Leveraging social capital to build the cumulative triple-A supply chain sand cone model

In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen
Le e aging social capi al o build he cumula i e iple-A
supply chain sand cone model
Jou nal:
In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen
Manusc ip ID
IJPDLM-12-2023-0449.R4
Manusc ip Type:
Resea ch Pape
Keywo ds:
iple-A SC, agili y, adap abili y, alignmen , sand cone model, inancial
pe o mance, social capi al
In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen
In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen
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Le e aging social capi al o build he cumula i e iple-A supply chain sand cone model
Abs ac
Pu pose — D awing on he cumula i e capabili y pe spec i e, his s udy es s he sand cone
model o he iple-A supply chain (SC) (i.e., AAA: SC-alignmen , SC-adap abili y, SC-agili y),
including i s inancial pe o mance implica ions. Besides, his s udy in es iga es social capi al
as AAA’ enable .
Design — S uc u al equa ion modeling and boo s apping analysis a e used o examine
hypo heses using da a om 216 companies in China ha cap u e i ms’ supply chain
managemen p ac ices in ela ion o hei majo supplie s.
Findings — We iden i ied a cumula i e sand cone sequence o h ee As: alignmen -
adap abili y-agili y o e ec i ely de elop a iple-A SC. Fu he mo e, based on his sequence,
SC adap abili y can enhance inancial pe o mance indi ec ly h ough SC agili y, and SC
alignmen can imp o e inancial pe o mance indi ec ly h ough SC adap abili y and SC agili y,
which di ec ly and posi i ely a ec s inancial pe o mance. Fu he mo e, cogni i e, s uc u al,
and ela ional capi al play di e en oles in imp o ing AAA.
O iginali y/ alue — This s udy con ibu es o iple-A SC li e a u e by iden i ying he
cumula i e sand cone sequence o alignmen -adap abili y-agili y and hus u he ex ends he
cumula i e capabili y pe spec i e in ope a ions and supply chain managemen . Besides, his
s udy: a) deepens ou unde s anding o pe o mance implica ions o iple-A SC capabili ies
based on he sand cone model; b) con ibu es o e ealing social capi al as an impo an enable
o iple-A SC capabili ies om he complex adap i e sys em pe spec i e; (c) speci ies
di e ence in he pa e n o iple-A SC sand cone model ac oss di e en le els o ma ke
u bulence.
Keywo ds T iple-A SC (SC-agili y, SC-adap abili y, SC-alignmen ), Sand cone model,
Financial pe o mance, Social capi al
Pape ype Resea ch pape
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1. In oduc ion
The amewo k o supply chain agili y, adap abili y, and alignmen ( iple-A SC o AAA SC
he ea e ) was i s p oposed by Lee (2004). I sugges s enhancing SC wi h iple-A capabili ies
o be e add ess he e ol ing business en i onmen a he han ocusing solely on e iciency
enhancemen and cos sa ings. The impo ance o iple-A SC is highligh ed in he e a o pos -
Co id-19 and geopoli ical ensions (Pa ucco and Kähkönen, 2021; Khan e al., 2023). Fo
ins ance, amids he China-US ade con lic , Huawei's SC has exhibi ed ema kable AAA
capabili ies and achie ed inancial g ow h despi e na iga ing high en i onmen al unce ain y.
T iple-A SC has been gaining inc easing academic in e es . Al hough ex an empi ical
esea ch on iple-A SC conside s he e ec o 3As ac ing simul aneously, i mainly ocuses on
hei pe o mance ou comes (e.g., Khan e al., 2023; Machuca e al., 2021; Dubey and
Gunaseka an, 2016) and an eceden s (I anmanesh e al., 2023; Ga ido-Vega e al., 2023).
Compa ed wi h hese s udies, only e y ew s udies on iple-A SC conside he
in e ela ionships among AAA (e.g., Ecks ein e al., 2015; Dubey and Gunaseka an, 2016;
I anmanesh e al., 2023) (see supplemen a y ma e ial, Appendix A[1]) despi e he in e es and
implica ions ha his may ha e. Appendix A shows e idence ha he exis ing li e a u e on
iple-A SC mainly ocused on ela ionships be ween wo As along wi h a lack o in eg a ed
heo e ical amewo k.
The e o e, his esea ch p oposes ha he cumula i e sand cone model enables us o
explain sa is ac o ily he in e ela ionships o all iple-A SC dimensions, hus con ibu ing o
he li e a u e on his opic. Some exis ing heo e ical pe spec i es (e.g., ade-o model,
complemen a i y pe spec i e, ambidex ous pe spec i e) p o ide implica ions o managing
in e ela ed esou ces and capabili ies. Fo ins ance, he ade-o model o Skinne (1969)
sugges s ha mul iple capabili ies mus be aded o as he imp o emen in one capabili y mus
come a he expense o ano he capabili y due o esou ce cons ain s. Howe e , he sand cone
model ha was oo ed in he cumula i e capabili y heo y asse s ha i ms could ob ain
imp o emen s in mul iple capabili ies, wi h imp o emen s occu ing in a pa icula sequence
(G ößle and G übne , 2006) and cumula i ely ein o cing each o he (Rosenzweig and Roh,
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2004; Flynn and James Flynn, 2004). On he one hand, he e may be a sca olding e ec whe e
he ounda ional capabili y (e.g., one A in ou case) helps he ensuing capabili y (e.g., ano he
A) mo e easily and e ec i ely de elop and wo k (Chen e al., 2023). On he o he hand, i ms
can gain long-las ing imp o emen s i all iple-A SC dimensions a e de eloped cumula i ely
(Schoenhe and Na asimhan, 2012; Nand e al., 2024). The e o e, his s udy p oposes he i s
esea ch ques ion (RQ1): Wha is he sequence o AAA in de eloping a cumula i e iple-A SC
based on he sand cone model o imp o e i m pe o mance?
In es iga ing he enable s o h ee As is impo an o p o iding a mo e comp ehensi e
unde s anding o iple-A SC (Feizabadi e al., 2019). Howe e , compa ed wi h p io s udies
examining he pe o mance implica ions o iple-A SC, ela i ely sca ce esea ch concu en ly
explo ed he an eceden s o h ee As, le alone he e ec i e enable s o de eloping a cumula i e
iple-A SC sand cone model (see supplemen a y ma e ial, Appendix A[1]). We hus u he
p opose ha social capi al could be an impo an d i e o cumula i ely de eloping iple-A
SC capabili ies. The cumula i e de elopmen o iple-A SC equi es esou ces ha canno be
p o ided by a single manu ac u e (Zhang e al., 2023). Social capi al (comp ised o s uc u al,
cogni i e, and ela ional capi al) p o ides unique access o in o ma ion, knowledge, and
esou ces ha span i m bounda ies and a e embedded in in e - i m ela ionships (Tsai and
Ghoshal, 1998; Inkpen and Tsang, 2005), which is i al o ocal i ms o uni e ex e nal pa ne s
o cumula i ely de elop AAA capabili ies (Rod igo-Ala cón e al., 2018). Mo eo e , some
p e ious s udies ha e indi ec ly e ealed he po en ial o social capi al in p omo ing iple-A
SC capabili ies (Gölgeci and Kui alainen, 2020; Vachon e al., 2009). Howe e , he po en ial
o social capi al (comp ised o s uc u al, cogni i e, and ela ional capi al) in di ec ly enhancing
all iple-A SC dimensions has no been ho oughly in es iga ed and con i med by empi ical
esea ch. The e o e, his s udy p oposes he second esea ch ques ion (RQ2): How do di e en
dimensions o social capi al p omo e he de elopmen o iple-A SC capabili ies?
This s udy subs an ially con ibu es o he exis ing body o li e a u e in se e al aspec s.
Fi s , i p o ides a u he unde s anding o he iple-A SC and sheds ligh on he sand cone
sequence o AAA, ex ending he cumula i e capabili y pe spec i e in ope a ions and supply
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chain managemen (OSCM). Second, i p o ides a deepe unde s anding o he associa ion
be ween cumula i e iple-A SC and inancial pe o mance based on he sand cone model.
Thi d, i en iches he ex an li e a u e conce ning bo h iple-A SC and social capi al by
e ealing he mechanisms o how social capi al dimensions enable iple-A SC. This s udy also
p o ides impo an guidelines o manage s o e ec i ely es ablish iple-A SC cumula i ely
by le e aging di e en ypes o social capi al, allowing hem o achie e long-las ing success
in iple-A SC de elopmen o add ess changes and gain supe io inancial pe o mance despi e
esou ce cons ain s.
2. Theo e ical backg ound and hypo hesis de elopmen
2.1 Cumula i e capabili y heo y and sand cone model
The ela ionship be ween mul iple manu ac u ing capabili ies is an impo an elemen o
ope a ions s a egy. The ade-o model, o iginally a icula ed by Skinne (1969), posi s ha
mul iple capabili ies a e incompa ible and need o be aded o as he a ainmen o supe io
pe o mance in one capabili y mus come a he sac i ice o ano he due o he sca ci y o
esou ces (Na asimhan and Schoenhe , 2013). The ade-o model has been ques ioned as
Flynn and James Flynn (2004) no ed ha he ade-o s a e no longe easible in he compe i i e
global en i onmen since i ms a e p essu ed o cumula e along mul iple capabili ies o handle
a iabili ies and compe e e ec i ely. Besides, he complemen a i y pe spec i e indica es ha
mul iple capabili ies could in e ac and ope a e in a complemen a y manne as combined
bundles o enhance he e ec i eness o each o he (Misangyi and Acha ya, 2014). The
ambidex ous pe spec i e emphasizes ha a i m can each an e icien equilib ium be ween
exploi a ion and explo a ion ac i i ies/capabili ies by esol ing ensions be ween hem
(And iopoulos and Lewis, 2009).
Unlike he abo e-men ioned heo e ical pe spec i es ha manage in e connec ed
capabili ies wi hou p obing in o he sequence among hem, he cumula i e capabili ies
pe spec i e posi s ha i ms a e able o achie e imp o emen s on mul iple capabili ies as hese
imp o emen s could ein o ce each o he in a cumula i e sequence. The bes -known sequence
o cumula i e capabili ies is examined h ough he “sand cone model” p oposed by Fe dows
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and De Meye (1990), which ad oca es ha mul iple manu ac u ing capabili ies could be
accumula ed in a speci ic sequence, wi h quali y as he ounda ion ollowed by deli e y,
lexibili y, and cos . The main hypo hesis o he sand cone model is ha i ms can exploi
cumula i e e ec s o achie e maximum imp o emen s in mul iple capabili ies i hey ollow a
speci ic sequence o de elop hese capabili ies in a manne ha ein o ces each o he (Flynn
and James Flynn, 2004; Nand e al., 2024). Acco ding o Fe dows and De Meye (1990),
cumula i e sand cone sequencing can be ex emely impo an . On he one hand, he e migh be
a sca olding e ec , whe e a basic and undamen al capabili y needs o be in place, and based
on his, imp o emen s in subsequen capabili ies can be made mo e easily (Flynn and James
Flynn, 2004). As such, i ms can o e come he limi a ion ha necessi a es ade-o s among
mul iple capabili ies unde esou ce sca ci y. On he o he hand, i ms can obse e long-las ing
imp o emen s in manu ac u ing compe i i eness i he capabili ies a e buil up cumula i ely in
a pa icula sequence (Schoenhe and Na asimhan, 2012) since capabili ies de eloped in a
cumula i e sequence can a oid ade-o s and enhance each o he , hus c ea ing cumula i e
bene i s in capabili y imp o emen s and u he logically ansla ing in o highe i m
pe o mance.
This cumula i e sand cone model has been widely examined in he OSCM li e a u e. Fo
example, Whi e e al. (2010) applied he sand cone model o de i e an e ec i e sequence o
he implemen a ion o JIT managemen p ac ices o supe io ope a ional pe o mance. Gold
e al. (2017) in eg a ed sus ainabili y in o he adi ional sand cone model, which ini ially
encompasses he quali y-deli e y- lexibili y-cos sequence. Mo e ecen ly, Chen e al. (2023)
ex ended he sand cone model o iden i y he mos app op ia e implemen a ion sequence o
he g een SCM, showing ha he sand cone sequence allows i ms o build long-las ing
ad an ages. Molina o e al. (2024) shed ligh on he cumula i e e ec s o he h ee
sus ainabili y pilla s, wi h en i onmen al pe o mance a he base, ollowed by social and
inancial pe o mance based on he sand cone model. This s udy seeks o explo e he
cumula i e sand cone sequence o AAA o e ec i ely de elop a iple-A SC in a mu ually
ein o cing manne .
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2.2 T iple-A SC and i s dimensions
The iple-A SC has been iden i ied as he d i e o sus ained compe i i e ad an age.
Manu ac u e s build iple-A SC capabili ies wi h SC pa ne s by in eg a ing and coo dina ing
hei business s a egies and p ocesses (alignmen ), adjus ing and econ igu ing SC s uc u es
o add ess long- e m ma ke shi s (adap abili y), and esponding quickly o sho - e m changes
in demand and supply (agili y) (Lee, 2004; Al alla-Luque e al., 2018; Sodhi and Tang, 2021).
Speci ically, SC alignmen e e s o he capabili y ha manu ac u e s align and in eg a e
s a egies, goals, and p ocesses wi h hei SC pa ne s o achie e be e pe o mance o bo h
pa ies (Lee, 2004; Flynn e al., 2010; Feizabadi e al., 2021; Sodhi and Tang, 2021). SC
adap abili y is he capabili y ha manu ac u e s and hei SC pa ne s e ec i ely econ igu e
hei s a egies, esou ces, p oduc s, echnologies, and ou ines in esponse o long- e m
changes (Al alla-Luque e al., 2018; Ma in-Ga cia e al., 2018; Feizabadi e al., 2021). SC
agili y in his esea ch is he capabili y o manu ac u e s o quickly espond o sho - e m
changes in demand and supply wi h hei SC pa ne s (Lee, 2004; Al alla-Luque e al., 2018;
Ma in-Ga cia e al., 2018; Feizabadi e al., 2021).
SC alignmen , agili y, and adap abili y in e ela e and co-exis in combina ion o p o ide
i ms wi h supe io pe o mance and compe i i e ad an ages (Aslam e al., 2018). Especially
in a dynamic and unce ain en i onmen augh wi h isks, h ee dimensions o iple-A SC
a e o g ea impo ance ha i is in easible o i ms o de elop only one o wo dimensions a
he expense o he o he s, which leads o a ade-o in he de elopmen o AAA. Howe e ,
wi hin his u bulen en i onmen , he de elopmen o iple-A SC encoun e s nume ous
challenges, pa icula ly in he ealms o in e - i m coo dina ion, collabo a ion, and he
cons ain s imposed by esou ce limi a ions, among o he ac o s. Consequen ly, i ms a e o en
con on ed wi h he di icul y o how o e ec i ely de elop all h ee As. Based on he sand
cone model, as p oposed by di e en au ho s (Fe dows and De Meye , 1990; Flynn and James
Flynn, 2004; Rosenzweig and Eas on, 2010), we de end ha i ms could cumula i ely de elop
all h ee As by ollowing a pa icula sequence, as he imp o emen in a ounda ional capabili y
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would u he ein o ce he ensuing capabili ies o c ea e cumula i e e ec s. In con as , we
conside ha he ade-o model is igid and inapp op ia e in an en i onmen wi h ex eme
unce ain ies because adeo s among dimensions o he iple-A SC may lessen i ms’
al e na i es and would be isky o dis up SCs (Schonbe ge , 2007). Al hough p e ious s udies
also applied he complemen a i y pe spec i e (Feizabadi e al., 2021) o ambidex ous
pe spec i e (Wamba e al., 2020) o in es iga e h ee As, hei in e ac ions, and pe o mance
implica ions, hey demons a e inadequacy in e ealing whe he h ee As could be ela ed
cumula i ely as he sand cone model implies. This c ea es a g ea oppo uni y o us o analyze
a sand cone sequence o he iple-A SC, h ough which all h ee As could mo e e ec i ely be
de eloped, and ou pe o m compe i o s unde he unce ain en i onmen .
Fu he mo e, combined wi h he p emises unde lying he sand cone model, he e idence
o he applicabili y o a iple-A SC sand cone sequence has also come om p e ious indings
on he posi i e ela ionships be ween wo As. Fo ins ance, SC alignmen has been alida ed
o enhance SC adap abili y by p e ious s udies (e.g., Dubey and Gunaseka an, 2016;
I anmanesh e al., 2023; Tickle e al., 2024), and Feizabadi e al. (2019) summa ized alignmen
as an an eceden o adap abili y in hei e iew a icle. In addi ion, SC adap abili y is ound o
posi i ely impac SC agili y (e.g., Ecks ein e al., 2015; Aslam e al., 2018). As such,
in eg a ing hese pa ial esul s p o ides a s ong g ound o p oposing ha he sand cone model
can be pe inen o he iple-A SC con ex ; ha is, all iple-A SC dimensions can be imp o ed
cumula i ely o ein o ce each o he ( a he han being aded o o weaken each o he ) by
ollowing a pa icula sequence, which is commen ed in he ollowing sec ion.
2.3 The cumula i e ela ionships o iple-A SC dimensions
As manu ac u e s a e suscep ible o en i onmen al changes, i is impe a i e o hem o de elop
capabili ies ha enable hem o e ec i ely add ess bo h long- e m changes (adap abili y) and
sho - e m luc ua ions (agili y) wi h hei SC pa ne s o ensu e con inuous ope a ions o he
en i e SC. Howe e , i is no a s aigh o wa d ask o de elop hese capabili ies, necessi a ing
sus ained commi men and con inuous e o o bo h pa ies-manu ac u e and i s SC pa ne -
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o me iculously e alua e cus ome demands, iden i y eme ging ma ke s, and c ea e lexible
p ocesses and ou ines (Whi en e al., 2012; Pa ucco and Kähkönen, 2021). SC alignmen
could be conside ed as he undamen al base o he cumula i e iple-A SC sand cone model
because i will no be easy o main ain esponsi eness and adap abili y o he changing
en i onmen i manu ac u e s and hei SC pa ne s a e no s a egically aligned in e ms o
s a egies, objec i es, and p ocesses (Whi en e al., 2012; Tickle e al., 2024). Fi s , SC
alignmen could enhance SC adap abili y. SC alignmen ensu es a s a egically in eg a ed
ela ionship be ween he manu ac u e and i s SC pa ne , which acili a es join wo king and
esou ce bundling o hei join econ igu a ions and adjus men s o SC s uc u e owa ds
long- e m changes. Second, he imp o emen in SC adap abili y p omo es SC agili y. SC
adap abili y p o ides al e na i e solu ions and ich p ac ical expe ience based on a ious
s uc u al econ igu a ions owa ds long- e m changes, which acili a es quick esponse
owa ds sudden sho - e m changes wi h ease and p o iciency.
SC alignmen is he base o he iple-A SC sand cone model, which expands o
cumula i ely imp o e SC adap abili y. Manu ac u e s’ abili y o s a egically align and
coo dina e wi h pa ne s ega ding s a egies, p ocesses, and ou ines p omo es hei join
econ igu a ions and adjus men s o SC s uc u e owa ds long- e m shi s (adap abili y)
(Dubey and Gunaseka an, 2016). Speci ically, such in eg a ion and coo dina ion no only
enable hem o join ly p edic o sense changes bu also enhance in e -o ganiza ional lea ning,
which con ibu es o add essing long- e m changes h ough he econ igu a ion o s uc u es,
p ocesses, and esou ce bases wi h SC pa ne s (Ma in-Ga cia e al., 2023). In addi ion, he
alignmen ensu es join wo king and planning, he eby acili a ing he manu ac u e and i s SC
pa ne s o collabo a i ely econ igu e owa d long- e m changes (Whi en e al., 2012; Ma in-
Ga cia e al., 2018). Finally, by gaining a comp ehensi e unde s anding o each o he 's
s a egies, objec i es, and plans, he manu ac u e can ini ia e app op ia e and e ec i e
econ igu a ions wi h SC pa ne s o add ess any long- e m changes ha may a ise (Ma in-
Ga cia e al., 2023). P e ious s udies u he co obo a e ha SC alignmen se es as a
undamen al enabling capabili y and p ecedes he imp o emen o adap abili y (e.g., Dubey
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la e esponses by compa ing se e al i m cha ac e is ics (e.g., indus y ype, i m owne ship,
numbe o employees, ixed asse s) and key cons uc s used in his s udy collec ed a wo imes
(A ms ong and O e on, 1977). The - es esul s indica ed no signi ican di e ence, showing
ha non- esponse bias was no a se ious conce n in ou esea ch.
Since single sou ce da a was collec ed in his s udy, common me hod bias (CMB) can
po en ially jeopa dize indings. We ollowed p e ious esea ch o inco po a e se e al
app oaches o check and minimize such bias (Tang and Wen, 2020). Fi s , we ied o p e en
CMB du ing he esea ch design phase by posi ioning concep ually ela ed a iables a apa
in he ques ionnai e o con ol he consis ency o esponses. Mo eo e , we p omised he
anonymi y o answe s o alle ia e esponden s’ conce ns and included e e se-sco ed i ems o
p e en habi ual sco ing by he esponden s. Second, Ha man’s one- ac o es employing
explo a o y ac o analysis (EFA) was u ilized o assess he p esence o common me hod bias.
We ound se en dis inc ac o s wi h eigen alues exceeding 1.0, and he i s ac o accoun ed
o 15.15% o he o al a iance, which did no occupy a majo i y o he o al a iance
(Podsako e al., 2003). Thi d, we conduc ed confi ma o y ac o analysis (CFA) o Ha man’s
one- ac o es (Sanchez and B ock, 1996). The model fi indices (χ2 = 3179.29 wi h deg ees o
eedom = 434, which yields χ2/d = 7.32; non-no med i index (NNFI) = 0.73, compa a i e
i index (CFI) = 0.75; oo mean squa e e o o app oxima ion (RMSEA) = 0.22; and
s anda dized oo mean squa e esidual (SRMR) = 0.14) a e ound o be unaccep able acco ding
o Sche melleh e al. (2003), and g ea ly wo se compa ed o hose o he measu emen model.
These esul s indica e ha common me hod bias is no an issue in his s udy. Fu he mo e, he
a iance in la ion ac o s (VIFs) a e also sugges ed o be used o assess he collinea i y (Wang
e al., 2023). VIFs among a iables in his s udy we e all below 3.3 (highes VIF-2.347),
sugges ing ha he e is no se ious pa hological collinea i y and CMB is less likely o
con amina e ou model (Kock, 2015; Wang e al., 2023). Finally, acco ding o Lindell and
Whi ney (2001), we used he enu e o esponden s as a ma ke a iable, which is no
heo e ically ela ed o cons uc s in ou s udy, o u he assess he CMB po en ial. We used
he alue o he smalles posi i e co ela ion ( = 0.02) be ween he ma ke a iable and o he
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la en a iables o adjus he co ela ions be ween he a iables. All signi ican co ela ions
emained signi ican a e he pa ial co ela ion adjus men s. The e o e, CMB is no a se ious
p oblem in his s udy.
4.2 Reliabili y and alidi y
A wo-s ep me hod is used o assess he eliabili y o cons uc s (Na asimhan and Jaya am,
1998). Fi s , he esul s o EFA (a ailable upon eques ) sugges ha all i ems possess g ea e
loadings on he in ended cons uc s hey a e designed o measu e while ea u ing low c oss-
loadings on o he ac o s, he eby demons a ing unidimensionali y. Then, composi e eliabili y
and C onbach’s alpha o each cons uc a e compu ed o check he in e nal consis ency
eliabili y (Wang e al., 2023), wi h all alues exceeding he h eshold o 0.70 sugges ed by
Hai e al. (2010). Appendix B in he supplemen a y ma e ial shows de ailed in o ma ion abou
eliabili y esul s[1].
Con e gen alidi y is assessed using CFA, whe e each i em is linked o i s espec i e
cons uc , and he co a iance is es ima ed wi hou cons ain s. The model exhibi s a a o able
le el o i , as alues o indices sa is y es ablished h eshold alues (Sche melleh e al., 2003;
Hu and Ben le , 1999): χ2 = 752.80 wi h deg ees o eedom = 413, which yields χ2/d = 1.82,
RMSEA = 0.066 (accep able i ), NNFI = 0.98 (good i ), CFI = 0.98 (good i ), and SRMR =
0.050 (good i ). Fu he mo e, all ac o loadings exceed he h eshold o 0.50 ( ange: 0.74–
0.91) and demons a e s a is ical signi icance a he 0.01 le el. The ob ained esul s p o ide
e idence o con e gen alidi y (Fo nell and La cke , 1981). In addi ion, he a e age a iance
ex ac ed (AVE) o each cons uc su passes 0.50 ( ange: 0.644 o 0.788), which se es as
addi ional e idence o con e gen alidi y (see Appendix B) (Flynn e al., 2010; Wang e al.,
2023).
In assessing disc iminan alidi y, squa e oo s o AVE su pass he co ela ion coe icien s
be ween he ocal cons uc and all o he cons uc s (see supplemen a y ma e ial, Table II[1]).
As such, disc iminan alidi y is ensu ed (Fo nell and La cke , 1981). Besides, we employed
he he e o ai -mono ai a io (HTMT) o co ela ions app oach as an addi ional app oach o
e alua e disc iminan alidi y in his s udy (Hensele e al., 2015), which is also highly
ecommended by ecen esea ch (e.g., Wang e al., 2023). HTMT se es as a me hod o
compa e he a e age alues o he e o ai -he e ome hod co ela ions (i.e., co ela ions be ween
indica o s ac oss dis inc cons uc s) and mono ai -he e ome hod co ela ions (i.e.,
co ela ions be ween indica o s wi hin he same cons uc ). Findings in Table III e eal ha he
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HTMT a io o co ela ions alls below he p ede e mined h eshold o 0.85, mee ing he
HTMT0.85 c i e ia and p o iding u he e idence o disc iminan alidi y (Hensele e al., 2015;
Cla k and Wa son, 2016) (see Table III in he supplemen a y ma e ial[1]).
4.3 Hypo hesis es ing
Following p e ious s udies, we applied s uc u al equa ion modeling (SEM) wi h he maximum
likelihood es ima ion me hod using LISREL 8.80 so wa e o in es iga e di ec and indi ec
e ec s and es hypo heses in his s udy. Speci ically, ollowing Sch oede e al. (2011) and
Bo olo i e al. (2015), we es ed whe he he p oposed sand cone model o iple-A SC (Model
1 in Table IVa) could be conside ed supe io o ano he possible model (H1a, H1b, and H1c).
The e o e, we es ed ano he al e na i e SEM model (Model 1a in Table IVa), which
encompasses all pa hs om Model 1 along wi h adding a di ec pa h om alignmen o agili y
(see Model 1 and Model 1a in Table IVa in supplemen a y ma e ial[1]).
To assess he model i , a a ie y o i measu es we e used, including RMSEA, SRMR,
NFI, CFI, Akaike in o ma ion c i e ion (AIC), Bayesian in o ma ion c i e ion (BIC), and
Akaike weigh s (AW). I he sand cone model is alid, we expec Model 1 o demons a e a
supe io model i since i p ecisely speci ies he cumula i e sequence o iple-A SC, as he
sand cone model p oposed. Fi s a is ics in Table IVa sugges ha indices o Model 1 (i.e., sand
cone model) well sa is y he es ablished h eshold alues (Hu and Ben le , 1999; Sche melleh
e al., 2003): χ2 = 823 wi h d = 469, which yields χ2 /d = 1.75 (good i ); RMSEA = 0.059
(accep able i ); TLI = 0.93 (good i ), CFI = 0.94 (good i ); and SRMR = 0.049 (good i ).
The e o e, he sand cone sequence in Model 1 i s well wi h he da a (see supplemen a y
ma e ial, Table IVa[1]).
In addi ion, as bo h models a e nes ed, i is essen ial o epo he alue o he χ2 di e ence
es and AIC alues in his kind o model compa ison (Sche melleh e al., 2003; Wang e al.,
2023). We hus conduc ed he χ2 di e ence es o calcula e he ma ginal change achie ed by
in oducing an addi ional di ec pa h compa ed o he sand cone model in Model 1. In he
p esen case, he χ2 di e ence be ween Model 1 and Model 1a (
χ
2
di
(
d
di
)
= 823.07 - 822.59
= 0.48 and
d
di
= 469 - 468 = 1, p- alue = 0.4884) is non-signi ican , sugges ing ha Model
1 should be e ained (Sche melleh e al., 2003). Mo e impo an ly, Model 1 also has lowe AIC
and BIC alues han Model 1a, as well as highe AW alues, which means highe s a is ical
con idence o Model 1 ( he one displaying he lowes AIC alue) (Wagenmake s and Fa ell,
2004; Wang e al., 2023). These esul s sugges ha he model esul ing om adding he di ec
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pa h om alignmen o agili y (Model 1a) de e io a es he model i , u he con i ming ha
Model 1 be e ep esen s he sand cone sequence (see supplemen a y ma e ial, Table IVa[1]).
Fo he sequence o be cumula i e, he ela ionship be ween any wo adjacen As should
be signi ican ly posi i e, and he ela ionship be ween non-adjacen As (di ec e ec ) canno
be obse ed as signi ican ly nega i e. Figu e 2 shows signi ican pa hs wi h s anda dized
coe icien s. The esul s indica e a posi i e ela ionship be ween SC alignmen and SC
adap abili y and a posi i e associa ion be ween SC adap abili y and SC agili y. Con ol
a iables ( i m size, indus y ype) exhibi no s a is ically signi ican e ec s on p oposed
ela ionships wi hin ou model. As such, he indings indica e ha all pa hs linking adjacen
As a e posi i e and s a is ically signi ican . Thus, he abo e esul s p o ide suppo o H1a
and H1b. Then, he magni ude o di ec and indi ec pa hs linking alignmen and agili y is
compa ed. The logic is ha h ee As a e sequen ially de eloped when he indi ec e ec
be ween wo non-adjacen As (alignmen and agili y) su passes hei di ec e ec . In Table IVa,
he alignmen -agili y indi ec e ec (0.13) media ed by adap abili y is la ge han he
co esponding di ec e ec (0.06), suppo ing ou iple-A SC sand cone model hypo hesis H1c.
====== Inse Figu e 2 abou he e ======
Mo eo e , o examine H2, he boo s apping analysis was applied o analyze he e ec s
o he iple-A SC sand cone model on inancial pe o mance (P eache and Hayes, 2008). Fi s ,
a posi i e ela ionship can be ound be ween SC agili y and inancial pe o mance (Figu e 2).
Fu he mo e, he indi ec e ec de i ed om he boo s apping analysis is deemed s a is ically
signi ican when 0 is excluded be ween he lowe and uppe limi s o he con idence in e al.
The e o e, based on he esul s in Table IVb, we can conclude ha SC alignmen exe s a
posi i e and s a is ically signi ican e ec on inancial pe o mance h ough he media ion o
SC adap abili y and agili y (see Table IVb in supplemen a y ma e ial[1]). Likewise, SC
adap abili y gene a es a posi i e and s a is ically signi ican impac on inancial pe o mance
h ough SC agili y. The e o e, H2 is suppo ed.
Resul s also indica e he ollowing s a is ically signi ican posi i e ela ionships (Figu e
2): ela ional capi al posi i ely impac s SC adap abili y, cogni i e capi al posi i ely impac s
SC alignmen and SC agili y, and s uc u al capi al posi i ely impac s SC alignmen while
nega i ely impac ing SC agili y (con a y o hypo hesis). The emaining ela ionships
conce ning he e ec s o social capi al on iple-A SC a e no s a is ically signi ican . Thus,
H3a, H4a, H4c, and H5b a e suppo ed, while H3b, H3c, H4b, H5a, and H5c a e ejec ed.
Fu he mo e, ela ional, cogni i e, and s uc u al capi al demons a e signi ican indi ec
e ec s on iple-A SC capabili ies (see supplemen a y ma e ial, Table IVb[1]).
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4.4 Robus ness checks
We conduc ed se e al obus ness checks o u he suppo ou esea ch esul s (see Appendix
C in he supplemen a y ma e ial[1]). Speci ically, we conduc ed an al e na i e model analysis
o assess whe he he p oposed iple-A SC sand cone sequence could be conside ed supe io
o o he possible al e na i e sequences ega ding AAA. The esul s demons a ed ha he
p oposed sand cone sequence (alignmen -adap abili y-agili y) is s a is ically alida ed o be a
supe io sand cone sequence o iple-A SC han he o he i e al e na i e models as i has he
lowes model AIC and BIC alues and he highes AW alues (see de ails in Appendix C1 in
he supplemen a y ma e ial[1]).
In addi ion, we ollowed ecen esea ch o u he educe conce ns wi h endogenei y by
applying he Gaussian copula app oach implemen ed by Pa k and Gup a (2012) and desc ibed
by Hul e al. (2018). We conduc ed he Gaussian copula analysis using Sma PLS 4, and he
esul s sugges ed ha endogenei y is no a se ious conce n in his model (see de ails in
Appendix C2 in he supplemen a y ma e ial[1]). In addi ion, we also included con ol a iables
such as i m size and indus y ype in o ou model o u he ule ou he exis ence o
endogenei y de i ed om omi ed a iables (Hul e al., 2018). The SEM esul s demons a ed
ha he con ol a iables ha e no e ec on ou s udy's p oposed ela ionships. We can conclude
ha ou model esul s a e no unduly a ec ed by endogenei y issues.
We also conduc ed obus ness checks wi h di e en subsamples. We ound ha ou
p esc ibed sand cone sequence o iple-A SC in di e en indus ies, classi ied as he me al,
mechanical, and enginee ing indus y and he elec onics and elec ici y indus y, we e bo h
suppo ed (see Appendix C3 in he supplemen a y ma e ial[1]).
Finally, we conduc ed a obus ness check using al e na i e inancial pe o mance
measu es. Speci ically, we pe o med addi ional analysis o H1 and H2 by spli ing he
inancial pe o mance i ems in o long- e m (i.e., p1- p3) and sho - e m (i.e., p4 and p5)
ca ego ies. We ound consis en esul s o H1 and H2, hus enhancing he obus ness o ou
indings in e ms o sho - e m and long- e m inancial pe o mance (see Appendix C4 in he
supplemen a y ma e ial[1]).
4.5 Addi ional analysis
We addi ionally conduc ed Necessa y condi ion analysis (NCA) using R 4.3.3 so wa e o
be e unde s and he ela ionships be ween social capi al and iple-A SC by p edic ing he
necessi y o social capi al dimensions o de eloping ce ain le els o iple-A SC capabili ies
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(see Appendix D in he supplemen a y ma e ial[1]). On he one hand, ou esul s showed ha
social capi al dimensions a e necessa y o de eloping iple-A SC capabili ies in gene al,
excep ha cogni i e capi al is ound o be unnecessa y o de eloping agili y (Appendix D1
in he supplemen a y ma e ial). The e ec size esul s u he e ealed ha di e en social
capi al dimensions demons a ed di e en necessi y e ec s o de eloping iple-A SC (see
de ails in Appendix D2 in he supplemen a y ma e ial). On he o he hand, we calcula ed he
bo leneck able o p esen he ceiling line esul s in a abula o m. The esul s clea ly ou lined
he necessi y le els o he h ee condi ions - s uc u al, cogni i e, and ela ional capi al -
equi ed o a ain a ce ain le el o AAA (see de ails in Appendix D3 in he supplemen a y
ma e ial). Fo ins ance, achie ing 60% o adap abili y necessi a es 2.9% o s uc u al capi al
and 4.1% o ela ional capi al; howe e , cogni i e capi al only becomes necessa y when i ms
aim o a ain an 80% o highe le el o adap abili y. Besides, i is impo an o no e ha
cogni i e capi al has always been unnecessa y o building agili y. Agili y o en equi es
lexibili y o espond o dynamic ma ke condi ions. Howe e , sha ed cogni i e capi al os e s
consis ency in decision-making p ocesses, which may in oduce igidi y o esis ance o change
i SC pa ne s a e o e ly commi ed o speci ic men al models o alues. Thus, oo much
emphasis on cogni i e capi al is no needed o espond quickly o a changing en i onmen .
Fu he mo e, we conduc ed a he e ogenei y es by examining he iple-A SC sand cone
model ac oss di e en ma ke u bulence (MT) le els (Appendix E in he supplemen a y
ma e ial[1]). MT indica es changes in cus ome s' composi ion and p e e ences (Paladino, 2008).
The esul s showed ha he alignmen -adap abili y-agili y sequence was suppo ed unde a low
le el o MT (Table E1 o Appendix E) bu no longe holds unde a high le el o MT (Table E2
o Appendix E). In con as , alignmen -agili y-adap abili y was s a is ically sa is ied and
supe io o o he possible al e na i e sequences ega ding AAA unde high MT (Table E3 o
Appendix E). In addi ion, we also conduc ed a he e ogenei y es by examining he iple-A SC
sand cone model ac oss a ying i m ages (Appendix F in he supplemen a y ma e ial[1]). The
esul s demons a ed ha ou p oposed alignmen -adap abili y-agili y sequence was suppo ed
in younge i ms. Con e sely, in olde i ms, alignmen -agili y-adap abili y was in es iga ed
o be he only s a is ically sa is ied sequence ega ding AAA.
5. Discussion and implica ions
5.1 Theo e ical con ibu ions
Ou esul s sugges ha he iple-A SC dimensions can be cumula i ely de eloped in a
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pa icula sand cone sequence by le e aging di e se o ms o social capi al and hus achie e
supe io inancial pe o mance. The esea ch indings he e o e ca y subs an ial heo e ical
con ibu ions and hold no able manage ial implica ions.
5.1.1 Suppo o he sand cone model o iple-A SC
Fi s , his s udy con ibu es o iple-A SC and cumula i e capabili y li e a u e by empi ically
in es iga ing and iden i ying an e ec i e sand cone sequence o iple-A SC. This s udy hus
answe s calls o mo e empi ical esea ch on he in e ela ionships be ween h ee As o
e ec i ely de elop a iple-A SC (e.g., Ecks ein e al., 2015; Al alla-Luque e al., 2018).
Meanwhile, his s udy especially con ibu es o he SC adap abili y li e a u e, which is he leas
explo ed o he h ee As in he academic li e a u e as p oposed by Phadnis (2024). This s udy
in es iga ed i s enable s, as well as i s in e ela ionships wi h alignmen and agili y based on
he cumula i e sand cone model. Speci ically, ou s udy applied he cumula i e capabili y
heo y o e eal ha hese iple-A SC dimensions can be cumula i ely de eloped in an
e ec i e sand cone sequence o s a wi h alignmen , hen adap abili y, and inally agili y. This
esul indica es ha SC alignmen is he ounda ional basis o he iple-A SC sand cone model,
ha is, he capabili y o be s a egically aligned wi h SC pa ne s p o ides possibili ies and
oppo uni ies o i ms and hei SC pa ne s o collabo a i ely add ess long- e m changes
(Whi en e al., 2012). Subsequen ly, manu ac u e s ha e he po en ial o cumula i ely enhance
hei SC agili y by imp o ing SC adap abili y, a o ding oppo uni ies and app oaches o
e icien ly manage sho - e m ope a ional luc ua ions p omp ly (Ecks ein e al., 2015).
Fu he mo e, his s udy e ealed ha he iden i ied sand cone sequence (alignmen -
adap abili y-agili y) was consis en in di e en indus ies, which is a sign o he obus ness and
eliabili y o he p oposed model.
Fu he mo e, ou esul s con ibu e o iden i ying con ingencies in he pa e n o he iple-
A SC sand cone model. P e ious s udies called o conside ing con ingen ac o s (e.g., Flynn
and Flynn, 2004; Nand e al., 2024) o p o ide a mo e comp ehensi e unde s anding o he
cumula i e sand cone sequence. The inco po a ion o con ingencies seems especially insigh ul,
as e idenced by ou he e ogenei y es . Fi s , ou esul s e ealed a di e ence in he pa e n o
he iple-A SC sand cone model by ma ke u bulence le els. The alignmen -adap abili y-
agili y sand cone sequence holds unde a low le el o ma ke u bulence bu no longe holds
unde a high le el o ma ke u bulence. In a highly unce ain ma ke , he alignmen -agili y-
adap abili y sequence was conside ed s a is ically alid and supe io o o he al e na i e
sequences. Since in a ma ke en i onmen cha ac e ized by subs an ial unce ain y,
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manu ac u e s a e i s compelled o cul i a e agili y h ough he enhancemen o alignmen o
p e en he loss o ma ke oppo uni ies and o de s. As such, ou esul s add new knowledge
on iden i ying con ex ual con ingencies in he pa e n o he iple-A SC sand cone model as
shaped by ma ke u bulence. Second, we ound ha he alignmen -adap abili y-agili y sand
cone sequence was suppo ed in younge i ms, while in olde i ms, he alignmen -agili y-
adap abili y sequence was suppo ed. These esul s a e logical as olde i ms, wi h mo e
edundan esou ces and well-es ablished SC pa ne ships, a e be e posi ioned o build agili y
wi h supplie s o quick esponse o changes wi hou con lic s and ime penal ies. On he o he
side, younge i ms a e mo e lexible in pa ne ship and in e - i m coope a ion wi hou oo
many en enched ou ines, making i easie o supplie s o econ igu e and adjus SC s uc u e
wi h he i ms o adap o undamen al changes.
Thi d, ou esul s ex end he cumula i e capabili y pe spec i e in OSCM esea ch by
demons a ing ha iple-A SC capabili ies can be cumula i ely de eloped in a pa icula sand
cone sequence. Mos p io esea ch employed a cumula i e capabili y pe spec i e o
in es iga e he sand cone sequence o manu ac u ing capabili ies (quali y- lexibili y-deli e y-
cos ) (Flynn and James Flynn, 2004). This s udy inno a i ely applied he cumula i e capabili y
pe spec i e o explo e and iden i y he exis ence o a sand cone sequence o AAA. T iple-A
SC capabili ies can be cumula i ely de eloped by i ms adhe ing o he alignmen -adap abili y-
agili y sequence. The indings indica e ha he de elopmen o ea lie As accumula es
esou ces and compe encies, which ac as he ounda ion o imp o emen s o be achie ed mo e
easily and e ec i ely in subsequen As. This s udy hus ex ends he cumula i e capabili y
pe spec i e o e eal ha he imp o emen s o iple-A SC capabili ies can cumula i ely
ein o ce each o he and he e o e e ec i ely achie e in sequence o add ess he changing
en i onmen and imp o e i m pe o mance despi e esou ce cons ain s (Rosenzweig and
Eas on, 2010).
5.1.2 Imp o ing inancial pe o mance h ough cumula i e iple-A SC capabili ies
This s udy con ibu es o e ealing he pe o mance implica ions o cumula i e iple-A SC
capabili ies based on he sand cone model. Mos p e ious s udies explo ed he cumula i e sand
cone sequence o mul iple manu ac u ing capabili ies and called o inco po a ing pe o mance
ou comes in o he sand cone model esea ch (e.g., Na asimhan and Schoenhe , 2013). This
s udy hus in es iga ed he impac s o he cumula i e iple-A SC capabili ies on inancial
pe o mance. The esul s indica e ha SC agili y p omo es inancial pe o mance, suppo ed
by p e ious s udies (Swa o d e al., 2008; Gligo e al., 2015). Fu he mo e, ou esul s
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demons a e ha SC adap abili y imp o es inancial pe o mance h ough SC agili y, aligning
wi h he conclusions o Ecks ein e al. (2015). Finally, SC alignmen also exhibi s a posi i e
indi ec impac on inancial pe o mance h ough adap abili y and agili y. The indings sugges
ha he alue o alignmen is ansla ed h ough adap abili y and agili y when add essing
changes o enhance inancial ou comes, which is consis en wi h he a gumen s o Pa ucco and
Kähkönen (2021), who p oposed ha SC alignmen should be pai ed wi h SC adap abili y and
agili y o p o ide a compe i i e ad an age in an unce ain wo ld. The indings e eal ha he
impac s o cumula i e iple-A SC capabili ies on inancial pe o mance also adhe e o he sand
cone sequence (H2), hus enhancing ou unde s anding o he pe o mance implica ions o
iple-A SC based on he cumula i e sand cone model.
5.1.3 De eloping iple-A SC capabili ies h ough social capi al
Fi s , his s udy con ibu es o iple-A SC and social capi al esea ch by e ealing he
mechanisms o how social capi al dimensions acili a e iple-A SC capabili ies. I hus answe s
he call o p e ious s udies (e.g., Ecks ein e al., 2015; Ga ido-Vega e al., 2023) o in es iga e
e ec i e enable s o iple-A SC. The esul s o e a comp ehensi e unde s anding o he
dis inc oles ha s uc u al, cogni i e, and ela ional capi al play in os e ing h ee As. F om
he CAS pe spec i e, he social capi al embedded in he complex SC ne wo k (encompassing
mu ually sha ed alues and no ms, e icien in o ma ion and esou ce exchanges, and
us wo hy in e -o ganiza ional ela ionships) is c i ical o i ms o egula e hei complex
ela ionships and o e come en i onmen al unce ain ies/changes.
Fi s , he esul s demons a e ha SC alignmen can be imp o ed by cogni i e and
s uc u al capi al. Sha ed alues and ideologies can con ibu e o o ming consis en and
in eg a ed s a egies and goals. F equen social in e ac ions and e ec i e in o ma ion sha ing
allow o join planning, wo king, and decision-making be ween he manu ac u e and i s SC
pa ne s (Flynn e al., 2010; Skipwo h e al., 2015). Howe e , ela ional capi al does no seem
o be an e ec i e enable o SC alignmen in ou s udy, which is inconsis en wi h p e ious
esea ch (e.g., Inkpen and Tsang, 2005; Zhao e al., 2011). One possible eason may be ha
us is necessa y bu insu icien in his s udy o mo i a e manu ac u e s and hei SC pa ne s
o in es signi ican ly in hei ela ionship, sha e c ucial business s a egies, and make join
plans o p epa e o un o eseen changes. Speci ically, us alone may all sho in econciling
he dispa a e objec i es among a ious en i ies wi hin he SC, especially in he absence o
mu ually ecognized alues os e ed by cogni i e capi al and heigh ened anspa ency achie ed
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h ough s uc u al capi al. Besides, high ela ional capi al is likely o e oke oppo unis ic isk,
pa icula ly in a ola ile en i onmen , as Villena e al. (2011) no ed, he eby de e ing
ha monious alignmen .
Second, he esul s show ha SC adap abili y is di ec ly imp o ed by ela ional capi al.
An exchange ela ionship ounded on us and commi men enhances he long- e m
ela ionship o ien a ion, enabling join econ igu a ions o ackle long- e m changes. In
addi ion, he boo s apping esul s demons a ed ha s uc u al and cogni i e capi al can
cumula i ely p omo e adap abili y h ough alignmen (see supplemen a y ma e ial, Table
IVb[1]). The eason could be ha manu ac u e s o en encoun e obs acles in econ igu ing
SCs wi h pa ne s, which equi es subs an ial in es men s and con inuous e o s om bo h
pa ies, po en ially leading o new luc ua ions and isks (Yang e al., 2022). In such scena ios,
seeking o become s a egically aligned wi h hei SC pa ne s h ough equen in e ac ions
and sha ed alues can make a b oad ange o join econ igu a ions easible as he willingness
o sha e in e es s/ isks g ows.
Thi d, ou esul s demons a e ha SC agili y is di ec ly imp o ed only by cogni i e
capi al. Simila philosophies and pe cep ions allow manu ac u e s and hei SC pa ne s o
main ain awa eness and espond quickly wi hou dispu e. Howe e , he esul s show ha
s uc u al capi al nega i ely a ec s SC agili y. This is in line wi h Mau e and Ebe s (2006),
which s a es ha densely in e connec ed coope a i e s uc u e may os e ine ia and ela ional
lock-in, p e en ing manu ac u e s om esponding quickly wi h pa ne s. Besides, equen
and di e se in e ac ions wi h SC pa ne s can lead o in o ma ion o e load, making i ime-
consuming o p ocess in o ma ion, he eby impeding p omp decision-making in esponse o
changes (Skippe and Hanna, 2009). Howe e , he boo s apping analysis esul s showed ha
s uc u al capi al could cumula i ely imp o e agili y h ough alignmen and adap abili y based
on he sand cone sequence, which can help a enua e i s ha m. Mo eo e , we also ound ha
cogni i e capi al could cumula i ely enhance agili y h ough alignmen and adap abili y, and
ela ional capi al cumula i ely imp o es agili y h ough adap abili y based on he sand cone
model (see supplemen a y ma e ial, Table IVb[1]). These esul s u he con i med he
p oposed sand cone model and e ealed he ole o social capi al in cumula i ely imp o ing
iple-A SC capabili ies.
5.2 Manage ial implica ions
This s udy also o e s p ac ical insigh s o manage ial decision-making. Fi s , we sugges
manu ac u e s de elop AAA SC capabili ies wi h hei pa ne s ac oss he SC. De eloping
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Figu e 1.
Concep ual model.
Figu e 2.
Es ima ed s uc u al equa ion model (only signi ican ela ionships).
Sou ce: Au ho s' own elabo a ion
No es: *p < 0.05; **p < 0.01; ***p < 0.001.
Financial
pe o mance
0.46***
S uc u al
capi al
Cogni i e
capi al
Rela ional
capi al
Agili y
Adap abili y
Alignmen
0.29**
0.18*
0.45***
0.43***
0.25**
0.73***
-0.19*
Social capi al
Sand cone o T iple-A SC
Sand cone model o T iple-A SC
SC Adap abili y
SC Alignmen
SC Agili y
Financial
pe o mance
H1a
H1b
Cogni i e
capi al
Rela ional
capi al
S uc u al
capi al
H4a-b-c
H3a-b-c
H5a-b-c
H2
Social capi al
Con ol a iables
Fi m size
Indus y ype
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Supplemen a y ma e ial o “Le e aging social capi al o build he cumula i e iple-A
supply chain sand cone model”
Table 1. Company and esponden p ofiles.
% esponden s
% esponden s
Region
Numbe o employees
Bohai Bay Economic Rim
31.5
100-199
27.8
Yangzi Ri e Del a
26.4
200-499
28.2
Pea l Ri e Del a
24.1
500-999
19.9
O he a eas in China
18.1
1,000-4,999
18.5
5,000 o mo e
5.6
Indus y
Me al, mechanical & enginee ing
41.2
Fixed asse (mRMB)
Elec onics & elec ical
19.0
<5
5.1
Tex iles & appa el
13.0
5-10
8.8
Chemicals & pe ochemicals
8.8
10-20
7.9
Building ma e ials
4.6
20-50
18.5
Publishing & p in ing
4.6
50-100
18.1
Rubbe & plas ics
3.7
100 o mo e
41.7
Food, be e age, alcohol & ciga e es
3.7
Pha maceu ical & medicals
1.4
Posi ion
Tenu e o cu en posi ion (yea s)
Top manage (e.g., p esiden s, CEO,
di ec o , and depu y o hese posi ions)
17.6
≤1
8.3
Middle manage (e.g., manage o
pu chasing, ma ke ing, and
p oduc ion)
77.8
2–5
34.3
O he s (e.g., pu chase and salesman)
4.6
6–10
36.1
11–15
13.4
≥16
7.9
Sou ce: Au ho s' own elabo a ion
Table II.
Co ela ions, means, and s anda d de ia ions.
Cons uc
Mean
S.D.
1
2
3
4
5
6
7
8
1. S uc u al capi al
4.61
1.383
0.88
2. Cogni i e capi al
4.69
1.301
.67**
0.89
3. Rela ional capi al
5.24
1.127
.57**
.61**
0.87
4. Alignmen
4.15
1.333
.58**
.61**
.46**
0.83
5. Adap abili y
5.00
1.047
.48**
.44**
.55**
.46**
0.80
6. Agili y
4.89
1.112
.37**
.42**
.50**
.44**
.73**
0.84
7. Financial pe o mance
4.02
1.214
.19**
.22**
.29**
.19**
.39**
.42**
0.84
8. Ma ke a iable
7.71
5.042
-0.10
-0.14
-0.08
-0.11
-0.09
-0.05
0.021
-
No es: **p<0.01. The squa e oo o AVE is shown on he diagonal o he ma ix in bold.
Sou ce: Au ho s' own elabo a ion
Table III.
HTMT esul s.
Cons uc
1
2
3
4
5
6
7
1. S uc u al capi al
2. Cogni i e capi al
0.737
3. Rela ional capi al
0.622
0.659
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4. Alignmen
0.659
0.691
0.517
5. Adap abili y
0.520
0.474
0.595
0.514
6. Agili y
0.402
0.460
0.536
0.487
0.789
7. Financial pe o mance
0.210
0.243
0.309
0.209
0.424
0.460
Table IV.
Resul s o sand cone sequence.
(a) SEM esul s associa ed wi h hypo hesized iple-A SC sand cone sequence.
Pa hs
Model 1
Model 1 (a)
Alignmen →Adap abili y
0.25
0.25
Alignmen →Agili y
—
0.06 (n.s.)
Adap abili y→Agili y
0.73
0.72
Fi indices
χ2 (d )
823.07(469)
822.59(468)
χ2/d
1.75
1.76
RMSEA
0.059
0.059
SRMR
0.049
0.049
TLI
0.93
0.93
CFI
0.94
0.94
AIC
18718.08
18719.59
BIC
19139.99
19144.88
AW
0.68
0.32
Compa ing di ec and indi ec e ec s
Alignmen →Agili y di ec
—
0.06 (n.s.)
Alignmen →Agili y indi ec
0.14
0.13
(b) Boo s apping esul s o indi ec e ec s.
Cons uc
Adap abili y
Agili y
Financial pe o mance
Indi ec e ec s
Rela ional capi al
0.29 (0.183, 0.420)
Cogni i e capi al
0.09 (0.039, 0.189)a
0.07 (0.026, 0.154)
S uc u al capi al
0.06 (0.012, 0.146)
0.05 (0.008, 0.124)
Alignmen
0.14 (0.044, 0.267)
0.06 (0.019, 0.125)
Adap abili y
0.34 (0.221, 0.496)
No es: a The numbe in pa en heses indica es he 90% con idence in e al (LLCI, ULCI) o n=1000
boo s ap; (LLCI, ULCI): Lowe and uppe le els o he con idence in e al o indi ec e ec
coe icien .
Ali
Ada
Agi
Ali
Ada
Agi
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Appendix A. Li e a u e e iew o empi ical iple-A SC esea ch
S udy
An eceden
Consequence
In e - ela ionships
Me hod
Theo y
Al alla-Luque e al.
(2018)
Compe i i e ad an age
(cos -CA; quali y-CA;
deli e y-CA; lexibili y-
CA; inancial-CA)
Su ey
Resou ce-based iew (RBV);
Dynamic capabili y heo y
(DCT)
Aslam e al. (2020)
SC adap abili y;
SC alignmen
SC agili y;
SC esilience
SC adap abili y-SC agili y
SC alignmen -SC agili y
Su ey
DCT
A ia (2015)
SC pe o mance
Su ey
A ia (2016)
O ganiza ional pe o mance
Su ey
Dubey e al. (2015)
SC adap abili y
SC agili y;
Human pe o mance;
Logis ics pe o mance
SC adap abili y-SC agili y
Su ey
Dubey e al. (2018)
SC isibili y
Su ey
RBV
Dubey and Gunaseka an
(2016)
SC alignmen ;
SC adap abili y
SC agili y;
SC adap abili y;
Humani a ian SC
pe o mance
SC alignmen -SC agili y
SC alignmen -SC adap abili y
SC adap abili y-SC agili y
Su ey
Feizabadi e al. (2019)
Fi m pe o mance
( inancial pe o mance;
ma ke pe o mance)
Su ey
Resou ce ad an age heo y
(RAT); Resou ce o ches a ion
heo y (ROT)
Feizabadi e al. (2021)
Ma ke pe o mance;
Financial pe o mance;
Cycle ime pe o mance
Su ey
Complemen a i y heo y
Ga ido-Vega e al.
(2023)
Compe i i e
en i onmen ;
Business s a egy
Su ey
Con ingency heo y
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Gligo e al. (2020)
Fi m pe o mance
Su ey
RAT; ROT
Huma and Ahmed (2022)
Visibili y; Flexibili y;
Veloci y
Agili y-Adap abili y
Adap abili y-Alignmen
Su ey
RBV
I anmanesh e al. (2023)
Ope a ional SC
anspa ency
Blockchain adop ion
in en ion
SC alignmen -SC adap abili y;
SC adap abili y-SC agili y
Su ey
RBV; Con ingency heo y
Je msi ipa se and
Kampoomp ase (2019)
Supply chain pe o mance
SC alignmen -SC agili y
SC alignmen -SC adap abili y
SC adap abili y-SC agili y
Su ey
Khan e al. (2023)
SC analy ics
Pos pandemic dis up ion
pe o mance
Su ey
DCT
Machuca e al. (2021)
Compe i i e ad an age
Su ey
Con ingency heo y
Ma in-Ga cia e al. (2018)
Su ey
Sheel and Na h (2019)
Compe i i e ad an age;
Fi m pe o mance
Su ey
RBV; DCT
Whi en e al. (2012)
SC pe o mance
Su ey
DCT; Complex adap i e
sys em (CAS)
Wilujeng e al. (2022)
SC pe o mance
Su ey
Danesh a Kakhki e al.
(2023)
Da a analy ics
dynamic capabili ies
Ope a ional pe o mance;
S a egic pe o mance
Me a
analysis
(su ey
s udies)
DCT
Ma in-Ga cia e al. (2023)
Compe i i e ad an age
Alignmen -Adap abili y;
Adap abili y-Agili y
Su ey
ROT
Mohaghegh e al. (2024)
Digi al ans o ma ion
Sus ainable pe o mance
Su ey
ROT
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Appendix B. Measu es, eliabili y, and alidi y
Please indica e he ex en o which you ag ee o disag ee wi h he p esen ed s a emen s ega ding you social capi al wi h majo supplie ,
wi h “1” indica ing “s ongly disag ee” and “7” indica ing “s ongly ag ee”. The majo supplie e e s o he supplie ha p o ides he
highes dolla alue in e ms o you p ocu emen .
Fac o
loading
- alue
Rela ional capi al (Villena e al., 2011)
C onbach's alpha=0.928; Composi e eliabili y (CR)=0.928; AVE=0.762
Rel1. The ela ionship be ween us and ou majo supplie is cha ac e ized by mu ual us a mul iple le els
0.91
56.60
Rel2. The ela ionship be ween us and ou majo supplie is cha ac e ized by mu ual espec a mul iple le els
0.85
39.46
Rel3. The ela ionship be ween us and ou majo supplie is cha ac e ized by mu ual iendship a mul iple le els
0.90
55.02
Rel4. We and ou majo supplie sha e ecip oci y
0.83
34.50
Cogni i e capi al (Villena e al., 2011)
C onbach's alpha=0.914; CR =0.918; AVE=0.788
Cog1. We and ou majo supplie sha e simila business ision
0.82
32.39
Cog2. We and ou majo supplie ha e simila co po a e cul u e/ alues and managemen s yle
0.93
62.70
Cog3. We and ou majo supplie ha e simila philosophies/app oaches o business dealings
0.91
55.97
S uc u al capi al (Villena e al., 2011)
C onbach's alpha=0.905; CR =0.910; AVE=0.771
S 1. The e is equen and in ensi e in e ac ion be ween he pe sonnel o us and ou majo supplie
0.81
29.27
S 2. The e is an in e ac ion be ween he pe sonnel ac oss di e en le els (e.g., manage s and enginee s) o us and ou majo supplie
0.91
53.29
S 3. The e is an in e ac ion be ween he pe sonnel ac oss di e en unc ions (e.g., logis ics and ma ke ing) o us and ou majo supplie
0.91
53.34
Please indica e he ex en o which you ag ee o disag ee wi h you iple-A supply chain s a emen s, wi h “1” indica ing “s ongly
disag ee” and “7” indica ing “s ongly ag ee”.
Alignmen (González-Beni o, 2007; Sande s, 2008)
C onbach's alpha=0.859; CR =0.865; AVE=0.681
Ali1. We and ou majo supplie pa icipa e in each o he ’s business s a egy o ma ion
0.86
33.18
Ali2. We and ou majo supplie ha e a good knowledge o each o he ’s business objec i es
0.74
20.26
Ali3. We make s a egic plans wi h ou majo supplie oge he
0.87
34.78
Adap abili y (Swa o d e al., 2006)
C onbach's alpha=0.927; CR =0.927; AVE=0.644
Ada1. We and ou majo supplie can change ma e ial olumes (ma e ials p o ided o us) o adap o dis u bance (changes in he en i onmen )
0.75
22.96
Ada2. We and ou majo supplie can change he ma e ial mix o adap o dis u bance
0.81
31.47
Ada3. We and ou majo supplie can implemen enginee ing changes o adap o dis u bance
0.80
30.09
Ada4. We and ou majo supplie can change supplie - ela ed human esou ces o adap o dis u bance
0.77
25.97
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Ada5. We and ou majo supplie can change supplie - ela ed plans o adap o dis u bance
0.87
44.73
Ada6. We and ou majo supplie can educe h oughou imes o adap o dis u bance
0.81
30.73
Ada7. We and ou majo supplie can adjus supplie - ela ed p ocesses o adap o dis u bance
0.80
29.08
Agili y (B aunscheidel and Su esh, 2009)
C onbach's alpha=0.932; CR =0.933; AVE=0.700
Agi1. We and ou majo supplie can quickly espond o changes in ou inpu
0.83
36.16
Agi2. We and ou majo supplie can quickly o ecas changes in ou inpu
0.80
29.88
Agi3. We and ou majo supplie can quickly espond o changes in supplie - ela ed plans
0.89
52.43
Agi4. We and ou majo supplie can quickly espond o changes in hei cus ome se ice o us
0.89
53.90
Agi5. We and ou majo supplie can quickly espond o changes in supplie - ela ed p ocesses
0.86
41.31
Agi6. We and ou majo supplie can quickly espond o changes in supplie - ela ed human esou ces
0.74
22.79
Please e alua e you company inancial pe o mance (in ecen i e yea s) ela i e o you p ima y/majo compe i o s, wi h “1” meaning
“much wo se” and “7” meaning “much be e ”.
Financial pe o mance (F ohlich and Wes b ook, 2001; Na asimhan and Kim, 2002; Vicke y e al., 2003)
C onbach's alpha=0.924; CR =0.923; AVE=0.706
Fpe 1. G ow h in sales
0.74
21.96
Fpe 2. G ow h in p ofi
0.87
44.42
Fpe 3. G ow h in ma ke sha e
0.80
29.44
Fpe 4. G ow h in e u n on in es men s
0.90
51.62
Fpe 5. G ow h in e u n on sales
0.88
46.99
Ma ke Tu bulence (Paladino, 2008)
MT1: Cus ome s in ou ma ke s a e e y ecep i e o new p oduc ideas
0.60
11.10
MT2: In ou ma ke s, cus ome s' p e e ences change ela i ely as
0.74
16.27
MT3: New cus ome s end o ha e p oduc - ela ed needs ha a e di e en om hose o exis ing cus ome s
0.67
13.70
MT4: We add ess di e en cus ome base compa ed wi h ha we did in he pas
0.71
14.92
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Appendix C. Robus ness checks o he main esul
Pu pose
Analysis
Resul s
Main analysis
alignmen -adap abili y-agili y
Model 1a in Table IV
Tes s o al e na i e models
Compa ison be ween 6 al e na i e sequences ega ding AAA
Consis en (Model 1 in Table C1)
Endogenei y analysis
Endogenei y es using Gaussian copula app oach
Consis en (Gaussian Copula Model 7 in Table C2a
and Model 14 in Table C2b)
Di e en subsample analysis
Subsample: i ms in Me al, mechanical, and enginee ing indus y
Consis en (Model 1 in Table C3(1))
Subsample: i ms in Elec onics and elec ici y indus y
Consis en (Model 1 in Table C3(2))
Al e na i e measu es o inancial pe o mance
Sho - e m inancial pe o mance
Consis en (Model1 in Table C4-1, Table C4-2)
Long- e m inancial pe o mance
Consis en (Model1 in Table C4-3, Table C4-4)
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Appendix D1. NCA sca e plo o s uc u al, cogni i e, ela ional capi al o iple-A SC
Figu es D1-1, D1-2 and D1-3 depic sca e plo s o social capi al dimensions o iple-A SC
capabili ies wi h emp y spaces abo e he ceiling lines o CR. This esul sugges ed ha social
capi al is necessa y o de eloping iple-A SC capabili ies in gene al, excep ha cogni i e capi al
was no deemed necessa y o agili y in Figu e D1-3.
Figu e D1-1. NCA sca e plo o s uc u al, cogni i e, ela ional capi al o alignmen .
Figu e D1-2. NCA sca e plo o s uc u al, cogni i e, ela ional capi al o adap abili y.
Figu e D1-3. NCA sca e plo o s uc u al, cogni i e, ela ional capi al o agili y.
Appendix D2. NCA e ec size o social capi al o iple-A SC.
We calcula ed he accu acy, ceiling zone, scope, and e ec size. As shown in Table D2, a medium
e ec is obse ed wi h ega d o he necessi y o s uc u al capi al (d = 0.24), cogni i e capi al (d =
0.29), and ela ional capi al (d = 0.27) o achie ing alignmen . Simila ly, he e is a medium
necessi y e ec o s uc u al capi al (d = 0.10) and ela ional capi al (d = 0.10) o adap abili y and
s uc u al capi al
alignmen
cogni i e capi al
ela ional capi al
alignmen
alignmen
agili y
agili y
agili y
s uc u al capi al
cogni i e capi al
ela ional capi al
adap abili y
s uc u al capi al
cogni i e capi al
ela ional capi al
adap abili y
adap abili y
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a small necessi y e ec o cogni i e capi al o adap abili y (d = 0.05). Finally, s uc u al capi al
demons a es a small necessi y e ec o agili y (d = 0.07), and ela ional capi al exe s a medium
necessi y e ec (d = 0.13), while cogni i e capi al is ound o be unnecessa y o de eloping agili y
(d = 0.00).
Table D2. Accu acy, ceiling zone, scope, and e ec size esul s
Cons uc
Me hod
Accu acy (%)
Scope
Ceiling
zone
E ec size
(d)
P- alue
Ou come a iable: alignmen
S uc u al capi al
CR-FDH
100%
36
8.778
0.244
0
CE-FDH
96.3
36
9.904
0.275
0
Cogni i e capi al
CR-FDH
100
36
10.333
0.287
0
CE-FDH
97.7
36
8.989
0.25
0
Rela ional capi al
CR-FDH
100
31.5
8.583
0.272
0
CE-FDH
94.9
51.5
9.674
0.307
0
Ou come a iable: adap abili y
S uc u al capi al
CR-FDH
100%
32.571
3.381
0.104
0.011
CE-FDH
98.1
32.571
3.116
0.096
0.007
Cogni i e capi al
CR-FDH
100
32.571
1.571
0.048
0.481
CE-FDH
100
32.571
0.786
0.024
0.565
Rela ional capi al
CR-FDH
100
28.5
2.964
0.104
0.096
CE-FDH
99.1
28.5
2.281
0.08
0.154
Ou come a iable: agili y
S uc u al capi al
CR-FDH
100%
36
2.556
0.071
0.132
CE-FDH
99.1
36
1.97
0.055
0.191
Cogni i e capi al
CR-FDH
100
36
0
0
1
CE-FDH
100
36
0
0
1
Rela ional capi al
CR-FDH
100
31.5
4.208
0.134
0.006
CE-FDH
97.2
31.5
3.465
0.11
0.018
Appendix D3. NCA bo lenecks able
We calcula ed he bo leneck able o p esen he ceiling line esul s in a abula o m, hus clea ly
ou lining he necessi y le els o he h ee condi ions - s uc u al, cogni i e, and ela ional capi al -
equi ed o a ain a ce ain le el o AAA. Fo simplici y, we ocused on esul s using he CR ceiling
line. Fi s , Table D3-1 shows ha i ms will no ha e o pu s uc u al, cogni i e, and ela ional
capi al in place unless hei desi ed le els o alignmen exceed 30%, which equi es a leas 0.4%
o s uc u al capi al and 1.3% o ela ional capi al. Fu he mo e, i ms ha aim o achie e alignmen
exceeding 50% equi e low o high le els o cogni i e capi al (10.2%-88.3%), s uc u al capi al
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(22.6%-78.2%), and ela ional capi al (25.6%-86.4%). Second, as Table D3-2 illus a es, achie ing
60% o adap abili y necessi a es 2.9% o s uc u al capi al and 4.1% o ela ional capi al; howe e ,
cogni i e capi al only becomes a necessa y condi ion when i ms aim o a ain an 80% o highe
le el o adap abili y. Thi d, Table D3-3 sugges s ha only s uc u al capi al demons a es a
bo leneck when pu suing 70% o agili y. Then, 23.2% o ela ional capi al and 13.4% o s uc u al
capi al a e equi ed o de elop 80% o agili y, while cogni i e capi al has always been unnecessa y
o building agili y.
Table D3-1. NCA bo lenecks able o social capi al dimensions o alignmen using CR (in %)
Alignmen (%)
S uc u al capi al
Cogni i e capi al
Rela ional capi al
0
NN
NN
NN
10
NN
NN
NN
20
NN
NN
NN
30
0.4
NN
1.3
40
11.5
NN
13.5
50
22.6
10.2
25.6
60
33.7
25.9
37.8
70
44.9
41.5
49.9
80
56.0
57.1
62.1
90
67.1
72.7
74.2
100
78.2
88.3
86.4
No e(s): NN indica es No Necessa y.
Table D3-2. NCA bo lenecks able o social capi al dimensions o adap abili y using CR (in %)
Adap abili y (%)
S uc u al capi al
Cogni i e capi al
Rela ional capi al
0
NN
NN
NN
10
NN
NN
NN
20
NN
NN
NN
30
NN
NN
NN
40
NN
NN
NN
50
NN
NN
NN
60
2.9
NN
4.1
70
11.9
NN
11.9
80
23.8
5.2
19.7
90
34.3
10.9
27.5
100
44.7
16.7
35.4
Table D3-3. NCA bo lenecks able o social capi al dimensions o agili y using CR (in %)
Agili y (%)
S uc u al capi al
Cogni i e capi al
Rela ional capi al
0
NN
NN
NN
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10
NN
NN
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20
NN
NN
NN
30
NN
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40
NN
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70
4.4
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80
13.4
NN
23.2
90
22.4
NN
49.9
100
31.3
NN
76.6
Appendix E. He e ogenei y es s ac oss di e en ma ke u bulence (MT) le els.
We examined he iple-A SC sand cone model ac oss a ying ma ke u bulence (MT) le els. MT
indica es changes in he composi ion o cus ome s and hei p e e ences (Paladino, 2008). We i s
de eloped high and low g oups based on hei MT scale sco es (see Appendix B). Fi ms sco ing
abo e he mean (i.e., 4.532) we e ca ego ized in o he “high” MT g oup (n = 105), while i ms
sco ing below he mean we e ca ego ized in o he “low” MT g oup (n = 116). Subsequen ly, we
conduc ed pa h analysis o di e en MT g oups. The esul s in Tables E1 and E2 demons a ed ha
ou p oposed sand cone model o iple-A SC (alignmen -adap abili y-agili y) was s ill alida ed o
be s a is ically sa is ied unde a low le el o MT bu no longe holds unde a high le el o MT as
he ela ionship be ween alignmen and adap abili y becomes insigni ican . As such, we ook a s ep
u he o in es iga e he sand cone sequence unde a high MT. The esul s in Table E3 showed ha
alignmen -agili y-adap abili y was s a is ically sa is ied and supe io o o he possible al e na i e
sequences ega ding AAA unde high MT.
Table E1. Tes o hypo hesized iple-A SC sand cone sequence unde a low MT.
Pa hs
Model 1
Model 1 (a)
Alignmen →Adap abili y
0.22
0.22
Alignmen →Agili y
—
0.02 (n.s.)
Adap abili y→Agili y
0.78
0.78
Fi indices
χ2 (d )
8.79 (8)
8.69 (7)
χ2/d
1.10
1.24
RMSEA
0.030
0.047
SRMR
0.028
0.028
TLI
0.98
0.97
CFI
0.99
0.99
AIC
1242.41
1244.30
BIC
1312.86
1317.46
AW
0.72
0.28
Compa ing di ec and indi ec e ec s
Alignmen →Agili y di ec
—
0.02 (n.s.)
Alignmen →Agili y indi ec
0.17
0.17
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Table E2. Tes o hypo hesized iple-A SC sand cone sequence unde a high MT.
Pa hs
Model 1
Model 1 (a)
Alignmen →Adap abili y
0.15 (n.s.)
0.15 (n.s.)
Alignmen →Agili y
—
0.14
Adap abili y→Agili y
0.56
0.54
Fi indices
χ2 (d )
7.62 (8)
4.84 (7)
χ2/d
0.95
0.69
RMSEA
0.000
0.000
SRMR
0.027
0.027
TLI
1.01
1.05
CFI
1.00
1.00
AIC
1097.27
1096.50
BIC
1166.28
1168.15
AW
0.40
0.60
Compa ing di ec and indi ec e ec s
Alignmen →Agili y di ec
—
0.14
Alignmen →Agili y indi ec
0.07 (n.s.)
0.07 (n.s.)
Table E3. Pa h analysis esul s o iple-A SC sand cone sequence unde a high MT (alignmen -
agili y-adap abili y).
Pa hs
Model 1
Model 1 (a)
Alignmen →Agili y
0.18
0.18
Alignmen →Adap abili y
—
0.01 (n.s.)
Agili y→Adap abili y
0.57
0.56
Fi indices
χ2 (d )
12.92 (8)
12.88 (7)
χ2/d
1.62
1.84
RMSEA
0.077
0.089
SRMR
0.033
0.033
TLI
0.90
0.87
CFI
0.97
0.96
AIC
1102.58
1104.54
BIC
1171.58
1176.20
AW
0.72
0.28
Compa ing di ec and indi ec e ec s
Alignmen →Adap abili y di ec
—
0.01 (n.s.)
Alignmen →Adap abili y indi ec
0.10
0.10
Appendix F. He e ogenei y es s ac oss di e en i m ages.
We examined he iple-A SC sand cone model ac oss di e en i m ages. We i s de eloped young
and old g oups based on he median o i m age wi hin he sample. Fi ms sco ing abo e he median
(i.e., 17) we e ca ego ized in o he “old i ms” g oup (n = 110), while i ms sco ing below he
median we e classi ied in o he “young i ms” g oup (n = 106). The pa h analysis esul s in Tables
F1 and F2 showed ha ou p oposed iple-A SC sand cone model (alignmen -adap abili y-agili y)
was suppo ed in younge i ms. Howe e , his sequence no longe holds in olde i ms since he
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di ec e ec o alignmen on adap abili y and he indi ec impac o alignmen on agili y bo h
became insigni ican . Subsequen ly, we ook a s ep u he o explo e he sand cone sequence in
olde i ms. Resul s in Table F3 showed ha alignmen -agili y-adap abili y was s a is ically sa is ied
and supe io o o he possible al e na i e sequences ega ding AAA in olde i ms.
Table F1. Tes o hypo hesized iple-A SC sand cone sequence in younge i ms.
Pa hs
Model 1
Model 1 (a)
Alignmen →Adap abili y
0.32
0.32
Alignmen →Agili y
—
0.09 (n.s.)
Adap abili y→Agili y
0.68
0.66
Fi indices
χ2 (d )
8.028 (8)
7.043 (7)
χ2/d
1.00
1.24
RMSEA
0.006
0.008
SRMR
0.027
0.026
TLI
1.000
0.999
CFI
1.000
1.000
AIC
1140.405
1141.420
BIC
1209.655
1213.333
AW
0.68
0.32
Compa ing di ec and indi ec e ec s
Alignmen →Agili y di ec
—
0.09 (n.s.)
Alignmen →Agili y indi ec
0.21
0.20
Table F2. Tes o hypo hesized iple-A SC sand cone sequence in olde i ms.
Pa hs
Model 1
Model 1 (a)
Alignmen →Adap abili y
0.14(n.s.)
0.14 (n.s.)
Alignmen →Agili y
—
0.12 (n.s.)
Adap abili y→Agili y
0.64
0.63
Fi indices
χ2 (d )
5.991 (8)
4.145 (7)
χ2/d
0.75
0.59
RMSEA
0.000
0.000
SRMR
0.022
0.020
TLI
1.003
1.054
CFI
1.000
1.000
AIC
1215.886
1216.040
BIC
1286.098
1288.952
AW
0.68
0.32
Compa ing di ec and indi ec e ec s
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Alignmen →Agili y di ec
—
0.12 (n.s.)
Alignmen →Agili y indi ec
0.07 (n.s.)
0.06 (n.s.)
Table F3. Pa h analysis esul s o iple-A SC sand cone sequence in olde i ms (alignmen -agili y-
adap abili y).
Pa hs
Model 1
Model 1 (a)
Alignmen →Agili y
0.15
0.15
Alignmen →Adap abili y
—
0.01 (n.s.)
Agili y→Adap abili y
0.56
0.56
Fi indices
χ2 (d )
10.27 (8)
10.21 (7)
χ2/d
1.28
1.46
RMSEA
0.051
0.089
SRMR
0.031
0.031
TLI
0.96
0.94
CFI
0.99
0.98
AIC
1220.16
1222.11
BIC
1290.38
1295.02
AW
0.72
0.28
Compa ing di ec and indi ec e ec s
Alignmen →Adap abili y di ec
—
0.01 (n.s.)
Alignmen →Adap abili y indi ec
0.08
0.08
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