3 The above logic
ots. Transient Transaction Hazards (Entry Year) and Pre- suggests a moderating effect of entry time on the rela-
nm Entry Experience. As industries evolve, entrants can
maynfor rely on an increased stock of industry-specific knowl- tionships predicted in H2A and H2B.
@i edge (Gort and Klepper 1982), which has important Hypothesis 3A (H3A). The positive relationship be-
e
filns implications not only for changes in the relevance of tween pre-entry experience in an activity and the deci-
eo pre-entryexperience(BayusandAgarwal2007,Klepper sion to internalize that activity is weaker for later
hsi
Ts 2002) but also for the availability of specialized external entrants.
mi
ers.er partners or suppliers (Jacobides and Winter 2005). As Hypothesis 3B (H3B). The positive relationship be-
bp discussed above, entry timing also relates to changes in
scrito asset specificity and uncertainty, and thus it should also tiwnteeernnablieziengaavadliuveerscihfyaiinngaecnttirvaitnyt iasndwethaekedrecfoisriolnatetor
subolicy hbaevtweeaennipmrpe-oerntatrnyt ceoxnpteirnigeenncceyanefdfebcotuonndathrye rcehloatiicoenss.hip entrants.
op
ts The industry evolution literature documents temporal Enduring Transaction Hazards and Pre-Entry Expe-
blethi changes in the technological and demand conditions rience. Our organizational capabilities hypotheses also
a
ailng in industries (Abernathy and Utterback 1978, Agarwal imply that firms may differ in their ability to man-
avrdi and Bayus 2004, Gort and Klepper 1982). At the early age enduring transaction hazards. We now examine the
ega stages, characterized by uncertainties in technology, impact of both a firm’s experience with a given activ-
de
ar demand, and government policy, resources and capa- ity and its overall experience on its ability to manage
m
ns bilities compete for value creation potential. Few, if transactions related to that activity in a new setting.
so
histi any, supplier firms offer standard or easily customized The nascent literature on dual governance—that is,
ce applications for incumbent firms. Therefore, firms with the combination of making and buying similar inputs—
hiu
wq transferable pre-entry activity experience are likely to suggeststhatorganizationswithinternalcapabilitiesdeal
n,ny be able to create competitive advantage. Given a small with market transactions and manage external supplier
oa
sid poolofsuppliers,evenimperfectlytransferablepre-entry relationships better (Mayer and Salomon 2006). First-
ern experience may make a firm the “least incapable” of hand experience with an activity helps a firm assess
ve
e s the available suppliers (Hoetker 2004). However, as the the performance of an outside supplier, reducing the
c e
n s industry evolves, industry-specific knowledge develops risk of opportunism and hold-ups (Mayer and Salomon
a a
v e (Gort and Klepper 1982). Particularly after a dominant 2006, Parmigiani 2007). Knowledge about the activity
d Pl
A design is set, product features become largely pre- also allows the firm to credibly threaten to internalize
ticlesin or’ssite. diUnitcgtteawrbbelaelcl,-kkann1od9w7in8n)np.orovPdaruteic-oetninostnraytreercehmsnoaouilnroclgyeiseasda(dnA-dobnecsranpatoathbyielxiatiisnetds- i(tG,Wrraeondrtukc1i9no9gn6,thdKeuoafiglrumgto’asvnevdrunZlananencrdaeebrihli1ats9y9tg2oe).neexrtaelrlnyalesxuapmpliineerds
r h
A ut from other industries, even if they are related, are less simultaneous or near-simultaneous production of com-
hisea likely to fit the norms of the focal industry; hence, the ponents in the same industry value chain. However, to
th value creation potential and relevance of pre-entry expe- the degree that value chain activities are similar in an
ot
tg rience decrease over time (Bayus and Agarwal 2007, entrant’s prior and new industries, a firm that internal-
ghtdin Ganco and Agarwal 2009). The increasing availability ized that stage in its prior industry or the founder of a
pyriclu of capable suppliers means pre-entry experience loses start-up with similar prior experience should be able to
coin value over time, and overall, pre-entry experience in an apply lessons learned to management of external sup-
se, activity becomes less of a differentiator as an industry pliers in the new industry. Accordingly, prior internal
oldbsit develops. experience in a stage of the value chain will lower the
hwe Similarly, interfaces between value chain activities cost of managing a given level of transaction hazards.
S
Mr simplify over time because product features are gradu- In H2A, we argue pre-entry activity experience makes a
e
Rh ally standardized. The organization of each value chain firm more likely to integrate. Suppose two firms face a
Oot
F activity becomes more modular than it was early in transaction hazard, and one has experience but the other
Ny
Ian industry evolution, which increases the pool of poten- does not. If transaction hazard goes up by some degree
ht: n tial external partners specializing in various value chain of change, or “delta,” the inexperienced firm is subject
g o aspects (Jacobides and Winter 2005). Early in an to thatentire delta in additionalhazard. The experienced
ri d
y e industry’s life, integrative capabilities allow diversify- firm, however, because it has knowledge and capabili-
op ost ingentrantstogaincompetitiveadvantagebyorganizing ties relevant to the situation, is only subject to a fraction
C p
transactionswithintheirboundariesmoreefficientlythan of the delta. The marginal effect of transaction hazards
is possible in the nascent market. However, the stan- is therefore less for experienced firms. Given the lower
dardization and modularization of the value chain over marginal cost of dealing with an increase in transaction
Qian,Agarwal,andHoetker: ConfigurationofValueChainActivities
OrganizationScience,ArticlesinAdvance,pp.1–20,©2011INFORMS 7
Figure1 TheoreticalFrameworkofHypotheses
eg.
br Organizational Organizational
o
ots. capabilities economics
nm H4B (–)
yor
manf H4A (–)
@i
e Pre-entry Transaction hazard of
filns H2A (+) a value chain activity
eo Activity level
bers.Thpermissi experience H2C (+) H3A (–) ainL vtieakrleunloeiahf lciozhoiandign H1A( +) (Endurhinagz atrradnss)action
scrito Diversifying H2B( +) activity H1B( –) Entry year (Transient
by entrant
uc transaction hazards)
soli
op Integrative H3B( –)
ts capabilities
blethi
a
ailng
avrdi hazards, we predict that prior activity-level experience Data and Methodology
a
eg will mitigate the transaction hazards posed by external
de Empirical Context: The U.S. Bioethanol Industry
ar sourcingoftheactivityandthusnegativelymoderatethe
ms We tested our hypotheses with data from extant
n transaction hazard–internalization relationship.
so bioethanol producers that entered this evolving indus-
histi Under the same logic, diversifying entrants’ integra- try over 1978–2009. The industry dates back to the “oil
ce
whiqu tiveexperienceprovidesknowledgeaboutreducingcom- shock” in the 1970s, which sparked interest in renew-
munication and coordination costs that can be leveraged
n,ny to manage the transaction costs of external sourcing. able energy sources. In 1978, President Jimmy Carter
oa asked ADM (Archer Daniels Midland) to develop an
ersind Firm-level experience in monitoring, identifying inter- alternative to OPEC (Organization of the Petroleum
vse nalmilestones,andcreatingroutinestocoordinateactiv- Exporting Countries) oil. Subsequently, research fund-
ce e ities can also be leveraged to draw up contracts that ing, subsidies, and tax incentives from federal and state
n s
a a safeguardagainsttransactionhazards.Collectively,these governments fueled the growth of the bioethanol indus-
v e
d Pl factorsmakeadiversifiedentrant’sdecisionsaboutinter- try. Additional support stemmed from various agencies
A
n e. nalization of an activity less sensitive to the transaction related to environment protection, agricultural devel-
si sit hazards associated with that activity than the decisions opment, and economic development. Recently, societal
cle r’s of a start-up. Accordingly, we posit two moderating attention to environmental and economic issues related
rti ho hypotheses. to fossil fuels has provided an additional boost to the
A ut use of alternative energies (e.g., renewable fuels, solar
hisea Hypothesis 4A (H4A). The positive relationship be- energy, and wind). In the United States, bioethanol is
otth tween the enduring transaction hazards posed by an one of the two most important biofuels (bioethanol and
httng activity and the decision to internalize that activity is biodiesel) and is made primarily from amylaceous grain
gdi less for firms that have pre-entry experience in it. plants (e.g., wheat, corn). The bioethanol industry is an
riu
pycl ideal context for our study, given its clear demarcation
coin Hypothesis 4B (H4B). The positive relationship be- of value chain activities, recent evolution, and entrants’
oldsbsite, tawcteievnitythaendentdhueridnegcistrioannstaoctiinotnerhnaazliazredsthpaotsaecdtivbiytyains hetFeirgougreen2eoruesprporde-uecnetsrytheexpvearliueencceh.ain activities related
he
w less for diversifying entrants than for start-ups. to ethanol production as depicted by the U.S. Environ-
S
Mr mental Protection Agency, originating with feedstock
e
FORoth fraTmoewsourmk.mOaruizre,firFstigsuerte o1f hpyrpeosethnetssesouerxatmheinoerestitchael pinrdoudsutcrtyiorneqaunidreesndinincgurwriintgh ctohnesucmappittaiolnc.oEsntstryofinbtouitlhde-
Ny
n effects of enduring (H1A) and transient (H1B) trans-
Ia ing a plant using one of two dominant technologies:
ht: n action hazards on the decision to internalize a value dry or wet mill processing.4 As Figure 2 shows, four
rig do chain activity, and the second set of Hypotheses (H2A, key value chain activities complement ethanol produc-
y e H2B, and H2C) focuses on the role of organizational tion: (1) feedstock procurement, (2) technology devel-
op ost capabilities in this decision. The third (H3A and H3B) opment, (3) bioethanol marketing and distribution, and
C p
and fourth (H4A and H4B) sets integrate consideration (4) coproduct marketing and distribution. Accordingly,
of transaction hazards and organizational capabilities to wecouldidentifywhetherproducerschosetointernalize
predict contingent effects on boundary choices. any of these complementary activities.
Qian,Agarwal,andHoetker: ConfigurationofValueChainActivities
8 OrganizationScience,ArticlesinAdvance,pp.1–20,©2011INFORMS
Figure2 ValueChainActivitiesRelatedToEthanolProduction
eg.
br
o
ots.
nm Feedstock Feedstock Biofuel Biofuel Biofuel
maynfor production logistics production distribution end use
@i
e
filns
eo
hsi
Ts
mi
ers.er
bp
rio Ag crops
sct Ag residues
by Transportation fuels
suolic EFonreersgty r ecrsoidpuses Harvesting and Technology Coproduct (in light and heavy duty
top Wastes collecting development distribution voeffh-rioclaeds vaenhdi ctlreusc,ks,
blethis SPtroerpargoecessing Biological, (by barge,truck, ltoeccohmnooltoivgeiess, ,flight
vailading Transportation cbhioecmheicmali,cal, and Sratiol, raangde pinip etalinnek)s bPooawtse/sr haipnsd) generators
aar thermochemical Dispensing Chemical
deeg conversion Feedstocks for
ar Manufacturing
m
s
n Source. Adaptedfromhttp://www.epa.gov/Sustainability/images/biofuel_chain.png.
so
chiesti Figures 3–5 depict the evolution of key industry vari- rate of about 13.33% for the last 25 years and accelera-
whiqu ables.Theindustryhasexperiencedrapidgrowthinboth tionsince2000toabout17.4%.Furthermore,aggressive
n,ny sales and the number of firms, and it now ranks as the government incentives and policies have fostered entry
oa largestintheworldintermsoftotalproduction(Renew- by both diversifying entrants and start-ups (Figure 4).
sid
ern able Fuels Association 2008). Figure 3 shows a clear Asbefitstheearlyandgrowthphasesoftheindustrylife
ve
s growth trend over the last three decades, with an annual cycle (Agarwal and Gort 1996), not many exit events
e
c e
n s
a a
v e
Ad Pl Figure3 HistoricalU.S.FuelEthanolProduction Figure4 NumberofEntrantsbyYear
n e. 25
si sit 12,000 Diversifying entrant
cle r’s Millions of gallons Start-up entrant
ti o
r h
Asaut 10,000 nts 20
thihe ntra
ot e
Sholdscopyrighttwebsite,including al ethanol production 68,,000000 diversifying and start-up 1105
Mer nu of
ORoth An 4,000 ber
F m
Nny Nu 5
Ia
:
ht n 2,000
g o
ri d
y e
Cop post 0 0 978 980 982 984 986 988 990 992 994 996 998 000 002 004 006 008
0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2
98 98 98 98 98 99 99 99 99 99 00 00 00 00 00 Entry year
1 1 1 1 1 1 1 1 1 1 2 2 2 2 2
Source. http://www.ethanolrfa.org/pages/statistics/. Source. Calculationandcompilationperformedbyauthors.
Qian,Agarwal,andHoetker: ConfigurationofValueChainActivities
OrganizationScience,ArticlesinAdvance,pp.1–20,©2011INFORMS 9
Figure5 NumberofInternalizationsDividedbyNumberof information from the Renewable Fuels Association
Entrants (RFA) and a consulting firm (BBI International)6 that
eg.
bor 4.5 specializes in renewable fuels and publishes the lead-
ots. ing annual directory of the industry. Additional infor-
nm
maynfor 4.0 mboautinodnarryegcahrdoiincegstifmoringvaolufeencthrayi,npraec-teivnittriyese,xpanerdiepnrcoe-,
@i
e duction capacity was compiled from various resources,
filns 3.5 including U.S. Securities and Exchange Commission
eo
hsi (SEC) filings, firm websites, shareholder newsletters,
Ts
mi 3.0 press releases, industry magazines (Ethanol Producer
ers.er Magazine),7 and various regional news reports on agri-
ribop 2.5 cultureandbiofuels.8 Thesesecondarydatasourceswere
sct complemented by interviews with managerial personnel
by
suolic 2.0 who provided expert opinions on one of our key vari-
op ables: enduring transaction hazards. The data contain 90
t
blethis 1.5 boiboseetrhvaantioolnsp.roducing companies and 360 firm-activity
a
ailng
avrdi 1.0 Estimation
a
madesreg 0.5 N2-uymeabremroofviinntgeranvaelrizaagteio(nnsu÷mnbuemrobfeirnotefrennatlrizaanttisons Tdehceisuionnitaotfthaneatliymsies oinf ionudrussttruydyenitsrythfeorinetearcnhaloizfaftoiounr
son ÷numberofentrants) specificvaluechainactivities.Wereporttheresultsfrom
histi 0 probitestimationofthedecisiontointernalizewithclus-
ce
hiu 1978 1983 1988 1993 1998 2003 2008 tering by firm to account for each firm’s generation of
wq
Source. Calculationandcompilationbyauthors. four observations, one for each activity. Thus, our esti-
n,ny mationtechniquecomputesrobuststandarderrorsforthe
oa
sid occurred in the industry in studied period. The pre- coefficients and accounts for inherent heteroskedasticity.
ern
ve entryexperienceofindustryentrantsvariessignificantly.
s
ce e The firms and the founders of start-ups that entered the Variable Definition
an as bioethanolindustrycamefromavarietyofbackgrounds. The dependent variable, internalization, indicates a
v e
d Pl For example, diversifying entrants included agricultural firm’s governance choice for feedstock procurement,
A
n e. products companies that had engaged in grain process- technology development, bioethanol marketing and dis-
si sit ing and distribution (e.g., ADM, Bunge North America, tribution, or coproduct marketing and distribution (1=
cle r’s Cargill), firms who had engaged in electricity or coal internalization; 0=external development). We collected
ti o related production (e.g., Great River Energy, Headwa- information on boundary choices at founding from a
r h
A ut ters), and firms that had related production technology firm’s first filed 10-K (or 10-KSB), which has required
sa
hie (e.g., ICM). Among the start-ups, founders’ pre-entry sections for each of the four complementary activi-
otth experiencevariedconsiderablyaswell.Somewerefarm- ties. This information, checked against the firms’ web-
tg ers (e.g., the founder of Central Indiana Ethanol Plant sites and industry publications, outlined whether these
htn
gdi was a fourth-generation Hoosier farmer). Other start- activities were conducted in-house or externally sourced
riu
pycl ups’ founders had been employed in incumbent firms through agreements with specialized suppliers or with
coin but “spun out” to build new plants (e.g., a cofounder of other integrated ethanol producers. We also ensured,
se, Pacific Ethanol had managed a smaller-scale plant mak- from information for later years, that these bound-
oldbsit ing bioethanol from beer and soft-drink syrup before he ary choices remained unchanged, and we observed no
he
w founded Pacific Ethanol, which has much larger scale switchesbetweeninternalandexternalmodeswithinour
S
Mr and uses corn as its primary feedstock). sample firms. Table 1 shows the internal/external break-
e
ORoth Importantly, Figure 5 shows a declining trend in the down for each value chain activity in our sample.
F numberofinternalizedactivitiesperfirm.Thus,theevo-
Ny
Ian lution of the U.S. bioethanol industry is also evident in
: Table1 FrequencyofInternalization(1)andExternal
ght on acodnescisrteeanstewinithinStetgigrlaetred(1fi9r5m1)saonvderotuirmHe,yapottrheensdisth1aBt.is Development(0)byActivity
ri d
y e Internal External
Cop post Data Sources 1. Feedstockprocurement 62 28
Our data represent all extant bioethanol producers5 in 2. Technologydevelopment 19 71
the United States that entered from 1978 to 2009. We 3. Ethanolmarketinganddistribution 36 54
4. Coproductmarketinganddistribution 44 46
compiled information on the firms by cross-referencing
Qian,Agarwal,andHoetker: ConfigurationofValueChainActivities
10 OrganizationScience,ArticlesinAdvance,pp.1–20,©2011INFORMS
Thevariableenduringtransactionhazardswasusedto Following extant literature, we used a dichotomous
capturetransactionhazardsforeachactivitythatendures measurefordiversifyingentrant(1=firmwasinanother
eg.
bor throughout the industry’s life and is based on six indus- industry prior to bioethanol industry entry; 0=firm was
ots. try experts’ responses to survey questions about per- founded as a bioethanol start-up). Joint ventures repre-
nm
yor ceived frequency and uncertainty (see Appendices A sentahybridorganizationstructurebetweendiversifying
manf and B).9 The experts were randomly selected from entrantsandstart-ups(HelfatandLieberman2002).Four
@i
e among top managers in the industry. For each activity, joint ventures were present in our sample and were clas-
filns the experts assigned a value from 1 (low) to 5 (high) sifiedasdiversifyingentrants,giventhattheycouldben-
eo
hsi for a typical bioethanol producer. We averaged these efitfromtheirparents’firm-levelexperienceandtransfer
Ts
ers.ermi sinixterrerastpeornrseelsia,buisliintygathnedfaogllroeweminegntp.roceduretoascertain ovefnrtouurteisnews.erReeesxucltlsudreemdafrinoemdtlhaergaenlyalsyismisi.lar when joint
bp
rio We first checked the interrater reliability by calculat- Pre-entry activity-level experience indicated whether
sct ing intraclass correlation coefficients (ICCs).10 For the a firm or its founder had pre-entry experience related
by
suolic 24 ratings of activity frequency (four activities and six to a given ethanol value chain activity (1=related pre-
op ratings for each activity), the ICC for average measures entry experience; 0 = no related experience). Because
t
ablethis wICaCs 0fo.9r7a.vFeorargtehem2e4asruarteisngwsaosf0a.9c3ti.viWtyeutnhceenrtcaoinmtyp,uttehde bcaomthetfhreomdivdeivrseirfsyeiningdeunsttrraynstsettainndgsfoanudndbearcskogfrosutanrdt-su,pins
ailng Fleiss’s kappa statistics,11 obtaining 0.54 for the fre- coding this variable we relied on detailed information
vdi
aar quency questions and 0.47 for the uncertainty questions about their experience, particularly focusing on whether
deeg on the four activities. These statistics show moderate a firm or founder had prior experience in (1) handling
mar agreement among respondents. For all eight questions grains(e.g.,graintransportation,merchandizing,ortrad-
s
n ing); (2) research and development activities related
so together, the Fleiss’s kappa is 0.61, showing substantial
histi agreement. Thus, the six responses to our questions on to ethanol production (e.g., development of technol-
ce
hiu the frequency and uncertainty of four value chain activ- ogy for corn milling, distiller grain drying, or corn
wq oil extraction); (3) fuel sales, transportation, or storage;
n,ny ities showed strong interrater reliability and moderate to and (4) storage and transportation of agricultural pro-
oa substantial agreement among raters. Having established
versisend tthaientrye,liwabeilcitoymopfiloeudramseinasgulerems eoafsfurreequoefnecaychanadctuivnictye’rs- cinegssreeddiegnotso,dasnd(ea.gn.i,mfaolofdeeadnsd).1i4nWduestcrioaml bsitnaercdhkees,ywfooordd
e and content searches of descriptions of firms’ divisions,
c e transaction hazards, averaging the six experts’ ratings
an as on both variables. The measure for enduring transac- product offerings, services, histories, and so forth in the
dv Ple tion hazards is the sum of these two averages for each above-mentioned various data sources to address these
A
four primary inquires mapping onto the four value chain
n e. valuechainactivity.12 Table2presentsthesefourvalues.
si sit Robustness checks revealed no change in results when activities.
rticle hor’s wvaeluuesserdepaorrtaendkinorTdaebrleof2.the activities rather than the theFyorhdaidveorpsiefryaitnegdeinntrapnritos,rwtoe cehnetrcyk;edfowrhsattarbtu-usipnse,sswees
Asaut Entry year (transient transaction hazards) was ascertainedthepre-entryworkexperienceoftheprimary
hie defined as the year in which a firm started bioethanol founders. For example, a firm with prior experience in
th petroleum distribution and marketing would be coded 1
ot production. For a firm with more than one plant, we
tg for bioethanol marketing and distribution. For start-ups,
htn used the founding year of its first operating plant, or
gdi we relied on company websites, news releases at found-
riu the earliest available year. We subtracted 1977 from the
scopye,incl clihersotneonltorygiycaelaryienarouorfseanmtrpyles.oTthhiastm1eraespurreesecnatpstuthreesetahre- idinfegtaa,ilfasonuodnndtferoarudwnedaijsnoguartnefaaalrmmasec’rc,porwuionertsec,xowpdeehrditcish1e.fpForroovrtihedexeadfimrrpmilce’hs,
oldbsit effect of transient transaction hazards, because industry procurement value chain activity and 0 for all others.
he experts agreed asset specificity had reduced over time
w Similarly, if a founder had oil refinery experience, we
S (see Appendix B).13 Robustness checks confirmed that
Mr coded 1 for this firm’s bioethanol distribution and 0 for
Rhe the results did not change when a logarithm of entry all other activities. For firms with multiple founders, we
Oot year was used.
F compiled pre-entry experience by accounting for all the
Ny
Ian founders’ experience; thus, a start-up could have related
:
ht n Table2 EnduringTransactionHazardsbyActivity prior experience in multiple activities, even if any one
g o of its founders had experience in only one.
yri ed Feedstock Technology Ethanol Coproduct Controlvariablesincludedproductioncapacity,avari-
op ost Activity procurement development marketing marketing able capturing firm size, representing the potential pro-
C p
Enduring 9.16 5.5 8 7 duction of bioethanol in a firm’s founding year, or the
transaction earliest available information about production capacity
hazards
(in billion gallons per year). Information was collected