18 No wegian Jou nal o de elopmen o he In e na ional Science No 168/2025
A CLUSTER ANALYSIS OF ARMENIA’S STRATEGIC ROLE IN THE GLOBAL BLOCKCHAIN
LANDSCAPE
Tumanyan A.
Associa e p o esso a he Chai o In o ma ion Sys ems and Business In o ma ion Technologies, A menian
S a e Uni e si y o Economics, PhD in Economics
G igo yan M.
Associa e p o esso a he Chai o In o ma ion Sys ems and Business In o ma ion Technologies, A menian
S a e Uni e si y o Economics, PhD in Economics
Gals yan L.
Associa e p o esso a he Chai o In o ma ion Sys ems and Business In o ma ion Technologies, A menian
S a e Uni e si y o Economics, PhD in Economics
Khacha yan K.
Associa e p o esso a he Chai o In o ma ion Sys ems and Business In o ma ion Technologies, A menian
S a e Uni e si y o Economics, PhD in Economics
h ps://doi.o g/10.5281/zenodo.17608952
Abs ac
The aim o he s udy is o quan i a i ely assess A menia’s s a egic ole in he global blockchain ecosys em,
e ealing he coun y’s posi ion ela i e o global compe i o s. The s udy was conduc ed on a selec ion o 50
coun ies, using a numbe o o he elemen s: c yp ocu ency adop ion, inno a ion po en ial, and digi al in as uc-
u e. Fo p ac ical analysis, he K-means clus e ing me hod was used, which allows classi ying coun ies in o ho-
mogeneous g oups, as well as P incipal Componen Analysis (PCA).
As a esul o he clus e analysis, h ee main clus e s we e iden i ied: “Global Leade s”, “Eme ging In eg a-
o s” and “New En an s”. A menia alls in o he “Eme ging In eg a o s” clus e , which is cha ac e ized by an
abo e-a e age inno a ion po en ial (≈ 0.53) and a s ong digi al in as uc u e (≈ 0.8), bu a ela i ely low c yp o
adop ion a e (≈ 0.019).
In addi ion o clus e analysis, an op imiza ion model was also used, which showed ha A menia’s ansi ion
o he “Global Leade s” clus e will equi e capi al in es men s, a sha p imp o emen in he legisla i e amewo k,
and human esou ce de elopmen .
This esea ch ills a scien i ic gap by p o iding a quan i a i e assessmen o A menia’s posi ion on he global
blockchain map and o ming a p ac ical basis o de eloping a ge ed s a e policies, op imal esou ce alloca ion,
and echnological in eg a ion s a egies.
Keywo ds: Blockchain, clus e analysis, inno a ion index, digi al economy, a menia, s a egic posi ioning.
In oduc ion: Blockchain echnology, based on
he immu abili y p inciple o Dis ibu ed Ledge Tech-
nology (DLT), has expanded beyond c yp ocu encies
in he las decade o become a key d i e o he global
digi al economy[10]. Global ma ke analysis shows
ha by 2030, he olume o in es men s in blockchain
echnologies will inc ease d ama ically, o ming he in-
as uc u e o a new digi al economy. In his con ex ,
he Republic o A menia, which has a s ong enginee -
ing adi ion and a de eloping echnological ecosys em,
aspi es o become a egional digi al cen e . Acco ding
o o icial epo s, A menia’s IT sec o is showing a
s eady g ow h o mo e han 20% annually, and he In-
no a ion Index (0.534) and Digi al In as uc u e Index
(0.8) co espond o an abo e-a e age le el compa ed o
a sample o 50 coun ies. These indica o s indica e ha
A menia has he echnological eadiness o he adop-
ion and sp ead o blockchain.
Howe e , he e is a signi ican dispa i y be ween
A menia's high- ech po en ial and he ma ke adop ion
o blockchain: he C yp o Adop ion Sco e is only
0.019, which p o es ha echnological capabili ies a e
no ye ully e lec ed in he le el o ma ke in eg a ion.
A s udy o global blockchain s a egies shows ha
he key success ac o s o la ge and small economies
a e s a egic go e nmen suppo , an open egula o y
en i onmen , and policies aimed a human capi al de-
elopmen . Howe e , o small and de eloping econo-
mies like A menia, wha is needed is no a di ec ans-
e o he expe ience o wo ld leade s, bu a comp e-
hensi e quan i a i e modeling ha will allow
unde s anding he in e ac ion be ween he coun y's in-
no a ion po en ial and in es men cons ain s[4]. In
his ega d, his s udy ills a scien i ic gap by p o iding
a quan i a i e assessmen o A menia's posi ion on he
global blockchain map based on mul i a ia e s a is ical
analysis and op imiza ion modeling.
The main objec i es o he esea ch a e:
• o quan i a i ely assess A menia’s in eg a ion
in o he global blockchain ecosys em,
• o iden i y he ac o s ha shape he coun y’s
posi ion in di e en clus e s,
• o de elop he s uc u e o s a egic in es men s
ha will ha e he g ea es impac on A menia’s le el o
blockchain in eg a ion.
The e o e, h ee main esea ch ques ions a e o -
mula ed:
1. Wha is A menia’s le el o in eg a ion in o he
global blockchain ecosys em compa ed o 50 coun-
ies?
2. By wha ac o s do di e en clus e s (e.g. “S a-
egic T ans o ma ion Nodes” and “Adjus able Po en ial
No wegian Jou nal o de elopmen o he In e na ional Science No 168/2025 19
Le e s”) di e acco ding o he main componen s iden-
i ied by PCA?
3. Wha a e he op imal in es men di ec ions ha
will ha e he g ea es impac on A menia’s blockchain
in eg a ion and s a egic posi ioning?
Li e a u e e iew: The li e a u e has ex ensi ely
explo ed he heo e ical ounda ions o blockchain and
i s ans o ma i e po en ial[5][9]. Recen esea ch has
ocused on quan i ying he economic impac o block-
chain. Tapsco and Tapsco [10] highligh he im-
po ance o c ea ing an “In e ne o Value” ha enables
decen alized and in e media y- ee ansac ions.
Schola ly sou ces documen ha blockchain adop ion
is posi i ely co ela ed wi h a coun y’s GDP g ow h
and digi aliza ion indices[3]. Howe e , his impac a -
ies g ea ly depending on a coun y's digi al in as uc-
u e, le el o human capi al, and egula o y en i on-
men [1]. A signi ican numbe o s udies analyze he
ole o blockchain in inc easing inancial inclusion[1]
and ensu ing anspa ency in supply chains[7]. The
gene al consensus is ha ac i e go e nmen suppo
and “open” egula ion a e i al o he blockchain eco-
sys em o lou ish. A emp s o map he global block-
chain landscape ha e been made in bo h academic and
co po a e ci cles. Co po a e epo s o en use mul i-a -
ibu e indices o ank coun ies by blockchain eadi-
ness c i e ia (e.g., egula o y amewo k, alen , und-
ing). Academic s udies ha use clus e analysis ypi-
cally classi y coun ies in o ou o i e main clus e s:
“Leade s”, “Followe s”, “Po en ial”, and “Needs De-
elopmen ”[8]. These s udies o en use P incipal Com-
ponen Analysis (PCA) o Fac o Analysis o educe
mul iple indica o s o one o wo main dimensions (e.g.,
“Inno a i e Capaci y” and “Regula o y Suppo ”). This
me hodology allows o a quan i a i e de e mina ion o
a coun y’s posi ion, ee om subjec i e assessmen s.
Pa icula a en ion has been paid o he ole o
small coun ies, which o en ha e lexible egula o y
bodies and small communi ies o highly quali ied IT
p o essionals. Fo example, Mal a, Swi ze land (C yp o
Valley), and Singapo e a e seen as small economies
ha ha e become global blockchain hubs hanks o
hei egula o y app oaches[2][6]. The successes o
hese coun ies demons a e ha economic size is no
necessa ily a ba ie i he e is s a egic go e nmen
suppo and an inno a ion cul u e. Howe e , egional
(Caucasus) compa a i e analyses using quan i a i e
clus e models emain limi ed.
Despi e he gene al mapping o he global
blockchain landscape, a speci ic scien i ic gap is
obse ed in he quan i a i e assessmen o A menia’s
s a egic posi ion. The exis ing li e a u e mainly
ocuses on de eloped coun ies o on indi idual
echnical aspec s o blockchain. The e is a lack o a
model ha would apply clus e analysis o ank
A menia among global compe i o s based on mul iple
socio-economic and echnological indica o s.
Mo eo e , he e a e no publica ions ha would apply
op imiza ion modeling o p o ide clea indica ions on
how A menia can maximize (max) i s in eg a ion index
gi en he a ailable esou ces and cons ain s (e.g.
budge , in as uc u e). This s udy aims o ill his gap
by p o iding a quan i a i e, me hodologically igo ous
analysis ha will se e as a basis o in o med policy
de elopmen .
Me hodology: The me hodology o he p esen
s udy is based on quan i a i e and compa a i e
analy ical app oaches, he pu pose o which is o e eal
he posi ion and s a egic ole o A menia in he global
blockchain ecosys em. The s udy was conduc ed in
h ee s ages: da a collec ion, clus e analysis, and
op imiza ion assessmen .
In he i s s age, open da a was collec ed om
a ious in e na ional sou ces, including he annual
epo s o he Wo ld Bank, OECD, S a is a, and
Chainalysis. The da a included he le el o blockchain
echnology adop ion, digi al asse u no e , in es men
clima e indica o s, as well as assessmen s o he legal
and ins i u ional en i onmen o di e en coun ies.
In he second s age, clus e analysis was
pe o med using he K-Means and Hie a chical
Clus e ing me hods, aiming o classi y coun ies by he
le el o blockchain de elopmen and adop ion. The
da a was no malized using he Z-sco e me hod o
educe he impac o di e en doses. The op imal
numbe o clus e s was de e mined using he Elbow
Me hod and Silhoue e Sco e c i e ia.
A he inal s age, an op imiza ion analysis was
conduc ed o assess he ac o s whose imp o emen
could con ibu e o A menia’s ansi ion o a mo e
ad anced clus e , he so-called “Global Leade s”
g oup. Fo his pu pose, a baseline eg ession and
mul i-c i e ia op imiza ion model was buil ha
assesses he impac o in es men s, legisla i e e o ms,
and echnological inno a ions on he compe i i eness
o A menia’s blockchain sec o .
The esea ch me hodology is aimed a ensu ing he
compa abili y and objec i i y o he esul s ob ained,
allowing o he o ma ion o a scien i ic jus i ica ion
o A menia’s s a egic posi ion and de elopmen
po en ial in he global blockchain en i onmen .
Analysis: The aim o he s udy is o quan i a i ely
assess A menia’s posi ion in he global blockchain eco-
sys em based on key indica o s o digi al eadiness, in-
no a ion po en ial, and c yp ocu ency adop ion. Fo
his pu pose, a compa a i e analysis amewo k was se-
lec ed, co e ing 50 coun ies ep esen ing di e en le -
els o economic and echnological de elopmen .
The s udy is based on ou key me ics, which a e
conside ed key ac o s in blockchain in eg a ion and
echnological eadiness[7][4].
1. C yp o Adop ion Sco e - e lec s he deg ee o
mass adop ion o c yp ocu encies by he popula ion o
a gi en coun y. I se es as he bes p oxy o he i-
nancial ac i i y o blockchain.
2. Inno a ion Index - shows he de elopmen o
he coun y's o e all inno a ion ecosys em, which is di-
ec ly ela ed o he abili y o c ea e and implemen
blockchain and o he inno a i e echnologies.
3. Digi al In as uc u e Index - e lec s he physi-
cal and echnical ounda ions (e.g. ne wo k quali y,
da a cen e s) necessa y o he ope a ion o any digi al
echnology, including blockchain.
4. In e ne Pene a ion (%) - shows he pe cen age
o he popula ion ha has in e ne access, which is a
20 No wegian Jou nal o de elopmen o he In e na ional Science No 168/2025
undamen al p e equisi e o mass adop ion o block-
chain.
The indica o s calcula ed o A menia a e:
• C yp o Adop ion Sco e -0.019 (Low sco e, indi-
ca ing limi ed mass adop ion).
• Inno a ion Index - 0.534 (Abo e a e age, con-
sis en wi h he coun y's IT ole).
• Digi al In as uc u e Index - 0.80 (High, indica -
ing a s ong echnical ounda ion).
These da a indica e a gap be ween a high- ech
ounda ion and ma ke adop ion, which was u he
con i med by he esul s o he cas e analysis.
To ensu e he accu acy o he mul i a ia e analy-
sis, all a iables we e s anda dized be o e conduc ing
he K-means clus e ing and PCA analyses. The Z-Sco e
me hod was used, which b ings each indica o o he
same scale, ha ing a ze o mean and one s anda d de i-
a ion. This p ocess p e en s he si ua ion whe e a ia-
bles wi h a la ge alue ange domina e he analysis.
To di ide he coun ies in he da a se in o homo-
geneous g oups (clus e s), he K-means clus e ing al-
go i hm was used, which aims o minimize he sum o
squa ed e o s (SSE) wi hin he g oups. The op imal
numbe o clus e s was de e mined using he Elbow
me hod.
Figu e 1 : Elbow me od klas e s
Examina ion o he g aph shows ha he Elbow
o ms a K=3, indica ing ha he sepa a ion o h ee
clus e s p o ides an op imal balance be ween in a-
clus e homogenei y and he numbe o clus e s. The e-
o e, he alue o K=3 was chosen o u he analysis.
PCA was applied o wo main pu poses:
a) To educe he mul idimensionali y o he ou
indica o s
b) To show how he baseline indica o s clus e in o
new P incipal Componen s (PCs).
The applica ion o he K-means algo i hm e-
ealed h ee dis inc g oups o 50 coun ies based on
blockchain adop ion, inno a ion, digi al in as uc u e,
and in e ne access.
Figu e 2: Mean indica o s o each clus e
The igu e allows us o cha ac e ize each clus e .
The “Global Leade s” clus e shows he highes
a e age alues o all ou indica o s, especially in
e ms o C yp o_Adop ion_Sco e and Inno a ion_In-
dex. They ep esen he mos ad anced coun ies in he
implemen a ion o blockchain and digi al echnologies.
The “Eme ging In eg a o s” clus e occupies an
a e age posi ion, ha ing a ai ly high In e ne access
No wegian Jou nal o de elopmen o he In e na ional Science No 168/2025 21
indica o , bu hey a e in e io o he leade s in e ms o
C yp o_Adop ion_Sco e and Inno a ion_Index. The “New En an s” clus e is cha ac e ized by he
lowes alues o all ou indica o s, which indica es
ha hey a e a he e y ini ial s age o de elopmen .
Table 1:
Classi ica ion o Coun ies by Clus e
Global
Leade s
Eme ging
In eg a o s
New En an s
A menia Saudi A abia Es onia
Aze baijan UAE La ia
Geo gia Li huania
Russia Tu key
Uk aine Is ael
Kazakhs an Qa a
Uzbekis an Ge many
Ky gyzs an F ance
Tajikis an I aly
Moldo a Spain
Bela us Poland
I an Ne he lands
India Sweden
Indonesia No way
Philippines Finland
Vie nam Swi ze land
B azil China
Mexico Japan
A gen ina Sou h Ko ea
Nige ia Singapo e
Sou h A ica Malaysia
Egyp Thailand
Kenya USA
Canada
Chile
1. Global Leade s. This clus e is cha ac e ized by
he highes a e age alues in all ou indica o s, espe-
cially in he C yp o Adop ion Sco e and Inno a ion In-
dex. The g oup includes Eme ging Ma ke s (e.g. India,
Nige ia, B azil), whe e high c yp o in es men is
d i en no by ins i u ional ac o s bu by inancial ins a-
bili y and he need o money ans e s (P2P mo i es).
2. Eme ging In eg a o s. This clus e (Saudi A a-
bia, UAE) has excellen digi al in as uc u e indica o s
(Digi al In as uc u e Index), bu a mode a e a e age
le el o c yp o in es men . I ep esen s coun ies wi h
high esou ces and go e nmen suppo , which a e
eady o in eg a ion, bu cu en ly exhibi a cau ious,
cen alized managemen model.
3. New En an s. The clus e consis s o highly de-
eloped coun ies in No h Ame ica and Wes e n Eu-
ope. I has he lowes a e age C yp o Adop ion Sco e
and a medium Inno a ion Index (compa ed o K0), in-
dica ing he dominance o egula o y amewo ks and
ins i u ional cau ion be o e mass adop ion. This g oup
is in he ini ial, ounda ional s age o echnological in-
eg a ion.
P incipal Componen Analysis (PCA) was used o
educe he dimensionali y o he o iginal ou indica o s
and iden i y he independen ac o s unde lying hem.
Explained Va ianceThe i s P incipal Componen
(PC1) explains 40.81% o he o al a iance, and he
second P incipal Componen (PC2) explains an addi-
ional 28.80%. Thus, he i s wo componen s oge he
explain mo e han 69.61% o he o al a iance o he
da a se , which allows u he analysis o ocus only on
PC1 and PC2 wi hou signi ican loss o in o ma ion.
22 No wegian Jou nal o de elopmen o he In e na ional Science No 168/2025
Figu e 3: P incipal componen analysis
The s udy o PC Loadings allows us o make sense
o and cha ac e ize he essence o he newly ob ained
componen s.
“Basic Digi al In as uc u e and Inno a ion
Readiness”. Axis PC1 exhibi s he highes nega i e
loadings on he “Digi al In as uc u e Index” (-0.7107)
and “Inno a ion Index” (-0.4825) indica o s. Al hough
he loadings a e nega i e, hei magni ude indica es a
s ong ela ionship. This componen e lec s he le el
o Basic Digi al Capabili ies and Inno a ion Ma u i y
o a coun y. Tha is, coun ies wi h a high Digi al In-
as uc u e Index and Inno a ion Index will ha e a low
(nega i e) sco e on PC1, and ice e sa.
“C yp o In es men In ensi y”. Axis PC2 has he
highes posi i e loadings on he “C yp o Adop ion
Sco e” (0.6952) and “Inno a ion Index” (0.6696). PC2
e lec s he in ensi y o ac ual adop ion o blockchain
and digi al asse s, especially in coun ies whe e inno a-
ion is di ec ly ela ed o he decen aliza ion o inan-
cial se ices. This axis di ides coun ies based on he
dynamism o hei c yp o ma ke . This phase o he
analysis con i ms ha he digi al eadiness o coun ies
can be e ec i ely measu ed by wo main independen
dimensions: one ela ed o he basic in as uc u e and
inno a ion capaci y (PC1), and he o he o he in ensi y
o echnological (c yp o) in es men i sel (PC2). Thus,
A menia’s cu en posi ion can be cha ac e ized as an
inno a ion-po en ial bu ma ke -cau ious economy ha
has no ye ealized i s ull po en ial in he ield o
blockchain in eg a ion.
Op imiza ion model cons uc ion - based on he
esul s o clus e analysis, an op imiza ion model was
de eloped, he pu pose o which is o maximize he
s a egic in luence in he blockchain sec o o A menia,
in condi ions o limi ed esou ces, ha is, i is necessa y
o ind ou how and whe e (in which clus e s) he
maximum s a egic in luence can be achie ed in he
global blockchain h ough join in es men s and
poli ical s eps, aking in o accoun esou ce cons ain s
(budge a y, ene gy, human capi al, legisla i e isk,
e c.).
In he model, in es men di ec ions a e di ided
in o i e main sub-sec o s: Mining, Node Hos ing,
S a ups, R&D and Exchanges.
• The main pa ame e s and cons ain s o he
model a e:
• To al in es men ≤10 million USD[11][12[13],
• To al ene gy consump ion ≤4 million
USD[14][15][16],
• Jobs c ea ed ≥ 80[17][18],
• Regula o y isk ≤0.4 (weigh ed
a e age)[19][20],
• Talen eadiness ≥ 0.6[21],
• Minimum R&D unding ≥ 1 million USD.
The o mula ion o he objec i e unc ion is o
maximize A menia’s s a egic impac , aking in o
accoun he equi alen e iciency o one million USD
in es men in each sec o .
Op imiza ion calcula ions ha e shown ha he
maximum esul is achie ed by ocusing abou 80% o
in es men s on s a up de elopmen and wi h minimal
manda o y alloca ions o he R&D sec o .
As a esul , he alue o he calcula ed objec i e
unc ion was 68.5 poin s, which ep esen s he
maximum le el o s a egic impac o A menia’s
blockchain in es men po olio.
The sea chable esul s in he model a e: he
amoun o annual in es men s di ec ed by A menia o
he i- h clus e , i=1,2,…,k (k is he numbe o clus e s),
he o al amoun alloca ed (million USD).
The op imali y c i e ion o he p oblem is o
maximize A menia's s a egic in luence and we will
calcula e i using he ollowing o mula:
𝐹𝑚𝑎𝑥 =∑𝑐𝑖𝑥𝑖
𝑘
𝑖=1
Whe e cj is he s a egic impac o e e y $1
million in es ed in clus e I. The ma hema ical model
o he p oblem is:
1․ Budge cons ain : ∑𝑥𝑖≤ 𝐵
𝑘
𝑖=1 ` o al
in es men s canno exceed he budge , meaning ha he
Republic o A menia has limi ed inancial esou ces o
in es men s.
2. Human esou ce cos s (hou s / alen a ailabil-
i y): ∑𝑗𝑖𝑥𝑖≥𝐽𝑚𝑖𝑛
𝑘
𝑖=1 : he minimum numbe o jobs
ha mus be c ea ed o ensu e employmen .
3. Ene gy cons ain : ∑𝑒𝑖𝑥𝑖≤ 𝐸𝑚𝑎𝑥
𝑘
𝑖=1 : The o al
ene gy consump ion canno exceed he cons ain , i.e.
he ene gy capabili ies o he IT in as uc u e a e lim-
i ed. 4. Legisla i e isk: ∑𝑟𝑖𝑥𝑖≤ 𝑅𝑚𝑎𝑥 ∙∑𝑥𝑖
𝑘
𝑖=1
𝑘
𝑖=1 , a
small ma ke in es men con idence indica o .
5. Weigh ed a e age alen : ∑𝑡𝑖𝑥𝑖≥𝑇𝑚𝑖𝑛 ∙
𝑘
𝑖=1
∑𝑥𝑖
𝑘
𝑖=1 , will ensu e he educa ional and echnological
eadiness o he Republic o A menia.
6. The condi ion o non-nega i e illing o a ia-
bles is: 𝑥𝑖≥ 0, since i is a mone a y quan i y.
The ini ial da a o he p oblem is p esen ed in he
able (Table 3).
No wegian Jou nal o de elopmen o he In e na ional Science No 168/2025 23
Table 2:
Nume ical model o he p oblem
Clus e
S a egic impac pe $1M in-
es ed (economic/s a egic
uni s pe 1 million USD)
(sco e pe $1M)
Ene gy in ensi y — MW e-
qui ed pe $1M in es ed
(MW / $1M)
Jobs c ea ed pe $1M in-
es ed (di ec + indi ec )
(jobs / $1M)
Regula o y isk sco e (0 = no
isk, 1 = e y high isk)
Talen eadiness sco e (0 =
low, 1 = high)
Mining[22]
7.5
0.33
12
0.6
0.5
Node Hos ing (Da a cen e s / alida o
nodes / hos ing)[23]
6
0.043
8
0.2
0.7
S a ups (DeFi, apps, in ech s a ups)[24,
pp. 5–9][25, Table 2.4][26, p. 12]
7
0.06
10
0.3
0.65
R&D (Uni e si ies, aining, g an s, alen
de elopmen )[27, pp. 17–23][28]
5
0.02
18
0.1
0.8
Exchanges (exchanges, c yp o se ices, cus-
odial se ices)[39, pp. 32–34] [30]31, Ta-
ble 3.2]
6.2
0.05
6
0.4
0.6
As a esul o sol ing he p oblem, we ound he
ollowing op imal dis ibu ion:
• S a ups (DeFi, FinTech, applica ions) - abou
80% o in es men s,
• R&D ( esea ch and educa ion) - minimum man-
da o y in es men - 10%,
• The emaining di ec ions - Mining, Node Hos -
ing and Exchanges - we e no included in he op imal
plan due o high ene gy and egula o y isks.
As a esul o his dis ibu ion, he maximum alue
o he objec i e unc ion was 68.5 poin s, which ep e-
sen s he peak o he s a egic impac o he A menian
blockchain in es men po olio.
A mo e de ailed analysis also showed he sensi i -
i y o esou ce cons ain s.
• An inc ease in budge a y esou ces by one mil-
lion dolla s inc eases he s a egic impac by abou 7
poin s,
• An inc ease in human capi al by one poin in-
c eases he alue o he objec i e unc ion by an a e -
age o 3.3 poin s,
• Reducing R&D in es men s, on he con a y, has
almos no e ec on e iciency, bu has an impo an
s uc u al signi icance in e ms o main aining he edu-
ca ional base.
The da a ob ained indica e ha he mos sca ce e-
sou ce in A menia's blockchain s a egy is inancial e-
sou ces, and he second impo an ac o is he limi ed
po en ial o human capi al.
Conclusions: In summa y, his s udy aimed o
quan i a i ely assess A menia’s s a egic posi ion in he
global blockchain ecosys em based on a combina ion o
digi al in as uc u e, inno a ion po en ial, and c yp o-
cu ency adop ion. Using K-means clus e analysis and
P incipal Componen Analysis (PCA), i was possible
o map he dis ibu ion o coun ies wi hin he global
digi al economy and iden i y h ee main clus e s:
“Global Leade s,” “Eme ging In eg a o s,” and “New
En an s.”
A menia was classi ied as an “Eme ging In eg a-
o ” clus e , demons a ing a high digi al in as uc u e
(0.8) and an abo e-a e age inno a ion index (0.534),
bu a ela i ely low le el o c yp o adop ion (0.019).
This combina ion indica es ha he coun y’s echno-
logical po en ial is no ully ansla ed in o ma ke ac-
i i y.
The applica ion o he op imiza ion model showed
ha in condi ions o limi ed inancial, ene gy and hu-
man esou ces, he maximum s a egic impac is
achie ed by ocusing in es men s on he de elopmen
o s a ups (abou 80%), and wi h minimal manda o y
alloca ions o he R&D sec o (abou 10%). The lack o
human capi al and budge a y cons ain s ha e been
o med as he main limi ing ac o s o A menia's block-
chain s a egy. As a esul , he alue o he calcula ed
objec i e unc ion was 68.5 poin s, which ep esen s
he maximum s a egic impac o A menia unde con-
di ions o op imal esou ce alloca ion.
Speaking abou he main esul s, we will say ha
• A menia is a an a e age le el o blockchain in-
eg a ion, ha ing a s able echnological base, bu weak
ma ke pene a ion,
• Clus e analysis showed ha inno a ion po en ial
and digi al in as uc u e a e he main ad an ages o
A menia.
• The op imiza ion model e ealed ha he mos
e ec i e in es men di ec ion is he de elopmen o
s a ups, while he issues o he egula o y en i onmen
and human esou ces a e p io i y a eas o imp o e-
men .
• Budge a y cons ain s and he lack o human cap-
i al a e he mos in luen ial cons ain s on A menia’s
blockchain s a egy.
The esea ch combines clus e analysis and op i-
miza ion modeling, which allows no only o quan i a-
i ely classi y A menia on he global blockchain map,
bu also o o mula e an op imal esou ce alloca ion
s a egy. This is a new gia ic app oach in he analysis
24 No wegian Jou nal o de elopmen o he In e na ional Science No 168/2025
o A menia’s digi al economy, which ills he exis ing
heo e ical and applied gaps. The ob ained jus i ica-
ions can se e as a poli ical and p ac ical basis o
s eng hening A menia’s posi ion in he global digi al
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