Delios, And ew e al.
A icle — Published Ve sion
The insigh s om he c owd: D awing in e ences
om many app oaches o key empi ical ques ions in
in e na ional business
Jou nal o In e na ional Business S udies
Sugges ed Ci a ion: Delios, And ew e al. (2025) : The insigh s om he c owd: D awing in e ences
om many app oaches o key empi ical ques ions in in e na ional business, Jou nal o In e na ional
Business S udies, ISSN 1478-6990, Palg a e Macmillan UK, London, Vol. 56, Iss. 9, pp. 1102-1124,
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The insigh s om hec owd: D awing in e ences ommany
app oaches okey empi ical ques ions inin e na ional business
And ewDelios· TianyouHu· ShuYu· NanZhou· E icUhlmann, e al.[ ull au ho de ails a he end o he a icle]
Recei ed: 1 Augus 2024 / Re ised: 29 May 2025 / Accep ed: 11 June 2025 / Published online: 5 No embe 2025
© The Au ho (s) 2025
Abs ac
In his c owdsou ced ini ia i e, 57 independen analys s used he same longi udinal da ase o add ess ou majo empi ical
ques ions in in e na ional business. Fo all ou esea ch ques ions, di e en analys s ob ained subs an ial es ima es in oppo-
si e di ec ions, meaning ha hey could ha e d awn any conclusion a all had hey conduc ed he p ojec alone. Agg ega ing
ac oss he esul s ob ained by di e en analys s poin ed o an o e all answe o wo o he ou esea ch ques ions, al hough
o one o he wo ques ions, he e idence was mo e sugges i e han conclusi e. Tha said, he a iabili y in esul s was
no simply andom, and could in some cases be meaning ully explained. Choices ega ding how o ope a ionalize a iables
played an impo an ole in de e mining he empi ical esul s, and expe analys s we e mo e likely o epo la ge posi-
i e e ec s. Ra he han exhibi ing a bias o con i m hei p e-exis ing belie s, analys s appea ed o a ionally upda e hei
belie s conside ing he e idence. O e all, hese indings empi ically demons a e he ole o subjec i e esea che choices
in shaping esul s in in e na ional business esea ch ye also show ha i is s ill possible o d aw meaning ul conclusions in
science. We ad oca e o an open science o in e na ional business in which he consequences o subjec i e analy ic choices
a e ende ed as anspa en as possible.
Keywo ds C owdsou cing· Many analys s· Open science· En y mode s a egy· Mul ina ional i ms
In oduc ion
Since 2010, we ha e seen inc easing conce ns abou publica-
ion incen i es and esea che con i ma ion bias nega i ely
a ec ing he eliabili y o hescien i ic li e a u e (Aguinis
e al., 2020; Nelson e al., 2018; Nosek e al., 2022). Simu-
la ions demons a e ha gi en su icien choice poin s in a
da ase , esea che s could, in p inciple, selec he pa h ha
suppo s wha e e hey a e (consciously o pe haps uncon-
sciously) biased o conclude (Mu phy & Aguinis, 2019;
Simmons e al., 2011). In pa , because o such oppo u-
ni ies o “p-hack”, p alue dis ibu ions in published a i-
cles e lec subs an ial numbe s o s a is ically implausible
indings (Fanelli, 2010; Gold a b & King, 2016; Ioannidis,
2005; Simonsohn e al., 2014). Indeed, one p edic o o he
esul s o an empi ical scien i ic in es iga ion is he p io
in ellec ual commi men s o he esea ch eam (Be man &
Reich, 2010).
The consequen e o m mo emen o inc ease he ep o-
ducibili y, eplicabili y, and obus ness o scien i ic indings
ac oss disciplines holds alue o esea ch on in e na ional
business (IB) jus as i does o o he ields (Aguinis e al.,
2017, 2022, 2023). Al hough less p one o he small samples
and unde powe ed es s ha cha ac e ize many beha io al
expe imen s (Schimmack, 2012; Weinga en e al., 2016),
in e na ional business s udies o en ely on complex lon-
gi udinal da ase s wi h many choice poin s and de ensible
analy ic app oaches. Me a-scien i ic in es iga ions sugges
conce ning a es o scien i ic e o s (Be gh e al., 2017),
di icul ies ep oducing he same indings om he same
And ew Delios, Tianyou Hu, Shu Yu, and Nan Zhou sha e i s
au ho ship. They, oge he wi h E ic Luis Uhlmann, o med he co e
p ojec eam ha designed and conduc ed he me a-scien i ic p ojec
and w o e he pape . The emaining 64 co-au ho s se ed as analys s
who ca ied ou he analyses o he ou esea ch ques ions.
Co espondence o: Tianyou Hu, Facul y o Business
Adminis a ion, Uni e si y o Macau, E22, A enida da
Uni e sidade, Taipa, Macau. Email: iany[email p o ec ed]; Nan
Zhou, School o Economics and Managemen & Ad anced Ins i u e
o Business, Tongji Uni e si y, 1500 Siping Road, Shanghai, China,
200092. Email: [email p o ec ed].
Accep ed by A jen an Wi eloos uijn, A ea Edi o , 11 June 2025.
This a icle has been wi h he au ho s o ou e isions.
1103Jou nal o In e na ional Business S udies (2025) 56:1102–1124
da a (Be gh e al., 2017; Delios e al., 2022), and e ec
size o e es ima ion (Bosco e al., 2016; Gold a b & King,
2016) in s a egy and in e na ional business—jus as has
been obse ed ac oss all disciplines examined hus a (e.g.,
Fanelli, 2010; Ioannidis, 2005; Open Science Collabo a ion,
2015). In his s udy, we demons a e how empi ical es i-
ma es a e con ingen on subjec i e analy ic choices, which
we sugges ep esen s a mo e signi ican challenge o he
science o in e na ional business han p-hacked o subop-
imal analyses.
P io heo e ical schola ship has aised conce ns abou
epis emic unce ain y in managemen esea ch (Ke oki i
& Man e e, 2010; King e al., 2021; Man e e & Ke oki i,
2013), and he dependency o esul s on analy ic choices
has been di ec ly demons a ed o a ew speci ic claims
(Be chicci & King, 2022; Gold a b & King, 2016; Gold-
a b & Yan, 2021; Nandiala h & Rogmans, 2019). Build-
ing on Gold a b and King (2016), who es ima e s a is ically
ha alse posi i es a e common in s a egic managemen
esea ch (as in many ields), we examine whe he esul s
ha a e ue-posi i es unde a gi en speci ica ion may no
always be obus o he al e na i e speci ica ions o he
capable schola s migh ha e used. By o ganizing a mass
collabo a ion, we p o ide a bi ds-eye empi ical iew o he
impo ance o con ex in his space as heo ized by Ke oki i
and Man e e (2010).
We selec ed ou unanswe ed esea ch ques ions ha a e
in insically in e es ing in-and-o - hemsel es o exploi he
insigh s o he c owd o cla i y he na u e o he ela ion-
ships be ween key a iables in in e na ional business s udies,
such as in angible asse s, policy unce ain y, owne ship, and
i m pe o mance. This i s -o de con ibu ion o add ess
he esea ch ques ions hemsel es, some o which a e he
subjec o dozens o p io publica ions ha ende ed mixed
and con lic ing esul s, is a key alue-add beyond he sec-
ond-o de con ibu ion o u ning a me a-scien i ic lens on
he scien i ic p ocess i sel .
Ou s udy shows ha IB as a discipline is no immune
o he subjec i i y challenge unco e ed in o he ields. A
he same ime, ou a ge ques ions a ied in he deg ee o
heo e ical consensus in he ield ega ding wha pa e n
“should” eme ge, allowing us o examine c oss-ques ion di -
e ences in empi ical conclusions. In addi ion o agg ega ing
esul s ac oss dispa a e app oaches in an a emp o d aw
subs an i e in e ences (Landy e al., 2020; Nandiala h &
Rogmans, 2019), we examine meaning ul mode a o s (Be -
chicci & King, 2022; Ke oki i & Man e e, 2010; King e al.,
2021) such as heo e ically laden a iable ope a ionaliza-
ions, esea che expe ise, and belie s, wi h mo e success
han in any p io many-analys s ini ia i e om any ield.
In he Discussion, we p esen a ypology o in e en ions
designed o ackle subjec i i y in science while also discuss-
ing hei downsides. We ou line an op imis ic ision o an
open science o IB ha conside s bo h he bene i s and cos s
o po en ial e o ms as well as p omising s a egies o deal-
ing wi h he e ogeneous es ima es, such as agg ega ion and
mode a o iden i ica ion.
Resea ch backg ound
Subjec i i y inscience
E en absen analy ic mis akes, di icul ies ep oducing ind-
ings, publica ion p essu es, o an in ellec ual commi men o
a heo y, he e emains some inhe en subjec i i y in scien-
i ic app oaches o a p oblem (Landy e al., 2020; Silbe zahn
e al., 2018). Fo complex da ase s, his is ende ed anspa -
en by a mul i e se in which a solo esea che o small eam
conduc s many analyses (Sala-i-Ma in, 1997; Simonsohn
e al., 2020; S eegen e al., 2016; Young & Hols een, 2017)
o a c owdsou ced app oach in which many scien is s use he
same da ase o es he same esea ch ques ion (Silbe zahn
e al., 2018). C owdsou cing is a less e icien han a mul-
i e se ye i e eals he na u ally eme ging s a egies o eal
esea che s. A many-analys s app oach can di ec ly add ess
he ques ion o wha would ha e happened i ano he in es-
iga o had analyzed he same da a and u he examined
which o hei cha ac e is ics (deg ee o opic o s a is ical
expe ise, belie s abou he hypo hesis, e c.), made a di e -
ence in he ou comes o hei empi ical es s.
In one ea ly many-analys s ini ia i e, 29 esea ch eams
es ed he ela ionship be ween he skin one o socce play-
e s and whe he hey ecei ed ed ca ds om e e ees. The
es ima es ob ained om he 29 esea ch eams anged om
la ge posi i e es ima es, consis en wi h he conclusion ha
e e ees a e biased agains da ke -skinned playe s, o small
nega i e e ec s e lec ing he opposi e di ec ional bias (Sil-
be zahn e al., 2018). Mos subsequen c owd collabo a ions
ind an e en g ea e he e ogenei y in esul s ac oss inde-
penden analys s (Bo inik-Neze e al., 2020; B eznau e al.,
2022; Menk eld e al., 2024; Schweinsbe g e al., 2021).
Schweinsbe g e al. (2021) e e o i as “ adical e ec size
dispe sion” when di e en esea che s analyzing he same
da ase o add ess he same esea ch ques ion e u n di e en
e ec size es ima es in opposi e di ec ions.
Fo example, one analys may conclude ha including
mo e women in a scien i ic deba e inc eases he likelihood
ha each indi idual woman will speak, whe eas ano he
inds ha he p esence o mo e women supp esses emale
pa icipa ion (Schweinsbe g e al., 2021). Rela ed wo k
e eals such a wide dispe sion o esul s ac oss expe imen al
s udies designed by independen esea ch eams (Ba ibaul
e al., 2018; Hube e al., 2023; Landy e al., 2020; Tie ney
e al., 2025). To he ex en ha such a pa e n eme ges e-
quen ly o he hypo heses es ed in a scien i ic ield, he
1104 Jou nal o In e na ional Business S udies (2025) 56:1102–1124
implica ions ega ding he p oduc s o adi ional science
done in small eams a e conside able. When eading a ypi-
cal published pape , he eade is hence le unsu e whe he
o us he esul s; i ano he eam had pu sued he same
idea, hey migh ha e eached he e e se conclusion.
Two means o managing heunce ain y:
Agg ega ion e suspa sing
When di e en scien is s using dispa a e app oaches each
di e en conclusions, how can we adjudica e? The wo
majo app oaches o managing a iabili y and unce ain y
in science a e agg ega ion and pa sing (Be chicci & King,
2022; Cy us-Lai e al., 2022). Agg ega ion in ol es mechan-
ically a e aging ac oss he esul s om he many analyses o
many designs, o example, ia me a-analysis (Landy e al.,
2020) o Bayesian model a e aging (Hinne e al., 2020;
Nandiala h & Rogmans, 2019). Pa sing means iden i ying
meaning ul empi ical mode a o s o he di e en es ima es
ac oss di e se app oaches (Be chicci & King, 2022). Pe -
haps di e en esea che s in e p e ed he esea ch ques ion
di e en ly (Auspu g & B üde l, 2021; B eznau e al., 2022;
Kumme eld & Jones, 2023) o ob ained di e gen esul s
because hey ope a ionalized a iables in dis inc ways (Sch-
weinsbe g e al., 2021). The associa ed di e ences in esul s
could be non-a bi a y and hold heo e ical implica ions ha
a e los ia agg ega ion. Pa sing is consis en wi h he pe -
spec i is hesis ha he opposi e o a g ea u h is also
ue (McGui e, 1983), and ha he ask o he scien is is o
un a el his web o heo e ically ich mode a ion.
Al hough pe spec i ism o e s a beau i ul ision o scien-
i ic inqui y, he many-analys s p ojec s hus a ha e s ug-
gled o quan i a i ely explain much a iance in es ima es.
Silbe zahn e al. (2018) ailed o iden i y eliable mode a o s
o he esul s ac oss analysis eams, inding only null e ec s
o expe ise and o he a iables. Schweinsbe g e al. (2021)
demons a ed ha heo e ically laden ope a ionaliza ions o
a iables (e.g., how s a us is concep ualized) accoun ed o a
meaning ul po ion o he esul s, bu only a small amoun in
absolu e e ms. B eznau e al. (2022) conside ed nume ous
p edic o a iables, which combined explained only 4% o
he a iabili y in esul s ac oss analys s. Hube e al. (2023)
coded ea u es o hei c owdsou ced many designs, which
oge he cap u ed 3.1–6.4% o he a iance in es ima es.
Ba ibaul e al. (2018) had mo e success, demons a ing ha
sup aliminal p iming designs ha p esen ed s imuli abo e
he h eshold o conscious pe cep ion p oduced eliable
p iming e ec s, whe eas subliminal designs did no .
Pas c owd ini ia i es ha e p o ed g oundb eaking,
bu also hold majo limi a ions, especially wi h ega ds o
c acking he pa sing p oblem. Some p io in es iga ions
used a bi a ily selec ed esea ch ques ions, such as pos-
sible acial bias among socce e e ees (Silbe zahn e al.,
2018; see also Landy e al., 2020). O he s gene a ed and
selec ed hypo heses o es le e aging a c owd o scien is s
(Schweinsbe g e al., 2021) o chose esea ch ques ions he
p ojec coo dina o s conside ed impo an o hei espec i e
ields (e.g., B eznau e al., 2022; Menk eld e al., 2024). We
conside hese majo imp o emen s in he selec ion p ocess
o esea ch ques ions, since he ex emely ine icien p o-
cess o o ganizing and coo dina ing a small a my o col-
labo a o s is be e jus i ied by a high-s akes ou come (Isage
e al., 2021). Posing mo e han one esea ch ques ion in he
same p ojec (Landy e al., 2020; Schweinsbe g e al., 2021)
u he a o ds he oppo uni y o explo e whe he he ques-
ion i sel ma e s. Fo example, a gene al esea ch claim
wi h a high la i ude o cons ual could be in e p e ed di e -
en ly by di e en scien is s, inc easing he di e si y o me h-
odological s a egies used o ackle he p oblem (Auspu g &
B üde l, 2021; Kumme eld & Jones, 2023).
Al hough he numbe o collabo a o s in ol ed was a
highe han in a small eam p ojec , he numbe s o da a ana-
lys s o ma e ials designe s in ea ly c owd ini ia i es we e
small in absolu e e ms (e.g., 29 analysis eams in Silbe -
zahn e al., 2018, and 23 indi idual analys s in Schweinsbe g
e al., 2021; 15 o ewe eams o ma e ials designe s pe
hypo hesis in Landy e al., 2020). Silbe zahn e al. (2018)
ound ha eam leade s’ p io belie s abou e e ee acial
bias did no p edic hei es ima es, bu wi h 29 uni s o
obse a ion, his does no p o ide s ong e idence agains
esea che con i ma ion bias. Such samples make i di icul
o assess he ole o indi idual di e ences, o example, in
opic expe ise o s a is ical skills, unless he ela ionship is
e y s ong. False nega i es become uncom o ably likely.
Mo e ecen ly, Menk eld e al. (2024) obse ed a small
co ela ion be ween expe ise and analy ic es ima es in hei
sample o 164 eams, in con as o ea lie es ima es ha
we e close o ze o. Despi e he ex ao dina y coo dina ion
e o equi ed, mo e c owd p ojec s, including as many ana-
lys s as possible, a e necessa y o add ess he pa sing chal-
lenge. In pa icula , does he analys ma e , and i so, which
aspec s o an analys ’s p o ile a e mos impo an ?
The p esen esea ch
We c owdsou ced es s o ou unanswe ed esea ch ques-
ions in in e na ional business s udies wi h a g oup o 57
collabo a o s using a common complex longi udinal da a-
se . A eliable inding ha is obus o di e en analy ic
app oaches is necessa y, bu no su icien , o scien i ic
c edibili y. A c edible inding should also be heo e ically
and p ac ically ele an (RRBM, 2024). We selec ed hese
ou ques ions because hey a e impo an ones in IB, and
each has had many p io in es iga ions a ge ed a hem
wi h a di e si y o empi ical ou comes and no unequi ocal
1105Jou nal o In e na ional Business S udies (2025) 56:1102–1124
conclusion. The answe s, i a ainable, a e bo h heo e i-
cally in e es ing o academics and o p ac ical impo ance
o managemen p ac i ione s. As emphasized ea lie , he e
is an in insic alue in a emp ing o answe he speci ic
esea ch ques ions hemsel es, abo e and beyond he
me a-scien i ic ques ion o a iabili y in es ima es due o
esea che choices.
Fu he , al hough hese ou esea ch ques ions ha e been
add essed in much empi ical esea ch, p io es s ha e a -
ied in sample size, sample cons i u ion (geog aphy, indus y,
and ime pe iod) and key a iables (Shen e al., 2017; Zhao
e al., 2004). Thus, i is use ul o explo e i such a iance
in esul s is main ained when da a and esea ch ques ions
a e ixed ac oss esea che s. In ou esea ch collec i e o 57
collabo a o s, mos con ibu o s had di ec opic expe ise in
s a egic managemen o in e na ional business, and many
had ex ensi e s a is ical expe ience.
Epis emic unce ain y is inc easingly ecognized in man-
agemen esea ch (Ke oki i & Man e e, 2010; King e al.,
2021; Man e e & Ke oki i, 2013), as in o he ields such as
psychology (Nosek e al., 2022) and medicine (Ioannidis,
2005; Musani e al., 2007). I is he e o e aluable o empi -
ically examine whe he he e is a dispe sion in es ima es
when a c owd o capable esea che s a emp s o ackle he
same ques ions wi h he same da ase . We do no claim ha
he p oblem is mo e se e e in in e na ional business s udies
han elsewhe e, no do we make ield-by- ield compa isons.
Howe e , quan i a i e esea ch in in e na ional business
o en elies on complex se s o obse a ions ha in ol e
nume ous analy ic choices. The p esen me a-scien i ic
insigh s a e ele an o any ield o sub ield ha likewise
depends on opic expe s o na iga e hei way h ough a
ga den o o king pa hs (Gelman & Loken, 2014) o each
an empi ical conclusion.
Adding u he alue, he ou ocal esea ch ques ions
a ied in he le el o heo e ical consensus ega ding he
expec ed ou come based on he pas li e a u e. This allows
us o explo e c oss-ques ion di e ences, such as whe he
subjec i e heo e ical consensus is ela ed o he objec i e
deg ee o dispe sion in empi ical es ima es ac oss analys s.
E en i high in ellec ual consensus does no necessa ily
ansla e o consis ency in empi ical esul s, agg ega ing
ac oss widely dispa a e es ima es could s ill yield di ec ional
answe s o ques ions whe e es ablished heo y poin s o wha
“should” happen. Thus, he p ojec ’s alue-add in po en ially
answe ing some o he esea ch ques ions hemsel es is
in e wined wi h he me a-scien i ic con ibu ion o ex end-
ing he many-analys app oach o s a egic managemen .
Resea ch ques ion 1 Wha is he ela ionship be ween
en y mode and o eign subsidia y pe o mance?
The ela ionship be ween en y mode and o eign sub-
sidia y pe o mance is one o he mos s udied in in e -
na ional business esea ch (B ou he s, 2002; Chen & Hu,
2002; Shen e al., 2017; Wu e al., 2022). En y mode
choice is an impo an s a egic decision in in e na ional
expansion because i has implica ions o a ious c i ical
issues, such as con ol o e o eign ope a ions, in es men
isk, and esou ce commi men (Zhao e al., 2004). Pas
s udies sugges i u he in luences manage ial sa is ac-
ion ega ding subsidia y pe o mance (B ou he s e al.,
2003), subsidia y su i al (Sha e , 1998), and he objec-
i e p o i abili y o he subsidia y (Chen & Hu, 2002).
Di e en heo ies and pe spec i es could explain he
ela ionship be ween en y mode and subsidia y pe o -
mance, such as ansac ion cos heo y (B ou he s e al.,
2003), in e na ionaliza ion heo y (Johanson & Vahlne,
1977; Wiede sheim-Paul & Johanson, 1975) and c oss-
na ional dis ance (Tihanyi e al., 2005). T ansac ion cos
heo y would p edic ha sole owne ship leads o be e
pe o mance due o educed ansac ion cos s. In e na ion-
aliza ion heo y would expec ha sha ed owne ship would
lead o be e pe o mance due o he educ ion o isk and
oppo uni ies o lea ning and esou ce sha ing o ei he
scale esou ces o link esou ces along he alue chain.
The e o e, we expec ed a high le el o heo e ical con-
sensus among schola s in he ield ha en y mode should
ma e , bu con lic ing p edic ions ega ding he di ec ion
o he e ec .
Resea ch ques ion 2 Wha is he ela ionship be ween
in angible asse s and a i m’s le el o owne ship in i s o -
eign subsidia ies?
The possession o in angible asse s ha a e subjec o
ma ke ailu e is one o he easons ha i ms in es ab oad
(Buckley & Casson, 1976). In angible asse s a e a he co e
o in e naliza ion heo y, which has been widely accep ed
in he in e na ional business li e a u e (Henisz, 2003;
Mo ck & Yeung, 1992; Zeng e al., 2019). The e is li le
heo e ical con o e sy ha a highe le el o in angible
asse s should lead o a highe le el o owne ship because
o eign subsidia ies need o p o ec hei in angible asse s
om leaking o pa ne s (Guillén, 2003; Ma in & Salo-
mon, 2003). Thus, we an icipa e a high le el o in ellec ual
consensus among he collabo a o s ha he e should be
a posi i e ela ionship be ween in angible asse s and he
le el o owne ship. This consensus should be g ea e han
ha o esea ch ques ion (RQ1).
Resea ch ques ion 3 Wha is he ela ionship be ween
policy unce ain y and a i m’s le el o owne ship in i s o -
eign subsidia ies?
1106 Jou nal o In e na ional Business S udies (2025) 56:1102–1124
Ins i u ional heo y posi s ha i m s a egy is in luenced
by he ex e nal ins i u ional en i onmen (No h, 1990;
Sco , 2008). Policy unce ain y in he hos coun y inc eases
he isk o ope a ions, and i ms could educe hei le el o
owne ship o educe isk (Delios & Henisz, 2000; Henisz,
2000a). Howe e , his may no always be he case since
some i ms may be able o deal wi h policy unce ain y by
o he s a egic means (Lee, 2018; Sun e al., 2016). Thus,
ou eading o he li e a u e is ha he le el o academic
consensus o RQ3 is no as high as o RQ2.
Resea ch ques ion 4 How does he le el o policy unce -
ain y mode a e he ela ionship be ween in angible asse s
and a i m’s le el o owne ship in i s o eign subsidia ies?
When he le el o policy unce ain y is high, he isk o
exp op ia ion by pa ne s is high. An oppo unis ic local
pa ne would use all a ailable means, such as he manipu-
la ion o he poli ical sys em o seize oppo uni ies o exp o-
p ia ion (Delios & Henisz, 2000). In such a si ua ion, a o -
eign subsidia y would end owa d ull owne ship o educe
he isk o exp op ia ion by a local pa ne (Henisz, 2000a).
Acco dingly, we expec he ela ionship be ween in angible
asse s and le el o owne ship o be s onge when he le el
o policy unce ain y is high. Howe e , his ela ionship
is a ely es ed in he li e a u e and does no ca y s ong
explici assump ions on he pa o mos schola s (Delios &
Henisz, 2000; Henisz, 2000b). Thus, e en mo e so han o
RQ3, we ea RQ4 as an open empi ical ques ion.
Finally, a a me a-scien i ic le el, ou p ojec has se e al
alue-adds ha e lec lea nings and bes p ac ices gleaned
om pas c owd ini ia i es. To ou knowledge, his is only
he second such p ojec , a e Silbe zahn e al. (2018),
o assess belie s abou he esea ch ques ions among ou
esea che s bo h be o e and a e ca ying ou he analyses.
We a e in e es ed in pos -analysis belie s because scien is s’
iews ega ding he hypo hesis a e likely o be shaped by he
esul s hey ound, and indeed no ma i ely ough o be. I
pos -belie s a e mo e s ongly co ela ed wi h esul s han
p e-belie s, his sugges s belie upda ing—changing one’s
belie s o all in line wi h he empi ical esul s, some hing a
good scien is should do.
Ou design hus allows us o po en ially cap u e no only
con i ma ion bias bu also a ional upda ing o belie s con-
side ing he e idence. We p o ide a new es o whe he
analys s and hei cha ac e is ics ma e , examining he
po en ially dynamic in e play be ween belie s and e idence
wi h a sample size app oxima ely double ha o Silbe zahn
e al. (2018). Building on Schweinsbe g e al. (2021), we
lea e a iable ope a ionaliza ions uncons ained and es
hei po en ial impo ance in explaining dispe sion in esul s
ac oss analys s. We empi ically compa e he impo ance o
esea ch ques ion and esea che , as in Landy e al. (2020),
bu wi h 57 uni s o obse a ion a he han 15. Ou aim is
o p o ide in o ma i e es s o he ou un esol ed esea ch
ques ions in in e na ional business esea ch, and a he same
ime shed unique new ligh on he na u e o social scien i ic
inqui ies based on complex da a.
Me hods
Da a andcode a ailabili y
All da a and code used in his pape a e publicly a ailable
on he Open Science F amewo k (OSF) a h ps:// os . io/
e w3 z/? iew_ only= 52ab5 518ad a34e0 ca104 6aeab 08a51 22.
Ou p ima y supplemen a y documen ha con ains Supple-
men s 1–9 is accessible on he JIBSwebpage o his pape
(h ps:// doi. o g/ 10. 1057/ s41267- 025- 00808-9).
P e‑ egis a ion
Ou me hods and analyses we e p e- egis e ed a h ps://
os . io/ 4euaj. See ou o iginal plan in Ma e ial 1 in “Fu he
Resul s and Ma e ials”, which is pos ed on he OSF eposi-
o y. Supplemen 1, loca ed in he supplemen a y documen
(on he JIBS webpage o his pape ), summa izes de ia ions
om and addi ions o he o iginally planned analyses.
Da ase
The "O e seas Japanese Companies" da ase , p o ided by
Toyo Keizai Inc, is widely used in in e na ional business
esea ch. The e sion o he da ase we used in his s udy
includes in o ma ion on Japanese i ms' o eign subsidia ies
ope a ing in 155 coun ies du ing he pe iod om 1991 o
2009. This e sion o he da ase co e s o e 2100 i ms wi h
mo e han 26,000 subsidia ies ope a ing in a ious indus ial
sec o s. Acco ding o a epo om Wes e n Uni e si y in
Oc obe 2022, his da ase has been used in a leas 161 pee -
e iewed publica ions ac oss ields such as managemen and
o ganiza ion esea ch, in e na ional business, poli ical sci-
ence, accoun ing, inance, and o he social sciences (I ey,
2015).
Analys s
Le e aging ou pe sonal ne wo ks and email ad e isemen s
(Ma e ial 10 on OSF), we ec ui ed a c owd o 57 esea ch-
e s based in 20 di e en coun ies and e i o ies. Thei ages
anged om 24 o 57, wi h a mean o 34.88 yea s (SD =
8.16). Twen y- wo (38.60%) sel -iden i ied as women and 35
(61.40%) as men. The egions wi h he mos analys s we e
mainland China (13 analys s, 22.81%) and he Uni ed S a es
(12 analys s, 21.05%), ollowed by Aus alia ( i e analys s,
1107Jou nal o In e na ional Business S udies (2025) 56:1102–1124
8.77%), Singapo e ( ou analys s, 7.02%), India ( h ee ana-
lys s, 5.26%), New Zealand ( h ee analys s, 5.26%), he
Czech Republic ( wo analys s, 3.51%), Hong Kong ( wo
analys s, 3.51%), and Taiwan ( wo analys s, 3.51%). Aus-
ia, B azil, Colombia, Denma k, Ge many, G eece, I eland,
Japan, Malaysia, Thailand, and he Uni ed Kingdom had one
analys each. See Supplemen 8 o a summa y o he ana-
lys s’ academic p o iles and hei demog aphics.
Al hough analys s came om a a ie y o disciplina y
backg ounds, such as economics, o ganiza ional beha io ,
and inance, he majo i y (82.46%) o hem we e om s a-
egic managemen o in e na ional business. They epo ed
an a e age o 7.01 yea s o expe ience wi h da a analysis
(SD=4.39). Fo y (70.18%) o hem had i e o mo e yea s
o expe ience wi h da a analysis and 34 (59.65%) analys s
pe o med da a analyses a leas once pe week. The 57 ana-
lys s included h ee Full P o esso s (5.26%), eigh Associ-
a e P o esso s (14.04%), 22 Assis an P o esso s (38.6%),
20 doc o al s uden s (35.09%), one pos -doc o al s uden ,
wo Mas e s s uden s and one eaching ellow. Ou sample
o analys s was skewed owa ds junio academics. Senio
ep esen a ion also d opped om 24 o 19% o e he cou se
o he p ojec , pe haps in pa due o he highe oppo uni y
cos s o ime o Full and Associa e P o esso s. The s a is-
ics (Supplemen 8) ob ained om he p e-su ey (Ma e ial
2 on OSF) show ha 37 (64.91%) analys s had published
a leas one scien i ic pape , and 12 (21.05%) analys s had
published i e o mo e pape s in s a egic managemen o
in e na ional business. A o al o 11 (19.30%) analys s had
published a leas one pape wi h a p ima y con ibu ion in
me hodology o s a is ics, and eigh (14.04%) had augh a
leas one s a is ics class.
P ojec websi e
The p ojec websi e p o ided de ailed in o ma ion abou
he p ojec o colleagues po en ially in e es ed in aking
pa (Ma e ial 9 on OSF). In he backg ound sec ion, we
in oduced he pu pose o he p ojec and he da abase
ha would be employed o es he esea ch ques ions. The
da a desc ip ion po ion included an o e iew and de ails
ega ding each a iable in he da abase. In he da a analysis
sec ion, we p o ided da a in h ee al e na i e o ma s, spe-
ci ically STATA, SPSS, and Excel. In he FAQ sec ion, we
p o ided answe s o common ques ions analys s migh ha e
in mind, alongside one au ho ’s con ac in o ma ion i hey
had any u he que ies.
P o ocol
We asked he da a analys s o pe o m hei esponsibili ies
independen ly by comple ing i e s eps: (1) comple e he
p e-su ey, (2) access he esea ch ques ions, he da a and
he da a desc ip o , (3) unde ake he analyses o add ess
he ou esea ch ques ions, (4) comple e he pos -su ey,
and (5) upload hei esul s and s a is ical code om hei
analyses using an online po al.
In he i s s ep, we ecei ed 158 esponses o he p e-
su ey, which asked o an analys ’s belie s abou each
esea ch ques ion as well as hei demog aphic cha ac e is-
ics (Supplemen 8 and Ma e ial 2 on OSF). Ou e iew o
hese 158 esponses showed ha 35 esponses had an accom-
plishmen pe cen age below 50%, which mean a leas hal
o he ques ions we e unanswe ed in hei esponses. In addi-
ion, in he comple ed answe s, he e we e ou esponden s
who submi ed he p e-su ey wice. Ul ima ely, 119 unique
analys s success ully comple ed he p e-su ey.
In S eps 2 and 3, analys s we e p o ided wi h pooled lon-
gi udinal da a, which hey used o es ima e a ious i ing
models, including o dina y leas squa es, logis ic models,
obi models, and gene alized linea models, among o he s.
Membe s o ou c owd wo ked indi idually—consis en
wi h in es iga ions ha a e solo-au ho ed and scien i ic col-
labo a ions ha include a single da a analys —bu in con-
as o he b oad o e all end ac oss ields owa ds la ge
eams o collabo a o s (Wuch y e al., 2007), p esumably
wi h mul iple membe s con ibu ing o he analyses. Pas
many-analys p ojec s ha e ec ui ed bo h eams and indi-
iduals, wi h compa able o e all esul s (e.g., Menk eld
e al., 2024; Schweinsbe g e al., 2021; Silbe zahn e al.,
2018), bu he use o solo analys s does s and as a limi a ion
o his esea ch.
In S ep 4, we ecei ed 93 esponses o he pos -su ey.
Wi hin hese esponses, ou esponden s p o ided an in a-
lid ID numbe and name; eigh esponden s submi ed hei
answe s wice, and h ee esponden s did no inish hei su -
ey. As such, we had 78 analys s wi h comple e in o ma ion
inclusi e o he pos -su ey, which collec ed indi idual ana-
lys s’ belie s ega ding each esea ch ques ion, and de ails on
how hey conduc ed hei analyses. Fo example, we asked
analys s on hei me hods and heo e ical a ionale o ope a-
ionalize impo an a iables (such as En y Mode, In an-
gible Asse s, Policy Unce ain y, Pe o mance, and Le el
o Owne ship), as well as hei easons o he selec ion o
hei s a is ical echnique and con ol a iables (Ma e ial 3
on OSF).
In S ep 5, we ound ha 72 o hese 78 analys s submi -
ed bo h esul s and coding iles. We checked each submis-
sion and ca e ully ep oduced all he analyses and esul s.
Amongs hese 72 analys s, wo analys s did no submi
comple e s a is ical so wa e codes, and 13 had submissions
ha a e i ep oducible due o un ecognizable codes, miss-
ing eg essions, o ambiguous a iables (see Ma e ial 14 on
OSF o de ails). As such, ou ocal analyses a e based on
he 57 analys s whose submissions a e comple e and ep-
licable. Among hese analys s, ou used SPSS o p ocess
1108 Jou nal o In e na ional Business S udies (2025) 56:1102–1124
hei analysis while he emaining 53 used S a a. These
analys s o en p o ided mul iple analyses o one o mo e
esea ch ques ions; hence, we had 227, 126, 98, and 135
speci ica ions o RQ1–4, espec i ely. Ins i u ing quali y
con ol measu es, such as excluding esul s han canno be
ep oduced om he same da a and code, helps ensu e ha
he he e ogenei y in es ima es does no eme ge as an a i ac
o subs anda d wo k by some membe s o he c owd. This
p esen s a conse a i e es o he “many-analys s” phenom-
enon in which c owds o analys s ob ain dispa a e es ima es
using he same da a o es he same hypo hesis. Tha said,
i is impo an o be anspa en abou wha speci ica ions
and esul s we e excluded and why. We ha e pos ed on he
OSF all analys s’ submissions, including hei model speci-
ica ions and esul s, along wi h he da a hey used and ou
o e all p ojec analysis code. The easons o exclusions a e
clea ly s a ed (ei he “incomple e submission” o “analy-
sis no ep oducible”) in Ma e ial 14. In he spi i o open
science, we welcome u he pe spec i es om addi ional
colleagues.
Resul s
E ec size dispe sion ac ossanalys s
Analys s used a wide a ie y o speci ica ions o conduc
hei analyses. No wo analys s adop ed he same app oach
when we conside he a iables, me hod, and sample selec-
ion simul aneously. Ma e ial 4 on OSF p o ides a de ailed
summa y able desc ibing he speci ica ions employed by
each analys o each esea ch ques ion. Supplemen 2 sum-
ma izes he con ol a iables used by he analys s.
To achie e a s anda dized measu e o he e ec sizes o
he independen a iables on dependen a iables ac oss all
analyses, we compu ed he ma ginal e ec sizes (B eznau
e al., 2022; Fey e al., 2023), which ep esen he inc ease
in a dependen a iable o a uni inc ease in an independen
a iable. In Fig.1, we p esen he dis ibu ion g aphs o he
e ec size es ima es ound in he sepa a e analyses o each
esea ch ques ion. Fo all ou esea ch ques ions, he ange
o es ima es encompasses bo h nega i e and posi i e alues
and c osses ze o.
The mos used h eshold o conside ing an indi idual
es ima e s a is ically signi ican is he a bi a y alue o p <
0.05. Whe he o no an es ima e is associa ed wi h a p alue
below o abo e 0.05 gi es a sense o wha an indi idual
Fig. 1 Analys s’ epo ed ma ginal e ec sizes om hei es s o ou in e na ional business esea ch ques ions. No es: Qua iles o he numbe
o analyses and 95% con idence in e als o he e ec sizes a e as indica ed in he igu e
1109Jou nal o In e na ional Business S udies (2025) 56:1102–1124
s and-alone analysis o a pape buil a ound one p ima y
analysis migh ha e concluded ega ding each esea ch ques-
ion. As seen in Table1, he c owd o analys s p oduced
a leas a ew posi i e es ima es ha c ossed he p < 0.05
h eshold and a leas a ew nega i e es ima es ha c ossed
he p < 0.05 h eshold o all ou esea ch ques ions. Thus,
di e en esea che s, gi en he same esea ch ques ion and
he same da ase came o di ec ly opposi e conclusions o
each o he ou ques ions posed o he c owd. As shown in
Table2, he agg ega ion o analyses exhibi s a high le el o
he e ogenei y in es ima es ha is al eady appa en isually.
All RQs a e associa ed wi h a high alue o Coch an's Q
and I2.
Agg ega ing ac ossdi e se app oaches and esul s
Agg ega ing ac oss all he speci ica ions e ealed an o e all
di ec ional e ec wi h a 95% con idence in e al excluding
ze o o RQ1 (mean=0.044, 95% CI=[0.011, 0.077]) and
RQ2 (mean=23.817, 95% CI=[11.967, 35.668]), bu no
o RQ3 and RQ4 (Table2). No e ha he con idence in e -
als o he agg ega ed es ima e o RQ1 almos includes
ze o, which means we d aw a cau ious conclusion on a
di ec ional e ec (Benjamin e al., 2018). I is impo an o
no e ha his in e ence ha we d aw om agg ega ing e ec
sizes is no based on double-coun ing, as can happen in a
me a-analysis ha includes mul iple esul s om he same
Table 1 Resul s o esea ch ques ions 1–4 based on di ec ion and whe he hey c oss he con en ional p < .05 h eshold
All ou esea ch ques ions we e s a ed o analys s in a non-di ec ional manne . Fo RQ1, a posi i e e ec size means he analys es ima es a
posi i e ela ionship be ween wholly-owned subsidia y (WOS) and o eign subsidia y pe o mance, a he han join en u e (JV) o acquisi ion.
A wholly-owned subsidia y is en i ely owned and managed by a pa en o eign company. A join en u e is a i m c ea ed by local i ms and
o eign i ms, gene ally wi h sha ed owne ship, e u ns, isks, and go e nance. An acquisi ion is a ansac ion in which a o eign i m pu chases
mos o all o ano he company’s sha es o gain con ol o ha company. Fo RQ2, a posi i e e ec size means he analys es ima es a posi i e
ela ionship be ween in angible asse s and a i m's le el o owne shipin i s o eign subsidia ies. Fo RQ3, a posi i e e ec means he analys
es ima es a posi i e ela ionship be weenpolicy unce ain y and a i m's le el o owne shipin i s o eign subsidia ies. Fo RQ4, a posi i e e ec
means he analys inds ha high policy unce ain y makes he ela ionship be ween in angible asse s and a i m’s le el o owne ship in i s o -
eign subsidia ies mo e posi i e.
Resea ch ques ion Posi i e e ec
size & p < 0.05
Posi i e e ec
size & p > 0.05
Nega i e e ec
size & p > 0.05
Nega i e e ec
size & p < 0.05
1. Wha is he ela ionship be ween en y mode and o eign subsidia y
pe o mance?
27.31% (n=62) 28.19% (n=64) 24.23% (n=55) 20.26% (n=46)
2. Wha is he ela ionship be ween in angible asse s and a i m’s le el
o owne ship in i s o eign subsidia ies?
67.72% (n=86) 5.51% (n=7) 8.66% (n=11) 18.11% (n=23)
3. Wha is he ela ionship be ween policy unce ain y and a i m’s
le el o owne ship in i s o eign subsidia ies?
32.65% (n=32) 12.24% (n=12) 25.51% (n=25) 29.59% (n=29)
4. How does he le el o policy unce ain y mode a e he ela ion-
ship be ween in angible asse s and a i m's le el o owne ship in i s
o eign subsidia ies?
20.59% (n=28) 37.50% (n=51) 32.35% (n=44) 9.56% (n=13)
Table 2 He e ogenei y s a is ics o he ou esea ch ques ions
He e ogenei y es s a e conduc ed wi h andom-e ec s models. Repo ed e ec sizes a e agg ega ed in means. The b acke s con ain he 95%
con idence in e als o he espec i e s a is ic. Gi en he na u e o he di e se choice o a iables and measu es, means p o ide an o e iew o
collec i e esul s. They sugges ha he ocal subsidia y will ha e, on a e age, a chance o 4.4% o be “p o i able” when i is a wholly owned
en u e (RQ1), a 1% inc ease in he a io o R&D expendi u e o o al sales o a pa en i m will lead o a 23.77% inc ease in i s owne ship in i s
subsidia ies (RQ2), and so o h. Coch an’s Q is he weigh ed sum o squa ed di e ences be ween he e ec sizes o indi idual analyses and he
pooled e ec sizes ac oss analyses. All he Q alues in his able ha e a p alue ha is less han 0.001 and e lec a high le el o he e ogenei y. I2
is he pe cen age o a iance ac oss analyses ha is due o he e ogenei y a he han chance. I2 alues highe han 75% a e usually conside ed o
indica e high he e ogenei y. S a is ics o Bayes Fac o Bound a e gene a ed using p alues (Benjamin & Be ge , 2019). The means o he pa ial
co ela ion coe icien s be ween ocal independen and dependen a iables a e s a ed as a e e ence o a s anda diza ion o e ec sizes (Fi zge -
ald, 2024; S anley & Doucouliagos, 2012). The numbe s o analyses used o his s anda diza ion a e in pa en heses.
Resea ch
ques ions
Numbe
o ana-
lys s
Numbe
o analy-
ses
Repo ed e ec size means Q I2Bayes ac o bound means Pa ial co ela ion coe icien
means
RQ1 52 227 0.044 [0.011, 0.077] 22,835.295 99.05% 138.544 [50.670, 226.417] 0.018 [0.010, 0.026] (222)
RQ2 54 127 23.817 [11.967, 35.668] 3,159.866 96.01% 819.416 [309.150, 1329.681] 0.028 [0.018, 0.038] (72)
RQ3 52 98 −0.246 [−1.692, 1.201] 5,976.930 98.38% 88.484 [15.739, 161.230] 0.009 [−0.008, 0.025] (61)
RQ4 52 136 −3.808 [−12.829, 5.213] 578.095 76.65% 13.178 [7.667, 18.690] 0.002 [−0.005, 0.009] (60)
1116 Jou nal o In e na ional Business S udies (2025) 56:1102–1124
Po en ial coun e measu es
Add essing bo h s a egic p-hacking and inhe en subjec-
i i y in da a analysis is c i ical o ensu ing he eliabili y
and obus ness o ou science. Al hough bo h in ol e an
unce ain ga den o o king pa hs (Gelman & Loken, 2014)
h ough a scien i ic inqui y, we belie e he mos e ec i e
coun e measu es a e di e en . In Table5, we o e a lis
and ypology o in e en ions and classi y hem as e ec i e
agains ei he s a egic o inhe en elas ici y in esea che
decision making. Se e al in e en ions a e in p inciple e ec-
i e a cu ailing p-hacking, in pa icula p e- egis a ion o
analyses (Wagenmake s e al., 2012), c ea ing a es and
holdou sample (Al man, 1968) and blind analyses (Mac-
Coun & Pe lmu e , 2015). In he complex a chi al da a
commonly used in in e na ional business s udies, da a mus
o en be explo ed as pa o he p ocess o unde s anding,
ende ing p e- egis a ion and blind analyses po en ially
o e - es ic i e (King e al., 2021). Thus, a es -holdou
app oach could be compa a i ely mo e use ul in quan i a-
i e esea ch using complex da ase s, o allow explo a ion
while a he same ime p e en ing p-hacking (Gold a b &
Yan, 2021).
Al hough inhe en elas ici y is a guably he g ea e chal-
lenge o science, i is no in e pe sonally in lamma o y like
allega ions o p-hacking since he o me does no call he
e hical in eg i y o he scien is in o ques ion in any way.
Demons a ing ha suba eas o he schola ly li e a u e
a e cha ac e ized by publica ion bias, p-hacking by some
esea ch eams, and, in some cases, a lack o e iden ia y
alue helps o he scien is s iden i y he mos eliable bodies
o knowledge upon which o build (Egge e al., 1997; Gold-
a b & King, 2016; Simonsohn e al., 2014; S anley, 2005).
A he same ime, accusa ions o s a egic analyses agains
indi idual pape s and esea ch eams can be p oblema ic
and un ai —i we un publica ion bias o p-hacking es s on
enough indi idual a icles, ine i ably some will be lagged
o con aining p oblema ic da a, e en i none o hem do
(Simonsohn, 2013).
Mo ing o wa d, i is also wo h conside ing ou collec-
i e in e es in maximizing he c edibili y o wha appea s
in he published li e a u e. We he e o e sugges ha wi h
ega d o indi idual u u e in es iga ions, i is ypically be -
e o cu ail s a egic elas ici y be o e he ac han a emp
o expose i a e he ac . P oposed new p ac ices, such as
p e- egis a ion and es -holdou also ha e sho comings,
such as educed e iciency o he equi emen o e y la ge
samples and hus should be deployed on a case-by-case
basis. The ul ima e objec i e should be an imp o ed esea ch
ecosys em, no he una ainable goal o scien i ic pe ec ion.
In con as , inhe en elas ici y, such as ha e ealed he e,
may be ine i able in scien i ic inqui y. Di e en scien is s, ac -
ing en i ely in good ai h, will plan ou di e en app oaches in
ad ance, analyze da a blindly in di e en ways, and explo e
di e en pa hs h ough a es sample. The bes a ailable
op ions a e o ende inhe en elas ici y anspa en h ough
mul i e se and c owd analyses, expanded obus ness es s, and
he open pos ing o da a o e-analysis by colleagues. Open
da a sha ing should be encou aged by in e na ional business
jou nals on publica ion, al hough his, o cou se, is no pos-
sible o da ase s en ailing con iden iali y conce ns and p o-
p ie a y da ase s ob ained h ough ag eemen s wi h pa ne
i ms and da a p o ide s. To he ex en ha da a can be made
open, he communi y can collec i ely p obe he obus ness
o indings using al e na i e speci ica ions (Gold a b & Yan,
2021) and wo k oge he o iden i y issues pos -publica ion.
We he e o e p esen a ision o an open science o in e na-
ional business (Meye e al., 2020) in which in e en ions a e
conside ed o p e en p-hacking be o e i happens when his
is a signi ican conce n, a single pape epo s mo e analy ic
app oaches and obus ness es s han is cu en ly he no m
(Sala-i-Ma in, 1997; S eegen e al., 2016), and he da a and
code a e made publicly a ailable o he communi y when pos-
sible. When aced wi h a wide dispe sion in es ima es ac oss
di e en speci ica ions, in e na ional business schola s should
seek o an icipa e and de ec heo e ically in o ma i e mode a-
o s, and ailing his, conside agg ega ion o each a en a i e
conclusion. The p inciples o open science u he encou -
age esea che s o eplica e analyses ac oss di e en se s o
obse a ions, especially al e na i e ime pe iods and coun ies.
This helps de elop a no m o examining he gene alizabil-
i y e sus he con ex sensi i i y o esea ch indings (Delios
e al., 2022). The app oaches we ad oca e ha e ad an ages and
disad an ages, as do adi ional p ac ices. Howe e , open sci-
ence leads o mo e anspa en esul s and accu a e in e ences
(Nosek e al., 2022) on behal o he s akeholde s we se e.
Tes ing hypo heses in many ways is conside ably mo e
wo k han social scien is s a e used o doing o make a sin-
gle claim. Mul i e se analyses, c owd analyses, agg ega ing
ac oss nume ous obus ness es s, and assessmen s o gene -
alizabili y ac oss ime and geog aphies ep esen depa u es
om s anda d scien i ic p ac ices and incen i e s uc u es.
Howe e , a adi ional small science pape epo ing a single
p ima y analysis o he esul s o one esea ch design should
no be solely elied on o make s ong claims o de e mine
policy, e en in he absence o any esea che bias owa ds
a desi ed conclusion. The esea ch communi y will need
o inco po a e mo e e o med p ac ices i we wish o d aw
s ong in e ences (Pla , 1964).
Conclusions
The majo con ibu ions o his i s c owd science ini ia-
i e in he ield o in e na ional business a e mul i ace ed
and in e ela ed. We add ess ou impo an un esol ed
1117Jou nal o In e na ional Business S udies (2025) 56:1102–1124
Table 5 App oaches o p e en ing and de ec ing s a egic and inhe en elas ici y in analy ic p ocedu es
A longe e sion o his able is a ailable in Supplemen 9
App oach Desc ip ion Aimed a inhe -
en o s a egic
elas ici y
P e en s o de ec s
Tes -holdou sample app oach The da ase is spli in o wo pieces, wi h explo a o y
analyses conduc ed on he i s piece and con i ma-
o y analyses on he second piece (e.g., Al man, 1968;
O ù e al., 2020; Sh es ha e al., 2021)
S a egic P e en s
P e- egis a ion Resea che speci ies he analy ic plan be o e collec ing
o ob aining he da a (Wagenmake s e al., 2012)
S a egic P e en s
Da a-blind analysis O iginal esea che elabels o ecodes some o he key
a iables be o e conduc ing he analysis, such ha she
is unawa e o he di ec ion and na u e o he obse ed
ela ionships (MacCoun & Pe lmu e , 2015)
S a egic P e en s
Independen e-analysis Independen esea che uses an al e na i e analy ic
app oach o es he same heo e ical idea (e.g.,
Simonsohn, 2011)
Bo h De ec s
E o analysis Independen esea che iden i ies e o s in da a han-
dling o analysis, and may show ha hese e o s sys-
ema ically a o he o iginal hypo hesis (Rosen hal,
1978)
Bo h De ec s
Tes s o publica ion bias and ques ionable
esea ch p ac ices
Me a-scien i ic analysis o a s udy se o indi idual
s udy o es o omi ed s udies and da a pa e ns con-
sis en wi h ques ionable esea ch p ac ices (Ioannidis
& T ikalinos, 2007; Simonsohn e al., 2014)
S a egic De ec s
“Ch ysalis” analysis Compa ison o ea lie s. la e e sions o esea ch
epo s (e.g., doc o al disse a ions s. published
epo s o he same wo k) (O’Boyle e al., 2017)
S a egic De ec s
Conduc ing an independen eplica ion s udy Independen esea ch eam collec s new da a using he
same me hodology and uns he same analyses as
desc ibed in he o iginal pape (e.g., Open Science
Collabo a ion, 2015). P-hacked o iginal indings a e
less likely o eplica e, bu ailed eplica ions can
occu o many o he easons as well
S a egic De ec s
Red eam app oach Independen esea ch eam a emp s o debias and op i-
mize an o iginal in es iga ion be o e i is submi ed
o publica ion (Lakens, 2020)
S a egic P e en s
Re ospec i e e-assessmen O iginal esea che decides a e - he- ac ha he analy-
sis was sub-op imal and a di e en app oach would
ha e been be e (Roh e e al., 2021)
Bo h De ec s
Compa ing employed analyses A single esea che su eys he a ious analy ic
app oaches used in di e en s udies on he same opic,
iden i ies inconsis encies wi hin and ac oss esea ch
g oups, and po en ially applies all app oaches o he
same da a (e.g., Elson e al., 2014; Ha is e al., 2013)
S a egic De ec s
S a egic analysis A c owd o analys s is asked o ei he p oduce s a is i-
cally signi ican suppo o he o iginal hypo hesis
(di ec ional goal) o p o ided wi h no goal when
analyzing he da a (ongoing c owd ini ia i e ia he
Uni e si y o Pennsyl ania’s Ad e sa ial Collabo a-
ion P ojec )
S a egic De ec s
Mul i e se analysis o speci ica ion cu e One analys employs nume ous speci ica ions (Simon-
sohn e al., 2020; S eegen e al., 2016)
Inhe en De ec s
Many analys s Many esea che s analyze he same da a o es he
same hypo hesis (Silbe zahn e al., 2018)
Inhe en De ec s
1118 Jou nal o In e na ional Business S udies (2025) 56:1102–1124
ques ions in he ield by le e aging he insigh s o 57 inde-
penden schola s. The insigh s collec i ely sugges a di ec-
ional answe o wo esea ch ques ions, each he subjec o
nume ous p io in es iga ions ac oss decades o s udy ha
only yielded mixed and con lic ing esul s.
A he same ime, we ake s ock o he small bu g ow-
ing li e a u e on he many-analys s phenomenon (Silbe zahn
e al., 2018). Choice poin s ma e in esea ch using com-
plex a chi al da a, bu we do no ye ully unde s and he
easons o and consequences o choice poin e ec s. The
p esen empi ical esul s indica e ha he e ogenei y in na u-
ally eme ging app oaches and esul ing es ima es poses a
signi ican challenge o managemen schola ship (Ke oki i
& Man e e, 2010; King e al., 2021; Man e e & Ke oki i,
2013). In e ms o pa sing his a iance in es ima es, we
epo he s onges e idence ye ha a iable ope a ionali-
za ions and esea che expe ise ma e . A he same ime,
we explo e c oss-ques ion di e ences, inding ha a high
heo e ical consensus ega ding wha “should” happen is no
gua an ee o empi ical consis ency in es ima es. Encou ag-
ingly, howe e , whe e heo y is s ong, as o he di ec ion
o asse s and owne ship hypo heses, agg ega ion e eals
an o e all es ima e consis en wi h heo y, pending u he
e idence. Likewise, suppo ing he idea o a communi y
building knowledge oge he , schola s upda ed hei belie s
conside ing he empi ical e idence a he han allowing hei
p io con ic ions o bias hei analyses and conclusions.
To u he s eng hen his communi y, we ad oca e in e-
g a ing he knowledge gains om me a-scien i ic c owd
p ojec s like his one in o he e e yday p ac ices o adi-
ional small eams o esea che s. To acili a e open sci-
ence p ac ices in in e na ional business s udies, we p esen
a compendium o in e en ions agains analy ic elas ici y
(see Table5) and a no el ypology based on whe he hey
a ge s a egic o inhe en elas ici y and a e p e en ion o
de ec ion o ien ed.
Al hough ou p esen c owd ini ia i e demons a es he
c i ical ole o subjec i e esea che choices in shaping
esul s in in e na ional business esea ch, i also unde sco es
ha meaning ul in e ences emain possible in science. By
sampling analyses and s imuli widely, and le e aging he
insigh s de i ed om a di e si y o app oaches, we can make
eal p og ess on he key scien i ic ques ions o in e es o a
ield.
Supplemen a y In o ma ion The online e sion con ains supplemen-
a y ma e ial a ailable a h ps:// doi. o g/ 10. 1057/ s41267- 025- 00808-9.
Acknowledgemen s This p ojec was suppo ed by he Mul i-Yea
Resea ch G an om he Uni e si y o Macau (Re e ence No.: MYRG-
GRG2024-00140-FBA), awa ded o Tianyou Hu, and he Na ional Na -
u al Science Founda ion o China (P ojec No.: 72572121,72122016),
awa ded o Nan Zhou. E ic Uhlmann is g a e ul o unding om he
INSEAD R&D commi ee. We acknowledge he ollowing analys s
as co-au ho s, wi h whom we ha e un o una ely los con ac : Liang
Hu (Jinan Uni e si y), Mahdi Fo ghani Bajes ani (Old Dominion Uni-
e si y), Aqi Liu (The Chinese Uni e si y o Hong Kong), Fa emeh
Aska zadeh (Old Dominion Uni e si y), Benjamin K ebs (Pade bo n
Uni e si y), and Jiao Li (China Eu ope In e na ional Business School).
We a e g a e ul o esea ch assis ance om Jimmy Chen, Yuehan Chu,
Nihong Li, Yixiao Han, Yize Liu, Ra ael Goldszmid , Ke ui Gao, Lix-
uan Xu, and Zhiyi Wu o hei ma e ial o ganiza ion, da a p epa a ion,
and adminis a i e assis ance.
Da a A ailabili y S a emen Ou me hods and analyses we e p e-
egis e ed a h ps:// os . io/ 4euaj. The supplemen a y documen o
hispape is accessiblea h ps:// doi. o g/ 10. 1057/ s41267- 025- 00808-
9.Fu he Resul s and Ma e ials a e pos ed on he Open Science
F amewo k a h ps:// os . io/ ew3 z/? iew_ only= 52ab5 518ad a34e0
ca104 6aeab 08a51 22.
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Publishe 's No e Sp inge Na u e emains neu al wi h ega d o
ju isdic ional claims in published maps and ins i u ional a ilia ions.
And ew Delios (PhD, I ey Business School, Wes e n Uni e si y o
On a io) is P o esso and Vice Dean (MSc P og ams), NUS Business
School, Na ional Uni e si y o Singapo e. He is a Fellow o he Acad-
emy o In e na ional Business and he Asia Academy o Managemen .
His esea ch in e es s a e s a egy and he in e na ionaliza ion p ocess,
wi h a ocus on i ms ope a ing in he Asia-Paci ic egion.
Tianyou Hu is Assis an P o esso in he Facul y o Business Admin-
is a ion a he Uni e si y o Macau. He ecei ed his unde g adua e
deg ee om Peking Uni e si y and his PhD om Na ional Uni e si y
o Singapo e. His cu en esea ch in s a egy and in e na ional busi-
ness ocuses on i m s a egy wi hin alliances and ne wo ks, and how
mul ina ional i ms na iga e ins i u ional en i onmen s in o eign di ec
in es men .
Shu Yu (PhD, Na ional Uni e si y o Singapo e) is Senio Resea ch
Fellow a he Suzhou Indus ial Pa k Monash Resea ch Ins i u e o
Science and Technology and Senio Lec u e a he Monash Uni e si y
Suzhou Campus. He esea ch in e es s lie in co po a e and in e na-
ional s a egy, wi h a ocus on eme ging economies.
Nan Zhou is P o esso a he School o Economics and Managemen ,
Tongji Uni e si y in China. She ecei ed a PhD om he Wha on
School, Uni e si y o Pennsyl ania. He esea ch add esses ques ions
ha in e sec he ields o co po a e s a egy and in e na ional business,
ocusing p ima ily on unde s anding how i m g ow h is in luenced by
i m esou ces and ins i u ional en i onmen s in he con ex o eme g-
ing ma ke s.
E ic Uhlmann is P o esso o O ganiza ional Beha io a INSEAD.
His esea ch cen e s on s e eo yping and disc imina ion, mo al judg-
men s and beha io s, c oss-cul u al simila i ies and di e ences, and
c owdsou cing science. He ecei ed a PhD in Social Psychology om
Yale Uni e si y in 2006 and was a pos doc o al schola a he Kellogg
School o Managemen .
1123Jou nal o In e na ional Business S udies (2025) 56:1102–1124
Au ho s and A ilia ions
And ewDelios1· TianyouHu2· ShuYu3· NanZhou4· FaisalM.Ahsan5· MonaBahl6· TaoBai7· Madhu imaBasu8·
HanokuBa hula9· Geo giosBa sakis10· Jo geCa nei o11· Dwa kaChak a a y12· DanyangChen13·
WeihongChen14· Ying‑YuChen15· LuisAl onsoDau16· ShuDeng17· Desisla aDiko a18· XiaominFan19·
ViswaP asadGada20· DongdongHuang21· HyunGonKim22· KyungjoongKim23· AlešKubíček24·
ChengguangLi25· WenHelenaLi26· YiLi27· YuanyuanLi28· Yung‑ChihLien29· HuanchenLiu30· WeiLiu31·
G igo ijLjubownikow32· RachealLouisVincen 33· OndřejMachek34· Pa ulManocha35· YoichiMa sumo o36·
A haphonMumi37· ChaoNiu38· N.Nu uzzaman39· VidyaSukuma aPanicke 40· K.P a eenPa bo eeah41·
WeiQiao42· XiaoleQiao43· ShyamalaSe hu am44· AoShen45· LeiShi46· E isSinani47· SandeepSi akuma 48·
Pei‑ShanSoon49· MaximilianS allkamp50· P ii Tini s51· DanielTols oy52· And anikTumasjan53·
MayankVa shney54· And esVelez‑Calle55· Ch isWagne 56· PengWang57· XingangWang58· YongWang59·
LiangWen60· TaoWu61· SandeepYada 62· JiajuYan63· JingYuYang64· MeganZhang65· WeihaoZhang66·
YamengZhang67· YangZhao68· E icUhlmann69
* Tianyou Hu
ian[email p o ec ed]
* Nan Zhou
[email p o ec ed]
1 Na ional Uni e si y o Singapo e, Singapo e, Singapo e
2 Facul y o Business Adminis a ion, Uni e si y o Macau,
E22, A enida da Uni e sidade, Taipa, Macau
3 Monash Uni e si y & Suzhou Indus ial Pa k Monash
Resea ch Ins i u e o Science andTechnology, Suzhou, China
4 School o Economics andManagemen & Ad anced
Ins i u e o Business, Tongji Uni e si y, 1500 Siping Road,
Shanghai200092, China
5 Xa ie School o Managemen , Jamshedpu , India
6 Illinois S a e Uni e si y, No mal, USA
7 The Uni e si y o Queensland, B isbane, Aus alia
8 Symbiosis Ins i u e o In e na ional Business & Symbiosis
In e na ional (Deemed Uni e si y), Pune, India
9 The Uni e si y o Auckland, Auckland, NewZealand
10 The Ame ican College o G eece & B unel Uni e si y
o London, Uxb idge, UK
11 FGV EAESP Sao Paulo, School o Business Adminis a ion,
SãoPaulo, B azil
12 San Diego S a e Uni e si y, SanDiego, USA
13 Shanghai Uni e si y o Finance andEconomics, Shanghai,
China
14 Guangxi Uni e si y, Nanning, China
15 Na ional Dong Hwa Uni e si y, Taiwan, China
16 No heas e n Uni e si y, Bos on, USA
17 The Uni e si y o Mississippi, Ox o d, USA
18 Vienna Uni e si y o Economics andBusiness, Vienna,
Aus ia
19 Nanjing Uni e si y o Science andTechnology, Nanjing,
China
20 Glasgow Caledonian Uni e si y, Glasgow, UK
21 Nankai Uni e si y, Tianjin, China
22 The S a e Uni e si y o New Je sey, NewB unswick, USA
23 No hwes Missou i S a e Uni e si y, Ma y ille, USA
24 P ague Uni e si y o Economics andBusiness, P ague,
Czechia
25 Technical Uni e si y o Munich, Munich, Ge many
26 Uni e si y o Technology Sydney, Ul imo, Aus alia
27 The Uni e si y o Sydney, Campe down, Aus alia
28 Cali o nia S a e Uni e si y, Los Angeles, LosAngeles, USA
29 Na ional Taiwan Uni e si y, Taipei, Taiwan
30 Nanjing Uni e si y o Ae onau ics andAs onau ics, Nanjing,
China
31 Qingdao Uni e si y, Qingdao, China
32 The Uni e si y o Auckland, Auckland, NewZealand
33 Monash Uni e si y Malaysia, SubangJaya, Malaysia
34 P ague Uni e si y o Economics andBusiness, P ague,
Czechia
35 Uni e si y o Alabama a Bi mingham, Bi mingham, USA
36 Keio Uni e si y, Mina o, Japan
37 Mahasa akham Uni e si y andNa ional Ins i u e
o De elopmen Adminis a ion, KhamRiang, Thailand
38 Ci y Uni e si y o Hong Kong, Kowloon, HongKong
39 Uni e si y o Manches e , Manches e , UK
40 Loughbo ough Business School, Loughbo ough, UK
41 Uni e si y o Wisconsin – Whi ewa e , Whi ewa e , USA
42 Xiamen Uni e si y, Xiamen, China
43 Chang’an Uni e si y, Xi’an, China
44 Ba uch College, NewYo k, USA
45 Xidian Uni e si y, Xi’an, China
46 Uni e si y o In e na ional Business andEconomics, Beijing,
China
47 Copenhagen Business School, F ede iksbe g, Denma k
48 Indian Ins i u e o Managemen Raipu , Raipu , India
49 Sunway Uni e si y, SubangJaya, Malaysia
1124 Jou nal o In e na ional Business S udies (2025) 56:1102–1124
50 Eas Ca olina Uni e si y, G een ille, USA
51 Pe manen Rep esen a ion o Es onia o heOECD, Pa is,
F ance
52 S ockholm School o Economics, S ockholm, Sweden
53 Johannes Gu enbe g Uni e si y Mainz, Mainz, Ge many
54 Indian Ins i u e o Managemen Ahmedabad, Ahmedabad,
India
55 Uni e sidad EAFIT, Medellín, Colombia
56 ananki.ai GmbH, MenloPa k, USA
57 Beijing No mal-Hong Kong Bap is Uni e si y, Zhuhai,
China
58 The Uni e si y o Auckland, Auckland, NewZealand
59 Sou hwes Jiao ong Uni e si y, Chengdu, China
60 Xi’an Jiao ong-Li e pool Uni e si y, Suzhou, China
61 The Chinese Uni e si y o Hong Kong, Shenzhen, China
62 Indian Ins i u e o Managemen Bangalo e, Bengalu u, India
63 Baylo Uni e si y, Texas, USA
64 The Uni e si y o Sydney, Sydney, Aus alia
65 Independen Resea che , NewYo k, USA
66 Guangxi Uni e si y, Nanning, China
67 Xi’an Jiao ong-Li e pool Uni e si y, Suzhou, China
68 No hwes No mal Uni e si y, Lanzhou, China
69 INSEAD, Singapo e, Singapo e
