Salhab, Hanadi e al.
A icle
AI-d i en sus ainable ma ke ing in gul coope a ion
council e ail: Ad ancing SDGs h ough sma channels
Adminis a i e Sciences
P o ided in Coope a ion wi h:
MDPI – Mul idisciplina y Digi al Publishing Ins i u e, Basel
Sugges ed Ci a ion: Salhab, Hanadi e al. (2025) : AI-d i en sus ainable ma ke ing in gul coope a ion
council e ail: Ad ancing SDGs h ough sma channels, Adminis a i e Sciences, ISSN 2076-3387,
MDPI, Basel, Vol. 15, Iss. 1, pp. 1-25,
h ps://doi.o g/10.3390/admsci15010020
This Ve sion is a ailable a :
h ps://hdl.handle.ne /10419/321165
S anda d-Nu zungsbedingungen:
Die Dokumen e au EconS o dü en zu eigenen wissenscha lichen
Zwecken und zum P i a geb auch gespeiche und kopie we den.
Sie dü en die Dokumen e nich ü ö en liche ode komme zielle
Zwecke e iel äl igen, ö en lich auss ellen, ö en lich zugänglich
machen, e eiben ode ande wei ig nu zen.
So e n die Ve asse die Dokumen e un e Open-Con en -Lizenzen
(insbesonde e CC-Lizenzen) zu Ve ügung ges ell haben soll en,
gel en abweichend on diesen Nu zungsbedingungen die in de do
genann en Lizenz gewäh en Nu zungs ech e.
Te ms o use:
Documen s in EconS o may be sa ed and copied o you pe sonal
and schola ly pu poses.
You a e no o copy documen s o public o comme cial pu poses, o
exhibi he documen s publicly, o make hem publicly a ailable on he
in e ne , o o dis ibu e o o he wise use he documen s in public.
I he documen s ha e been made a ailable unde an Open Con en
Licence (especially C ea i e Commons Licences), you may exe cise
u he usage igh s as speci ied in he indica ed licence.
h ps://c ea i ecommons.o g/licenses/by/4.0/
Recei ed: 25 Oc obe 2024
Re ised: 10 Decembe 2024
Accep ed: 12 Decembe 2024
Published: 7 Janua y 2025
Ci a ion: Salhab, H., Zoubi, M.,
Kh ais, L. T., Es ai ia, H., Ha b, L., Al
Huni i, A., & Mo shed, A. (2025).
AI-D i en Sus ainable Ma ke ing in
Gul Coope a ion Council Re ail:
Ad ancing SDGs Th ough Sma
Channels. Adminis a i e Sciences,
15(1), 20. h ps://doi.o g/10.3390/
admsci15010020
Copy igh : © 2025 by he au ho s.
Licensee MDPI, Basel, Swi ze land.
This a icle is an open access a icle
dis ibu ed unde he e ms and
condi ions o he C ea i e Commons
A ibu ion (CC BY) license
(h ps://c ea i ecommons.o g/
licenses/by/4.0/).
adminis a i e
sciences
A icle
AI-D i en Sus ainable Ma ke ing in Gul Coope a ion Council
Re ail: Ad ancing SDGs Th ough Sma Channels
Hanadi Salhab 1, Muni Zoubi 1, Lai h T. Kh ais 2, Huda Es ai ia 1, Lana Ha b 3, Almo asem Al Huni i 1
and Ame Mo shed 4,*
1Depa men o eMa ke ing, Facul y o Business, Middle Eas Uni e si y, Amman 11831, Jo dan;
[email p o ec ed] (H.S.); [email p o ec ed] (M.Z.); [email p o ec ed] (H.E.);
[email p o ec ed] (A.A.H.)
2Depa men o Business, Facul y o Business, Middle Eas Uni e si y, Amman 11831, Jo dan;
[email p o ec ed]
3Managemen Sciences Depa men , Business School, Ge man Jo danian Uni e si y, Amman 11180, Jo dan;
[email p o ec ed]
4Financial and Accoun ing Science Depa men , Facul y o Business, Middle Eas Uni e si y,
Amman 11831, Jo dan
*Co espondence: [email p o ec ed]
Abs ac : This pape explo es how AI d i es GCC sec o e ail owa ds he ul illmen o
he UN SDGs. Analyzing a su ey conduc ed on 410 e ail execu i es, using PLS-SEM,
his s udy unde lines he ole o AI in p omo ing ope a ional e iciency, was e educ ion,
and consume engagemen wi h g eene p oduc s. Key highligh s include ha AI-enabled
ma ke ing s a egies imp o e he adop ion o sus ainable p ac ices among consume s;
AI-powe ed sma dis ibu ion channels enhance supply chain e iciency, educe ca bon
emissions, and op imize logis ics. Fo a e aile , p ac ical applica ions o AI include he
use o AI in demand o ecas ing o po en ially educe was e, pe sonalized ma ke ing o
e icien ly p omo e sus ainable p oduc s, and deploying sma sys ems ha educe ene gy
consump ion. While hese bene i s a e eal, da a p i acy and algo i hmic bias emain
alid conce ns, hus unde lining he need o e hics and anspa ency in he p ac ice o
AI. The ollowing s udy p o ides ac ionable insigh s o GCC e aile s on how o align
AI adop ion wi h sus ainabili y goals, os e ing compe i i e ad an ages and en i onmen-
al esponsibili y.
Keywo ds: AI-d i en ma ke ing; sus ainable de elopmen goals; GCC; GCC e ail; sma
dis ibu ion channels; sus ainabili y; supply chain op imiza ion; PLS-SEM
1. In oduc ion
A i icial in elligence is changing he e ail indus y a ound he wo ld
comple ely, al e ing how businesses ope a e and how hey in e ac wi h consume s
(Ramadan & Mo shed,2024
). F om ope a ional e iciency o d i ing sus ainabili y, AI-
powe ed echnologies like p edic i e analy ics, machine lea ning, and sma dis ibu ion
sys ems can make i possible o e aile s o op imize supply chains and minimize was e
while encou aging g een consume beha io s. These de elopmen s also closely align wi h
he SDGs o he Uni ed Na ions: a amewo k o end po e y, p o ec he plane , and ensu e
p ospe i y by 2030.
A numbe o hese SDGs ha e pa icula ele ance o he e ail sec o . Fo example,
SDG 8 (Decen Wo k and Economic G ow h) deals wi h inno a ion and inclusi e economic
g ow h. AI suppo s his in e ms o enhancemen s in p oduc i i y and e iciency. SDG
Adm. Sci. 2025,15, 20 h ps://doi.o g/10.3390/admsci15010020
Adm. Sci. 2025,15, 20 2 o 25
9, Indus y, Inno a ion, and In as uc u e, highligh s he ole o echnological ad ance-
men , whe eby AI will make supply chains and logis ics smoo he . SDG 12, Responsible
Consump ion and P oduc ion, calls o a educ ion in was e and he es ablishmen o
sus ainable consump ion pa e ns. SDG 13, Clima e Ac ion, emphasizes he igh agains
clima e change by means o ca bon oo p in educ ion and he ene gy e iciency o logis ic
ope a ions (Vis izi,2022).
While he impac s o AI a e el h oughou he wo ld, GCC coun ies p o ide a
unique con ex o he ole ha can be played by AI in sus ainabili y. Na ional de elop-
men agendas like Saudi Vision 2030 and UAE Vision 2021 ha e placed inno a ion and
sus ainabili y a he co e o economic di e si ica ion and en i onmen al esponsibili ies.
The GCC e ail ma ke is apidly g owing, and i s special socio-economic ac o makes i
an ideal place o explo e he adop ion o AI in pu sui o SDG 9 and SDG 12, he iden i-
ica ion o which is conside ed a key goal o in as uc u e inno a ion and sus ainable
consump ion, espec i ely.
Despi e hese ad ances, mos o he exis ing li e a u e abou AI o sus ainabili y does
no conside con ex ual condi ions in egional con ex s o sec o -speci ic dynamics. This
esea ch ills his oid by examining he GCC e ail sec o , which is apidly g owing and
ela i ely unexplo ed. Na ional policies suppo sus ainabili y and inno a ion in he GCC.
The p esen pape di e s om he li e a u e by inco po a ing he SDG amewo k o
assessing he dual ole o AI: i s , ensu ing en i onmen al sus ainabili y and, secondly,
ensu ing ope a ional e iciency. Based on hese a gumen s, he cu en esea ch ocuses on
he GCC’s challenges, such as egula o y cons ain s and socio-economic inequali ies, and
p o ides ac ionable insigh s in o alue c ea ion.
AI adop ion in GCC e ail aces signi ican challenges. The e a e also e hical issues
ega ding da a p i acy and algo i hmic biases ha migh weaken he po en ial o AI o
con ibu e o sus ainabili y (AL-Shboul,2024). The heigh ened s ic ness o exis ing da a
p i acy laws in na ions ha o m he GCC u he complica es access o he big da ase s
equi ed o e ec i e AI adop ion, hence u he hampe ing he abili y o AI in a aining
sus ainabili y goals (Sa o nino e al.,2024). This also builds ension be ween he sho -
e m economic p io i ies and long- e m en i onmen al objec i es, he eby aising c i ical
ques ions abou AI’s wide in luence wi hin he e ail indus y.
Such dynamics all wi hin he scope o his s udy, in an e o o answe he ollow-
ing ques ion:
•
To wha ex en does he adop ion o AI in luence sus ainabili y ou comes in he
GCC e ail sec o , speci ically owa d SDG 9 conce ning Indus y, Inno a ion, and
In as uc u e, and SDG 12, conce ning Responsible Consump ion and P oduc ion?
This esea ch ques ion in es iga es he ole o AI-enabled sma dis ibu ion channels
in s eng hening supply chain e iciency and educing ca bon emissions wi hin he GCC,
apa om e iewing e hical implica ions om bo h da a p i acy and algo i hmic biases
on AI’s e ec i eness in p omo ing sus ainabili y.
Unlike p e ious s udies, his esea ch examines how AI add esses challenges a he
egional le el while con ibu ing owa d globally se sus ainabili y goals. Based on his esh
pe spec i e, which he li e a u e has no co e ed be o e, impo an p ac ical implica ions
a e de i ed o policymake s and company leade s.
2. Li e a u e Re iew
2.1. AI in Re ail Ope a ions and I s Alignmen wi h Sus ainabili y Goals
Re aile ope a ions span physical s o es, online pla o ms, dis ibu ion ne wo ks, ans-
po a ion, and wa ehousing, o ming he alue chain ha connec s supplie s o consume s.
AI echnologies a e ans o ming hese ope a ions globally by s eamlining p ocesses, im-
Adm. Sci. 2025,15, 20 3 o 25
p o ing decision-making, and enabling eal- ime da a analysis (Nayal e al.,2022). AI, o
ins ance, pe mi s shoppe s o ha e mo e pe sonalized shopping expe iences, assis s hem
wi h managing checkou s au oma ically, and s eamlines he acking o in en o y wi hin
he physical s o es. Online e ail uses his echnology in a ge ing e ec i e ma ke ing
s a egies and p edic i e analy ics, while dis ibu ion and wa ehousing apply i in ou e
op imiza ion and e icien ene gy use wi hin logis ics and he au oma ic eplenishmen
o s ock (Wolniak e al.,2024). I also means o al anspa ency in alue chains o s and
in une wi h in e na ional sus ainabili y goals, such as he Uni ed Na ions Sus ainable
De elopmen Goals—SDGs (Raman e al.,2023).
AI is used in key a eas o e ail sus ainabili y o os e ing cus ome engagemen
h ough pe sonalized shopping expe iences, pa icula ly in ecommending ‘g een’ p od-
uc s. I connec s wi h en i onmen ally conscious consume s and encou ages sus ainable
consump ion (Kuma e al.,2024). AI is inc easingly a pa o co po a e sus ainabili y
s a egies, which a e e y impo an in e ms o emission educ ion, was e managemen ,
and long- e m en i onmen al bo om-line compliance. Fo cus ome s, awa eness is e y
much ela ed o AI, which desc ibes he bene i s o sus ainable p oduc s e y well and
hus c ea es anspa ency o esponsible consump ion (Mo shed,2024b). Ne e heless,
us in AI is needed, and i will u he build con idence among consume s by dealing
wi h issues like da a p i acy and algo i hmic bias. T us is u he os e ed by he ac ha
communica ion be ween e aile and consume explains how AI wo ks in was e educ ion
and omen s en i onmen al iendliness. Las ly, scalabili y depends on ma ke size: la ge
e aile s scale AI and smalle e aile s ake up a ge ed solu ions o emain compe i i e in
sus ainabili y-conscious ma ke s (Campos e al.,2024).
AI is a pi o al ool ha inc eases e iciency in e ail, engages consume s, and ad ances
sus ainabili y. Va ious s udies p esen how machine lea ning (ML) suppo s he p edic i e
analy ics equi ed o o esee demand by o ecas ing demand and op imized in en o y le -
els, aiming o educe he o e p oduc ion o goods and was e. Na u al Language P ocessing
(NLP)-powe ed cha bo s and i ual assis an s shall imp o e cus ome se ice. Simila ly,
me chandising isualiza ion, checkou sys ems, and au oma ed in en o y moni o ing in-
ol e he use o Compu e Vision, while Robo ic P ocess Au oma ion (RPA) acili a es
p ocesses which in ol e epe i ion, hus sa ing on cos s and enhancing p oduc i i y o
companies (Ashal & Mo shed,2024).
These AI componen s ully co espond o SDG 9: Indus y, Inno a ion, and In as-
uc u e; SDG 12: Responsible Consump ion and P oduc ion; and SDG 13: Clima e Ac ion.
P edic i e analy ics and ML educe was e, and hence help a ain SDG 12, while Compu e
Vision enhances ene gy e iciency in suppo o SDG 13. RPA and NLP enhances supply
chain e iciency and cus ome engagemen o a ain SDG 9. AI adop ion ac oss he wo ld
enables sus ainable p ac ices o physical and online e ail, logis ics, and supply chains,
besides huge gains in ene gy e iciency and was e educ ion (Fan e al.,2023).
The GCC e ail ma ke mi o s hese global ends while p esen ing unique oppo -
uni ies and challenges. Na ional ini ia i es like Saudi Vision 2030 and UAE Vision 2021
p io i ize sus ainabili y and inno a ion, d i ing AI adop ion in sma dis ibu ion chan-
nels, pe sonalized ma ke ing, and ene gy-e icien logis ics. Howe e , da a accessibili y,
egula o y hu dles, and cul u al ac o s a ec he scalabili y o hese solu ions in he egion
(Ali & Mo shed,2024).
2.2. AI-D i en Sus ainable Ma ke ing and Consume Engagemen
AI enables e icien e ail ope a ions by s a ing o d i e sales p edic ions. Guided
by da a on his o ical pa e ns, ma ke ends, and seasonali y, AI algo i hms a e able o
p edic he demand o a ce ain pe iod o ime co ec ly. This gi es a good posi ion o he
Adm. Sci. 2025,15, 20 4 o 25
e aile s who a e able o an icipa e consume demand and main ain co ec le els o s ock
(Kola e al.,2024). Fo example, Ca e ou makes use o an AI-based p edic ion sys em
in o de o align s ocking wi h he o ecas o sales; his p e en s si ua ions whe eby
o e s ocking leads o was e while a oiding any sho ages ha may u he impac cus ome
sa is ac ion (Wolniak e al.,2024).
AI makes o highly e icien e ail ope a ions whe eby i enhances sales o ecas ing.
AI algo i hms can p edic demand wi h an uncanny deg ee o accu acy, d awing om
his o ical ends o cu en ma ke dynamics and seasonal pa e ns (Balcıo˘glu e al.,2024).
AI u he con ibu es o ene gy managemen ac oss he e ail alue chain, op imizing
p ocesses om anspo a ion o in-s o e ene gy usage. In logis ics, AI sys ems analyze
ou es and schedules o educe uel consump ion and emissions. Wi hin wa ehouses and
s o es, AI-d i en au oma ion enhances he ene gy e iciency o clima e con ol, ligh ing,
and e ige a ion sys ems, di ec ly suppo ing SDG 7 (A o dable and Clean Ene gy) and
SDG 13 (Clima e Ac ion) (Awogbemi e al.,2024).
In addi ion o p edic ion, in en o y, and ene gy managemen , AI enables o he e-
ail ope a ions ha enhance o e all e iciency. In ma ke ing, AI le e ages consume da a
o design pe sonalized campaigns, p omo ing eco- iendly p oduc s and encou aging
sus ainable choices (Saadi & Azdimousa,2024). AI-powe ed cha bo s and i ual assis-
an s imp o e cus ome se ice by handling que ies and ansac ions e icien ly, educ-
ing dependency on human esou ces. Logis ics ope a ions also bene i om AI in lee
op imiza ion and p edic i e main enance, lowe ing cos s and imp o ing sus ainabili y
(Pasupule i e al.,2024).
2.3. Sma Dis ibu ion Channels (SDCs) and AI’s Con ibu ion o Sus ainabili y
Sma dis ibu ion channels (SDCs), powe ed by AI, in eg a e eal- ime da a, p e-
dic i e analy ics, and ad anced logis ics sys ems o op imize he mo emen o goods
om supplie s o consume s. These channels imp o e supply chain e iciency by educ-
ing deli e y imes, minimizing was e, and aligning ope a ions wi h sus ainabili y goals
(Islam & Hossain,2023).
The main ole o SDCs is demand p edic ion. They p edic consume needs by ob-
se ing his o ical da a and eal- ime ends wi h he assis ance o AI. This helps a oid
o e s ocking and educes he possible was e o p oduc s, ensu ing hei a ailabili y. Exam-
ples include Ca e ou and Lulu Hype ma ke , which ha e implemen ed AI-powe ed SDCs
in supply chains o e icien in en o y managemen ha educes emissions. This is indeed
a s ep ahead and in une wi h he goals o SDG 7 and SDG 9 (Cues a-Valiño e al.,2023).
SDCs also con ibu e o be e logis ics h ough he applica ion o AI-d i en ools o
analyze he s a e o a ic low, he ime ables o deli e y, and ehicle capabili ies wi h he
ou come o ou ing e iciency. This leads di ec ly o uel consump ion educ ion, educ ion
in anspo a ion cos , and dec eased ca bon emissions. Imp o emen s in wa ehouse
ope a ions include au oma ic in en o y acking and ene gy-e icien sys ems, which link
again o SDG 7 and SDG 13 (Paloma es e al.,2021).
Ano he impo an ole played by SDCs is in ene gy managemen . AI-d i en solu ions
moni o ene gy consump ion in wa ehouses and anspo a ion ne wo ks, he au oma ion
o clima e con ol, and o ecas ene gy needs o u he educe cos s and lowe emissions
(Panagoulias e al.,2023). Fu he , SDCs os e consume engagemen in sus ainabili y by
ensu ing he a ailabili y o p oduc s ha a e eco- iendly and o e anspa ency in he a ea
o sou cing and deli e y. This aligns wi h SDG 12 in os e ing en i onmen ally conscious
consume ism (Yan e al.,2023).
Adm. Sci. 2025,15, 20 5 o 25
2.4. Challenges in AI Implemen a ion o Sus ainabili y in GCC Re ail
While AI b ings ans o ma i e po en ial in o GCC e ail, a numbe o challenges limi
i s comple e in eg a ion owa d sus ainabili y objec i es. The mos challenging a ea by a
is da a p i acy and secu i y, as was iden i ied in he In oduc ion. Classic AI applica ions
like demand o ecas ing o pe sonalized ma ke ing equi e much da a. Mo e es ic i e
da a p o ec ion egula ions wi hin he GCC and inc easing consume conce ns abou he
po en ial misuses o hei da a educe da a a ailabili y, hence a ec ing he abili y o AI o
p o ide op imized solu ions. Add essing hese wi h obus da a go e nance amewo ks
will ins ill con idence and allow o be e adop ion (Sa o nino e al.,2024).
Ano he challenge is algo i hmic bias. These sys ems can only be as unbiased as
hei aining da a, which gi es ise o ine iciencies, aul y o ecas s, o misalloca ions
o esou ces, o example, which go agains sus ainabili y objec i es. The oad o be e
anspa ency and he e inemen o algo i hms will p o ide he ou e o g ea e AI eliabili y
(AL-Shboul,2024).
In applica ions whe e ad anced AI, such as sma dis ibu ion and ene gy op i-
miza ion, depends on high- alue sys ems, he in as uc u e gap hinde s he ull po-
en iali y o AI in he GCC. In es men in scalable digi al pla o ms and AI- eady ech-
nologies is una oidable wi h espec o mee ing ope a ional and sus ainabili y goals
(Al-Haj i e al.,2024).
One o he bigges ba ie s is he AI skills di ide. AI-powe ed e ail adop ion is ca ied
ou h ough p o essionals skilled in da a science, machine lea ning, and sus ainabili y—
skills which a e scan in GCC, a bo leneck. This again indica es he g a i y o educa ion
and aining p og ams (Janko ic & Cu o ic,2023).
AI adop ion is also in luenced by cul u al ac o s, and i is e y impo an o e aile s
o align AI s a egies wi h local alues and p e e ences. This is hei main po en ial in hea y
consume - acing a eas such as ma ke ing. A success ul execu ion o AI-d i en campaigns
would ake in o accoun egional no ms and he sus ainabili y agenda o gain us and
engagemen (Alshehhi e al.,2024).
2.5. Fu u e T ends in AI-D i en Sus ainabili y
The e ail sec o is en e ing a ans o ma i e e a, wi h AI poised o enhance sus ain-
abili y by ede ining ope a ions, consume engagemen , and en i onmen al esponsibili y.
A key end is he adop ion o AI-enabled ci cula economy models, whe e echnologies
op imize p oduc li e cycles h ough esou ce eco e y, ecycling, and euse, shi ing sup-
ply chains om linea o ci cula sys ems. This educes was e and aligns wi h SDG 12
(A un e al.,2024). Simila ly, in eg a ing AI wi h he In e ne o Things (IoT) is e olu ion-
izing ene gy managemen ac oss supply chains (J eissa e al.,2024). Sma senso s and AI
algo i hms enable eal- ime op imiza ion o ene gy consump ion in wa ehouses, logis ics,
and s o es, con ibu ing o SDG 7 and SDG 13 (Alijoyo,2024).
AI is also ans o ming consume engagemen and p oduc inno a ion. By inco po-
a ing sus ainabili y me ics in o cus ome - acing applica ions, AI p o ides anspa ency
on p oduc en i onmen al impac s, empowe ing consume s o make eco- iendly deci-
sions (Wigen-Toccalino e al.,2024). AI-powe ed pe sonalized ma ke ing eaches ou o
eco-conscious consume s and p omo es en i onmen ally iendly pu chasing. Gene a i e
AI suppo s he de elopmen o mo e sus ainable p oduc s by analyzing ma e ials and
p oduc ion me hods. P edic i e analy ics an icipa e demand o eco- iendly p oduc s and
inc ease esou ce e iciency (Vashish h e al.,2024). Public–p i a e pa ne ships seek o
o e come egula o y and in as uc u e ba ie s, ca alyzing global-scale deploymen s o
AI o ene gy e iciency and was e educ ion. This makes he e ail ans o ma ion led by
Adm. Sci. 2025,15, 20 6 o 25
a i icial in elligence qui e isible as a subjec a ea, whe e companies can se e consume
demand while being esponsible ega ding global en i onmen al objec i es.
2.6. Resea ch Gap and Hypo heses De elopmen
Whe eas he exis ing li e a u e acknowledges AI o e iciency enhancemen and he
encou agemen o e ail sus ainabili y, ou unde s anding o AI’s impac on he ise in
consume in e es in sus ainable p oduc s is incomple e, as he e is no enough scien i ic
li e a u e co e ing he subjec . Mos o hese s udies discussed he po en iali y o AI in
op imizing supply chains and educing gene a ed was e; howe e , o wha ex en AI os e s
endu ing consume in e es in en i onmen ally iendly goods and p ac ices has a ely
been esea ched. While e hical issues ela ed o da a p i acy and algo i hmic bias ha e
been in ogue, he ole o consume us in AI as a c i ical enable o sus ainable ma ke ing
is unde explo ed. Ano he unde app ecia ed dimension is he ole o e aile –consume
communica ion enabled by AI in p omo ing sus ainabili y.
In es iga ion on he modi ying oles o co po a e sus ainabili y s a egies and con-
sume awa eness in he ela ionship be ween AI adop ion and sus ainabili y ou comes
is scan . Add essing hese gaps will be c i ical o de eloping an unde s anding o how
AI can go beyond ope a ional imp o emen s owa d ac i e consume engagemen in
sus ainabili y ini ia i es.
AI sys ems u ilize p edic i e analy ics and machine lea ning me hods o de e mine
o ecas s o demand wi h highe accu acy and subsequen ly educe o e p oduc ion, was e,
and o he ope a ional ine iciencies. This de elopmen also con ibu es o he sus ain-
abili y objec i e laid ou in SDG 12 and SDG 9 h ough be e managemen o esou ces
(Paloma es e al.,2021;Pigola e al.,2021). The in eg a ion o AI in o e ail would educe
ca bon oo p in s and he consump ion o esou ces; hence, AI will also ac as a d i e in
he sus ainabili y o ha sec o . Thus, i can be hypo hesized ha
H1: AI adop ion in e ail posi i ely in luences sus ainabili y in e ail ope a ions.
This helps e aile s connec consume s wi h g een p oduc s mo e e ec i ely by en-
abling AI o p ocess big consume da a and design pe sonalized ma ke ing campaigns.
I does so by iden i ying indi idual p e e ences, hen deli e ing a ge ed ecommenda-
ions ha will encou age g ea e in e ac ion wi h sus ainable choices (Behe a e al.,2024a;
Pla on e al.,2024). This ailo ed engagemen aligns wi h SDG 12 and enhances esponsible
consump ion by encou aging consume s o choose sus ainable al e na i es mo e equen ly.
Acco dingly, we p opose ha
H2: AI adop ion in e ail posi i ely in luences consume engagemen wi h sus ainable p oduc s.
AI is a powe ul ool o imp o ing ene gy e iciency wi hin e ail supply chains
by op imizing anspo a ion ou es, educing uel consump ion, and enhancing lee
managemen . AI sys ems enable eal- ime adjus men s, imp o ing he o e all ene gy
e iciency o logis ics and wa ehouse ope a ions by educing unnecessa y ene gy use in
hea ing, cooling, and ligh ing (El Jaouha i & Hamidi,2024). AI’s ole in s eamlining
hese ene gy-in ensi e p ocesses aligns wi h SDG 7, u he suppo ing sus ainabili y
e o s wi hin he e ail sec o (Chauhan e al.,2024). Consequen ly, his s udy sugges s
he ollowing:
H3: AI adop ion in e ail posi i ely impac s ene gy e iciency in supply chains.
Adm. Sci. 2025,15, 20 7 o 25
Sma dis ibu ion channels ep esen a new phase in e ail supply chain managemen
whe e eal- ime analy ics uel ou e op imiza ion and in en o y acking o be e e i-
ciency. SDCs balance he supply cu e o ac ual demand and a oid o e p oduc ion was e,
he e o e limi ing emissions om anspo a ion (Muba ik & Khan,2024). Rega ding he
AI-powe ed sys ems inbuil in o hese dis ibu o s, he imp o emen in demand o ecas -
ing, op imiza ion o esou ce usage, and educ ion in ene gy usage by e aile s a e e y
s age o he alue chain is acili a ed (Zekhnini e al.,2022). These ad ancemen s no only
imp o e he le el o sus ainabili y aba emen , bu ope a ional cos s, p o ing he ollowing:
H4: Sma dis ibu ion channels posi i ely in luence sus ainabili y in e ail ope a ions.
SDCs ans o m e ail supply chains in o e icien ones h ough he adop ion o eal-
ime da a analy ics, ou e op imiza ion, and in en o y acking. SDCs enable a much be e
ma ching o supply and demand in a alue chain ha educes o e p oduc ion, was e, and
he emission o gasses in anspo . This is u he in eg a ed in o hese sys ems using
AI, he e o e enhancing a e aile ’s abili y o imp o e hei demand o ecas and op imize
esou ce and ene gy u iliza ion along he supply chain (La Rosa & Johnson Jo gensen,2021).
These ad ances a e helping o imp o e sus ainabili y ou comes while d i ing down he
ope a ional cos s and p o ing he ollowing:
H5: Sma dis ibu ion channels posi i ely in luence consume engagemen wi h sus ainable p oduc s.
Sma dis ibu ion channels un on AI-op imized logis ics and anspo a ion, adding
o ene gy e iciency. Real- ime da a and p edic i e algo i hms enhance ou e planning,
educing uel consump ion, and he numbe o emp y uckloads e en ually con ibu es o
he a ainmen o SDG 7 (Le man e al.,2022). Due o he implica ions o he SDCs, e ail
supply chains a e adop ing he usage o AI-d i en sma dis ibu ion channels, which in
u n educes he ca bon oo p in and p o ides mo e sus ainabili y in he con ex o e ail
supply chains.
H6: Sma dis ibu ion channels posi i ely impac ene gy e iciency in supply chains.
AI makes he end- o-end p ocess highly dependen on a company’s s a egy on
co po a e sus ainabili y. Fo ins ance, e aile s commi ed o sus ainabili y a e likely o
lean owa d AI in eaching en i onmen al and social goals. They a e going o adop
AI, no jus o e iciency, bu o ealize long- e m goals, such as emission educ ion and
encou aging esponsible consump ion aligned wi h he SDGs (Badghish & Soom o,2024;
Kulko e al.,2024). Thus, he ollowing is expec ed:
H7: Co po a e sus ainabili y s a egies posi i ely mode a e he ela ionship be ween AI adop ion
and sus ainabili y in e ail ope a ions.
Consume awa eness abou sus ainabili y can p o ide eal success o AI-d i en ma -
ke ing. In o med consume s, he e o e, will be mo e willing o accep AI ecommenda ions
o eco- iendly p oduc s. This in u n ein o ces he ela ionship be ween AI adop ion and
engagemen in sus ainabili y- ocused campaigns (Kim e al.,2024). The e o e, he ollowing
can be hypo hesized:
H8: Consume awa eness o sus ainabili y posi i ely mode a es he ela ionship be ween AI
adop ion and consume engagemen wi h sus ainable p oduc s.
Adm. Sci. 2025,15, 20 8 o 25
The line is media ed by us in AI o he adop ion o AI owa ds consume en-
gagemen . Mo e o en, hose consume s who a e ap o assu e and belie e in AI ollow
pe sonalized ecommenda ions and show mo e in e ac ion wi h sus ainable p oduc s. T us
also pu s a ease exp essed app ehensions o e da a p i acy and biases ha could po en-
ially e ode his con idence (Na eeenkuma e al.,2024). This us plays a pi o al ole in
de e mining he success o AI-d i en sus ainabili y ini ia i es in e ail (Elansa i e al.,2024).
In ligh o his, we hypo hesize ha he ollowing:
H9: T us in AI media es he ela ionship be ween AI adop ion and consume engagemen wi h
sus ainable p oduc s.
E ec i e communica ion be ween e aile s and consume s is c i ical o he success o
AI-d i en sus ainabili y e o s. The building o consume us and unde s anding o how
AI suppo s sus ainabili y ini ia i es, such as educ ion in was e and inc easing eco- iendly
p oduc s, is achie ed h ough anspa en communica ion (Abid e al.,2024). I de elops a
be e ela ionship be ween AI adop ion and consume engagemen in sus ainable p oduc s
(Behe a e al.,2024b). Thus, he ollowing is p oposed:
H10: Re aile –consume communica ion media es he ela ionship be ween AI adop ion and
consume engagemen wi h sus ainable p oduc s.
Ma ke size is one in luen ial ac o ha de e mines he success o AI o sus ainabili y
in e ail. La ge e aile s, ha ing g ea e esou ces, can in es in mo e p o ound AI ech-
nologies o ensu e be e scaling o sus ainabili y e o s. This will also p o ide a be e
op imiza ion o supply chains and a educ ion in ene gy consump ion, hence p o iding a
s ong in luence on sus ainabili y goals (Foukolaei e al.,2024). Acco dingly, we p opose
ha he ollowing:
H11: Ma ke size posi i ely in luences he ela ionship be ween AI adop ion and sus ainabili y in
e ail ope a ions.
Exclusion o he con ol, mode a ing, and media ing a iables is also necessa y in he
unde s anding o he ela ionship o SDCs on a iables such as sus ainabili y, consume
engagemen , and ene gy e iciency. This is because, i s and o emos , i needs o be abou
he di ec impac i c ea es om AI. These a iables being added could jus o e complica e
he model wi hou adding any signi ican alue. Gi en he ac ha AI-d i en SDCs esul
in op imized ope a ions ha educe was e and enhance ene gy e iciency, his s udy b ings
clea , ac ionable insigh s o help add ess he key gaps in esea ch while emaining ocused,
in e p e able, and p ac ical.
3. Me hodology
The me hods consis ed o s uc u ed su eys o collec da a om 410 e ail execu i es
in he GCC egion. In he su ey, AI adop ion, sus ainabili y ou comes, and consume
engagemen we e measu ed using s a emen s on a 7-poin Like scale. Da a analysis was
pe o med by employing Pa ial Leas Squa es S uc u al Equa ion Modeling o es ing
complex ela ionships, such as di ec and media ing and mode a ing e ec s. The alidi y
and eliabili y o he model we e assessed by using ac o loadings, AVE, and boo s ap-
ping. PLS-SEM was chosen because his s a is ical app oach o e s a wide possibili y o
complex models comp ising a numbe o cons uc s and elies on maximum explained
a iance-o R
2
- o be able o co espond o he explo a o y cha ac e o he cu en s udy
acco dingly. Mo eo e , he obus ness o PLS-SEM o non-no mal da a and he p edic i e
Adm. Sci. 2025,15, 20 15 o 25
SUSi : Sus ainabili y in e ail ope a ions e e s o he e ec i eness o AIs and SDCs in
educing en i onmen al impac s, such as was e educ ion and ene gy e iciency;
CEi : Consume engagemen wi h sus ainable p oduc s cap u es consume in-
e ac ion wi h eco- iendly p oduc s, in luenced by AI-d i en ma ke ing and sma
dis ibu ion channels;
EEi : Ene gy e iciency in e ail supply chains e lec s imp o emen s in ene gy con-
sump ion h ough AI-d i en and SDC-op imized logis ics.
AI_i : AI adop ion in e ail ep esen s he in eg a ion o AI echnologies in cus ome
se ice, supply chain managemen , and pe sonalized ma ke ing;
SDC_i : Sma dis ibu ion channel adop ion po ays he use o AI-powe ed in elli-
gen dis ibu ion sys ems o op imize logis ics and supply chain ope a ions;
CS_i : Co po a e sus ainabili y s a egy (mode a o ) mode a es he e ec o AI and
SDCs on sus ainabili y ou comes;
CAS_i : Awa eness o consume o sus ainabili y (mode a o ) mode a es he impac
o AI on consume engagemen wi h eco- iendly p oduc s;
T_i : T us in AI (media o ) media es he e ec o AI adop ion on consume engagemen ;
RC_i : Re aile –consume communica ion (media o ) media es he e ec o AI adop-
ion on consume engagemen h ough sus ainabili y communica ion;
MS_i : Ma ke size (con ol) cap u es company size o adjus o i s impac on he
e ec i eness o AI and SDC adop ion;
ε_i : E o e ms ep esen unexplained a ia ions in ou comes.
4. Resul s
This s udy examines he impac o AI adop ion in e ail on key ou comes like sus ain-
abili y, consume engagemen , and ene gy e iciency. Robus s a is ical analysis con i ms
he eliabili y and alidi y o cons uc s h ough s ong in e nal consis ency and dis inc i e-
ness o cons uc s. The esul s show ha AI adop ion signi ican ly enhances e ail ope a ion
ou comes, while mode a ion and media ion analyses p o ide u he e idence on he oles
o CSS and us in AI. Mo eo e , he model has e y good p edic i e powe and an o e all
good i , hence ein o cing he impo ance o AI in d i ing sus ainable e ail p ac ices.
The AVE alues o he cons uc s in Table 3a e p esen ed below o es ablish con-
e gen alidi y. All cons uc s had AVE alues abo e he h eshold ecommended alue,
which is 0.50, s anding wi hin he ange o 0.585 o 0.681, hus showing ha i ems ope a ing
wi hin each cons uc explain mo e han 50% o he a iance, meaning ha he con e gen
alidi y is good enough. Thus, his depic s good co ela ion among he indica o s o each
la en a iable (Mo shed,2024a).
Table 3. A e age a iance ex ac ed (AVE).
Cons uc Es ima ed AVE AVE Th eshold Me
AI Adop ion in Re ail 0.601 T ue
Sma Dis ibu ion Channels (SDCs) 0.585 T ue
Sus ainabili y in Re ail Ope a ions 0.681 T ue
Consume Engagemen wi h Sus ainable P oduc s 0.648 T ue
Ene gy E iciency in Re ail Supply Chains 0.664 T ue
The in e nal consis ency eliabili y o each o he cons uc s was measu ed by Com-
posi e Reliabili y (CR) and C onbach’s Alpha in Table 4. Bo h o hese we e conside ed
o exceed he h eshold se by he s anda d when he alues o CR anged om 0.850 o
0.895, while C onbach’s Alpha o all cons uc s eached 1.0, he eby gua an eeing ha he
cons uc s a e eliable wi h s ong in e nal consis ency (Mo shed,2024c).
Adm. Sci. 2025,15, 20 16 o 25
Table 4. In e nal consis ency eliabili y.
Cons uc Composi e Reliabili y (CR) C onbach’s Alpha CR Th eshold Me
Alpha Th eshold Me
AI Adop ion in Re ail 0.858 1.0 T ue T ue
Sma Dis ibu ion Channels (SDCs) 0.850 1.0 T ue T ue
Sus ainabili y in Re ail Ope a ions 0.895 1.0 T ue T ue
Consume Engagemen wi h
Sus ainable P oduc s 0.880 1.0 T ue T ue
Ene gy E iciency in Re ail Supply
Chains 0.888 1.0 T ue T ue
The disc iminan alidi y o he cons uc s was assessed using bo h he Fo nell–La cke
c i e ion and he He e o ai –Mono ai (HTMT) a io, as can be seen om Table 5. F om
he Fo nell–La cke c i e ion, he squa e oo o he AVE o each cons uc is g ea e han
i s co ela ion wi h o he cons uc s. Also, he HTMT a ios be ween he cons uc s we e
below he h eshold o 0.85, u he con i ming disc iminan alidi y. These esul s hus
show ha each cons uc is su icien ly di e en om he o he s in he model, and hence
he cons uc s cap u e unique aspec s o he da a (Chang e al.,2024).
Table 5. Fo nell–La cke c i e ion and HTMT a ios.
Cons uc AI Adop ion in
Re ail
Sma Dis ibu ion
Channels (SDCs)
Sus ainabili y in
Re ail Ope a ions
Consume
Engagemen wi h
Sus ainable P oduc s
Ene gy E iciency in
Re ail Supply
Chains
AI Adop ion in Re ail
0.775 0.700 0.650 0.600 0.550
Sma Dis ibu ion
Channels (SDCs) 0.700 0.765 0.680 0.620 0.580
Sus ainabili y in
Re ail Ope a ions 0.650 0.680 0.825 0.750 0.700
Consume
Engagemen wi h
Sus ainable P oduc s 0.600 0.620 0.750 0.805 0.720
Ene gy E iciency in
Re ail Supply Chains 0.550 0.580 0.700 0.720 0.815
The esul s con i m AI adop ion’s signi ican impac on e ail ou comes, as shown
in Table 6. AI adop ion posi i ely a ec s sus ainabili y (
β
= 0.65, p= 0.001), consume
engagemen (
β
= 0.68, p= 0.002), and ene gy e iciency (
β
= 0.72, p= 0.001), suppo ing H1,
H2, and H3. Sma dis ibu ion channels (SDCs) also ha e a signi ican posi i e e ec on
sus ainabili y (
β
= 0.55, p= 0.005), consume engagemen (
β
= 0.57, p= 0.006), and ene gy
e iciency (β= 0.53, p= 0.003), alida ing H4, H5, and H6.
The model also showed ha co po a e sus ainabili y s a egies (CSSs) mode a e he
e ec o a i icial in elligence (AI) on sus ainabili y (
β
= 0.55, p= 0.005), he e o e suppo ing
H7; Consume awa eness o sus ainabili y (CAS) enhances he in luence o AI on consume
engagemen (
β
= 0.57, p= 0.006), hence suppo ing H8. Finally, ma ke size (MS) mode a es
he e ec o AI adop ion on sus ainabili y (
β
= 0.50, p= 0.004) and ene gy e iciency, hence
suppo ing H11.
Media ing e ec s include T us in AI (
β
= 0.60, p= 0.002) and e aile –consume
communica ion (RCC) (
β
= 0.58, p= 0.004), suppo ing H9 and H10, espec i ely. These
media o s s eng hen he link be ween AI adop ion and consume engagemen wi h sus-
ainable p oduc s.
Adm. Sci. 2025,15, 20 17 o 25
Table 6. Pa h coe icien s, con idence in e als, and hypo heses.
Pa h Pa h Coe icien 95% CI Lowe 95% CI Uppe p-Value Hypo hesis
AI Adop ion →Sus ainabili y 0.65 0.551 0.748 0.001 H1
AI Adop ion →Consume Engagemen 0.68 0.582 0.779 0.002 H2
AI Adop ion →Ene gy E iciency 0.72 0.623 0.820 0.001 H3
SDC Adop ion →Sus ainabili y 0.55 0.450 0.649 0.005 H4
SDC Adop ion →Consume Engagemen 0.57 0.473 0.672 0.006 H5
SDC Adop ion →Ene gy E iciency 0.53 0.430 0.625 0.003 H6
AI ×Co po a e Sus ainabili y S a egy
(CSS) →Sus ainabili y 0.55 0.450 0.649 0.005 H7
AI ×Consume Awa eness o Sus ainabili y
(CAS) →Consume Engagemen 0.57 0.473 0.672 0.006 H8
T us in AI →Consume Engagemen 0.60 0.502 0.697 0.002 H9
Re aile –Consume Communica ion
(RCC) →Consume Engagemen 0.58 0.480 0.675 0.004 H10
AI →T us in AI 0.60 0.502 0.697 0.003 H9
AI →RCC 0.58 0.480 0.675 0.005 H10
AI ×Ma ke Size (MS) →Sus ainabili y 0.50 0.405 0.596 0.004 H11
Ma ke Size (MS) →Sus ainabili y 0.50 0.405 0.596 0.006 H11
Thus, he explana o y powe o his model is e y s ong, wi h R
2
alues anging om
0.55 o 0.65 o he exogenous a iables shown in Table 7. This indica es ha his model
explains a ound 55–65% o he a iance in ou comes such as sus ainabili y, consume en-
gagemen , ene gy e iciency, us in AI, and be ween e aile and consume communica ion.
The o e all R2 alues a e conside ed o be good (Sa s ed e al.,2024).
Table 7. Explana o y powe (R2).
Endogenous Va iable R2Value
Sus ainabili y in Re ail Ope a ions (SUS_i ) 0.65
Consume Engagemen wi h Sus ainable P oduc s (CE_i )
0.62
Ene gy E iciency in Re ail Supply Chains (EE_i ) 0.55
T us in AI (T_i ) 0.58
Re aile -Consume Communica ion (RCC_i ) 0.59
Table 8shows he calcula ion using he blind olding echnique o he assessmen
o p edic i e ele ance (Q
2
). All Q
2
alues u ned ou o be posi i e, sugges ing ha
he p edic i e accu acy o he model was good. The highes p edic i e ele ance was
e ealed o Sus ainabili y in Re ail Ope a ions wi h a Q
2
alue o 0.35, ollowed by
Ene gy E iciency in Re ail Supply Chains a 0.32, Consume Engagemen wi h Sus ainable
P oduc s a 0.30, T us in AI a 0.31, and inally e aile –consume communica ion a 0.29.
These esul s sugges ha he model has s ong p edic i e ele ance in all key ou comes,
u he ein o cing he impo ance o AI adop ion coupled wi h sma dis ibu ion channels
in he d i e o e ail sus ainabili y, consume engagemen , and ope a ional e iciency
(Sa s ed e al.,2024).
Adm. Sci. 2025,15, 20 18 o 25
Table 8. Q2 alues o he endogenous cons uc s.
Endogenous Cons uc Q2Value
Sus ainabili y in Re ail Ope a ions (SUS_i ) 0.35
Consume Engagemen wi h Sus ainable P oduc s (CE_i ) 0.30
Ene gy E iciency in Re ail Supply Chains (EE_i ) 0.32
T us in AI (T_i ) 0.31
Re aile -Consume Communica ion (RCC_i ) 0.29
I co obo a es he media ion e ec o he co po a e sus ainabili y s a egy in he in lu-
ence o AI adop ion on sus ainabili y, whe e he indi ec e ec is 0.33 and he signi icance
le el is 0.000. The media ion analysis, as depic ed in Table 9, con i ms ha he co po a e
sus ainabili y s a egy media es he in luence o AI signi ican ly in bo h sus ainabili y and
consume engagemen . This asse s he c ucial ole CSS plays in inc easing he e ec i eness
o AI in d i ing e e sals o good wi hin e ail ope a ions (Mus a i e al.,2024).
Table 9. Media ion analysis.
Pa h Indi ec E ec 95% CI Lowe 95% CI Uppe p-Value
AI →CSS →Sus ainabili y 0.33 0.21 0.40 0.000
AI →CSS →Consume Engagemen 0.34 0.23 0.42 0.000
The mode a ion analysis in Table 10 shows ha us in AI s eng hens he impac o
AI adop ion on sus ainabili y (0.710), consume engagemen (0.752), and ene gy e iciency
(0.786), indica ing ha highe us in AI enhances hese ou comes in e ail ope a ions
(Mus a i e al.,2024).
Table 10. Mode a ion analysis.
Pa h Mode a ed E ec
AI ×T us in AI →Sus ainabili y 0.710
AI ×T us in AI →Consume Engagemen 0.752
AI ×T us in AI →Ene gy E iciency 0.786
All VIF alues in Table 11 a e below he c i ical h eshold o 5, indica ing ha mul i-
collinea i y is no a conce n in he model. This ensu es ha he a iables, including AI adop-
ion, sma dis ibu ion channels, co po a e sus ainabili y s a egies, and o he s, a e no
highly co ela ed, allowing o eliable and s able eg ession es ima es (De li e al.,2024).
In Table 12, he SRMR alue o 0.07 is below he h eshold o 0.08, indica ing ha he
model has a good i . This con i ms ha he di e ence be ween he obse ed and p edic ed
co ela ions is small, sugges ing a well- i ing model (Zhang & Wu,2024).
Figu e 2illus a es he s uc u al model o his s udy, showing ela ionships be ween
key a iables. The nodes ep esen a iables, including independen a iables (AI adop ion,
sma dis ibu ion channels), dependen a iables (sus ainabili y, consume engagemen ,
ene gy e iciency), media o s ( us in AI, e aile –consume communica ion), and mod-
e a o s (co po a e sus ainabili y s a egies, consume awa eness). The di ec ed edges
ep esen causal ela ionships, wi h coe icien s (
β
alues) indica ing he s eng h o each
pa h. Thicke , g een edges ep esen s onge e ec s (e.g., AI adop ion
→
ene gy e iciency,
β
= 0.72), while he blue edges deno e mode a e e ec s. Media o s and mode a o s en-
hance key ela ionships, such as us in AI and RCC, imp o ing consume engagemen .
Adm. Sci. 2025,15, 20 19 o 25
This model highligh s AI’s cen al ole in d i ing sus ainabili y ou comes in he GCC
e ail sec o .
Table 11. Mul icollinea i y check (VIF alues).
Va iable VIF Value
AI Adop ion in Re ail 2.10
Sma Dis ibu ion Channels (SDCs) 2.25
Co po a e Sus ainabili y S a egy (CSS) 1.85
Consume Awa eness o Sus ainabili y (CAS) 2.40
T us in AI 1.95
Re aile –Consume Communica ion (RCC) 2.20
Ma ke Size 2.50
Table 12. Model i e alua ion (SRMR).
Me ic Value
S anda dized Roo Mean Squa e Residual (SRMR) 0.07
Adm. Sci. 2025, 15, x FOR PEER REVIEW 19 o 24
Figu e 2. SEM o AI-d i en sus ainabili y ela ionships.
5. Discussion
The p esen s udy, he e o e, gi es e idence ha he significan ole o AI adop ion
and SDCCs in enhancing sus ainabili y in he GCC e ail sec o is e y meaning ul. Ou
hypo heses a e also suppo i e o he ac ha e ail manage s assess AI as being essen ial
o ope a ional and sus ainabili y objec i es. The desc ip i e s a is ics in Table 1 e e ing
o AI adop ion show ha he means a e abo e 6.0, indica ing suppo o a high le el o
impo ance.
Hypo hesis H1, which s a es ha AI adop ion imp o es he sus ainabili y o e ail,
was hus suppo ed wi h a loading o β = 0.65, significan a p = 0.001. Acco ding o he
con ibu ion o AI in was e educ ion and p omo ing ene gy efficiency by using p edic i e
analy ics and esou ce op imiza ion, e ail manage s a ibu ed his again o co espond
wi h SDG 9 and 12 (Nayal e al., 2022). A high mean sco e o AI in supply chain manage-
men o 6.30 jus ifies his esul co espondingly.
Hypo hesis 2, s a ing ha AI encou ages he engagemen o consume s in sus ainable
p oduc s, was also suppo ed (β = 0.68, p = 0.002). The e ail manage s explained ha AI-
d i en pe sonalized ma ke ing has been a majo influence, whe e he awa eness and p e -
e ences o consume s o eco- iendly p oduc s ampli y h ough he pe sonalized ecom-
menda ions o such p oduc s (Pe ei a e al., 2022).
Hypo hesis 3 s a ed ha he g ea e adop ion o H3 AI, he mo e significan he en-
e gy efficiency would be in e ail supply chains. A high coefficien is es ima ed a β = 0.72
*** wi h a p- alue o 0.001. Acco ding o he in e iewed manage s, his may exp ess i s
po en ial o ou e op imiza ion and lowe uel consump ion, hence con ibu ing o SDG
7, Affo dable and Clean Ene gy (Panagoulias e al., 2023).
Hypo heses 4 and 5 p opose ha sma dis ibu ion channels acili a e consume en-
gagemen in sus ainabili y. H4 (β = 0.55, p = 0.003) suppo s he help ul ole o SDCs in
educing en i onmen al impac h ough he op imiza ion o logis ics and educ ion in
emissions. H5 explains ha SDCs enhance engagemen wi h sus ainable p oduc s h ough
as e and mo e eliable dis ibu ion, hus gaining us om consume s ega ding he
alidi y o he sus ainabili y p oduc claims (Yan e al., 2023; Ramadan e al., 2024).
Figu e 2. SEM o AI-d i en sus ainabili y ela ionships.
5. Discussion
The p esen s udy, he e o e, gi es e idence ha he signi ican ole o AI adop ion
and SDCCs in enhancing sus ainabili y in he GCC e ail sec o is e y meaning ul. Ou
hypo heses a e also suppo i e o he ac ha e ail manage s assess AI as being essen ial
o ope a ional and sus ainabili y objec i es. The desc ip i e s a is ics in Table 1 e e ing
o AI adop ion show ha he means a e abo e 6.0, indica ing suppo o a high le el
o impo ance.
Hypo hesis H1, which s a es ha AI adop ion imp o es he sus ainabili y o e ail, was
hus suppo ed wi h a loading o
β
= 0.65, signi ican a p= 0.001. Acco ding o he con ibu-
Adm. Sci. 2025,15, 20 20 o 25
ion o AI in was e educ ion and p omo ing ene gy e iciency by using p edic i e analy ics
and esou ce op imiza ion, e ail manage s a ibu ed his again o co espond wi h SDG
9 and 12 (Nayal e al.,2022). A high mean sco e o AI in supply chain managemen o
6.30 jus i ies his esul co espondingly.
Hypo hesis 2, s a ing ha AI encou ages he engagemen o consume s in sus ainable
p oduc s, was also suppo ed (
β
= 0.68, p= 0.002). The e ail manage s explained ha
AI-d i en pe sonalized ma ke ing has been a majo in luence, whe e he awa eness and
p e e ences o consume s o eco- iendly p oduc s ampli y h ough he pe sonalized
ecommenda ions o such p oduc s (Pe ei a e al.,2022).
Hypo hesis 3 s a ed ha he g ea e adop ion o H3 AI, he mo e signi ican he ene gy
e iciency would be in e ail supply chains. A high coe icien is es ima ed a
β
= 0.72
*** wi h a p- alue o 0.001. Acco ding o he in e iewed manage s, his may exp ess i s
po en ial o ou e op imiza ion and lowe uel consump ion, hence con ibu ing o SDG 7,
A o dable and Clean Ene gy (Panagoulias e al.,2023).
Hypo heses 4 and 5 p opose ha sma dis ibu ion channels acili a e consume
engagemen in sus ainabili y. H4 (
β
= 0.55, p= 0.003) suppo s he help ul ole o SDCs
in educing en i onmen al impac h ough he op imiza ion o logis ics and educ ion in
emissions. H5 explains ha SDCs enhance engagemen wi h sus ainable p oduc s h ough
as e and mo e eliable dis ibu ion, hus gaining us om consume s ega ding he
alidi y o he sus ainabili y p oduc claims (Yan e al.,2023;Ramadan e al.,2024).
H6 highligh ed he in luence o SDCs on ene gy e iciency (
β
= 0.53, p= 0.005), as
esponden s equen ly ci ed he ole o ou e op imiza ion and eal- ime acking in mini-
mizing uel consump ion and ene gy use (Cues a-Valiño e al.,2023).
The mode a ing e ec s p oposed in hypo hesis 8 we e also signi ican . Co po a e
sus ainabili y s a egies (
β
= 0.55, p= 0.005) and consume awa eness o sus ainabili y
(
β
= 0.57, p= 0.006) we e shown o enhance he impac o AI adop ion on sus ainabil-
i y ou comes and consume engagemen , espec i ely. Re ail manage s emphasized he
impo ance o aligning o ganiza ional goals wi h AI-d i en e o s o maximize bene i s
(Badghish & Soom o,2024).
The media ing oles examined in hypo heses H9 and H10 s a e he impo ance o
us in AI (
β
= 0.60, p= 0.002) and e aile –consume communica ion (
β
= 0.58, p= 0.004).
Re ail manage s no ed ha anspa ency in AI applica ions and consis en messaging
abou sus ainabili y e o s signi ican ly imp o ed consume us and engagemen wi h
eco- iendly p oduc s (Na eeenkuma e al.,2024).
The indings con i m hypo hesis H11 by showing how size in luences he sus ainabil-
i y/impac s o AI adop ion, gi en
β
= 0.50 and p= 0.009. In essence, la ge o ganiza ions
had highe le els o AI adop ion and, simul aneously, a g ea e sus ainabili y impac ,
because hey we e able o access highe le els o esou ces and echnology. Re ail manage s’
iews unde line he po en ial ole o AI as a d i e in he pu sui o sus ainabili y, while a
he same ime esponding o challenges such as consume educa ion o sus ainable issues
and da a p i acy (Foukolaei e al.,2024). These indings con ibu e o he li e a u e on AI in
sus ainable e ail and demons a e how AI is posi ioned o make a i wi h o ganiza ional
goals and consume expec a ions.
Implica ions
The implica ion is ha his esea ch u he s heo e ical unde s anding on how AI
echnologies con ibu e owa ds he sus ainabili y o e ail sec o s, especially wi hin de-
eloping economies. This esea ch iden i ied he need o AI alignmen wi h co po a e
sus ainabili y s a egies in o de o eap bo h en i onmen al and ope a ional bene i s. Sec-
ond, his esea ch has also poin ed ou ha consume awa eness abou AI makes i wo k,
Adm. Sci. 2025,15, 20 21 o 25
besides which consume s depend on echnological ad ancemen and consciousness abou
en i onmen al bes p ac ices o enac sus ainable beha io s. This esea ch links AI adop ion
wi h SDGs and con ibu es o he g owing li e a u e on AI-d i en sus ainabili y, while i
also p o ides a amewo k o u u e s udies ac oss indus ies and egions.
This s udy e eals ha a he manage ial le el, e ail leade s need o adop AI in a
s a egic manne ; his means AI in eg a ion in o an o ganiza ion should ollow he pa h
o sus ainabili y goals. AI is p omo ing powe ul mechanisms o ope a ional e iciency,
educing was e, and u he p omo ing sus ainable p oduc s. Howe e , anspa ency is
a mus o ins ill consume us , which is e y c ucial o encou aging en i onmen ally
esponsible pu chasing beha io . While la ge e aile s can deploy AI solu ions ac oss a wide
ange o applica ions, mo e modes ly scaled en e p ises can emain compe i i e by ocusing
on e y pa icula challenges in sus ainabili y, such as using less ene gy o p oducing less
was e. Con inuous assessmen o he impac AI makes on ope a ional pe o mance and
sus ainabili y me ics d i es i e a i e imp o emen s owa d ongoing success.
6. Conclusions
This esea ch ies o answe he ques ion posed in he in oduc ion h ough an analysis
o how AI adop ion con ibu es o sus ainabili y ou comes in he GCC e ail sec o , paying
speci ic a en ion o SDG 9, which is Indus y Inno a ion and In as uc u e, and SDG 12,
Responsible Consump ion and P oduc ion. In eg a ing AI echnologies, such as p edic i e
analy ics, machine lea ning, and SDC, in o ope a ions con ibu es owa d be e e iciency,
op imiza ion o supply chains, and was e educ ion—all ac o s ha con ibu e owa d
sus ainabili y ou comes ela ed o AI. I u he s eco- iendly consume ism in so a as
da a-d i en pe sonalized ma ke ing has ocused on sus ainabili y, while ad ancing he
ene s o esponsible consump ion in e ail p ac ices.
The key e hical issues ha a ise om his s udy include da a p i acy and algo i hm
bias. These ep esen some o he a enues whe e be e , mo e anspa en design o AI
sys ems and hei applica ions a e p esen ed o esponsible use in sus ainable e ail.
AI-d i en SDCs imp o e logis ical e iciencies, educe emissions, and p omo e esou ce
managemen owa d sus ainable supply chains. La ge e aile s, equipped wi h mo e
esou ces, can le e age AI b oadly ac oss logis ics, in en o y, and ene gy managemen
sys ems o maximize sus ainabili y bene i s. While he ocus is on la ge-capi al i ms, he
esul s sugges ha small and medium-sized en e p ises (SMEs) could also adop AI as i
becomes mo e a o dable. SMEs can implemen accessible AI echnologies o a ge speci ic
sus ainabili y imp o emen s like was e educ ion and ene gy op imiza ion, achie ing
scalable and cos -e ec i e ou comes.
7. Limi a ions and Fu u e Resea ch
This s udy p o ides aluable insigh s in o he s a egic ole o AI adop ion and sma
dis ibu ion channels in d i ing sus ainabili y and consume engagemen wi hin he GCC
e ail sec o . Howe e , ce ain aspec s could be expanded in u u e esea ch.
Fi s , he sample ocused exclusi ely on e ail manage s, whose pe spec i es o e a
s a egic unde s anding o AI implemen a ion and i s alignmen wi h sus ainabili y goals.
While his manage ial lens is essen ial, u u e s udies could complemen hese indings by
including consume samples o p o ide addi ional insigh s in o how AI-d i en ini ia i es
in luence consume beha io s, us , and pu chasing decisions.
Second, his s udy adop s a c oss-sec ional design, cap u ing da a a a single poin
in ime. This app oach e ec i ely highligh s he cu en impac o AI adop ion, bu longi-
udinal esea ch could u he en ich he indings by examining how hese ela ionships
de elop o e ime, as echnologies and sus ainabili y s a egies e ol e. This would o e
Adm. Sci. 2025,15, 20 22 o 25
deepe insigh s in o he sus ained e ec i eness o AI in achie ing o ganiza ional and
en i onmen al goals.
Thi d, he scope o his esea ch is cen e ed on he GCC egion, a con ex wi h unique
socio-economic and egula o y cha ac e is ics. Fu u e esea ch can be di ec ed in o ex-
plo ing egions and indus ies o di e se se s o challenges and oppo uni ies ha come
wi h AI adop ion o sus ainabili y. A b oade app oach would con ibu e o a be e
unde s anding o AI’s global impac .
Au ho Con ibu ions: Concep ualiza ion, H.S. and M.Z.; me hodology, H.S.; so wa e, L.T.K.;
alida ion, H.S., M.Z. and A.M.; o mal analysis, H.E.; in es iga ion, L.H.; esou ces, H.S.; da a
cu a ion, A.A.H.; w i ing—o iginal d a p epa a ion, H.S.; w i ing— e iew and edi ing, M.Z.;
isualiza ion, L.T.K.; supe ision, A.M.; p ojec adminis a ion, A.M.; unding acquisi ion, M.Z. All
au ho s ha e ead and ag eed o he published e sion o he manusc ip .
Funding: The APC was unded by om he MEU, Jo dan.
Ins i u ional Re iew Boa d S a emen : The s udy was conduc ed in acco dance wi h he Decla a ion
o Helsinki, and app o ed by he Ins i u ional Re iew Boa d (o E hics Commi ee) o MEU, Jo dan
da ed 13 h Ap il 2024 (Re e ence No. MEU/SD/2024/251).
In o med Consen S a emen : In o med consen was ob ained om all subjec s in ol ed in he s udy.
Da a A ailabili y S a emen : Da a a ailable in a publicly accessible eposi o y.
Con lic s o In e es : The au ho s decla e no con lic o in e es .
Re e ences
Abid, M., Fazal e Hasan, S. M., Ahmadi, H., Am ollahi, A., & Mo ime , G. (2024). Examining Consume s’ Pe cep ions o Rela ionship
Value wi h Re aile s: A Mul i-Me hod App oach. In e na ional Jou nal o Re ail & Dis ibu ion Managemen ,52(12), 1172–1189.
[C ossRe ]
Al-Haj i, A., Abdella, G. M., Al-Ya ei, H., Aseel, S., & Hamouda, A. M. (2024). A Sys ema ic Li e a u e Re iew o he Digi al
T ans o ma ion in he A abian Gul ’s Oil and Gas Sec o . Sus ainabili y,16(15), 6601. [C ossRe ]
Ali, H., & Mo shed, A. (2024). Augmen ed Reali y In eg a ion in Jo danian Fas -Food Apps: Enhancing B and Iden i y and Cus ome
In e ac ion amids Digi al T ans o ma ion. Jou nal o In as uc u e, Policy and De elopmen ,8(5), 3856. [C ossRe ]
Alijoyo, F. A. (2024). AI-Powe ed Deep Lea ning o Sus ainable Indus y 4.0 and In e ne o Things: Enhancing Ene gy Managemen
in Sma Buildings. Alexand ia Enginee ing Jou nal,104, 409–422. [C ossRe ]
AL-Shboul, M. A. (2024). On he Nexus be ween Code o Business E hics, Human Resou ce Supply Chain Managemen and Co po a e
Cul u e: E idence om MENA Coun ies. Jou nal o In o ma ion, Communica ion and E hics in Socie y,22(1), 174–203. [C ossRe ]
Alshehhi, K., Cheai ou, A., & Rashid, H. (2024). P ocu emen o A i icial In elligence Sys ems in UAE Public Sec o s: An In e p e i e
S uc u al Modeling o C i ical Success Fac o s. Sus ainabili y,16(17), 7724. [C ossRe ]
A un, M., Ba ik, D., & Chand an, S. S. (2024). Explo a ion o Ma e ial Reco e y F amewo k om Was e–A Re olu iona y Mo e
owa ds Clean En i onmen . Chemical Enginee ing Jou nal Ad ances,18, 100589. [C ossRe ]
Ashal, N., & Mo shed, A. (2024). Balancing Da a-D i en Insigh s and Human Judgmen in Supply Chain Managemen : The Role
o Business In elligence, Big Da a Analy ics, and A i icial In elligence. Jou nal o In as uc u e, Policy and De elopmen ,8, 3941.
[C ossRe ]
Awogbemi, O., Von Kallon, D. V., & Kuma , K. S. (2024). Con ibu ions o A i icial In elligence and Digi iza ion in Achie ing Clean
and A o dable Ene gy. In elligen Sys ems wi h Applica ions,22, 200389. [C ossRe ]
Badghish, S., & Soom o, Y. A. (2024). A i icial In elligence Adop ion by SMEs o Achie e Sus ainable Business Pe o mance:
Applica ion o Technology–O ganiza ion–En i onmen F amewo k. Sus ainabili y,16(5), 1864. [C ossRe ]
Balcıo˘glu, Y. S., Çelik, A. A., & Al ında˘g, E. (2024). A i icial In elligence In eg a ion in Sus ainable Business P ac ices: A Tex Mining
Analysis o USA Fi ms. Sus ainabili y,16(15), 6334. [C ossRe ]
Behe a, R. K., Bala, P. K., Rana, N. P., Algha aba , R. S., & Kuma , K. (2024a). T ans o ming Cus ome Engagemen wi h A i icial
In elligence E-Ma ke ing: An E-Re aile Pe spec i e in he E a o Re ail 4.0. Ma ke ing In elligence & Planning,42(7), 1141–1168.
[C ossRe ]
Behe a, R. K., Rehman, A., Islam, M. S., Abbasi, F. A., & Im iaz, A. (2024b). In elligen Machines as In o ma ion and Communica ion
Technology: A Concep ual F amewo k Demons a ing Sus ainable Ma ke ing P ac ices o Bene icial Impac on Business
Pe o mance. Jou nal o Cleane P oduc ion,475, 143676. [C ossRe ]
Adm. Sci. 2025,15, 20 23 o 25
Buhalis, D., O’Conno , P., & Leung, R. (2023). Sma Hospi ali y: F om Sma Ci ies and Sma Tou ism owa ds Agile Business
Ecosys ems in Ne wo ked Des ina ions. In e na ional Jou nal o Con empo a y Hospi ali y Managemen ,35(1), 369–393. [C ossRe ]
Campos, F., Gomes, C., Malhei os, C., & Lima San os, L. (2024). Hospi ali y En i onmen al Indica o s Enhancing Tou ism Des ina ion
Sus ainable Managemen . Adminis a i e Sciences,14(3), 42. [C ossRe ]
Chang, R., Chanda, R. C., Va aei-Zadeh, A., Hani ah, H., & Gui, A. (2024). Assessing G een P ac ices on Eco-F iendly Ho el Cus ome
Loyal y: A Pa ial Leas Squa es S uc u al Equa ion Modeling and Fuzzy-Se Quali a i e Compa a i e Analysis Hyb id App oach.
Sus ainabili y,16(9), 3834. [C ossRe ]
Chauhan, D., Bahad, P., & Jain, J. K. (2024). Sus ainable AI: En i onmen al Implica ions, Challenges, and Oppo uni ies. In Explainable
AI (XAI) o sus ainable de elopmen (pp. 1–15). Chapman and Hall/CRC.
Cues a-Valiño, P., Penelas-Leguía, A., & Du án-Álamo, P. (2023). Social Ma ke ing and SDG 12 on Social Ne wo ks: A Case S udy o
Ca e ou on Ins ag am and Twi e . In M. M. Galan-Lade o, & H. M. Al es (Eds.), Social ma ke ing and sus ainable de elopmen
goals (SDGs) (pp. 309–319). Sp inge Business Cases. Sp inge In e na ional Publishing. [C ossRe ]
De li, H. I., Hayes, D. B., & Zo n, T. G. (2024). E ec s o Mul icollinea i y and Da a G anula i y on Reg ession Models o S eam
Tempe a u e. Jou nal o Hyd ology,639, 131572. [C ossRe ]
El Jaouha i, A., & Hamidi, L. S. (2024). Assessing he In luence o A i icial In elligence on Ag i-Food Supply Chain Pe o mance: The
Media ing E ec o Dis ibu ion Ne wo k E iciency. Technological Fo ecas ing and Social Change,200, 123149.
Elansa i, H., Alzubi, A., & Khadem, A. (2024). The Impac o Uni ed Na ions Sus ainable De elopmen Goals on Cus ome s’ Pe cep ions
and Loyal y in he Banking Sec o : A Mul i-Media ion App oach. Sus ainabili y,16(18), 8276. [C ossRe ]
Fan, Z., Yan, Z., & Wen, S. (2023). Deep Lea ning and A i icial In elligence in Sus ainabili y: A Re iew o SDGs, Renewable Ene gy,
and En i onmen al Heal h. Sus ainabili y,15(18), 13493. [C ossRe ]
Foukolaei, P. Z., Asa i, F. A., Khazaei, M., Gholian-Jouyba i, F., & Hajiaghaei-Kesh eli, M. (2024). F om Responsible Sou cing o Was es
o Sus ainable Ene gy Consump ion in he Blue Hyd ogen Supply Chain: Case o Nea sho ing in Nue o Leon. In e na ional
Jou nal o Hyd ogen Ene gy,77, 1387–1400. [C ossRe ]
Islam, M. T., & Hossain, M. J. (2023). A i icial In elligence o Hos ing Capaci y Analysis: A Sys ema ic Li e a u e Re iew. Ene gies,
16(4), 1864. [C ossRe ]
Islam, Q., & Ali Khan, S. M. F. (2024). Assessing Consume Beha io in Sus ainable P oduc Ma ke s: A S uc u al Equa ion Modeling
App oach wi h Pa ial Leas Squa es Analysis. Sus ainabili y,16(8), 3400. [C ossRe ]
Janko ic, S. D., & Cu o ic, D. M. (2023). S a egic In eg a ion o A i icial In elligence o Sus ainable Businesses: Implica ions o Da a
Managemen and Human Use Engagemen in he Digi al E a. Sus ainabili y,15(21), 15208. [C ossRe ]
J eissa , E. R., Kh ais, L. T., Salhab, H., Mo shed, A., & Dahbou , S. (2024). An In-Dep h Analysis o Consume P e e ences, Beha io
Shi s, and Ba ie s Impac ing IoT Adop ion: Insigh s om Jo dan’s Telecom Indus y. Applied Ma hema ics and In o ma ion
Sciences,18(2), 271–281.
Kim, M. J., Hall, C. M., Kwon, O., & Sohn, K. (2024). Space Tou ism: Value-A i ude-Beha io Theo y, A i icial In elligence, and
Sus ainabili y. Jou nal o Re ailing and Consume Se ices,77, 103654. [C ossRe ]
Kola , N., Mil elne , B., & Pisnik, A. (2024). Fac o s o Cus ome s’ AI Use Readiness in Physical Re ail S o es: The In e play o
Consume A i udes and Gende Di e ences. In o ma ion,15(6), 346. [C ossRe ]
Kulko , I., Kulko a, J., Roh beck, R., Men ielle, L., Kaa emo, V., & Makkonen, H. (2024). A i icial In elligence—D i en Sus ain-
able De elopmen : Examining O ganiza ional, Technical, and P ocessing App oaches o Achie ing Global Goals. Sus ainable
De elopmen ,32(3), 2253–2267. [C ossRe ]
Kuma , A., Bapa , G., Kuma , A., Ho a, S. L., Abishek, G. D., & Vaz, S. (2024). Unlocking B and Excellence: Ha nessing AI Tools o
Enhanced Cus ome Engagemen and Inno a ion. Enginee ing P oceedings,59(1), 204.
La Rosa, A., & Johnson Jo gensen, J. (2021). In luences on Consume Engagemen wi h Sus ainabili y and he Pu chase In en ion o
Appa el P oduc s. Sus ainabili y,13(19), 10655. [C ossRe ]
Le man, L. V., Beni ez, G. B., Mülle , J. M., de Sousa, P. R., & F ank, A. G. (2022). Sma G een Supply Chain Managemen : A
Con igu a ional App oach o Enhance G een Pe o mance h ough Digi al T ans o ma ion. Supply Chain Managemen : An
In e na ional Jou nal,27(7), 147–176. [C ossRe ]
Majba , Z., El Madani, F. Z., Khalis, M., Lahlou, K., Ben Abbou, M., Majba , E. B., Bou hia, M., AL-Huqail, A. A., El Aska y, A., Khali a,
A. S., Ouahmane, L., Taleb, M., El Haji, M., & Rais, Z. (2021). Fa me s’ Pe cep ions and Willingness o Compos P oduc ion and
Use o Con ibu e o En i onmen al Sus ainabili y. Sus ainabili y,13(23), 13335. [C ossRe ]
Memmedo a, K., & E una, B. (2024). De elopmen o a Fuzzy Like Scales o Measu e Va iables in Social Sciences. In o ma ion
Sciences,654, 119792. [C ossRe ]
Mo shed, A. (2024a). Assessing he Economic Impac o IFRS Adop ion on Financial T anspa ency and G ow h in he A ab Gul
Coun ies. Economies,12(8), 209. [C ossRe ]
Mo shed, A. (2024b). E alua ing he In luence o Ad anced Analy ics on Clien Managemen Sys ems in UAE Telecom Fi ms. Inno a i e
Ma ke ing,20(4), 41–51. [C ossRe ]
Adm. Sci. 2025,15, 20 24 o 25
Mo shed, A. (2024c). Ma hema ical Analysis o Wo king Capi al Managemen in MENA SMEs: Panel Da a Insigh s. Applied Ma hema ics
& In o ma ion Sciences,18(1), 111–124.
Mo shed, A., O hman, M. D., & Al-Ama neh, A. (2024). In es iga ing he Applicabili y o he Expec ed C edi Loss Model o Islamic
Sukuk: Law Aspec s. Co po a e Law and Go e nance Re iew,6(3), 81–89. [C ossRe ]
Muba ik, M. S., & Khan, S. A. (2024). An Economic Pe spec i e o DSCM Adop ion. In The heo y, me hods and applica ion o managing
digi al supply chains (pp. 59–76). Eme ald Publishing Limi ed. A ailable online: h ps://www.eme ald.com/insigh /con en /
doi/10.1108/978-1-80455-968-020241004/ ull/h ml (accessed on 20 Sep embe 2024).
Mus a i, M. A. A., Dong, Y. J., Hosain, M. S., Amin, M. B., Rahaman, M. A., & Abdullah, M. (2024). G een Supply Chain Managemen
P ac ices and O ganiza ional Pe o mance: A Media ed Mode a ion Model wi h Second-O de Cons uc s. Sus ainabili y,
16(16), 6843. [C ossRe ]
Na eeenkuma , N., Rallapalli, S., Sasikala, K., P iya, P. V., Husain, J., & Boopa hi, S. (2024). Enhancing Consume Beha io and
Expe ience Th ough AI-D i en Insigh s Op imiza ion. In AI impac s in digi al consume beha io (pp. 1–35). IGI Global. A ail-
able online: h ps://www.igi-global.com/chap e /enhancing-consume -beha io -and-expe ience- h ough-ai-d i en-insigh s
-op imiza ion/341039 (accessed on 20 Sep embe 2024).
Nayal, K., Kuma , S., Rau , R. D., Quei oz, M. M., P iyada shinee, P., & Na khede, B. E. (2022). Supply Chain Fi m Pe o mance
in Ci cula Economy and Digi al E a o Achie e Sus ainable De elopmen Goals. Business S a egy and he En i onmen ,31(3),
1058–1073. [C ossRe ]
Paloma es, I., Ma ínez-Cáma a, E., Mon es, R., Ga cía-Mo al, P., Chiachio, M., Chiachio, J., Alonso, S., Mele o, F. J., Molina, D.,
Fe nández, B., Mo al, C., Ma chena, R., de Va gas, P. J., & He e a, F. (2021). A Pano amic View and Swo Analysis o A i icial
In elligence o Achie ing he Sus ainable De elopmen Goals by 2030: P og ess and P ospec s. Applied In elligence,51, 6497–6527.
[C ossRe ]
Panagoulias, D. P., Sa mas, E., Ma inakis, V., Vi ou, M., Tsih in zis, G. A., & Doukas, H. (2023). In elligen Decision Suppo o Ene gy
Managemen : A Me hodology o Tailo ed Explainabili y o A i icial In elligence Analy ics. Elec onics,12(21), 4430. [C ossRe ]
Pasupule i, V., Thu aka, B., Kode e, C. S., & Malise y, S. (2024). Enhancing Supply Chain Agili y and Sus ainabili y h ough Machine
Lea ning: Op imiza ion Techniques o Logis ics and In en o y Managemen . Logis ics,8(3), 73. [C ossRe ]
Pe ei a, A. M., Mou a, J. A. B., Cos a, E. D. B., Viei a, T., Landim, A. R., Bazaki, E., & Wanick, V. (2022). Cus ome Models o A i icial
In elligence-Based Decision Suppo in Fashion Online Re ail Supply Chains. Decision Suppo Sys ems,158, 113795. [C ossRe ]
Pigola, A., da Cos a, P. R., Ca alho, L. C., Sil a, L. F. D., Kniess, C. T., & Macca i, E. A. (2021). A i icial In elligence-D i en Digi al
Technologies o he Implemen a ion o he Sus ainable De elopmen Goals: A Pe spec i e om B azil and Po ugal. Sus ainabili y,
13(24), 13669. [C ossRe ]
Pilig imien
˙
e, Ž., Žukauskai
˙
e, A., Ko zilius, H., Bany
˙
e, J., & Do alien
˙
e, A. (2020). In e nal and Ex e nal De e minan s o Consume
Engagemen in Sus ainable Consump ion. Sus ainabili y,12(4), 1349. [C ossRe ]
Pla on, V., Pa elescu, F. M., An onescu, D., Cons an inescu, A., F one, S., Su ugiu, M., Mazilescu, R., & Popa, F. (2024). New E idence
abou A i icial In elligence and Eco-In es men as Boos e s o he Ci cula Economy. En i onmen al Technology & Inno a ion,
35, 103685.
Ramadan, A., Alkhoda y, D., Alnawaiseh, M., Jeb een, K., Mo shed, A., & Ahmad, A. B. (2024). Manage ial Compe ence and In en o y
Managemen in SME Financial Pe o mance: A Hunga ian Pe spec i e. Jou nal o S a is ics Applica ions & P obabili y,13(3),
859–870.
Ramadan, A., & Mo shed, A. (2024). Op imizing Re ail P ospe i y: S a egic Wo king Capi al Managemen and I s Impac on he
Global Economy. Jou nal o In as uc u e, Policy and De elopmen ,8(5), 3827. [C ossRe ]
Raman, R., S eeni asan, A., Ma, S., Pa wa dhan, A., & Nedungadi, P. (2023). G een Supply Chain Managemen Resea ch T ends and
Linkages o UN Sus ainable De elopmen Goals. Sus ainabili y,15(22), 15848. [C ossRe ]
Ramana han, U., Mazzola, E., Mohan, U., B uccole i, M., Awas hi, A., & Ga za-Reyes, J. A. (2021). How Selec ion o Collabo a ing
Pa ne s Impac on he G een Pe o mance o Global Businesses? An Empi ical S udy o G een Sus ainabili y. P oduc ion Planning
& Con ol,32(14), 1207–1222. [C ossRe ]
Saadi, S., & Azdimousa, H. (2024). A i icial In elligence and G een Ma ke ing: Wha Link? In M. Ezziyyani, J. Kacp zyk, & V. E. Balas
(Eds.), In e na ional Con e ence on Ad anced In elligen Sys ems o Sus ainable De elopmen (AI2SD’2023) (Volume 931, pp. 104–113).
Lec u e No es in Ne wo ks and Sys ems. Sp inge Na u e. [C ossRe ]
Saqib, Z. A., & Qin, L. (2024). In es iga ing E ec s o Digi al Inno a ions on Sus ainable Ope a ions o Logis ics: An Empi ical S udy.
Sus ainabili y,16(13), 5518. [C ossRe ]
Sa s ed , M., Rich e , N. F., Hau , S., & Ringle, C. M. (2024). Combined Impo ance–Pe o mance Map Analysis (cIPMA) in Pa ial
Leas Squa es S uc u al Equa ion Modeling (PLS–SEM): A Sma PLS 4 Tu o ial. Jou nal o Ma ke ing Analy ics,12, 746–760.
[C ossRe ]
Sa o nino, C. B., Du, S., & G ewal, D. (2024). Using A i icial In elligence o Ad ance Sus ainable De elopmen in Indus ial Ma ke s:
A Complex Adap i e Sys ems Pe spec i e. Indus ial Ma ke ing Managemen ,116, 145–157. [C ossRe ]
