In sea ch o a sui able way o
deploy T iple-A capabili ies
h ough assessmen o AAA
models’compe i i e ad an age
p edic i e capaci y
Juan A. Ma in-Ga cia
ROGLE-DOE, Uni e si a Poli 
ecnica de Val
encia, Valencia, Spain, and
Jose A.D. Machuca and Ra aela Al alla-Luque
GIDEAO-Depa amen o de Econom
ıa Financie a y Di ecci
on de Ope aciones,
Uni e sidad de Se illa, Se illa, Spain
Abs ac
Pu pose –To de e mine how o bes deploy he T iple-A supply chain (SC) capabili ies (AAA-agili y,
adap abili y and alignmen ) o imp o e compe i i e ad an age (CA) by iden i ying he T iple-A SC model wi h
he highes CA p edic i e capabili y.
Design/me hodology/app oach –Assessmen o in-sample and ou -o -sample p edic i e capaci y o T iple-A-
CA models (conside ing AAA as indi idual cons uc s) o ind which has he highes CA p edic i e capaci y. BIC,
BIC-Akaike weigh s and PLSp edic a e used in a mul i-coun y, mul i-in o man , mul i-sec o 304 plan sample.
Findings –G ea e di ec ela ionship model (DRM) in-sample and ou -o -sample CA p edic i e capaci y
sugges s DRM’s g ea e likelihood o achie ing a highe CA p edic i e capaci y han media ed ela ionship
model (MRM). So, DRM can be conside ed a benchma k o esea ch/p ac ice and he T iple-A SC capabili ies
as independen le e s o pe o mance/CA.
Resea ch limi a ions/implica ions –DRM eme ges as a e e ence o analysing how o igge he h ee
T iple-A SC le e s o be e pe o mance/CA p edic i e capaci y. The e o e, MRM p oposals should be
compa ed o DRM o de e mine whe he hei pe o mance is signi ican ly be e conside ing he s udy’s aim.
P ac ical implica ions –Resul s wi h ou sample jus i y how manage s can sui ably deploy he T iple-A SC
capabili ies o imp o e CA by implemen ing AAA as independen le e s. Single capabili y deploymen does
no equi e le els o be eached in o he s.
O iginali y/ alue –Fi s esea ch conside ing T iple-A SC capabili y deploymen o be e imp o e
pe o mance/CA ocusing on model’s p edic i e capabili y (essen ial o decision-making), u he highligh ing
he lack o heo y and con as ed models o Lee’s T iple-A amewo k.
Keywo ds T iple-A supply chain, AAA, Agili y, Adap abili y, Alignmen , PLSp edic , BIC, Akaike weigh s,
Compe ing model assessmen , Pe o mance, Compe i i e ad an age
Pape ype Resea ch pape
1. In oduc ion
Supply chain (SC) adap abili y, alignmen and agili y a e dynamic capabili ies ha enable global
SCs o espond o hei changing business en i onmen s (Machuca e al., 2021).These capabili ies
IJPDLM
53,7/8
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© Juan A. Ma in-Ga cia, Jose A.D. Machuca and Ra aela Al alla-Luque. Published by Eme ald Publishing
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ıa).
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Recei ed 26 Ma ch 2022
Re ised 30 Oc obe 2022
29 Decembe 2022
Accep ed 3 Feb ua y 2023
In e na ional Jou nal o Physical
Dis ibu ion & Logis ics
Managemen
Vol. 53 No. 7/8, 2023
pp. 860-885
Eme ald Publishing Limi ed
0960-0035
DOI 10.1108/IJPDLM-03-2022-0091
can be de ined as ollows (Ma in-Ga cia e al.,2018): SC agili y is he SC’s abili y o apidly de ec
and espond o sho - e m changes in eal demand and supply;SC adap abili y is heSC’sabili y
o adap i s s a egies, p oduc s and/o echnologies o s uc u al ma ke changes, and SC
alignmen is he SC’s abili y o sha e in o ma ion, esponsibili ies, oles and incen i es wi h SC
membe s o synch onise and coo dina e p ocesses and ac i i ies. This se o dynamic capabili ies
was p oposed by Lee as a concep ual amewo k called he T iple-A SC (Lee, 2004) and has
become one o he mos in luen ial o all amewo ks o SC p ac i ione s and esea che s (Mak
and Shen, 2021). I a gues ha SCs should s i e o imp o e by implemen ing he T iple-A
capabili ies a he han ocusing exclusi ely on e iciency and cos imp o emen s (Lee, 2004).
The main easons o he ele ance o his opic include he inc easingly impo an ole o
SCs in he wo ld economy and he signi ican in es men s in esou ces and e o s equi ed in
he design o global SCs and in he deploymen and implemen a ion o he T iple-A (also
called AAA) SC capabili ies o imp o e pe o mance/compe i i e ad an age (CA). These
capabili ies demand complex esou ces whose implemen a ion in SCs migh be di icul ,
expensi e and ha d o eplica e (Whi en e al., 2012). The e o e, hey could gene a e a
supe io le el o pe o mance/CA. Toge he wi h he ac ha i ms ha e limi ed esou ces,
hei expensi e implemen a ion makes inding he mos sui able way o deploy he T iple-A
SC capabili ies o imp o e CA pa icula ly ele an a he han i ial (Machuca e al., 2021).
Howe e , nei he he ini ial concep ual amewo k p oposed by Lee (2004) no his mo e
ecen a icle on he opic (Lee, 2021a) hypo hesises abou how he AAA-SC can be bes
ela ed o deployed o ob ain an “op imum” esul , e en hough, as men ioned abo e, inding
an answe o his is ele an no only o esea che s bu also o manage s, who would hen
ha e a guide as o how o adequa ely deploy he T iple-A SC capabili ies in hei SC design o
be e imp o e SC pe o mance/CA. Mo eo e , he lack o heo y and a con as ed model o
his concep ual amewo k has led o he appea ance o di e en app oaches o i s
de elopmen . Ne e heless, esea ch esul s ega ding he mos sui able implemen a ion o
AAA capabili ies o he ela ionship be ween he T iple-A SC capabili ies and pe o mance/
CA o he possible linkages be ween he capabili ies a e s ill sca ce and inconclusi e (Dubey
and Gunaseka an, 2016), which lea es a majo esea ch gap in he (s ill de eloping) T iple-A
esea ch a ea, whe e he opic con inues o be conside ed unde - esea ched (Machuca e al.,
2021). Resol ing his issue is also ele an o p ac ice as i will indica e a sui able way o
implemen AAA-SC o imp o e pe o mance/CA.
To ill his gap, he p esen esea ch ocuses on iden i ying how o deploy he T iple-A SC
capabili ies o bes imp o e SC pe o mance/CA. This is done by ocusing on models’
p edic i e capabili y (as well as hei p ac ical ele ance). This is impo an because a model’s
p edic i e capabili y is he mos impo an condi ion o i s ele ance o decision-making
and p o iding ecommenda ions o business p ac ice (Chin e al., 2020;Shmueli e al., 2016).
This is ano he impo an gap illed by his esea ch as, despi e he ele ance o he model’s
p edic i e capabili y, he p e ious li e a u e on T iple-A SC has mos ly ocused on model i .
This is in line wi h he call ega ding he in e es in esea ch s udies “ ha use PLS-SEM and
ela ed me hods o add ess he in e play be ween explana ion and p edic ion in o de o
ad ance ou unde s anding and knowledge o he LSCM ield”(Cheah e al., 2022).
Fo all he abo e easons, iden i ying he model (ou o he models p oposed in he li e a u e)
wi h he highes p edic i e capabili y ha shows how o bes deploy he T iple-A SC capabili ies o
imp o e CA should be conside ed an impo an o iginal con ibu ion. This model should be a
benchma k o esea che s and manage s. This leads o he ollowing esea ch ques ion,
which is he ocus o he p esen esea ch.
RQ1. Does a T iple-A SC capabili ies–pe o mance/CA ela ionship model wi h a highe
p edic i e capabili y exis han he o he s p oposed in he li e a u e ha could be
conside ed a benchma k o deploying AAA-SC?
P edic i e
capaci y o
T iple-A SC
models
861
Rega ding he RQ1, wo main app oaches o AAA-SC and pe o mance/CA models ha e been
ound in he li e a u e. Some wo ks ha e modelled his ela ionship conside ing he T iple-A
SC as an agg ega e high-o de cons uc (HOC) o he T iple-A SC capabili ies di ec ly ela ed
o pe o mance/CA (e.g. Whi en e al., 2012;A ia, 2015;Al alla-Luque e al., 2018;Machuca
e al., 2021). O he models conside T iple-A SC capabili ies as indi idual cons uc s (IC) and
ha e analysed hei in luence on pe o mance/CA as such (e.g. Dubey and Gunaseka an,
2016;Al alla-Luque e al., 2018;Yang, 2021). This a icle’sRQ1 de e mines ha only he
second g oup o models should be conside ed in his esea ch, as he i s modelling me hod
(T iple-A SC as an HOC) does no allow he in luence o each indi idual T iple-A SC capabili y
on pe o mance/CA o he ela ionships be ween he AAA-SC capabili ies hemsel es o be
analysed. Ne e heless, i mus also be s a ed ha he e is no consensus ega ding he second
g oup as a ious models ha e been p oposed o ep esen he men ioned ela ionships ei he
di ec ly (e.g. Al alla-Luque e al., 2018;A ia, 2016;Yang, 2021) o h ough media ion (e.g.
Dubey and Gunaseka an, 2016;Dubey e al., 2015).
The e o e, ega ding he RQ1, i is ele an o de e mine whe he any o he models in he
li e a u e ha p opose di e en deploymen s o he T iple-A SC capabili ies (di ec ly o h ough
media ion) p edic pe o mance/CA be e han he o he s. To da e, he e is no consensus on
modelling he ela ionships be ween he AAA-SC capabili ies and pe o mance/CA (Dubey
and Gunaseka an, 2016), and in addi ion, as p e iously s a ed, he p e ious esea ch has no
analysed which model p o ides he highes pe o mance/CA p edic i e capabili y. So, u he
esea ch is needed o ill his majo gap. Fo his, we need o compa e he T iple-
A→pe o mance/CA models p oposed in he li e a u e based on hei p edic i e capabili y
(and no hei i , al hough a good i is a p e-condi ion o e e y model conside ed in his
esea ch). Compa ing he exis ing models o iden i y he model ha can se e as a benchma k
o guide u he esea ch could be conside ed a (me hodological) con ibu ion o he opic ield
(especially i we conside ha he opic is s ill in de elopmen ) ha acili a es heo y
de elopmen and en ails impo an manage ial implica ions.
Based on he abo e, we conside ha his esea ch p o ides o iginal con ibu ions ha can
acili a e he heo e ical de elopmen o he T iple-A SC esea ch opic. Speci ically, he
con ibu ion o his pape o esea che s is h ee old. Fi s ly, o iginal insigh s a e o e ed abou
he model wi h he highes p edic i e capabili y in he ela ionships be ween he AAA-SC
capabili ies and pe o mance/CA o heo y de elopmen and p og ess on he T iple-A SC opic.
Secondly, he esul o he compa ison o he di e en ypes o T iple-A SC–CA models p oposed
in he li e a u e (wi h AAA-SC capabili ies as IC) has led o a u he con ibu ion, as hese models
a e anked by pe o mance/CA p edic i e capabili y, hus p o iding a guide o u he esea ch.
Las ly, conce ning he me hodology, as a as we know, his is he i s ime ha mul iple PLS-
ela ed me hods (BIC, BIC-Akaike weigh s and PLSP edic ) ha e been used o complemen each
o he in a single pape o assess CA p edic i e capabili y and ein o ce he eliabili y o he
conclusions, hus enhancing ou knowledge on his ma e and p o iding guidelines o
p edic i e model selec ion in managemen a eas. Fu he mo e, his esea ch uses a wide mul i-
coun y, mul i-in o man , mul i-sec o da abase ha o e s a high gua an ee o eliable esul s.
The indings also ha e clea implica ions o manage s, who a e p o ided wi h a guide o
he e ec i e design o hei SC s a egies o seek highe pe o mance/CA in a highly
compe i i e global con ex h ough he app op ia e deploymen o he AAA capabili ies.
De e mining he key d i e s and how hey should be ela ed can guide i ms wi h limi ed
esou ces ha wish o ind an app op ia e way o achie e he mos e ec i e in luence on
pe o mance/CA (Al alla-Luque e al., 2018). This is pa icula ly ele an due o he
signi ican in es men s in esou ces and e o equi ed o he implemen a ion o he T iple-
A SC capabili ies (Al alla-Luque e al., 2018).
The emainde o his pape is o ganised as ollows. Sec ion 2 analyses he heo e ical
backg ound o his esea ch. Sec ion 3 desc ibes he sample and he me hodology used.
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Sec ion 4 epo s he analysis o he da a and he esul s. Las ly, Sec ion 5 p esen s he mos
impo an conclusions and speci ies he pape ’s con ibu ions, implica ions o manage s and
academics, limi a ions and possible u he esea ch.
2. Theo e ical backg ound
2.1 T iple-A capabili ies
The concep ual amewo k de eloped by Lee (2004) p oposes he T iple-A SC capabili ies as
d i e s o achie ing a sus ainable SC–CA and is gaining ele ance in he cu en ma ke s
cha ac e ised by inc easing unce ain y, u bulence, high compe i i e in ensi y and a complex
SC (Ga ido-Vega e al., 2021). In line wi h he esou ce-based iew (RBV) heo y (Ba ney, 1991)
and he dynamic capabili ies iew (DCV) (Teece e al., 1997), he AAA-SC a e dynamic
capabili ies ha help companies gain SC compe i i eness bu demand complex esou ces
whose implemen a ion migh be di icul , expensi eand ha d o eplica e (Machuca e al., 2021;
Whi en e al., 2012). As Lee (2021a) s a es, “ he AAA concep is s ill applicable, and winning
SCs should s ill be agile, adap able, and aligned”. In his line, he Co id-19 pandemic has
e ealed he inc easing need o e i e he AAA-SC capabili ies o allow SCs o be e espond
o dis up ions and disas e s (Khan e al., 2022). Fo his, i ms should concen a e hei e o s
on e alua ing he dep h and s eng h o AAA capabili ies so as o be be e equipped o he
h ea s p o ided by he ex e nal en i onmen (Pa ucco and K€
ahk€
onen, 2021). Howe e ,
despi e i s impo ance, he T iple-A SC is s ill an unde - esea ched ield (Machuca e al., 2021)
ha needs u he de elopmen wi h new heo e ical and empi ical s udies.
In he concep ual a ea, Lee (2004) nei he de eloped no alida ed scales o he T iple-A
SC capabili ies, and hei de ini ion and measu es a e sca ce and di e se in he li e a u e
(Ma in-Ga c
ıae al., 2018). In addi ion, only a ew s udies ha e analysed he T iple-A SC
amewo k and om di e en pe spec i es. Some wo ks ha e p oposed de ini ions and
dimensions o he h ee As based on a concep ual iew (e.g. A ana-Sola es e al., 2011), some
empi ical pape s ha e de eloped scales (e.g. Whi en e al., 2012;Dubey e al., 2015) and a e y
small numbe o pape s ha e ocused on de eloping and alida ing a T iple-A SC
measu emen model (e.g. Ma in-Ga c
ıae al., 2018;Feizabadi e al., 2019a). Consequen ly,
al hough he eplica ion o scales in di e en samples is sugges ed o cons uc ing heo y, a
a ie y o scales ha e been used in p e ious esea ch o measu e he T iple-A SC capabili ies.
2.2 T iple-A and pe o mance: models in p e ious esea ch
The key ques ion in mos o he sca ce empi ical esea ch on he opic ocuses on he
ela ionship be ween he T iple-A SC and pe o mance o CA, wi h he conside a ion o one o
o he o he wo ways o model his ela ionship men ioned in Sec ion 1. The i s conside s
he T iple-A SC as a single HOC composed o all 3 As ha in luences pe o mance/CA. The
second conside s he 3 As singly, as indi idual a iables ha in luence pe o mance/CA. The
esul s o he esea ch using HOC sugges ha he e is a posi i e ela ionship be ween
he T iple-A SC and pe o mance (A ia, 2015;Whi en e al., 2012)o CA(Al alla-Luque e al.,
2018;Machuca e al., 2021), al hough he au ho s also ag ee on he need o u he esea ch.
Speci ically, Whi en e al. (2012) s a e ha a T iple-A SC-based s a egy has a posi i e
in luence on SC pe o mance and ha he e is a media ed posi i e in luence o SC
pe o mance on inancial pe o mance. A ia (2015) concludes ha he e is a posi i e
ela ionship be ween T iple-A SC and SC pe o mance, and be ween SC pe o mance and
o ganisa ional pe o mance. Al alla-Luque e al. (2018) s a e ha he T iple-A SC has a
posi i e and signi ican ela ionship wi h mos CA componen s (cos -CA, deli e y-CA,
lexibili y-CA and inancial p oxy-CA). Finally, an analysis by Machuca e al. (2021) o wo
sepa a e samples o eme ging and de eloped coun ies concludes ha he e is a signi ican
posi i e ela ionship be ween he T iple-A SC and CA in bo h con ex s.
P edic i e
capaci y o
T iple-A SC
models
863
In he second way o model he AAA-SC capabili ies (IC), he concep ual amewo k
es ablished by Lee (2004) does no hypo hesise abou he possible in luence o he indi idual
T iple-A SC capabili ies on pe o mance/CA o how hey can bes be ela ed o ob aining an
“op imum o be e esul ”. Rega ding hei in luence, mos o he p e ious esea ch suppo s
a posi i e ela ionship be ween each o he indi idual As and pe o mance/CA (Machuca
e al., 2021), al hough in some cases he esul s a e no clea ly conclusi e o all he
ela ionships (e.g. Al alla-Luque e al., 2018;Dubey and Gunaseka an, 2016;Dubey e al.,
2015). Howe e , ega ding ob aining an op imum esul , he di e en models ha ha e been
heo ised do no show conclusi e esul s o he mos sui able se o ela ionships be ween he
h ee T iple-A SC capabili ies and pe o mance/CA o he linkages ha migh exis be ween
he capabili ies (Dubey and Gunaseka an, 2016), and his con inues o be an impo an gap as
i is a key ques ion o esea che s and manage s. The e o e, as he i s s ep in ou esea ch, a
li e a u e analysis is pe o med o he di e en models used, and hese will be compa ed in a
second s ep o de e mine whe he any model has a g ea e p edic i e capabili y o
pe o mance/CA. Should his be he case, he “winne ”will be conside ed he mos
app op ia e model o bes indica e how o deploy he T iple-A SC capabili ies.
The abo e is ela ed o esou ce o ches a ion heo y (ROT), which is oo ed in he RBV bu
o e comes i s limi a ions in ou esea ch. While he RBV does no speci y how o deploy he
esou ces o c ea e e ec s ha could acili a e he de elopmen o CA (Si mon e al., 2011;G ube
e al., 2010), ROT complemen s RBV by p oposing ha supe io pe o mance is p o ided by “a
ce ain combina ion o esou ces, capabili ies and manage ial acumen”(Chadwick e al.,2015),
and ha his unique combina ion allows di e en ia ion in he ma ke place (Ke chen e al.,2014).
In his line, manage s should use he T iple-A amewo k o o ches a e he deploymen o he
AAA-SC o ob ain sus ainable CA. As al eady s a ed by some au ho s (Feizabadi e al.,2019a,b;
Gligo e al.,2020), ROT is embodied in Lee’s(2004) amewo k.
As p e iously s a ed, wo main ypes o models ha e been ound in he li e a u e o he
deploymen o he AAA-SC capabili ies: di ec ela ionship models (DRMs) and media ed
ela ionship models (MRMs) (Figu e 1).
2.3 T iple-A and pe o mance: di ec ela ionship models
DRMs could be conside ed o be di ec ly de i ed om seminal Lee’s (2004) s a emen ha
“only SCs ha a e agile, adap able and aligned p o ide companies wi h sus ainable CA”.
They do no conside any media ing e ec be ween he h ee As o achie e pe o mance
(A ia, 2016;Lussak, 2020;Yang, 2021;Khan e al., 2022)o CA(Al alla-Luque e al., 2018;
Sheel and Na h, 2019), which would imply ha manage s could de elop each A independen ly
as none has been es ablished o le e age any o he o imp o e pe o mance/CA. This is in line
wi h he conclusions o Machuca e al. (2021), who used a DRM o a wide sample o de eloped
and eme ging coun ies and con i med ha he e a e no signi ican di e ences in he
impo ance o SC adap abili y, SC agili y and SC alignmen as le e s in he T iple-A SC–CA
ela ionship as he e ec s o he h ee capabili ies a e summa i e.
Mos esea ch using DRM inds ha he T iple-A SC capabili ies ha e a posi i e in luence on
pe o mance/CA. Fo example, A ia (2016) con i ms ha each o he h ee capabili ies has a
signi ican in luence on o ganisa ional pe o mance in a sample o Egyp ian manu ac u ing
i ms. The same esul is ob ained by Lussak (2020) o SC pe o mance in he Indonesian
se ice indus y. Yang (2021) con i ms a signi ican ela ionship be ween SC agili y,
adap abili y and alignmen and ope a ional and ela ional pe o mance imp o emen s in a
sample o USA manu ac u ing i ms. Las ly, Al alla-Luque e al. (2018) epo simila esul s o
mos o hei analyses o a sample o manu ac u ing i ms in eigh de eloped coun ies, wi h
con i ma ion o signi ican posi i e ela ionships be ween SC agili y and lexibili y and inancial
CA; SC adap abili y and cos , quali y, deli e y, lexibili y and inancial CA; SC alignmen and
cos , quali y, deli e y and inancial CA. Howe e , no posi i e ela ionship is con i med be ween
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864

Figu e 1.
T iple-A capabili y
ela ionship models
P edic i e
capaci y o
T iple-A SC
models
865
SC agili y and cos , quali y o deli e y CA, o be ween SC alignmen and lexibili y. A mo e
ecen analysis by Khan e al. (2022) o he e ec s o AAA-SC capabili ies on pos -co id
dis up ion pe o mance ina sample o Pakis ani ex ile i ms concludes ha hese a e posi i ely
and signi ican ly ela ed o pe o mance. I is in e es ing o no e ha he majo i y o DRM
esea ch ocuses on he impac o he AAA capabili ies on pe o mance/CA wi hou including
any o he a iable as an an eceden o he o me , as he p ima y issue o be sol ed is he
ela ionship be ween AAA and pe o mance/CA. Only wo ecen pape s, Yang (2021) and
Khan e al. (2022), each include one an eceden o he AAA in he T iple-A SC amewo k,
speci ically Knowledge Managemen Capabili y and Supply Chain Analy ics, espec i ely.
2.4 T iple-A and pe o mance: media ed ela ionship models
Rega ding he o he ype o model, MRMs conside some ela ionships be ween he As. In his
sense, Feizabadi e al.’s (2019a) li e a u e e iew s a es ha p e ious esea ch has conside ed
alignmen cons uc s as an eceden s o SC agili y. I also s a es ha “while no esea ch has
di ec ly assessed alignmen as an an eceden o adap abili y, all o he consequences o
adap abili y a e sha ed wi h alignmen , sugges ing alignmen as an an eceden o
adap abili y”. In ou sea ch o he T iple-A models conside ed in his esea ch, wo main
MRM p oposals ha e been ound in line wi h his s a emen .
The i s ype is a media ed model (MRM1) ha p oposes a sequence in which SC alignmen
leads o SC adap abili y, which u he leads o SC agili y and hen, o pe o mance, as well as
sa u a ed media ion, which conside s all possible di ec and indi ec links wi h pe o mance
(Figu e 1) (e.g. Dubey and Gunaseka an, 2016;Je msi ipa se and Kampoomp ase , 2019).
Ma in-Ga cia e al. (2018) s a e ha in he i s s ep o he sequence, incen i e, in o ma ion and
p ocess alignmen be ween SC pa ne s a o ds coope a ion, communica ion and sha ed
goals, and isks and ewa ds, which bene i s he key dimensions o SC adap abili y (e.g.
o ganisa ional design and he use o echnology in he SC, and medium- and long- e m ma ke
knowledge). Consequen ly, alignmen is conside ed o be an an eceden o adap abili y. In he
same line, Lee (2004) s a es ha SC alignmen implies ha in o ma ion and knowledge a e
exchanged eely along he SC o clea ly es ablish oles, asks and esponsibili ies, and
equi ably sha e isks and cos s be ween all pa ne s. This u he s knowledge abou supplie s
and ma ke s and helps o iden i y he needs o end consume s, enabling he c ea ion o lexible
p oduc designs and he de e mina ion o echnology and p oduc li e cycles. This signi ies
ha he alignmen o ups eam and downs eam SC pa ne s in luences he capabili y o he
SC o add ess long- e m changes (adap abili y), which indica es ha i could be conside ed an
enable o SC adap abili y. The e o e, i can be conside ed ha SC adap abili y is buil upon
he ounda ion o SC alignmen and ha he in luence o SC alignmen on pe o mance could
be media ed by SC adap abili y.
The nex s ep in he sequence means ha an aligned and adap able SC could a ou he
ob en ion o SC agili y o ecognise and espond o sho - e m changes h ough he
achie emen o a ie y and olume lexibili y. SC agili y esponds o unan icipa ed changes
in u bulen ma ke s (Cha les e al., 2010;Abdallah e al., 2021). Fo example, he c ea ion o
lexible p oduc designs ha make he SC mo e adap able could gene a e a design based on
pos ponemen ha acili a es SC agili y (Lee, 2004). Consequen ly, he achie emen o an agile
SC could media e he in luence o SC adap abili y on pe o mance.
Rega ding he comple e MRM1 sequence, Dubey and Gunaseka an (2016) and
Je msi ipa se and Kampoomp ase (2019) p opose an in e p e i e s uc u al model o
he con ex o humani a ian SC whe e SC alignmen ac s as he enable a he beginning o he
sequence, di ec ly ollowed by SC adap abili y, which is in u n ollowed by SC agili y, which
leads o pe o mance a he op le el. Dubey and Gunaseka an (2016) con i m posi i e
ela ionships be ween SC alignmen and SC adap abili y, SC adap abili y and SC agili y, and
SC alignmen and SC agili y. Rega ding he ela ionship wi h SC pe o mance, SC agili y and
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adap abili y a e ound o ha e posi i e signi ican ela ionships wi h SC pe o mance bu SC
alignmen is no . Je msi ipa se and Kampoomp ase (2019) also con i m he p oposed
MRM1 sequence and, in hei case, he h ee T iple-A SC capabili ies a e posi i ely associa ed
wi h SC pe o mance.
The second ype o media ed model (MRM2) ound in ou li e a u e sea ch p oposes a
di ec ela ionship be ween he h ee As and pe o mance/CA, and he media ion o SC agili y
be ween SC adap abili y and pe o mance/CA (Figu e 1). The conside a ion o SC
adap abili y as an an eceden o SC agili y has been analysed in he li e a u e. In his
sense, Sha ma and Bha (2014) sugges ha adap abili y is an enable o an agile SC as, when
i is p esen , he SC can adjus o long- e m changes (e.g. demog aphic ends, poli ical shi s,
economic p og ess, e c.), enabling an agile SC ha can eac app op ia ely o sho - e m
changes. Swa o d e al. (2006) also con i med SC adap abili y as an an eceden ha posi i ely
impac s SC agili y. In he same line, Ecks ein e al. (2015) s a e ha adap i e capabili ies
p o ide a s uc u al basis ha ac s as an enable o de eloping agile capabili ies, in he sense
ha he abili y o adap he SC design o ma ke s uc u al changes and de elop new supply
bases and ma ke s (adap abili y) enables he SC o de elop agile capabili ies ha allow a
quick eac ion o sho - e m changes in supply o demand.
Following he MRM2 model, he agile capabili ies c ea ed on he basis o adap able
capabili ies could ansla e in o imp o ed pe o mance. In his sense, he Ecks ein e al. (2015)
analysis o he ela ionships be ween SC adap abili y and SC agili y and pe o mance
concludes ha bo h ha e posi i e e ec s on cos and ope a ional pe o mance and ha he e
is a pa ial media ing ole o SC agili y on he links be ween SC adap abili y and bo h cos and
ope a ional pe o mance. Following Ecks ein e al. (2015),Aslam e al. (2018) con i m ha SC
agili y signi ican ly media es he ela ionship be ween SC adap abili y and pe o mance (SC
e iciency and SC esponsi eness).
Apa om including he media ed in luence o SC agili y be ween SC adap abili y and
pe o mance, he MRM2 model also includes he di ec e ec o SC alignmen on pe o mance.
Rega ding his e ec , Gligo e al. (2020) s a e ha SC alignmen may in i sel be su icien o
ob ain s ong i m pe o mance wi hou he need o he media ion o he o he As.
Las ly, Dubey e al. (2015) analyse humani a ian SC in India wi h he MRM2 model and
conclude a posi i e di ec ela ionship be ween he h ee As and human and logis ics
pe o mance, excep o he ela ionship be ween SC adap abili y and human pe o mance.
They also show ha SC agili y ully media es he ela ionship be ween SC adap abili y and
human pe o mance and, pa ially, logis ics pe o mance.
A his poin , i is wo h commen ing ha he ini ial models p oposed in he li e a u e a e
“di ec ela ionship models (DRMs)”(by an HOC o IC), which seem o be mo e in line wi h
Lee’s (2004) p oposal since hese models a e summa i e, i.e. an inc ease in he le el o any o
he As esul s in highe pe o mance/CA. The “media ed ela ionship models (MRMs)” ha
la e eme ged as modi ica ions o DRMs a e mo e complex and impose cons ain s on
p ac ical deploymen . Ne e heless, in con as wi h DRMs, no jus i ica ion is gi en o MRM
amewo ks (Dubey and Gunaseka an, 2016;Je msi ipa se and Kampoomp ase , 2019;
Aslam e al., 2018), which would ha e been desi able. In ou opinion, mo e complex models,
which a e less pa simonious, would be jus i ied i hey a o ded a highe p edic i e capaci y,
a he e y leas . This implies ha i is sensible o ake DRM as he “ e e ence”model agains
which MRMs should be compa ed when he la e ’s p edic i e capaci y is analysed.
2.5 T iple-A models’compe i i e ad an age p edic i e capaci y
In summa y, h ee main ypes o models ha e been p oposed in he p e ious esea ch (DRM,
MRM1, MRM2). All ha e been alida ed and mos ind a posi i e e ec o AAA capabili ies
on pe o mance/CA. Howe e , he ques ion o be answe ed o espond o ou RQ1 is: Which, i
P edic i e
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867
any, o hese models has he highes p edic i e capabili y o p edic CA? In esponse o he
RQ1, his pape analyses he h ee men ioned models’p edic i e capabili y o CA. As
p e iously s a ed, al hough all h ee models appea o con i m a posi i e ela ionship
be ween he 3 As and CA in gene al e ms, he adop ion o one o ano he would ha e
di e en implica ions o he design o he T iple-A SC and he app op ia e deploymen o
each o he As o be e CA. The e o e, i is impo an o de e mine whe he any o hese
models can be conside ed mo e sui able om he poin o iew o i s p edic i e capabili y as
his would gene a e majo implica ions o esea ch and p ac ice. To achie e his objec i e, a
se o assessmen echniques is used o measu e he a ious aspec s o he p edic i e
capabili y o he models unde s udy (see Me hodology sec ion).
3. Me hodology
3.1 Sample and da a collec ion
This esea ch uses a da ase aken om he High-Pe o mance Manu ac u ing (HPM)
esea ch p ojec da abase (4 h ound). A andom sample o plan s (wi h ≥100 employees) was
aken om 14 di e en coun y mas e lis s ha included a selec ion o plan s in he
machine y, elec onics and au omo i e componen s indus ies. The men ioned sec o s we e
selec ed because hey ace in ense global compe i ion in di e en en i onmen s and ha e a
la ge numbe o plan s in e e y coun y (Ga ido-Vega e al., 2015;Mo i a e al., 2018). They
a e also p esen in global ne wo ks and sha e p ac ices ele an o his esea ch. In his sense,
o he esea che s ha e also used hese sec o s ei he sepa a ely (e.g. D oge e al., 2004;O ega
e al., 2012) o join ly (Danese e al., 2019;Mo i a e al., 2018). The global selec ion o coun ies
imp o es he gene alisabili y o he esul s, which is mo e es ic ed when he sample is
ob ained a a na ional o egional le el.
Local esea ch eams con ac ed he plan s and assis ed esponden s wi h comple ing he
ques ionnai e. Ini ially, pe sonal app oaches we e made o he plan CEOs, and HPM
esea che s subsequen ly isi ed he plan s o explain he pu pose o he p ojec . To d i e up
in ol emen , plan manage s we e o e ed de ailed copies o he su ey esul s in e u n o
hei plan ’s pa icipa ion. The su ey epo was based on HPM da a and included plan
assessmen s based on he OM p ac ices ha hey had implemen ed and he pe o mance ha
hey had achie ed compa ed o he a e age sco es o na ional and in e na ional compe i o s
in hei indus ies. An app ox. 65% esponse a e ensu ed ha non- esponse bias was limi ed.
Each o he 12 ques ionnai es in his esea ch was ailo ed o he expe ise o he ocal
in o man . In pa icula , he con ac pe son in each plan (see sample p ocedu e abo e)
dis ibu ed he ques ionnai es con aining he ques ions ela ed o his s udy o wo SC
manage s and he plan manage , so he e we e mul iple esponden s pe ques ion (Danese
e al., 2019). Also, esponses o dependen and independen a iables we e gi en by di e en
people. Fo mo e in o ma ion, see Ma in-Ga cia e al. (2018),Danese e al. (2019),Machuca
e al. (2021).
The HPM ou h- ound ques ionnai e was e iewed and upda ed om p e ious HPM
ounds. A panel o expe s e iewed he i ems o gua an ee con en alidi y and, las ly, i was
pilo ed in se e al plan s (see, o example, Sch oede and Flynn, 2001;Flynn e al., 1995;
Machuca e al., 2021). Ques ionnai es ha e been e iewed o e he HPM p ojec ’s a ious
ounds. The i ems and scales had p e iously been used and alida ed in se e al OM s udies,
and new scales we e duly alida ed wi h p esc ip i e eliabili y, alidi y and in e nal
consis ency analyses (Ahmad and Sch oede , 2002;Flynn e al., 1995;Ma in-Ga cia e al.,
2018;Sakakiba a e al., 1997). Ambiguous o complex i ems we e a oided in he design phase.
I ems we e pilo ed o check o hei cla i y and eadabili y and a di e en choice o scale
ancho s was used wi h i ems in he same scale included in di e en pa s o he ques ionnai e
(i ems we e no g ouped by scale bu andomly lis ed o p e en i em p oximi y om
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Howe e , o he au ho s ha e p oposed media ed models (MRM1 and MRM2) ha es ablish
media ion ia some o he capabili ies (e.g. Dubey and Gunaseka an, 2016;Dubey e al., 2015),
bu hey do no assess whe he hei p oposed sequence could be conside ed mo e sui able
han DRM o imp o ing pe o mance/CA. The e o e, a compa ison o hese models is
necessa y o asce ain which has he highes pe o mance/CA p edic i e capabili y, i any.
The i s s ep o he analysis o he h ee ypes o models (Figu e 1), di ec (DRM) and
media ed (MRM1 and MRM2) equi es e i ying whe he hey all mee he equi emen s o be
good measu emen models o he analysed sample. The esul s show ha hey all do. Thus,
all h ee models sa is y he essen ial condi ion o be conside ed alid, as in e e y case he
indica o s o he cons uc s a e ele an , eliabili y is adequa e and, in gene al, he di ec and
indi ec pa hs a e signi ican .
Once he h ee measu emen models ha e been alida ed, he second s ep is o compa e
hei in-sample p edic i e capabili y. This was done using R
2
, BIC and Akaike weigh s. DRM
was consis en ly p o en o ha e he highes p edic i e capabili y, ollowed by MRM2 and,
las ly, MRM1, which leads o he conclusion ha DRMs a e he mos ad an ageous models as
a as in-sample p edic ion o pe o mance/CA is conce ned. I is in e es ing o emembe ha
R
2
alues alone a e no ecommended o assessing he adequacy o heo e ical models as
hey may end o gene a e o e i and no di e en ia e he ela ionships om he noise
inhe en in any da ase (Chin e al., 2020). This is why i is use ul o use BIC and Akaike
weigh s, which ha e a lesse endency o su e om o e i . The co esponding esul s
indica e ha he DRM model p oduces ewe p edic ion e o s in da a used o es ima e model
pa ame e s and maximises he likelihood o coincidence wi h he unde lying da a.
As he esul s o he in-sample p edic ion measu es a e no clea ly gene alisable o o he
da a samples, he hi d s ep is he measu emen o ou -o -sample p edic i e capabili y. Fo his,
PLSP edic was used, which en ails a ade-o be ween explana ion and p edic ion o p e en
o e i (Chin e al., 2020). All h ee models we e shown o achie e adequa e Q2 p edic alues
o all he CA indica o s. The esul s ob ained om he compa ison o he h ee models e eal
be e ou -o -sample p edic ion o DRM han o MRM1 and MRM2. This means ha ,
al hough all h ee models pe o m be e in p edic ion han he simple mean o da a o a linea
eg ession model, DRM gi es a lowe p edic ion e o han he compe ing models (MRM1 and
MRM2). In o he wo ds, DRM ha e a g ea e p edic i e capabili y o pe o mance indica o s
and so can be conside ed mo e gene alisable beyond he cu en sample o es ima e PLS pa h
models.
In he con ex o he p oposed models and ou sample, he con ol a iables (plan size,
indus y and coun y con ex ) do no ha e a su icien ly ele an in luence o explain he
a ia ion in pe o mance. In ela ion o he plan size and indus y con ol a iables, se e al
pape s ha e ound no di e ences be ween he manu ac u ing and se ice sec o s (e.g.
Ma inez-Sanchez and Lahoz-Leo, 2018;Liu e al., 2013) o plan / i m sizes (Mandal, 2016;Liu
e al., 2013). In he same line, he p e ious esea ch has no con i med he in luence o he
coun y con ex (usually de eloped s de eloping coun ies) in he ela ionship be ween
T iple-A SC and pe o mance/CA (Machuca e al., 2021).
The abo e esul s show ha al hough T iple-A esea ch wi h MRM has added mo e
ela ionships o he o iginal DRM models, he in o ma ion p o ided by ou esul s shows ha
he inclusion o complexi y in he DRM model does no seem o ha e been success ul in
imp o ing bo h he in-sample and ou -o -sample in o ma ion. The use o PLSp edic has
enabled esea ch on T iple-A o educe he unce ain y a ound model choice by compa ing
he di e en al e na i e models and iden i ying DRM as he model wi h he highes p edic i e
powe . On he manage ial side, whe e he ocus is on inding gene alisable app oaches/
models ha could be use ul o business o p oduce p edic i e powe (Ruddock, 2017), he use
o PLSp edic has allowed us o choose he model wi h he lowes gene alisa ion e o , which
P edic i e
capaci y o
T iple-A SC
models
875

enables manage s o make decisions ha will be mo e likely o wo k in o he se ings (Chin
e al., 2020).
Summa ising, DRM has equen ly been used in p e ious T iple-A SC esea ch (e.g.
Al alla-Luque e al., 2018;A ia, 2016;Lussak, 2020;Sheel and Na h, 2019;Yang, 2021) and
demons a es a g ea e p edic i e capabili y (bo h in-sample and ou -o -sample) o
p edic ing pe o mance/CA han MRM1 and MRM2. In o he wo ds, he p obabili y o
ob aining a highe pe o mance/CA p edic i e capaci y is g ea e wi h DRM han wi h MRM1
and MRM2, which means ha i can be conside ed a benchma k model o esea ch and
p ac ice when he speci ic goal is o ob ain he highes pe o mance/CA p edic i e capabili y
possible. In addi ion, i mus be s a ed ha media ed models a e also less pa simonious han
DRMs and hei use adds o he complexi y o T iple-A capabili y deploymen o manage s.
G ea e complexi y migh be accep able i hey also had a highe p edic i e capabili y, bu
his is no suppo ed by he esul s.
The choice o DRM as he benchma k model implies ha he AAA-SC capabili ies can be
conside ed independen le e s o achie ing CA. As shown below and in he ollowing
sec ion, his has impo an manage ial implica ions. I means ha , unlike in he case o MRM
models, no speci ic AAA capabili y deploymen sequence needs o be ollowed when seeking
be e pe o mance/CA. I is wo h highligh ing a his poin ha he AAA-SC capabili ies
ocus on di e en SC aspec s ha can be de eloped independen ly and could complemen
each o he . In his sense, SC alignmen , adap abili y and agili y conno e long-, medium- and
sho - e m pe spec i es, espec i ely (Tang and Tomlin, 2008). Each T iple-A capabili y has
a ole o play in company s a egy and all a e needed, especially nowadays as wha e e he
s a egy a company adop s, he T iple-A will always be a ec ed by he compe i i e
en i onmen (Ga ido-Vega e al., 2021). The h ee capabili ies conside he di e en planning
le els o ocus he whole SC on se ing he end cus ome and achie ing CA. In addi ion, in he
cu en unce ain and complex en i onmen , companies inc ease he alue ha hey o e
cus ome s by aising hei le els o SC agili y, adap abili y and alignmen o con end wi h
highe compe i i e in ensi y (Ga ido-Vega e al., 2021). Doing his could also help o mi iga e
o educe he isks o SCs (Tang and Tomlin, 2008) ha de i e om unexpec ed si ua ions
such as he Co id-19 pandemic.
P oposing DRM, in o he wo ds, a simul aneous AAA deploymen ins ead o a speci ic
sequence (MRM1 o MRM2) when seeking o ob ain a highe alue o p edic i e pe o mance/
CA, implies ha : (1) DRM is summa i e, which means ha he soone all he AAA a e
implemen ed and each high alues, he highe he CA ha will be ob ained; (2) he absence o
any o he AAA a any gi en momen esul s in an un a ou able compe i i e posi ion. This is
in line wi h Khan e al. (2022), who s a e ha he SC’s abili y o ou pe o m he compe i ion in
sensi i e imes depends on i s membe s’abili y o simul aneously deploy he AAA. In his
con ex , simul aneously does no indica e ha a i m needs o de elop all AAA abili ies a he
same poin in ime and wi h he same in ensi y (which is no in line wi h he business
p ac ices), bu i implies ha he i ms need o possess o es ablish all he AAA capabili ies in
e e y compe i i e si ua ion. This ma e is be e cla i ied in he subsec ion Implica ions o
manage s.
I is also wo h s a ing ha i is he only c i e ion o analysis used in he p e ious esea ch
and ha ou wo k goes u he by using p edic i e capabili y, which is indispensable due o
i s ele ance o decision-making and p o iding ecommenda ions o business p ac ice (Chin
e al., 2020;Shmueli e al., 2016). The e o e, he explici implica ions p oposed in his esea ch
add o iginali y and alue o esea che s and manage s.
Las ly, we would s ess ha he dependen a iables in he analysed models a e
ope a ional measu es. This means ha di e en esul s migh be ob ained in o he esea ch
con ex s mo e ocused on o he aspec s such as inancial o sus ainabili y measu es, o
example, and in some o hese cases, MRMs migh ha e a be e p edic i e capaci y.
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6. Implica ions, limi a ions and u he esea ch
6.1 Implica ions o esea ch
The analysis de eloped in his s udy has some ele an implica ions o esea che s. Fi s ly,
he di e en T iple-A SC models p oposed in he li e a u e ha e been join ly analysed and
compa ed in he same sample o he e y i s ime. The esul s ob ained iden i y DRM as he
model wi h he highes pe o mance/CA (in-sample and ou -o -sample) p edic i e capabili y
and so i can be conside ed a e e ence model o u u e esea ch o analyse and iden i y how
o igge he T iple-A SC le e s (agili y, adap abili y, alignmen ) when he main objec i e is
o imp o e pe o mance/CA. As such, when he use o a media ed model is p oposed in
esea ch, i would be app op ia e o compa e his wi h he di ec model (DRM) o de e mine
whe he he esul s o he media ed model a e signi ican ly be e o he speci ic aim o he
s udy in ques ion. Secondly, gi en BIC, Akaike weigh s and PLSP edic all iden i y DRM as
he bes op ion o pe o mance/CA p edic i e capabili y, when he sole pu pose o a s udy is
o de e mine he e ec o he T iple-A SC on CA (i.e. wi hou de e mining how o deploy he
AAA-SC capabili ies), i would make sense o g oup hei e ec s in a HOC. This would
p oduce a mo e pa simonious model o iden i y he con ibu ion made by T iple-A o CA.
This esea ch amewo k has been used in some p e ious pape s (e.g. Al alla-Luque e al.,
2018;A ia, 2015;Machuca e al., 2021;Whi en e al., 2012). Wi h espec o he me hodology,
as a as we know, his is he i s ime ha PLSP edic has been used o compa e T iple-A SC
models, and also he i s ime ha BIC, BIC Akaike weigh s and PLSP edic ha e been used
in he same pape as complemen a y me hods o assess he models’CA in-sample and ou -o -
sample p edic i e capaci y. This ep esen s p og ess in he use o hese echniques o
enhance ou knowledge and p o ide guidelines o p edic i e model selec ion no only in
ope a ions and SC managemen bu also in o he managemen a eas. Finally, i can be s a ed
ha his esea ch ep esen s an ad ance in he conside a ion o he RBV, DCV and ROT as
impo an heo ies o unde s anding he ole o AAA-SC capabili ies as key ac o s in CA in
he T iple-A SC amewo k.
6.2 Implica ions o manage s
This pape also has ele an implica ions o manage s, o whom ha ing a clea e
unde s anding o he mos sui able way o deploy he T iple-A SC capabili ies o e s an
oppo uni y o imp o e CA (Lee, 2004). Co ec ly de eloping a T iple-A SC is e en mo e
c i ical in he cu en unce ain y en i onmen as i minimises he e ec s o SC in e up ions
on ma e ial and in o ma ion lows (Khan e al., 2022). In his con ex , he limi ed business
esou ces and signi ican in es men s needed o p ope ly implemen he AAA capabili ies
(Machuca e al., 2021) make ou indings especially ele an . In line wi h ROT, company
esou ces mus be o ches a ed o any po en ial ad an age o be ob ained (Chi ico e al.,
2011) and, in his sense, his esea ch p oposes a sui able way o manage s o deploy he
T iple-A SC capabili ies wi h he g ea es likelihood o ob aining highe pe o mance/CA.
As DRM is iden i ied as he bes op ion o ob ain a highe pe o mance/CA p edic i e
capabili y, AAAs can be conside ed independen le e s and, as a esul , could be igge ed
sepa a ely gi en hei addi i e na u e. The e o e, CA could be imp o ed wi h any o he
capabili ies wi hou he need o each p io le els in he o he wo. I should be possible o ake
long-, medium- and sho - e m decisions o imp o e pe o mance/CA wi hou he need o
ollow a speci ic sequence, as is sugges ed should be done when using media ed models
(MRM). Implemen ing he AAA capabili ies independen ly seems o be mo e e ec i e wi h
DRM han ollowing a p e-es ablished sequence. Ne e heless, i should be bo ne in mind ha
he g ea es po en ial is achie ed when all h ee capabili ies a e deployed, as o he au ho s
(e.g. Lee, 2004;Khan e al., 2022) ha e also s a ed. In addi ion, his implemen a ion is less
complex as DRMs a e mo e pa simonious han MRMs. The e o e, based on he business aims
P edic i e
capaci y o
T iple-A SC
models
877
and conside ing ha each A capabili y has a dis inc impac on he a ious pe o mance
measu es (Al alla-Luque e al., 2018), manage s can decide whe he i is mo e expedien o
s a wi h one speci ic A o wi h all h ee in unison.
Fu he mo e, he cu en highly unce ain con ex demands ha manage s need o be
conscious o he g owing impo ance o co ec ly implemen ing he AAA capabili ies, as SCs
ha e o be mo e agile, adap able and aligned han be o e (Lee, 2021a). SCs need o espond o
g owing unce ain ies and dis up ions by enhancing SC agili y in o de o su i e and be
compe i i e This can be done by apidly iden i ying unce ain ies and esponding wi h a
quick and lexible design (Lee, 2021a). Manage s also need o s ay ab eas o medium- and
long- e m ma ke changes by adap ing hei SC p ocesses and s uc u e o ma ke changes
and in oducing new echnologies based on he de ec ion o echnological cycles (Al alla-
Luque e al., 2018). Finally, highe SC alignmen is needed in e ms o incen i e alignmen
(de ining he oles, asks and esponsibili ies o SC pa ne s), in o ma ion alignmen (sha ing
isks, cos s and bene i s equi ably) and p ocess alignmen (sha ing knowledge and impo an
and co ec in o ma ion o planning, con ol and decision-making) (Ma in-Ga cia e al., 2018).
I is also impo an o s a e ha alignmen should no longe be ocused only on he
pa ne ship be ween selle s and buye s along he SC, as an expanded iew is needed ha
conside s s akeholde s’ecosys ems, which include mul iple in e dependen SCs and new
ac o s wi h an in e es in en i onmen al and social issues, such as local go e nmen s, NGOs
and communi ies (Lee, 2021). Las ly, ega ding his las poin , manage s mus also ake no e
o he g owing impo ance o social aspec s in SC design, which Lee (2021b) calls “SC wi h a
conscience”and en isages he ex ension o he SC iew o he SC ecosys em iew (Sodhi and
Tang, 2021).
6.3 Limi a ions
The e a e some limi a ions o he p esen s udy ha can be used as he basis o u he
esea ch. Fi s ly, he da a used co espond o only h ee indus ies (elec onics, machine y
and au omo i e componen s) and a de eloping/de eloped coun y sample, so, he esul s
ha e o be in e p e ed in he con ex o hese sec o s and a eas. None heless, he con ol
a iables a e seen o ha e li le in luence on he esul s, which could be ega ded as a sign o
obus ness. Be ha as i may, i would be in e es ing o ex end he s udy o an analysis o
o he samples om di e en sec o s and coun y con ex s (Al Humdan e al., 2020). Mo eo e ,
as his sample has analysed he CA p edic i e capaci y o T iple-A SC models, u he
esea ch could also explo e his issue o he case o pe o mance.
Ano he limi a ion is also ound in mos s udies in his a ea: he use o c oss-sec ional
analysis, which does no allow o obse e change and eac ions o change in p ac ice. Fu he
esea ch using a longi udinal s udy would allow o obse e he way ha he a iables e ol e
and, hus, o analyse he e olu ion o he le els o he a iables and hei ela ionships wi h
CA. I would hen be possible o con i m whe he T iple-A SCs ha e sus ainable CAs (Lee
(2004)). Hope ully, he da abase o he nex ound o he HPM p ojec will allow his esea ch.
6.4 Fu he esea ch
New empi ical esea ch is also encou aged o eplica e he analysis de eloped he e wi h o he
samples o obse e whe he he conclusion ha DRM is he model wi h he highes p edic i e
capabili y o pe o mance/CA is gene alisable. I con i med, he ole o he DRM model would
be s eng hened bo h as a e e ence poin in he T iple-A SC esea ch amewo k and as a
guide o manage s o implemen each o he AAA-SC capabili ies independen ly and in no
speci ic o de .
I should be no ed ha ecen esea ch add esses he analysis o he le els o each T iple-A
SC capabili y equi ed o su icien o achie e ce ain le els o CA (e.g. Gligo e al., 2020;
IJPDLM
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878
Feizabadi e al., 2021). This could be conside ed a p omising esea ch s eam o complemen
he p esen s udy’s indings, as he ac ha each o he As on i s own could lead o an
imp o emen in CA does no p eclude he possible exis ence o a join e ec ha enables
syne gy o be ob ained when all he 3 As a e achie ed. Fu u e esea ch should shed some
ligh on his issue, no only wi h he use o di e en samples bu also using di e en
analy ical me hods.
One las commen is ela ed o s a is ical ools o he assessmen o ou -o -sample
p edic i e capaci y. While we ha e chosen o use he well-es ablished PLSp edic me hod
(Shmueli e al., 2016,2019), his could be complemen ed by he use o p omising ools such as
CVPAT (Chin e al., 2020;Liengaa d e al., 2021;Sha ma e al., 2022). This will be he subjec o
u he esea ch on his opic, as sub le di e ences in he gene a ion o he PLS-SEM
p edic ions can be impo an o models’p edic i e pe o mance (Danks, 2021).
No e
1. Annex and supplemen a y ma e ial (Appendixes) ci ed in he ex ha e been pu in addi ional
ma e ial downloadable om h ps://doi.o g/10.5281/zenodo.7486519
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