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 1 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 Page 1 o 57 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 2 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, Page 2 o 57In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 3 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 Page 3 o 57 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 4 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 Page 4 o 57In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 5 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 . Page 5 o 57 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 6 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 Page 6 o 57In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 7 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 - Page 7 o 57 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 8 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 Page 8 o 57In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 15 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 Page 15 o 57 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 16 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 Page 16 o 57In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 17 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 Page 17 o 57 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 18 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]). Page 18 o 57In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 19 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 Page 19 o 57 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 20 (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 Page 20 o 57In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen Page 21 o 57 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 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, 21 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 22 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 Page 22 o 57In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 23 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 Page 23 o 57 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 24 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. 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Page 32 o 57In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 33 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 Page 33 o 57 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 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 Page 34 o 57In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 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 Page 35 o 57 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 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 Page 36 o 57In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 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 Page 37 o 57 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 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 Page 38 o 57In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 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 Page 39 o 57 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 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) Page 40 o 57In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 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 Page 47 o 57 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 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 Page 48 o 57In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen (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 Page 49 o 57 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 10 NN NN NN 20 NN NN NN 30 NN NN NN 40 NN NN NN 50 NN NN NN 60 NN NN NN 70 4.4 NN NN 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 Ali Ada Agi Ali Ada Agi Page 50 o 57In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 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 Ali Ada Agi Ali Ada Agi Ali Agi Ada Ali Agi Ada Page 51 o 57 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 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 Ali Ada Agi Ali Ada Agi Ali Ada Agi Ali Ada Agi Page 52 o 57In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 In e na ional Jou nal o Physical Dis ibu ion & Logis ics Managemen 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.) 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