Jou nal o Resea ch and De elopmen
Pee Re iewed In e na ional, Open Access Jou nal.
ISSN : 2230-9578 | Websi e: h ps://j d b.o g Volume-17, Issue-8| Augus - 2025
267
E alua ing Mode n Rec ui men and Selec ion S a egies: A S udy o
Talen Acquisi ion in he Supply Chain Indus y
P o . Vishwana h. R Ha alappagol1 , M . Va un Gowda J2
1Associa e P o esso & Resea ch Supe iso , Depa men o Managemen S udies, Vis es a aya Technological
Uni e si y-Belaga i, Cen e o Pos -G adua ion S udies, Muddenahalli, Chikkaballapu , India,
2S uden , Depa men o Managemen S udies (MBA), Cen e o Pos G adua e S udies, Muddenahalli, Chikkaballapu ,
Vis es a aya Technological Uni e si y, Belaga i, Ka na aka S a e, India,
Email: a ungow[email p o ec ed]m
Manusc ip ID:
JRD -2025-170850
ISSN: 2230-9578
Volume 17
Issue 8|
Pp. 267-273
Aug 2025
Submi ed:19 July. 2025
Re ised: 02 Aug. 2025
Accep ed: 20 Aug. 2025
Published: 31 Aug. 2025
Abs ac This sys ema ic li e a u e e iew examines mode n ec ui men and selec ion s a egies in he
supply chain indus y h ough analysis o 67 pee - e iewed s udies (2019-2025) and de elopmen o an
in eg a ed heo e ical amewo k. Using PRISMA me hodology, we syn hesized empi ical e idence on i e
key s a egic dimensions: AI-enabled sou cing, compe ency-based assessmen , employe b anding,
di e si y ini ia i es, and analy ics-d i en op imiza ion. Ou heo e ical amewo k, g ounded in Pe son-
En i onmen Fi Theo y and Resou ce-Based View, p oposes ha ec ui men s a egy e ec i eness is
media ed by o ganiza ional capabili ies and mode a ed by con ex ual ac o s. Me a-analysis o 34
quan i a i e s udies e eals signi ican e ec s: AI-sou cing (d=0.42 o ime- o- ill educ ion), skills-based
assessmen (d=0.38 o quali y-o -hi e imp o emen ), and in eg a ed employe b anding (d=0.31 o o e
accep ance a es). The amewo k con ibu es o ec ui men li e a u e by p o iding sec o -speci ic
insigh s and es ablishes an empi ical agenda o supply chain alen acquisi ion esea ch. P ac ical
implica ions include p io i ized implemen a ion pa hways and ROI benchma ks o ec ui men
mode niza ion ini ia i es.
Keywo ds: ec ui men s a egies, supply chain, alen acquisi ion, sys ema ic e iew, human esou ce
managemen
In oduc ion
The global supply chain indus y aces an unp eceden ed alen c isis, wi h p ojec ed
sho alls o 2.1 million wo ke s by 2028 (Global Supply Chain Ins i u e, 2024). Digi al
ans o ma ion has in ensi ied compe i ion o specialized skills while adi ional blue-colla
oles e ol e owa d echnology-enabled posi ions equi ing new compe encies (B ynjol sson &
McA ee, 2024). Supply chain dis up ions du ing 2020-2023 highligh ed he s a egic
impo ance o esilien alen pipelines, pa icula ly in e-comme ce logis ics whe e hi ing
olumes luc ua e by 200-400% seasonally (McKinsey Supply Chain Repo , 2024).Mode n
ec ui men s a egies p omise solu ions h ough a i icial in elligence, compe ency- based
selec ion, and da a-d i en op imiza ion. Howe e , empi ical e idence o e ec i eness emains
agmen ed ac oss disciplines, wi h limi ed supply chain-speci ic esea ch.
This sys ema ic e iew add esses h ee c i ical esea ch ques ions:
1. To iden i y and e alua e mode n ec ui men s a egies ha demons a e empi ical
e ec i eness in he supply chain con ex , using KPIs such as ime- o- ill, cos -pe - hi e,
quali y o hi e, ea ly a i ion (90/180 days), and candida e expe ience.
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A ibu ion-NonComme cial-Sha eAlike 4.0 In e na ional Public License, which allows o he s o emix,
weak, and build upon he wo k noncomme cially, as long as app op ia e c edi is gi en and he new
c ea ions ae licensed unde he iden ial e ms.
Add ess o co espondence:
P o . Vishwana h R Ha alappagol, Associa e P o esso & Resea ch Supe iso , Depa men o
Managemen S udies, Vis es a aya Technological Uni e si y-Belaga i, Cen e o Pos -G adua ion
S udies, Muddenahalli, Chikkaballapu , India,
How o ci e his a icle:
Ha alappagol, V. R., & J, V. G. (2025). E alua ing Mode n Rec ui men and Selec ion S a egies: A S udy
o Talen Acquisi ion in he Supply Chain Indus y. Jou nal o Resea ch and De elopmen , 17(8), 267–273.
O iginal A icle
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268
2. To examine how o ganiza ional capabili ies—such as ATS/HRIS sophis ica ion, analy ics ma u i y, hi ing-manage
enablemen , p ocess s anda diza ion, and employe b anding—media e he ela ionship be ween ec ui men
s a egies and hi ing ou comes.
3. To de e mine he mode a ing e ec s o con ex ual ac o s—including sub-sec o (logis ics, wa ehousing,
p ocu emen , planning), i m size, geog aphy, labo ma ke igh ness, and ole amily (blue- s. whi e-colla )—on
he e ec i eness o ec ui men s a egies.
Theo e ical Founda ion
Pe son-En i onmen Fi Theo y
Pe son-En i onmen Fi (P-E Fi ) heo y posi s ha cong uence be ween indi idual cha ac e is ics and
en i onmen al demands leads o posi i e ou comes (Edwa ds & Shipp, 2007). In supply chain con ex s, his mani es s
ac oss mul iple dimensions:

Pe son-Job Fi : Alignmen be ween indi idual KSAs and job equi emen s

Pe son-O ganiza ion Fi : Cong uence wi h o ganiza ional cul u e and alues

Pe son-Team Fi : Compa ibili y wi h wo k g oup dynamics

Pe son-En i onmen Fi : Adap a ion o physical and ope a ional en i onmen
Resou ce-Based View o Rec ui men
The Resou ce-Based View (RBV) sugges s ha sus ainable compe i i e ad an age s ems om aluable, a e,
inimi able, and o ganized esou ces (Ba ney, 1991). Applied o ec ui men , ad anced capabili ies in AI-enabled
sou cing, alida ed assessmen sys ems, and analy ics ma u i y ep esen s a egic esou ces ha a e:
VRIO
C i e ia
Rec ui men Applica ion
Supply Chain Con ex
Valuable
Reduces ime- o- ill, imp o es quali y-o -
hi e
C i ical in igh labo ma ke s o
logis ics oles
Ra e
Ad anced AI/analy ics capabili ies
Few supply chain i ms ha e ma u e TA
echnology
Inimi able
O ganiza ional lea ning and p ocess
in eg a ion
Embedded capabili ies di icul o
eplica e
O ganized
In eg a ed s a egy execu ion
Coo dina ed ac oss dis ibu ed ope a ions
1.1 In eg a ed Theo e ical F amewo k
Me hodology
Sys ema ic Re iew P o ocol
Following PRISMA 2020 guidelines, we conduc ed comp ehensi e sea ches ac oss mul iple da abases:

Da abases: ABI/In o m, PsycINFO, Business Sou ce P emie , Web o Science

Sea ch Te ms: (" ec ui men " OR "selec ion" OR " alen acquisi ion") AND ("supply chain" OR "logis ics" OR
"wa ehousing" OR "p ocu emen ")

Da e Range: Janua y 2019 - Ma ch 2025

Language: English only

S udy Types: Empi ical esea ch (quan i a i e, quali a i e, mixed-me hods)
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269
S udy Selec ion and Quali y Assessmen
Ini ial sea ch yielded 1,247 a icles. A e emo ing duplica es and sc eening i les/abs ac s, 312 a icles unde wen
ull- ex e iew. Final inclusion c i e ia equi ed:
1. Empi ical esea ch in supply chain/logis ics ec ui men
2. Clea me hodology and esul s epo ing
3. Pee - e iewed publica ion
4. Quali y sco e ≥ 6/10 on adap ed JBI checklis
Final Sample: 67 s udies (34 quan i a i e, 21 quali a i e, 12 mixed-me hods)
Resul s And E idence Syn hesis
Me a-Analysis Resul s
Quan i a i e syn hesis o 34 s udies wi h ex ac able e ec sizes:
Rec ui men
S a egy
Numbe
o
S udies
E ec
Size
(Cohen's
d)
95%
CI
P ima y
Ou come
AI-Enabled
Sou cing
[0.28,
Time- o- ill
educ ion
8
0.42**
0.56]
Jou nal o Resea ch and De elopmen
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Skills-Based
Assessmen
[0.31,
Quali y-o -
hi e
imp o emen
12
0.38**
0.45]
S uc u ed
In e iews
[0.22,
15
0.35**
0.48]
P edic i e
alidi y
Employe
B anding
[0.15,
O e
accep ance
a e
9
0.31*
0.47]
[0.11,
Di e se
hi ing
ou comes
DEI P ac ices
6
0.28*
0.45]
*p < 0.05, **p < 0.01
S a egy-Speci ic E idence
AI-Enabled Sou cing and Sc eening Key Findings:

42% a e age educ ion in ime- o- ill ac oss 8 s udies

35% inc ease in quali ied candida e pool (Chen e al., 2023)

Risk o algo i hmic bias equi es ongoing moni o ing (Rod iguez & Kim, 2024)
Skills-Based Assessmen Sys ems E idence Summa y:

38% imp o emen in quali y-o -hi e me ics

Wo k samples show highes p edic i e alidi y ( = 0.54) o echnical oles

Si ua ional judgmen es s e ec i e o supe iso posi ions ( = 0.48)
In eg a ed S a egy E ec s
O ganiza ions implemen ing 3+ s a egies simul aneously showed ampli ied e ec s:
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Theo e ical F amewo k Valida ion
Media ion Analysis Resul s
Pa h analysis o a ailable da a suppo s he p oposed media ion model:
Pa hway
S anda dized Coe icien
Signi icance
Media ion E ec
S a egies → Capabili ies
0.67
p < 0.001
S ong
Capabili ies → Ou comes
0.54
p < 0.001
Mode a e
Di ec E ec (S a egies →
Ou comes)
0.23
p < 0.05
Pa ial Media ion
Mode a o Analysis
Limi a ions And Fu u e Resea ch
S udy Limi a ions

Publica ion Bias: Sys ema ic o e - ep esen a ion o posi i e esul s

C oss-Sec ional Da a: Limi ed causal in e ence capabili y

Geog aphic Bias: 78% o s udies om No h Ame ican/Eu opean con ex s

Measu emen Va ia ion: Inconsis en ou come de ini ions ac oss s udies

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Fu u e Resea ch Recommenda ions
1. Design and me hods
Conduc longi udinal and quasi-expe imen al e alua ions o ec ui men in e en ions ac oss plan s/si es o
es ima e causal e ec s on ime- o- ill, quali y o hi e, and ea ly a i ion.
Run ield expe imen s on candida e expe ience le e s (communica ion SLAs, in e iew scheduling au oma ion,
pay anspa ency) o quan i y e ec s on con e sion and o e accep ance.
A/B es ATS con igu a ion changes and assessmen -led selec ion o isola e he inc emen al impac o wo k low
au oma ion, s uc u ed in e iews, and wo k samples.
2. AI and ai ness
Implemen independen audi s o AI sc eening ools o in e sec ional bias using es ablished ai ness me ics and
publish mi iga ion playbooks wi h human-in- he-loop sa egua ds.
Compa e AI-only e sus hyb id human-in- he-loop pipelines on quali y-o -hi e, di e si y ou comes, and candida e
us using mul i-si e ials.
3. Measu es and da a
De elop and alida e supply-chain–speci ic scales o quali y-o -hi e composi es and candida e NPS o enable
c oss- i m benchma king.
Link HRIS hi ing da a wi h pos -hi e pe o mance, sa e y inciden s, and p oba ion ou comes o quan i y
downs eam business impac .
Build ROI models o TA mode niza ion ha in eg a e cos -pe -hi e, p oduc i i y amp, e en ion gains, and
cus ome /se ice KPIs.
4. Con ex ual he e ogenei y
Compa e s a egy e ec i eness ac oss sub-sec o s (wa ehousing, anspo , planning, p ocu emen ), including
seasonal peak hi ing and shi -based ope a ions.
Con as blue- e sus whi e-colla pa hways o assess whe he skills- i s selec ion and job- ele an assessmen s
di e en ially educe ea ly a i ion.
Examine geog aphy and labou -ma ke igh ness e ec s by con as ing Indian ma ke s wi h global hubs o guide
localiza ion o TA playbooks.
5. Capabili y building and go e nance
Tes he impac o hi ing-manage enablemen and ec ui e upskilling on p ocess ma u i y, unnel e iciency, and
decision consis ency.
E alua e TA ope a ing models (in-house, RPO, hyb id) o high- olume supply chain hi ing, ocusing on speed,
quali y, and cos ade-o s.
6. Fu u e skills and oles
Map eme ging supply chain oles and compe encies o selec ion ools o ensu e alidi y o digi al and analy ics-
hea y job amilies.
Assess he e ec i eness o skills axonomies and success p o iles in p edic ing pe o mance in digi ized,
au oma ed supply chain en i onmen s.
7. Pipelines and communi ies
Measu e long- un e ec s o s uc u ed e e als, alumni pools, and alen communi ies on candida e quali y and
hi ing eloci y.
E alua e pa ne ships wi h skilling p o ide s and app en iceship p og ams o pipeline esilience in logis ics and
planning oles.
8. E hics, egula ion, and anspa ency
Design go e nance amewo ks o AI in ec ui ing co e ing audi equency, explainabili y s anda ds, candida e
consen , and egula o y compliance.
Tes he in luence o pay anspa ency policies and sala y-band disclosu e on applica ion quali y and o e
accep ance in compe i i e sub-sec o s.
9. Repo ing and benchma king
S anda dize dashboa ds acking unnel con e sion, ime- o- ill, ea ly a i ion, quali y-o -hi e, and candida e
expe ience o enable con inuous imp o emen .
C ea e anonymized da a collabo a i es o benchma k TA ou comes ac oss i ms and iden i y high-impac p ac ices
by ole amily and con ex .
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Conclusions
This e iew o 67 esea ch s udies p o ides clea guidance o supply chain companies looking o imp o e
hei hi ing p ocesses. The e idence shows ha mode n hi ing me hods wo k, bu success depends on how well
companies implemen and combine di e en app oaches.
Key akeaways o manage s:

Skills es ing and compu e -assis ed candida e sea ching o e he bigges imp o emen s

Combining mul iple me hods p oduces be e esul s han using any single app oach

Company eadiness ( aining, echnology, p ocesses) de e mines whe he new me hods succeed

Di e en ypes o supply chain companies need di e en hi ing app oaches

Fo esea che s and academics:

This s udy es ablishes a ounda ion o unde s anding supply chain hi ing and iden i ies impo an a eas needing
u he in es iga ion.The e idence s ongly suppo s in es ing in mode n hi ing me hods, pa icula ly o
companies expe iencing high u no e , long hi ing imes, o di icul y inding quali ied candida es. Howe e ,
success equi es sys ema ic implemen a ion wi h adequa e aining and ongoing measu emen .

Supply chain manage s should iew hi ing capabili y as a compe i i e ad an age ha equi es he same s a egic
a en ion gi en o o he ope a ional capabili ies. Companies ha excel a inding and selec ing alen will be be e
posi ioned o handle u u e dis up ions and g ow h oppo uni ies.
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