Accoun and Financial Managemen Jou nal e-ISSN: 2456-3374
Volume 10 Issue 11 No embe 2025, Page No.-3838-3842
DOI: 10.47191/a mj/ 10i11.03, Impac Fac o : 8.167
© 2025, AFMJ
3838
Takou io Feudjio i ginie1, AFMJ Volume 10 Issue 11 No embe 2025
The E ec o Loan Di e si ica ion on The Pe o mance o Mic o inance
Ins i u ions in Bamenda Came oon
Takou io Feudjio i ginie1, Ouma ou Bobbo2
1,2Depa men o banking and inance, Uni e si y o Bamenda, Bambili Came oon
Co espondence: Takou io Feudjio i ginie
Facul y o Economic and Managemen Sciences, The Uni e si y o Bamenda
ABSTRACT: Di e si ica ion in inancial sec o is c ucial in he sense ha i helps o educe he le el o isk acing by he sec o
especially in he a ea o loan. In he inancial sec o , mic o inance ins i u ions a e mo e expose o he p oblem o loan managemen ,
i is o ha eason ha his a icle ied o in es iga e and analyze he e ec o loan di e si ica ion on he pe o mance o
mic o inance ins i u ions in Bamenda Came oon. To each ou objec i e, he a icle make use o p ima y da a collec ed om 35
mic o inance ins i u ion in bamenda. 70 s uc u ed ques ionnai es we e adminis e ed o manage and loan o ice who we e selec ed
andomly. Da a we e analyzed using desc ip i e and in e en ial analysis.The indings indica e ha MFIs wi h loan di e si ica ion
end o exhibi be e inancial pe o mance and lowe isk le els, sugges ing ha di e si ica ion is a iable s a egy o enhancing
he s abili y and p o i abili y o mic o inance ins i u ions.
KEYWORDS: loan di e si ica ion, mic o inance ins i u ions, inancial pe o mance, isk managemen , Bamenda Came oon,
p o i abili y
1.INTRODUCTION
A majo h ea o MFI sus ainabili y lies in hei lending
ac i i ies, as e lec ed in he quali y o hei loan po olios.
In 2005,Da id and Dionne a gue ha depending on he
di e si ica ion a iable, MFIs should build a po olio wi h
ou s anding loans ha ha e epaymen p obabili ies wi h low
co ela ions. Fo all inancial ins i u ions i is impe a i e o
pu sue some so o loan po olio di e si ica ion which
consis s o well hough ou s a egic implemen a ions ha
aim o op imize he isk- ewa d adeo . In an e ol ing
inancial landscape, he signi icance o loan di e si ica ion
canno be o e s a ed. Di e si ica ion is undamen al abou
sp eading isk and ensu ing ha an ins i u ion's o unes a e
no ied o he pe o mance o a single sec o o demog aphic
g oup. Recen s udies ha e shown ha MFIs wi h di e si ied
po olios end o exhibi highe le els o s abili y and a e
be e equipped o wi hs and economic shocks (Wagne ,
2013). This is pa icula ly c ucial o ins i u ions ope a ing in
he ola ile ma ke s o de eloping coun ies, whe e economic
down u ns, sec o -speci ic c ises, o localized na u al
disas e s can quickly lead o a su ge in de aul a es. Despi e
he ecognized po en ial o loan di e si ica ion o enhance
he pe o mance and s abili y o MFIs, he sec o is s ill aced
by he p oblem o loan managemen . This a icle aims o
shed ligh on how MFIs can go h ough loan di e si ica ion
o imp o e hei inancial pe o mance.
2. CONCEPTUAL LITERATURE
2.1 Loan Di e si ica ion
Loan Di e si ica ion is a s a egic app oach employed by
Mic o inance Ins i u ions (MFIs) and o he inancial en i ies
o sp ead hei c edi isks ac oss a wide a ay o loan ypes,
sec o s, and demog aphics. This s a egy is designed o
minimize he impac o speci ic, isola ed isks ha migh
o he wise disp opo iona ely a ec he ins i u ion's inancial
heal h (Wagne , 2013). Acco ding o Aa lo and A nega d
(2017) di e si ica ion is a be e ool o imp o es
pe o mance.
Di e si ica ion in a loan po olio is a s a egic ha educe
c edi isk on loan acco ding o some loan cha ac e is ics
such as: hei ma u i ies, hei sec o s, and geog aphic a ea.
Acco ding o he ma u i ies o loan, MFIs o e a ange o
loan p oduc s wi h di e en e ms and ma u i ies. Sho - e m
loans, like wo king capi al loans, migh ca y highe isks o
de aul bu p o ide quicke e u ns, while long- e m loans,
such as hose o equipmen inancing, o e mo e s abili y
bu ha e longe eco e y pe iods. Cull e al. (2017) sugges
ha o e ing a mix o hese p oduc s allows MFIs o balance
hei sho - e m liquidi y needs wi h long- e m in es men s,
op imizing hei o e all po olio pe o mance.
Ano he aspec o di e si ica ion is sec o al di e si ica ion,
whe e MFIs sp ead hei lending ac oss di e en indus ies
such as ag icul u e, e ail, and manu ac u ing sec o . Each
sec o has i s own economic cycles and isk p o iles. Fo
“The E ec o Loan Di e si ica ion on The Pe o mance o Mic o inance Ins i u ions in Bamenda Came oon”
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Takou io Feudjio i ginie1, AFMJ Volume 10 Issue 11 No embe 2025
ins ance, while e ail migh boom du ing es i al pe iods,
ag icul u e's p o i abili y may be seasonal, based on ha es
imes. Ha a ska & Nadolnyak (2012) highligh ha by
lending ac oss a ious sec o s, MFIs can ensu e a mo e
consis en low o loan epaymen , he eby mi iga ing he
isk ha a down u n in one indus y will signi ican ly impac
hei en i e po olio.
Geog aphic di e si ica ion is c ucial, especially o MFIs
ope a ing in egions p one o localized economic down u ns,
na u al disas e s, o poli ical un es .
2.2 Pe o mance o MFIs
The p o i abili y is a way o measu e a company’s
pe o mance. I is he capaci y o make p o i , and a p o i is
wha is le o e om income ea ned a e you deduc ed all
cos s and expenses ela ed o ea ning he income. As indica o
o p o i abili y o pe o mance, we ha e Re u n on Equi y
(ROE) and Re u n on Asse s (ROA). B igham and Hous on
(2012) desc ibe ROE as a key indica o o inancial
pe o mance ha measu es how e ec i ely a company is
using i s equi y base o gene a e p o i s. Ross & Ja e (2013)
emphasize he impo ance o ROE in e alua ing he e u ns
gene a ed on he equi y in es ed by he owne s o he
company. Higgins (2015) no es ha ROE is a comp ehensi e
measu e o a i m's p o i abili y, p o iding insigh s in o how
well he i m is le e aging i s equi y o gene a e p o i s
Using p ima y da a, inancial pe o mance is cap u e in his
a icle by he g ow h o e olu ion in Re u n on equi y and
G ow h o e olu ion in Re u n on asse s.
3. METHODOLOGY
3.1 Sou ce and echnique o da a analysis
The p ima y da a used in his a icle was collec ed h ough
he use o sel - epo ing ques ionnai es. The ques ionnai e
was add essed o loan o ice s and manage s o mic o inance
ins i u ions in Bamenda ci y.
3.2 Model Speci ica ion
Empi ical Model
This empi ical model made use o he mul i linea eg ession
echnique. This is because i enables he p edic ion o one
a iable on he basis o se e al o he a iables. In o de o
measu e he ela ionship be ween he wo a iables loan
po olio di e si ica ion as an independen a iable and
pe o mance o MFIs as dependen a iable. This model
he e o e exp esses he pe o mance o MFI as a unc ion o
loan po olio di e si ica ion (loan e m di e si ica ion
(LTD), geog aphic di e si ica ion (GD), and sec o al
di e si ica ion (SD) which we e independen a iables o
loan po olio di e si ica ion. This unc ional ela ionship
can be exp essed as ollows:
P=ƒ (LTD,GD,SD,)………………… ………………..(3.1)
The abo e ela ionship can he e o e be pu in he linea ized
o m, aking ca e o e o e m and he cons an e m; he
abo e unc ional ela ionship becomes an econome ic model
as ollows;
P = β0 + β 1LTDi+ β 2GDi + β3SDi + β 4AGEi + β 5SIZEi + εi
……………………..(3.2)
Whe e β0 is he cons an e m, β1, 2, 3 a e he pa ame e s o be
es ima ed, ε is he e o e m componen ha cap u es all he
omissions and e o commi ed in he p ocess o analyzing
he da a.
P: Pe o mance o MFI
LTD: Loan Te m Di e si ica ion
GD: Geog aphic Di e si ica ion
SD: Sec o al Di e si ica ion
(AGE) and (SIZE), a e con ol a iable.
4. RESULTS
4.1 Ques ionnai es analysis
Table 4.1: Ques ionnai e esponse a e
F equency
Pe cen
Cumula i e Pe cen
Valid
Re u ned Ques ionnai e
64
91.4
91.4
Un e u ned Ques ionnai e
06
8.6
100
To al
70
100
Sou ce: Au ho (2025)
Table 4.1 shows, he numbe o ques ionnai es ha we e adminis e ed. Ou o he 70 ques ionnai es ha we e issued, 64
ques ionnai es we e e u ned gi ing a pe cen age o 91.4%.
4.2 P esen a ion o In e en ial s a is ics
4.2.1 Tes o Reliabili y (C onbach Alpha)
Table 4.2: C onbach Alpha
I em
Obs
Sign
i em- es
co ela ion
i em- es
a e age
co ela ion
in e i em
co a iance
alpha
Pe o mance
64
+
0.8777
0.7662
0.3449653
0.7365
Loan e m di e si ica ion
64
+
0.6618
0.5478
0.5370288
0.7937
“The E ec o Loan Di e si ica ion on The Pe o mance o Mic o inance Ins i u ions in Bamenda Came oon”
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Takou io Feudjio i ginie1, AFMJ Volume 10 Issue 11 No embe 2025
Geog aphical di e si ica ion
64
+
0.8190
0.7521
0.4873016
0.7630
Sec o ial di e si ica ion
64
+
0.3407
0.1747
0.6526042
0.8496
Age o he ins i u ion
64
+
0.6960
0.5374
0.4838542
0.7923
Numbe o b anches
64
+
0.8851
0.7869
0.3490079
0.7286
Tes scale
0.4757937
0.8132
Sou ce: Au ho (2025)
Resul s indica e good in e nal consis ency gi en ha he o e all alue o C onbach alpha (0.8132) > 0.7 which is g ea e han he
bench ma k o 0.7. So, hese p ima y indica o s ha e good in e nal consis ency, hus can be used o o m o build ou index o
a iable.
4.2.2 Fac o Analysis (Va iance In la ion Fac o Tes )
Table 4.3: VIF Tes
Va iable
VIF
1/VIF
Numbe o b anches
2.47
0.404183
Geog aphical di e si ica ion
2.12
0.470911
Age o he ins i u ion
1.61
0.619496
Loan e m di e si ica ion
1.50
0.668826
Sec o ial di e si ica ion
1.18
0.850526
Mean VIF
1.78
Sou ce: Au ho , 2025
The VIF esul s p esen ed abo e e eals a mean VIF o 1.78 which is below he gene al accep ed cu -o o VIF which is 2.5. Also,
no indi idual VIF was ound o be g ea e han 10. Thus he esul s o he eg ession a e eliable and p edic able. This means ha
ou analyses a e oid o mul icollinea i y which alida es he indings o his s udy.
4.2.3. Pai wise Co ela ion Analysis
Table 4.4: Co ela ion Ma ix
Va iable
Loan e m
di e si ica ion
Geog aphical
di e si ica ion
Sec o ial
di e si ica ion
Age o he
ins i u ion
Numbe o
b anches
Loan e m
di e si ica ion
1.0000
Geog aphical
di e si ica ion
0.3011
1.0000
Sec o ial
di e si ica ion
0.1378
0.1237
1.0000
Age o he ins i u ion
0.2500
0.0924
-0.3551
1.0000
Numbe o b anches
0.3752
0.1542
-0.0149
0.7512
1.0000
Sou ce: Au ho (2025)
F om he co ela ion ma ix, all he co ela ion coe icien s along he diagonal a e uni a y indica ing ha each a iable has a pe ec
posi i e co ela ion wi h i sel . The able e eals ha many o he independen a iables a e posi i ely co ela ed and has a low
mul icollinea i y alues which a e less han 0.8. Hence, he e is no s ong co ela ion be ween he a iables. The e o e, i can be
concluded ha he e is no s ong ela ionship be ween he a iables and hence he a iables can be subjec ed o o he empi ical
es ing.
4.2.4. Analysis o Va iance
Table 4.5: Analysis o Va iance
Sou ce
SS
d
MS
F
sig
Model
103.589108
5
20.7178215
247.84
0.01a
Residual
4.84839241
58
.083592973
To al
108.4375
63
1.72123016
Sou ce: Au ho s, 2025
“The E ec o Loan Di e si ica ion on The Pe o mance o Mic o inance Ins i u ions in Bamenda Came oon”
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Takou io Feudjio i ginie1, AFMJ Volume 10 Issue 11 No embe 2025
The esul s p esen ed on Table 4.5 e eals ha he pe o mance o MFIs model is globally signi ican since he F-s a is ic i.e. F (5;
58) = 247.84 has a p- alue o 0.001 which is less han 0.05.
4.2.5. Reg ession Analysis
In o de o es he ela ionship be ween loan po olio di e si ica ion and he pe o mance o MFIs in he No h wes egion o
Came oon, he OLS eg ession model was used.
Table 4.6: OLS Reg ession Analysis
Reg ession Analysis
Pe o mance
Coe .
S d. E .
P>
[95% Con . In e al]
Loan e m
di e si ica ion(LTD)
.5680503***
0.1846378
-3.08
0.003
-0.9376431
-0.1984575
Geog aphical
di e si ica ion( GD )
1.2671***
0.1777833
7.13
0.000
0.911228
1.622972
Sec o ial
di e si ica ion(SD)
-0.2596312
0.1782155
-1.46
0.151
-0.6163683
0.0971059
Age o he ins i u ion
(AGE)
0.6319098***
0.0715905
8.83
0.000
0.4886058
0.7752137
Numbe o b anches(Nb)
0.2277015**
0.0935317
2.43
0.018
0.0404774
0.4149256
Cons an e m
0.0721648
0.1194825
0.60
0.548
-0.1670055
0.3113351
Numbe o obs = 64, F(5, 58) = 247.84, P ob > F = 0.0000,
R-squa ed = 0.9553, Adj R-squa ed = 0.9514, Roo MSE = 0.28912
No e: *, ** and *** ep esen s 10%, 5% and 1% le el o signi ican espec i ely
Sou ce: Au ho , 2025
P = 0.0721648 - 0.2596312LTDi+ 1.2671GDi + -0.2596312SDi + 0.6319098AGEi + 0.2277015 Nbi + εi
5. DISCUSSION OF THE RESULTS
The able abo e p esen s he eg ession analysis on he
ela ionship be ween loan po olio di e si ica ion (loan e m
di e si ica ion, geog aphical di e si ica ion and sec o ial
di e si ica ion) and he pe o mance o MFIs in Bamenda .
F om he analysis, we ob ained he F-s a is ics o 247.84 wi h
a P- alue o 0.0000. his shows ha loan po olio
di e si ica ion has a s a is ically signi ican e ec on he
pe o mance o MFIs in he No h Wes egion o Came oon.
The esul e eals an R-squa e alue o 0.9553 indica ing ha
95.53% o a ia ion in he pe o mance o MFIs in Bamenda
is explained by a ia ion in he independen a iables.
Speci ically, he esul e ealed ha Loan e m di e si ica ion
has a coe icien o 0.5680503 indica ing ha loan e m
di e si ica ion has posi i e and signi ican e ec on he
pe o mance o MFIs in Bamenda. This shows he impo ance
o loan e m di e si ica ion in mic o inance ins i u ions.
F om ha esul , he null hypo hesis is ejec ed and we
conclude ha loan e m di e si ica ion has a signi ican e ec
on he pe o mance o MFIs in Bamenda. This esul is in
con o mi y o ap io i expec a ion and con i ms he agency
heo y inno a ion di usion heo y by Jensen & Meckling,
(1976).
Geog aphical di e si ica ion has a coe icien o 1.2671
indica ing ha geog aphical di e si ica ion has a posi i e
ela ionship wi h he pe o mance o MFIs in Bamenda. This
shows ha i MFIs in Bamenda ex en hei loans o u al
a eas, and no only concen a e in u ban a eas, i will inc ease
hei pe o mance by app oxima ely 1.27%. The coe icien
is ound o be signi ican a 1% since he P- alue is less han
1%. The e o e, we ejec he null hypo hesis and conclude
ha geog aphical di e si ica ion has a signi ican e ec on
he pe o mance o MFIs in Bamenda. This esul is in
acco dance o he inding o Hughes, Lang, Mes e , and
Moon (1996a) who ound ha when an e icien bank is mo e
geog aphically di e si ied, i epo s highe e u ns, bu also
highe le els o isk.
Sec o ial di e si ica ion has a coe icien o -0.2596312 and
i is ound o be insigni ican since he P- alue is mo e han
10%. This esul indica e ha MFIs in Bamenda. Doesn’
g an hei loan base on sec o s. We conclude ha sec o ial
di e si ica ion does no ha e a signi ican e ec on he
pe o mance o MFIs in Bamenda.
6. CONCLUSION
The main objec i e o his a icle was o e alua e he e ec o
loan po olio di e si ica ion on he pe o mance o MFIs in
Bamenda. Desc ip i e s a is ics wi h he aid o ables o
equencies and pe cen ages whe e use o analyzed he da a.
O dina y leas squa e was used o un he eg ession . Resul s
om eg ession indica e ha he e is a posi i e signi ican
e ec o Loan e m di e si ica ion and Geog aphical
di e si ica ion on he pe o mance MFIs in Bamenda. Hence
“The E ec o Loan Di e si ica ion on The Pe o mance o Mic o inance Ins i u ions in Bamenda Came oon”
3842
Takou io Feudjio i ginie1, AFMJ Volume 10 Issue 11 No embe 2025
we can conclude ha loan po olio di e si ica ion has a
posi i e e ec on he pe o mance o MFIs in Bamenda.
7. RECOMMENDATIONS
Based on he indings, he ollowing ecommenda ions can be
o mula ed;
➢ MFIs in Bamenda should pay mo e a en ion o loan
e ms di e si ica ion o lay emphasis on expanding loan
po olio sec o s
➢ MFIs o Bamenda should c ea e a conduci e
en i onmen and educa e employees on he ad an ages
o loan po olio di e si ica ion
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